notebooks/assorted-predictions.ipynb

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  "metadata": {
    "colab": {
      "name": "Assorted predictions",
      "provenance": [],
      "collapsed_sections": [
        "yElDWWhHmM4b"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oughtinc/ergo/blob/notebooks-readme/assorted-predictions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yElDWWhHmM4b",
        "colab_type": "text"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8yH0BYFmLqH",
        "colab_type": "code",
        "outputId": "808ffbe1-054c-4a95-ac1a-127f1eb0040b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "!pip install --quiet poetry  # Fixes https://github.com/python-poetry/poetry/issues/532\n",
        "!pip install --quiet git+https://github.com/oughtinc/ergo.git@submit_mixture\n",
        "!pip install --quiet pendulum seaborn requests\n",
        "!pip install --quiet torch"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 225kB 2.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 112kB 9.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 92kB 6.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.7MB 8.8MB/s \n",
            "\u001b[?25h  Building wheel for pyrsistent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 61kB 1.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 522kB 7.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 7.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 29.3MB 105kB/s \n",
            "\u001b[K     |████████████████████████████████| 153kB 41.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 491kB 47.7MB/s \n",
            "\u001b[?25h  Building wheel for ergo (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for country-converter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42KVEbypmg-k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext google.colab.data_table"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvWn9iJqmilB",
        "colab_type": "code",
        "outputId": "220f3ecf-56ce-4c90-dde3-e86db74d3d86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import ergo\n",
        "import pendulum\n",
        "import pandas\n",
        "import seaborn\n",
        "import requests\n",
        "import re\n",
        "import numpy as np\n",
        "from scipy import stats"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8x4jgmDOVP67",
        "colab_type": "text"
      },
      "source": [
        "# Set up Metaculus API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IY2_EPj6VSIO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "metaculus = ergo.Metaculus(username=\"ought\", password=passwords[\"ought\"], api_domain=\"pandemic\")\n",
        "# metaculus = ergo.Metaculus(username=\"oughttest\", password=passwords[\"oughttest\"], api_domain=\"pandemic\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lzL9I-cDCkOi",
        "colab_type": "text"
      },
      "source": [
        "# Utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mI0zqOWsDBVK",
        "colab_type": "code",
        "outputId": "5a09c4b9-accc-4224-b47e-d3e2906dd5c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def will_x_happen_prediction (model_uncertainty, start, p_at_start, end):\n",
        "  start_to_end = (end - start).days\n",
        "  days_remaining = (end - pendulum.now()).days\n",
        "  proportion_time_remaining = days_remaining / start_to_end\n",
        "  model_prediction = p_at_start * proportion_time_remaining\n",
        "\n",
        "  return model_prediction + model_uncertainty\n",
        "\n",
        "# should be 1\n",
        "will_x_happen_prediction(0.5, pendulum.now(), 0.5, pendulum.datetime(2020,7,7))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfU-SaV5mqqe",
        "colab_type": "text"
      },
      "source": [
        "# Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQq8q8U3m5im",
        "colab_type": "text"
      },
      "source": [
        "## JSON"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCJwi2JVmwEv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "question_infos = [\n",
        "    {\n",
        "        \"name\": \"What will the Seattle Police Department report as the total number of criminal offenses in March 2020?\",\n",
        "        \"id\": 3924\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"What will Washington state’s Department of Revenue report as the 2020 Q1 gross business income?\",\n",
        "        \"id\": 3923\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Will the US federal government shut down all non-essential services by 2020-04-19?\",\n",
        "        \"id\": 3921\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Will the Emergency Telework Act (S.3561) become law by 4/25/20?\",\n",
        "        \"id\": 3918\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"By May 1 will there be an iOS or Android app that shares an individual's COVID-19 infection status with more than 1M other users?\",\n",
        "        \"id\": 3915\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"By June 1, how many tests for COVID-19 will have been administered in the US?\",\n",
        "        \"id\": 3916\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"[short fuse] How many total confirmed deaths of novel coronavirus will be reported in the state of New York by April 2nd?\",\n",
        "        \"id\": 3934\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"What will be the US unemployment rate for March 2020?\",\n",
        "        \"id\": 3922\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"How many days will the city of New Orleans spend under lockdown between 2020-03-25 and 2020-04-15?\",\n",
        "        \"id\": 3930\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Will Florida go under lockdown between 2020-03-25 and 2020-04-25?\",\n",
        "        \"id\": 3926\n",
        "    }\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FyyLhLqYlRnn",
        "colab_type": "text"
      },
      "source": [
        "## Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gs9BAHl8M28N",
        "colab_type": "code",
        "outputId": "01052dae-68a5-45b1-a3e2-3a52a30373a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "questions = [metaculus.get_question(question_info[\"id\"], name=question_info[\"name\"]) for question_info in question_infos]\n",
        "ergo.MetaculusQuestion.to_dataframe(questions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>0</th>\n",
              "      <td>3924</td>\n",
              "      <td>What will the Seattle Police Department report...</td>\n",
              "      <td>What will the Seattle Police Department report...</td>\n",
              "      <td>2020-04-26 06:59:00+00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3923</td>\n",
              "      <td>What will Washington state’s Department of Rev...</td>\n",
              "      <td>What will Washington state’s Department of Rev...</td>\n",
              "      <td>2020-04-26 06:59:00+00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3921</td>\n",
              "      <td>Will the US federal government shut down all n...</td>\n",
              "      <td>Will the US federal government shut down all n...</td>\n",
              "      <td>2020-04-19 06:59:00+00:00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3918</td>\n",
              "      <td>Will the Emergency Telework Act (S.3561) becom...</td>\n",
              "      <td>Will the Emergency Telework Act (S.3561) becom...</td>\n",
              "      <td>2020-04-26 06:59:00+00:00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3915</td>\n",
              "      <td>By May 1 will there be an iOS or Android app t...</td>\n",
              "      <td>By May 1 will there be an iOS or Android app t...</td>\n",
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              "      <th>5</th>\n",
              "      <td>3916</td>\n",
              "      <td>By June 1, how many tests for COVID-19 will ha...</td>\n",
              "      <td>By June 1, how many tests for COVID-19 will ha...</td>\n",
              "      <td>2020-06-01 07:00:00+00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3934</td>\n",
              "      <td>[short fuse] How many total confirmed deaths o...</td>\n",
              "      <td>[short fuse] How many total confirmed deaths o...</td>\n",
              "      <td>2020-04-02 04:00:00+00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3922</td>\n",
              "      <td>What will be the US unemployment rate for Marc...</td>\n",
              "      <td>What will be the US unemployment rate for Marc...</td>\n",
              "      <td>2020-04-03 07:17:00+00:00</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>How many days will the city of New Orleans spe...</td>\n",
              "      <td>How many days will the city of New Orleans spe...</td>\n",
              "      <td>2020-04-16 06:59:00+00:00</td>\n",
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              "      <th>9</th>\n",
              "      <td>3926</td>\n",
              "      <td>Will Florida go under lockdown between 2020-03...</td>\n",
              "      <td>Will Florida go under lockdown between 2020-03...</td>\n",
              "      <td>2020-04-01 17:38:00+00:00</td>\n",
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              "0  3924  ... 2020-04-26 06:59:00+00:00\n",
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              "2  3921  ... 2020-04-19 06:59:00+00:00\n",
              "3  3918  ... 2020-04-26 06:59:00+00:00\n",
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        }
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZR00LQjU21V",
        "colab_type": "text"
      },
      "source": [
        "# Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAOaDLlcODai",
        "colab_type": "text"
      },
      "source": [
        "## 0. What will the Seattle Police Department report as the total number of criminal offenses in March 2020?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3924/what-will-the-seattle-police-department-report-as-the-total-number-of-criminal-offenses-in-march-2020/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQCz0QZcOBR0",
        "colab_type": "code",
        "outputId": "8e6e95fc-5317-452b-e814-25b44aacc5d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def crime_model():\n",
        "  feb_total = 5090 # https://www.seattle.gov/police/information-and-data/crime-dashboard\n",
        "  mar_difference_multiplier = ergo.lognormal_from_interval(0.3, 2)\n",
        "  prediction = feb_total * mar_difference_multiplier\n",
        "  ergo.tag(prediction, \"mar_total\")\n",
        "\n",
        "crime_samples = ergo.run(crime_model, 1000)\n",
        "\n",
        "questions[0].samples = crime_samples.mar_total\n",
        "\n",
        "# questions[0].show_submission(questions[0].samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:00<00:00, 2322.84it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqnvA9Erh4LM",
        "colab_type": "text"
      },
      "source": [
        "#### Apr 4\n",
        "* We're already predicting a broader range than the community on this one, and our median is close to the community median. I don't see any obvious reason to change anything\n",
        "* TODO:\n",
        "  * This one is ripe for reference-class comparison, which I haven't done at all:\n",
        "    * what does data from other states, or early data from WA, show re: impact of coronavirus on crime?\n",
        "    * what impact did other catastophes have on crime in the past?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyZG_XS3w78I",
        "colab_type": "text"
      },
      "source": [
