Copy_of_Copy_of_SI_370_FA_2017_Lecture_4_1_Take_2.ipynb

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      "name": "Copy of Copy of SI 370 FA 2017 Lecture 4.1 Take 2.ipynb",
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        "[View in Colaboratory](https://colab.research.google.com/github/cteplovs/si999/blob/master/Copy_of_Copy_of_SI_370_FA_2017_Lecture_4_1_Take_2.ipynb)"
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      "source": [
        "# SI 370 FA 2017\n",
        "# Lecture 4.1+2 - For the Love of Statistics\n",
        "\n",
        "## September 27, 2017"
      ]
    },
    {
      "metadata": {
        "id": "t9Up2PErR_A6",
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      "cell_type": "markdown",
      "source": [
        "# Overview of today \n",
        "\n",
        "- Announcements\n",
        "- Comments/Questions/Concerns\n",
        "- Last class: Yeah, about that\n",
        "- An experiment: embedding the lab in the lecture\n",
        "- Today (and next week): Statistics\n",
        "- Today: Univariate Statistics Take 2; Intro to multivariate?\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "iQ-WLyV_R_A7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# So, you want to explore your data...\n",
        "* How can we describe it?\n",
        "* What does it look like?\n",
        "* What sorts of \"preliminary checks\" can we perform on our data? \n",
        " * Why would we want to to this?"
      ]
    },
    {
      "metadata": {
        "id": "ss-Xk-t_R_A8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Eyeballing your data: plotting\n",
        "* Introducing [Seaborn](http://seaborn.pydata.org/)\n",
        "> Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n",
        "\n",
        "Um, ok.  How about [some examples](http://seaborn.pydata.org/examples/index.html)?"
      ]
    },
    {
      "metadata": {
        "id": "fbE8QEEER_A9",
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      "cell_type": "markdown",
      "source": [
        "# Think about plotting the relationship between X and Y\n",
        "![](AnscombeData.png)\n",
        "(https://en.wikipedia.org/wiki/Anscombe's_quartet)"
      ]
    },
    {
      "metadata": {
        "id": "0Modw9l9R_A-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# A nice linear relationship, right?\n",
        "![](AnscombeQ1.png)\n",
        "(https://en.wikipedia.org/wiki/Anscombe's_quartet)"
      ]
    },
    {
      "metadata": {
        "id": "DdwWEt6UR_A-",
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      "cell_type": "markdown",
      "source": [
        "# Anscombe's Quartet\n",
        "![](AnscombePlot.png)\n",
        "(https://en.wikipedia.org/wiki/Anscombe's_quartet)"
      ]
    },
    {
      "metadata": {
        "id": "5yApJ0a8R_A_",
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      "cell_type": "markdown",
      "source": [
        "# Why do we care?\n",
        "\n",
        "* Statistical analysis requires good inputs\n",
        " * Remember Anscombe’s quartet\n",
        " * Which model should we use?\n",
        " * Tendencies/trends/patterns in data are important in picking the right models\n",
        "* Anomalies are important to detect and understand\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "-nggDvFuR_A_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Purpose of EDA (broadly)\n",
        "* maximize insight into a data set\n",
        "* uncover underlying structure\n",
        "* extract important variables\n",
        "* detect outliers and anomalies\n",
        "* test underlying assumptions\n",
        "* develop parsimonious models and\n",
        "* determine optimal factor settings.\n",
        "\n",
        "(National Institute of Standards and Technology)"
      ]
    },
    {
      "metadata": {
        "id": "50MDfmtVR_BA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Our primary goal in this class: understand the _downstream_ statistical analyses enough so that we can make good decisions"
      ]
    },
    {
      "metadata": {
        "id": "TJQZBHAYR_BB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Spherical cows\n",
        "![Spherical cow](Spot_the_cow.gif)\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "7L7Kcs7jR_BC",
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      "cell_type": "markdown",
      "source": [
        "## The average weight of a Jersey cow is 1,000 lbs.\n",
        "![](jersey.jpg)"
      ]
    },
    {
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          "height": 196
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        "outputId": "7fc5b73d-77d2-40a3-bf27-cb5b2bd3a4db"
      },
      "cell_type": "code",
      "source": [
        "# This step is optional -- it just makes our tables a bit clearer\n",
        "from IPython.core.display import HTML\n",
        "css = open('style-table.css').read() + open('style-notebook.css').read()\n",
        "HTML('<style>{}</style>'.format(css))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-6fab06cda22c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'style-table.css'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'style-notebook.css'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'<style>{}</style>'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'style-table.css'"
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    {
      "metadata": {
        "id": "6b-NmDkZR_BH",
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      "cell_type": "markdown",
      "source": [
        "# Let's make some spherical cows..."
      ]
    },
    {
      "metadata": {
        "id": "Jo6ANtHRR_BJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.stats import norm\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tN55nrm3R_BL",
        "colab_type": "code",
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        },
        "outputId": "59fc8676-637b-42a7-ec2c-0119d10bb305"
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      "cell_type": "code",
      "source": [
        "# The weights of a herd of 80 cows\n",
        "measures = (np.random.standard_normal(80)*150+1000).astype(int)\n",
        "measures"
      ],
      "execution_count": 0,
      "outputs": [
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          "output_type": "execute_result",
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              "array([1077,  976, 1026, 1042, 1103,  973,  866, 1276,  607,  963, 1085,\n",
              "       1068, 1081,  994,  970,  915,  800, 1094,  945, 1144, 1195, 1001,\n",
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              "        744,  832,  822])"
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      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">BEGIN Q1: Create some objects of your own\n",
        "Think of some object and some property of that object.  I used cows and their weights.  Pick something that you know something about, and create a NumPy array of some number of them (ideally between 20 and 50000), recording some property.  So you might choose something like the height of undergraduate students, etc.  Pick somethink that will *likely* have a normal distribution (which is probably most things you can think of.</font>\n",
        "\n",
        "First, pick the number that you want and assign it to numberOfObjects, then pick the mean value and assign it to meanOfProperty, and finally pick the variance and assign it to varianceOfProperty.  It doesn't matter what you pick, but if you're unsure pick 1/5 of the mean."
      ]
    },
    {
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        "id": "gqBwqEhAR_BO",
        "colab_type": "code",
        "colab": {}
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      "cell_type": "code",
      "source": [
        "numberOfObjects = 1000    # change 0 to some number between 20 and 50000\n",
        "meanOfProperty = 100     # change 0 to the mean value of the property you're interested in\n",
        "standardDeviationOfProperty = 5 # change 0 to the variance of the property -- try meanOfProperty/5 if you have no idea"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8lirpwDSR_BQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now create some data by asking for a random sample from a normal distribution, scaled so that it matches the mean and variance you want:"
      ]
    },
    {
      "metadata": {
        "id": "NtTLaMQVR_BR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1310
        },
        "outputId": "6d58958b-7938-4f54-e7c7-749f50073feb"
      },
      "cell_type": "code",
      "source": [
        "things = np.random.standard_normal(numberOfObjects)*standardDeviationOfProperty+meanOfProperty\n",
        "# And let's say we want integers instead of floats:\n",
        "things = things.astype(int)\n",
        "things"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        97, 102,  97,  99,  97,  95, 102, 100, 104, 101, 101, 104, 102,\n",
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              "       104, 101,  97, 101,  92, 100, 106, 107, 104, 100, 106, 103, 100,\n",
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              "        96,  92,  94,  97,  91,  94, 104,  94, 101, 102, 101, 104, 104,\n",
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              "       103,  99, 105,  97,  94, 101,  96, 109, 101,  96,  95, 101, 100,\n",
              "       107,  99,  93, 103, 100, 103, 100,  99,  89,  94,  95, 102,  98,\n",
              "        91,  94,  99, 100,  94, 105,  97,  94, 103, 102, 109,  99, 104,\n",
              "       100, 105,  94,  97, 108,  97,  98, 106,  96,  95,  93,  89, 101,\n",
              "        99,  96,  99,  97,  99,  95, 100, 103, 102,  89,  90, 105,  98,\n",
              "       103,  93,  99, 100, 101,  98,  95, 103,  97,  97, 100,  99,  92,\n",
              "       102,  98,  96,  95,  97,  97,  98, 109,  94,  96,  98,  98, 102,\n",
              "       100,  95, 106,  98, 101, 103, 107,  99,  97, 101, 103, 105,  94,\n",
              "        91, 102,  99,  99, 106,  95,  92, 100, 100,  96, 105,  99, 101,\n",
              "       101,  95, 101, 102,  98,  99,  86,  96,  99, 100, 103,  94, 101,\n",
              "        96, 107,  97, 110, 100,  93, 102, 111, 102, 102, 103,  95,  98,\n",
              "       110,  95,  97,  92,  87,  98, 104,  99,  99, 102,  98, 102,  98,\n",
              "       101, 108, 103,  95,  94,  99,  97, 100,  94, 104,  93, 101, 104,\n",
              "        97,  97, 100,  96,  98,  89, 103,  97, 109, 100,  96, 103, 101,\n",
              "       107, 106, 105, 102,  98,  99,  97,  92, 104, 104, 100, 103,  96,\n",
              "        99, 100,  89, 102,  98,  94,  97,  96,  91,  91,  94,  97,  93,\n",
              "        98, 101,  92,  95,  99, 103, 106, 110,  93,  96, 103,  97, 102,\n",
              "       104,  93,  96,  99,  95,  98, 102, 100,  97,  91,  98,  91, 102,\n",
              "        95,  95,  95,  91,  93,  94,  96,  99,  94,  95,  99,  97, 102,\n",
              "       100, 101,  91, 100, 101, 106, 102, 101, 101,  97,  98, 100,  99,\n",
              "       100, 101, 106,  98, 110,  97,  95, 102, 104,  99,  96, 109, 105,\n",
              "        95,  98, 101, 103, 107, 104,  96, 107, 105,  96, 103, 100, 103,\n",
              "       104, 100, 105,  97, 101,  92, 100,  99, 107,  99, 106, 109, 108,\n",
              "       106,  99,  94,  97, 102, 106, 109, 103, 104, 102,  96, 101, 109,\n",
              "       102,  92, 102, 102, 100, 108,  88,  96,  97,  97, 100, 105,  96,\n",
              "       101,  97, 103, 102, 100, 104, 101,  92,  98, 105,  95, 101, 103,\n",
              "        81,  90, 105, 104, 101, 102, 100, 100, 100, 102,  97,  98,  92,\n",
              "       104,  94,  97, 107, 109, 101,  99,  92,  95,  97, 102,  95, 101,\n",
              "       102, 104,  98, 101, 105,  93, 104, 111, 105, 100, 107,  94,  97,\n",
              "        95,  92, 109,  93,  91,  95, 103, 102, 101,  98, 112, 100,  98,\n",
              "        96, 111, 109, 100,  97, 105, 106, 105,  95, 102,  89,  91, 104,\n",
              "        99, 105, 101, 102, 101,  90,  95, 102, 101,  96,  97, 106, 102,\n",
              "        96, 106, 107, 107, 107,  99,  97, 101, 105, 104, 103, 101,  97,\n",
              "       102,  92, 101,  93,  97,  95,  91,  96, 100,  98,  98,  98,  91,\n",
              "       111, 103,  89, 104, 101,  97,  97, 107, 102, 102,  86, 101, 104,\n",
              "       105, 101, 103,  89,  96, 109,  96,  93,  93, 101,  96, 103, 107,\n",
              "       100,  87, 101, 102, 107,  95, 108,  98, 103,  99, 103, 109,  99,\n",
              "       101,  99,  87, 104, 106, 101,  94,  93, 102,  94, 100,  99,  99,\n",
              "        98, 104, 103, 114, 111,  96,  97,  97, 104,  89,  92,  98,  97,\n",
              "        88,  99, 100, 101,  94,  96,  96, 105, 101,  99,  97,  93,  97,\n",
              "        99,  98,  99,  99,  99,  95, 106,  99,  96, 102,  96,  96,  97,\n",
              "       101,  96,  98, 105, 114,  97, 101, 109,  93,  90,  94, 106, 106,\n",
              "       102,  98,  98,  94, 109, 103,  99,  94, 102,  96, 104, 105, 104,\n",
              "        97,  97, 108, 101, 102, 100,  95,  91, 100,  98,  99,  90, 102,\n",
              "        87,  97,  97, 103,  99,  92,  95, 102, 101,  97,  99,  96])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "UjQQDtQlR_BT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">END Q1</font>"
      ]
    },
    {
      "metadata": {
        "id": "rk7-fmX2R_BU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Measures of central tendency\n",
        "* Mean\n",
        "* Median \n",
        "* Mode"
      ]
    },
    {
      "metadata": {
        "id": "zRDZA4MgR_BV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Mean\n",
        "\n",
        "Add up all the values and divide by the number of values:\n",
        "\n",
        "$$mean = \\frac {\\sum{x_i}} {n}$$\n"
      ]
    },
    {
      "metadata": {
        "id": "aBjq9tIRR_BV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec333b6e-54ad-4cc5-b8cf-310dea6dea42"
      },
      "cell_type": "code",
      "source": [
        "sum(measures)/len(measures)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "980.6625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "AalVLrahR_BY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bae6b465-7148-4262-8aeb-e95e8fabc26a"
      },
      "cell_type": "code",
      "source": [
        "np.mean(measures) # find the mean"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "980.6625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "VxEjBr--R_Bc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Median\n",
        "\n",
        "sort all the numbers and find the one in the middle"
      ]
    },
    {
      "metadata": {
        "id": "2QIZVbb1R_Bd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "e524cb54-7d6a-4bb7-b682-0560a171f9aa"
      },
      "cell_type": "code",
      "source": [
        "measures = np.sort(measures)\n",
        "measures"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 542,  607,  720,  744,  748,  752,  754,  756,  761,  764,  778,\n",
              "        800,  806,  809,  818,  821,  822,  832,  841,  866,  872,  886,\n",
              "        887,  890,  895,  905,  914,  915,  940,  945,  950,  962,  963,\n",
              "        969,  970,  971,  972,  972,  973,  976,  986,  989,  994, 1001,\n",
              "       1002, 1007, 1008, 1015, 1016, 1026, 1027, 1028, 1036, 1040, 1042,\n",
              "       1059, 1059, 1068, 1076, 1076, 1077, 1081, 1085, 1094, 1103, 1103,\n",
              "       1132, 1136, 1144, 1188, 1195, 1195, 1208, 1216, 1258, 1272, 1276,\n",
              "       1279, 1308, 1480])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "s7f8v5ZkR_Bg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fccab288-17a0-420e-d2f8-4d5968fe9bcc"
      },
      "cell_type": "code",
      "source": [
        "measures[len(measures)//2]  # find middle value"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "LFC4RP6GR_Bj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e43bd418-d4c9-447a-8814-af1c3e2eae42"
      },
      "cell_type": "code",
      "source": [
        "np.median(measures)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "981.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "JAYFB_taR_Bm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Another alternative: Mode\n",
        "\n",
        "* mode = most common value\n",
        "* Unfortunately not in default numpy\n"
      ]
    },
    {
      "metadata": {
        "id": "tkbXjxXaR_Bm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "938ffc97-a959-488d-cbc2-d3ab81743e06"
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from scipy import stats\n",
        "stats.mode(measures)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ModeResult(mode=array([972]), count=array([2]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "92A_Z_0lR_Bp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">BEGIN Q2: Calculate the mean, median and mode of your \"things\"\n",
        "\n",
        "Step 1: Just run the following cell (assumes you have some data in an np array called \"things\")"
      ]
    },
    {
      "metadata": {
        "id": "aGjbP0cxR_Bp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0836eb02-da48-44f7-e64e-286116d39eea"
      },
      "cell_type": "code",
      "source": [
        "from scipy import stats # just in case we didn't already do it\n",
        "\n",
        "thingsMean = np.mean(things)\n",
        "thingsMedian = np.median(things)\n",
        "thingsMode = stats.mode(things)\n",
        "\n",
        "print(thingsMean, thingsMedian, thingsMode)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "99.43 99.0 ModeResult(mode=array([101]), count=array([89]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mK7KXB2pR_Br",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Step 2: Now, to demonstrate what happens to mean, median and mode when you add an outlier, append some crazy big value to the end of your things.  But let's not mess up our things array, so let's copy it first"
      ]
    },
    {
      "metadata": {
        "id": "ppJcF3VFR_Br",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d42e2355-38dd-471a-cc16-83104fe09864"
      },
      "cell_type": "code",
      "source": [
        "things2 = things.copy()\n",
        "things2 = np.append(measures,[10000])\n",
        "things2Mean = np.mean(things2)\n",
        "things2Median = np.median(things2)\n",
        "things2Mode = stats.mode(things2)\n",
        "\n",
        "print(things2Mean, things2Median, things2Mode)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1092.0123456790122 986.0 ModeResult(mode=array([972]), count=array([2]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "T-x9dSyKR_Bt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Step 3: Record, in your own words, what happened to each of the mean, median and mode when you added that value:"
      ]
    },
    {
      "metadata": {
        "id": "OGjzmsYBR_Bt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<font color=\"red\">INSERT YOUR ANSWER HERE</font>"
      ]
    },
    {
      "metadata": {
        "id": "C77ARbBUR_Bv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">END Q2</font>"
      ]
    },
    {
      "metadata": {
        "id": "Z7qxWo2BR_Bv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Measures of dispersion\n",
        "\n",
        "* Percentile cutoffs\n",
        " * Interpercentile range\n",
        "* Variance\n",
        "* Standard Deviation"
      ]
    },
    {
      "metadata": {
        "id": "az6iw_d-R_Bw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Percentiles\n",
        "\n",
        "* In a *sorted* list, find the threshold so that data is split\n",
        " * 5th percentile -- bottom 5% of measures below threshold\n",
        " * 25th percentile -- bottom 25% of measures below\n",
        " * 97th percentile -- bottom 97% of mesures below"
      ]
    },
    {
      "metadata": {
        "id": "gZYRO6NnR_Bx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Numpy does this well\n",
        "\n",
        "`np.percentile(array,percentile,\n",
        "               interpolation='linear')`\n",
        "\n",
        "* linear: i + (j - i) \n",
        "* fraction, where fraction is the fractional part of the index surrounded by i and j.\n",
        "* lower: i.\n",
        "* higher: j.\n",
        "* nearest: i or j, whichever is nearest.\n",
        "* midpoint: (i + j) / 2.\n"
      ]
    },
    {
      "metadata": {
        "id": "368OwUMJR_Bx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d7b6f286-b655-42a8-b830-46a059dd52ed"
      },
      "cell_type": "code",
      "source": [
        "np.percentile(measures,25) # the 25th percentile"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "870.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "L8KemUc-R_B1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f1107d35-1189-4ece-da62-2d86e3bcf690"
      },
      "cell_type": "code",
      "source": [
        "np.percentile(measures,25,interpolation='higher') # bump it up to the next higher real value from the data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "872"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "EehCSx0bR_B5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Interpercentile Range\n",
        "\n",
        "* Sometimes we want to some range\n",
        " * e.g., 5th -- 95th percentile\n",
        " * clearly, 90% of measures sit here"
      ]
    },
    {
      "metadata": {
        "id": "Wer9dsshR_B5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "82057a73-1c9c-4dec-837e-b3014ab21416"
      },
      "cell_type": "code",
      "source": [
        "print(np.percentile(measures,5),\"-\",np.percentile(measures,95))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "747.8 - 1272.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DTh7jXb-R_B9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Variance\n",
        "\n",
        "How does the data spread around the mean?\n",
        "\n",
        "$$ variance = \\frac{\\sum{(x_i - \\mu)^2}}{n}$$\n",
        "\n",
        "where, $\\mu$ is the mean\n",
        "\n",
        "$$ mu = \\frac{\\sum{x_i}}{n}$$"
      ]
    },
    {
      "metadata": {
        "id": "7e7_R9SlR_B-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Standard Deviation\n",
        "\n",
        "* Measure of dispersion \n",
        "\n",
        "![d](https://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/640px-Standard_deviation_diagram.svg.png \"sd\")"
      ]
    },
    {
      "metadata": {
        "id": "GmCvvPq0R_B_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bNRE1L6oR_CB",
        "colab_type": "code",
        "colab": {},
        "outputId": "3edbc9db-d13f-48ba-f471-79ff1f186487"
      },
      "cell_type": "code",
      "source": [
        "print(np.percentile(measures,5),\"-\",np.percentile(measures,95))\n",
        "print(np.var(measures))\n",
        "print(np.std(measures))  # this should be the square root of variance"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "687.85 - 1261.25\n",
            "25731.274375\n",
            "160.409707858\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lXIWAOvPR_CD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">BEGIN Q3: Measures of dispersion</font>\n",
        "Run the following cell.  Does the output for standard deviation match what you asked for when you first generated the data in Q1? What's the relationship between the 5th-to-95th percentile range and the standard deviation? Answer below"
      ]
    },
    {
      "metadata": {
        "id": "89zS8J0IR_CE",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "print(np.percentile(things,5),\"-\",np.percentile(things,95))\n",
        "print(np.var(things))\n",
        "print(np.std(things))  # this should be the square root of variance"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "J8K6HBQZR_CG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<font color=\"red\">INSERT YOUR ANSWER HERE</font>"
      ]
    },
    {
      "metadata": {
        "id": "8BF1ySbtR_CG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">END Q3</font>"
      ]
    },
    {
      "metadata": {
        "id": "OzmuHIz9R_CG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Visualizing data with Seaborn\n",
        "* Visualization package built on top of matplotlib\n",
        "* It's meant to make your life better\n",
        "* Plays well with pandas, numpy, scipy, and statsmodels\n",
        "* Many different visualization are included:\n",
        " * Strip plots, Swarm plots, Violin plots\n",
        " * Box plots\n",
        " * Histograms\n",
        " \n",
        " We need to import the package, and it's typically imported as sns:\n",
        " \n",
        " ```\n",
        " import seabord as sns\n",
        " ``` \n",
        " \n",
        " and don't forget to inline matplotlib (that's a jupyter thing):\n",
        "\n",
        " ```\n",
        " %matplotlib inline\n",
        " ```\n",
        "\n",
        "[seaborn.pydata.org](http://seaborn.pydata.org)"
      ]
    },
    {
      "metadata": {
        "id": "T6YiNbPeR_CH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Strip Plot"
      ]
    },
    {
      "metadata": {
        "id": "km9ZDDl0R_CH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d037ea71-fa9f-4916-f38a-948a3132c803"
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import seaborn as sns  # you might need to do: conda install seaborn\n",
        "sns.stripplot(x=measures)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb6de3cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h7ByctvyNwMMhhFT+9bSeg8vik26SJPOBTwB3Am8C3gr8LfClEMIWoAH4/SRJZpEL8uuB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\nf/US96bsFH3iAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb6de3a20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Mo2kflTIR_CK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Swarm Plot"
      ]
    },
    {
      "metadata": {
        "id": "LNyb5WuVR_CK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "63ce0a55-efd8-401d-d747-be1ae9f21bcb"
      },
      "cell_type": "code",
      "source": [
        "sns.swarmplot(x=measures)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb6dead30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xw/095Nqk0B1jg9WuRmtcY0pibPQmL6NtsN/HSr+E8aWXaqTNkoXZyVglmdhn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\nRMQgha6IiEEKXRERgxS6IiIG/X+C3Z9UL/zTYwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb6d51630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VZjiCRKsR_CM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Violin Plot\n",
        "* If we have too much data"
      ]
    },
    {
      "metadata": {
        "id": "61G2YmPeR_CN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "f816752c-701a-4b1e-8669-f2563b59f4d2"
      },
      "cell_type": "code",
      "source": [
        "sns.violinplot(x=measures,color=\"#d7d0f3\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:588: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  kde_data = remove_na(group_data)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:816: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  violin_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb6d2d780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UlEJVVQ2ZmdlGlyaWkYRuCCktLWdiYpyenm5udjRQXrpXxu1WiUeP+2nruMTs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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4cd6d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5oKzrGAgR_CP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Box Plot"
      ]
    },
    {
      "metadata": {
        "id": "7JVGAxmGR_CQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "31cf82cd-074c-496e-bf30-a8b5215df091"
      },
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=measures) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  box_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb4c3dcc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD4CAYAAABPLjVeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nleJAaAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4c3da58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "sg5dZEFNR_CV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here's a quick explanation of what a box and whisker plot means:\n",
        "![d](boxplot.png \"boxplot\")"
      ]
    },
    {
      "metadata": {
        "id": "rk2eO3EXR_CV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Histogram\n",
        "\n",
        "We're going to use this a lot.  Seaborn puts a nice smooth line over a distribution.  We'll talk about that soon, but for now just think about it as an extrapolation:  if we had a bunch more data, the distribution would eventaully snooth out to something that looks like the line."
