data_visualization_of_big_data.ipynb

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      "name": "data_visualization_of_big_data.ipynb",
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      "authorship_tag": "ABX9TyOqcqeWtgU/E8MiUBarturz",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Blackman9t/Advanced-Data-Science/blob/master/data_visualization_of_big_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDhWnhLqkwW_",
        "colab_type": "text"
      },
      "source": [
        "## Data Visualization of Big Data\n",
        "\n",
        "Since plotting more than 3 dimensions is hard, we'd learn how to reduce dimensionality as part of this course.<br>**Note that:**\n",
        "\n",
        "In 2 dimensional space data boundaries can be defined through lines and circles.<br>\n",
        "In 3 dimensional space as spheres and planes.<br>\n",
        "In more than 3 dimensions as hyper-spheres and hyper-planes... These are multidimensional spheres and planes which cannot be visualised anymore, are hard to imagine, but still do the job to separate multidimensional point clouds in multi-dimensional vector space from each other."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPCR5FAplUkS",
        "colab_type": "text"
      },
      "source": [
        "### Plotting with Apache Spark and Python Matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SyE7RiIyA4s",
        "colab_type": "text"
      },
      "source": [
        "One important aspect when plotting is on data size. Know that plotting libraries run on a single machine and expect a rather small input datsset in the form of matrcs, vectors, rows and columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZaYnH_Cymlg",
        "colab_type": "text"
      },
      "source": [
        "Too much data leads to either main memory problems or performance problems. The solution is sampling."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3YSqCcCzGj2",
        "colab_type": "text"
      },
      "source": [
        "### Sampling:\n",
        "\n",
        "Sampling takes only a subset of your original data, but due to inherent randomness in selecting the values, it preserves most properties of the original dataframe.<br>Sampling reduces computational costs because at downstream data processing steps, only a fraction of data has to be considered"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3qlZOWjntA4",
        "colab_type": "text"
      },
      "source": [
        "### Let's install spark related dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxYRL13hmKOX",
        "colab_type": "code",
        "outputId": "529baa21-f481-4506-b7fb-c3bb3b39399a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "!wget -q http://apache.osuosl.org/spark/spark-2.4.4/spark-2.4.4-bin-hadoop2.7.tgz\n",
        "!tar xf spark-2.4.4-bin-hadoop2.7.tgz\n",
        "\n",
        "!pip install -q findspark\n",
        "!pip install pyspark\n",
        "# Set up required environment variables\n",
        "\n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = \"/content/spark-2.4.4-bin-hadoop2.7\""
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyspark\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/87/21/f05c186f4ddb01d15d0ddc36ef4b7e3cedbeb6412274a41f26b55a650ee5/pyspark-2.4.4.tar.gz (215.7MB)\n",
            "\u001b[K     |████████████████████████████████| 215.7MB 62kB/s \n",
            "\u001b[?25hCollecting py4j==0.10.7\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e3/53/c737818eb9a7dc32a7cd4f1396e787bd94200c3997c72c1dbe028587bd76/py4j-0.10.7-py2.py3-none-any.whl (197kB)\n",
            "\u001b[K     |████████████████████████████████| 204kB 47.8MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-2.4.4-py2.py3-none-any.whl size=216130387 sha256=9de7979b125a3fdcd253f803681a967cddfe966392fdfc284bf6d1bcedd628eb\n",
            "  Stored in directory: /root/.cache/pip/wheels/ab/09/4d/0d184230058e654eb1b04467dbc1292f00eaa186544604b471\n",
            "Successfully built pyspark\n",
            "Installing collected packages: py4j, pyspark\n",
            "Successfully installed py4j-0.10.7 pyspark-2.4.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BfSCRPgksTj",
        "colab_type": "code",
        "outputId": "c2665e8b-2329-447d-ceb0-faf7edb13146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from pyspark import SparkConf, SparkContext\n",
        "try:\n",
        "    conf = SparkConf().setMaster(\"local\").setAppName(\"My App\")\n",
        "    sc = SparkContext(conf = conf)\n",
        "    print('SparkContext Initialised Successfully!')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SparkContext Initialised Successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lawFjid7m__f",
        "colab_type": "code",
        "outputId": "086ce760-bc80-4cd1-b985-6ac397dafb82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "sc"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://e04b9c64d40e:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v2.4.4</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>My App</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        "
            ],
            "text/plain": [
              "<SparkContext master=local appName=My App>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nSwk-TmkwSX",
        "colab_type": "text"
      },
      "source": [
        "### Next, we set up a spark session"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njrrKvdqnYtk",
        "colab_type": "code",
        "outputId": "4dc9c4b3-3c1f-4e16-966c-059c6e4eaa7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.appName('My App').getOrCreate()\n",
        "spark"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://e04b9c64d40e:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v2.4.4</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>My App</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
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            "text/plain": [
              "<pyspark.sql.session.SparkSession at 0x7f5fdba4b0b8>"
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          "metadata": {
            "tags": []
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          "execution_count": 4
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BzjN1_HKrAaF",
        "colab_type": "text"
      },
      "source": [
        "Now it is time to grab a PARQUET file and create a dataframe out of it. Using SparkSQL you can handle it like a database."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfjrAobmq_M_",
        "colab_type": "code",
        "outputId": "0f5d0a74-7d9a-416d-cbfc-ce4f7bf76418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "!wget https://github.com/IBM/coursera/blob/master/coursera_ds/washing.parquet?raw=true\n",
        "!mv washing.parquet?raw=true washing.parquet"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-01-20 10:10:39--  https://github.com/IBM/coursera/blob/master/coursera_ds/washing.parquet?raw=true\n",
            "Resolving github.com (github.com)... 140.82.118.4\n",
            "Connecting to github.com (github.com)|140.82.118.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/IBM/coursera/raw/master/coursera_ds/washing.parquet [following]\n",
            "--2020-01-20 10:10:39--  https://github.com/IBM/coursera/raw/master/coursera_ds/washing.parquet\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/IBM/coursera/master/coursera_ds/washing.parquet [following]\n",
            "--2020-01-20 10:10:40--  https://raw.githubusercontent.com/IBM/coursera/master/coursera_ds/washing.parquet\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 112048 (109K) [application/octet-stream]\n",
            "Saving to: ‘washing.parquet?raw=true’\n",
            "\n",
            "\rwashing.parquet?raw   0%[                    ]       0  --.-KB/s               \rwashing.parquet?raw 100%[===================>] 109.42K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-01-20 10:10:40 (9.01 MB/s) - ‘washing.parquet?raw=true’ saved [112048/112048]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qI23LbxroIz",
        "colab_type": "text"
      },
      "source": [
        "Now let's read the parquet file to a Spark DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3sD_P_orQTs",
        "colab_type": "code",
        "outputId": "71cb2626-168a-40e7-a3c5-887e45090333",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        }
      },
      "source": [
        "df = spark.read.parquet('washing.parquet')\n",
        "df.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\n",
            "|                 _id|                _rev|count|flowrate|fluidlevel|frequency|hardness|speed|temperature|           ts|voltage|\n",
            "+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\n",
            "|0d86485d0f88d1f9d...|1-57940679fb8a713...|    4|      11|acceptable|     null|      77| null|        100|1547808723923|   null|\n",
            "|0d86485d0f88d1f9d...|1-15ff3a0b304d789...|    2|    null|      null|     null|    null| 1046|       null|1547808729917|   null|\n",
            "|0d86485d0f88d1f9d...|1-97c2742b68c7b07...|    4|    null|      null|       71|    null| null|       null|1547808731918|    236|\n",
            "|0d86485d0f88d1f9d...|1-eefb903dbe45746...|   19|      11|acceptable|     null|      75| null|         86|1547808738999|   null|\n",
            "|0d86485d0f88d1f9d...|1-5f68b4c72813c25...|    7|    null|      null|       75|    null| null|       null|1547808740927|    235|\n",
            "|0d86485d0f88d1f9d...|1-cd4b6c57ddbe77e...|    5|    null|      null|     null|    null| 1014|       null|1547808744923|   null|\n",
            "|0d86485d0f88d1f9d...|1-a35b25b5bf43aaf...|   32|      11|acceptable|     null|      73| null|         84|1547808752028|   null|\n",
            "|0d86485d0f88d1f9d...|1-b717f7289a8476d...|   48|      11|acceptable|     null|      79| null|         84|1547808768065|   null|\n",
            "|0d86485d0f88d1f9d...|1-c2f1f8fcf178b2f...|   18|    null|      null|       73|    null| null|       null|1547808773944|    228|\n",
            "|0d86485d0f88d1f9d...|1-15033dd9eebb4a8...|   59|      11|acceptable|     null|      72| null|         96|1547808779093|   null|\n",
            "|0d86485d0f88d1f9d...|1-753dae825f9a6c2...|   62|      11|acceptable|     null|      73| null|         88|1547808782113|   null|\n",
            "|0d86485d0f88d1f9d...|1-b168089f44f03f0...|   13|    null|      null|     null|    null| 1097|       null|1547808784940|   null|\n",
            "|0d86485d0f88d1f9d...|1-403b687c6be0dea...|   23|    null|      null|       80|    null| null|       null|1547808788955|    236|\n",
            "|0d86485d0f88d1f9d...|1-195551e0455a24b...|   72|      11|acceptable|     null|      77| null|         87|1547808792134|   null|\n",
            "|0d86485d0f88d1f9d...|1-060a39fc6c2ddee...|   26|    null|      null|       62|    null| null|       null|1547808797959|    233|\n",
            "|0d86485d0f88d1f9d...|1-2234514bffee465...|   27|    null|      null|       61|    null| null|       null|1547808800960|    226|\n",
            "|0d86485d0f88d1f9d...|1-4265898bb401db0...|   82|      11|acceptable|     null|      79| null|         96|1547808802154|   null|\n",
            "|0d86485d0f88d1f9d...|1-2fbf7ca9a0425a0...|   94|      11|acceptable|     null|      73| null|         90|1547808814186|   null|\n",
            "|0d86485d0f88d1f9d...|1-203c0ee6d7fbd21...|   97|      11|acceptable|     null|      77| null|         88|1547808817190|   null|\n",
            "|0d86485d0f88d1f9d...|1-47e1965db94fcab...|  104|      11|acceptable|     null|      75| null|         80|1547808824198|   null|\n",
            "+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-17T9pUwa_h",
        "colab_type": "code",
        "outputId": "2292539e-28f9-40ad-cb7d-986fe4fe2592",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Shape of the Dataset is {} rows, {} columns.\".format(df.count(), len(df.columns)))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the Dataset is 2058 rows, 11 columns.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFvP1QoLtRkJ",
        "colab_type": "code",
        "outputId": "9d1ca845-5c8d-487a-ad9c-a9b4c53e042e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Let's see if the washing table is now part of the tables in our spark session\n",
        "spark.catalog.listTables()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5I5XlPJstiWc",
        "colab_type": "text"
      },
      "source": [
        "So let's add the washing table to our spark session tables so that we can use spark.sql() commands and methods on it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJTFuXQQsj_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.createOrReplaceTempView('washing')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPUY81A-u370",
        "colab_type": "code",
        "outputId": "25358107-5a76-4eb5-d5e8-1cedd9dc1e9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "spark.catalog.listTables()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Table(name='washing', database=None, description=None, tableType='TEMPORARY', isTemporary=True)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4trJ3djwGEn",
        "colab_type": "text"
      },
      "source": [
        "Let's inspect the voltage column and select it's values except null values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FmlFZ8uvBK9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "voltage_vec = spark.sql(\"select voltage from washing where voltage is not null\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plQf4D3_vx1o",
        "colab_type": "code",
        "outputId": "423b4980-ea5c-4cf7-fbfd-b0ab892cc0e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "print(voltage_vec.count(),'Rows.')\n",
        "voltage_vec.show()\n",
        "\n",
        "# this shows that of the 2048 rows in voltage columns, only 448 are not null or NaN."