        "### 1. What will Washington state’s Department of Revenue report as the 2020 Q1 gross business income?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3923/what-will-washington-states-department-of-revenue-report-as-the-2020-q1-gross-business-income/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8T8N-Ax48Sr8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wa_df = pandas.read_csv(\"https://gist.githubusercontent.com/brachbach/5dc01125a44ce28e067a2dddb18f8a02/raw/12b16deecef9848c1f75432cf9aca5b61b1fd26a/WAGrossBusiness.csv\")\n",
        "wa_df[\"Total Gross\"] = wa_df[\"Total Gross\"].apply(lambda x: int(re.sub('[\\$,]', '', x)))\n",
        "wa_df[\"year\"] = wa_df[\"Year\"].apply(lambda x: int(x.split()[0]))\n",
        "quarters = wa_df[wa_df[\"Year\"].str.contains(\"Quarter\")]\n",
        "recent = quarters[quarters[\"year\"] >= 2017]\n",
        "recentish = quarters[quarters[\"year\"] >= 2008]\n",
        "worst_quarters = recentish.nsmallest(10, \"Total Gross\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZO0uSqW-k2IV",
        "colab_type": "code",
        "outputId": "b2edbcb6-e609-49c5-cec7-907b852e6d88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "recentish"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"2019 Quarter 3\",\n\"All NAICS\",\n{\n            'v': 237687289560,\n            'f': \"237687289560\",\n        },\n\"246,154\",\n{\n            'v': 2019,\n            'f': \"2019\",\n        }],\n [{\n            'v': 1,\n            'f': \"1\",\n        },\n\"2019 Quarter 2\",\n\"All NAICS\",\n{\n            'v': 235895076961,\n            'f': \"235895076961\",\n        },\n\"244,453\",\n{\n            'v': 2019,\n            'f': \"2019\",\n        }],\n [{\n            'v': 2,\n            'f': \"2\",\n        },\n\"2019 Quarter 1\",\n\"All NAICS\",\n{\n            'v': 217177570561,\n            'f': \"217177570561\",\n        },\n\"234,591\",\n{\n            'v': 2019,\n            'f': \"2019\",\n 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\"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>NAICS</th>\n",
              "      <th>Total Gross</th>\n",
              "      <th>Units</th>\n",
              "      <th>year</th>\n",
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              "      <th>0</th>\n",
              "      <td>2019 Quarter 3</td>\n",
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              "      <td>237687289560</td>\n",
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              "      <th>1</th>\n",
              "      <td>2019 Quarter 2</td>\n",
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              "      <th>4</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2018 Quarter 1</td>\n",
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              "      <td>221,748</td>\n",
              "      <td>2018</td>\n",
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              "      <th>8</th>\n",
              "      <td>2017 Quarter 4</td>\n",
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              "      <th>9</th>\n",
              "      <td>2017 Quarter 3</td>\n",
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              "      <td>210867803892</td>\n",
              "      <td>229,472</td>\n",
              "      <td>2017</td>\n",
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              "      <td>2017 Quarter 2</td>\n",
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              "      <td>204263367379</td>\n",
              "      <td>226,214</td>\n",
              "      <td>2017</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2017 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>189078920976</td>\n",
              "      <td>218,514</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2016 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>209405913782</td>\n",
              "      <td>355,108</td>\n",
              "      <td>2016</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2016 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>198026055719</td>\n",
              "      <td>216,585</td>\n",
              "      <td>2016</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2016 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>194474826628</td>\n",
              "      <td>214,831</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2016 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>179452983176</td>\n",
              "      <td>208,489</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2015 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>200561377790</td>\n",
              "      <td>349,096</td>\n",
              "      <td>2015</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2015 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>193662328659</td>\n",
              "      <td>209,694</td>\n",
              "      <td>2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2015 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>186987899223</td>\n",
              "      <td>207,533</td>\n",
              "      <td>2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2015 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>171817467356</td>\n",
              "      <td>202,020</td>\n",
              "      <td>2015</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2014 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>203681828671</td>\n",
              "      <td>338,400</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2014 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>186812248776</td>\n",
              "      <td>203,803</td>\n",
              "      <td>2014</td>\n",
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              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2014 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>178795195550</td>\n",
              "      <td>202,636</td>\n",
              "      <td>2014</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2014 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>164210241281</td>\n",
              "      <td>198,809</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2013 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>187167334879</td>\n",
              "      <td>335,239</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2013 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>172536822829</td>\n",
              "      <td>200,065</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2013 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>167195720642</td>\n",
              "      <td>198,780</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2013 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>155975197202</td>\n",
              "      <td>193,715</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2012 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>177137590454</td>\n",
              "      <td>324,314</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2012 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>163867207578</td>\n",
              "      <td>196,503</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2012 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>156196240317</td>\n",
              "      <td>195,134</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2012 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>146762378611</td>\n",
              "      <td>190,344</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2011 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>167503635871</td>\n",
              "      <td>317,090</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2011 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>155649960983</td>\n",
              "      <td>191,767</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2011 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>151713565602</td>\n",
              "      <td>191,159</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>142981766513</td>\n",
              "      <td>187,160</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2010 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>166293823954</td>\n",
              "      <td>308,520</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2010 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>147264405798</td>\n",
              "      <td>187,064</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2010 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>139269736881</td>\n",
              "      <td>186,512</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2010 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>132621379249</td>\n",
              "      <td>181,869</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2009 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>162530197651</td>\n",
              "      <td>301,683</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2009 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>138473535728</td>\n",
              "      <td>185,643</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2009 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>133652503413</td>\n",
              "      <td>184,713</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2009 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>127874475307</td>\n",
              "      <td>180,577</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>2008 Quarter 4</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>164289498251</td>\n",
              "      <td>299,859</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>2008 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>157908370961</td>\n",
              "      <td>189,197</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>2008 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>155833533382</td>\n",
              "      <td>189,098</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2008 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>145982311778</td>\n",
              "      <td>185,348</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Year      NAICS   Total Gross    Units  year\n",
              "0   2019 Quarter 3  All NAICS  237687289560  246,154  2019\n",
              "1   2019 Quarter 2  All NAICS  235895076961  244,453  2019\n",
              "2   2019 Quarter 1  All NAICS  217177570561  234,591  2019\n",
              "3   2018 Quarter 4  All NAICS  257804331180  352,024  2018\n",
              "4   2018 Quarter 3  All NAICS  236068790931  233,228  2018\n",
              "5   2018 Quarter 2  All NAICS  219719073510  228,626  2018\n",
              "6   2018 Quarter 1  All NAICS  203079048068  221,748  2018\n",
              "8   2017 Quarter 4  All NAICS  227663229937  351,078  2017\n",
              "9   2017 Quarter 3  All NAICS  210867803892  229,472  2017\n",
              "10  2017 Quarter 2  All NAICS  204263367379  226,214  2017\n",
              "11  2017 Quarter 1  All NAICS  189078920976  218,514  2017\n",
              "13  2016 Quarter 4  All NAICS  209405913782  355,108  2016\n",
              "14  2016 Quarter 3  All NAICS  198026055719  216,585  2016\n",
              "15  2016 Quarter 2  All NAICS  194474826628  214,831  2016\n",
              "16  2016 Quarter 1  All NAICS  179452983176  208,489  2016\n",
              "18  2015 Quarter 4  All NAICS  200561377790  349,096  2015\n",
              "19  2015 Quarter 3  All NAICS  193662328659  209,694  2015\n",
              "20  2015 Quarter 2  All NAICS  186987899223  207,533  2015\n",
              "21  2015 Quarter 1  All NAICS  171817467356  202,020  2015\n",
              "23  2014 Quarter 4  All NAICS  203681828671  338,400  2014\n",
              "24  2014 Quarter 3  All NAICS  186812248776  203,803  2014\n",
              "25  2014 Quarter 2  All NAICS  178795195550  202,636  2014\n",
              "26  2014 Quarter 1  All NAICS  164210241281  198,809  2014\n",
              "28  2013 Quarter 4  All NAICS  187167334879  335,239  2013\n",
              "29  2013 Quarter 3  All NAICS  172536822829  200,065  2013\n",
              "30  2013 Quarter 2  All NAICS  167195720642  198,780  2013\n",
              "31  2013 Quarter 1  All NAICS  155975197202  193,715  2013\n",