      ]
    },
    {
      "metadata": {
        "id": "morwVSVWR_CW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b075d67b-47e7-4c80-ba72-64875850695d"
      },
      "cell_type": "code",
      "source": [
        "# x axis = value, y axis = count (frequency)\n",
        "sns.distplot(measures); "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FQOStSDTOt395HMOAO25Zk/O3tFwoGTcQ2VCY7x6xJPx8z1k6Lo6y88qGgr55vNtpp66y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\nBzo3UsV4GSIsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4bdcf60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Uq3_5xNvR_CY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "d4952fae-3111-4377-95f1-1cbe8641db5e"
      },
      "cell_type": "code",
      "source": [
        "sns.distplot(measures, rug=True); # show a strip plot on bottom -- we call it a \"rug\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+X2+VmYe0GKgFOdbaz2QwzI1Gb2+53BQ47dy2uY4osP9YN2GdzC6vaDFQC3Lo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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4b74438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7Dnni64fR_Cc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">BEGIN Q4: Test driving Seaborn</font>\n",
        "Your turn:  run the following cells on your data to generate the plots described above.  "
      ]
    },
    {
      "metadata": {
        "id": "F2oQIEfwR_Cc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import seaborn as sns  # you might need to do: conda install seaborn\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ly0VRWDqR_Ce",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f3a5fa56-e408-470f-962f-48c00632f0d1"
      },
      "cell_type": "code",
      "source": [
        "sns.stripplot(x=things)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb49ca5f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UOVeS3RUl7/2J+As8UVGKc9tXsZ8ulHS7uc+wsyR1iv/EudY59z27LU9Lta/3vjbFaE6f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\n+j+kYkFMLLOfLAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4b74f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IiVWbCX-R_Cf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "b0012d8f-ad40-4bde-89a7-bc340260c5d4"
      },
      "cell_type": "code",
      "source": [
        "sns.swarmplot(x=things)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb49fca58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TTJ8/la+libRk4sDZXknrxM6BCRzj+yUxJKYM/RrgxhVTXmgdkUyEjtlc19RP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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb49d5048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "V0n23FRIR_Cq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "fca193f1-fab1-49b7-823f-d75587604e86"
      },
      "cell_type": "code",
      "source": [
        "sns.violinplot(x=things)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:588: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  kde_data = remove_na(group_data)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:816: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  violin_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb4c2d518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0O7loNMrrr7/C++//DcMwcOYMwZkzRKaDiTNSFBVnth+7rxeh8g3s2LGVX/zi/zBt2gxm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\nCBNJ6QohhImkdIUQwkRSukIIYaL/D7vfHKVXITaVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4987160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y2WTcF-yR_Ct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "650cae37-2999-4310-bb03-9be5ab2045f3"
      },
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=things)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:454: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  box_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb495a4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD4CAYAAABPLjVeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nByrlZWFNpIHoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb492d208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0NaBqNeER_Cv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5cb5e4e6-4b32-4818-8d22-0201c76bf701"
      },
      "cell_type": "code",
      "source": [
        "sns.distplot(things)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb48c36a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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G8rNTLvnxnBbLP0OxYqI2Gi/8oxmi2QLcC2CMWQ3UWms7AKy1p4EcY0y1McZL+ILqlsif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\n8CIiCer/A5s861/VFMsdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb48e0240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5abN-mtUR_Cz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">END  Q4</font>"
      ]
    },
    {
      "metadata": {
        "id": "lG3pgdDSR_C1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Expressiveness, Effectiveness, Scale\n",
        "\n",
        "* Expressiveness\n",
        " * What facts can we extract?\n",
        " * What facts *can't* we extract?\n",
        "* How well can we extract them?\n",
        "* How do they work with more data?\n",
        " * Consider different dimensions\n",
        " * More samples\n",
        " * More univariate data"
      ]
    },
    {
      "metadata": {
        "id": "APmxK0VQR_C1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Why do we care about distributions?\n",
        "## World is not normal\n",
        "* Many other kinds of distributions\n",
        "* We can tell what they are by looking at distribution"
      ]
    },
    {
      "metadata": {
        "id": "qy_6BwkKR_C3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Uniform"
      ]
    },
    {
      "metadata": {
        "id": "-Kax1ZTYR_C4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "474375bc-d089-4142-fdf0-110ca0bb0158"
      },
      "cell_type": "code",
      "source": [
        "uniform = np.random.uniform(-2,2,1000)  # low,high,count\n",
        "sns.distplot(uniform,kde=False,norm_hist=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb4832400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb486d9b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "icjPl_7ER_C-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Bimodal"
      ]
    },
    {
      "metadata": {
        "id": "uBbObqQrR_C_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5c4f1f52-3d73-416e-da12-3d1d6bd03638"
      },
      "cell_type": "code",
      "source": [
        "bimodal = np.append(np.random.normal(-20,10,100),\n",
        "                    np.random.normal(20,10,100))\n",
        "sns.distplot(bimodal,kde=False,norm_hist=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb47d1278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb478b0b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "07eOwHdoR_DK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Poisson"
      ]
    },
    {
      "metadata": {
        "id": "0Ttr1TGRR_DM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "$$ P(k~events~in~interval) = \\frac{\\lambda^ke^{-\\lambda}}{k!} $$\n",
        "\n",
        "$\\lambda$ is the event rate\n",
        "\n",
        "Examples\n",
        "* Meteor strikes\n",
        "* Arrival of patients to hospital"
      ]
    },
    {
      "metadata": {
        "id": "kV6wYUIWR_DN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "08054d2e-96ec-401d-e2a4-66fc36c75f1f"
      },
      "cell_type": "code",
      "source": [
        "# as lambda goes up --> looks more normal\n",
        "pois = np.random.poisson(4,100000) # lambda, count \n",
        "sns.distplot(pois,kde=False,bins=10,norm_hist=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfb470ab00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fbfb4807eb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "r-QZzYs0R_DU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Power/Zipf/Pareto\n",
        "\n",
        "$$ P = \\frac{x^{-a}}{\\zeta(a)}$$\n",
        "\n",
        "\"long tail\"\n",
        "* degree distribution\n",
        "* movie/music popularity\n",
        "* words\n",
        "\n",
        "## Note:  both axes are log-transformed"
      ]
    },
    {
      "metadata": {
        "id": "-Pt9UWQmR_DY",
        "colab_type": "code",
        "colab": {},
        "outputId": "a2a04504-64c1-41aa-e6de-bdfb2feb56e0"
      },
      "cell_type": "code",
      "source": [
        "# bit of a hack (seaborn)\n",
        "power = np.random.zipf(2,100000)\n",
        "ax = sns.plt.plot(np.histogram(power,bins=400)[0])\n",
        "ax[0].axes.set_xscale(\"log\")\n",
        "ax[0].axes.set_yscale(\"log\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6elObvP7plB+spyIC8wLtIZCRwqHaRs0JSLtI9wTuwhsCzwE3RbhtEcfy9wQ6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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12e19ed30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XYZb1XAUR_Df",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visual Tests on Data"
      ]
    },
    {
      "metadata": {
        "id": "Aw95q5HqR_Dh",
        "colab_type": "code",
        "colab": {},
        "outputId": "f902eb03-3287-4bbe-e5f3-1efd0b6aff62"
      },
      "cell_type": "code",
      "source": [
        "testdata = (np.random.standard_normal(500)*20+150).astype(int)\n",
        "sns.distplot(testdata)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x11d8505c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5vR7mhIvp7Y/Q1G5jNnKdJQNjUtTa2c/uo83Mrw4xu3JqdhxfaE6VcxwnG7pcjsS4zZKB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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x11cf6d400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wj06sRFZR_Dp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Run Sequence\n",
        "* Run Sequence (index versus value)\n",
        "* flat and non-drifting\n",
        " * fixed-location assumption holds\n",
        "* vertical spread same over the entire plot, \n",
        " * then the fixed-variation assumption holds."
      ]
    },
    {
      "metadata": {
        "id": "Rcp3py6gR_Ds",
        "colab_type": "code",
        "colab": {},
        "outputId": "f6005243-b2f8-4970-84e9-5fd2c6b0d3af"
      },
      "cell_type": "code",
      "source": [
        "ax = sns.regplot(np.arange(len(testdata)),testdata,fit_reg=False)\n",
        "ax.set_ylim(0,250)\n",
        "ax.set_ylabel(\"val\")\n",
        "ax.set_xlabel(\"i\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x12d5e07b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VTE/t6Co7l/7+C4efBwypSarO8rlrJtr6aN751j0hnwexoyhNarRpmaYB0zSQy5qYnRnD\nqxdLiccWd3rolVM5ifYXNZalUlWeODRK5XowhnYb1v42wpGixfQckzT3pn52VPkaILzxtRN0+v3v\nub4Yiu4Lrp0z8clDx7FRa8BxPWm1MqTQW1ytYv++Yuiz9PyljZpcs+o8HxvJysbWwKZOj5s1ZbY7\nferRiklCiYcpKIKFB+SkW12XR33P19QajodsxsRIVib9bMZmTg5o1Sbquh4ajgfHdWAYCJl79M/a\nrCNUnXDq5jI9mS4EVd8w8zkTN+2aSD0u8mv8tRJtde9b9+BH//0SPM/bdNHGqHwCWqgU4aObRmgD\nSkIa7U8fy8Offabl/uge6f7abVjthOPjT74cOd40Wq762aqyo54WVK27k2Ye9V2oY795rognnj4l\n155/SPIgZYfneZjZUQiUhtmZcZw+vxyyDNgNF6vrTXNnFJvZWKOe981zxcTh13GnT9UPkiaUeJjq\nnLHwgNxcaWM3YAQCxHE8FHfIRZPEuRa3sI8eOxvKOl4q1dBwXRgGkM2Y8LSoE/2zNusI7cWE62UC\nIyAFiBqaCwBnLpZ6XrRRHzc84PXlDcAATEN6ZtUNKGk+SBTqJhV3MpHfZblFgOQyZmjDpetFnZ7i\nzCS9cJjTdY8cPQnHlcEU+WyrT4xIq5nrY3/00PHgWXjwYJCz3ANcD1hcrWGtsoAT1gIO7NuN5yLq\neFHJ+35trLr5N43ZKO70qfpB4kKJjxw9GVvYVGcQBU1ZeEBururGXrMdGDCQyTQzXdNkGOua1nKp\nGvI1AIAB6SB0HA/IyI2MJo/+WZ0ivDqZT3ox4bYigZHuUz2hyc/pvmijPu7ieA7zvmnSzBrK68l7\nbnTapNptMAf27W7Rnunz1ftL63uJmmdp565Ktd7ADddOBCYiMnPev//W0LiSBBl0ypMgZYEUNzqB\nGHQS8RAkiE5N5KWVwM9dUvNAtmJj7eZ0o3+Xv/9vjgaWjlzWRM12QiZyQJ5Ozi2s46ZdE6EAntmZ\nMdgNt2drbjOw8EBrPaOxQhZ1W54M5q6Z6NpmGuVreH15Aw3XRTZjwjQMuK4XCBDDQKQPIm6BAkik\nBfUiBLWXCYxxqPfZrmhjGvRxjxVyyGSqcFwPUKJCF1ergYa72XyQTgmOD737TThy9CRenZfmmJuv\nL7Zsymlo59NKel11g19eqyHvr4MkZk7aHNVs+qO+wxxoP0dnZ8ZR3qhjcbWGjGmg4TYd/JmM/HZI\neXjq2Fn8wg07uvIHJaWTMrbZ041qIgeAWt2B7bgwIO+XzNelso1cxmwJ4FlcraI4nkNhJJsqyKUf\nXPXCI6qeEeVDUPRLt0T5GuYXK/DqXmAyUZ3Cb741XtuM0kSjQoMrVRuf+eqPg82RwkA3W9KjlwmM\n7ehltBMQPW4qXqefcEjDpXEQSXILgOYmlSS8M+2GntSJGrXZtyvct2euiOf+fgGvnFsJTizNSLhm\n5YFK1cZzJxfw8GefiU1+jcoPcVwXGVNWN6jbbqCgHTl6MsiToGdZKttAXfp/MplwiDY9vwfvvS0U\nUk9h4q8vb+D0hRXsnCyEQmXTbKxJTFKbPd2oJnLX9dBwyWQHGIYRmK9pDyppUYm242KskMP0ZKGn\n664brnrhEVXPyDCA8oYd0qA6TcCohR61icjrG62vOy5uniumykTVr1+p2nh9uYqG6yLvO/rVMNC4\nyZZk0XRzeulHPHraa0aNW/YyMVoWpqrhAnJunL6wgtJ6PdjE4nIL1E2qH8l6cQX/4uYZIDfbdoX7\nyFFL07HZ9VAKUjJJkvaby5qh3jBAc37E5YfUGw7gOVjfQODjsxsuXjm/EjrltfMJqM9P9cmcW1j3\nI7OAat1Bte4EPrhuogiTmKQ2c4o/YS3guZMLsH2rhuvb50xDmrBHcplAeZ29ZhyLKxsoV+3AhG4a\nRuD/G4aSJ1e98NDrGdHEr9pOsFDUejxJtC5aXHpSHoCgbIL0FzSFlWkYOPztk4H2F2eCijMvAFL4\nyVDHsHBSw0CjSLJo2pnOokpqHzl6MqTN9iIePc2GSvc1v1hGIZ9t0UYB4FOPH48MNaa8HAAo+SYG\nNaABCOcW6Gymz3fcdwu0FvxrN88Audm2K9xH1O1wOQ+CbPD0Pj3JTp0fUfkhrtv0XwBSINF8N2Dg\nC4efxwceuDPST9ju+e0Vu3D02FnctGtCnuS9cPj7WCHbVYRVEpNUEh9PFME9KX4c1/MC8zWFowPA\nRq0B226E/EAU/UnfwaA7fgIsPFq0RJr4ahFAqsdTHM9FZp7Hbb5RkNZL9mS6vuvPKn2j0iN41EWV\nzxqhv6VQ46yWN0JhoEnLSJA54NzCeqhfSadFrpbULmkJkVH3k5YkFVSjynrTxqpro3feuqttXg6N\nn1CDAzr1VO9kJtTRn6VqOqKw4kpVJpTqWfxx7N+3OxS+21K4jzZ27SDsQfpO7IYXZH5TGKmK+gyi\n8kMoZ6MlbdEDslkDpUo9UqFIskHTnN1sWLdK0hOjHn2VJGyX5q5qKqUkWDNrhARz3XYxNSF/DgXw\nmOkDePrJVS88dC1Rj2snYVKzHSyuNieqmnkep7HUG05oE8lnMyjks1haq2J1vY58LgO74WFmquBv\nsE1oo1IXgb55krnNbrgwTTkB1yp16EV/cxkT+WwmURkJ1eSQy5ptTwztSmrrizrqftJCiZxq6Q2q\noKomjjl+QMJYIZyDogsu/bsPbOi2g41aA8XxfChsWI2G6dRT/eB9t6eySevPkj53qVRV8h88GJ7R\ncgrS55l6ulpeqwUlccgcBTSVI7shQ3HVOZPLmBgr5HD3HXM4c7GEi6+XA2WAzFh6M7So/BAPHrJZ\nEw3HDV3fAEImmCiFopNfiOZsL8O6054Y04TtqnPX8AM1MqYBx/NaBDPlhanWkFLZRsNxgwAeQD77\n0xdkYMlIzoytPdYvrnrhoWs5tGHQlxnEoHseXE9qUx68YMPqVJBOjUSJyn4FPIyOZFEqm62aIcKL\nIC7iyTQNfPqD78AJawFf/PpPIsNA49DLSKimDfV9UQs8zqcDozWbPup+0qBHqdCJxvU8ZEwjdM+O\nK5MvdQ1dF1zqd3/6wkpQLZhqQy2uVjE+mo2slNqpp3pUjH4S/5XqBKa5Rv4rsn0DwFKpGswZ+p5U\nYUXzLZ9t5rK4fhCFaZCm6/kNwpqnG7vhYs9cEW8Wu4L6UKQth0rqoNnwiMy6VBJkJO+P1xdWWdOU\njmEPfoRbOIqq3ak4jn6Edav+lCASbrYY+/dxJ2G1Vh2ZUNW563nyUVy7cxTTxQKmi4XIvDCChAhV\nbqDvVlX01gB43krwvq0QIFe98ADCWs7hp07iiadPYXG1GtLWPA9wFNsq/AQmw1jB7/32HR01lrhN\nhkwk+iKgjUq9RpJs3rgw0HbZx+omem6hNU+A/k7lhLUQ0mrp71WfTqf7ScORoyfRcFzUG07IgSh7\nq7QGIHgeML9UwdhINraVrLph1WxXuWev6fC13SD8dWpiBIV8FmuVOj55SPpLHMfFzslCi2+CYvSB\naI2Uao1RccSMIe+JNmbT9Dd9xwsEZCZjoKGYmzy/Ckkua8Y6sPVgkEI+g5tmJxX/T7PmE9WY2ivC\nRT7Va5TKdRTyzWfaUmbdV4ruvmNORnGdX8FIPoPJnMwJclwP+byJaeWZtTsVt4s+BHoX1k3fx6nz\nK6j7p7HpYgHVeiNxtnewmSu16sgnpa8H+TzrLRGdSfJ16LtVFT263mZNw2lg4aFwwlrAD1+4iOJ4\nDkulGirVBjzPQy5ntthtSQOs224qG62OekQFENL+9Bj9JMfqqON+3EZfqdqwG14o/NIDElWl1bVa\nMqWoPh26n1rdQSZjYKPWwJGjJ3Hk6MlUPZ5PWDKUFJBarON6aDguCrkMJibyWK80ncXkpDWAoNRJ\nVCtZ3eSwul6D43hBiOj4qMz1qdkO7EYWUxN5ZDMGrLPLaDhuIMAcx8PrKxuYHMsFoahkNtOhRU0n\nxNdXqnAcebqgCZbNmqEw7kJGavGeJ08MWbNZ+t4wEdqE4xzYaviuaRq4f/+tUrtdLMMDIr+DqPyY\nsUIO5xbWA/8Sfb9Aa6+MVy+W8In3/2qoHMncNRORta3i6LQJxpm2qByNbtKJqx+lntjJ3FazHby+\nsoFrd4zGbsid/KXBs5gv4brpseBv9GrB6rhpTo6PZlEq11Gu2iiO5fFb73hjy3fbS39PN7DwUFBP\nB57XjIySpw+p8RlGU+sFgLwfN5/URquj985QtT+dbiI94jZ6Mj/MTBVC4Zd33zHXNodBfU5qVn7d\ndrGwXME/uHkab/4HuwJtdro4GmwYlaoN6+xysElfulzG6fMrHfNpjh47G5jBKC8GADIZE//wDdcE\nxQZtRy78XNaE3I/ljhzVSlYPLaVN3HEA25BCYHxULo+pibw8Tby2DteTTmAXHtyGvL7b8PzoKDkX\nHNeDYbSWp6dFffTYWVmiRt1w/ROu60oHKp2lHEeaSHO+UDFNA4YrQ16zphl7OmxXkC+Jlq8nzpLC\nUchlML9YacmO1jfMuJwWvbaV7tSPukZS6PTwyrkVGAaCelkePCwsb+BHL14KhIHeI4U2fk9REx3H\na+ur6+Qv1dGLVs5dE/bJqZW9yxsNZEwTGVPOpx++cBG33LgjSKzstb+nG1h4KJBEV4+DruvB8WT4\nq2HKCqCe1wzv3Dk5kig3o1Oxu6THzLRJdHHmCxIcuhb46sVSx0ghXSsNBK0hI5t++MLFILLp0UPH\ng2gndcOkTXpxtYojT53s6A+IOvbbjtuSaX/uNX/DNICZqdGQU1m/JkFNiBq+j6H5eh27djY1Rvpd\nVJFzCr0cyWVa8iQIWtTzi2XU7dbiiHTtmeJIqNS94TbL2KjXJ02zkwM7CXpUn544SwpHJmOgWneC\n12lc+oYZt4FFzV/dxt/pGlGQkjS/WAEAv78MgjwQclRLRaa5aVNNLLpXtbYd+TaB9t0A4/ylxM2z\nxchQat2EG7X/AGip7N0Pf083sPBQIIlOE4kyQClMruG68Nxm2CL1ZkjSJUyfaKSlpklE7IY488W5\nhfVI8wH5QJKeotSJrmqfNNHVz1c3THWTfjWiRHnr53nB55HWu+f6YmB2KOSzODNfkiajbNimDrRu\nRHQPVOGUTEeGf6rJZU24ttcSOBGH6fefmJ0ZC2zfuimHFrWsDrvacg06beibx0guEwho9fp6+Qpy\nYFO0V5QSkETLj0qczWVNOI6Ha3eOopBvRrY1za7NTPRS2cZGrREK8wbiEzzTRDnFZctTXkyzyCKC\n/6sesbr2PTZrYkkBopZIMWAEz1jvBqjnfj14720AEHkf9x+QBUA7WQz0/YfQEwN77e/pFhYeCjSJ\n6Tjo+JOIzFQjmQyyGRNrlTpuuXFH6i5hUZFXQH8busSZL2RZ8oi/T6DtqYtdneiq9kkTPe7zDfh1\nvVyp3ekbTdTn6cf++w/cGnqWxbEcGg0XtbqDpZL0c1AUkb6ZHdi3uzUyzZDhk+Tv8DwP84sVFMfz\noTDXKDx4KFft4O/VPAl9sziwbzeefWkeNTt8GspmTcxdIzOs1eRFcuCTMFKbeL16qRQZ4EB1tLrR\n8vXEWeLcwnrk6xu1BuaumQhFrI2OhE1DQOc6bJ0213bZ8pQXE/U9BaHOHqCfG+mzqFilaRrIQvrV\nMhkDe+aKgNfMFZIKo8y9WFjewK6dYyGTLykxBmSwypv9PDASmA/ee1vsGtf3HyIqMTCtBaIfsPBQ\nUEP1XjnvO2gzTQfmdFFqs6ZpYP++3fjk48dh227L4u1kq91MSe+0xGl19751T6hVJ5HkyKsu9teW\nKvA8tGxeNNHVz8/nzGDDNAwEdX1GsplUpza1pwKV6M7nzCBSKWvK3ILXlzeQyRqYniy0bGZ7xS5M\nF0dRKttwPenryGQMwANW1pqBBeWNRhAS3FaA+P4wMvHMTBXwgQfeFKtE3H/gVhw5elJqwh4wkjOx\nsziC9/7WP2rZ4CtVaZJqOC5eW6rg5uuLgVnw4c8+40echYmag0kr76ZVOKi+mdqeWOWpY2dj+xnS\nnE+yGbbLljcMebL14JuYEd9DUfVFkZBqV6ySquCq4dOeJ3v+XPJPQGOFbJCsOus7x5dWN/DUsbPB\nZyUt304mxOgcAAAgAElEQVT7Ty4ja4KVynUsrlZRyGcTtw3YClh4RLCzWJCJeyty09AXWuB09Gen\nnkXdSXvvVIvoyNGTsM4uB+04kziWu+nLfcuNOzqa0dpdN+oURailJOjzN2pyIzYA1OxmxNK030GQ\n/k53sOqaG4CWk0+l2ghOiKZpAIoTVzepfOrx47jz1l1YXqsGkUP0O8riJtMkmWgqtUZQe8gJzBpU\n0A6BAHC9aAe9zgP7bw09f13bVjVsOh3t2jmGsUIWVaVzXtIoOromCaJKtYFKtYHds5N4z2/+w9io\nPjX5UprMWrPbkxSEjBO6aZziaqKd3XADYV+u2s0y7oAf0tz8QANSsBsGkM9KywHNOXW+f+L9v9ry\nmWp+kSo4VGp+XS0DCE6eVBkXaO13k6R8e+D894XIzFT7sOFBwMJDoSWRb0o6LnWtmtAdVnH9OHQ6\nRcKcX1hXInGa7ThVx7Ju+1VDIJP25e5kRjt1fiV0OonSnJKYHNTPp2ibv3tpPmSWoQie15YqgXbV\nrmYYoWYzO06zR0fOL8BHBu9QaWujtZkVmWGoDXEzo1qaaF5bquC66TGce20d2QwCk5vnB1NksgZu\n2NXsf/HK+ZW2pjj9ueiv0zN99uV5OK4spqlmeR85ehLVeiNxFJ3teHhtsRzqVWMaBi6vbMR+PhUf\npM1LPmsnthx4uzykJCHg7dCTROmeVQFOyXcwgPHRHOoNGQlGfo5c1sRoIYuNWiNx3TW1Cq6nnWVk\nOHjzZw9hRVKdf0Sn6sTEXtGs3yXnVD3IPaOKxIOmr8JDCPHLAP6tZVn3CCHeDOAbAF7xf/3nlmX9\nJyHE+wA8BKAB4OOWZX2jn2NqR6fyH1FORzouA4BpRvfj0IkzJa1t2Fhc2QiiWQhaMC//fAknfDON\n+n4KqVRLVgDJ22/GmdH++gdngho7Kvp1u4kWIxOH3q9ALYseNy41bj6qA6R8PR8q/hfl2FebWanj\nyJhGy2mSImb0kGHatHLZsPO6U2kXnagT3v59u/F3P51HxmyWEqExkTBLEkVXqdqYX2wKCbXQnu24\nseVBaPNSaVcOPG5e3+yXfd9Moyp1Ewfkd+Q6XsghTlV7KXDhwuvrcN3m9+04MqTa8E+lSequqY3i\nFpY3glOn/ECEbGOqnCj5p0GVJNWJVX52YQWLq1Vp6vUFY8128OLPFvGBTz2N92onxq2mb8JDCPHH\nAA4CoHPpWwB8xrKsTyt/MwvgDwDcBend/J4Q4tuWZdX0620Fncp/EEePnQ11g6NJQou8E3E2/MPf\nPtn2fY7r4YtffyFk5gGawkUPDU1qEogzN5Qq9UjhEXfdNH4a2mj0yCK1LHrcuFRCuSZaTkK1Lk0z\n515bD8wczdIc4WZWz51cQC5rYny0WX4DaD5TiphRTQmqM1sXVlGlXfSscuq1AkQ7kwv5bIvzlMak\nVvXvFEVXKtt+HklYc3YcGVoc932mbXwUN6/p9LqZRlVqKXMDAPwQZkCapOA1A1vU8FpVEaONn54D\nnR7nFysY808k9Hmt1Y1z2LUToURCUlioKrb6eG3HxUxxBKpIaVedGEDIkrBWqePS5UpQtl3n3Pxa\n0GphUAKknyeP0wDeDeCQ//NbAAghxLsgTx9/CGAfgO/7wqImhDgF4A4Az7a7sBDiEQAf6/WAk1bV\n7NRLOcmXqWvrjx46rrTjjHb2GYbcONYq9UDzBpqmGz00NKlJIO6+9YSwuDIfQPe9nePKos8vlWMT\n1fS4edo81R4bVISykM+gVLZRbziwGzKCRjX/kLNXdTyroah6d0cSAmEznSzx8XcvzceWdomqO0a9\nVlRlQC/wWBzPR2YS/+KNOyIj/aKc2lT8kBzKhAcPxfF87Dzppi9J1LwmknQl1IkqZS6r/jYbJcWV\nwpkYy/mZ2nbwDLMZGUmlNmICmn3sDz91MmSqdV0X84vVUOWBSrUBx5WCVy36SFF6dsNFcSyPh979\nJgBNYSrNj9IEXSo354naAoCiyGjuRW0E8mWvY6uFftM34WFZ1l8JIfYoLx0D8CXLsn4shPgw5Ob/\nPAA14H0NwFSCaz8C4BH1Nf+zfr6ZMSeNN98rdrXtpayTRCNXE+HInKBiAMHree04TO/Ts3yTmgSi\nHKS1uhMoTaZhoFZ3MF+tIJMxIqM+uu3tHFcWPZ/NRCaqAQUcvO/24NqqhquWnljWymDU6k6QpxNV\nskTdKNVNbu6aiUiTTtQ9xUUbUV8N/ZQFhJUB3YTnekB5oxHalCi/5f4DtyaOoiOBVq03sLJWD+W0\nLK5WMV2sRkbx9Kt9MZDsVHzCWsDnDz+PNf+k5XrNDpylcj2yFI5a2oei1ui7JEUokzVCOUdqoUbV\nVNvM9DaCUjeADBGnYIef/vwyVtfrMAx5TdoHohSOTx46Hjmf7UazBUAo090zoiP8/OKS1GphUGyl\nw/wJy7JW6N8APg/gbwFMKn8zCWBFf+NWkcT5S4JgsVRFg7oCZprXiCq+l0Qj1xPhGtWmRmMa4U5i\nURmrhgE4bjOUM84kEGc6OXjf7SEHaSZjBOUyPISTJaOiPrrdJNplQkclquWyZtvoq0rVxnNWGeWq\nHYRQlsp1v/yInzXckPWO1IioPXNFPGcttJ5yUnR3bLfZPv7ky5GJhqoyoAsXKpVet91QPSnqIwN0\njqKbnR7H3XfM4YcvXPSjxzJBnkI+J5Mp46J4kuZftKPd6aWdUkXrhvI3PPnlwTSlUDUMtGj2UaV9\n1O+DlKyxQhY1u9YsT58zAj/RuYX1QHjQ90HRezNFaXb7z985hTtv3YU9c0VcvLyOkVwmmKOlcj0Y\nQ4v5KxdtglRNw3qmu1rLTCWTkXvBIJtCbaXw+BshxL+0LOsYgP0Afgx5GvkzIUQBsh7nbQBe3MIx\ntaBHBqmNXvb49ttK1YbT8HwbajMaStVk6b1k3iL7OE0ytYsa0JoIRy1lqYQ2URzPh+zvpy+soGbL\nDYiqu1Iop0oQAvzqcpAAZRpGqE0t0IxSCkIhES56pzoM1VNFt5sEgJbEKrUKsPo8lko1nH1tDYur\n1VCUDEVfqZq7AXlaqtlOi+aWyRhwPdnrgp4NFcRUN4Hb3jAdaPEklPRGYPrcoeeib7ZSCy63bB6q\nMqD/jkqlr1XsyGRD+j8VOaTTX9TJSBUodsPDtFJGQw1hphLkauHKpH1J6HtWCxLunCxEhvfePFds\nq1TRvag+H9M0kMmYuGFmLHQi1JU7VdirpsxbbtyJ295g4u9enJehu5BzQe28qZr91O/DAFqi9Z59\naR5AM2CCBNCrF0uxzb30U+TURB6/cMOOYO3Q/WZMA47r37NnNMPDjWbuWXE8P9CmUFspPP5XAJ8X\nQtgA5gH8nmVZJSHE5wB8F4AJ4MOWZVXbXWSriDoxPGctBA2I1ExUx/GQyRlB3/MjR08GobM0AV9f\n3giiMxzXw+XVDXzy0HH89q/dggf2t2qSUouSUSpRiUsnrAVU67J4Wi4jJ3CnDoTzi5UgVp2ibUw/\nBJQKyqm0hEL64bC0GdCpol3yWS5nBsf1XNZEecMOCZmgIZXvwyHBp/o75KlKPmcDrZFQFH2lau4Z\n04DteC3F2g0gODG+tlTGo4eOY7kkp5yeOf3CK5eDooiqOUltBJbUpHVAyWJWUZWBLxx+HqVKvcX/\nc8uNOyM38DR+JnVcqn8n6GTp+wB++vNFGDCwY3IEnuelihaL7jEBAF5LeG8nMyedZHV/BikycaVL\n9Odx6fJ6yIT06KHjoRIvBAVGqGa/qGAFAEHJEgp7zmXNkFn3taWKX+FAeY9/Lf0USc2dgioJ/v2a\npoHJ8VxgXrvxOko8lNelvYDuabPJxN3QV+FhWdYZAL/i//sEgLdF/M1jAB7r5zi6IWpyk0aqakKm\nacD1ZOIQ9T1XQ2fVUice1QoHgk3wiadPBdUyozaeB/wJEje+qGNwXAdCihIhqAWm7bh4db4UWixq\nKKQKOZzJca4uWD2a5s1iF554+lSknbdd1rEUvk1/R+Do9WQJD/1eCfVZUEFBNYQ37H/0YBgynPbc\na+uRRSIp2qxToTqi3Qlrr2ifxQwAH3jgzlQ+Bn2OdjpB6KfEStXGwvJGOKHOVxJW1moo5DOxIaxx\nY4nqMTE7M9YS3tupxhaNcayQQ7XuNHuBZDO4+465yPEk8buppVdovHpgBJ3SNmoNrK7XURzPhzp9\nUmQdzS3V+e44UmC+cm4lNKdIKKineKqN9/iTL8vTsyHn7XRxFDAQmUujspVljqLgJEEffeH/7MIK\nRkfCjyeXNYPy2OpGJYVCM6NZDZ1VE4zUmHTVJNRNxAQtgiQd+/S/VauGAuG8h1A1Vxle7jsMW0Mh\nVQ1SjRKikuhnLpYiNbdSuR6ZdRxkedcaGBvJBkf8uu0Emz5VcVXHoedgEFRQ0PH9NYEARzMfgJ6L\nLoiApgmjU6E6IL6fu1o8LyqLWTWzUAb7Yklm4U9PFTA5mo8tnqn6mVRN2vW84BSpFksE5Kai1vUK\n5S0AQSVBiuZJ2jo4bY+JuEg6vayNXp58ZqoQKk8eNYaWsWml6k+fXw4JDdfzsFFr4JOPHw98gCTo\nKLJurbIQKsOzuFoNTEvqM6QItqVSFQvLFWkZ8O9PrXeWz2ZCxRbp/3F5YlGKSTdBKr0kWWLCFQ4t\n/EuX14PkndX1epBtTBTHc75JIRd63TDCbTWpl8D6Bml24cgOtV5WLmN2FTExOzMejAmQjm274cK2\nXcwvVnDzXDHybzNm05BDtt58To5HdkYE6E9kZFUGb5grYnZmNNhsi2P5YJLPL5abVWT9jaNWd/DS\nmSX86MVLcFy3JVa9ZjtYLtVw6XIZ84sVv1SGcg0PQcJbPmcGpSUASnCT16RNnBz+e64vBi1wSeuj\nng7k5zH836v5HsXxXKRT8t637pHfUUt0W2uhuqhTwOJqDWculUIJYZTkCYTnXXmjjlfOrcjyFmN5\nTI7lMH+54vcwj34/fa9AWONX8znU3JOnFJ/IdHHU75QpHyzNCnonRfPo9xkHjUV/VlE9JuJKvi+V\n5H8Pf/YZHD12FnffMQe74bV8p+q9RI2h5XXls/fMFYN55roeqnUnmCO27QY+QHrOe8UufOjgXfjf\nf/cuzM6MNc2xWROmaWDHZNNHYsDw14g0sZJQaZ64DXzggTvx6Q++Azv98Oz5xQrOvbYerIOo+4ra\nnw598yX87EJ0bBE3g9pCoiS4mn1MjBVy2L/vZrx6sRQqhVy3HX/ReDi/sB5KTCKTgGlAVm1VGknJ\nz4mPs29nBlEd7NW6E4RgjuQzKI7nQ9qZ+rfX7mw2bxrJZ7BrehS27QKQC0mNo9cLWjcc1xeK9aBl\n5/JaLbDDUq0h6lfd8LzA5p0xpdCkxCwZOdXsja0mveX9nimA3PyoTLZpGL62JxfnnpvDZh81B4Oc\ntjsm87IjYF2aFHNZE/lsJuRTGCvkMF0cbeklTSYMPTGQ3qdGYl1aLKM41vxdXIc9VSuMK/S3VKqi\n4UhT58JyJahppb8/rrqxoTxM9fPVTUWehsaC7HO1myF9l7TxR0Wc0fjpOXuei1rdbYkoiuoxEVXy\nneptqWXPL11eRz5nRpbo2IgICkkSWnzmYinIEan419DLjFD+BI01yvm+5/pi4NckB7jrCwvK5s9m\nzOA0rNc7o+zx4HvyBYxhtJbq79TCWoebQW0hcZnlhiFzGgIb9WwRt9y4o8UPobexjMIDMDGak5Pe\nNEIbUVLnn/rz0WNnUak1YNsuNmoOxgpZ6MlpasVS+jmqV7XeSpOirT7wwJ0A4Nvp14IOgK4LWK8u\nwzq7jMmxXGD+IkHZbAPbrDnkuB5MUxrvdkyOhMa5VKqhWnf847yMAqIsb2nnNpGFiawfJTWSz2Bq\nIo9P/G+thezoWa+uS427kM9iuhj2iyyuVlsStaKip4Bw/a+4zGn6UNWJT/OANmAyyZ1bWA9qXqnz\njv5eT15zXC90XVUAqN+rWt1Y9cupuT/qpqL6FHZMSuUDhsyWHslm4HpSS3+z2NVS3+yLX/8JyIOk\nOp3JzDgxloNpGLE9JqJKvs8vViJPf1IwhZ3blNCn56YkCS2W5UayMiz3tXXUPcf/+prSw3Zc/PTn\nl3FCCd2mQA/VrERzggp+UjdITzm/qWtSbUhWi2kGVtfK9KvPS4d6qehwM6gtJC7MdKcfA0/Z3O3i\n4am8t96xDkDT5OIB102P4Y037GiZ3Hqhw3OvraFad1qil6ggHuAXbxyRmtJOZUOmsNafXSzhxEe/\niZliAZNj+cD2ri6ouIlJn0FhkyO5DGxDale20zRFVaqy2iw5tUlgAICJ5knBg4cdEyPI58wWX5Ln\nSR9LLiNPHGpinOO2lrwHWlt4AmGBS5unuvFWqjYWS7UgXl+Py29Hu8xpoOkvotMqaZzF8XC0llrz\nqpDPBs9ZDawgcyKVwACawQG6VqkKt6Zy0dzUSfMn5ywV5NszVwzm/HSxEGTiT02MhDZ8/T7lWFqT\nHYFmPsrcNRNtw3uj1lvgK9PI58xAEVCJq+YQF+0W9dmqD1AN1zUALK/Vg1bGdkP2sr92x2joM9XP\n+pN/972gFXLGNGAYRhDJSCH6aqfHuI0/6vWkLay7ycPZDCw80D5RLYqoSUtmgPnFSqhENEE2ZCqH\noRLV5KbecGRGuRKhBMhILtUJRxuVWuDv9eVqkNRXqzk4+9oashkT1+4YbXGeqpE3cQUKqU2nrhXT\nfe+cHIHnyU3QdtzA5k5mLLr/9Y068nYGl1eqwfhpI6JihLSQK9UGbtw1ESS46URpV+rxXnWe07Mp\nlW3kMmYod4SSvs5cLKUKc9SFLmnQlJOhmjXOL6w3780I95Mg1MCKbMb0zX9eS65NnFapa91qxI7u\nnCWTkJ4DEbXxRCkXQbVYbY7HOcd1otYbzQdCba3ruC4ME6H2z+0c+XHmXj2kPJ8zUasboecM+Imx\nbjMykkLbl9aqsZ9J65/GTmtpo9YIOlWO5DI4fX4Fly6vY3qyAHjNvC95/zm88YYdbZ8XKYZ128WF\n19exVKrGnpr7DQsPxB93k7TsJGgTLo7nmm1NFciG3GnTo83UgBGE0gLSDu64chI7rhPShorjOSyW\nasH71Q6Ijm9Kchwv5MPR+yG3K1BIbTopgCBUhtqTmyGF6Zom9YtW/B+gEicuGr4DlISiS04WQwqQ\nhuPB9f0vtuPhlht3tO17oW4Uqt9BjRyjTc1uuL4vJ7pEu36qbOdzitIGxwq5UE4GJWaq/bTrtovX\nVzZw7Y5RmKYRahU7XRzFuQX/xJkxMZnLhWoldarYrGvdeqKqHGNzyb96sdQxATDqPtUTgmqmjXKO\nx40TCK83VUnQW+uWyrIRUyZjBKdFwMMtN+5suXaS9gI0V8sbDcztkmNV8ydOn19BwzFa1nDddhPV\nAVOLdTq+ry7rn6pJESzksy15RUC0ckDPK8p8rCb5brUAYeHhE3XcjWvZmc9mWhyIqlOabMiuJ8Py\nMqaMwvjtX7sl8guOsn1Tz3RC7cAHhLWh6ckRWcRwqYJKrRFor6ZhoAE3+Pso5ymNp12BwgfvvQ2n\nzy/HnKhobAZmZ8Zw8L7bcer8Cp54+hQqtUbQO4ImvOlHTtGpyjSMQKt0fWeJjC0wsLiyEfTjTpIk\nF/Y7NB2yhiHNXKqZqFPv9U4x9Emcs3vFLhw5etJ3YDfDo+l7U/N7Yu/JJ0mp/7hnE2XCA5JF5cTV\nPctkjJBvB4h2jsfRLgueKhyr1ZHLVRuOawQnysXVGvbvK7ZcN0l7Ab12mT63fucj30TG9IJe5ipx\n96bPh7GCrFaQy3qhABmgWRV552ShpbJC3Hesm4/16w2iQCILjza0WzjUkpY2lYP33R5okaYpy3wn\nSfQBou2wptnsma5WBFW1ec8DqjUHl+oVjOQyspyF12xoBDST5AwYsc7TvSJcoLDi9+Ku+U7s/+eJ\nFzBdHMWKEr6sl6G2G26wwVGU0qcePy7LpGdM1OAEi8gDgiP+Rq2ByytS61Pj5TMZIzYZj9A3Ct3v\nQJsEjStqQ5Xva+293imGPolzFgDOzJeCaDGVuu22lLKJiurp1o7dyYQHRNdhiztpqXXPrt05CrLj\nd3KOp4Geq95at267QTVcVcF59WKp5Rq9aC+w5/oiXjm3ElSQoPVz067WIpnq2IHwfKAkQz2IpmY7\nWF2vY3Qk21JZoR1kPtYZVIFEFh4dKOSzOHWBwnKbBQN1Le6pY2exf99ueJCb+s5iIZENPcoOSxOE\nBBSAoLENDCALM5Q7Qkfi15c34Hqe/K8R7jOQyRihTVLXoNSkLLVcRdYwcXmlCs8Ddkzkg9OCysxU\nARNjuZYgAlUgUUVTIKzpv/GGHdg5WcWZSyXUG+Eud1HJeCqd/A5xtaCS9F5PknDWyTkLwC/mZ7Rs\nRCM5M/aEo0f1dIM6/igTHhCeA51OWnGNoTo5x7tBN5XRnBsZyYRKe7QzH+vE9V+PMkPdf+BWfPHr\nP2nxR7z3t/5R23FHBVVElaTxPK+lpwfQObmPzMdRSauDKJB41QuPds41WjxZ04SXATy3tSw0bTxq\nTX4gWamAw0+dDMp3GAbgOC7KDRez14xhciwfOrWotadk6Y2mKcQ0jCAKSsXzABcedu0cw7U7R9ue\ngujnLxx+PsgwVpMZqczE2IjsZaCXoo+avOrJLZ8zA1+Q6jSmDYxqb0WdCOL6h0T179b9DjrR0UlN\nokq0q6RdpKTFUikbghyjabKE0zTbUgMhSmU7qI6cy2aCekrqe5PWmlKpVG08+/I8fucj3wxVaE4r\n9PT7UiPBAIQi19TP1vu0x5kTK1U7CM1N0slwr5ClZJL42eK+B1IKKfHWAIL+9hP5XEvABJAs0CCu\nPtogCiRe1cKjnbalLqaoUiRAWIuLS9iJ0yZOWAuhuk9qct7N1xVDmx9lu9IGQElg2YwZlA5RTT6G\nX++fSpiL3fGbqcpesQs7JkcwNZHHude0UEr/XifGci2htgDammCe+/sFlKt+tjjkIooKkY1LxtMX\nBn1vriudyfWGg0q1gR2TI5gujiS2uQPxZqde9LIAWrVYOrQtr1Xx6KHjkWVwgNaNJG0dI7UECeCX\nFYeBnZMjiaOq1HHownSpVMVyqQYPgGnICsx6heao0v9Rm2zUySsuIQ9oOtT1Pu3qs6DvlSLN7IYT\nKC2VagO7ZyfxnjZtXONOlUm+h6h6b3bDxS/u3hHqM6KTJNCgU320rSRWeAgh/nm7N1qW9R97P5yt\npZ22pS4m1WasZu+q5pe4uO04beLosbNt6z4R6kS8dmchmIhj41mMF3KgmP6o6C4gvT2UNgm9ThTd\na7vY8riNoJDPtpg8AIRs1nHJeFEb3dFjZ0ONehyXajHV8D/+6hsSL6R2ZqekPo0kn0Fa7OkLK0Gh\nvdGRbFAGx/PQoonqG0naOkZq7pF+Sox6T6eT1p65Ip59ad5P2lPT4MI90alC81JpI7Jroh4VFNun\nXosEU+eF3fAiC1mq/ij6jEcPHQ+NhSLFLq9s4NT5lVBuFRBdSFIdAzWn0k8wcZUD9O6JAGIrUO/f\nt7vjqSaRqXSLaHfy+LU2v/MAbHvh0U7bUheTajMeyWWCvg9TEyOBCaBdZBYlEKka2PxiObLks+2E\nwwHjJqIaOQQgqI6q1vsH0ttDSePWS2GrkTRxEzh2I/BLputECbUki2N+sRxq1BOYgwxEOlG7pVcL\nla4T1WkwqgwO0HrC6abZlpp70Ok97U5acuN+tdkDPObzHMdDDQ7+/tUlUKCgWo4nKioo6X2p34Xu\nUG93X+pcUanZDp54+hSK47mgOVa7UvR6cyq9LYD62XH3pJq2o/q5A9G97NVxDBOxwsOyrPfE/U4I\nMdqf4Wwt7bQttca+GvapZ+CqRNlal0rVoNQGEO5brW/QAFpyQeImYr3htOQIXLy8HvosIL09VNW4\n1fpdSSJp4sZK6JV391zfGmqZhNmZcZydX2t5vdsik5shjR8irgwORd+UKnUUx/K49617Up8Mokjz\nHvV7P60EiBw9dhbLparM4TEMmFkD9UazwZZa/cz1PMCh15sla0iZoVNwS4c9pTFVr+8rbq54noea\n7WBx1W2ajtFaih5AKFdGbwsblfkfVzFYNW1H9XOPyuYHtq5Kblo6+jyEEP8UwP8BYAJynmQAjAIY\nvrtJSTttSzdbxGXgElFmjqWSjCLSKZXrfiRVa5tVPRek3UKJyhHohT20W407bqw3zxZbzBh2w8VS\nKbp3dicO7NsdtIxVaVdkkkiz2XcirR8i6vlUqjZqdRezM4UglDSq5Hg3Ppi076HPu/jN9WCTo34n\naqCI2iNFJ5OReU1BDxY0+8bkMrIopRr+vl6po2Y7Qah5O0d2N/cVN1cMwwj8l+q9qKXo1ZOCWntM\n7e6pZ/7HVQwGCh1N25vp9z4IkjjMHwXwLwD8EYA/A/AbAK7p56C2ik527bSbqP73D3/2mdi4bP3k\nEGdTT7NQBm0PjRvr/QduxZGjJzva35Nu7HvFLvz2r90iAw6067XbdHrdPCetHyLq+ZTKdqKwTTXf\nQi3U2Y5u/DZR95TLmtJE6m+YGdOQ5Ts8wDABeL6JNGfgmimZAyJL5IT7xujRUqRMZE0Zeq6acNqN\nMc19xc2VUrneTLwNNQxr5hepJwU1/0pNctUz/6MqBlNF3Z3FQtsTU68i/LaKJMJj2bKsp4UQbwMw\nZVnWI0KIH/d7YFtFPzfcTnHZST47KnoEQGyDIJ1eatqdaLeoZR/4ePt72o39gf23ti1bEkWvm+ek\n1RSjns9GrZEo2oqIKtR56vwKzlwshUJd6WdyBFM0n06SJmjF8Vyw0QLNvJVsxsD01Ghg0lT9fmrp\nf9NEEGlEJX9UP4RpGoAhk/DIhNOJNOs2aq7cPFcMoh3VJE61FL16UlBNzGqSq56PE1UxGJBm5k6K\nYNqT4lau7SiSCI8NIcStAF4GcI8Q4r8BmOrvsK4MehWXHZeb0GmDHUSbyrhF3Umrarex0+/1RZJW\n8AKRGGYAAA/fSURBVPfaLKDfk1rMj6qn6mZF9T7279sN7xgSa5tRz6hStfHE06eCzez0+WUc++l8\n0I/l7HyzRpT+/UfNj6jor7FCDmK3dHN2MomqfkLaPHXN/NLl9UTdGXtJ3FyhE8lIJhOEke+ZK7aE\n1EaVu4lSVjqZmYH2lo52v1cZdAtaIJnw+DCAjwP4XQD/GsBDAL7Uz0FtJzbbtzoNaTXnQbepVOmk\nVSWJUAE2t0g2YxaI+p718jVqMb8kG/Whb76Eu++YixxTlHIR9YzIFKj+LP9f1/6utShm0iZoABIl\n/yXZ/OiZ6ZGGUQmh/dask5xe9XpVarmbKDrN804KT1KFaBjWdhLh8WlIB/nDAN4NYN2yrOW+jmqb\nkET699IsllZzHiYHXKeNJW5jT5t82Y64hR3VKU8/MUR9z2o9M72Ynz7OdvkMSXxfQPRJp1JtAIYs\n/1IczzcdtTL5JSCqKKY+P4L6bX70V5p6VfpG/+C9t8X6IIDOCaFbpVm3W5/d+Iy6eU83QnIY1nZH\n4WFZ1i8JIW4B8DsA/iuAJSHEIcuy/qLvoxtytlr6p9Wch80B126hxm3saZMvO30+gBbbt94pT9+k\n2n3PHzp4F/aK1mJ+xOkLK3j00HEce2m+ZaOk+0iqYMSddKgXCrXzpb4XQDNKKKoopjo/1OuN5DJB\n5FdSwZFmo1cFyJn5EtYqdUxPFUIn8mHQrGms/YxWpC6k5Fy/dLmM0+eX8dC739T2GsOwtqNXpoZl\nWacAfAbAvwEwCWm+uurZaul/IMZP0q5MdJq/HyR7xS4cvO92zF0zEVQlPnjf7fiFiOY4QPeLZK/Y\nhQ8dvAuf/uA78KGDd+FMTFLhU8rmleR7np1pHU+lamN1vS4z9pXGXmrV4zT3oT6jtYqNXNbEjsl8\nS6FKQJqBiuO50M+E6qAlVAe2+rdPxWziKp38VTokbKr1Bmanx3Dd9FhLVdlh0Ky3giNHT4baV1O5\n+SNHT7Z93zCs7SR5Hu+GPHX8MoBvAPiXlmX9oN8D2w5stfRPeyTu5gg9SOI0tl7UmIojqWDo9D13\nCsNVo3VUn0La+6BnpJ50Cvlw8uU/ueeWoC6U2lGwnYP23EJruQz9OcSRdqNPcqoYBs16K4jKAwOa\ngQlxDMPaTuLzeBDAIQD/zLKs6ObFVylqGXN18d59x1zfPjPtMTru7zfjjExaVbQXzs5+L5J2mxTd\nw+kLKyj5NanUjVVv/KSPUw3DVaN1Go4bG63Tzbj15kYP+KUukkDzI6p0CpBss0670ScRNr0qTDn0\ntB4aE9PPNIMkJPF5/NOtGMh2ZK/YFXTNs51mKYKnjr2K5/5+oW2RtUGyGWdk2qqiaa8fRT8XSTsn\nehB2OpL1+03XYRhGrBNZH6e+IdMm34seGL3eXDdzvbTvTSJs0ioNg8556JY9s8Wg1YLKzV2W7tlK\nruqS7L3gzMVSKPmNHI+lso3ZmbGhLG62GWdkkvcOi7MzCXGblH4P3Wz8/dSee30i28z10r436XNJ\nqjQMQ85Dt8iS/S+gVK6HMuDvT3F6HBQsPDaJfgQnx6PemGmYNs7NOCOTvHe7OTujNinKhNZJcw/9\nNrmp4ybNW2byd6d5b+aEl+a9vX4u20lZ0dkrduGhd9/R1bMY9GmLhccmiWqZCYRDI4Hh2jg344xM\n8t4rwdnZq3vYCrt0N5UHBm3iGWT+07DRzbMYhtNWolBdJh49ZI6azejF7oZp49xMmF+S9w5DGOFm\n2U73kCZUljadS5fXQ134qFvldiQqTBoYrjXXa9KGR/cDPnlsEv0IrrfMJIZp0+m3fXsYwgg3y3a6\nhzSat7rpqFGCXzj8PD7wwJ1Dd39JTklXTWQWms+jXdLpVsHCowfox84krVQHTb/t22mcnYM2ocQx\n6FDIpKQxsZGgUTPKAaBUqQ+dkzmpaWY7CfrNoD4PNemUuhkCW3vaYuHRB7bLpgMMdvPerN12mAXP\nVpJG8yZBo7dmJR/dMDmZ0zjCt9Oa6xb1efQq6XQzsPC4ihm0020zUTKDHvswkUbzJkET1YURGC4n\n83Z3hPca9Xn0Oum0G1h4XMUMOsRxM5vDoMc+bCTVvOlvvnD4eZQq9Ra7+TA5ma+EqL1eoj+PXiad\ndkNfo62EEL8shPiO/+9bhBDfE0J8Vwjx50II03/9fUKI40KIHwkh/qd+jocJM2jNbjNRMoMe+3Zm\nr9iFDzxwJ27aNYHZmbHYkiuDZjtFvG0Fw/Y8+iY8hBB/DNk0quC/9BkAH7Es6+2QFV3eJYSYBfAH\nAN4G2Rv9E0KIiO4NTD8YdIjjZhbDoMe+3dkroqsYD9OpbTuMcSsZtufRT7PVacjmUYf8n98C4Bn/\n308C+HUADoDvW5ZVA1ATQpwCcAeAZ/s4LsZn0CGOm4mSGfTYrwS2g5N5O4xxKxmm59E34WFZ1l8J\nIfYoLxmWZVG3nDXIPuhFAKvK39DrbRFCPALgY70Z6dXLMIQ4drsYhmHsDHM1s5UOczW8YxLACoCS\n/2/99bZYlvUIgEfU13xB9fNNjvGqY5g0mbRs57EzzHZnK8uTPCeEuMf/9zsBfBfAMQBvF0IUhBBT\nAG4D8OIWjolhGIbpgq08efwRgMeEEHkALwP4mmVZjhDic5CCxATwYcuyqls4JoZhGKYL+io8LMs6\nA+BX/H+fBPCOiL95DMBj/RwHwzAM01u4qi7DMAyTGhYeDMMwTGpYeDAMwzCpYeHBMAzDpIaFB8Mw\nDJMaFh4MwzBMalh4MAzDMKlh4cEwDMOkhoUHwzAMkxoWHgzDMExqWHgwDMMwqWHhwTAMw6SGhQfD\nMAyTGhYeDMMwTGpYeDAMwzCpYeHBMAzDpIaFB8MwDJMaFh4MwzBMalh4MAzDMKlh4cEwDMOkhoUH\nwzAMkxoWHgzDMExqWHgwDMMwqWHhwTAMw6SGhQfDMAyTGhYeDMMwTGpYeDAMwzCpYeHBMAzDpIaF\nB8MwDJMaFh4MwzBMalh4MAzDMKlh4cEwDMOkhoUHwzAMkxoWHgzDMExqWHgwDMMwqWHhwTAMw6Qm\nu9UfKIQ4AaDk//hzAH8G4CsAPAAvAni/ZVnuVo+LYRiGSc6WCg8hRAGAYVnWPcpr/wXARyzL+o4Q\n4j8AeBeAJ7ZyXAzDMEw6tvrk8SYAY0KIb/mf/acA3gLgGf/3TwL4dXQQHkKIRwB8rH/DZBiGYdqx\n1cKjAuBTAL4E4BchhYVhWZbn/34NwFSni1iW9QiAR9TXhBB7IM1gDMMwTJ/ZauFxEsApX1icFEIs\nQp48iEkAK1s8JoZhGCYlWx1t9V4AnwYAIcQcgCKAbwkh7vF//04A393iMTEMwzAp2eqTx18A+IoQ\n4nuQ0VXvBXAZwGNCiDyAlwF8bYvHxDAMw6RkS4WHZVl1AP8s4lfv2MpxMAzDMJuDkwQZhmGY1LDw\nYBiGYVLDwoNhGIZJDQsPhmEYJjUsPBiGYZjUsPBgGIZhUsPCg2EYhkkNCw+GYRgmNSw8GIZhmNSw\n8GAYhmFSw8KDYRiGSQ0LD4ZhGCY1LDwYhmGY1LDwYBiGYVLDwoNhGIZJDQsPhmEYJjUsPBiGYZjU\nsPBgGIZhUsPCg2EYhkkNCw+GYRgmNSw8GIZhmNSw8GAYhmFSw8KDYRiGSQ0LD4ZhGCY1LDwYhmGY\n1LDwYBiGYVLDwoNhGIZJDQsPhmEYJjUsPBiGYZjUsPBgGIZhUsPCg2EYhkkNCw+GYRgmNSw8GIZh\nmNSw8GAYhmFSw8KDYRiGSU120AMAACGECeDfA3gTgBqAf2FZ1qnBjophGIaJY1hOHv8EQMGyrLsB\n/GsAnx7weBiGYZg2DMXJA8CvAvhrALAs60dCiLu6uEYGAObn53s5LoZhmCsaZc/MpHnfsAiPIoBV\n5WdHCJG1LKsR9cdCiEcAfCzqdw8++GDvR8cwDHPlcz2A00n/eFiERwnApPKzGSc4AMCyrEcAPKK+\nJoQYAfBLAC4BcLoYw88BvKGL921n+J6vDvierw66vecMpOB4Ns2bhkV4fB/AbwI4LIT4FQD/Pe0F\nLMuqAfhetwMQQsCyrDPdvn87wvd8dcD3fHWwyXtOfOIghkV4PAHgHwshfgDAAPCeAY+HYRiGacNQ\nCA/LslwAvz/ocTAMwzDJGJZQXYZhGGYbwcKjyf856AEMAL7nqwO+56uDLb1nw/O8rfw8hmEY5gqA\nTx4MwzBMalh4MAzDMKlh4cEwDMOkhoUHwzAMkxoWHgzDMExqWHgwDMMwqRmKDPNBcjU0ohJC/DKA\nf2tZ1j1CiFsAfAWAB+BFAO+3LMsVQrwPwEMAGgA+blnWNwY24E0ghMgB+DKAPQBGAHwcwEu4su85\nA+AxAALyHn8fQBVX8D0TQohdAH4M4B9D3tNXcAXfsxDiBGQhWUAWQvwzDOie+eRxhTeiEkL8MYAv\nASj4L30GwEcsy3o7ZB2xdwkhZgH8AYC3AfgNAJ/wqxRvR34XwKJ/f/cC+AKu/Hv+TQCwLOttAD4C\nuaFc6fdMisIXAWz4L13R9yyEKAAwLMu6x//vPRjgPbPw0BpRAeimEdUwcxrAu5Wf3wLgGf/fTwI4\nAGAfgO9bllWzLGsVwCkAd2zpKHvHEQAf9f9tQGpeV/Q9W5b1nwH8nv/jzQBWcIXfs8+nAPwHABf9\nn6/0e34TgDEhxLeEEP/Nr0A+sHtm4RHTiGpQg+k1lmX9FQBbecmwLIvKCqwBmELrM6DXtx2WZa1b\nlrUmhJgE8DVITfyKvmcAsCyrIYT4/wB8HsBXcYXfsxDifwHwumVZf6O8fEXfM4AKpMD8DUjT5EC/\nZxYeKRtRXQG4yr8nIbVU/RnQ69sSIcRNAJ4GcMiyrL/EVXDPAGBZ1v8M4FZI/8eo8qsr8Z7fC9nG\n4TsA7gTwHwHsUn5/Jd7zSQCPW5blWZZ1EsAigOuU32/pPbPwkI2o7gOAbhtRbTOeE0Lc4//7nQC+\nC+AYgLcLIQpCiCkAt0E637YdQojrAHwLwL+yLOvL/stX+j0fFEL8if9jBVJYHr+S79myrP/Bsqx3\nWJZ1D4DnAfxzAE9eyfcMKTA/DQBCiDnIE8a3BnXPV4x5ZhNcbY2o/gjAY0KIPICXAXzNsixHCPE5\nyIlnAviwZVnVQQ5yE/wpgJ0APiqEIN/HBwF87gq+568D+H+FEH8LIAfgDyHv80r+nqO40uf2XwD4\nihDie5DRVe8FcBkDumeuqsswDMOkhs1WDMMwTGpYeDAMwzCpYeHBMAzDpIaFB8MwDJMaFh4MwzBM\nalh4MMwWIIS4SwjxpUGPg2F6BYfqMgzDMKnhkwfDbAFCiHv8UhoMc0XAwoNhGIZJDQsPhmEYJjUs\nPBiGYZjUsPBgGIZhUsPCg2EYhkkNh+oyDMMwqeGTB8MwDJMaFh4MwzBMalh4MAzDMKlh4cEwDMOk\nhoUHwzAMkxoWHgzDMExqWHgwDMMwqfn/AWBM8higfNWsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12f4dec50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cbj4j_9lR_D0",
        "colab_type": "code",
        "colab": {},
        "outputId": "3c910459-7896-4f9c-9b88-97a6023be5d2"
      },
      "cell_type": "code",
      "source": [
        "drifting = np.array([(testdata[i]+i*.1) \n",
        "                     for i in np.arange(len(testdata))])\n",
        "sns.distplot(drifting,kde=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x12d931160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L7p9LD0QEwL7M3BERdwH76f6F87uZ+WJ7SfvLzMMTlvNG4J6I+A26f3ZenZlfn7CMAB8A\n7o+I48DLwAcn4L28le4RGLdFxMl53OuBuyJiJd0ppz2Z+UqLOU/NuAL4EeA/mKz9p997uTkzv9P7\noAn9P38/LexDnvVSkgooM2cvSZVZ9pJUgGUvSQVY9pJUgGUvSQVY9pJUgGUvSQX8P3Ju3860R0V6\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12d5283c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "54ZslPlGR_D8",
        "colab_type": "code",
        "colab": {},
        "outputId": "de8c0849-f9b9-4139-a8b0-8e48bfd68f66"
      },
      "cell_type": "code",
      "source": [
        "ax = sns.regplot(np.arange(len(drifting)),drifting,fit_reg=False)\n",
        "ax.set_ylim(0,300)\n",
        "ax.set_ylabel(\"val\")\n",
        "ax.set_xlabel(\"i\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x12e47a470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YsHHi5BmcOHkGs4sNVJdaKBZstBwfF6YW8aycxjt/+iDuPXIrjh7ep5Pgtm0hD1uHpIpF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\ng2EYhskMGw+GYRgmM/8/zXyhsGlyYv8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12e46fc18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_zga4g9JR_EG",
        "colab_type": "code",
        "colab": {},
        "outputId": "54585002-32a8-45d1-ce27-014f83195a12"
      },
      "cell_type": "code",
      "source": [
        "expanding = np.array([(testdata[i]+i*np.random.randint(-1,1)*.2)\n",
        "                     for i in np.arange(len(testdata))])\n",
        "sns.distplot(expanding,kde=False,norm_hist=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x12e4390b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IkooMCUlSkSEhSSoyJCRJRYaEJKnIkJAkFRkSkqQiQ0KSVGRISJKKDAlJUpHXbuqCbl+b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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12e448438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yByjkQ1LR_EQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "9699d280-8810-40a5-d5f1-37df53178dcd"
      },
      "cell_type": "code",
      "source": [
        "ax = sns.regplot(np.arange(len(expanding)),expanding,fit_reg=False)\n",
        "ax.set_ylim(0,300)\n",
        "ax.set_ylabel(\"val\")\n",
        "ax.set_xlabel(\"i\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x12e0d1c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wV8Ae4zV7gJ6d+xzHeQB4wHysLah+2Me6fKI2JcuCZsvzxZtBzS9//YdUNuq0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\nRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12e4905f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eE9_i4_mR_Eb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Lag Plot\n",
        "\n",
        "* Plot point $y_i$ versus $y_{i-1}$\n",
        "* If the lag plot is structureless\n",
        " * randomness assumption holds."