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "448 Rows.\n",
            "+-------+\n",
            "|voltage|\n",
            "+-------+\n",
            "|    236|\n",
            "|    235|\n",
            "|    228|\n",
            "|    236|\n",
            "|    233|\n",
            "|    226|\n",
            "|    235|\n",
            "|    220|\n",
            "|    225|\n",
            "|    239|\n",
            "|    230|\n",
            "|    236|\n",
            "|    244|\n",
            "|    235|\n",
            "|    234|\n",
            "|    224|\n",
            "|    225|\n",
            "|    223|\n",
            "|    236|\n",
            "|    226|\n",
            "+-------+\n",
            "only showing top 20 rows\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtlrhUG65iDQ",
        "colab_type": "code",
        "outputId": "afc22c7b-424b-41a7-ecd0-a1e64421d4a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "voltage_vec.take(5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Row(voltage=236),\n",
              " Row(voltage=235),\n",
              " Row(voltage=228),\n",
              " Row(voltage=236),\n",
              " Row(voltage=233)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlalKmcC6X-q",
        "colab_type": "text"
      },
      "source": [
        "This shows that the values are somehow wrapped in a row object. So let's get rid of those. As mentioned earlier, DataFrames are wrappers of the RDD, so we should now access the wrapped RDD in the RDD API inorder to extract the containing values in the row wrapper object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjDMtD237mFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "voltage_vec = voltage_vec.rdd.map(lambda row: row.voltage)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4jpoS3L8g5e",
        "colab_type": "code",
        "outputId": "f4d375f2-2dca-4c7c-8ce0-e2355cb510f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Let's see the first 10 numbers\n",
        "voltage_vec.take(10)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[236, 235, 228, 236, 233, 226, 235, 220, 225, 239]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cl7ySl6i8p-m",
        "colab_type": "text"
      },
      "source": [
        "**Sampling the Data**\n",
        "Let's now Sample this data.\n",
        "This step is the most important. Imagine if this RDD has millions of rows of data. There is no way we can pass that volume to a plotting library, since plotting is only done on a single machine. So Sampling is the answer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6a2PkvPHBUo9",
        "colab_type": "text"
      },
      "source": [
        "Let's use a random fraction of 10% in this case. But if we really have a lot of data, then 0.01 or 0.001 or even less percent will be appropriate.<br>\n",
        "As a rule of thumb, not more than 100 data points should be plotted"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgtygyD58pGa",
        "colab_type": "code",
        "outputId": "6c742f2a-d49f-4d16-9b7d-95d169144e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "sample_voltage_vec = voltage_vec.sample(False, 0.1).collect()  # We sample with just 10% of the data in this case.\n",
        "sample_voltage_vec"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[234,\n",
              " 236,\n",
              " 237,\n",
              " 239,\n",
              " 233,\n",
              " 229,\n",
              " 228,\n",
              " 227,\n",
              " 225,\n",
              " 238,\n",
              " 237,\n",
              " 221,\n",
              " 223,\n",
              " 236,\n",
              " 237,\n",
              " 223,\n",
              " 227,\n",
              " 232,\n",
              " 232,\n",
              " 224,\n",
              " 226,\n",
              " 236,\n",
              " 246,\n",
              " 229,\n",
              " 230,\n",
              " 226,\n",
              " 231,\n",
              " 232,\n",
              " 257,\n",
              " 223,\n",
              " 238,\n",
              " 234,\n",
              " 222,\n",
              " 220,\n",
              " 220,\n",
              " 231]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xH3awKjDKA_",
        "colab_type": "text"
      },
      "source": [
        "This looks fine, the sampled data is the subset of all voltage values coming from the cloud and couchDB NOsql database.<br>\n",
        "Now we have a meaningful array containing integer values reflecting the voltage of the power source of a washing machine in different points in time, accessible to a local spark driver in a python array...Let's plot it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6U_4AGzJ3W-E",
        "colab_type": "text"
      },
      "source": [
        "### Boxplots:\n",
        "\n",
        "Boxplots show many statistical moments of your data at the same time:- mean, median, skew, standard deviation and outlier content. Boxplots tell us about the distribution of our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hhz2BT1TERzh",
        "colab_type": "code",
        "outputId": "e21e0e67-a959-4d51-cc9a-df10871d897e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.boxplot(sample_voltage_vec)\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAFlCAYAAAA+t0u5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAATXElEQVR4nO3df6hn9X3n8dd7dUigmo3BW5KMMxkb\nTNAMqWYvrsW0VBbaNIRKaf9QiqHtwNRiXGVdAnHYNNviX2kNJGFTLBOWwqzdgjaEZQpxYeh2YLXM\nTMcfM5MW05pGdzY7UVFDfuDoe/+4x3qdjN7vOHfy/eR7Hw+45NzP95zvfX//SJ6e8z2eVHcHABjD\nv5r3AADAq4QZAAYizAAwEGEGgIEIMwAMRJgBYCDnz3uAJLn44ot727Zt8x4DAH5sDh48+J3uXjp1\nfYgwb9u2LQcOHJj3GADwY1NV3zzdukvZADAQYQaAgQgzAAxEmAFgIMIMAAMRZgAYiDADwECEGQAG\nIswAMBBhhg3q3nvvzfbt23Peeedl+/btuffee+c9EpBBHskJ/Hjde++92bVrV3bv3p0Pf/jD2b9/\nf3bs2JEkufHGG+c8HWxs1d3zniHLy8vtWdnw47N9+/Z84QtfyHXXXfcva/v27cutt96axx57bI6T\nwcZRVQe7e/lH1oUZNp7zzjsvP/jBD7Jp06Z/WXvxxRfz1re+NS+99NIcJ4ON4/XC7Dtm2IAuv/zy\n7N+//zVr+/fvz+WXXz6niYBXCDNsQLt27cqOHTuyb9++vPjii9m3b1927NiRXbt2zXs02PDc/AUb\n0Cs3eN166605duxYLr/88tx1111u/IIB+I4ZAObAd8wA8BNAmAFgIMIMAAMRZgAYiDADwECEGQAG\nIswAMBBhBoCBrBnmqtpSVfuq6mhVHamq26b1z1TVU1V1ePr56LS+raq+v2r9T871hwCARTHLIzlP\nJrmjuw9V1YVJDlbVA9Nrn+vuPzrNMd/o7ivXbUoA2CDWDHN3H09yfNp+oaqOJdl8rgcDgI3ojL5j\nrqptSa5K8tC09ImqeqSqvlxVF63a9dKq+ruq+uuq+vnXea+dVXWgqg6cOHHizcwOAAtn5jBX1QVJ\n7ktye3c/n+RLSd6b5MqsnFH/8bTr8SRbu/uqJP8hyX+rqred+n7dfU93L3f38tLS0ll+DABYDDOF\nuao2ZSXKe7r7/iTp7m9390vd/XKSP01y9bT+w+5+eto+mOQbSd53LoYHgEUzy13ZlWR3kmPdffeq\n9Xet2u3Xkjw2rS9V1XnT9s8kuSzJP67n0ACwqGa5K/vaJDclebSqDk9rdya5saquTNJJnkjyu9Nr\nv5DkD6rqxSQvJ7m5u59Z16kBYEHNclf2/iR1mpf2vs7+92XlsjcAcIY8+QsABiLMADAQYQaAgQgz\nAAxEmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMADESYAWAgwgwAAxFmABiIMAPAQIQZ\nAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADAQYQaAgQgzAAxEmAFgIMIM\nAAMRZgAYyJphrqotVbWvqo5W1ZGqum1a/0xVPVVVh6efj6465lNV9XhV/X1V/fK5/AAAsEjOn2Gf\nk0nu6O5DVXVhkoNV9cD02ue6+49W71xVVyS5IckHkrw7yf+sqvd190vrOTgALKI1z5i7+3h3H5q2\nX0hyLMnmNzjk+iR/3t0/7O5/SvJ4kqvXY1gAWHRn9B1zVW1LclWSh6alT1TVI1X15aq6aFrbnORb\nqw57Mm8ccgBgMnOYq+qCJPclub27n0/ypSTvTXJlkuNJ/vhM/nBV7ayqA1V14MSJE2dyKAAsrJnC\nXFWbshLlPd19f5J097e7+6XufjnJn+bVy9VPJdmy6vBLprXX6O57unu5u5eXlpbO5jMAwMKY5a7s\nSrI7ybHuvnvV+rtW7fZrSR6btr+a5IaqektVXZrksiR/u34jA8DimuWu7GuT3JTk0ao6PK3dmeTG\nqroySSd5IsnvJkl3H6mqv0hyNCt3dN/ijmwAmM2aYe7u/UnqNC/tfYNj7kpy11nMBQAbkid/AcBA\nhBkABiLMADAQYQaAgQgzAAxEmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMADESYAWAg\nwgwAAxFmABiIMAPAQIQZAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADCQ\n8+c9APDmVdW8R0iSdPe8R4CFIczwE2w9glhVwgoDcSkbAAYizAAwkDXDXFVbqmpfVR2tqiNVddsp\nr99RVV1VF0+//2JVPVdVh6efT5+r4QFg0czyHfPJJHd096GqujDJwap6oLuPVtWWJL+U5J9POeZv\nuvtj6z0sACy6Nc+Yu/t4dx+atl9IcizJ5unlzyX5ZBJ3jgDAOjij75iraluSq5I8VFXXJ3mqux8+\nza4/V1UPV9VfVdUHzn5MANgYZv7XparqgiT3Jbk9K5e378zKZexTHUrynu7+blV9NMlXklx2mvfb\nmWRnkmzduvXMJweABTTTGXNVbcpKlPd09/1J3pvk0iQPV9UTSS5Jcqiq3tndz3f3d5Oku/cm2fTK\njWGrdfc93b3c3ctLS0vr9HEA4CfbmmfMtfJood1JjnX33UnS3Y8m+elV+zyRZLm7v1NV70zy7e7u\nqro6K/F/+lwMDwCLZpZL2dcmuSnJo1V1eFq7czobPp3fSPJ7VXUyyfeT3NAeKwQAM1kzzN29P8kb\nPpC3u7et2v5iki+e9WQAsAF58hcADESYAWAgwgwAAxFmABiIMAPAQIQZAAYizAAwEGEGgIEIMwAM\nRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADAQYQaAgQgzAAxEmAFgIMIMAAMRZgAYiDADwECEGQAG\nIswAMBBhBoCBCDMADESYAWAgwgwAAxFmABiIMAPAQIQZAAYizAAwkDXDXFVbqmpfVR2tqiNVddsp\nr99RVV1VF0+/V1V9vqoer6pHqupD52p4AFg058+wz8kkd3T3oaq6MMnBqnqgu49W1ZYkv5Tkn1ft\n/ytJLpt+/m2SL03/CQCsYc0z5u4+3t2Hpu0XkhxLsnl6+XNJPpmkVx1yfZI/6xUPJnl7Vb1rfccG\ngMV0Rt8xV9W2JFcleaiqrk/yVHc/fMpum5N8a9XvT+bVkAMAb2CWS9lJkqq6IMl9SW7PyuXtO7Ny\nGftNqaqdSXYmydatW9/s2wDAQpnpjLmqNmUlynu6+/4k701yaZKHq+qJJJckOVRV70zyVJItqw6/\nZFp7je6+p7uXu3t5aWnp7D4FACyIWe7KriS7kxzr7ruTpLsf7e6f7u5t3b0tK5erP9Td/zfJV5N8\nfLo7+5okz3X38XP3EQBgccxyKfvaJDclebSqDk9rd3b33tfZf2+SjyZ5PMn3kvz2WU8JABvEmmHu\n7v1Jao19tq3a7iS3nPVkALABefIXAAxEmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMA\nDESYAWAgwgwAAxFmABiIMAPAQIQZAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkA\nBiLMADAQYQaAgQgzAAxEmAFgIMIMAAM5f94DwEb1jne8I88+++y8x0iSVNVc//5FF12UZ555Zq4z\nwCiEGebk2WefTXfPe4whzPsfDGAkLmUDwECEGQAGsmaYq2pLVe2rqqNVdaSqbpvW/7CqHqmqw1X1\ntap697T+i1X13LR+uKo+fa4/BAAsilm+Yz6Z5I7uPlRVFyY5WFUPJPlsd/+nJKmqf5/k00luno75\nm+7+2DmZGAAW2JpnzN19vLsPTdsvJDmWZHN3P79qt59K4i4WADhLZ3RXdlVtS3JVkoem3+9K8vEk\nzyW5btWuP1dVDyf5P0n+Y3cfWY9hAWDRzXzzV1VdkOS+JLe/crbc3bu6e0uSPUk+Me16KMl7uvtn\nk3whyVde5/12VtWBqjpw4sSJs/kMALAwZgpzVW3KSpT3dPf9p9llT5JfT5Lufr67vztt702yqaou\nPvWA7r6nu5e7e3lpaelNfwAAWCSz3JVdSXYnOdbdd69av2zVbtcn+fq0/s7pmFTV1dPfeHo9hwaA\nRTXLd8zXJrkpyaNVdXhauzPJjqp6f5KXk3wzr96R/RtJfq+qTib5fpIb2uONAGAma4a5u/cnOd3z\n8va+zv5fTPLFs5wLADYkT/4CgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADAQYQaAgQgz\nAAxEmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMADESYAWAgwgwAAxFmABiIMAPAQIQZ\nAAYizAAwEGEGgIEIMwAMRJgBYCDnz3sA2Kj699+WfOZfz3uMIfTvv23eI8AwhBnmpP7z8+nueY8x\nhKpKf2beU8AYXMoGgIEIMwAMZM0wV9WWqtpXVUer6khV3Tat/2FVPVJVh6vqa1X17mm9qurzVfX4\n9PqHzvWHAIBFMcsZ88kkd3T3FUmuSXJLVV2R5LPd/cHuvjLJ/0jy6Wn/X0ly2fSzM8mX1n9sAFhM\na4a5u49396Fp+4Ukx5Js7u7nV+32U0leuYvl+iR/1iseTPL2qnrXOs8NAAvpjO7KrqptSa5K8tD0\n+11JPp7kuSTXTbttTvKtVYc9Oa0dP7tRAWDxzXzzV1VdkOS+JLe/crbc3bu6e0uSPUk+cSZ/uKp2\nVtWBqjpw4sSJMzkUABbWTGGuqk1ZifKe7r7/NLvsSfLr0/ZTSbaseu2Sae01uvue7l7u7uWlpaUz\nmxoAFtQsd2VXkt1JjnX33avWL1u12/VJvj5tfzXJx6e7s69J8lx3u4wNADOY5Tvma5PclOTRqjo8\nrd2ZZEdVvT/Jy0m+meTm6bW9ST6a5PEk30vy2+s6MQAssDXD3N37k9RpXtr7Ovt3klvOci4A2JA8\n+QsABiLMADAQYQaAgQgzAAxEmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMADESYAWAg\nwgwAAxFmABiIMAPAQIQZAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADAQ\nYQaAgQgzAAxEmAFgIOfPewDYyKpq3iMM4aKLLpr3CDAMYYY56e55j5Bk5R8ORpkFmOFSdlVtqap9\nVXW0qo5U1W3T+mer6utV9UhV/WVVvX1a31ZV36+qw9PPn5zrDwEAi2KW75hPJrmju69Ick2SW6rq\niiQPJNne3R9M8g9JPrXqmG9095XTz83rPjUALKg1w9zdx7v70LT9QpJjSTZ399e6++S024NJLjl3\nYwLAxnBGd2VX1bYkVyV56JSXfifJX636/dKq+ruq+uuq+vmzmhAANpCZb/6qqguS3Jfk9u5+ftX6\nrqxc7t4zLR1PsrW7n66qf5PkK1X1gdXHTMftTLIzSbZu3Xp2nwIAFsRMZ8xVtSkrUd7T3fevWv+t\nJB9L8ps93dbZ3T/s7qen7YNJvpHkfae+Z3ff093L3b28tLR01h8EABbBLHdlV5LdSY51992r1j+S\n5JNJfrW7v7dqfamqzpu2fybJZUn+cb0HB4BFNMul7GuT3JTk0ao6PK3dmeTzSd6S5IHpIQkPTndg\n/0KSP6iqF5O8nOTm7n5m3ScHgAW0Zpi7e3+S0z2eaO/r7H9fVi57AwBnyLOyAWAgwgwAAxFmABiI\nMAPAQIQZAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAhBkABiLMADAQYQaAgQgzAAxE\nmAFgIMIMAAMRZgAYiDADwECEGQAGIswAMBBhBoCBCDMADESYAWAgwgwAAxFmABiIMAPAQIQZAAYi\nzAAwEGEGgIEIMwAMZM0wV9WWqtpXVUer6khV3Tatf7aqvl5Vj1TVX1bV21cd86mqeryq/r6qfvlc\nfgAAWCSznDGfTHJHd1+R5Jokt1TVFUkeSLK9uz+Y5B+SfCpJptduSPKBJB9J8l+q6rxzMTwALJo1\nw9zdx7v70LT9QpJjSTZ399e6++S024NJLpm2r0/y5939w+7+pySPJ7l6/UcHgMVz/pnsXFXbklyV\n5KFTXvqdJP992t6clVC/4slp7dT32plkZ5Js3br1TMYAJlU1xPt097rMAZxBmKvqgiT3Jbm9u59f\ntb4rK5e795zJH+7ue5LckyTLy8v+Ww1vgiDC4pkpzFW1KStR3tPd969a/60kH0vy7/rV/4V4KsmW\nVYdfMq0BAGuY5a7sSrI7ybHuvnvV+keSfDLJr3b391Yd8tUkN1TVW6rq0iSXJfnb9R0bABbTLGfM\n1ya5KcmjVXV4WrszyeeTvCXJA9P3Uw92983dfaSq/iLJ0axc4r6lu19a/9EBYPGsGebu3p/kdHeG\n7H2DY+5KctdZzAUAG5InfwHAQIQZAAYizAAwEGEGgIEIMwAMRJgBYCDCDAADEWYAGIgwA8BAaoT/\nd5qqOpHkm/OeAzaoi5N8Z95DwAb0nu5eOnVxiDAD81NVB7p7ed5zACtcygaAgQgzAAxEmIF75j0A\n8CrfMQPAQJwxA8BAhBk2oKr6clX9v6p6bN6zAK8lzLAx/dckH5n3EMCPEmbYgLr7fyV5Zt5zAD9K\nmAFgIMIMAAMRZgAYiDADwECEGTagqro3yf9O8v6qerKqdsx7JmCFJ38BwECcMQPAQIQZAAYizAAw\nEGEGgIEIMwAMRJgBYCDCDAADEWYAGMj/BycCwbayMcK+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZME-ZzEJvZ2",
        "colab_type": "text"
      },
      "source": [
        "### Run Charts:\n",
        "\n",
        "This is another way of visualizing data. Ideally Time-series data.\n",
        "<br>Run charts are the default charts we may have seen for plotting stock market data. And guess what... Stock market data is a Time-series data, just as IOT sensor data.<br>Run charts always define the x-axis as time and it runs from the left to the right.<br>On the y-axis we can see the observed values over time. Sometimes also multiple values. Cos we could plot multiple dimensions using different colors, especially if the value ranges of each dimension are similar."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-WIULWHMJrG",
        "colab_type": "text"
      },
      "source": [
        "To create a Run chart we need to fetch an additional dimension from our data set, namely the time dimension."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rjMpSjhMI1q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# First we run the spark.sql select statement for the two cols we want to display.\n",
        "time_volt_vec = spark.sql('select voltage,ts from washing where voltage is not null order by ts asc')\n",
        "# Next we use the underlying rdd attribute to sample the data and choose the ts and voltage values.\n",
        "result_array = time_volt_vec.rdd.sample(False,0.1).map(lambda row: (row.ts, row.voltage))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CK44FPuIR20K",
        "colab_type": "code",
        "outputId": "69c264a1-3350-495b-d8b1-cc18309a03fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "# Lets see the first 10 values\n",
        "result_array.take(10)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(1547808725914, 223),\n",
              " (1547808734922, 234),\n",
              " (1547808755932, 224),\n",
              " (1547808767938, 224),\n",
              " (1547808776948, 235),\n",
              " (1547808779950, 220),\n",
              " (1547808800960, 226),\n",
              " (1547808909090, 239),\n",
              " (1547808924097, 225),\n",
              " (1547808927099, 234)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKvaGe0_SZgT",
        "colab_type": "text"
      },
      "source": [
        "We've rightly received a list of tiples of timestamp(ts) and voltage values. Let's turn these into python arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MtMzl0JoMBPN",
        "colab_type": "code",
        "outputId": "d1b02fa5-c8b3-42f3-ea62-69bb1c35fd00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "voltage_array = result_array.map(lambda ts_voltage: ts_voltage[1]).collect()\n",
        "# Let's see the first 5 values\n",
        "voltage_array[:5]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[223, 234, 224, 224, 235]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxkjlzhXVDCf",
        "colab_type": "code",
        "outputId": "b81e1859-a8a1-435f-8a0e-7e9f533e7581",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ts_array = result_array.map(lambda ts_voltage: ts_voltage[0]).collect()\n",
        "# Let's see the first 5 values\n",
        "ts_array[:5]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1547808725914, 1547808734922, 1547808755932, 1547808767938, 1547808776948]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rs86T8W7Vctj",
        "colab_type": "text"
      },
      "source": [
        "With our time stamp and voltage arrays, now we plot the Run chart."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_NUy7VAViJ3",
        "colab_type": "code",
        "outputId": "997f2fb0-4609-4f7c-bbdf-8f833cd4db28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.plot(ts_array,voltage_array)\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Voltage')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OyNiaBHRBaHF0W8q9XVORQVFNyl3TCMM9cUxIDd0zM8n0hoY4QMbWJKALQouTkzn0wFBK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\nRi1/+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPnatXRWiQP7",
        "colab_type": "text"
      },
      "source": [
        "But this time we want to only plot data worth of one hour. Therefore we first have to find out in which date range we have data available:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UI5eApdciTQb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "2dc5adfc-fa58-4b30-8c37-6dd65d984175"
      },
      "source": [
        "spark.sql(\"select min(ts), max(ts) from washing\").show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "+-------------+-------------+\n",
            "|      min(ts)|      max(ts)|\n",
            "+-------------+-------------+\n",
            "|1547808720911|1547810064867|\n",
            "+-------------+-------------+\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qbui8NlHjF4o",
        "colab_type": "text"
      },
      "source": [
        "Now lets repeat the previous steps but only show data for hour. We've found out the low and high boundary of data available and we know that \"ts\" stand for \"timestamp\". Timestamp are the number of millisecons passed since the 1st of Jan. 1970"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoNGVcD_i_vA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "db48dfe3-c170-4bdd-cca5-845fed23c215"
      },
      "source": [
        "result = spark.sql(\n",
        "\"\"\"\n",