              "33  2012 Quarter 4  All NAICS  177137590454  324,314  2012\n",
              "34  2012 Quarter 3  All NAICS  163867207578  196,503  2012\n",
              "35  2012 Quarter 2  All NAICS  156196240317  195,134  2012\n",
              "36  2012 Quarter 1  All NAICS  146762378611  190,344  2012\n",
              "38  2011 Quarter 4  All NAICS  167503635871  317,090  2011\n",
              "39  2011 Quarter 3  All NAICS  155649960983  191,767  2011\n",
              "40  2011 Quarter 2  All NAICS  151713565602  191,159  2011\n",
              "41  2011 Quarter 1  All NAICS  142981766513  187,160  2011\n",
              "43  2010 Quarter 4  All NAICS  166293823954  308,520  2010\n",
              "44  2010 Quarter 3  All NAICS  147264405798  187,064  2010\n",
              "45  2010 Quarter 2  All NAICS  139269736881  186,512  2010\n",
              "46  2010 Quarter 1  All NAICS  132621379249  181,869  2010\n",
              "48  2009 Quarter 4  All NAICS  162530197651  301,683  2009\n",
              "49  2009 Quarter 3  All NAICS  138473535728  185,643  2009\n",
              "50  2009 Quarter 2  All NAICS  133652503413  184,713  2009\n",
              "51  2009 Quarter 1  All NAICS  127874475307  180,577  2009\n",
              "53  2008 Quarter 4  All NAICS  164289498251  299,859  2008\n",
              "54  2008 Quarter 3  All NAICS  157908370961  189,197  2008\n",
              "55  2008 Quarter 2  All NAICS  155833533382  189,098  2008\n",
              "56  2008 Quarter 1  All NAICS  145982311778  185,348  2008"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKz9EUyy8bxU",
        "colab_type": "code",
        "outputId": "5902d6f9-56f8-4a9c-a086-51ba927cadcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "worst_quarters"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 51,\n            'f': \"51\",\n        },\n\"2009 Quarter 1\",\n\"All NAICS\",\n{\n            'v': 127874475307,\n            'f': \"127874475307\",\n        },\n\"180,577\",\n{\n            'v': 2009,\n            'f': \"2009\",\n        }],\n [{\n            'v': 46,\n            'f': \"46\",\n        },\n\"2010 Quarter 1\",\n\"All NAICS\",\n{\n            'v': 132621379249,\n            'f': \"132621379249\",\n        },\n\"181,869\",\n{\n            'v': 2010,\n            'f': \"2010\",\n        }],\n [{\n            'v': 50,\n            'f': \"50\",\n        },\n\"2009 Quarter 2\",\n\"All NAICS\",\n{\n            'v': 133652503413,\n            'f': \"133652503413\",\n        },\n\"184,713\",\n{\n            'v': 2009,\n            'f': \"2009\",\n        }],\n [{\n            'v': 49,\n            'f': \"49\",\n        },\n\"2009 Quarter 3\",\n\"All NAICS\",\n{\n            'v': 138473535728,\n            'f': \"138473535728\",\n        },\n\"185,643\",\n{\n            'v': 2009,\n            'f': \"2009\",\n        }],\n [{\n            'v': 45,\n            'f': \"45\",\n        },\n\"2010 Quarter 2\",\n\"All NAICS\",\n{\n            'v': 139269736881,\n            'f': \"139269736881\",\n        },\n\"186,512\",\n{\n            'v': 2010,\n            'f': \"2010\",\n        }],\n [{\n            'v': 41,\n            'f': \"41\",\n        },\n\"2011 Quarter 1\",\n\"All NAICS\",\n{\n            'v': 142981766513,\n            'f': \"142981766513\",\n        },\n\"187,160\",\n{\n            'v': 2011,\n            'f': \"2011\",\n        }],\n [{\n            'v': 56,\n            'f': \"56\",\n        },\n\"2008 Quarter 1\",\n\"All NAICS\",\n{\n            'v': 145982311778,\n            'f': \"145982311778\",\n        },\n\"185,348\",\n{\n            'v': 2008,\n            'f': \"2008\",\n        }],\n [{\n            'v': 36,\n            'f': \"36\",\n        },\n\"2012 Quarter 1\",\n\"All NAICS\",\n{\n            'v': 146762378611,\n            'f': \"146762378611\",\n        },\n\"190,344\",\n{\n            'v': 2012,\n            'f': \"2012\",\n        }],\n [{\n            'v': 44,\n            'f': \"44\",\n        },\n\"2010 Quarter 3\",\n\"All NAICS\",\n{\n            'v': 147264405798,\n            'f': \"147264405798\",\n        },\n\"187,064\",\n{\n            'v': 2010,\n            'f': \"2010\",\n        }],\n [{\n            'v': 40,\n            'f': \"40\",\n        },\n\"2011 Quarter 2\",\n\"All NAICS\",\n{\n            'v': 151713565602,\n            'f': \"151713565602\",\n        },\n\"191,159\",\n{\n            'v': 2011,\n            'f': \"2011\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"Year\"], [\"string\", \"NAICS\"], [\"number\", \"Total Gross\"], [\"string\", \"Units\"], [\"number\", \"year\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/html": [
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>NAICS</th>\n",
              "      <th>Total Gross</th>\n",
              "      <th>Units</th>\n",
              "      <th>year</th>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2009 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>127874475307</td>\n",
              "      <td>180,577</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2010 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>132621379249</td>\n",
              "      <td>181,869</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2009 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>133652503413</td>\n",
              "      <td>184,713</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2009 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>138473535728</td>\n",
              "      <td>185,643</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2010 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>139269736881</td>\n",
              "      <td>186,512</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2011 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>142981766513</td>\n",
              "      <td>187,160</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2008 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>145982311778</td>\n",
              "      <td>185,348</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2012 Quarter 1</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>146762378611</td>\n",
              "      <td>190,344</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2010 Quarter 3</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>147264405798</td>\n",
              "      <td>187,064</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2011 Quarter 2</td>\n",
              "      <td>All NAICS</td>\n",
              "      <td>151713565602</td>\n",
              "      <td>191,159</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Year      NAICS   Total Gross    Units  year\n",
              "51  2009 Quarter 1  All NAICS  127874475307  180,577  2009\n",
              "46  2010 Quarter 1  All NAICS  132621379249  181,869  2010\n",
              "50  2009 Quarter 2  All NAICS  133652503413  184,713  2009\n",
              "49  2009 Quarter 3  All NAICS  138473535728  185,643  2009\n",
              "45  2010 Quarter 2  All NAICS  139269736881  186,512  2010\n",
              "41  2011 Quarter 1  All NAICS  142981766513  187,160  2011\n",
              "56  2008 Quarter 1  All NAICS  145982311778  185,348  2008\n",
              "36  2012 Quarter 1  All NAICS  146762378611  190,344  2012\n",
              "44  2010 Quarter 3  All NAICS  147264405798  187,064  2010\n",
              "40  2011 Quarter 2  All NAICS  151713565602  191,159  2011"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9LMbzCMzkCf",
        "colab_type": "code",
        "outputId": "fb280793-f92b-42a5-9685-2b9e210063dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def wa_rev_model():\n",
        "  proxy_gross = ergo.random_choice(recent[\"Total Gross\"].to_list())\n",
        "  # fuzzed to be more likely to be worse than normal rather than better\n",
        "  fuzzed = ergo.normal_from_interval(proxy_gross * 0.5, proxy_gross * 1.2)\n",
        "  ergo.tag(fuzzed, \"mar_gross\")\n",
        "\n",
        "wa_rev_samples = ergo.run(wa_rev_model, 1000)\n",
        "\n",
        "questions[1].samples = wa_rev_samples.mar_gross\n",
        "\n",
        "# questions[1].show_submission(questions[1].samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:00<00:00, 1808.48it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6_9R26EkMcq",
        "colab_type": "text"
      },
      "source": [
        "* IMO our prediction is grossly overconfident\n",
        "  * why do I think so:\n",
        "    * much more confident than the community\n",
        "    * my dumb model can't provide that much certainty\n",
        "    * I think that the prediction for how things would go in the absence of coronavirus should be relatively close to the 90th percentile of our estimate, which it is not\n",
        "  * TODO:\n",
        "    * I think for now, just increase the fuzz factor\n",
        "* The model predicts that revenue will turn out to have been much lower than expected for Q1 2020. It's not obvious why that'd be true\n",
        "  * stay-at-home order was not issued until 23 Mar, i.e. almost the end of the quarter\n",
        "  * TODO:\n",
        "    * switch to basically assuing that Q1 2020 will be a normal quarter, not an abnormally bad one\n",
        "    * figure out what my view on this should really be:\n",
        "      * what exactly is \"gross business income\"?\n",
        "      * how much should we expect it to have been hurt by corona before stay-at-home order?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGUwrOJRDBTo",
        "colab_type": "text"
      },
      "source": [
        "### 2. Will the US federal government shut down all non-essential services by 2020-04-19?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3921/will-the-us-federal-government-shut-down-all-non-essential-services-by-2020-04-19/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5djPXU0mB0C",
        "colab_type": "text"
      },
      "source": [
        "#### Thinking about the question\n",
        "\n",
        "- As far as what people usually think of as \"government shutdowns\", seems like all have been caused by budgetary brinksmanship: https://www.thebalance.com/government-shutdown-3305683\n",
        "\n",
        "- Seems like no budget showdowns coming up before the question expires: http://www.crfb.org/blogs/upcoming-congressional-fiscal-policy-deadlines\n",
        "\n",
        "- So really we're just talking about coronavirus\n",
        "\n",
        "- I feel like this might resolve ambiguously: \"The president or other federal official formally announces a government shutdown\"\n",
        "\n",
        "- Also it's unclear whether no work _with pay_ would count as a \"furlough\" or not , per: \"And / or at least 200,000 federal employees are furloughed for at least 1 week\"\n",
        "\n",
        "- Not much on Google about this possibility: https://www.google.com/search?q=us+federal+government+shutdown+coronavirus&tbm=nws\n",
        "\n",
        "- Also not much about state shutdowns/furloughs\n",
        "\n",
        "- So I think this is pretty unlikely, predicting 15%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pH9hu-PajpIG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "shutdown_prediction = will_x_happen_prediction(0.05, pendulum.datetime(2020,4,4), 0.08, pendulum.datetime(2020,4,19))\n",
        "\n",
        "questions[2].binary_prediction = shutdown_prediction"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aG_fVg2J3lEd",
        "colab_type": "code",
        "outputId": "97a4861e-399b-49f3-bfa3-1448aa1390e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "shutdown_prediction"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07133333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17iLdSvl2Ims",
        "colab_type": "text"
      },
      "source": [
        "#### Apr 4\n",
        "* Now several days later, not seeing any signs of this becoming more likely\n",
        "  * so we should lower p\n",