      ]
    },
    {
      "metadata": {
        "id": "zzomnl2CR_Ed",
        "colab_type": "code",
        "colab": {},
        "outputId": "36bdddb6-2463-4ba6-d977-62c2ea4d09db"
      },
      "cell_type": "code",
      "source": [
        "lag = testdata.copy()\n",
        "lag = np.array(lag[:-1])\n",
        "current = testdata[1:]\n",
        "ax = sns.regplot(current,lag,fit_reg=False)\n",
        "ax.set_ylabel(\"y_i-1\")\n",
        "ax.set_xlabel(\"y_i\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x12e650550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eUJVqoyVwspOgVr/aJdun3RpgVMGRZEDhqARtDiOBBnPO+QUA/weAX+/w2KcB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\n2F5YueRgDQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12f0d37f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xqF7BBihR_El",
        "colab_type": "code",
        "colab": {},
        "outputId": "331471b2-ae5a-4dfc-dfaf-13e97795c0c1"
      },
      "cell_type": "code",
      "source": [
        "connected = np.array([testdata[i]+testdata[i-1] for i in np.arange(500)])\n",
        "sns.distplot(connected,kde=False,norm_hist=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x12f0f88d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1eeFakjqZjhe1nO73qHAmIUkqMiQkSUWGhCSpyJCQJBUZEpKkIkNCklRkSEiSigwJSVKR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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12f6af6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "3U1mGAA7R_Et",
        "colab_type": "code",
        "colab": {},
        "outputId": "35d47c82-3f28-4756-be95-cae0b227548c"
      },
      "cell_type": "code",
      "source": [
        "lag = connected.copy()\n",
        "lag = np.array(lag[:-1])\n",
        "current = connected[1:]\n",
        "ax = sns.regplot(current,lag,fit_reg=False)\n",
        "ax.set_ylabel(\"y_i-1\")\n",
        "ax.set_xlabel(\"y_i\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.text.Text at 0x12fc13518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TQojPAt/G8cF8wjCMxnKrexC50pMr1mYryHbo94+tUzNRN0lkl66ucO65BTfp\nTv49NpJkc8tJ5lvHqR2l14RsmPlDnleuXC0LbBwl4L0HUYr3vLGI8fKKLyPbDlmI24Bs23HXm/a7\n1zIzmSG/WWFprUg6NQbYbjkXuUt6+13zLOeLLK8XKZWrroCUemQkk2Rjqx6OW7XsUMd7u4l1UcmF\nVcsiN5Zp6CwYFlkWnKPg/W1sjevU15JhwTLCazqXpWw2lm4Jzq5l+f6sX8s2d9uDkIzY7zHEpjwM\nw9gEHggcno547xdwwnoVHuRKL9ihL79Zbiq4omjnxxZXNJZXyBeKJs8Yi5x7boE33TLF/SeOdJ1Z\nHGYjd/515sj7d8KTfGHbNmhag+1czqk8r3QmJ3QN03JW8XpSc88fpXjPnLuCHVGkQ6uZmyzbxrad\ncN8Hfv4Ilz0Oca/PY22j7IYJe+33a09d5sDsuKPMbECDbDpBNp10w4STuu5EY2GT0DXX8R62W7Oh\nlgQZTlRyYTKhs7lV8WXJQ/NFSdT9PXnfnb4Q39sP7otsjdsMx8yYYjRbCTw7FTeRcbsMgn+vn2NQ\nGeYDjFzpydWXRNp/u9kxNPuxxRn6V69S67dBX76e31ZmcdCPE7SRy7+rlo03YVuadoK2czmn8rzy\ndV3XSKLXOiA6CqBZJNnC0mbtOxpFcTKhc2iu7ubTdY0Hjh/x1fvyKolK1XLNNN65W84XubG2RbKW\npwGOn+HYT+7n5et5ltaK6LrmdiR0d2OvrvHioxfc87/y2jrf++F1bqrV1goWOPSGzcodhxennbBd\nK3GiuQIe8LWwbbcpU1RyYifUnx1/3xh5P3dD+Hi/URnmA4zsPTCaTflCN3Oj6VhCYOMM/ZMPczAs\nVD7M3WYWB/szBHthyL8Tut+rnEklmJnMkhtLh4YVy/N6beu6rjGaTXLLzeO87UjzFd/czBiZVIJk\nQneLKYKjdDIBX0CwT4dUsK7i0hylsZz3W3OtWnhTtepXUBdeuMHJ++50TD0hbYE3CmXf+csVC7Nq\nsbzuP/8jgVBfx2ymUa1FV3nPO5pNsS+XcTPTgaZhwnEHb9SfnSQzk1nnPmqOMtkt4eP9Ru08Bhiv\n/ddrxojrx9/sgd6uOStYpVYihXM7mcXQ6Nx3HKM/cO3vmgaWVe+F4bWZS5+Hc9xxREfNpZz74MpV\nOlxbrVxPHLuVS1dXWFqz0JOO8rAs2xVgXoJ9OrwK1rIc30zZrGLbjlJ0e5vXTFVB89h6ocxRMcsH\nH3hrqI/7+ZMiAAAgAElEQVSrFHBwy88Ho6Nevp7n5ulR37HRbJKKmfaZ0RaWClRMi5wnObHVznFu\nZoxLV1cbsuODznyINqU2+036n516lJz3fg9Chvgwo5THALPTDrEogZ1OJbZtzgpWqZVIYRymFNp3\n7tfzL5z6UwnXoeq1mctoq3QqwW0HJpvO5VEx61aSlc718dFk7Xyt78FRMcsHfvGneOTMRV5eyGNW\nbbLpBJZtc2OtSHXFIqHrTIwmXUEbzLvQNOeaNE1D1zSqtk2laqHbMDWeoWpZbiSRl4nRtO98wd/P\nvzr1NBWPJzkYrSWJ8n2kU47CD5ogU0nNvWetdhaH53Oce27BPR6VRR9lSm3WL8Vrmo16dvqdnb0b\niFQeQohrwM0hL2mAbRhG53F4io7plUOsnVVWlMCOkiKdhDu6VWrPXuSFK6ukkjrplF6LKCqRTSd9\nyVvtKs6o/IvpXDayNEY7nDcW+e6Fa75Oh0BLW37wmuUK+fOPXmA5X6RUqbrzqWs2GwWTS1dXXaXt\nzbtYWCpg2xaWbWPZnm5+tsbmlulGUyUSfuVx7z2HG8bg5fAbnBBfiQwGSAfCXw/P5VjOF0N3B8eP\n3crnTjvFIoLJhWfPXWm5c5RZ9MF+J8Es+qgdzBNPXQ6tNeb9TTZ7dnYiYXa302zn8dPAnwPvNQwj\nRKIohoV2V1lRAjuYuCXp1D7tFaay1Hkq4djNi2WzYUztKM64bOdRwuWRMxd9HRDbWbE+cuaiz8cg\ndbFZtUgnE27klhRcUom7zv6aT0P2B5FJhtl0kmM/OceFF26wXigzMZrm3nsOu6XTo7j/xBGfqS+T\nTjCRSjF/07ivFPuLV1cja2w1Kxa5sLzJ++69o+nOsd0s+qj7my+UI7+7HVTC7PaJVB6GYbwqhPg/\ngX8G3L9zQ1L0mjBBWCiafO70s0xNZJp2/JOf7zS5sNlOR9rEb5kdb/hcOys/77lX1ksNyX6FYoWK\nabesutrsvNdvbPpW05KXFxr9AM3Gfd5Y5K9eXq4VL/S/ZtuO07tStSgUK75e4G+/a561py6TL5Sd\nTHI0N9kvoWvM5LKMj6b4yC8fbXldQaRJ7ey5Kx5Tns6+iWxDRFSz3UGz3UWrnWO7Pq2o9+VGGxVH\n2Oej2K0JsztJU5+HYRiPAY/t0FgUMRFcZXlt1a061UHnyYXt7HS6XfkFz51O6u61jGZTbg7CzGTz\nqqth5/WuxquWRalc5aZ9AJpruqlaFoViYxXdsHGfPnuRr/33FzGrUd4DZ/eR1HReXy1i2zZXFta5\nfmOTS1dXufeew3z3wjWuLm5QKlfdHUu1avP66hbTk9mmc9UMORfXHttwW88G56rV7sD7u/AmDXpN\nkFFz3u5vKup9cm5afT6KfifM7gY6cpgLIX7DMIz/ENdgFPEQXGXJaJ5giYeo1bPXeZwvlMnVzCPb\nsSd326fBe26ZC2HZdq0Qn0bFtBuS1eR3y8+H7YYc01I9l0bTNMyqxY3VopsTAo7pyJthLgkrJf7i\n1VVquYiRnQUBqqaFTd0sJc1DzxiLnLzvTv7lH3/f53ayAdO03MoD3dJORFSz1bl37l5Z3GhqggzS\nrk+r2ftuPzjVdTBJJ8EoKiornE6jrX4TUMpjyAiusuqd6hpXlGEPCtDgPP7uhWvcfnAq9CFqtasI\nq98ETi7DzGSm6Y5Bntu7e5JlRkYyScCs/etPtHv19U1evLrqCvzg+S9f9ztqdU1zKtbW/BLSZOOM\ns+T6KSSylLjk8vU8pUqVZEInmdAxawrCi1b7nqplk0rq7nVIXr6e56iYdbLhA9jA4spW6Dx7aSb4\nWt2ndlbn3Zggg2N63713NBXGUTuY7QaTtPN5FZUVTafKI6SSvmLQCa6ycmNpUiFFAdPJ8JDcbDr8\nZxIlHFo1+gmr35RNJ5iZzDQtVOg9dzDZUO6iyhWLkUxjGGnFrIbuGNzzh/yynYq1Grfc3CgY1wsV\nXzZ1cBVfMS20mp8ildRJBkKUvcrCxmpQHA3nCnm5ElGQUNJK8LW7s+ik5pXveERJ92ESxioqK5pO\nlccfxDIKRex4V1nBB7gVlxfyzIU4iaP8E35beH31n00neeRMPXrHa09/bbnQoDjCvkNWSy2UTGzb\nyXHQNMc09MprG+g6bl0nLzIXIrhjcPMO5vzhq5KJkcYxjWZTbr7HmXNX+PLjP+L60ia50Xq13VRS\nx7KdBklQL51u2zTsMrKphFv2xMuhOSfnIZnUqZYbFUXQ7BikleBrd2fRSkh24nweNmGsorKiaZbn\n8bcNw/i6EOJXPYe35N+GYfxx7KNTNNAL+2unIblRa2KvcAiO6+13zfOMseiapmYmMxTLJq+8thG6\nw4gizJdQLJvoGlRtJzvaskCvZWI7xQ9tp4QG9RwE6cyVNa+C1+CEr15o6Dl+/NitoY7ZoJkK219t\n18lst9wSJZWqhaZpjI8ksW3c70inEmyVTKxa8cJ62fcU959wQm5vPzCFcWXFjbaS1XNvO9CYje2l\nleDrVRJqs8VCsPHSsAnjuKKydoMfpVWex9eBvxbymg0o5bHD9HLL30lI7qE35CiWzIbjcoUaNq7r\nNzbIpp26QvnNcs1PUEHTYDlf8oV/plPOSrzuA6nvSMJ8CbIUxnqhXjHVG0o6mk1RMe2GPICltZJb\n88p7DafPXuTxpy6ztuE43SdGU/zkT8w0dcyeOXfFF2GkaU7YrdzZyPFPT464uRPL+aIvR6RQNHl9\ndcvZQWn1UuqH35DzVRq+/8QR/u1Xz5PfLFO1QNdhYtRRLt6eI6VayO1tB6Y4PJ9jZb0U2gfGK/h6\nkYTqTQANLhaCv89hC5GNIypr2Ex3UTTL8/hk7d+/F/UeIcR/MAzjN6JeV/SWuLf8UQ/K/bWks6gV\natS4Lr266uunUDGdAnyWZWPXNh6lSpXNYoV9E5m6osmXmM6NcP+JI6G+BICtotmQ+V4xLZbzJUBr\naEQUJsyDiXDSlLRRqHBoPtc0WfHzX7vgi86ybcCGqmWxVapSrlRJBXIngkJjOV90QnX1mhlLmrK0\nRiGSTScpVyx3x5JNJ90SHVIJyZ3J6ytbfO+H15kYTWHZNoWiyWaxQibllG3pRThq2Mp530SWW24e\nd3cfS2slUkmdR85edK9n2EJke7U78zJsprsotlvbqvv6D4qOiXvL3+pBifphR42rYlq+HhrgCFld\n15waV1UL27JJJnTKFYvpXL3MyHQuy1Ex22BKk7WxShVnp6FZ9TBYTXMc5ktrRd506xT3Hz/S8qH/\nvVqJjSCyn3gUYS1Wdd1RWiMZ538QvqqUY3IVh94YZeUlqgSLLNEhlZBE7oQ2ChWfgnXe0yRmuE2i\nVs6Fkukx39XH8sKV1Ya8j342UeqUXuzOvAyb6S4KVRhxiGhny79dW2o3D8rczBjP/XiJ/Gap1jdD\nIzfmlJC3AjLWyZDWmZtxHPCvvOZcT7DarnyQgtccrHLrbecqzy+Pt3Mtsm94u8clmZTOmmW7jZY0\nNBK6Ftm3W64qvWP63377sYbrDiOqg5/s81GOOEfZdHwurnLSnB3Ydle4jsnObKh5VTEttkLMm6mk\n3nbNqb3AsJnuooizh3kCpzugwHnEfxMoAv9P7e8fAv/IMAxLCPF+4AOACXzKMIyvxzWuYabVlr+Z\nLRWch97bx/uNB6YilUsnSiid0lnJFz01m2xW8kVu2jdCMqH5/BuaVk+GA0+P6kDkkHyQZHSVV1CN\njaSoVi2KlSqarqEjO+DVu+VF9dUOMjGaDk22m4gofyHnZr1Qwaxasip6rRl5Y3l0Sdiq0lug0PIo\nomw64XM0R3Xwk30+wvJAwpD+nu2ucH/86mrD7mJprcj4aCpUGebGUkO3qo6TYTPdRRHnzuM9AIZh\nvEMI8XPAQziP2G8bhvEXQog/AP6OEOK7wIdwTGBZ4EkhxDcMwyhFnHfP0mrLH1nM7+xFiiXTJ3zW\nAdv2V3OVdOrQe/r51xpEpg1sbFWY3z/m+hsKxQrL+RKlSpWFpQK5sZS7kwgmLHofpGLZpFg2qVZt\nymaVrZLJwdlxKqblKhVwBHAiobG0VqJi2g2RPmG8+57DvuJ/4Di+R7PJ0Ex3OTflStUpl177TCLh\nlE3PJMOLTYetKmWBwuV8CdNy8kKSCUc5eud/JV/k5YV1x+xkO9nviYTm9iiRLW0lCd35O2AxdJMx\nt7vCLUXsrnRN4023TLlBDd4AhmFbVcfJMJruwmipPIQQP20YxvcjXo7MbDIM40+FEHIHcQhYBU4A\nstfm48DfAKrAd2rKoiSEeBG4C4j6zj1Nsy1/lC1VNvUJtg8NVnOVdOrQWy9UQpPYiuWq24/60qur\n5DcrtZaluI7xN9XKe0f1qH7kzEU2t0wnGklzBKRl27y2XGA6l2H/1AjXb2y6vS+kDE0l9bYiWKRf\n44mnLrNeKJNJJ0jUfDJhme5ybmTvcemktm2bmakRXykTL2GrSlmg8HOnn8UulN3wYOnbqCt9x5dQ\n1w/OfiebTpJNJ1hZLzu7oFreSyadIJ3SmRhLs7BUaDjvdle4sp9H2PH7TxzZFavquNkNprt2dh7/\nUghxE05o7inDMBY8r32j2QcNwzCFEF8C3gv8EvDzhmHIR2AdmARywJrnY/J4JEKIB4FPtjH2PUV4\nvagKWyUnv8JxYNd7Wsuch3bLYEeZHnQdqiGLUW/DuzXXNGTXkgPrjnEpwKWp7MuP/4gz565w4tit\nXL6ex7LshvyMYi1p7tabJ/h77/lJPnf6WfIhArgd+/4Dx4+4Y3j41NOh9mh5Hjk3qaTu5GfUJLpZ\ntSmWq7zljU6Ib7uryqMiurR5XemXnV7kloZtO9V5zarF4kqB2X2j/PQdN0d+53ljsecr3NsOTIG9\n0lBt97YDU7tmVa1oTUvlYRjGXxdCHAJOAv9VCPEKjt/i/zMM4/9q4/O/JoT4OPCXwIjnpQmc3Ui+\n9t/B483O+SDwoPeYEOIw8FKr8exmgrZUaaZKJnR31WpaFkmcCB9pA29WBtvrGPW2GfVy4KZxrry2\n3jCemckRdzyVmqlDms2kKctb7yrMVFY2q5hBr3sNpxjiKh8TdzftLdEJC0uboc7goANf02jICl9Z\nL5JK6R2vKqMcqPLs0izn5JLUX69atq/7no30/dSJY4V74titXL+x0ZDoKXcXu2FVrWhN8/oGNQzD\neBln5/GfgbcAHwZ+KIR4b9RnhBAnhRC/VfuzAFjA0zX/B8C7gW8D54B3CiGyQohJ4A4cZ7qiQ46K\nWU7edyfz+8fR9XqFWWkqkh3npNCTNvCwMthQrw8lhZdsM3reWPS9/9d/4S3sm8iQ0OuJbrqmuX4W\n57P1n5rXfCYVV5SpzG4RjCSjm+Zmwm3qndraM6mE75qlM1jmjci5CUYVaRokdZ0LL9xwj503Fnn4\n1NN89DPf5Ld+/0l+6/ef5KOf+SYPn3raN4cnIkw6h2vlSeTcBf3iMjjgmZrivX5jw2dqC96nXhH8\nnc3vH4/sBa/YvbTj8/gHOLuONwBfAn7GMIyrQoh54BngaxEffRT4ohDiW0AK+AjwI+ALQoh07b//\nxDCMqhDisziKRAc+YRhGMeKcigBhUVGy/epHP/NNTyRO1q33ZNs2+6dGuO3AJIfmc66paG5mjMPz\nOS5fy1MomdyoRfLIchneNqNeQXFUzPLhXz7q6w4oHeHSvOQNsfVG5Hg7y4VSi84Ki+Ipm1U2tiqc\nNxbjj2CpTaO87n/2h99zampRr1llWhZL+aIrtL0lO64sODuzmckM12/YDf22v/XMVb797DUqZpVU\nMsE73zrPz77tIKcee96dO6d8CW5uiCzzIs1bQeJOOgvb6TRjN5TkUNRpx+fxs8AnDcP4C+9BwzCu\nCSH+j6gPGYaxCTwQ8tK7Qt77BZywXkUHdFI11ZtkNr9/nI+dvLvh85eurnDuuQW3H4aGE9kT7KgX\nZgoKK82d39RrEVGVWl6Ho8A0zRlDO53lpsadfJHFlULNOV0nmdBJ6M5u6OR9d7rO+e3Y2kuVamj3\nPG/o71Exy3Quy/pmGcuyfWY1XaOhErG3SGN+s9KghE+fvciTz15Dw6lsDPDks9eYv2ncvSZNW+PG\n6haWZZNJ+5V5lPCOKzy2m/Iau6Ukh6JOOz6PX23y2v/b2+EoOmG7VVODn5fmJBmFJXMwvAIPwk1B\n541Fnrm4SKVS9xPIFbPcNUgFFmbicPI5ftAgtGXHuNl9o+65bGy3pIc0vZ09d4WPnby7qSAKrnzT\nKZ0fePp/v/uewzUlZjfY84PX/O57DvPV/2r4HPkakBtz2vJ529V6d00yTLliWry2XOC8scjjT10O\nHe8TT13mj37nb7Sshnx4LuermRU25l6u+rspr9HJZ9QOZThQGeZDzHarpgY/79r5q7JZlF/4S4Km\nIFeo2fXzOI2dssxMZqmYlq/3RbQg0Hz/gMbtB6fcwoSlyhLL+aKbEDg24kRVFYqmr/93mLAJCt7n\nfrzEynrRNQGtb5Y5/Y2L/Mxb5xtG5eSnFH3nv/3gFBNjTmkQia5rZNONeR5SCVu2TbVqU9Gc+bRt\nZ5eytlEK7ecRzHKPup9AQyKlrAgcdu3bXfV3U16j3c+oHcrwoJTHENNOmYNmkS/Bz6eSeq1Xtu1W\nRx0bSZKoCdgo4S9XlU4Z8npuZ36zzNzMaFvO1Kj6TXJHAXDthtPLWyqzza2K+29UXkZwjPWxOeOs\nWravttSFF27wwQfeytlzV3jupRvkNyq1sNgtcmNpbNt2zVLTuQzlStWnXOWu7ZBnNyCVcLXqZMBL\n5K4pqhHUxGi6qU9L4vhYwtI0w69d0q1PpNXvLmzMrRqEyfevrJdIhzQqG7aigXsBpTyGmO06iYOf\nT6d0NosVXy/timnxwM8faVokUK4q5QPvLVMepjjChEvYynQ5X+Ly9Ty/9Ftfx7ZhfCQVoqBKJHS9\nIUM9KGyC55cRZ8FyIuuFMkeF07P9L59bcN5XC8tdWXe+dzqXdc1SubEUry1vuecrVaos50t8+Jfv\ndMexsLzJ9GSWS1dXsWwaclHGR5NsFBrNTne9aX/LVfh5Y5HfO/2sW3rd2yslmJsSpFufSLPfnXfn\nUCiaPGMscu65BeZmRqmYVsPiIFhy31sqppWfTdFflPIYYrabkBX8fEJ3SoiXK1VfQ6SXr+Wbnsfv\nmK87cuf3j7c0H3lb3Xrt9sv5EivrxVrBQafl6sp6lX0TWZ9D27JhNqS5VFDYBFe+Cd3pHa4FiiTI\nmlbSDxFULvnNshv6DE6yYjDfY32zzItXV3ng+BHf9UclIP7kT+zn0HzOzXKfGE1z7z2HuRwx71Ip\nyLmUAtcxF9bzaKKKS7pz0mXJkGa/u4dPPQ00tgGWJWiymaSvJH5wV9SJn03RX5TyGHK2m5Dl/bw/\ntLdOq1VfJzugKBNKEGlWkmYeDQ0bm/xmielczhUsaxvl0K6EQWETHGNuLMPKetFnRgK4957DQN3f\nIL9XIhWFNEs1tLutnS6spHuzeToqZhve/9HPfLPhvVC/H3IupcCVSMHrLS7Z6zDmqN+d3OUE58XZ\ndaSYnsj6zG7Bkvvt+tkU/UcpD4VLtyvUTnZAYSaUQtHkteUCE2NpKhWLdCqBZePrdZHQNcxa5Vkv\nMhorSFDYBMf4ljfOkEo5SX3e1b4U4BOjadbWS7W6VfXzJGuJlrJF7INf+K7ve2SL2RtrWw3Z+J3u\nFFvdDzmXwTL1UvB6M76jvrfXkU1yzEHhLxMdW+0IHbNWJ0EWin6hlEcMDGuoobf8uXSca5oW2ota\nErzW9917R9NrDQoLad5IJXVGM0lwIl0ZH0lRKtdzK3Rdc8uqBIVKWJvYsDF0skv7qTft58+ffgVw\ngr+k/pjOZX1+nFQyQcWsOoULA+cIc9x3MoZWO4a5mTEuXXVqTFm27baznRrPNPiawr43jsgmOebg\nbkj6pFrtCIHIcG7FYKGUR48Z/lBDG7NquSXCEwmnXWrYNXRzrUFhIc0bQYf3dC7L9Rv+Vaqua6HO\n+zhqKZUrFvsmsm6Dq2StwdWbD037vmtiNMVyvtqgOGQE1XaihFrtVA7P5zj33EL9+2rfee89h9v6\nzjjaobrVkM9e5IUrqw096VvtCNVOY3hQyqPHdPtADsJuxQmXTZHfrPhMNe2UbvcWE/zc6Wf54ANv\njYyyKpRM1zyFhi9CSJJO6Tzw80canMhhUV+dzl07719Y2mQ6l2E6l/EfD5hd7vyJGZ778VJDvoem\nOV0SZSLgdoRx1GcvX8u7fd87CXBwryWmdqhyzO1W9G1X+QfvmyylM2w7/N2CUh49ppsHclB2K3Ls\nQXt1q9Ltwcia/GY5NKRUHvOap/ZNRGdHe0ulR9Hp3J03Fn2Z7NdvbHLp6gof+MWf8r2/Xf/PiWO3\ncunqCsmE5vpjnKZU9WKGpx57nhevrvZc0C0sbbq5MbI97dJakfXCYlsKK+52qL3cETaW0lmtldJx\nFh7Dt8MffpTyaEI3u4FuHsg4zAfdIMcetFenEjqFoknFtHxZ1vL9+c2ymz0tW8EWihXf+NuNspK0\nG13Tztx57+PicoFC0az3NKmFtz5y5qJvrjuLUNJIJnWq5Sq27e+QlhtLUShW+Np/f9Ht2x7WHrgb\npeL4PFZZzhcpVao1M6PmKixoLki91+jtjd7Mx9UvGpM8y7V//SG9Kplw51DKI4JudwPdhEXGYT7o\nRvHJsQcT8dK1MuUzkxlfFvfb75rn+o0NSpWq0yK1hobm9tpodY1ls7qtgoat5i54H9cLFac2FvVI\nLsuy+auXl0PLm7QaVz0zfpxC0WRhueA0bMKuFZhMObWsavPjNe/9m6/8D8ZG6o2xOlUq0uchFb2N\njVm13VyVVoLU9U+cucgrixukEjozk1mKZXPgVvGRpXQCu2SVTLhzKOURQbe7gW4cgL02H3Sr+Lxj\n17RVyjW/RLlWaTbol3j5Wp6T993JQ188B1BL6Kt3Kix7el03u8btmDdazV3DfayFTsmyJLIqrqZp\noeVNWo3LK9RGs0lGM0lPIUhnvmS9qaB5b71QqZWsz/oyr2X7WUnU/XN8HhkWlrZql+bsPMoVJ0qt\nHUF6VDRWQ5YM0io+rJSOnFff+1Qy4Y6hlEcE290NeHsdvHh1tekqstdJXNsxg4UJzGbJg0fFLDdN\nZbmx2tiCRTZQgngS1do5b/A+ppM6pUrVTfyTfopgX+52Bac0Hcnw5qptY1s2iYRjuhvNpkglddKp\nhK+svAz/tSzbDUiQtNufw/F5pBjNVnwr8ErVCjUzRl1PXI7zXhI0sZlVi7JZRdNw5xlUMuFOopRH\nBN3uBrpx7PU6XLHXwqDVXLzxwBS2vdoQ9XPbgXoreu81Xnp1jXKlSiqlu4qu24S1VnMXHPt0Lsvr\nq1uOMqw5J5IJnemJesmRdqr0SqTpqLGvh8ZSvsR0boT/5S1zPPnsNV8/EvmvaVlQ8Z+z3f4c8tqC\nZkYNGsyMn3/0AtO5LKVKteGa4nac94IwE9toNkW5UnXn+f4TRwZmp7QXUMojgm5Xyt069noZmdJr\nYdBqLuo9rZOhr0vk9b346A/YKpnkNy3yG4tcurrC8WOHfJninUTPNJu74NhHs0lumhphOpelbFZZ\nyZecBMXa2L1Ji82q9Eqk6WhxxWM6SmqkUwnmZkad76lYTgfBpYJPM8hSJsFdXVh/jkKxQsW0fQpN\nXluwIGUmlWBsJOkel9ckqxwHryn2Low9opmJbTqXVYpjh1HKg+bO5U53A4Pg2Ou1MPDOxXMvLbFR\ncDKaf+/0s7zbk3vRzlw9cuaib5Uso53+7JuXuGnfSMP7t2t39+94pB9HZ18u6/YObydpMWoc0nSU\n0EskPJYvb3izbTv+j2A7Xdt2yq6kkglf1nxwTIVihaW1EjOTWZ9CC3ZPvP3gPo4fu5UvP/4jn0LK\newoneqOqgvk4w5CoNwwmtr1CLMpDCJEC/gg4jBPR/yngCvAHgAlcBP6BYRiWEOL9wAdqxz9lGMbX\n4xhTFK2cy50+QIPg2ItDGBwV9TLl4JhlZAMloKGCbBSXr4cnsK1vVUKVRy+EghzXtcc2GKnll0QJ\n4KikxahxNAtvBuc+27XvS+gaJn6zlK5r3HZgkk//o59pOLccU8W03dbAwdfDuieeOXfF9xuU49I0\n/OXsC+Vt/db7wTCY2PYKce08/i6wZBjGSSHENPAs8D+Af24YxmNCiK8Af0sI8X3gQ8DdQBZ4Ugjx\nDcMwSpFn7jG9zrForN6adstRe4nbJBCHMGjWLvX2g1Pt+SvC+x6hRxzvlVAIu8+FosnnTj/L1ETG\nrcsVFLytxhEV3iwbPTXsJLT6FCQTeq2sSON5vfev02rHwd9gULG5x2sKbpCiqloxLCa2vUBcyuMR\n4E9q/63h7CqeAaaFEBowgeMmPAZ8p6YsSkKIF4G7gO83O7kQ4kHgk70YaK+3wcFV/+0Hpzh+LMfL\n1/Khu4BWTuJBKFsi8bZF9SYF3ljb4vOPXgjNVwheZ7XqNJjyhvQCHPDYsb25EGEJa93MSfA+e8Nm\nJ8fTDbkrQaKEU1R4820HJht2e//6y09TteoFDDOpBLmxtBtaG0Wnq+3gb/DwfI7ltS2fcoO6ghsm\nk88wmdh2O7EoD8MwNgCEEBM4SuS3cXbrv1/77zXgL4Bfqv23ZB2YpAWGYTwIPOg9JoQ4DLzU6Vjj\n2AZ3UqunmcnMKaVR703tlNJY5QO/eFdsD0szwTwxmma9lk3uTQoEeH11i4TuZDfLHtpna6v9R85c\n5IVXnCJ5I5kkZrVCtepEO0kB+uvveYvz3rMX3Ra4M5OZhoS1bnNYgvdZZsXbdr3lbm4s5eaueIXT\nofkcZ85d4cuP/yhUWTW73975zKSTTIymG8xP3t9a2Px3s9oOjum8scjnTj/L6kbJVV7SF3L7wanI\n8wzS4kUyLCa23Y7e+i3dIYS4BfjvwCnDMP4T8G+BdxqG8Wbgj4HfBfI4uxDJBLAaPFecnIh4AHdi\nG7o1x5wAAByPSURBVNzMZAbSuVz0Od2X1oo8cvZiLOORgvn6jQ2fY9bpkQ3vrjVKqlb9JhRN05y4\n+4p/nM+9tMSpx553/RwV02Jzy2Ri1MmqzqYTHBWzrjI8KmbZN5HllpvHmZsZbYhOg9ZzFkXwPsus\neNlJUDruL726ylExy8dO3s3vfvhdHD92K9+9cC1yTjqZz3RSY2mtSKHoj6SSv7Wo+Qenne/8/nF0\nXWN+/3jHJcuPilnuvecwuqY5Pek1zb1Ph+ZzbY2/k2tX7H7icpjfDPw34IOGYZytHV7GURYA14B3\nAOeAh4QQWRzH+h3AD+MYUxT93Aa3MpldXgh3Lr8c4XTeLq38PzKq6j//17+iajmRQrmxNPnNMlW7\nsWXrRqHCvolMg729XLGYmxlF1zVfVzloPSfdmhmD91nTNLfZlFVrMmVjc2O16DOT+SsHR0cqhRGc\nT2/GebAnSbAXubeMeZRjvFNkWLG8Bu9uq53xS4bJR6KIj7h8Hv8E2Af8jhDid2rH3g98VQhhAmXg\n/YZhLAghPgt8G2cX9AnDMBpTlWOmX9vgliazqGyxmGhHMN9+cIqpiawr5LLpBOubTr50sB+4VXPy\nBh228r/DTIOt5sTbAMkrAG8/uC/yusIaVv2Hr13gxmrRSe6rWu5Ul80qn3/0grsbqlcOrkRGKkF4\nHaqw+RzNptB1jd/98Lt845O9yC3LplA02SxWyKQSTOcyXfkkwsxN9Yz09iLJVFisohlx+Tw+DHw4\n5KV3hLz3C8AX4hhHu/TLrtvKln34DTleeKXRindoLtzM0A7NrrWZ4D5vLLq+C01zFIM09SQSGqCT\nTGhYrs8jRcV0RHKwTaoMWw4zDbacE08DJKibm44fy4VeGxDqI9k3kcW2cQoZ1l7TkEUdHdPgUTHr\nqRzsTwOXkUqPnLnoS+jzmpra9afJFb6m4ctSL1eca5vONYYwNyPKL5RNJyPL34ehwmIVzdjzSYL9\n7KXRymR2/4kjvt4TUijL/tmd0upaowT3ofkcpx57noWlghNlZTqOZtkSdjSTZCybbFjRvv2ueb57\n4Zrbl1qWLzn8hlxkKYlWcxLVAOmZWuOh/GaZUqXKS9fW+N4PrzOSSTI+0jg2tFq/bLue6Q3UFGHd\nNOi05v0BhaLptOWtFX+UkUovL0TXoZKfDd6/oNIMW+HbNTNg2azyyuJ6RyXSe1X+XoXFKpqx55VH\nv+26zUxmjjP5p3rmj2l1rVGCW37OV3pdcwScrmmMj6T5jff+T6Hj9PYXd8KW26tX1ayIn2yA5OXS\n1TUsyw5EgtmsF8puL3SvAilXnFLw//d//B5Vq16RVtfCEk40dB3c02rOuZtRN+1ovn/CkjrkCt+2\nndwP02PiSyZ0iuVqRwuaXpW/V2GximbseeUx6HbdXvpjwq61UKw0FAEMOrG//PiPgMYaTNJBXq5U\nI8cpj58+e5HHn7rMXz6/wMRo2lfWpBOiTCkVs0pC1xsiweSQg7XFZCn4Nx+abmoalP06ZveNBrol\nOuc7FFKHSp6/3uujMTPcO1dyhS99Q5rm+JBkEmGnyXy9LH8/SGGxgxg2vJfZ88qjGwfsICIfrB+/\nukqpVr/ptgNT7gN23lhkZb3kieZJAzavrxaxbZsrC+uReSRSGGluIXEH6SAPljMPcvrsRbeMCcDa\neomvPP5XPPadl7jzJ2bcMUoFI3uWhymYE8du5d9+9RnymyWqll0zIWVIJnTX1ONF+mcKRdOTz5F2\nTS+tTINS4QZNb5rmhM8CkaYdqXSDBBcm3oqxL1x1FJmbfU57yXy+fJJUgkLRbFmocpgYlFbNijp7\nXnk0c8AOC/LBCjYbwl7h+o0NXry6yncvXCOd9OY0OErDrFokaytbbx5J2Mo4k05AGTesNZPS2ZfL\ncNuB6CQz8Jc18ZYuX9souULgW89c5cln61V1g3WzJC9eXa1FJcnzOe/dl8uwtlFGQ/MpkFRCxwZ0\nn36z3Xk7c+4KmqYxkkmSSdu1ciGaa6rzruK9u4j5/eO+OQoz7XRS6sS7Qzv9jYuUzarrW5HfGeWo\nDgpWZydkk80kKVequ8Lc1G/zsqKRPa88ohywUbHvg4h8sGTGsESaVp546jKT4+mG0t2VmuII2vmD\neSTBlXEmnfAJtVYrWm9ZE9l8KfjfXsXh5YmnLvuUx+NPXXYd9V5KZafb4Uq+RFH28669z7ZhZirr\nM1t5o6RGMkls23R7YIxmkh2VK4ky7XTqcD5vLPLdC9eYmcy6i4DNLZNs2rmPUZ8LE6yj2RTTE9kG\nE+SwMujm5b3InlceUQ7YYfpRygcrmIwn/84XykyOO6YPb5z/j6/lIxzEjUgBeb4W1dSJA1WWNQG/\nWSnhUQBls0o6mWj4rFfxhP0tKVWqfPR9/7Ov2VQ6pbO2WSY32pjb4I2SKhRNp8ufZbO4ssXsvrpz\nPaxcSbur+E4dzlIJBE1kFdNumlE+SII1Lr+EChsePPa88ujXj7KXD1lkWfBaPkVuNB36uYmRVK2H\ntp9meSRhNZMePvV0w3V4r29sJMXaegld13xmpdxYxj1PmOIAR/EE/17fbFQgE6Pp0B3Aw6eeDr2/\nEmnqk7ugqmW7yYCj2ZTbareda47C25K4GQ390GsLGl3XOqrb5R5v4zfcy99hnH4JFTY8eOx55dGP\nH6X3ISsUKzxjbHLuuQXedMtUV60062XB/cl4sgz8vfcc9nXpk/zCu27j7LmXm+aRNBMu/uswecZY\n5NxzC8zNjFIxLVf4pRIaE2NpqlWrVozQURzTubry+Jm3zoearu6t1dOSvPuewz7ne9T7gnPjpVA0\n0TWNV17boGo50U1SqckgAJkUGOzeB+FJh9BYQfjHr66ytlF2S420EqbdKoFuf8O9FvZx+iVU2PDg\nseeVRz9+lPIhC5a8uHw939XD6y8LXjfZ3HZgKjTfIioPI6xcfDPhUr8Ov6P+1dc3SOg6UG9gNJ3L\nML9/nI+dvDvS9DV/0zhPeKKt7g2JtpJ/P/HUZVY3Sk6eyWiKy9fyoYl0wfubTiUoFCuMjSQplqu1\nHYfjKJed/cAxgy2tWQ3d+7LpxkdG9gVJpXQ2CxUKJZNMKuGUPbHx7WTkWHrhI4m6xnZ/w70W9nGb\nzwYpbFihlAew8z9K+ZAFS17I1qXdPLytrqFVHkYYrYRL/Tr8ZiQnhNY57vUlSSES9Z0PHD/SVu7H\nA8ePcPvBqbZXzd7ve/jU0xRL9byMxZUtqpaNpsG+8QzlihNIoKOFdu8LZpRLxWlZTsZ9xbRcs5xZ\ntdzii948kyhhup2FTDe/4V4Le+WX2Fso5dEH5EMWdHDLZLDtrNR6ZcM+byzyjLFIqVx1zDlu86KU\nO76o65Cr90qg50cvhUi3q2a/XyHF7L7azkCD6VzWfa1QMhnNtH488r5AgLo/p1p1TGBVj1KRNJuH\nnVzI9FrYK7/E3kIpjz4QzCiWyGSwsIe3HaXQKxu2PI9ZtTyF+hyh+PpKlc0tk49+5pu1ZLRK6HVs\nbpmuMpR4+1ZsV8F1s2oOJkqmUzrliuXuGrZKVbcDYFSORjCjvN4f3FGY0ndi1zLEZakUbw/748du\nbZnU2SndzGmvhb3yS+wtlPLoA8G8CZlbEpU30a5S6JUNO+o8subSxlaF4sJ6TQAnmJnKsnCj4OtD\nkU1XmJ4caUhS65WC63TVLL83XRPipUqVzWLFNSvNTGYZySR8wi5MsMpgAikgc2NpUknNDTpIJDTM\n2q5D1zSyqQSJhM7keJr5/eO+nuZRSZ2dzkW3cxqHsFd+ib2DUh59opO8iXaVQnjtKrOhdlWrh1ue\nRxbqk73KHcNMvU9HxXT6kc/fNMav/723NLRuvXwtz8LSpi9EtVcKrtNVczCHYnGl4FwjNjOTI67i\nblUkUh73BhV8/tEfOJ0UzaqrNFIpp1zKbQcbI+gePvU0EJ3U2Stl3855vL9D2Wr3TK0isFICimYo\n5dFn2lmptWuiCa7G5co2ldQb2pq2kzcgzVF6sh6BhFavaSV5+Xredx3NVsK9ctJ2umoO5lAkdB1p\nVQtz6svvaE+Aao6StWy3P/jtB6e4/3h42HWrpM5O52K7c6rqRim6QSmPIaBdE01wNS5XtjLfQ9Jq\nRVrPG0n5QollMrrseRHFmXNXfC1bpTnr7LkrPXXSdmIiCX6vVIxeX0Q344iqnDs9kY0cW6ukzk7H\nsN05VXWjFN0QVw/zFPBHwGGc3uSfAr6H0zFwH5AAftUwjEtCiPcDHwBM4FOGYXw9jjENM+2aaIKr\ncTScWk1tth0NO4+mrTpOZdtmbcOmWrWoVm0szXar2GbTCV+OxaVXV31KRxab1LRVfuO9d/UlIic4\nhzKhMqhYOx1HN6t+b1Ln66tbrllQ05zdYqdj2K7je5DKmyiGh7h2Hn8XWDIM46QQYhp4Fvhz4CuG\nYZwWQvw14M1CiE3gQ8DdQBZ4UgjxDcMwSpFn3uU0i5ppx0QTzGnodkUaZobKpHReXyliWhaW5exE\nkrrO2EjSZ+YoV6zQc5YrVldO2l5EZwW/12lMlePla/ltOYu9q/5C0XTqUZkWubF0ZPc/N2Di7EVe\nX91C10FDr1U37rxx/XYd3yo/Q9ENcSmPR4A/qf23hrOreAdwQQhxBriM0+P8OPCdmrIoCSFeBO4C\nvh/TuAaaVrbnbsuWBOl0ZVt3Nqe4aV89sS6ha9y0L9uQOZ1J6ayHnCedSnR8Lb20x8cRCSTnOBg5\nlUpqTccpM/QPzU00vBZHkmgzVH6GohtiUR6GYWwACCEmcJTIbwNfAlYMwzghhPinwMeBi8Ca56Pr\nwGSr8wshHgQ+2eNh95XzxiK/d/pZX7OmYARQp/QqFDOYWJfQSx5nc93ss7C8yXljkVJF1rDyJhem\nue1Ay1sL+HcaK+slLMtyMr8D/pNBsMfLMXzu9LMAvnBlaH7vBsVcpPIzFN0Qm8NcCHEL8DXg3xmG\n8Z+EEP8G+LPay/8FeAh4GvAuvSaAxp6gAQzDeBB4MPB9h4GXtjvufiBX17JarGzKJGtDbUeY9GK1\n3a6zOZ1K1HIpNLepEuAqwnZWssGdxtp6CdOq9x3x+k+8n+lne9KjYpapiYxb9t5Ls3s3SOYilZ+h\n6JS4HOY3A/8N+KBhGGdrh58E7gNOAT8LPAecAx4SQmRxHOt3AD+MY0yDjDQLBaNvZG2oOIWJFLyX\nXnUc45mUzhsDmc4njt3qa9WqaU5HwKCzWZrrG5pOmVbTfhRegpE/3nIfMmQYcP0qvTBr9UL5dKMI\nlLlIMczEtfP4JzhRVb8jhPid2rFfA/5QCPEPcUxVv2IYxooQ4rPAtwEd+IRhGMXQM+5ipPkiGBor\na0PFJUzq7Wvr1X3XAdteDcl01tx/kgmd0WyC6dwIZbOeQR7Vs9tbzqMVXlNOoWjWciccJWLZttu8\nSvpPthtm2iufSjeKQJmLFMNMXD6PD+M4xIP8fMh7v4ATwrtnkavW4Io9N5pue8XeDfX2tf7qvnLH\nIwVwZC5Dzt/mVNaDCpaat23aFshyLqQD2klIdPYfZtUik04wPZF1/Sfb9Rv0KsehW0WgzEWKYUUl\nCQ4A3lWrbBNbKFaYzo3EWi4iMtO56s90bldAy+sIKiNZ8LEdgVw/h+P/SegapmWTqvk8krru859s\n12/QS6e1UgSKvYTe+i2KuDkqZjl5353M7x9H17VawyGNYtn0lRU5byz29HvnZhwBG3R8y2q4UgDL\n9zV8PiCg5XVoGqA55/X2xGhHIMtzSCtZJp1g30SGTM1MpWn4dmMnQsxChWKF5XyRj37mmzx86umm\n89butSkUCj9KeQwIR8UsHzt5N7/74XexL9fYhAiclXsvkYLX6/i2LBuzavHKaxss54ucNxZDBTSE\n2/OPilneemSWW2bHmZsZ9V1HuwL5qJjlbUdmueVm5xzTuSxzM6PccvM4bzviX91vV/F2cm0KhaKO\nUh4DSDumlPPGIg+ferqt1XUUUvDefnAf+6eyZNMJ1yE+M5mlWDZdc5pXQM/vH2/qi+mFQO70HDaO\nb2VhuRD6epTiDSqfVtemUCgclM9jAGllx48r6zqqnMnZc1f42Mm72z53L6KI2j1HcC7W3TLn2chq\nuWHfpZSFQtEZSnkMIK3CPsMihApFk8+dfpapicyOdueLoplAbjevoh2hHpwLmSsT7J8+7D6MfidC\nKhRBlPIYQFqtuoNC3ltXaXI8vWPd+bqh170jgnMhc2WC/dObmcwGXTCrfhuKQUQpjwGl2ao7KORl\nWGswairu7nydIAX0MxcXwcZX/6mbsUqCcyHPWTGdvuStTGbDIJhVvw3FIKKUxxASFPIyTyNYLiTu\n7nzt4hXQlVpZEZlEKIV9t/W7whTeaDbVdTkUySAJ5kEpoKhQeFHKYwgJCvncWJpUUmto+hR3d75m\nBCvjppNOcp+3fpfs2d3tWOV4ofv+INeXNsmNphtCowdJMA9SAUWFQqKUx5AS1qwpSL9yFcIioCzb\n6f9RtWyqVZtEQvNltm9nrK0UnldZZFIJlvNbdUVr46tgLBkkwawKKCoGEaU8dgGDVmAvaArSNDBN\ni2pVc/wyCadKbjLt5FXEOdbzxiKff/SC2+GvallomsZNU455SzrYg9FZgySYB+3+KhSglMeuYZBy\nFaJs9LK8uq5p6EmN2w5M+QorxsEjZy76OvxVLRuwWcmX3DpiAOuFSlsO9n4xSPdXoQClPBQxELTR\n27bT61wqD9ltr2xWYx/L5YW8728NDRubkqfP+mg2xe0H98WuyBSK3YQqT6LoOcHSIqmkjq5rzO4b\ncetVjWZTO+NXsP1/JhL1viReBslMpVAMA2rnoeg5QRv94fkcy2tbDdFgOyGwD78hh3FlhWrVxsZG\nw2mROz6SGmgzlUIx6CjloYiFoI3+vLHYF4fv2948i/Hyiu+Yrmn8wrtu44HjR2L/foVit6KURwz0\ns9zFoJba6JfD9/K1PDfty7rdGaW/5eVr+dYf7iGDel8Uim6JRXkIIVLAHwGHgQzwKcMw/qz22q8A\n/9gwjLfX/n4/8AHArL3v63GMaafoZ7mLYSi1sdMsLG36oqrc4zuYBKjui2I3EpfD/O8CS4ZhvBO4\nF/gcgBDibcDfp+auFELMAR8C3gH8TeDTQohMTGPaEZqVu9jN3z2oDEKnQHVfFLuRuMxWjwB/Uvtv\nDTCFEDPAvwA+Anyh9tox4DuGYZSAkhDiReAu4PvNTi6EeBD4ZAzj3jb9rEOkaiA1MgjZ2eq+KHYj\nsSgPwzA2AIQQEzhK5HeA/wh8FNjyvDUHrHn+Xgcm2zj/g8CD3mNCiMPAS92Pujf0sw6RqoHUyCBk\nZ6v7otiNxOYwF0LcAnwN+HfAC8CbgH8PZIE7hRCfAf4cmPB8bAJYjWtMO0E/V7qDsMoeRPqdna3u\ni2I3EpfD/GbgvwEfNAzjbO3wT9ZeOwx81TCMj9R8Hg8JIbI4jvU7gP+/vXuLlauq4zj+PS2IplSD\nEqANhvqgf6xG4yWKYkkfMNoSNdFElFoxkaJGFOILibYhEgEv8dJqokkViqSEBDWIJiZGQ/GSqBDl\noQT/KpFIQo9oQSxRqqXHh71PHesM7Tpz2WuG7+fp7Nn7pL+s6Zr/3muts2bvODJNSpd3ujXcZXep\n1hVNT/f3RbNpXE8eHwdOAbZFxLb2tQ2Z2TtkRWbOR8QO4Kc0k/efyMwnmHJd3ul2fZfdldpXND1d\n3xfNrnHNeVwOXD7g3APAOT3HO/nvBLq0JNPwpU7SLPGPBPU/ah36ORZXNEmTZfHQEbUP/TwVVzRJ\nk+Wuujpimv+Y7eidfBe5okkaD588dMQ0D/24okmaLIvHEkzrvMCxTGrop7T9jvd6VzRJk+OwVaHF\neYF9f32chYWFI/MCv86Hu442tEkM/ZS23yy3tzTNLB6Fpnle4FheGaexeeNaVp96MsuWzbH61JPZ\nvHHtSO/mS9tvlttbmmYOWxWa5nmB4zHuoZ/S9pv19pamlU8ehWrY4nualbaf7S3VyeJRyCWhwylt\nP9tbqpPDVoVcEjqc0vazvaU6WTyWwCWhwyltP9tbqo/DVpKkYhYPSVIxi4ckqZjFQ5JUzOIhSSo2\nS6utlgPMz893nUOSpkbPZ+bykt+bpeKxCmDTpk1d55CkabQKuP94L56l4nEXsA7YBzzZcZZFfwRe\n0HWIAcy2dDXnM9vS1ZxvnNmW0xSOu0p+aW5hYWE8cURELGTmXNc5+jHb0tWcz2xLV3O+GrM5YS5J\nKmbxkCQVs3hIkopZPMbrk10HeApmW7qa85lt6WrOV102J8wlScV88pAkFbN4SJKKWTwkScUsHpKk\nYhYPSVIxi4ckqdgsbYw4cRFxInA9sAY4CfhUZt7enrsI+Ehmvq493gJ8ADjUXvf9SWcDfgHsBE6h\n2QztvZl5fyXZ/gR8rc3wO+CSzDw86WxtvuU07RTAAvBB4AlgV3u8F/hwF/kGZDsB+DLNhqAHad7X\nP9eQLTP3tuc67Q+D8gEPU0efGPS+VtEn+vHJYzjvAfZn5jrgzcBXACLiFcD7gbn2+Azgo8C5wJuA\n6yLipA6yfRbYnZnnAVuBsyvKdhVwdWa+gaagXNBRNoC3AGTmuTTtdA3wBWBrm3kOeFtH+fpl207z\nwbwe+A5wZUXZaukPg/LV0if6ZaupT/wfi8dwbgW2tT/PAYci4nnAtcAVPde9Bvh5Zh7MzMeAPwAv\nm3Q2mv9wZ0bEj4BNwJ6Ksv0GeG5EzAErgX93lI3MvA24tD08C/gb8Crgzva1HwDnd5FvQLZ3ZeY9\n7Wsn0DwlVZGtov4wqO2q6BMDslXTJ/qxeAwhMx/PzAMRsRL4Fs0H4jeAjwEHei59NvBYz/EB4DkT\nzraVZpjo0cw8n2aY6MqKsv0e2AHcB5xO04knnq0n46GIuJFmOGg3MJeZi9sxLOboJN/R2TJzH0BE\nvB64DPhiJdluppL+MCDfbirpEwOyVdUnjmbxGFJEPB+4A7iJ5s1+IfBV4BZgbUR8Cfg7zZ3DopU0\ndxYTy5aZNwP7gdvb098DXl1Rtu3Ausw8G/gm8Pmusi3KzIuBF9GMRT+rT47O8vVmi4gVEXEhzfj4\nBZn5l0qy3Qa8nEr6Q598O9t/t4o+0SfbDirrE72cMB9CRJwO/BC4LDN/3L78kvbcGuCWzLyiHae8\nJiKeSTN2+WKaSddJZ/sZsJGm0J0H3Av8qpJsj9B0DICHaIYTJp6tzbcZODMzrwP+ARwG7o6I9Zm5\nB9hAU/i6aLt+2d5OM+SxPjMfaS+tIds8sDYz/9l1fxiQ7zDwE+roE/2yVdMn+nFjxCFExHbgQuC3\nPS9vOKqznNNeu4Wmgy8Drs3Mb3eQ7WLg68AKmkffizLz0UqybQM+TTP/8S9gS2Y+MOlsbb4VwA3A\nGcCJba77aO4Gn9H+vCUzn+yg7fplu4FmyGXxDvTOzLyqhmyZ+d323Bo67A+D8gH3UEef6JdtP/AZ\nKugT/Vg8JEnFnPOQJBWzeEiSilk8JEnFLB6SpGIWD0lSMYuH1JGIuDoi3tp1DmkpXKorSSrmk4c0\nQhFxU0Rc2nN8R0S8dsC1uyLifRMLJ42QxUMaretptpwnIs4CTsvMX3YbSRo997aSRmsPsLrdjmMz\nzYZ20szxyUMaoXbb9huBdwPvpNlwT5o5PnlIo7eLZgfjezPzoY6zSGPhk4c0Ypn5IPAgTRGRZpJL\ndaURar8ydBXNV9a+NDMPdhxJGguHraTRegfNN+d9KDMPRsTngDf2ue7uzLxkstGk0fHJQ5JUzDkP\nSVIxi4ckqZjFQ5JUzOIhSSpm8ZAkFfsP1Oru0j2xdG8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12d176978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MSVHkhPvR_Ey",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## QQ Plot\n",
        "* QQ Plots takes our n ordered data points\n",
        " * sorted from smallest to largest\n",
        "* Asks:\n",
        " * What is the relationship between quantiles from our data and quantiles from a theoretical distribution that we're assuming the sample is drawn from"
      ]
    },
    {
      "metadata": {
        "id": "2CAiWDm4R_FX",
        "colab_type": "code",
        "colab": {},
        "outputId": "3a5ffba9-bf8f-4932-c71e-483be29a7698"
      },
      "cell_type": "code",
      "source": [
        "qntls, xr = stats.probplot(testdata, fit=False)\n",
        "sns.regplot(xr,qntls)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x13032b4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wFxEpAtneCA0AY8w5wPettRcaY94P/BKIAFuBv7fWho0xXwC+CHQB37HWPpG1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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12d8dd080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y3rgii2aR_Fa",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "def random_snorm(n, mean = 0, sd = 1, xi = 1.5):\n",
        "    def random_snorm_aux(n, xi):\n",
        "        weight = xi/(xi + 1/xi)\n",
        "        z = np.random.uniform(-weight,1-weight,n)\n",
        "        xi_ = xi**np.sign(z)\n",
        "        random = -np.absolute(np.random.normal(0,1,n))/xi_ * np.sign(z)\n",
        "        m1 = 2/np.sqrt(2 * np.pi)\n",
        "        mu = m1 * (xi - 1/xi)\n",
        "        sigma = np.sqrt((1 - m1**2) * (xi**2 + 1/xi**2) + 2 * m1**2 - 1)\n",
        "        return (random - mu)/sigma\n",
        "\n",
        "    return random_snorm_aux(n, xi) * sd + mean\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cUU_pRi1R_Fc",
        "colab_type": "code",
        "colab": {},
        "outputId": "e0ad66e7-2769-460d-99d4-ba91cd49f35a"
      },
      "cell_type": "code",
      "source": [
        "rightskewed = random_snorm(1000,xi=2)*100\n",
        "sns.distplot(rightskewed,kde=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1302faf60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mfgsgIg4Bh+mE2o6hVNzeUurTUrup0I3AnohYCRwF9mXm6Yi4m85/ymXAxzLzpWEWeR63\nAq8BbouImWMDNwB3j3GfAP4B+JuI+FdgBZ3p4qOM92s126L97XkVUUkqbCSngyRJi8MQkKTCDAFJ\nKswQkKTCDAFJKswQkKTCDAFJKux/AZP5GHrziDh8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x12fdec6a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G7lxW9CWR_Fe",
        "colab_type": "code",
        "colab": {},
        "outputId": "4e3ff539-0b96-4cd0-898a-870482bd3cbe"
      },
      "cell_type": "code",
      "source": [
        "qntls, xr = stats.probplot(rightskewed, fit=False)\n",
        "sns.regplot(xr,qntls)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1306378d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KvTGmHPgNMBzoAG6x1n7o5sASVb11V9fza+Nn9yOGlujkrIhIVKrLOF8G3rbW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\nkQLw/wHbpLxeYMwLCAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1306566d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7Nqj5cI5R_Fk",
        "colab_type": "code",
        "colab": {},
        "outputId": "67c45392-85e5-47a8-b49d-3f535f59c608"
      },
      "cell_type": "code",
      "source": [
        "leftskewed = random_snorm(1000,xi=-2)*100\n",
        "sns.distplot(leftskewed,kde=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x13072e860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u65f8Z3wJZOYXgC+0j0XE88DHql/yEeBJ4GrefEOcYeAgrX3uJxufEyfL3/bYr1XHNf4R\neC99lv9U2SPi3cBXgD/JzJ3VX9J9lR1O/95PMfVmSn2RfwY6uglUnznRtjz5vp7q/6HnIuJcWpfa\nfyAzH4mIP297uKf5i9wdlJnnZeZYZo4BB4CrMvMV4LWI+Plqd9FKYBetG+WsjIh5EfFOWr8o/9Wz\n8EBE3BYRv1utvgocH5T8EfGLtKbH12bmDoBByX4qA5z/aeD98PqB7Y5vAtVDz7XNxlbTer+fAZZH\nxOLqCwjn0zro2lMRcTatPzRvzcwt1XDf5D/jZwId+jjwMK3btD2Zmd8BiIhdwG5apbmud/FetwX4\nYjWTmc8bN+8ZhPz30Dq4d3+1K+5QZq5hMLKfziDmH+SbQN0MbI6IhcBeYFtmHo+ITbQ+UOcBd2Tm\nkV6GrNxO65t8d0bE5LGBm4BN/ZDfq4hKUsGK3B0kSWqxBCSpYJaAJBXMEpCkglkCklQwS0CSCmYJ\nSFLB/h8zBpVgJ5AqxwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1304f4ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MMAAzN9qR_Fm",
        "colab_type": "code",
        "colab": {},
        "outputId": "ceb25b3f-e980-4cb3-8170-dc4ee3d0ce4f"
      },
      "cell_type": "code",
      "source": [
        "qntls, xr = stats.probplot(leftskewed, fit=False)\n",
        "sns.regplot(xr,qntls)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1307450b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EJEaSLuwh8btqQpp9AXYfOMlLb++l2Fa0e+68GSO585rpzJo8TL3yIhJzSRn2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\niEgK+P8c7j7q/gyUXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x130378a90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "z4_F9j_XR_Fs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">BEGIN Q5: Are we normal?</font>"
      ]
    },
    {
      "metadata": {
        "id": "AJyHg1MSR_Ft",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Now the serious plots... let's wrap them in a single function that we can call"
      ]
    },
    {
      "metadata": {
        "id": "hIaYq-4dR_Ft",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "def multiplePlots( series ):\n",
        "    \n",
        "    fig, axs = plt.subplots(2,2)\n",
        "    plt.tight_layout(pad=0.4, w_pad=4, h_pad=1.0)\n",
        "\n",
        "    # Histogram\n",
        "    sns.distplot(series, ax=axs[0,0])\n",
        "    \n",
        "    # Lag plot\n",
        "    lag = series.copy()\n",
        "    lag = np.array(lag[:-1])\n",
        "    current = series[1:]\n",
        "    ax = sns.regplot(current,lag,fit_reg=False, ax=axs[0,1])\n",
        "    ax.set_ylabel(\"y_i-1\")\n",
        "    ax.set_xlabel(\"y_i\")\n",
        "    \n",
        "    # QQ plot\n",
        "    qntls, xr = stats.probplot(series, fit=False)\n",
        "    sns.regplot(xr,qntls, ax=axs[1,0])\n",
        "    \n",
        "    # Run sequence\n",
        "    ax = sns.regplot(np.arange(len(series)),series, ax=axs[1,1])\n",
        "    ax.set_ylabel(\"val\")\n",
        "    ax.set_xlabel(\"i\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AEdejCLjR_Fu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Now run this on your \"things\""
      ]
    },
    {
      "metadata": {
        "id": "KFfpiDdZR_Fu",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "multiplePlots(things)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jo6bTK7UR_Fv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## <font color=\"red\">Do your data look normally distributed?</font>"
      ]
    },
    {
      "metadata": {
        "id": "CI36UA78R_Fv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<font color=\"red\">INSERT YOUR ANSWER HERE</font>"
      ]
    },
    {
      "metadata": {
        "id": "8EOfPvz0R_Fv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">END Q5</font>"
      ]
    },
    {
      "metadata": {
        "id": "JLYkUGHbR_Fw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Univariate Data -- Summary\n",
        "* Simple but valuable\n",
        "* We want to know how data is distributed\n",
        "* How does it fit known models/distributions\n",
        "* When does it not?\n",
        " * Visual and analytical tests"
      ]
    },
    {
      "metadata": {
        "id": "kiBE-nmkR_Fw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">BEGIN Q6: Investigating the unknown</font>\n",
        "In this section, we're going to load a dataframe that consists of a bunch of series.  Each of the series is pulled from a different distribution.  We're going to use visual and analytic methods to figure out some things about each of those series."