        "select voltage,ts from washing \n",
        "    where voltage is not null and \n",
        "    ts > 1547808720911 and\n",
        "    ts <= 1547810064867+3600000\n",
        "    order by ts asc\n",
        "\"\"\")\n",
        "result_rdd = result.rdd.map(lambda row : (row.ts,row.voltage))\n",
        "result_array_ts = result_rdd.map(lambda ts_voltage: ts_voltage[0]).collect()\n",
        "result_array_voltage = result_rdd.map(lambda ts_voltage: ts_voltage[1]).collect()\n",
        "plt.plot(result_array_ts,result_array_voltage)\n",
        "plt.xlabel(\"time\")\n",
        "plt.ylabel(\"voltage\")\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ysRRmRLNZniowRmL6qDGL+xnnvet0Ddl7xqI+c9BQ0/ksq22AgJGJqG89ukOP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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_favZWBWfm7",
        "colab_type": "text"
      },
      "source": [
        "The run chart preserves the time domain, so we can see the time and volt relationships in time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gss-E9adYARv",
        "colab_type": "text"
      },
      "source": [
        "### Scatter Plots:\n",
        "Scatter plots don't draw lines, they put individual data points to a 2 or 3 dimensional vector space.<br>Each point is reflecting a row in a data set and,<br>Each dimension reflects a column. Scatter plots can be used to define classification boundaries between 2 IOT events for example. Or clusters of similar data, or detecting outliers from normal behavior. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1qyHdU4b_ik",
        "colab_type": "text"
      },
      "source": [
        "For this scatter plot, we want to plot the values for the hardness, temperature and flowrate dimensions of the washing table or Dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "et1JzGW8X-aJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# First we run the spark.sql select statement for the three cols we want to display.\n",
        "result = spark.sql('select hardness,temperature,flowrate from washing where hardness is not null and temperature is not null and flowrate is not null')\n",
        "# Next we use the underlying rdd attribute to sample the data and choose the columns we need\n",
        "result_array = result.rdd.sample(False,0.1).map(lambda row: (row.hardness, row.temperature, row.flowrate))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY7kf-BRoWyl",
        "colab_type": "code",
        "outputId": "f3a06b49-9a10-45dd-b64f-0883f28e0c31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "# let's see the first 10 values\n",
        "result_array.take(10)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(73, 84, 11),\n",
              " (79, 96, 11),\n",
              " (80, 89, 11),\n",
              " (125, 88, 11),\n",
              " (72, 81, 11),\n",
              " (80, 80, 11),\n",
              " (75, 91, 11),\n",
              " (79, 86, 11),\n",
              " (72, 89, 11),\n",
              " (130, 94, 11)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kb6VN99uoqV3",
        "colab_type": "text"
      },
      "source": [
        "Next let's unpack the hardness, temperature and flowrate columns into separate arrays or lists"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjpyW6bTozAL",
        "colab_type": "code",
        "outputId": "07f60ae7-fb74-412e-8825-54185ca6cd54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "hardness_arr = result_array.map(lambda hardness_temperaure_flowrate: hardness_temperaure_flowrate[0]).collect()\n",
        "# Let's see the first 5 values\n",
        "hardness_arr[:5]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[73, 79, 80, 125, 72]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvNFizidpWup",
        "colab_type": "code",
        "outputId": "8dc9313d-8f86-45d3-f3dc-8828bae70496",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "temp_arr = result_array.map(lambda hardness_temperaure_flowrate: hardness_temperaure_flowrate[1]).collect()\n",
        "# Let's see the first 5 values\n",
        "temp_arr[:5]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[84, 96, 89, 88, 81]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMgju90bph_k",
        "colab_type": "code",
        "outputId": "5254d309-0d7d-432b-c7b2-b4d2456edf1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "flowrate_arr = result_array.map(lambda hardness_temperaure_flowrate: hardness_temperaure_flowrate[2]).collect()\n",
        "# Let's see the first 5 values\n",
        "flowrate_arr[:5]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[11, 11, 11, 11, 11]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlXD14-dp-r0",
        "colab_type": "text"
      },
      "source": [
        "Now let's plot the 3D plot of our data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EokUDxH0qDH4",
        "colab_type": "code",
        "outputId": "95a91ccc-6693-49b9-eaa8-fc27873738f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "fig = plt.figure(figsize=(10,8))\n",
        "ax = fig.add_subplot(111, projection = '3d')\n",
        "ax.scatter(hardness_arr, temp_arr,flowrate_arr, c='r', marker='o')\n",
        "ax.set_xlabel('Hardness')\n",
        "ax.set_ylabel('Temperature')\n",
        "ax.set_zlabel('Flowrate')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hw5idnUUqlYLL5dLuvH0+X0Wp3UR9MLIfJl+C/MTEBFKpVNUS5KuFWStTQH7B\nE4lE6nIzaBRI8BicYnOv1Hk6PT09mk9nbW3NcFvJAXbVomAwiFgsBkmScP78+brfubCu8MiyjKWl\nJYyMjGi/12Ivdg6HAw6HQ2tByLKMZDKJcDiM1dVVTE5OQhAEeDyeLDMqi/dwp/sEqonZwkM5jtPm\nBKmRGawT5KuFmSs8+Vpa1Y6VMDokeAyK3qejVnRynejUeTo+n2+TT4elSGG5e6hSYRCPx+H3+2Gx\nWOB2u3Ho0CEm66oUlhWeeDyOwcFB2Gw2nD17tuKWFMdxcLlccLlc6OzsBHA9sDIcDmcFVnq9Xq0V\nVqop2kwXYzNiNsEDbK6UsE6Qr9W6zYQoijnPGdUODjU6JHgMRqk+HUEQ8s7TYS14WFHuugRBwNjY\nGFZWVtDb24uWlha8/PLLzNZVKSwEjyzLiMViuHz5svYaC/1sJb8XfWClihpTEA6HMT09jWg0qk3o\nNYIpeqdjRsFTjO+okgT5alVhRFE0ZKutGPJVp0jwEIZA9elMTk6io6MDNputKJ+O2ivPBUvBw5JS\nKzyyLGNhYQFjY2PYs2cPzp07p51A1dk3RrgTq7RytbS0hOHhYXAcl/Ua81GNC1+umALVFB0OhxEM\nBguaommOUHUxo+Ap9/tZbIK8WpXUh6eyEEFmbmmJokgtrRyQ4DEAep/O0tISWltbN+1kkGUZc3Nz\nmJiYyPLpFGI7CJ5wOAy/3w+Px1NwSjILwVPpxaTci72+RXf69Gm89dZbhhBwKsWYotPpNJxOJyRJ\ngtfrRSaTMe3dsZHZSYInF7kS5CVJ0sJTWSbIG+VGqhwEQchrWt6psRIACZ66IkkSBEHI8unkEinq\nbJnGxkbcfPPNRV9IjCp4illXOp3GyMgIotEo+vr68n5JWcdCVNoiKmUtkiRhfHwci4uLWpCpLMum\nqJDkM0WPjY0hGo3i7bff1jwYahWomu2HncJOFzy54Hle+4yxTJA3e4UnX0uLdmkRNaWQT8dqtWpi\nQO/TKWe2TDWSt1mccAsJA1mWMT09jenpaRw8eBD9/f0Fn491LlclJ+ZSKjzBYBDDw8PYvXt3VrXO\nbBczFdUUrW4x3r17d5YpWt9+qPTOeydj5G3p+ahHpYRFgvx2rfAUskFsd0jw1JBi5ulYLBakUikM\nDw8X5dOpJaooqPSuJ59IWV1dRSAQQGtra9FTks2WdJ5IJOD3+8FxHE6fPg2n01nRcxoN/edZ78FQ\n2w96U/TU1JR20dHPB6qVKdoMlbSNGGnwYLEYRaRtlSCvF0E+nw+xWAzpdNqUVbV8Hp5oNIqDBw/W\nYUXGgARPDVDbFMXM04nH4xthpB4AACAASURBVFhcXMSBAwfKzr2qFmorioXg0V9skskkAoEARFHE\nDTfcUFKSr5EET6FjSJKEiYkJLCws4OjRo1kj+ncSue681cTuYkzROx0zXnyNLNIKJcivrKxgZmYG\no6OjzBPkq02+95x2aRFVpZjBgcB1nw7HcThw4AD27t1b45VuDWtPkCiKmJiYwOLiYtmVLJaCp1o5\nWCsrKwgEAti1a5fhRKwRyJXYrc9p2jgpmtV0XrOKB7Ot2WytIfXz6HA4MDAwAJvNljdBXj8osZQE\n+XpBu7SIqlDKPB21unHixAmsrKww/9KwOkmyEhdqteuVV15BV1dXRSKApWmZdYUnmUzC7/dDlmXc\neOONcLlclS7TFLBoFeXKaUokEtqd9/j4+I40RZPgqR36dbNOkK8XVOEhmFJK7tXY2BiWl5ezWhzr\n6+vIZDLM1sPKdwOwqfBEo1EEAgEIgoBz585V7GExUktLrfBIkoTJyUnMz88byoNVC6p1ctdP5801\nKXqjKVq94GxHU7SRLqDFsB0ETy7yJcir7dm5uTmkUqmcIqheRKPRrHbyToMEDyPUC10mk9GETj6f\njjpPZ8+ePZuqG1arFclkktm6WPlu9McqB0EQMDo6irW1NfT29mqp35VitJZWOp3Gr371K3R0dODc\nuXPbvuJQTyo1RZuxWmJGzCp4gNLFZa5xDbkS5J1OZ5YIYpkgX8gkThUeomJK9ek0NTXlnadjsVi0\nNhgL6h36qRd4e/fuxdGjR5leZIzS0lLbV4lEAhcuXCjJeE2wo1hTtN1u1wYkqnfhRHXYycIyX4J8\nMplEJBKpSoJ8vi3pAAkeEjwVIMuydjfZ0tKSt30Vj8cxNDQESZJw4sQJeDyevMdkbQxmebxSjxUK\nheD3++Hz+TYJPFaREPVuaUmShKmpKczOzuLQoUNIJpM7XuwYbbt3PlP0+vo6FhcXMTg4qN11szRF\nE9fZqYInF/og346ODgCbPWqVJMjn25Ku/t1O/lyT4CkD1aeTyWQQi8WwsLCQc5uxPuzyyJEjRW1F\ntlqthq3wFHusdDqNoaEhJBIJDAwM5LyjYBUJUc+W1traGvx+P9ra2rTW5NjYWMVrMTNmubA5HA60\ntrZidnYWp06d0i444XB4R5uiifqQy6NWboJ8PguD0W5E6gEJnhLI5dOx2WybBIosy5idncXk5OSm\nsMutMHKFZytxsbHa0dnZmfcCWO/KzEZKaWmlUikEAgFkMhmcPHmyYMWOMAf6C87GtO58puhi4gkI\nc2IEcVBugrwkSQUrQWa5KakGJHiKJJ9PRx8FAVyfFtzc3FxS7pWKkQVPoWOtrKxgaGhIq3ZsdSfM\nal21FE6yLGNqagozMzM4fPgwOjo66nry2MknLhZs5S3ZyhS9cTJvrSdFE9XDqEbrrRLkFxYWsLa2\npmU0qq1Zr9db9JygBx54AM8++yw6Ojpw5coVAMBTTz2Fxx57DIODg3j11Vdx9uzZvP9eFEWcPXsW\n3d3dePbZZ9m8cEaQ4NmCrebpqC0ovU+nkrv+jQKqUlgLnlQqlfWYGpUAoKQpySyFCott/FutZ319\nHYODg2htbS1K0FWKmY2eRrg7rhalmKL1lSAyRZsLMwWH6hPkAWBpaQmxWAytra2IRCL493//d3zt\na18DoHxWH3/8cZw9exbHjx/PuTvs/vvvx6c+9Sncd9992mPHjx/HM888g4ceemjL9Xz9619Hf38/\nwuEwo1fIDhI8eSh2cKAsy4jFYrh8+TKOHj2aNZiqHHieN6zg0YsCURQxPj6OpaWlsqISjNbSyufh\nUf1IyWSS2ldFUEqAar1hJSoLTYoOh8OYnZ1FOp3O2oXj8/mKMqAS9cFMgmcjqmlZ/Zx97GMfw8c+\n9jEMDw/j4YcfRiaTwTe+8Q288847sNlsePzxx3HixAnt3996662YmJjIOmZ/f39Rzz0zM4Of/OQn\n+NznPoe/+qu/YvmymEDfuByohuRCgwP1Ph2e53H+/HkmJ0/Wd/WsKzyCIGBhYQGjo6Po7u4ue0qy\n0VpaGz08+tT2rfxIrKEZMeYn36TonWaKNov43YhRW1rFIAhCTjEtSRK6urrwx3/8x9pjsViMqfB+\n9NFH8dWvfhWRSITZMVlCgicH6pc03wde9em0tLTg5ptvxmuvvWbYi1OuNlS5pNNpzM/PI5PJ4OzZ\nsxWV6Y1Y4VGPEwqFMDg4iObm5qJT21lS7I4xEkWVU0sRm8sUrXov5ufnEYlEwHEcvF7vtjFFm/Uz\navYKT65zc64ZPCwr1qrv58yZM7h48SKz47KEBE8OCs3TCQQCAErzq9QTFpWUTCaDkZERrK6uwufz\n4fjx4xWvy4iCJ5lM4te//jXi8TiOHz8Or9db8XHLwUxtoVyYZe31Xqfee1GsKVqSJFOJCLNWSsy6\nbiC/WKt2cOhLL72EH/3oR3juueeQTCYRDodx77334h//8R+r9pylQoInBxtPJmoswurqal6fDusv\nCKuTWiWCR9+2279/P7q7uzf1duuxLj0sBI8sy1hbW0MwGERfXx8GBgbqvvuKhYirB2a5EKsYbb2F\nTNGhUAipVAqvvfYa7HZ71s4wo5qizSoczFzhydfSqrbg+fKXv4wvf/nLAICLFy/iL/7iLwwldgAS\nPAXRX/ALxSKoO6tYfbEtFkvdAz/X19fh9/vR1NSktXXi8XhdYyqqcZxwOIzBwUFYrVb09PRoWz3r\nSTEVHrUKaaa7faNR7wpPsaim6MbGRqyuruLMmTNZpuiZmZlN0QRGMUWbVfCYdd1A5RWeu+++Gxcv\nXsTy8jJ6enrw+c9/Hi0tLXj44YcRDAZx55134tSpU/jpT3+Kubk5PPjgg3juueeq8VKYU/9vhEFR\n58q0tLRs6eNQzbysRnarx6uH4FGH6qXT6U1tHVYiheWxyj1OJpPB8PAwotEoBgYGkEgkEAqFKl4P\nC8ze0iKqg17cmsUUXSjI0siYucKTb+3F5mg9+eSTOR+/6667Nj3W1dWVU+y8733vw/ve976tF1tj\nSPDkIJlMYnp6umifjpFn5xS7zV2SJExOTmJubg5HjhxBe3v7phNVPXO58lGq4NGHmR44cAD9/f3g\nOA7JZJKZyKi06mJ2wWOWtZutOlZovcWYoqPRKABkmaI9Hk9V3wNZlk1ZKWFZsa81+Vpa0WhU+2zs\nVEjw5MDlcuHUqVNF/7yR86+KEWPBYBDDw8Po7OwsOFSPtRCrxcBAPZFIBFevXs0ZZspye/tOFjxm\nEhBmo9TPVammaNUTxHJStFlbQ1vFMxiZepmWzYA5f6MGg7XgYXm8QiIlHo/D7/eD53nceOONcLlc\nBY/F8kLMart8MUJFEAQMDw8jHA6jv78fPp+vrOOUsp5KTvJmFjxmYjtVeIplq0nRi4uLSCaTmila\nFUHlmqLNKnjybe02A/mqasW2tLYzJHhyUOpJxWz5V/oU997e3qwJsYVgeXFgNVFaNXjnQpZlzM/P\nY3x8HPv27UNfX1/BMFMWIoOFWClmLfF4HCMjI7Db7WhsbITP52PmISOMSbUEWqFJ0aFQCNPT02Wb\nos0qeMy67kKQ4CHBk5dSLlzVqPCwEjz61yHLMhYWFjA2NlZyijtrqm1ajkQiGBwchNfrLSrEldVW\ncBavq9BnT5IkjI+PY3FxEQcOHIAoilhbW8Pk5CQEQcgyqTY0NNTl90vVqepQy4pUsaZor9eriaBc\npmizCgczm5bzEY1Gc1a3dxIkeBhQDQ8Pq+OpJ0hVAHg8Htx00005Q+NqSaHKTClsFCqCIGBkZASh\nUAh9fX1ZpftCsPbwVHqMXGtZXV2F3+/Hrl27cP78eQiCAFmW0dHRAeB6rls4HMb8/DyGhobAcZwm\ngGqR5L3TWkS1pJ47ngqZotXPmzopWvUN+Xw+iKJoqvdYxaxCrRAkeEjwMMFisSCdTjM9HqsKTzqd\nRiKRwODgIPr6+gzzgWfV0lJPpvrq1d69e9Hb21uywdOoFZ50Oq2NCjh16lTenYNqLIHX680yqarz\nWkZHR7Ukb70Iqrf4JYrDaDueNqZ0A5tN0eqoB1EUtc+b0+k0vAgya4WnkCiORCKGOf/XCxI8eahn\nS4uFoVcffmm1WnHTTTcxO8mwuDNmOdNHFEW8/vrrcLlcZVevjOThUY+hH3xZboCpxWJBc3Mzmpub\ntcdy+TNYzmsxS0vLbBUeM6x3oyl6YWEBiUQCjY2NeU3RRhTdZq3w5NuSDijfe6fTWeMVGQsSPAyo\nhocnFouV/e/X1tbg9/vR2tqKc+fO4dVXX2W2NlWoVHr3w6KlpUZ+JJNJnDhxAk1NTWUfi5WHh5Xg\nicfjGB4eRkNDQ97Bl+U+Vy5/hr41oc5r0RtU3W53URdbo1+QzYwZBM9GJEkqyRStj8uo57Zws1Z4\ntlq32T4/rCHBwwCWnhv1eOW0e5LJJAKBAARBwMmTJ7UkXJYRBOraKj0ZVNLSkmUZi4uLGB0dxZ49\ne+B2uysSO+p6jNDSEkUR6+vrWFlZwYkTJ4r2IFVCvlaYulV5fHwc8XgcNptN2xFmxLvy7Y5ZBU+u\nSkkhU/Ty8jLGx8chSZJWeWxoaKjppOjtJnjUGyOzfX5YQ4InD6V8MFhPWi71eJIkYWJiAgsLC9qU\n5I3HEwSByQWq3sIgFothcHAQDodDa1/NzMwwWU+9W1rqAEibzYbDhw+zETuSBJRRmrdYLGhqasoS\nkrnuyt1ud9auMDNhNgFhtvUCxQ/w28oUPTc3h2g0uskUXa1J0WZtaYmiaNqBibWA3hkG1HOX1tLS\nEkZGRrSdO7m+pCz9MvWKhBBFEWNjY1heXkZfX1+WJ4UF9dyWnkwm4ff7AQCnT5/G1NRUxevgpqZg\n+cUvgGQS0sGDkG65BahwkFquu/J4PK55M0ZGRpDJZGC1WuFwOEpqhRFbY1bBU65w0Juiu7u7AVyv\nPEYiEW1StNVqzWq/sjBFG80gXiz5MhhTqZRpBymyhAQPA+rR0orFYvD7/bDZbDh9+nRBMxrL9bES\nPKUcZ2lpCcPDw+jp6ck7O8gocQ6lHEeWZUxNTWF2djarMlfxWtbWYHnhBcgtLUBzM/jRUcBigXTr\nrYAsg5uYALe4CLhckHw+oLERKHL4pB6O4+DxeODxeLSU+cXFRayuriKTyWS1wtSLUWNjo2FaYWYT\nEGZbL8B+K32uyuNWk6J3Uvs1X0srHA5nBUHvVEjw5KHULc0sd6YUEgOqUXd1dbXoSgfLlhvL+Tlb\nEY/HMTg4CJvNhrNnz+a9Q2GVX8WCYsVKKBTC4OAgWlpacO7cuayTVKWfJ25tTfmPayJY7uwEPzEB\n6dZbwV++DMsvfwlZFGF9/nkgkwF27ULmzjsh/s7vlP2c+rU7nU7s3btXeyydTmtb42dmZnK2wszo\nl6g1ZhQ8taiUlGqKVj9zhVo/ZtlpuJF8FR6asqxAgocBrE9CuVpk+piEvXv34ujRo0U/L8uWFqv5\nOYUQRRHj4+MIBoNFRV+wyK9ixVbvtZrrFYlEcPz48Zx3XUWJpnAYXDgMuN3AxmM4HOBEUTkGxwHx\nOODzAZIE/tIlSN3dsP7gB4q/x+2G1NoK67PPQu7thVRCaG6x2O12tLW1oa2tDUDuVhhwPcW7sbGx\nZq0wMwmIeg4eLJd6fS8LmaKDwSDGxsayTNHqOAYjnEMqIZ+Hh4JDFUjw5KGeJ5aNd/ihUAh+vx8N\nDQ1FxSRsxIgtrXyoxt2urq6ioy9YVZ1YkE+s6HeW7d+/v2Cu11Z+Is7vB/dv/warJIHnOIi33w65\nuVmp7DgckHftgtjbC8vQEGSeB6xWiO9/v9LOkmXIPA8uGFRaWakUYLEoxuZgsGqvf+PPbGyF6QfW\n5WqF+Xw+5h4Es93Fm9FXYpQbka1M0bOzs1mm6Ewmg2g0WjVTdLXIF3pKgkeBBA9DWJec0+k0hoaG\nkEgkMDAwUPYHlmVLi2W1SI+a3G6xWLb0JNVqTeWQay1qa06/s6wQBUVDNAru3/4NaG+HxPOQ43FY\nn3lGaV9lMuDfeAPc+jqkffsg/uZvQjpxQvHyXBtRIB47Bsvly5AbGsAtLACdnYqZWZKAaxWYeqAf\nWLdnzx4A2a2w2dlZpNNpuFwuNDY2aibVSlthZrqYmbGlZRTBk4t8pmh1a3w1TdHVIp+Hh1paCiR4\nGMFqIB+gnCTS6TRee+21sifs5lobC1hXePRhmL29vWhtbS35GEYSPHqxon9tfX19JaXS5xU88bjS\nprLblbaVywX+yhWIFy6AHxkBPzoK2eEAwmHY/vEfkfqDP4B1ZgZcJgPxxAlIN90EeDyQ3G5Y/+Vf\nIFss4GZmFHF0442s3gYm5GqFJRIJhEIhLC0tYXR0FLIsZ12MSrkjN2OFx6gX2nwYWfDkwmKxoKGh\nAS6XC8eOHQOgmKLD4TAikYhminY4HFmfO6OYovNNWqYKjwIJnjyUM8KfxbCqlZUVDA0NQZIknD9/\nnomAMmpLSxAEvPLKK9i9e3feLfXFYETBszHos5TXVlDwNDQAVisQi4FzuQDVoNzcDH5kBHJzMyAI\ngMcDORSC/amnIH7wg5AtFlguXQIXi4GbnQUXjUL80Icg3HCDUuXp7GTw6hXyrl0UgWhUqSiVMeJe\n35bI1QrT35GrXqBqtMLqhRkFjxnXvPHG1WazobW1NetmrFJTdLWgCk9hSPAwotLhfolEAoFAALIs\n44YbbsBbb73F7ETBMtyUxbESiQT8fj8ymQzOnj2bNwyzWIwkeCRJwtTUFGw2W8Ggz0IUFDwuF6SP\nfAT8888DKytAQwOE228HHwxCdjrBrawogsLhABeNQu7u1ubvyA0NsD7zDMR3vQtyTw+4lRVYr1yB\ncOJEJS9509pzsr4O649/DE4Nk7ztNkjX7qArYWN2E5C/FbbxYmS2i7HZ1guYr8IDKKJhqzUb1RSd\nT/BEIhFtivpOhgQPI8odPqjuSFpaWsLRo0e18r3qu2HxBWFp6q1kl5Z+IvTRo0eRTqeZTX9mVXUq\n96KiBn1OTU2ho6MDAwMDZV+cthyCuHs3pPvvhxCLQQwGwa+tAcPDQG8vrD//OWS3G/z0NOTubkg9\nPdePu7YG2WrVdnXJra3gZmeBRELZ7VVFrP/v/4FLJiF3dQHpNCw/+xnkXbsgl9HC3Ip8rTD1YqS2\nwux2OyRJMo05lQRPbSinUl+qKbpak6LztbRisRhVeECCJy/ltrSKRZZlbUpyd3f3praHerxSd2Tl\nW1u9W1orKysIBALo7OzUXuvExASTUeisKjzlzvOJRqO4evUqGhoacPDgQe1Y5cLz/Na/L56H5emn\nYfv+95V1+3xI/Y//gcwDD8ASCEC2WiGdOgXrv/87uOlpgOMg2+3A3r1Ky8tqBRIJyDYbUG3/gSSB\nW1xUxA6gPB/HAeEwUAXBs5F8F6PZ2dlN5lT9rjCjJUvTLq3awGrNhSZF52rBqiKoElN0oQqPz+er\n6PVsB0jwMKKUCk80GoXf74fD4cg7UI+lV4Z1haeUY6mxCbIs48Ybb4TL5Sr7WKzWtNVxij3ZiaKo\nDYHs7+9HY2MjZmdnkclkKlpHUXN4Ll+G5Z//GdLevYDNBiwuwv53f4f0X/81xJ4e8K+/Duvzz0N2\nuyG+971AQwPkjg7wQ0NK5ITFAnAchA9/WBE/DNm0dp6H3NYGrK8DqsdIljfPD6ohPM/D4/EglUrh\n8OHDAK6bU9XU+FQqBZfLhYaGBm1nWD1zilhtiqglZhQ81QwOzTcpWv3cVWqKzieKybSsQIKnAKWM\n+C9G8GQyGYyMjCAcDqO3t7dgwjfLfC7W4qmYY0mShMnJSczPz+cMNAXYChUWO25KWU8wGMTQ0JAW\nd6HekZUdCyEIwMyMMhywiHVwS0uKUFEvwO3t4K+FqPIvvgjra69Bam0FHwqBX1hA5vd/X9mddeQI\nuDffBLe+DuG97wX27y99rYXWlefOVPjN34T1Rz8CNzcHSBLEW2+FnOMzUU82mlPz+TLUAYlqS6JW\nF3QzVnioDbc1uT53qVQKkUiEmSmaTMsKJHgYUUgIqP6OyclJ7Nu3r+DQuWKOV87aaime1B1KHR0d\nm2ITSj1WMdRSOOmDPs+cObOp7VHWWtJp8E89BW5mBuA4eAHE3v9+4OWXwa+sQDpwADh+POufyLt3\ngxNFIJ1WTMkLC0q1B4Dl8mVIXV2A1QrZ4wE3MwNuYQFyezucv//74MfHIVsssH33u0j95V9Ces97\nSltvObS2Qrj3XnDhsLJt3gC5PltdjPO1wtRdYdPT04hGo5taYQ6HoyoXeTOKB8Bcs46A6lZ4ioHj\nODidTjidzk1BvZFIpKApOh/RaJRaWiDBU5BSKzy5Whnr6+vw+/1oamoqaUqyGVtaqVQKgUAAgiAU\ntUOJpVBh8V4VMgurQZ8zMzM4evRozoqVeoxSKzzclSvgpqYg79un/P/QEDq/9CVYo1Gl4rO0BPnG\nG5U/N9wAdHVBPnAAwu/+Lizf+x44AFJbG9L/7b8pa7Xbr/t0lMUDFgusTz2liJ1rz4OVFdi/+lUk\nayF4AMBmq4pJuZbwPK9dYFRytcKcTmeWCGLRCjOr4DEb9RY8udBPJy9kio7FYggEAloVyOVywWKx\nIBqNZu1kzMUDDzyAZ599Fh0dHbhy5QoA4KmnnsJjjz2GwcFBvPrqqzh79uymfzc9PY377rsPi4uL\n4DgOn/zkJ/HII4+wfxMYQIKHEVarFYlEQvv/VCqFoaEhpFKpvJlJWx3PLC0tdSu2mvrd0dFR1LGM\n6uHZiD7oc6vZSGWtJRyGrKsU8fE47IODkN/zHnDDw4DLBf611yAtLoL7139VMrSiUcidnUj9l/8C\n6bbbgN27NfOx9L73wfrjHyvCJ5OBvGePsg19eVmJmlBxu4Fr28RZYpaBfqwERK6WRDKZRDgcxsrK\nCsbHx5m0wkjw1Aaz+I42mqIlScKlS5fQ2dmJcDiMwcFBfPKTn4TX60Umk8Fzzz2HCxcuYN++fTk/\nR/fffz8+9alP4b777tMeO378OJ555hk89NBDeddhtVrxl3/5lzh9+jQikQjOnDmDD37wgxgYGKjK\n664EEjyMUNtG+ov/4cOH0dHRUdZJiqVIqaZ4Wltbg9/vR1tbW8mDElm2tFgdR3+xLibocyM5KzyR\nCLjBQSCdVqorzc2K2Lh2UpX37AH38stKcjnPgw8GIbpcsImiknXl8wFzc0BHB7i33waXTkNuaIDs\n8cD+ve8hfewYJLVqA0Dq60MmkQB36RLQ369MV7bZIN18M7h/+ifIsZgyqXlpCeIHP1jx+7bx9e90\nOI6Dy+WCy+VC57WBjhtbYbFYLKtaVMzuHBI8tUEURcNMTi4FQRBgs9k0U/TevXvx5ptvYnFxEXfd\ndReGh4fx5JNPYmJiAj09Pfj0pz+ND3zgA9q/v/XWWzExMZF1zP7+/i2fd/fu3dog0IaGBvT392N2\ndpYEj9ko5eRitVoRi8XwyiuvaFuvKymLshQ8ZRtpc6C2x9QKVjqdxsmTJ+G5ltVUCiwrM5XujAKu\nt7RKCfrMdwyNaBT8E0+AW18HlpfBBQKQBwYgezyQT54EjhyBfPIk5DvuAH/xIiCKSN12G6xzc8oU\nZUkClpYUgeRwAKEQ5N27lcdtNsBiAR8IKKLmGta//VvY/+qvlLaW3Y70//pfEO6+G+Jv/AZSf/In\nsP3934OLRCC85z1I/8//WfH7RmxNvlaYakxVd+cUaoWR4KkNZqnwbCRfK66jowMWiwV/9md/pl0L\nZmZmqtK2m5iYwJtvvolz584xPzYLSPAwIB6PY3h4GLFYDOfOncvael0uVqsVqVSKwerY33Unk0m8\n/vrrFVWwAHbeIpbCSf1d2u32ooI+cx1DLy45v18Z+NfcDP6ttyA3NoJ75x3lxDM2Bhw+DGluDlw8\nDnlhAWhpgXjmDIItLTjywx8q05QdDki7doGbnATa25V4Bp9PSUbPZJQt3yqDg7D/xV8ALpdSSYpG\nYf/c5yB86ENAUxPE++6DqCtZ72TqLSBsNhtaWlq0jLV8rTDVmMrqfEAUxogenmLYat36naRqQC9L\notEoPvrRj+Jv/uZvDGuQJsFTAaIoYmxsDMvLy9i/fz/m5uaYiB2AfUgnC9bX1zE4OAhJknDu3Dkm\nAwONsktLkiREIhGsrq7i2LFjRQd9bmRTNS2TUQI6YzFl2B4ArK8r5uNQCPK+feC/+U3FX9PcDCST\n8L3yClbuuw/yBz4A+bbbIB08CM7jAa5cARcIKLESdrsS+nnyJMTf+A3t6Sy//rViUlYN414vEAwC\nV68Ct9xS7ttTEmbx8BiNfK0w1ZgaiUQQiURgs9lKaoXVC7N+Dsxa4ck3ZZnVxP5CZDIZfPSjH8U9\n99yD3/qt36rqc1UCCZ4C5DuJyLKMhYUFjI2NaXNYRFHE9PQ0s+c2kuBJp9MYGhpCMpnEyZMncfny\nZSa7ToxiWla30fM8X1KqeTFrkQ8dAv/zn0NOJIBkEpwgAE1NSpyD1wtwHHi/H9INN2hbtfkrV7Dr\niSeAd79bmajs9ytVmbvvhgxAuvNO4KWXIHd1IfXhD4PXVaHEw4fBAZCTSSWgMx5XdmsdPVr2ayoF\nI15481HvCk8x6I2pah6S2+3OOahOL4JYTGivFDO8v7nYbhWeSCRS8qaZUpBlGR//+MfR39+PT3/6\n01V7HhaQ4CmRSCQCv98Pt9ud1fLgOI6ZMRhgazRWKfUEpPZ6p6amcOjQIXR2djI9gbEKNS1X8KTT\naQQCAaTTaZw6dQoz1wb3VUJWhWd1FVw4DPHDHwZ/9SrkVApyKAR+aQnc+jqkCxfATU1dT0BXX08i\nAcliAXRZUPylS5AOHwb/1FPg/8//Uao4a2vgfvhDSO9/P8Tbb1cG+Z06hfTHPw77t74FORIBLBak\nP/MZ7ViEeVG/v1ardVMrTE3vXl1dxeTkJARByNoVVovgyo2YtVJSi4pINSgkeIoZOnj33Xfj4sWL\nWF5eRk9PDz7/+c+jyEKtBgAAIABJREFUpaUFDz/8MILBIO68806cOnUKP/3pTzE3N4cHH3wQzz33\nHF566SV8+9vfxokTJ3Dq1CkAwJe+9CV8+MMfZv4aK4UET5Gk02mMjIwgEomgv79/U4+S9Z0M6wpP\nqTlR6lbs5uZmJu2rXNSrpaUfBKkXciwqTqppmRscBP/kk0obSxQh3XEH5P/8n8G98w7EWAyIxcBF\nIpD27oXc1QX+O9+B7PMp+VY+H1J6gSJJSujn6ir4v/97yJ2d4JaXAUmC9fXXIfT0gJ+fR/qP/gjw\nepF57DFkfud3YAkEIA4M1Ky6YzbMVoHIt179oDp1JESuGS1qtcjn86GxsbHqrTCzCh4zRngA+Vta\nxU5ZfvLJJ3M+ftddd216rKurC8899xwA4N3vfrdp2pckeAqgd7RPTU3hwIED6O/vr8lJkrXgUY+3\n1QkonU5jeHgY8Xi84FZsFheLerS09EGfG4Uci4gKnueBTAb8j3+smIldLkAQwP/gB8ALLyhBmleu\ngBsfh7xrF6T/9J8gffKTSgXmV78CWloQfe97If3TPwGzswDPgxNFSOfOKQZmSVJ2a62uKgbo5WXI\nLhe4RALc/DzkI0eUhfT3QyxiSylrWO4IJLIp5TuXK7hSEAStFba0tFT1VphZBY+ZW1r5BE81W1pm\nggRPAWKxGN54442qVjnywbqltVX6ur7qcfDgQQwMDOQ9uaoCo9KTQi13aeUK+txIoUnLxcJxHJBM\nKjN1VAO71QpuagrS3r3gBAHclStKxSaTAf/tbwOiCIgiOJsNcmcnsG8flu66C53XttpLx48rgwXj\ncaCxEVheBiwWCKurkDkOUasVnkSipBBQbmkJ3OioMp+nvx8oY6yA2dkuFZ5iKdQKW1tb01phHo8H\njY2NFbfCzCp4zLpuURRzBlFTUvp1SPAUwOFw4MSJEyXNmGF1Eq1WhScX6lTOxsbGooSdeqxKBU+t\nWlr5gj5LPQ6mp8E//bRSVTlyBNJHP6r4bzYcQ3A4FD/N0hLQ0QGEw4oAamsD99JLyg4ru12Zeuzz\ngf/2tyH91m9B7uoCRkfh+M530BSNgk8mAQDy8jLkgwchHzsG4YtfBP7sz5COx2FNpZC65RZIs7OY\n2rULswsL8MTjWssi38WKm5qC7VvfUqpFogj5xReR+cQndqToMROsBVo5rTCfzweXy1XUOiRJMpWg\nVDGrh0cQhIo8PPVAHcXAanfzVpDgKYDNZitJ7KhCgEUlqBaeoEwmg+HhYUSjUQwMDBT9pTDK7qqt\njrNV0GdJ64lEYPnWtyDb7ZC7u4GREfDf/S6kT3xC+fv5eXAjI7BYLOAEAdLv/R74//t/FVNyYyPE\n3/s98G+8AdnpBJdOAxaLsitrcVEZINjcrBynuxu2H/8YzvZ2yIcPg794EdwvfgH59GnIPT0YueMO\nLP73/46Bnh74BAHWqSnYfD64e3vRZbHkvFipF6rGxkY4HA5Y//VfIbtcwLU7fW5yEvzVq1nDCwnj\nUYuKVL5WWCQSQTgcxujoKBKJRFGtMDOmu6uYcd2FWlpGFDwrKyt44YUXcPXqVXzmM59BJpPBwsIC\njhw5UrX3nwQPQ9Q2VC1bX8WiFzyyLGNubg4TExNl+ZJYVZ+q1dIqNuhzIwVbWktLSszDtfko6OkB\nNzamtK/m5mD55jcBWYYtk8GuTAY4ehTyiROQ9+2D3NsLtLZCcrvBp1KQr15VYiWCQcDpVHw3kqQ8\nlkiAC4WQOnYMnN+vVF2sViSdTqy98w5cBw7g3Ec/qr1mcfdu7eTAAfB6vfB6vejq6gJw3bcRCoWw\nsLCAVCqFA+PjcPM8HC4XHA6HItIYDrk0i4fHjC2telyIrVYrmpub0ayKcihZgaFQaFMrTBVADQ0N\npm0NmZVCu7S2Cg6tB5/73OcAAD/84Q/xJ3/yJ7DZbPiDP/gDvPDCC1UTaMa7MpsYNU/LiKgiJRKJ\nYHBwEA0NDSWlt288lpFSzvWCp5Sgz1zHyRtR4XQqokQVJsmkYh622cD/y79A9nqVKo0kwf7ii7B8\n8YvK8L+1NaV6cvQo0NAA6cwZ4PbbwUUigCxDuuUWcG++Cf7FF5WqDwDxzBnwiQSQyUDiOCSiUaxF\nIujYvRuW9vbsANAtyOXbyMTj4J55BlFBwGosBms8jnUArvl5NDY2Ft2yIGqLkVpEDocDHR0dWitM\nlmWtujg3N4doNKqtd2FhoaRWGFEe+QRPNBqtymTlSvn5z3+OQCCAt956Cx6PBxaLBYlEYstKfCWQ\n4ClAqV9Oq9XKfFggq7tQjuO0O7Fc2+pLwUgTkvXrGRwcLCnos6T1dHdDete7FGHC84pYuece4Fp1\nRFaH/3EcrJEIOJ5XQkHVOAm/H+A4WIaGlCyts2chnz4N/qWXIDscEG+/HZzVCvnwYQjRKCxf/SqS\nsgxxZAR8fz+6urvBxWIQDx+u4J1SPgf2W28F73DAfekS4HAgfdtt4NvaEAqFNrUsVPOqEauWlWLG\nCo9R18tx3Kbq4tLSEoLBIFKplPa5stvtWa0wM4Z0GpVC29KNZloWRRH79u3DyMgIYrEYAGB2dhYc\nx1V1aOb2O4sxppQSPeudVeo26UpOcrIsY35+HrOzs2hvb8fp06crPmkaqaWlBn1Go1Hs3bu3pKDP\njWy1LV3+yEcgHjsGLhaD3NGhtbeks2fB/+AHkHftUsSPLENuagKufZHB80qe1smTSiDonj3gXngB\n3IsvAk1N4N95B9zaGqRTpyCfPo3UXXdh8M47sRvAoXQatrk5wG6HePfdQE9PWa9twwuFdOECpAsX\nACitsCYATU1NyuvU7d7RZzp5vV5NAHk8noKTyAn2GFnw5ILjOLjdbuzbt097TP1chUIhTE9PI5PJ\nZLXCvF5vXbeEm/mzW+ngwVrC8zwefvhhfOELX4Asy/jrv/5rfO9738Ojjz5a1eclwcMQ1i0t9Xjl\n3gVFo1EMDg7C7XZj//79sFqtzHaQGaGlFY/HMTg4CLvdDrfbjZ4KxcCW29I5Djh4EBtPifK73gUJ\nAP/665A9Hsz89m+jfXRUCflMpZQYCZ9PaYO5XIDNBm5qCvK5c4ooisWUCpHVitjLL2MyEgF/+jSO\nvuc9AIDK3+nSyLd7JxqNIhQKYXJyErFYDDabTRNA6t26mS7IZsNsgifXeh0OB9rb2zVfnb4VNj8/\nj0gkAo7jsnaFud3umr1us73HevJ5vIxoWhZFEefPn0dnZyd+/vOfI51O4/HHH9cmNVcLEjwMYd3S\nKvd4giBgZGQE6+vr2syZ2dnZ/P6UEql3S0uSJIyPj2NxcVHLvnr55Zfrth7wPOT3vAfiNYESffll\niB/8IPgXXlBm7CwvK0MDl5chv+tdwOQk5NZWwOMBNzcHOJ0Q43EEFxdhbWtDn9uNXxmshaTf7aWS\nTqcRCoUQCoUwNTUFQRDgcDiQyWQQDofrEmdQCma7uJltvcWYlnO1wlSvYTgcxtjYWE1bYWYdOlgI\nI7a0YrEYPvvZz+Kb3/wmbr75ZgDK+JA//dM/xRe/+MWqPa+xzqoGpJ4trVIrRvpQ071796K3t1c7\nQVosFiSvzXVhsS4Wgqeck7ca9Llr1y6cP3+e6QWVlacIALBnD6QHHlD+OxgEVlbA/frX4IJByHv2\nKK2sp5+GBCAzN4eky4XGo0fhDIch7d9f9GeunhdAu92edbcuSRKCwSCmp6cxMzODaDQKi8WiVYHU\nbfFE+Ww3wZMLi8WCpqYmrcUK5G6Fud3urJlTLITKdtxZZqQKjzoq5Gc/+xneeOMNTE9PIxqNoqen\nB4ODg/jlL39Z1ecnwcMQlqJCPV6xwkJtX7lcrqxQ03KOtRWsKjylsDHo0+12M38OFtESOWlvB9rb\nIff1ZbXD1mQZy88/j12ShAa3G1w0CvnYMci33gpculT584ZC4GdmlNlBBw9qu8CqBc/zcLvdcLvd\nGBgYAACt2hMKhTA3N4d0Ol2VC1U5mHlOjBlgKR5ytcLi8ThCoRDm5+cRjUYBoOJWmFkrPIXOW0aq\n8KRSKVy8eBHf+c53MD8/j3vuuQeJRALxeByiKOIT6lyzKkGChyGsW1rFiBRBEDA6Ooq1tTX09fVl\n3RWVeqxS1sWqPbYV+YI+qwHHcWj60Y9g+dM/BSwWSJ/4BOTbb2f+PJlMBoFAAEmnEwOf+xzcLhek\nlRXlL1talF1gFcLNzcH2v/83uGRSCS89fhyZP/zDkuInynreDb8bm82G1tZWtLa2Arh+odro2dAP\nR6x2qCVRGyRJqtruPo7j4PF44PF4crbCxsfHEY/HYbPZsj5bW7XCzFrhKTSyIJPJGKay2tjYiEcf\nfRR33HEH3n77bXzsYx9DIpEAgJpMWybBswWlnHhZt7QKCSh1d9Lo6Cj27NmDo0ePFlwra8HDspKV\nj0JBn9XA+c//DM8TT4Brb1eSyD/zGWSiUfALC+DW1yHdcIMigMrcNqn/nR04cAC7d+++/jvTp6Mz\nwPr974MDlPYZAP7tt8G/8w6kG29k+jylor9Q7d69G8D1Sb6hUAhLS0tIJBJwuVzaRaqhoaGqF06i\nOtRaPORqhak+s3A4jJmZmaxWmDogUV/RMWuFJ9+UZaPuOuvr60N7ezt+8YtfAFBujJxOJ7q7u4se\nFFsOJHgYUisPTywWw+DgIBwOR872Vb5jsRQ8zLwuOSgm6DMXlZo67T/5CVJuNxzXJsrKMzOwfu1r\nkN/9bsDjAf+Tn0BeXgb3xhvg3n4bcmsrpHvvBQYGIB8+XLBllEgkMDg4CJvNVvTvrBjyvWZuZQWy\nvm9vtSrDDg3Ixkm+ar5OKBRCMBjE6OgoZFlGQ0OD5gcqe+eOJCm75dxuw14MtgtGGJS40WemrzAu\nLi5iZGQEgDKhXG371HvN5bCVUDPKa1LPV2+88Qb+7u/+Ds888wysVis8Hg+mpqbwpS99CZ/5zGeq\n9vwkeBhS7cBPURQxNjaG5eVl9PX1ZY16r+XaWHt49BftYoM+c62p4gR3pxOc/nUlEoDdrgSAAsCe\nPeC/+lVlQGBDA/hf/hL8r34F6QMfgHzTTZAeemhTy0iNuZidnUVvb6/W2mEBx3F53x/x2DFY/+M/\nIO/dC6TTgCRBqsG0VRbREhzHweVyweVyYdeuXQCUz766LV5tV6g7d1QRtNXAMv7qVVifeAJcIgGp\nuxuWD33o+u+WYI4RPVK5Koz6z1YwGEQ8HkcsFsvaFWaUllA+8gkeo4l69Rz99NNP4+abb8Ztt92G\n5eVlPProo3jssceyZjZVAxI8W1DvllbqWsbR0tIShoeHNSFQ6omEdYWH1bHUC2QqlSop6HMjLARP\n5uMfB/df/yswO6tUApxOyIcOXf+BSATc6irkG29UQkHdbqVaYLGAu3wZ3OXLkM+c0f14BFevXkVz\nczPOnTtX01K5+JGPgEskYLl0CbLDgcy990I+cKBmz88adbeXvtqXK89p43BE7XuysgLb449DbmqC\n3N4OfmEBTd/9LsKPPFKnV7T9qXpLKxIBt7CgfE8rEPP6z5bD4UAikUBXVxfC4bAWxJtOp+FyubTP\n1sZWWL3JN2U5kUhUZYNHpaRSKbjdbqyvr2N2dhaAEguk/ne1IMHDkGq0tJLJJC5dugSbzYazZ8+W\nfadh1JYWz/OYmJjA/Px8SUGfuY5T9pokCZzfD97txujDD6MvGASsVkh33QX+Jz8BPzoK2W4Hl0xC\nbmwEBAHIZJQWliwrnh6O0yYry7KMoaEhrK2tlZRCzxSHA8K990K4+27FBG2QkjZLNuY5SZKEWCym\nzQWKxWKwWq1obGxE68IC2gQBvMcDAJB37YJtcJBZaCqxmWoKHm5mBra//VtwiQQgSRDf/W4Iv/u7\nFX/O1Zsmu92OtrY2tF3z1uVqhaltVrUKVGj6eLXJV+FR52EZBfXzcNNNN2H37t1wOp148cUX8eCD\nD2JkZATvuTbLrFqQ4NmCUj7ALOe4iKKIxcVFLC8v49SpU1r4Y7mwXBurllYoFEIkEoHP5ys56DPX\nmsp6faII/vHHwV26BBvPo3V1FfKXvgT56P9n78zj3Crr/f8+5ySZvbO0nb37dG+BbkOhgigWriig\neBVBRC4C+hMQBK+3XFzAtYK4IJcL3KJyEfEioF5AQW+1rLal7O3snX3fk0ky2c45vz/OnNNkJplJ\nMiczCeTzevUFk0mePMmc8zyf57t8PmsAUFauRH3tNXA4YPVqhHXrkH75Sy1N5HLB0qVQXIzQ349a\nWcnQ0BAul4vy8nKqq6vnP3eeRKfQREMURfLy8sjLyzNUt30+Hw6Hg7G+PixDQ7gBa1YW2YEAAasV\nJYG+Pe91JJLwWB5+WCvKr6wERUF64QWULVtQ1q2b1biRiMN0qTCHw2Goj1sslpA061ylwqYzDk0W\nDR44sZ9WV1dTUFBAUVERX//61zlw4AA33ngjmzZtSuj7pwmPiTBrcxsYGKCxsZHCwkJKSkpmTXbA\n3KK12UaLAoEAjY2NjI2NkZ+fz/Lly+MjO4qCUF8PTicZDkdchEeorUV47TVYvlyb2/g44sMPI3/n\nO+DzIT7xBMLBg5CTg1JejnLTTagbNmiFyy0t2olybAzPZz9LrcuFb2SEvLw8lixZMv9kZx5gRg2P\nmdBP6ixahDQywsI//xm/240vEKDpnHOwt7bS3dMT0rqckZHxnvzbmY1EEh6xr0/zswMtgimKYLfP\netxYWunDpVl1gj05FRbcFZaIjsNIKa1k89HSidmNN97IzTffzPvf/342bdqUcKKjI014kgh6J48k\nSWzduhW/309LS8t8T2sK4k1pBbdlL1u2jHXr1vH222/HR54UBXHfPoSDBxF8PtYcO4Zl9WrE005D\nufxyiLY42OXSnM0nNjg5M1Mz+ATEJ59E3L9fO0V6PEj33IN8wQWIBw6A2436/vcjX3QRvaOjNLe0\nsGrxYkpKSnj11VeTatNPQ4P8kY+gbNmCMDaGpbiYHLudxdnZFBUVGeKIvb29eL3eKW3xyVSvkSpI\nZJeWsnYtYm0takWF5lenqqgTkZfZYLZt6eFSYePj4zgcjpCOw2iNeGOZdySn9GRKaemfs7S0lLfe\neouqqioyMjKMf4myDNGRJjwzYC5OesHeUMGdPIqizLmicTSIJ6UVbPQZ3JYdbypKaGjQIi+VlQjP\nP4/o8yG1tSEUFSHdfTfyN74RlcieumSJdjp0OhGys8no70c9/3ztPV57TVtErVbtX1sb0n/+J+rm\nzbB4Mcpzz9HW2Ynzox+lurra6BJKtihHGieglpefULueiAhYLBaKioqMSGrwJhXcuqzXa+Tn55OV\nlTWnUaBUvJ4S2aXlv/RSrA8+iHj8OFit+C+/XOtInCXMjkrpjvHZ2dlGx6GiKIZA4uRUmP4v1qYN\nWZbDps/0koFkgX7PLFu2jB//+Mf83//9H2VlZUiSxNjYGHfeeafxPSUCacJjMnTH7WhvmsHBQRoa\nGigrK5viDWV2EbRZiIWkKIpCa2srvb29htFnMOIugHY6ESQJ1e2G8XHkBQuwer1QVobQ1QXDw9G1\nHJeXI193HeLDD0NXF2Pr17Pw0ku13+Xna6afmZlacbLdjpqRgZqdjd1uxyWKVPT0YJ0UjjXVkysF\nkYqbczDCbVK6iq/dbuf48eOMj4+TkZERUq+RSGHMVDMOhQR3aeXn47/pJq2OLiPDNAXxuRAeFEVx\nSios2IKlp6cnJMoYTSosEAiEnXeypbT0a3jXrl18+MMfNjotdd2tRJOzNOExGTpJmSk05/F4qK2t\nRRAEtm7dGpbRm63rEysZm26caKAbfZaUlEQ0+oy3AFpduhRVkjStHEVBcruRKyqwyLJGTmI4Iamb\nNiH/8IegqnS/8grLJyTOlU9/GvGuuxDa2rSQ+apV+EdG6OvqIjs7m/L8fCgrYzK1MSvCk4qbXCrN\nN5bvd7KKry6l4HA4GBoaoqWlBUVRTE9VxDPXZMGcKC1PdN2ZhflSWg5nwRKcCmtubjaur+CuMP37\nnS6llUyER0dzczNLlixhxYoVbNq0ac7mmCY8MyDWRUZXR45EeIIjHmvWrDFyveFgdqRAJ1CJXoRi\nMfqc6TOKDz2EePfd4POhnnkm8p13avo3xcUoN9yA+MtfohYWwtAQgcJCrJ2dyB//OMR6UpBlxMcf\nZ90jj2ApKUH+xCdQzz0X+bbbEJqbka1WjosiOb/4BWV2O1ZFQc3IQP70p2P+TNEg1Ta39xoEQSAz\nM5PMzMyQtnhdwE5PVVitVoMALViwIO4ahTThmRsky5wjpcL0rrBg2YUFCxbgcrkIBAJTrhOn05lw\nMb94MDw8zOOPP46qqlRVVbFlyxbOPvvstPBgqmE6/6uhoSHq6+unjXgEw+wFTic8MynSxot4jD6n\nTWn97W9I3/ueRl5ycxGee04rMu7uRmhvRy0vJ3DXXbB3L91/+Qt5Xi+2deuMlvJYIPzlL4j/+7/4\nCgtRi4qQfv1r5KIi1OpqBiSJ+vp6lixZQtlddyHW1iL7fJoDeRjCapbacCpucqkEU7/f8XGknh7y\ns7NZUFlp6MHoXk66NlAgECAnJ8cgQbm5uVFtsKl4LaTinJPZS0sURYM467ILeipscHCQtrY2mpqa\nyMrK4tChQ5SWljI6OjpjB9SVV17J008/TXFxMUePHgXgd7/7Hbfddhu1tbUcPnyY7du3h33ts88+\nyw033IAsy1x11VVR20J89atf5cYbb6SxsZH//d//5Zvf/CZXX311wmtW04QnCsSygYWru/F4PNTV\n1aGqKlu2bJkTV9hwMDtFFox4jT6nS2mJL7+s/Y8eti4sRPzDH6C8HBYuROjrw3LVVQT27yewdi0e\nWYbeXoTeXtQNGyAGEUPhzTdRi4q0mgCbDTU7G/mttziamYksyyHqz+opp0w/lkmEJ1lOm7Ei1Wt4\nYoXQ04PtzjsR7HZQFAK7d2uCj4IwxctJF0fUzSxdLheSJE1pi5+MVCQPkHqRylS75/RUWHt7O5s2\nbcJiseDxeDh69ChPP/00hw8f5umnn+app56iurqaU089lc2bN4esz1dccQXXXXcdl19+ufHYpk2b\nePLJJ/nCF74Q8b1lWebaa6/lr3/9K5WVlezYsYMLLriADRs2TDtnv9/P5ZdfTnd3N36/n3Xr1vH9\n73+fbUEq9YlCmvCYjGDDT0VRaGtro6enh9WrVyfUBTbauZntgaUoSlxGnzqmTf8UFGgWDzocDq0+\nR4+qLFyI0N8PNTVIeXnk33MP0tgYCAJqTo7WqRUsOa8oWkdWOCxaBBPdOADjIyO0DA9TUlJCSUmJ\neZ8pSqRqp1eqbXBmwPrggzA+jlJZCbKM5bnnUE4+GWXjxinPDRZHrKioAEILVru7u/H5fIajd35+\nPrm5uUlNeIS2Ns2stqREaxNPYSRzhGc66PPWfeguvfRSLr30Uq699lq+8IUvkJGRweHDh/npT39K\nSUkJd9xxh/HaM888k9bW1pDx1q9fP+N7Hj58mKqqKlauXAnApz/9af74xz/OSHg8Hg+dnZ2sXr2a\nj3/845x00kmUlpYmtOhfR5rwRIFYIzyyLDM8PEx9fT2LFy+etY+SWYud2QaiukBirEafk+cUiRwo\nn/0s4u9/j9DRoaUIBEHrnPL7tTbxQEAjMUVF5Lz0EtLAAKp+o/b2Ij75JMoNN8DICOJ//ifi0aOo\nixahfOlLqOvWaa9VFM1G4sILkWpqsNXXM9zfj1JezsprrsEag0GrDiMdVVeHUF+Pmp+PunNnTIXU\nqUp4TMXoKJY//xlheBjlpJOQ3/c+Uy0yzLqvxK4uFP06kSQQBIShoahfH65gVbcx6OnpYWxsDFVV\nCQQC9Pb2kp+fT2ZmZlIQIOmpp7A+/rh2kFBV/Fddpf2dUhSpFuHREUkCwOl0UlRUxPr169m+fTtf\n+tKXTHvPrq4ulgQdKCsrKzl06NCMr8vLy+PFF1+ktraWxx57jDvuuIPu7m7OO+88fv7zn5s2v3BI\nE54EoKWlBZvNxsknnzxr4zadpJjBfs0iPB6PB7fbTWdnZ1xGn8EQRRG/36/9/4MPIv7sZwgeD0pF\nBeqHP0zg5psRh4fB5UI580zExx9HevhhbeNTVZSLLoJ165D+8heU4O8oK0tLMQDS3XcjNDVpOh0O\nB9IPfoBaWIj4zDMAKOecg/+uu2i98kr6X3yRdRs2UBgjQZn8maSXXkJ65BFtA/T7Uf/+d+Rbb9Xc\n16PAe57wuFxkfO97MDCAkJmJ9MorMDSE/LGPzffMpkCuqkKsq0MtL9fIuKqizEIEL5yNwdjYGI2N\njXg8Hvr7+xkfH58ijjgXJ+SQeQ4MYH3iCe1zWyzg9WL91a+Qt22DeUrbm4FkIJJmIdl0eABGR0d5\n4oknGBsbY3R0lOzs7Lgi6fEgTXhMgqIotLe309HRwcKFC9m8ebMp4yYT4VFVlfb2djo7O8nMzGTD\nhg2zIjtwIv0j/PWvSN/9rlagPDKC2NEBb72FVFCAfNNNKOefj3D8OOq55xLYuROhuRl1+XLUc84B\nwL9pE1n794PTqbmXDw0hX3ABeL1alGXp0hMRotdf18YvK9NOpk89Ra/fT+BrX0PZupXs7dtnpesh\nAFmPPYZaUqIt/KqqCSUePYq6dWt0Y6Qo4TFr3mJ9PUJfnyY/AJCfj/Xpp5EvvNC0KI9ZER7/FVdg\n+9nPEDs7tZ8vuQR19epZjxsMURTJyMhg+YQFiqqqhnZJsIJvXl6eURCdnZ2d2M3b4dDuH/1eycgA\nWUZwuVDDEZ5AAMsf/oD00kuQnY3/0ktR5shS4L0Kp9OZMMJTUVFBR0eH8XNnZ6eRpp0OfX19PPPM\nM1x00UWcffbZpu2V0SBNeKLATIvGyMgIdXV1LFq0iDVr1jA+Pm7ae0/X9RUrZkN47HY7tbW1FBUV\nsXPnTt555x1T5qWntMSXXtIWz0BAc0C2WkFRUAsKkO68U/s9aI+dfDLyv/1bCClRNmxg6DOfIfsf\n/wCfD+XTn0aUg/D8AAAgAElEQVT90Ie0X2Zna3o92dma/1Z/P2RmolqteL1esNmoaGlBrapiZGTE\nlIJjweM5Ec0RBO2z+XwxjZGKhMc0qOpUYpNs34fHg/W730U6cgS1tBTv9dejrl0LCZDyn0zO9FqN\nrKysEHFEvS2+paUFt9uNzWYLEUc0s0NTLSnRiM3oKOTnIwwOoi5ahDqhVTQZlj/8AcsTT6CWlsLo\nKLY778T77W+jJlHbdCrec9PNeXx8fNZZhkjYsWMHjY2NtLS0UFFRwW9/+1t+85vfTPsaVVVZu3Yt\nTz75JG+99RZut5uamhqWLVtGjsmaSuGQJjyzgNfrpaGhAZ/Px0knnUROTg6Dg4OMjY2Z9h7BRdBm\njBUrSQk2+ty0aZPhy2KWRpDepaUWFmr1NLJ8YqOTJLDZEBwOlNxcKCrSoiVvvYXw1luo27Yh1NQg\n/cd/sKi7G3dhIcqFF8KKFZr9g+6P9YUvIN19t6aaLMuoq1ej1tXhdrmw2WzYAKW8HJkT3VGz+kyS\nhGfnTjIOH9aiPC4XZGXFdOqPlvAkczHrbKCsW4e6aBF0dkJ2NoLdTuCii5KqhifjS1/C8sILqLm5\n0NJCVm0t7meemRPCEw7hzCx1JduRkRHa2tqQZTmkLT5YvG46iAcPYv3DH0BVCXzkI8hnnAG5ufhu\nvhnbPfcgdHSgVlTgu+46sFjCXrvSK69oZCcrS/vncGgSD0lEeFIRM9UdzfT3veSSSzhw4ACDg4NU\nVlZy++23U1RUxPXXX8/AwAAf+chHOOWUU3juuefo7u7mqquu4k9/+hMWi4V77rmHc889F1mWufLK\nK9kYplA/GIIg0NXVxU9+8hP+/Oc/k5WVxfDwMO9///v5yU9+Ygh7JgppwhMHVFWlo6ODjo4Oqqqq\nKC4uNhYjszuhzBxPkiSjXmYmhDP6DF5wY5rX+Djir36FeOgQan4+ytVXo06EsnXipFxxhVag3Nxs\nkB61vBz6+7UNRQ/L6tGS8XHo60P63vdQs7MRXS7yX30V6bXXUFevRvn4x1E+8xnts+zYQWDvXoSO\nDnyZmTT09bH2X/+VXKdT+31+vlZfg3kdVs5PfpK8khLNj2vFCuTPfCZ6Q1NmJjyyLNPY2EhfXx8W\ni2VuUxlzgZwcvLfeiuXpp08ULZ911nzP6gScTiwvvaQ5douiphPV34/lhRc0YmYy4iVnGRkZFBcX\nTxFHnCxep187+fn5U8QRxTffxPazn2mRG0HAeu+9qDYbys6dqCtX4r3rLq12Keh14earZmdrxdx6\nuktR4q6TSwRSMboDkTvLov08jz76aNjHP/7xj095rLy8nD/96U/Gz+eddx7nnXdeVO+jE7OnnnqK\n3t5ejh07ZvzulltuYe/evezduzeqseJFmvBEgeAbd3R0lLq6OoqKisLqzZjtf2V2hMfj8cz4vEhG\nn5PHipbwiL/8peY6Xl4OTifS975H4Ec/gooKJEEgo6YGYWCAwL33Ih45Am+9hfj3vyOMj6OcdBLq\n9u2Ib7+ttby6XGC1oq5ahdDSonVqZWRgaWvDV1CA5PejVlQg/vGPKOeea7Swq+XldKoqbW1trN6y\nBcuBA8jPPguyjLJ7N0ykBURRnPXCJ4oiisWiEa4J0hXPGJHmoQtYVlZWsnPnTlRVNXyempubDZ8n\n/bQ/lwWtpqbiiooIBGmDJBUsFo18B0sdCIJmd5IAmNZRFka8zufz4XA4DG0gv99PTk6OQYCKXn5Z\nIyn6ocPnQ3rpJZSdO7WfBWFKMX64qEPg0kux3XEHjI1pqemlS5F37Jj1ZzILiqKkdEt6JCTb4cfr\n9VI40dUYCASwWCwUFhYyOjqa8PdOE54o4fP5aGhoYHx8PCS1MxlmE565rOGZyegzGLFEQsSXX0at\nrNQ2CZsNxsYQGhtRy8rIvu8+yv76V6TCQhAE5JtvRv3c50L9qcbH4Ze/RDxyBLWgAPkrX9EKjidS\nVPj9JzYfq1V7Hz0KBLhcLmpqasjNzQ0hqYpuEhoEM1JaZiotB8Pv91NfX4/X62XLli1kZmbi9/sR\nBCEklaEXtOo+PMePHwcwokDJ1NY8n5gVicjMxH/eeVifegrVZgO/H6WiAvnss82d5AQSmbq02Wws\nWrTIsLlRVdUQR+zq6sIxMMDC/n6wWrHZbGR6POGLkoMQjvAoGzbg/c53EGtrITNT6+aag7qNaJGq\nLemRjEN9Pt+cd+5Fg61bt/Lqq6/yox/9iF27dlFTU8PBgwf5TJyHw1iQfN9GEmJ4eJh33nknKruE\nZE9pRRorGqPPuOeVnw9ut3ZCVFWNpGRnI9TUYD14kLHSUs3Z3O1GuvdeAqeeGlqrkZWF8qUvTTHp\nVDdsQNm1C/Gll1AVBcnlQnnf+6C3F7W4GGXxYlqOH6e/v5/169dHlR82K6Vl9hj9/f00NjayYsUK\nysrKpiVVwQWtequnLMvGKb6xsdFoaw6OAqXi6XY+4bvzTtRVqxAPH9bqV266KSH1OzC3tVqCIJCb\nm0tubi7l5eUIX/wi1q9/HXlwEH8ggEOSaFy6FOGdd0La4oOvn0jkQV2yBDlYDDSJkMqig+GITTI5\npQfrBJ1xxhksXLiQn//85zz77LOUlZVxyy23sGMOon1pwhMFFixYELVdQqpFeGIx+pxprEiQr7oK\n6Yc/1Lo5FAV10ybULVsQXn8dQZIwtu2sLBgc1AhRmO9aaGhAvPdehMFBlG3bUK65BuWGG1A/8AG8\njY04n36aYkFAXbGCkUsv5dgbb1BcXMypp54a9cnNDMJjRlpMJzQ+n4/a2lpUVWX79u1TbAei3Qgl\nSaKwsNAIJetuzHa7nb6+PhobG41Ikb6JxSs5kCq1ELMmERYL/uuuM29C00BRlHmLyKmlpfh/8APE\nw4exKgrSjh1sLikJuX6aJlTK8/LyWLBgARkZGSkXQUzVCE8kopZMhEcQBA4ePGgIZy5evJjvfOc7\nRtF8OOPTRCBNeKJALK2cZmx2wZAkCV8M7cwzjaWTlHiMPoMRFTEYHUVoaACbjcD3vofQ2go5Oajb\nt2t1OCtWIFitiGNjWkFvd7fmUxWOWA4MIH3rW1rnVm4u4vPPg8eDcsstqKecgrx2LW1VVSzYvJmG\nhgZco6NG51wsMJOszBaDg4P09fVRVVVluihXsBuzLm4XCAQMi4Pe3l68Xi/Z2dkGCcrLy5tzw9s0\nNERS0p2z91+8GPkjHzF+FmDK9SPLslFL1tvby9jYGG+99VZIW3wyplh0pHKEJ9y8nU5nxNKL+cCR\nI0d4/vnnyczMxOl0EggEjLqp/v5+7r77bqqrqxM6h+S9+tIAEpPS0o0+J9e0xDrWtB1fHR1Y/v3f\nNSHAiaiOvGeP1q317LPI550Hp55K4NZbkW+/HXp7UbZvR4kgfS40NYHXCxOFllRWIh4+jCLLIEmI\nosj4+DiHDh1i2bJlrF+/Pq7N15S29CD16Hjg8XgYHBwkNzeX6urqhLnbT4bFYqGoqMio3dItDnSP\nJ6fTiSiKRhpMP8mnMlKFoKWC/IAkSRQUFFBQUEBRUREdHR2sXLkSu93O0NAQLS0tKIpCbm5uSFt8\nsnyuVC1a1gt/J8PpdCZNhAc0k9JPfvKT+P1+bDYbLpcLj8dDIBDA6XRG5d81W6QJTxSYzxvSzC4t\n0MKcR48ejcvoc/K8puv4kvbt0whKRYWmnfPmm1hPOgkGBxEA6Uc/0tJS55/P8WuuYeHu3dNrrEyI\nBhqCdF6v9pgo4vF4qKmpwePxsGvXrlltwvNp/KmqKp2dnbS3t7NgwQKWLl0aFdlJ1GYYbHFQXl4O\nhBpdBnf06CQolQhQqqTeIDUITzB08pCZmUlmZqYRodTb4u12O21tbbhcLqxWq0GAFixYELYrdC4g\ny3LKprSSvYYH4PHHH+eCCy6gpKSE5uZmli1bNucEM014EgSzFiizangGBgZoaGgAiNvoMxgzEQOh\nv/9EAacgaOms3l6E3FzweEBVEV9/HQoKWP7GG/ChD01LeNRNmzShwSNHDCPRwE030TFBEKqqqvD7\n/bPecE1rS4+RNLndbo4dO2ZE3XSrgOkgCMKcKzKHM7p0uVzY7XY6OjoYGxvD6/Vy/PhxgwTNVYTq\n3YxUJDzhyENwW7wOr9drkOj29nYCgUAIiY5WHHG2SOWUVrh1b2xsbFaHWrNx5513sn37doqKivj8\n5z/Pvn37WLVq1ZzWTqUJTwKgqwcng/+Vx+Ohrq4O0NoB33jjDVMWTltvL9a2Nli8GMJ0PylbtyI+\n9ZSWggoEwO1GkCQtQqOqWi2OLENFBdnHjsHAAATXqTQ3Iz34IMLQEMrOnSiXXop8yy0Ir74KY2O4\nSks56vGwwOUy3Oj19uvZYK7b0lVVpbW1lZ6eHtavX28UFZtdC5YoBHf0VFRUEAgEePPNN8nPzw/Z\nwPQ0hr6BJcPmnUokIpXmCrEVAGdkZLB48WIWL15svFZvi+/o6MDlciFJkkGUEhVJTNWi5elSWslU\nw1NSUsKmCcHZQCBgRI3n8jtPE54oEOtCo0dlzCA88UZ4go0+16xZYywmZkD89a8pePhhMj0erLff\njrppE8rq1bBqFcry5VBdjfLZz4Ldjvjii2C1In/iE1juv18jP6qq/TcnRyM9oBkP6hgYwLJnj9G+\nLj72mOaWfu21yKeeSnNzMwMDA2zYsMH0E8xcprTGxsaoqakx/MmCb/xU9tISRTFE1yVY3VdPYyTS\n4+ndiHcz4ZkMURTJy8sjLy/PMKMMTqV2d3fj8/nIzs42rqHc3NxZR2dSOcITqUtLV9hOBhw5coQ7\n7riDVatW0dTUxMsvv0xlZSU5OTnk5eUl3FYC0oQnasSyAemt6WacQuKp4Zls9GnmTSw0NiI++ihy\ncTHW118Hux2hpwfLk0+iiiIWUUS12VDXrEH58pcJ3HCDFs2ZiHpJDz6oER6bDeXkk6Gri4EzziA3\n6GIXjh3TRAN1592MDMS//pWhSy6htq6OsrKymFrNY4FZbenTjaEoCs3NzQwODrJhw4awbsZmRJqS\nBeHUfXWPp+HhYVpbW1EUxWhpzs/PnxN7jFQiEak0VzC/jT5cKtXtduNwOOjp6WFsbAxBEEKiQLGK\na6ZqhCcS4XG5XElVw/OLX/yCrq4u3n77bS677DL27dvH0NAQw8PDDA0N0dzcnPDvP014EgAztXhi\nSWlFMvo0FQMDIIoIsoxkt8OCBQhtbWC1IsgyeDwIbjfU1GD58pfxZ2aifvSjAMi33YZy1VWooogw\nMIDQ1YW6YgU9ssyK4Pew2bQC5QkoXi9On4/jzc0xaQXFg0SpJOuw2+3U1NRQUlJCdXV1xBs8lSM8\n0cw7nMdTsD2G2+0mMzNzXuwxkhGpSHgSuXkFF9QHyyro11B/f78hrhksjjjdNSTLckpGGiMpLSdb\n0fI///M/z/cU0oQnETCzlTyaWo6ZjD7NhFpZqXVd+f2aYKDXe6Imx+vVLB0mIjiqICA+9BDyRz+K\n8NprSN/5jmYDAcg33ojyqU9BZycZr7+OGgggTCxG6tatsGoVNDbiVRQ8DgfeL3+Zbdu2JXzR1+uv\nZjvG5OiMbvbpcDii0gdKVcIT798nuN0dtGtajwIl0h4jVUiEoigpRfjmQzdI92QKFtf0eDwh15Cq\nqhGNdlM5pRWpSytc9Pi9jNS5g+YZ8aS05gLRGH0GQ0+VxL0YLV2KfPPNCD/+MYHMTKx+v+aC7PFo\nZEdRTpgJer3Gf6Xvfler01m4EDwepLvuQv3DHxCPH2e9w4F08CDK7bdr7smZmThvu43uhx8mw+Wi\n5JxzyNm+Pb75xojZaujA1Gsl2Oxz7dq1UW2yqUp4zIIgCFNamoOF7cywx0ja71e3XwnaxNIRntgR\nbLFSOmEOrOuQ2e12WlpacLvd2Gw28vPzcblcSVXkGy2mEx5MpghPMiBNeBKAuSA8sRh9BkOPPsW8\nGPX3I3372whtbSjbt+P/6U9585132G61Irz4ItJvf4vgdILTqZkper1gsSBfe61mKeH1gj7HzEyE\nvj4EhwN17Vp8NhvCa68h/u53yJddRnt7O11dXaz5zGeMwte5gplKy36/n4aGBjweD1u2bCFrBsPF\ncGOkcQLBwnYw1R5DtzfQUxizsceYL4j19di+/32E/n6U5cvx3XoramVlShKeZIxISZIUEkkEQiKJ\nra2ttLW1GW3xujjifJO36RApmuZ0OtMRnklIvivyXQCzDUQnY2RkhNra2qiNPsPNLaZctdOJ9Zxz\ntJobUcTyt78h7dvH2uXL4Y47UPbuRfnud6GpCeHIEa0d3WJB/n//D04/HXw+rSPLbj9hJOp2o5aX\naxo9ooialYWvro7XDh+moKDAaDWfa5iltOx2uzl8+DDLly/XDBhj3KxSmfDM1byjscfweDzk5OSE\n1HHo90vSkQiHA9s3vgGqilpRgdjbS8Y3v4nngQeSb64zIBkiPNFCrycbGhoyuob0rsL29nZcLhcW\niyXEZ26+xBFjQTrCMxVpwhMlYllsLBYLXq/X1PdWFIVAIEBDQwNerzfu4t14yJj45z9DTw9qQQHC\n4KBWoDw6iiKKSN/9LoFf/UrT41m3DnXdOuTLLgsdwGZDvu02pNtug54esFhQPvlJxEOHtIVcVfEM\nDdGRkcH69evjPpXMOl3H7Lu0fD4fDQ0NuN1uTjvttLg79aIlPMlGjOZ7U47FHsPr9RrtzckAsasL\nweNBnUjhqYsWIfT2IgwNpQnPHEBPDYXrKvT5fGEVxoPb4pPt86YJz1SkCU8CYLFYcLlcpo0niiId\nHR10dnbGZfQZjLiiT36/VpejqlqNzoRooJqZCYqC0N2NOoPOj7pxI4GHH9bc0AsLtTG+/W3kQ4cQ\nxsZw79jByq99DWEWKQidrMwH4VFVld7eXpqbm1myZAmiKM5KluDd1JY+n5jOHmNwcJDjx48jy/K8\nKPtOhpqfr91fgYBWv+P1giCg5uaiulwpRXhSjaDB9CTNZrOFaEvpCuMOh4Ouri6DSAdrS82FY/x0\nh51U7TpLJNKEJwEwM6XldDqN8Gq8Rp+znZtyzjlIeXlaSmpiQVYXLkQVBM0YNFpRw8xMw/zT7/dz\n9JJLUM48E8liYcmOHbMiOzADWVFVzcg0O1sjW9OMEWvERPfyslqtVFdXEwgEGBwcjGmMcPOYq8L3\n9xp0TZfu7m6qqqrIzMwMscdwOp1GCkPfvOYihaGWl+O/+GKsjz6qXaOqiu/66yEnJ+UIRCpHeKJB\nsMK4TqSD06k9PT14PJ4QccRYi+qjQaTvOdWul7lCmvBEiVhTWrPdrGRZ5vjx4wwPD7NgwQJWrFgx\nd1YV4+MIL72E4PVq4oBLluB/6iks//qvqI2NCF4v6urVWEdHUfbsgYkbPhoER0JWrlxJ6aZN1NbW\nIpsQzYhIeHp6sNx6q+bnlZFB4JZbUM88M+wYsURWgs0+165da5z+ZFlOqJZPWCgK0rPPIr35Jkp5\nOYFPfALmKJwtDA3B2BhqWRmCzZZUKbZoMNkeA0JTGHNpjxH47GdRqqu1ouXKStQVmkJVqm1gqUh4\nZjvncOnUcEX1wW3xWVlZs/q7zkTSUumamQukCU8CMFuH84GBARobG6moqODUU0/l2LFjpkWMZiQ8\nLheW669HOH4cBAHJZiNw112omzcTeOop7TlDQwjd3dS2tLDt/POJ9pYaHx+npqaGjIyMkBZ6M9SN\npxvH8q1vQUeHViQ9Po7l29/G/4tfwNKlUY8xGZPNPoPJqFn2FLGMYb3vPqxPPIGamYnk9WI5eBDP\nz34WatmRAFj++7+xPvIIiCLqwoV4fvCDhL6fmZiORExOYej+TuFcvs22x1DWroW1a0MfM1m5ONF4\nLxKeyQhXVB8srXD8+HFDYDM4FRbLwTYS4Um162WukCY8CUC8/leTjT71llozU2QzjSXu34/Q1IS6\nZIn2wMgI0j33ELj/fvD5EO+9F/H//g9yc8n+4AejOnmqqkpbWxvd3d2sXbvWkIcPnlPCCI/Xi9DQ\ngKrbVGRlaXYYLS2oEQjPdBGK4M8SbPYZjESrNU+B14vl979HKSszUiFCczNiTQ3Kli2zmsd0EN9+\nG+vDD6MWF4PFgjAwQMbevXDFFQl7z/lCsL/TZHuMkZERwx4jOApkpj3GfAj5zQapSHgg8RGRcNIK\n+nU0NDRES0tLyHWkt8VHmlcklWWXyzWjuOl7EWnCEyUSmdKayegzpvFkGfE3v0F87jnIyUH+4hdR\ngza9GcmTw4EavFBlZmo6OoB4//1ITzyhbXBOJysfeADltNMQN2+eZjgHNTU1LFy4MGKruRnqxvo4\nUwiPzYZaUAAuF+TmaoJuiqIJIIbBdJGV6cw+Z5xHjIiJ8OjP069RQdBEIBMojQAgdHVp/6MrZBcV\nITY3R36BqiLt34/49tuolZUEzj9fI6DzhNmmiWayxxgfHycjI8PYuGI9vZs517lGOsIQHcIJbOpm\nu3a7ndbWVtxutxFN1K8jPToeSWXZ4XCkO7TCIE14EoBYIjLRGH3GMp74yCNI99+PWlQEg4NYbr6Z\nwP33o65ebYw1nZKwumWLtlk6nZCRgTA4iHzxxdrYf/876qJFmnqyzYbQ04PwxhsQhvDIskxTUxOj\no6Ns3Lhx2psvoSktQUD+5jex/Pu/a50usoz88Y+jbtwY9RjRmH2GvuUcR3gyM5F370b6y19Qc3MR\n3G7U0lKUDRtmNYeZoOq1WxNdRcLoKMry5RGfb33gAayPPopqsyH4fEgvvID3Jz+Bd0knyWR7DMCw\nNhgcHKR5ggzGU8ORaoRHVdWUtGlIBgS3xS+ZiLR7vd4pNWU5OTlGNmFyRC3dkh4eacITJWJZbKJ5\nbixGn7GkyMRnnkFduFDrRgLU7m6EgwdDCI9ncBDLWWchHjuGmpuL/+674fzztedv3Ij8zW8i3XMP\nuFzIF12EcvXV2uD5+dDbq0V9AFFVUXJymLysDQ4O0tDQQEVFBdXV1TN+HwlNaaF5c/kfekgrWi4o\nQF2z5kQ0JMwYwUQjWrPPYMw54QF8X/kKlrIyo2jZf8UVxjWQKCgnnYT/kkuw/s//aDU8BQX4brkF\n+vqmPtnjwfLYYyglJWCxaCmaujot7XbyyQmd53RINImYzh6jqakpanuMVCM86QiPucjIyGDx4sVG\n9F+vKdNb4o8cOYLFYqGrq4vR0VEWLVoUlU3GlVdeydNPP01xcTFHjx4FYHh4mIsvvpjW1laWL1/O\nY489FjZ1/7WvfY1nnnkGRVHYvXs3P/vZz5L+b54mPHOMeIw+Z4rKhCA7GxwO40dBlkPSBpIkseLi\nixHb2zWVY5cL2yWX4Hv+edi2TZvjWWcROOusKUPL11+P5V//FTo7QRDwLlmCumsX+vnc5/NRV1dH\nIBAIqUGaCQlNaekoKUEtKUFoakI4dAh15UqYSEUEQ09p6REqu90eldnn5DFmi5hJk9VK4LOfJfDZ\nz876vaOGIBC48krkj35U69IqL9eutXCEJxA4YTI78VoEQXt8nhDp+xXfeQfxyBHIyyNwzjlgojx/\nJHsMh8MxrT1GKhKeVKrhSbXOQr2mbMGCBeTm5lJZWYnf78fj8fDCCy9w+PBhOjs7ufjii9m5cyc7\nd+5ky5YtU9bkK664guuuu47LL7/ceGzv3r2cffbZ7Nmzh71797J3715++MMfhrzulVde4eWXX+bt\nt98G4H3vex/PP/88Z4XZN5IJacIzh4jV6FOHJEl4PJ6onitfcw2WW27R0jeKoqU2PvjBE2MBmW1t\nWt2FviD5/Uj/9V/IE4QnEtStW/Hv24f41luQmUn7okWUTyzGPT09tLS0xCWMaIZhpz7OdJEi8d57\nkX7zG1RJQhBFAnv3op566pQxvF4vhw4dorKykjVr1szLRhOtHlAybIJqcXFY8hiC3Fzk970Pywsv\noOTnI7hcqIsWaR1J84jJ35/0/PPYbrtNE9RUVSy//z2e++5LWIt/cCePbnCp67k4HA56e3vxer34\n/X56e3tZtGhRiD1GsiIqwuNwaOnXxYun1caaC6QaQdMRXMNjtVo5/fTTOf3003nqqac4duwYl112\nGQcPHuRXv/oVt956K/v37w+55s8880xaW1tDxvzjH//IgQMHAPjc5z7HWWedNYXwCIKAx+PB5/MZ\nvoF6FDOZkSY8USIeLyT9JorX6FNHVDU8qqoVHG/bRuDeexH+8Q/IyUE555wTpp0TY6mCMLWVPNpi\nyhUrUCa0QYTaWsbHx2lqaiIrK4vq6uq4WnMTndICEOrrkR55xFhcVZcL6RvfIPDcc0Z6KxAIUFdX\nh8fjYdeuXTGZfZqNZLOMMAO+f/931NJSxDffRKmowP/FL2qF5POEcN+v9b77UHNyIDcXFRA7O5Fe\nfBH5vPPmbF7h9FzefPNNLBbLFHsMPRI0G1XvRGDaiJSqanIGv/ylpiS9dCneH/0oegHTBCAW0cFk\nQiAQCPu3dzgc5Ofns379etavX8+//Mu/RD1mX1+f0UZfWlpKX5iI7WmnncYHPvABysrKUFWV6667\njvXr18f/QeYIacKTIOgkRS9KjsfoU8eMXVoDA1i++lWExkaQJOSbbkL5/Oe1bqRJkRPJZmPspJNY\n8NZbJ+wiMjKQv/rVmOakd6QMDAywefPmsDneaDEnKa2+Pu0UqS9qOTkIPT2akWlODgMDAzQ0NLB8\n+XLsdvu8kh14l1pLZGbi/9KX5nsW00Jwu1GDSLsKCOPj8zchtGtBkiRKS0uNzU23x7Db7XR1dRne\nTjoJSgZvp0iER3z9dWwPPqg1QFgsCG1t2H7wA7w//vEcz/AEUpXwROrSMqtoWRCEsH/HpqYmamtr\n6ezsBGD37t28+OKLnHHGGbN+z0QiTXhiQCynblEUDcHAeI0+dcwU4ZFuuw2hsVFLLfh8SHf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2IkkjxEssdwOByMjIzQ2tqKoighUaBodIFSMcIzna1EWnQwPNKrRoogFq0av99PY2MjLpdrysYK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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3mBAfrVlSIF",
        "colab_type": "text"
      },
      "source": [
        "You will notice that the individual points resemble in some sort of plane. But this is not a surprise. Actually we can draw the following conclusions from the plot:\n",
        "\n",
        "most of the data points are lying around hardness 60-80, temperature 80-100 and flowrate 80-100\n",
        "there are some outliers, especially when it comes to the range of hardness 100-200\n",
        "\n",
        "The data follows some narrow boundaries\n",
        "So lets double-check what's going on with hardness since it seems that it really sticks around 60-80 and very seldom creates values above that. We can use a histogram for that which bins together certain value ranges and counts the frequency of occurences of values within this range. Those frequencies are ordered and shown as a bar diagram, so let's plot it:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oskfAbBItTE7",
        "colab_type": "text"
      },
      "source": [
        "### Histograms\n",
        "\n",
        "Using Histograms, we can get an idea of the distribution of values within a single dimension. we can find regions of high and low value concentration within that particular dimension.<br>.Let's quickly plot the hardness array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ysycba2nuGZy",
        "colab_type": "code",
        "outputId": "a59c806e-80f3-4eb9-b434-ec82483811ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.hist(hardness_arr, color='r', edgecolor='black', linewidth=2)\n",
        "plt.title('Hardness Histogram', color='y')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAYOorITuUyY",
        "colab_type": "text"
      },
      "source": [
        "As with the scatter plot, we can see that hardness values are concentrated around 80, with a very low amount of exceptions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piCmXqyOvFyS",
        "colab_type": "code",
        "outputId": "cbdcc6eb-953b-44ac-901e-05c02f9a116f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# Let's plot temperature\n",
        "plt.hist(temp_arr, color='blue', edgecolor='black', linewidth=2)\n",
        "plt.title('Temperature Histogram',color='y')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko8AkxH_vxy5",
        "colab_type": "text"
      },
      "source": [
        "Temperature on the other hand is very evenly distributed with a peak around 100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuvqAv8twjqi",
        "colab_type": "code",
        "outputId": "21384981-b3b4-47f1-c4df-82cbc2d570f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# Let's plot flowrate\n",
        "plt.hist(flowrate_arr, color='magenta', edgecolor='black', linewidth=2)\n",
        "plt.title('Flowrate Histogram',color='y')\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMHZHTnhwvtt",
        "colab_type": "text"
      },
      "source": [
        "Just as we observed from the scatter plot, all values in the flowrate are 1. This is a unimodal distribution with only one value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTVt7h3Jw-NV",
        "colab_type": "text"
      },
      "source": [
        "## Summary:\n",
        "\n",
        "Low dimensional Data can be plotted using charts. we can easily visualize 1, 2 or 3 dimensional data at the same time.<br>\n",
        "Many statistical moments and properties can be visually represented using plots.\n",
        "<br>.Every plot has its clear purpose, but playing around with diff plots helps us to get more insight about our data set.\n",
        "\n",
        "We can look at the [ibm_notebook](https://github.com/IBM/coursera/blob/master/coursera_ds/a0_m4_exercice3.1_spark2.3_python3.5_cos.ipynb) for this exercise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_s2AUioiw8wK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}