        "  * ~~write some simple code to do this automatically going forward~~"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFMyypq_DTJ1",
        "colab_type": "text"
      },
      "source": [
        "### 3. Will the Emergency Telework Act (S.3561) become law by 4/25/20?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3918/will-the-emergency-telework-act-s3561-become-law-by-42520/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vJbQLPva6PW0",
        "colab_type": "text"
      },
      "source": [
        "#### Thinking about the question\n",
        "\n",
        "- looking at the question and the current comment, it seems like the thing to do is to model the procedure of bills becoming laws and then use that model to predict whether this one will\n",
        "  - the first step is to build/find a qualitative model of the steps from bill to law\n",
        "  - As the comment points out, it might require something really unusual for this to become law given that Congress is currently out of session, should look into that first\n",
        "- What do I find from a quick Google News search on this? \n",
        "  - nothing from less than a week ago: https://www.google.com/search?q=Emergency+Telework+Act&source=lnms&tbm=nws\n",
        "\n",
        "##### Qualitative model of bill to law\n",
        "\n",
        "[This](https://www.zerotothree.org/resources/728-how-a-bill-becomes-a-law) seems like a good overall summary\n",
        "\n",
        "And [this](https://www.usa.gov/how-laws-are-made) seems to have similar content\n",
        "\n",
        "1.   Various things happen that we don't care about because they already happened for this bill, then: the bill gets introduced into the Senate and referred to a committee: [\"Read twice and referred to the Committee on Homeland Security and Governmental Affairs.\"](https://www.congress.gov/bill/116th-congress/senate-bill/3561/all-actions?overview=closed&KWICView=false)\n",
        "2. Committee delegates to subcommittee or submits to floor\n",
        "3. debate on floor\n",
        "4. votes in full House and Senate (by this point there may be separate bills in the Senate and House; not sure how likely that is for this bill, vs. just having one version\n",
        "5. conference committee between House and Senate\n",
        "6. full House and Senate agree to the version that came out of conference committee\n",
        "7. President signs\n",
        "\n",
        "##### Prediction\n",
        "On further reflection, just seems unlikely to me that this will become law by the specified date given that it doesn't seem to have moved forward in the last week. Assigning 15% probability for now, may go back and build the model later\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZErE3q0hjspW",
        "colab_type": "code",
        "outputId": "8625e547-aff8-4814-a150-a97ba4405e54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "telework_prediction = will_x_happen_prediction(0.05, pendulum.datetime(2020,4,4), 0.08, pendulum.datetime(2020,4,25))\n",
        "\n",
        "questions[3].binary_prediction = telework_prediction\n",
        "\n",
        "telework_prediction"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08809523809523809"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EX30UrnioM3",
        "colab_type": "text"
      },
      "source": [
        "#### Apr 4\n",
        "* any new news on this?\n",
        "  * no\n",
        "  * so that's a slight further negative update, I'll move us down from 0.15 to 0.13\n",
        "* TODO:\n",
        "  * reference class forecasting. What's the right reference class of bills? How likely are they to become law? How long does it take them to become law?\n",
        "    * use this to build a model of the progress of the Emergency Telework Act towards becoming law, use that model to estimate p(becomes law) by 25 Apr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iud_nS11EPzz",
        "colab_type": "text"
      },
      "source": [
        "###\t4. By May 1 will there be an iOS or Android app that shares an individual's COVID-19 infection status with more than 1M other users?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3915/by-may-1-will-there-be-an-ios-or-android-app-that-shares-an-individuals-covid-19-infection-status-with-more-than-1m-other-users/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2PRSW_QBwd_",
        "colab_type": "text"
      },
      "source": [
        "#### Thinking about the question\n",
        "* Seems really hard to think about\n",
        "* Some related stuff:\n",
        "  * Apple has a coronavirus screening app: https://www.apple.com/covid19/ (does not seem to share your status)\n",
        "  * A friend of mine who values digital privacy but also other things posted on FB: \"Question: Under current employment law, what are the rules about whether you can require seeing the results of a medical test before hiring someone?\". This suggests to me that people would likely be open to an app like this, despite the sort of panoptic weirdness of it\n",
        "  * China:\n",
        "    * https://www.nytimes.com/2020/03/01/business/china-coronavirus-surveillance.html\n",
        "      * app assigns you a coronavirus risk assessment that's sent to the government\n",
        "      * doesn't report this to other users, so doesn't count\n",
        "  * Should look into what dating apps are doing/considering\n",
        "    * [didn't find too much](https://www.google.com/search?q=dating+app+share+coronavirus+status&source=lnms&sa=X&ved=0ahUKEwjZyIv9-MXoAhWYJzQIHYx-DGEQ_AUIDSgA&biw=1200&bih=1809&dpr=1)\n",
        "  * Seems likely that the app would star=t somewhere other than the US. East Asia seems particularly likely\n",
        "* One way to make a breakthrough on this question would be to learn about an app/feature that's in development\n",
        "* thinking about reference class, or at least just similar things -- what sort of info about themselves do users share with each other in this way?\n",
        "  * current prediction: 55%. Would have been at like 70% but surprised that we haven't already seen this come out of East Aia\n",
        "  * Can you report HIV or other STI status:\n",
        "    * on Grindr:\n",
        "      * [Yes](https://www.npr.org/sections/thetwo-way/2018/04/03/599069424/grindr-admits-it-shared-hiv-status-of-users)\n",
        "      * not sure about other STIs\n",
        "    * on Fetlife?\n",
        "      * seems like no, but that you also can't enter much other info for your profile\n",
        "    * on Tinder? \n",
        "      * No\n",
        "  * on OKCupid, you can answer a question about how you're feeling about coronavirus\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MU-GWUsjyIn",
        "colab_type": "code",
        "outputId": "cba583d1-c732-4b4a-c9a9-9306c9f3698b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "app_prediction = will_x_happen_prediction(0.05, pendulum.datetime(2020,4,5), 0.55, pendulum.datetime(2020,5,1))\n",
        "\n",
        "questions[4].binary_prediction = app_prediction\n",
        "\n",
        "app_prediction"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.38846153846153847"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqMeRhJQVdey",
        "colab_type": "text"
      },
      "source": [
        "#### 4 Apr update notes\n",
        "* checked the comments, nothing new + interesting\n",
        "* added in the consideration that you can answer on OKC re: how you're feeling about Coronavirus, which increased my p to 0.6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73AG3QWHEcdY",
        "colab_type": "text"
      },
      "source": [
        "### 5. By June 1, how many tests for COVID-19 will have been administered in the US?\t\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3916/by-june-1-how-many-tests-for-covid-19-will-have-been-administered-in-the-us/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEELoM0nJzAS",
        "colab_type": "text"
      },
      "source": [
        "Metaculus says that this is the best data source, seems reasonable: https://covidtracking.com/api/us/daily"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oq1QAM9iJ9OX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "daily_corona = pandas.read_csv(\"https://covidtracking.com/api/us/daily.csv\")\n",
        "daily_corona[\"date\"] = daily_corona[\"date\"].apply(lambda x: pendulum.parse(str(x)))\n",
        "daily_corona = daily_corona.sort_values(\"date\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Gj2Y3u0NTNn",
        "colab_type": "code",
        "outputId": "27c90376-4c57-44d0-b8d1-f28afc146d38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "daily_corona"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>2020-03-05T21:00:00Z</td>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1443</td>\n",
              "      <td>1246</td>\n",
              "      <td>1246</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>211.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>278.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2020-03-06 00:00:00+00:00</td>\n",
              "      <td>37</td>\n",
              "      <td>394</td>\n",
              "      <td>1588.0</td>\n",
              "      <td>458.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ea667c1aa458c1f53bedad18a3829e12319ec4fe</td>\n",
              "      <td>2020-03-06T21:00:00Z</td>\n",
              "      <td>26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2440</td>\n",
              "      <td>1982</td>\n",
              "      <td>1982</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>618.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>736.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2020-03-07 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>550</td>\n",
              "      <td>1839.0</td>\n",
              "      <td>602.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6d22e33c87ce6d5ec8869e1cc5fefb61b3b2263c</td>\n",
              "      <td>2020-03-07T21:00:00Z</td>\n",
              "      <td>27</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2991</td>\n",
              "      <td>2389</td>\n",
              "      <td>2389</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>407.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2020-03-08 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>731</td>\n",
              "      <td>2335.0</td>\n",
              "      <td>347.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>b824c409750148b3d403801cce72744e637f8611</td>\n",
              "      <td>2020-03-08T20:00:00Z</td>\n",
              "      <td>31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3413</td>\n",
              "      <td>3066</td>\n",
              "      <td>3066</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>677.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2020-03-09 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>1048</td>\n",
              "      <td>3344.0</td>\n",
              "      <td>313.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>864257af09b3ab892ea30784b03fb8e146813fe3</td>\n",
              "      <td>2020-03-09T20:00:00Z</td>\n",
              "      <td>35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4705</td>\n",
              "      <td>4392</td>\n",
              "      <td>4392</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>317.0</td>\n",
              "      <td>1326.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2020-03-10 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>1365</td>\n",
              "      <td>3812.0</td>\n",
              "      <td>469.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9d292c7d726064f220575190e75bc16d554e1c5e</td>\n",
              "      <td>2020-03-10T20:00:00Z</td>\n",
              "      <td>37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5646</td>\n",
              "      <td>5177</td>\n",