      ]
    },
    {
      "metadata": {
        "id": "eMJ5ei8CR_Fw",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "sample = pd.DataFrame.from_csv('sample.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5tzyD-25R_Fx",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "sample.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JoR4JZOOR_Fz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## <font color=\"red\">Q6a. So those are the basic state.  We're missing mode... so calculate it</font>"
      ]
    },
    {
      "metadata": {
        "id": "lh1hspQuR_F0",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "# insert your code here to generate the modes for each column.  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JViKXAxUR_F1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## <font color=\"red\">Q6b. Use our multiplePlots to generate some descriptive plots\n"
      ]
    },
    {
      "metadata": {
        "id": "m7ncnRIzR_F2",
        "colab_type": "code",
        "colab": {},
        "collapsed": true
      },
      "cell_type": "code",
      "source": [
        "# insert your code here to iterate through each series in the 'sample' DataFrame and call multiplePlots()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rUrWIqAGR_F5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Q6c.  For each column, fill in the following sentences.\n",
        "Based on the 4 plots, column ____________ looks like a (normal, uniform, power, Poisson, skewed, weird) distribution.  The mean is (less than, greater than, about the same as ) the median, and the standard deviation is (less than, greater than, about the same as ) the mean.   The run sequence plot suggests that the values ( do or do not ) change over the sequence of values, and in terms of the lag plot, it looks like there ( is or is not ) a relationship between a value and the previous one.  The QQ plot suggests that the data are ( not skewed, skewed to the left, skewed to the right )."
      ]
    },
    {
      "metadata": {
        "id": "hpVWEYcNR_F5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "1. Based on the 4 plots, column v0 looks like a ...\n",
        "1. Based on the 4 plots, column v1 looks like a ...\n",
        "1. Based on the 4 plots, column v2 looks like a ...\n",
        "1. Based on the 4 plots, column v3 looks like a ...\n",
        "1. Based on the 4 plots, column v4 looks like a ...\n",
        "1. Based on the 4 plots, column v5 looks like a ...\n",
        "1. Based on the 4 plots, column v6 looks like a ...\n",
        "1. Based on the 4 plots, column v7 looks like a ...\n",
        "1. Based on the 4 plots, column v8 looks like a ..."
      ]
    },
    {
      "metadata": {
        "id": "4jlyUN9kR_F5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# <font color=\"red\">END Q6</font>\n",
        "\n",
        "# <font color=\"red\">END LAB</font>\n",
        "\n",
        "# Your additional notes go here (or anywhere else)"
      ]
    },
    {
      "metadata": {
        "id": "tCbHt_XoR_F5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "Qvxc_bjiWATm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "703172f0-2f32-4ed4-c745-bb10ae8b40ef"
      },
      "cell_type": "code",
      "source": [
        "import pyspark"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-49d7c4e178f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpyspark\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyspark'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "E3XUv7u2WBYd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "9430c731-c6d7-4247-f24d-8a2c7465de31"
      },
      "cell_type": "code",
      "source": [
        "!pip install pyspark\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyspark\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/58/49/45370cc153a6adcf2c304a3c06e801ed3c9650d0f852e7fde04bd8ffb534/pyspark-2.3.0.tar.gz (211.9MB)\n",
            "\u001b[K    100% |████████████████████████████████| 211.9MB 82kB/s \n",
            "\u001b[?25hCollecting py4j==0.10.6 (from pyspark)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4a/08/162710786239aa72bd72bb46c64f2b02f54250412ba928cb373b30699139/py4j-0.10.6-py2.py3-none-any.whl (189kB)\n",
            "\u001b[K    100% |████████████████████████████████| 194kB 22.0MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyspark\n",
            "  Running setup.py bdist_wheel for pyspark ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/d9/db/ff/e6f3a8a564163ea64bc2072357e77b3404d10f91be48352796\n",
            "Successfully built pyspark\n",
            "Installing collected packages: py4j, pyspark\n",
            "Successfully installed py4j-0.10.6 pyspark-2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5-ZCQDe9WE6G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pyspark"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "t2_moqUqWQx2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3e93734b-b2f4-47ad-8e16-4850383d30e2"
      },
      "cell_type": "code",
      "source": [
        "import gensim"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-e70e92d32c6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgensim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gensim'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "pXggy2L4caWn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "81d3a034-88c7-475d-9521-8f9b07f5e740"
      },
      "cell_type": "code",
      "source": [
        "!pip install gensim"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gensim\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/33/33/df6cb7acdcec5677ed130f4800f67509d24dbec74a03c329fcbf6b0864f0/gensim-3.4.0-cp36-cp36m-manylinux1_x86_64.whl (22.6MB)\n",
            "\u001b[K    100% |████████████████████████████████| 22.6MB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.14.3)\n",
            "Collecting smart-open>=1.2.1 (from gensim)\n",
            "  Downloading https://files.pythonhosted.org/packages/4b/69/c92661a333f733510628f28b8282698b62cdead37291c8491f3271677c02/smart_open-1.5.7.tar.gz\n",
            "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.11.0)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (0.19.1)\n",
            "Collecting boto>=2.32 (from smart-open>=1.2.1->gensim)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bd/b7/a88a67002b1185ed9a8e8a6ef15266728c2361fcb4f1d02ea331e4c7741d/boto-2.48.0-py2.py3-none-any.whl (1.4MB)\n",
            "\u001b[K    100% |████████████████████████████████| 1.4MB 16.2MB/s \n",
            "\u001b[?25hCollecting bz2file (from smart-open>=1.2.1->gensim)\n",
            "  Downloading https://files.pythonhosted.org/packages/61/39/122222b5e85cd41c391b68a99ee296584b2a2d1d233e7ee32b4532384f2d/bz2file-0.98.tar.gz\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.18.4)\n",
            "Collecting boto3 (from smart-open>=1.2.1->gensim)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/0d/14f29bebb6f93362da511dd4299d0dee2fb19d294657ae2c1f4adfb930a6/boto3-1.7.28-py2.py3-none-any.whl (128kB)\n",
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            "\u001b[?25hRequirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (1.22)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2018.4.16)\n",
            "Collecting botocore<1.11.0,>=1.10.28 (from boto3->smart-open>=1.2.1->gensim)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2e/91/f0870d4de8eb78897ce781f3ff22fc492bbb9849b5c91f26db20b125ef36/botocore-1.10.28-py2.py3-none-any.whl (4.2MB)\n",
            "\u001b[K    100% |████████████████████████████████| 4.2MB 8.4MB/s \n",
            "\u001b[?25hCollecting s3transfer<0.2.0,>=0.1.10 (from boto3->smart-open>=1.2.1->gensim)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/14/2a0004d487464d120c9fb85313a75cd3d71a7506955be458eebfe19a6b1d/s3transfer-0.1.13-py2.py3-none-any.whl (59kB)\n",
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            "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1 (from boto3->smart-open>=1.2.1->gensim)\n",
            "  Downloading https://files.pythonhosted.org/packages/b7/31/05c8d001f7f87f0f07289a5fc0fc3832e9a57f2dbd4d3b0fee70e0d51365/jmespath-0.9.3-py2.py3-none-any.whl\n",
            "Collecting docutils>=0.10 (from botocore<1.11.0,>=1.10.28->boto3->smart-open>=1.2.1->gensim)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/36/fa/08e9e6e0e3cbd1d362c3bbee8d01d0aedb2155c4ac112b19ef3cae8eed8d/docutils-0.14-py3-none-any.whl (543kB)\n",
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            "\u001b[?25hRequirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.11.0,>=1.10.28->boto3->smart-open>=1.2.1->gensim) (2.5.3)\n",
            "Building wheels for collected packages: smart-open, bz2file\n",
            "  Running setup.py bdist_wheel for smart-open ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/b1/9e/7d/bb3d3b55c597e72617140a0638c06382a5f17283881eae163e\n",
            "  Running setup.py bdist_wheel for bz2file ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/81/75/d6/e1317bf09bf1af5a30befc2a007869fa6e1f516b8f7c591cb9\n",
            "Successfully built smart-open bz2file\n",
            "Installing collected packages: boto, bz2file, jmespath, docutils, botocore, s3transfer, boto3, smart-open, gensim\n",
            "Successfully installed boto-2.48.0 boto3-1.7.28 botocore-1.10.28 bz2file-0.98 docutils-0.14 gensim-3.4.0 jmespath-0.9.3 s3transfer-0.1.13 smart-open-1.5.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4PCkIR1ncdFb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import gensim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oV1WPIvochuR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "6b89b756-3a4e-4f8a-e100-5a6b88381a19"
      },
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "\n",
        "####\n",
        "# text to work with\n",
        "bltext = \"\"\"It was a dark and stormy night; the rain fell in torrents, except at\n",
        "occasional intervals, when it was checked by a violent gust of wind\n",
        "which swept up the streets (for it is in London that our scene lies),\n",
        "rattling along the house-tops, and fiercely agitating the scanty flame\n",
        "of the lamps that struggled against the darkness. Through one of the\n",
        "obscurest quarters of London, and among haunts little loved by the\n",
        "gentlemen of the police, a man, evidently of the lowest orders, was\n",
        "wending his solitary way. He stopped twice or thrice at different shops\n",
        "and houses of a description correspondent with the appearance of the\n",
        "quartier in which they were situated, and tended inquiry for some\n",
        "article or another which did not seem easily to be met with. All the\n",
        "answers he received were couched in the negative; and as he turned from\n",
        "each door he muttered to himself, in no very elegant phraseology, his\n",
        "disappointment and discontent. At length, at one house, the landlord, a\n",
        "sturdy butcher, after rendering the same reply the inquirer had hitherto\n",
        "received, added, \"But if this vill do as vell, Dummie, it is quite at\n",
        "your sarvice!\" Pausing reflectively for a moment, Dummie responded that\n",
        "he thought the thing proffered might do as well; and thrusting it into\n",
        "his ample pocket, he strode away with as rapid a motion as the wind and\n",
        "the rain would allow. He soon came to a nest of low and dingy buildings,\n",
        "at the entrance to which, in half-effaced characters, was written\n",
        "\"Thames Court.\" Halting at the most conspicuous of these buildings, an\n",
        "inn or alehouse, through the half-closed windows of which blazed out in\n",
        "ruddy comfort the beams of the hospitable hearth, he knocked hastily at\n",
        "the door. He was admitted by a lady of a certain age, and endowed with a\n",
        "comely rotundity of face and person.\"\"\"\n",
        "\n",
        "# split up into sentences\n",
        "sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\n",
        "blsentences = sent_detector.tokenize(bltext.strip())\n",
        "\n",
        "print(\"Split into sentences.\")\n",
        "print(blsentences[0])\n",
        "raw_input()\n",
        "\n",
        "###\n",
        "# split up each sentence into words, taking punctuation into account\n",
        "blsent_words = [nltk.word_tokenize(sent) for sent in blsentences]\n",
        "\n",
        "print \"Split into words.\"\n",
        "print blsent_words[0]\n",
        "raw_input()\n",
        "\n",
        "###\n",
        "# add part-of-speech tags: a list of sentences, each of which is a list of word/tag pairs\n",
        "blsent_tagged = [nltk.pos_tag(sent) for sent in blsent_words]\n",
        "\n",
        "print \"Added part-of-speech tags.\"\n",
        "print blsent_tagged[0]\n",
        "raw_input()\n",
        "\n",
        "###\n",
        "# add named-entity tags\n",
        "# blsent_ne: list of sentences, each of which is a list of word/tag/NEtag triples\n",
        "# (we do not go on using these below)\n",
        "blsent_ne = [nltk.chunk.tree2conlltags(nltk.ne_chunk(s)) for s in blsent_tagged]\n",
        "\n",
        "print (\"Added named-entity tags.\")\n",
        "print (blsent_ne[0])\n",
        "raw_input()\n",
        "\n",
        "###\n",
        "# chunking\n",
        "# IOB format\n",
        "# code from the NLTK book\n",
        "class UnigramChunker(nltk.ChunkParserI):\n",
        "    def __init__(self, train_sents): \n",
        "        train_data = [[(t,c) for w,t,c in nltk.chunk.tree2conlltags(sent)]\n",
        "                      for sent in train_sents]\n",
        "        self.tagger = nltk.UnigramTagger(train_data)\n",
        "             \n",
        "    def parse(self, sentence): \n",
        "        return nltk.chunk.conlltags2tree(self.parse_iob(sentence))\n",
        "    \n",
        "    def parse_iob(self, sentence):\n",
        "        pos_tags = [pos for (word,pos) in sentence]\n",
        "        tagged_pos_tags = self.tagger.tag(pos_tags)\n",
        "        chunktags = [chunktag for (pos, chunktag) in tagged_pos_tags]\n",
        "        conlltags = [(word, pos, chunktag) for ((word,pos),chunktag)\n",
        "                     in zip(sentence, chunktags)]\n",
        "        return conlltags\n",
        "\n",
        "from nltk.corpus import conll2000\n",
        "train_sents = conll2000.chunked_sents('train.txt', chunk_types=['NP'])\n",
        "chunker = UnigramChunker(train_sents)\n",
        "\n",
        "# blsent_chunked: triples (word, postag, chunktag)\n",
        "print \"Added chunk tags to word/tag pairs (version 1)\"\n",
        "blsent_chunked = [ chunker.parse_iob(s) for s in blsent_tagged ]\n",
        "print blsent_chunked[0]\n",
        "raw_input()\n",
        "\n",
        "# or, second option for chunking: via regular expressions\n",
        "grammar = r\"\"\"\n",
        "  NP: {<DT|PP\\$>?<JJ.*>*<NN.*>+}   # chunk determiner/possessive, adjectives and noun sequences\n",
        "\"\"\"\n",
        "chunker2 = nltk.RegexpParser(grammar)\n",
        "\n",
        "# blsent_chunked2: triples (word, postag, chunktag)\n",
        "blsent_chunked2= [nltk.chunk.tree2conlltags(chunker2.parse(s)) for s in blsent_tagged ]\n",
        "\n",
        "print \"Added chunk tags to word/tag pairs (version 2)\"\n",
        "print blsent_chunked2[0]\n",
        "raw_input()\n",
        "\n",
        "###\n",
        "#lemmatization\n",
        "lobj = nltk.stem.wordnet.WordNetLemmatizer()\n",
        "\n",
        "# a list of sentences, each of which is a list of  word/tag/chunktag/lemma tuples\n",
        "blsent_lemmatized = [ ]\n",
        "\n",
        "for sent in blsent_chunked:\n",
        "    onesentence = [ ]\n",
        "    \n",
        "    for word, postag, chunktag in sent:\n",
        "        if postag.startswith(\"V\"): shorttag = \"v\"\n",
        "        elif postag.startswith(\"N\"): shorttag = \"n\"\n",
        "        elif postag.startswith(\"J\"): shorttag = \"a\"\n",
        "        else: shorttag = \"n\"\n",
        "        onesentence.append( (word, postag, chunktag, lobj.lemmatize(word.lower(), shorttag)))\n",
        "        \n",
        "    blsent_lemmatized.append(onesentence)\n",
        "\n",
        "print \"Added lemmas.\"\n",
        "print blsent_lemmatized[0]\n",
        "raw_input()\n",
        "    \n",
        "###\n",
        "# positive and negative sentiment tags\n",
        "def read_sentiment_lexicon(filename):\n",
        "    f = open(filename)\n",
        "    words = [ ]\n",
        "    for line in f:\n",
        "        if not(line.startswith(\";\")) and not(line.strip() == \"\"):\n",
        "            words.append(line.strip())\n",
        "    f.close()\n",
        "    return set(words)\n",
        "\n",
        "poswords = read_sentiment_lexicon(\"/Users/katrinerk/Data/sentiment/Bing_Liu_opinion-lexicon-English/positive-words.txt\")\n",
        "negwords = read_sentiment_lexicon(\"/Users/katrinerk/Data/sentiment/Bing_Liu_opinion-lexicon-English/negative-words.txt\")\n",
        "\n",
        "blsent_sentiment = [ ]\n",
        "for sent in blsent_lemmatized:\n",
        "    onesentence = [ ]\n",
        "    \n",
        "    for word, postag, chunktag, lemma in sent:\n",
        "        if lemma in poswords:\n",
        "            onesentence.append( (word, postag, chunktag, lemma, \"POS\"))\n",
        "        elif lemma in negwords:\n",
        "            onesentence.append( (word, postag, chunktag, lemma, \"NEG\"))\n",
        "        else:\n",
        "            onesentence.append( (word, postag, chunktag, lemma, \"O\"))\n",
        "\n",
        "    blsent_sentiment.append(onesentence)\n",
        "\n",
        "print \"Added pos/neg sentiment tags\"\n",
        "print blsent_sentiment[0]\n",
        "raw_input()\n",
        "\n",
        "#how many positive and negative sentiment words in the text?\n",
        "num_pos = len([wtuple for sent in blsent_sentiment for wtuple in sent if wtuple[-1] == \"POS\"])\n",
        "num_neg = len([wtuple for sent in blsent_sentiment for wtuple in sent if wtuple[-1] == \"NEG\"])\n",
        "print \"Count of positive sentiment words:\", num_pos\n",
        "print \"Count of negative sentiment words:\", num_neg\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-8-f995aa7104b2>\"\u001b[0;36m, line \u001b[0;32m44\u001b[0m\n\u001b[0;31m    print \"Split into words.\"\u001b[0m\n\u001b[0m                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(\"Split into words.\")?\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "UhJFihspc0ek",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        " "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}