              "      <td>5177</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>468.0</td>\n",
              "      <td>317.0</td>\n",
              "      <td>785.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2020-03-11 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>1757</td>\n",
              "      <td>6106.0</td>\n",
              "      <td>563.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>c983eaf1f5557763f5aa336a3c228ef91fd5e8b5</td>\n",
              "      <td>2020-03-11T20:00:00Z</td>\n",
              "      <td>43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8426</td>\n",
              "      <td>7863</td>\n",
              "      <td>7863</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2294.0</td>\n",
              "      <td>392.0</td>\n",
              "      <td>2686.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2020-03-12 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>2221</td>\n",
              "      <td>8041.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>a09e368f11008925ddf306f3d80fd28a9e78e231</td>\n",
              "      <td>2020-03-12T20:00:00Z</td>\n",
              "      <td>51</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10935</td>\n",
              "      <td>10262</td>\n",
              "      <td>10262</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1935.0</td>\n",
              "      <td>464.0</td>\n",
              "      <td>2399.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2020-03-13 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>3095</td>\n",
              "      <td>13613.0</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4fa7df83c08a76fbb978b626babf444fe3f9fc38</td>\n",
              "      <td>2020-03-13T20:00:00Z</td>\n",
              "      <td>55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17838</td>\n",
              "      <td>16708</td>\n",
              "      <td>16708</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5572.0</td>\n",
              "      <td>874.0</td>\n",
              "      <td>6446.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2020-03-14 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>3846</td>\n",
              "      <td>17102.0</td>\n",
              "      <td>1236.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>d26500e1e44f93e9530f91a751a6c2fce2ec3856</td>\n",
              "      <td>2020-03-14T20:00:00Z</td>\n",
              "      <td>63</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22184</td>\n",
              "      <td>20948</td>\n",
              "      <td>20948</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3489.0</td>\n",
              "      <td>751.0</td>\n",
              "      <td>4240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2020-03-15 00:00:00+00:00</td>\n",
              "      <td>51</td>\n",
              "      <td>4902</td>\n",
              "      <td>22624.0</td>\n",
              "      <td>2242.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>b3ba00de27b8ad79294053003f51c724f45e8e82</td>\n",
              "      <td>2020-03-15T20:00:00Z</td>\n",
              "      <td>76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29768</td>\n",
              "      <td>27526</td>\n",
              "      <td>27526</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5522.0</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>6578.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2020-03-16 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>6182</td>\n",
              "      <td>36104.0</td>\n",
              "      <td>1691.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>eec53adbbabe91c32f98e286b156b107e23740b5</td>\n",
              "      <td>2020-03-16T20:00:00Z</td>\n",
              "      <td>97</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43977</td>\n",
              "      <td>42286</td>\n",
              "      <td>42286</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13480.0</td>\n",
              "      <td>1280.0</td>\n",
              "      <td>14760.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2020-03-17 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>8366</td>\n",
              "      <td>48053.0</td>\n",
              "      <td>1687.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>960e1e045192a42bc499965d7f6d209eab44fe56</td>\n",
              "      <td>2020-03-17T20:00:00Z</td>\n",
              "      <td>119</td>\n",
              "      <td>NaN</td>\n",
              "      <td>58106</td>\n",
              "      <td>56419</td>\n",
              "      <td>56419</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11949.0</td>\n",
              "      <td>2184.0</td>\n",
              "      <td>14133.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020-03-18 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>10939</td>\n",
              "      <td>67168.0</td>\n",
              "      <td>2526.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6eada9af2c163e98a586d4079ab48454d0ac2209</td>\n",
              "      <td>2020-03-18T20:00:00Z</td>\n",
              "      <td>142</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80633</td>\n",
              "      <td>78107</td>\n",
              "      <td>78107</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19115.0</td>\n",
              "      <td>2573.0</td>\n",
              "      <td>21688.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2020-03-19 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>15134</td>\n",
              "      <td>88939.0</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>307b3db9808b406ed85884f7410e66caf25b1dc6</td>\n",
              "      <td>2020-03-19T20:00:00Z</td>\n",
              "      <td>185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>107089</td>\n",
              "      <td>104073</td>\n",
              "      <td>104073</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21771.0</td>\n",
              "      <td>4195.0</td>\n",
              "      <td>25966.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2020-03-20 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>20908</td>\n",
              "      <td>118970.0</td>\n",
              "      <td>3330.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>455df435e6d9e3758a5088cb18047d8ca4494753</td>\n",
              "      <td>2020-03-20T20:00:00Z</td>\n",
              "      <td>247</td>\n",
              "      <td>NaN</td>\n",
              "      <td>143208</td>\n",
              "      <td>139878</td>\n",
              "      <td>139878</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30031.0</td>\n",
              "      <td>5774.0</td>\n",
              "      <td>35805.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2020-03-21 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>27435</td>\n",
              "      <td>157536.0</td>\n",
              "      <td>3468.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>fda2d9731fff9f561fb05887029b568e916b8dbf</td>\n",
              "      <td>2020-03-21T20:00:00Z</td>\n",
              "      <td>297</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>188439</td>\n",
              "      <td>184971</td>\n",
              "      <td>184971</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>38566.0</td>\n",
              "      <td>6527.0</td>\n",
              "      <td>45093.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2020-03-22 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>36391</td>\n",
              "      <td>195106.0</td>\n",
              "      <td>2842.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>aa401a3b01813e2a889f81ef3fe3cbda2a589961</td>\n",
              "      <td>2020-03-22T20:00:00Z</td>\n",
              "      <td>426</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>234339</td>\n",
              "      <td>231497</td>\n",
              "      <td>231497</td>\n",
              "      <td>129.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>37570.0</td>\n",
              "      <td>8956.0</td>\n",
              "      <td>46526.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2020-03-23 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>47066</td>\n",
              "      <td>240160.0</td>\n",
              "      <td>14571.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>3258.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>c92c4fe9e18d3e92107a2434b6a7b5d62229d256</td>\n",
              "      <td>2020-03-23T20:00:00Z</td>\n",
              "      <td>509</td>\n",
              "      <td>3258.0</td>\n",
              "      <td>301797</td>\n",
              "      <td>287226</td>\n",
              "      <td>287226</td>\n",
              "      <td>83.0</td>\n",
              "      <td>760.0</td>\n",
              "      <td>45054.0</td>\n",
              "      <td>10675.0</td>\n",
              "      <td>55729.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2020-03-24 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>57224</td>\n",
              "      <td>296585.0</td>\n",
              "      <td>14433.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>4091.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50c90e751f919cca012bfd34243f5fe5d4221bbd</td>\n",
              "      <td>2020-03-24T20:00:00Z</td>\n",
              "      <td>706</td>\n",
              "      <td>4091.0</td>\n",
              "      <td>368242</td>\n",
              "      <td>353809</td>\n",
              "      <td>353809</td>\n",
              "      <td>197.0</td>\n",
              "      <td>833.0</td>\n",
              "      <td>56425.0</td>\n",
              "      <td>10158.0</td>\n",
              "      <td>66583.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-03-25 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>69508</td>\n",
              "      <td>363191.0</td>\n",
              "      <td>51235.0</td>\n",
              "      <td>740.0</td>\n",
              "      <td>5436.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>74.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147.0</td>\n",
              "      <td>b97dd71634ee0b0162ef70099e2426762e24dd29</td>\n",
              "      <td>2020-03-25T20:00:00Z</td>\n",
              "      <td>931</td>\n",
              "      <td>5436.0</td>\n",
              "      <td>483934</td>\n",
              "      <td>432699</td>\n",
              "      <td>432699</td>\n",
              "      <td>225.0</td>\n",
              "      <td>1345.0</td>\n",
              "      <td>66606.0</td>\n",
              "      <td>12284.0</td>\n",
              "      <td>78890.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2020-03-26 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>86811</td>\n",
              "      <td>442281.0</td>\n",
              "      <td>60251.0</td>\n",
              "      <td>7387.0</td>\n",
              "      <td>9147.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>97.0</td>\n",
              "      <td>fa9bb1be9686aa1873eb5e6a6daf9a2ee4f17491</td>\n",
              "      <td>2020-03-26T20:00:00Z</td>\n",
              "      <td>1208</td>\n",
              "      <td>9147.0</td>\n",
              "      <td>589343</td>\n",
              "      <td>529092</td>\n",
              "      <td>529092</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3711.0</td>\n",
              "      <td>79090.0</td>\n",
              "      <td>17303.0</td>\n",
              "      <td>96393.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2020-03-27 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>105484</td>\n",
              "      <td>534249.0</td>\n",
              "      <td>60091.0</td>\n",
              "      <td>10511.0</td>\n",
              "      <td>11541.0</td>\n",
              "      <td>1792.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>324.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2422.0</td>\n",
              "      <td>447e0b7431d36f5ad623d2b5aa0b79089d83c5ae</td>\n",
              "      <td>2020-03-27T20:00:00Z</td>\n",
              "      <td>1574</td>\n",
              "      <td>11541.0</td>\n",
              "      <td>699824</td>\n",
              "      <td>639733</td>\n",
              "      <td>639733</td>\n",
              "      <td>366.0</td>\n",
              "      <td>2394.0</td>\n",
              "      <td>91968.0</td>\n",
              "      <td>18673.0</td>\n",
              "      <td>110641.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2020-03-28 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>124884</td>\n",
              "      <td>622779.0</td>\n",
              "      <td>65709.0</td>\n",
              "      <td>11872.0</td>\n",
              "      <td>13749.0</td>\n",
              "      <td>2174.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>390.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3148.0</td>\n",
              "      <td>8de9e06d8184836fdc79404c22c5c3db8d7726d3</td>\n",
              "      <td>2020-03-28T20:00:00Z</td>\n",
              "      <td>2001</td>\n",
              "      <td>13749.0</td>\n",
              "      <td>813372</td>\n",
              "      <td>747663</td>\n",
              "      <td>747663</td>\n",
              "      <td>427.0</td>\n",
              "      <td>2208.0</td>\n",
              "      <td>88530.0</td>\n",
              "      <td>19400.0</td>\n",
              "      <td>107930.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-03-29 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>144377</td>\n",
              "      <td>696372.0</td>\n",
              "      <td>65545.0</td>\n",
              "      <td>13501.0</td>\n",
              "      <td>16263.0</td>\n",
              "      <td>2456.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>439.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4061.0</td>\n",
              "      <td>18bbbdc64d3ec63e3c1325a029342ce5cee5f9f2</td>\n",
              "      <td>2020-03-29T20:00:00Z</td>\n",
              "      <td>2467</td>\n",
              "      <td>16263.0</td>\n",
              "      <td>906294</td>\n",
              "      <td>840749</td>\n",
              "      <td>840749</td>\n",
              "      <td>466.0</td>\n",
              "      <td>2514.0</td>\n",
              "      <td>73593.0</td>\n",
              "      <td>19493.0</td>\n",
              "      <td>93086.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-03-30 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>165597</td>\n",
              "      <td>793796.0</td>\n",
              "      <td>65369.0</td>\n",
              "      <td>15216.0</td>\n",
              "      <td>18511.0</td>\n",
              "      <td>3087.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>451.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4560.0</td>\n",
              "      <td>c7b61d5ebec84314465de213f0b14ff481202c0e</td>\n",
              "      <td>2020-03-30T20:00:00Z</td>\n",
              "      <td>2983</td>\n",
              "      <td>18511.0</td>\n",
              "      <td>1024762</td>\n",
              "      <td>959393</td>\n",
              "      <td>959393</td>\n",
              "      <td>516.0</td>\n",
              "      <td>2248.0</td>\n",
              "      <td>97424.0</td>\n",
              "      <td>21220.0</td>\n",
              "      <td>118644.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-03-31 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>190078</td>\n",
              "      <td>876287.0</td>\n",
              "      <td>59518.0</td>\n",
              "      <td>17353.0</td>\n",
              "      <td>22167.0</td>\n",
              "      <td>3487.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5666.0</td>\n",
              "      <td>b404bc25d6c91ff3410b1b13ceec3a1f00e1fa68</td>\n",
              "      <td>2020-03-31T20:00:00Z</td>\n",
              "      <td>3803</td>\n",
              "      <td>22167.0</td>\n",
              "      <td>1125883</td>\n",
              "      <td>1066365</td>\n",
              "      <td>1066365</td>\n",
              "      <td>820.0</td>\n",
              "      <td>3656.0</td>\n",
              "      <td>82491.0</td>\n",
              "      <td>24481.0</td>\n",
              "      <td>106972.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-04-01 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>215329</td>\n",
              "      <td>954517.0</td>\n",
              "      <td>59665.0</td>\n",
              "      <td>19408.0</td>\n",
              "      <td>26057.0</td>\n",
              "      <td>3937.0</td>\n",
              "      <td>407.0</td>\n",
              "      <td>561.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>7084.0</td>\n",
              "      <td>2e2412c6987aa80697b64e5d28e77affe3757c12</td>\n",
              "      <td>2020-04-01T20:00:00Z</td>\n",
              "      <td>4746</td>\n",
              "      <td>26057.0</td>\n",
              "      <td>1229511</td>\n",
              "      <td>1169846</td>\n",
              "      <td>1169846</td>\n",
              "      <td>943.0</td>\n",
              "      <td>3890.0</td>\n",
              "      <td>78230.0</td>\n",
              "      <td>25251.0</td>\n",
              "      <td>103481.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-04-02 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>243403</td>\n",
              "      <td>1048398.0</td>\n",
              "      <td>62097.0</td>\n",
              "      <td>21135.0</td>\n",
              "      <td>30198.0</td>\n",
              "      <td>4410.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>574.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>8586.0</td>\n",
              "      <td>03d3c731ca67c503f060f9e82445e33aaf6f597c</td>\n",
              "      <td>2020-04-02T20:00:00Z</td>\n",
              "      <td>5835</td>\n",
              "      <td>30198.0</td>\n",
              "      <td>1353898</td>\n",
              "      <td>1291801</td>\n",
              "      <td>1291801</td>\n",
              "      <td>1089.0</td>\n",
              "      <td>4141.0</td>\n",
              "      <td>93881.0</td>\n",
              "      <td>28074.0</td>\n",
              "      <td>121955.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-04-03 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>275457</td>\n",
              "      <td>1148355.0</td>\n",
              "      <td>61976.0</td>\n",
              "      <td>23825.0</td>\n",
              "      <td>33501.0</td>\n",
              "      <td>4811.0</td>\n",
              "      <td>486.0</td>\n",
              "      <td>605.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>10861.0</td>\n",
              "      <td>17df2588c0a2322d642a5a6c338fc2d8e48c69c7</td>\n",
              "      <td>2020-04-03T20:00:00Z</td>\n",
              "      <td>7026</td>\n",
              "      <td>33501.0</td>\n",
              "      <td>1485788</td>\n",
              "      <td>1423812</td>\n",
              "      <td>1423812</td>\n",
              "      <td>1191.0</td>\n",
              "      <td>3303.0</td>\n",
              "      <td>99957.0</td>\n",
              "      <td>32054.0</td>\n",
              "      <td>132011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-04-04 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>308993</td>\n",
              "      <td>1344087.0</td>\n",
              "      <td>15569.0</td>\n",
              "      <td>26948.0</td>\n",
              "      <td>37667.0</td>\n",
              "      <td>5383.0</td>\n",
              "      <td>554.0</td>\n",
              "      <td>656.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>12840.0</td>\n",
              "      <td>1656ed9a681a7b49df73747c53c2e83bce6e5ed6</td>\n",
              "      <td>2020-04-04T20:00:00Z</td>\n",
              "      <td>8379</td>\n",
              "      <td>37667.0</td>\n",
              "      <td>1668649</td>\n",
              "      <td>1653080</td>\n",
              "      <td>1653080</td>\n",
              "      <td>1353.0</td>\n",
              "      <td>4166.0</td>\n",
              "      <td>195732.0</td>\n",
              "      <td>33536.0</td>\n",
              "      <td>229268.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-04-05 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>334967</td>\n",
              "      <td>1440716.0</td>\n",
              "      <td>17303.0</td>\n",
              "      <td>28490.0</td>\n",
              "      <td>40223.0</td>\n",
              "      <td>5677.0</td>\n",
              "      <td>760.0</td>\n",
              "      <td>652.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>14542.0</td>\n",
              "      <td>0e8a973548efc72abf730aebd2dfd8a77897cce3</td>\n",
              "      <td>2020-04-05T20:00:00Z</td>\n",
              "      <td>9554</td>\n",
              "      <td>40223.0</td>\n",
              "      <td>1792986</td>\n",
              "      <td>1775683</td>\n",
              "      <td>1775683</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>2556.0</td>\n",
              "      <td>96629.0</td>\n",
              "      <td>25974.0</td>\n",
              "      <td>122603.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-04-06 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>363719</td>\n",
              "      <td>1561212.0</td>\n",
              "      <td>17283.0</td>\n",
              "      <td>32210.0</td>\n",
              "      <td>43198.0</td>\n",
              "      <td>6943.0</td>\n",
              "      <td>814.0</td>\n",
              "      <td>2961.0</td>\n",
              "      <td>147.0</td>\n",
              "      <td>16584.0</td>\n",
              "      <td>232e5dbbf4efde7a8345b043402278bb3f46da6d</td>\n",
              "      <td>2020-04-06T20:00:00Z</td>\n",
              "      <td>10720</td>\n",
              "      <td>43198.0</td>\n",
              "      <td>1942214</td>\n",
              "      <td>1924931</td>\n",
              "      <td>1924931</td>\n",
              "      <td>1166.0</td>\n",
              "      <td>2975.0</td>\n",
              "      <td>120496.0</td>\n",
              "      <td>28752.0</td>\n",
              "      <td>149248.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-04-07 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>394156</td>\n",
              "      <td>1678874.0</td>\n",
              "      <td>16548.0</td>\n",
              "      <td>39677.0</td>\n",
              "      <td>45500.0</td>\n",
              "      <td>9875.0</td>\n",
              "      <td>889.0</td>\n",
              "      <td>4076.0</td>\n",
              "      <td>151.0</td>\n",
              "      <td>18477.0</td>\n",
              "      <td>5cc01d5c306ebbdf28c7ecfbc179f6bc17dbfe84</td>\n",
              "      <td>2020-04-07T20:00:00Z</td>\n",
              "      <td>12646</td>\n",
              "      <td>45500.0</td>\n",
              "      <td>2089578</td>\n",
              "      <td>2073030</td>\n",
              "      <td>2073030</td>\n",
              "      <td>1926.0</td>\n",
              "      <td>2302.0</td>\n",
              "      <td>117662.0</td>\n",
              "      <td>30437.0</td>\n",
              "      <td>148099.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-04-08 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>424289</td>\n",
              "      <td>1788277.0</td>\n",
              "      <td>17219.0</td>\n",
              "      <td>41111.0</td>\n",
              "      <td>48917.0</td>\n",
              "      <td>9932.0</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>4131.0</td>\n",
              "      <td>216.0</td>\n",
              "      <td>21141.0</td>\n",
              "      <td>4bea4de1051b6959f4a5c455cb1265178421cef6</td>\n",
              "      <td>2020-04-08T20:00:00Z</td>\n",
              "      <td>14547</td>\n",
              "      <td>48917.0</td>\n",
              "      <td>2229785</td>\n",
              "      <td>2212566</td>\n",
              "      <td>2212566</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>3417.0</td>\n",
              "      <td>109403.0</td>\n",
              "      <td>30133.0</td>\n",
              "      <td>139536.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-04-09 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>458635</td>\n",
              "      <td>1916720.0</td>\n",
              "      <td>17622.0</td>\n",
              "      <td>46676.0</td>\n",
              "      <td>52051.0</td>\n",
              "      <td>12254.0</td>\n",
              "      <td>924.0</td>\n",
              "      <td>5794.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>24869.0</td>\n",
              "      <td>c28ccfa18a90ba72c5639b6e0c3b34daf3eb4624</td>\n",
              "      <td>2020-04-09T20:00:00Z</td>\n",
              "      <td>16424</td>\n",
              "      <td>52051.0</td>\n",
              "      <td>2392977</td>\n",
              "      <td>2375355</td>\n",
              "      <td>2375355</td>\n",
              "      <td>1877.0</td>\n",
              "      <td>3134.0</td>\n",
              "      <td>128443.0</td>\n",
              "      <td>34346.0</td>\n",
              "      <td>162789.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-10 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>493252</td>\n",
              "      <td>2036030.0</td>\n",
              "      <td>17435.0</td>\n",
              "      <td>48468.0</td>\n",
              "      <td>56342.0</td>\n",
              "      <td>12698.0</td>\n",
              "      <td>1185.0</td>\n",
              "      <td>5937.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>29054.0</td>\n",
              "      <td>f86beb2cd2a6ded90e984eac8dac94b95cf451f2</td>\n",
              "      <td>2020-04-10T20:00:00Z</td>\n",
              "      <td>18488</td>\n",
              "      <td>56342.0</td>\n",
              "      <td>2546717</td>\n",
              "      <td>2529282</td>\n",
              "      <td>2529282</td>\n",
              "      <td>2064.0</td>\n",
              "      <td>4291.0</td>\n",
              "      <td>119310.0</td>\n",
              "      <td>34617.0</td>\n",
              "      <td>153927.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-11 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>522843</td>\n",
              "      <td>2142823.0</td>\n",
              "      <td>16593.0</td>\n",
              "      <td>51409.0</td>\n",
              "      <td>58549.0</td>\n",
              "      <td>13563.0</td>\n",
              "      <td>1228.0</td>\n",
              "      <td>5978.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>31631.0</td>\n",
              "      <td>5e1d32c78661a97f79f0c5d122ab5e5f2342b775</td>\n",
              "      <td>2020-04-11T20:00:00Z</td>\n",
              "      <td>20355</td>\n",
              "      <td>58549.0</td>\n",
              "      <td>2682259</td>\n",
              "      <td>2665666</td>\n",
              "      <td>2665666</td>\n",
              "      <td>1867.0</td>\n",
              "      <td>2207.0</td>\n",
              "      <td>106793.0</td>\n",
              "      <td>29591.0</td>\n",
              "      <td>136384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-12 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>551826</td>\n",
              "      <td>2254066.0</td>\n",
              "      <td>16419.0</td>\n",
              "      <td>51413.0</td>\n",
              "      <td>61201.0</td>\n",
              "      <td>13917.0</td>\n",
              "      <td>1455.0</td>\n",
              "      <td>5986.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>34151.0</td>\n",
              "      <td>b66df37c6be1e91d8fb155d5612a9fb3202e8e52</td>\n",
              "      <td>2020-04-12T20:00:00Z</td>\n",
              "      <td>21919</td>\n",
              "      <td>61201.0</td>\n",
              "      <td>2822311</td>\n",
              "      <td>2805892</td>\n",
              "      <td>2805892</td>\n",
              "      <td>1564.0</td>\n",
              "      <td>2652.0</td>\n",
              "      <td>111243.0</td>\n",
              "      <td>28983.0</td>\n",
              "      <td>140226.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-04-13 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>576774</td>\n",
              "      <td>2358232.0</td>\n",
              "      <td>17159.0</td>\n",
              "      <td>50968.0</td>\n",
              "      <td>62673.0</td>\n",
              "      <td>13632.0</td>\n",
              "      <td>1628.0</td>\n",
              "      <td>6168.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>35442.0</td>\n",
              "      <td>171a7aa78e00daf2ddb2e32baedcdf1127162a17</td>\n",
              "      <td>2020-04-13T20:00:00Z</td>\n",
              "      <td>23369</td>\n",
              "      <td>62673.0</td>\n",
              "      <td>2952165</td>\n",
              "      <td>2935006</td>\n",
              "      <td>2935006</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>1472.0</td>\n",
              "      <td>104166.0</td>\n",
              "      <td>24948.0</td>\n",
              "      <td>129114.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-04-14 00:00:00+00:00</td>\n",
              "      <td>56</td>\n",
              "      <td>602473</td>\n",
              "      <td>2479147.0</td>\n",
              "      <td>16615.0</td>\n",
              "      <td>54215.0</td>\n",
              "      <td>67547.0</td>\n",
              "      <td>14039.0</td>\n",
              "      <td>1715.0</td>\n",
              "      <td>5975.0</td>\n",
              "      <td>221.0</td>\n",
              "      <td>37645.0</td>\n",
              "      <td>7ea156ba5cb34498d2798ce71d9470bdbb27201b</td>\n",
              "      <td>2020-04-14T20:00:00Z</td>\n",
              "      <td>25668</td>\n",
              "      <td>67547.0</td>\n",
              "      <td>3098235</td>\n",
              "      <td>3081620</td>\n",
              "      <td>3081620</td>\n",
              "      <td>2299.0</td>\n",
              "      <td>4874.0</td>\n",
              "      <td>120915.0</td>\n",
              "      <td>25699.0</td>\n",
              "      <td>146614.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date  ...  totalTestResultsIncrease\n",
              "46 2020-02-28 00:00:00+00:00  ...                       NaN\n",
              "45 2020-02-29 00:00:00+00:00  ...                       9.0\n",
              "44 2020-03-01 00:00:00+00:00  ...                      22.0\n",
              "43 2020-03-02 00:00:00+00:00  ...                      13.0\n",
              "42 2020-03-03 00:00:00+00:00  ...                      47.0\n",
              "41 2020-03-04 00:00:00+00:00  ...                     868.0\n",
              "40 2020-03-05 00:00:00+00:00  ...                     278.0\n",
              "39 2020-03-06 00:00:00+00:00  ...                     736.0\n",
              "38 2020-03-07 00:00:00+00:00  ...                     407.0\n",
              "37 2020-03-08 00:00:00+00:00  ...                     677.0\n",
              "36 2020-03-09 00:00:00+00:00  ...                    1326.0\n",
              "35 2020-03-10 00:00:00+00:00  ...                     785.0\n",
              "34 2020-03-11 00:00:00+00:00  ...                    2686.0\n",
              "33 2020-03-12 00:00:00+00:00  ...                    2399.0\n",
              "32 2020-03-13 00:00:00+00:00  ...                    6446.0\n",
              "31 2020-03-14 00:00:00+00:00  ...                    4240.0\n",
              "30 2020-03-15 00:00:00+00:00  ...                    6578.0\n",
              "29 2020-03-16 00:00:00+00:00  ...                   14760.0\n",
              "28 2020-03-17 00:00:00+00:00  ...                   14133.0\n",
              "27 2020-03-18 00:00:00+00:00  ...                   21688.0\n",
              "26 2020-03-19 00:00:00+00:00  ...                   25966.0\n",
              "25 2020-03-20 00:00:00+00:00  ...                   35805.0\n",
              "24 2020-03-21 00:00:00+00:00  ...                   45093.0\n",
              "23 2020-03-22 00:00:00+00:00  ...                   46526.0\n",
              "22 2020-03-23 00:00:00+00:00  ...                   55729.0\n",
              "21 2020-03-24 00:00:00+00:00  ...                   66583.0\n",
              "20 2020-03-25 00:00:00+00:00  ...                   78890.0\n",
              "19 2020-03-26 00:00:00+00:00  ...                   96393.0\n",
              "18 2020-03-27 00:00:00+00:00  ...                  110641.0\n",
              "17 2020-03-28 00:00:00+00:00  ...                  107930.0\n",
              "16 2020-03-29 00:00:00+00:00  ...                   93086.0\n",
              "15 2020-03-30 00:00:00+00:00  ...                  118644.0\n",
              "14 2020-03-31 00:00:00+00:00  ...                  106972.0\n",
              "13 2020-04-01 00:00:00+00:00  ...                  103481.0\n",
              "12 2020-04-02 00:00:00+00:00  ...                  121955.0\n",
              "11 2020-04-03 00:00:00+00:00  ...                  132011.0\n",
              "10 2020-04-04 00:00:00+00:00  ...                  229268.0\n",
              "9  2020-04-05 00:00:00+00:00  ...                  122603.0\n",
              "8  2020-04-06 00:00:00+00:00  ...                  149248.0\n",
              "7  2020-04-07 00:00:00+00:00  ...                  148099.0\n",
              "6  2020-04-08 00:00:00+00:00  ...                  139536.0\n",
              "5  2020-04-09 00:00:00+00:00  ...                  162789.0\n",
              "4  2020-04-10 00:00:00+00:00  ...                  153927.0\n",
              "3  2020-04-11 00:00:00+00:00  ...                  136384.0\n",
              "2  2020-04-12 00:00:00+00:00  ...                  140226.0\n",
              "1  2020-04-13 00:00:00+00:00  ...                  129114.0\n",
              "0  2020-04-14 00:00:00+00:00  ...                  146614.0\n",
              "\n",
              "[47 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJ7zEt3lM7PP",
        "colab_type": "code",
        "outputId": "869f51aa-6cfc-4afd-d5f4-78aefd70daa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        }
      },
      "source": [
        "seaborn.pointplot(daily_corona[\"date\"], daily_corona[\"totalTestResults\"], color=\"red\")\n",
        "seaborn.pointplot(daily_corona[\"date\"], daily_corona[\"totalTestResultsIncrease\"], color=\"green\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcfe28fecf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNyPwSuPOmAQ",
        "colab_type": "text"
      },
      "source": [
        "Original notes: Looks like there have been a constant number of new tests per day lately, so just assume that will continue to be the case"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUFBQ_JPOr33",
        "colab_type": "code",
        "outputId": "2d5f9ccc-7c8a-42b1-f288-acd19a33e918",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def tests_model():\n",
        "    latest_date = pendulum.instance(daily_corona.iloc[-1][\"date\"])\n",
        "    total_tests = daily_corona.iloc[-1][\"totalTestResults\"]\n",
        "    last_five_days = daily_corona.nlargest(5,\"date\")\n",
        "    avg_recent_daily_tests = last_five_days[\"totalTestResultsIncrease\"].mean()\n",
        "\n",
        "    tests_per_day_high = ergo.normal_from_interval(avg_recent_daily_tests * 1, avg_recent_daily_tests * 10)\n",
        "    tests_per_day_likely = ergo.lognormal_from_interval(avg_recent_daily_tests * 0.2, avg_recent_daily_tests * 2)\n",
        "    tests_per_day = ergo.random_choice([tests_per_day_high, tests_per_day_likely, tests_per_day_likely, tests_per_day_likely])\n",
        "    end_date = pendulum.date(2020,6,1)\n",
        "    for i in range((end_date - latest_date).days + 1):\n",
        "      date = latest_date.add(days=i)\n",
        "      total_tests = total_tests + tests_per_day\n",
        "      ergo.tag(total_tests, date.format('YYYY/MM/DD'))\n",
        "\n",
        "test_samples = ergo.run(tests_model, 1000)\n",
        "\n",
        "questions[5].samples = test_samples[\"2020/06/01\"]\n",
        "\n",
        "# questions[5].show_submission(questions[5].samples)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:10<00:00, 91.44it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aZCjmYHg5ug",
        "colab_type": "code",
        "outputId": "fad7854a-af12-421c-a168-2c664183d8e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "test_samples"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2020/04/14</th>\n",
              "      <th>2020/04/15</th>\n",
              "      <th>2020/04/16</th>\n",
              "      <th>2020/04/17</th>\n",
              "      <th>2020/04/18</th>\n",
              "      <th>2020/04/19</th>\n",
              "      <th>2020/04/20</th>\n",
              "      <th>2020/04/21</th>\n",
              "      <th>2020/04/22</th>\n",
              "      <th>2020/04/23</th>\n",
              "      <th>2020/04/24</th>\n",
              "      <th>2020/04/25</th>\n",
              "      <th>2020/04/26</th>\n",
              "      <th>2020/04/27</th>\n",
              "      <th>2020/04/28</th>\n",
              "      <th>2020/04/29</th>\n",
              "      <th>2020/04/30</th>\n",
              "      <th>2020/05/01</th>\n",
              "      <th>2020/05/02</th>\n",
              "      <th>2020/05/03</th>\n",
              "      <th>2020/05/04</th>\n",
              "      <th>2020/05/05</th>\n",
              "      <th>2020/05/06</th>\n",
              "      <th>2020/05/07</th>\n",
              "      <th>2020/05/08</th>\n",
              "      <th>2020/05/09</th>\n",
              "      <th>2020/05/10</th>\n",
              "      <th>2020/05/11</th>\n",
              "      <th>2020/05/12</th>\n",
              "      <th>2020/05/13</th>\n",
              "      <th>2020/05/14</th>\n",
              "      <th>2020/05/15</th>\n",
              "      <th>2020/05/16</th>\n",
              "      <th>2020/05/17</th>\n",
              "      <th>2020/05/18</th>\n",
              "      <th>2020/05/19</th>\n",
              "      <th>2020/05/20</th>\n",
              "      <th>2020/05/21</th>\n",
              "      <th>2020/05/22</th>\n",
              "      <th>2020/05/23</th>\n",
              "      <th>2020/05/24</th>\n",
              "      <th>2020/05/25</th>\n",
              "      <th>2020/05/26</th>\n",
              "      <th>2020/05/27</th>\n",
              "      <th>2020/05/28</th>\n",
              "      <th>2020/05/29</th>\n",
              "      <th>2020/05/30</th>\n",
              "      <th>2020/05/31</th>\n",
              "      <th>2020/06/01</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3109747.50</td>\n",
              "      <td>3137875.0</td>\n",
              "      <td>3166002.50</td>\n",
              "      <td>3194130.0</td>\n",
              "      <td>3222257.50</td>\n",
              "      <td>3250385.0</td>\n",
              "      <td>3278512.50</td>\n",
              "      <td>3306640.0</td>\n",
              "      <td>3334767.50</td>\n",
              "      <td>3362895.0</td>\n",
              "      <td>3391022.50</td>\n",
              "      <td>3419150.0</td>\n",
              "      <td>3447277.50</td>\n",
              "      <td>3475405.0</td>\n",
              "      <td>3503532.50</td>\n",
              "      <td>3531660.0</td>\n",
              "      <td>3559787.50</td>\n",
              "      <td>3587915.0</td>\n",
              "      <td>3616042.50</td>\n",
              "      <td>3644170.0</td>\n",
              "      <td>3672297.50</td>\n",
              "      <td>3700425.0</td>\n",
              "      <td>3728552.50</td>\n",
              "      <td>3756680.0</td>\n",
              "      <td>3784807.50</td>\n",
              "      <td>3812935.0</td>\n",
              "      <td>3841062.50</td>\n",
              "      <td>3869190.0</td>\n",
              "      <td>3897317.50</td>\n",
              "      <td>3925445.0</td>\n",
              "      <td>3953572.50</td>\n",
              "      <td>3981700.0</td>\n",
              "      <td>4009827.50</td>\n",
              "      <td>4037955.0</td>\n",
              "      <td>4066082.50</td>\n",
              "      <td>4094210.0</td>\n",
              "      <td>4122337.5</td>\n",
              "      <td>4150465.0</td>\n",
              "      <td>4178592.5</td>\n",
              "      <td>4206720.0</td>\n",
              "      <td>4234847.5</td>\n",
              "      <td>4262975.0</td>\n",
              "      <td>4291102.5</td>\n",
              "      <td>4319230.0</td>\n",
              "      <td>4347357.5</td>\n",
              "      <td>4375485.0</td>\n",
              "      <td>4403612.5</td>\n",
              "      <td>4431740.0</td>\n",
              "      <td>4459867.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3113232.75</td>\n",
              "      <td>3144845.5</td>\n",
              "      <td>3176458.25</td>\n",
              "      <td>3208071.0</td>\n",
              "      <td>3239683.75</td>\n",
              "      <td>3271296.5</td>\n",
              "      <td>3302909.25</td>\n",
              "      <td>3334522.0</td>\n",
              "      <td>3366134.75</td>\n",
              "      <td>3397747.5</td>\n",
              "      <td>3429360.25</td>\n",
              "      <td>3460973.0</td>\n",
              "      <td>3492585.75</td>\n",
              "      <td>3524198.5</td>\n",
              "      <td>3555811.25</td>\n",
              "      <td>3587424.0</td>\n",
              "      <td>3619036.75</td>\n",
              "      <td>3650649.5</td>\n",
              "      <td>3682262.25</td>\n",
              "      <td>3713875.0</td>\n",
              "      <td>3745487.75</td>\n",
              "      <td>3777100.5</td>\n",
              "      <td>3808713.25</td>\n",
              "      <td>3840326.0</td>\n",
              "      <td>3871938.75</td>\n",
              "      <td>3903551.5</td>\n",
              "      <td>3935164.25</td>\n",
              "      <td>3966777.0</td>\n",
              "      <td>3998389.75</td>\n",
              "      <td>4030002.5</td>\n",
              "      <td>4061615.25</td>\n",
              "      <td>4093228.0</td>\n",
              "      <td>4124840.75</td>\n",
              "      <td>4156453.5</td>\n",
              "      <td>4188066.25</td>\n",
              "      <td>4219679.0</td>\n",
              "      <td>4251291.5</td>\n",
              "      <td>4282904.0</td>\n",
              "      <td>4314516.5</td>\n",
              "      <td>4346129.0</td>\n",
              "      <td>4377741.5</td>\n",
              "      <td>4409354.0</td>\n",
              "      <td>4440966.5</td>\n",
              "      <td>4472579.0</td>\n",
              "      <td>4504191.5</td>\n",
              "      <td>4535804.0</td>\n",
              "      <td>4567416.5</td>\n",
              "      <td>4599029.0</td>\n",
              "      <td>4630641.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3157299.50</td>\n",
              "      <td>3232979.0</td>\n",
              "      <td>3308658.50</td>\n",
              "      <td>3384338.0</td>\n",
              "      <td>3460017.50</td>\n",
              "      <td>3535697.0</td>\n",
              "      <td>3611376.50</td>\n",
              "      <td>3687056.0</td>\n",
              "      <td>3762735.50</td>\n",
              "      <td>3838415.0</td>\n",
              "      <td>3914094.50</td>\n",
              "      <td>3989774.0</td>\n",
              "      <td>4065453.50</td>\n",
              "      <td>4141133.0</td>\n",
              "      <td>4216812.50</td>\n",
              "      <td>4292492.0</td>\n",
              "      <td>4368171.50</td>\n",
              "      <td>4443851.0</td>\n",
              "      <td>4519530.50</td>\n",
              "      <td>4595210.0</td>\n",
              "      <td>4670889.50</td>\n",
              "      <td>4746569.0</td>\n",
              "      <td>4822248.50</td>\n",
              "      <td>4897928.0</td>\n",
              "      <td>4973607.50</td>\n",
              "      <td>5049287.0</td>\n",
              "      <td>5124966.50</td>\n",
              "      <td>5200646.0</td>\n",
              "      <td>5276325.50</td>\n",
              "      <td>5352005.0</td>\n",
              "      <td>5427684.50</td>\n",
              "      <td>5503364.0</td>\n",
              "      <td>5579043.50</td>\n",
              "      <td>5654723.0</td>\n",
              "      <td>5730402.50</td>\n",
              "      <td>5806082.0</td>\n",
              "      <td>5881761.5</td>\n",
              "      <td>5957441.0</td>\n",
              "      <td>6033120.5</td>\n",
              "      <td>6108800.0</td>\n",
              "      <td>6184479.5</td>\n",
              "      <td>6260159.0</td>\n",
              "      <td>6335838.5</td>\n",
              "      <td>6411518.0</td>\n",
              "      <td>6487197.5</td>\n",
              "      <td>6562877.0</td>\n",
              "      <td>6638556.5</td>\n",
              "      <td>6714236.0</td>\n",
              "      <td>6789915.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3484130.25</td>\n",
              "      <td>3886640.5</td>\n",
              "      <td>4289151.00</td>\n",
              "      <td>4691661.5</td>\n",
              "      <td>5094172.00</td>\n",
              "      <td>5496682.5</td>\n",
              "      <td>5899193.00</td>\n",
              "      <td>6301703.5</td>\n",
              "      <td>6704214.00</td>\n",
              "      <td>7106724.5</td>\n",
              "      <td>7509235.00</td>\n",
              "      <td>7911745.5</td>\n",
              "      <td>8314256.00</td>\n",
              "      <td>8716766.0</td>\n",
              "      <td>9119276.00</td>\n",
              "      <td>9521786.0</td>\n",
              "      <td>9924296.00</td>\n",
              "      <td>10326806.0</td>\n",
              "      <td>10729316.00</td>\n",
              "      <td>11131826.0</td>\n",
              "      <td>11534336.00</td>\n",
              "      <td>11936846.0</td>\n",
              "      <td>12339356.00</td>\n",
              "      <td>12741866.0</td>\n",
              "      <td>13144376.00</td>\n",
              "      <td>13546886.0</td>\n",
              "      <td>13949396.00</td>\n",
              "      <td>14351906.0</td>\n",
              "      <td>14754416.00</td>\n",
              "      <td>15156926.0</td>\n",
              "      <td>15559436.00</td>\n",
              "      <td>15961946.0</td>\n",
              "      <td>16364456.00</td>\n",
              "      <td>16766966.0</td>\n",
              "      <td>17169476.00</td>\n",
              "      <td>17571986.0</td>\n",
              "      <td>17974496.0</td>\n",
              "      <td>18377006.0</td>\n",
              "      <td>18779516.0</td>\n",
              "      <td>19182026.0</td>\n",
              "      <td>19584536.0</td>\n",
              "      <td>19987046.0</td>\n",
              "      <td>20389556.0</td>\n",
              "      <td>20792066.0</td>\n",
              "      <td>21194576.0</td>\n",
              "      <td>21597086.0</td>\n",
              "      <td>21999596.0</td>\n",
              "      <td>22402106.0</td>\n",
              "      <td>22804616.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3267345.75</td>\n",
              "      <td>3453071.5</td>\n",
              "      <td>3638797.25</td>\n",
              "      <td>3824523.0</td>\n",
              "      <td>4010248.75</td>\n",
              "      <td>4195974.5</td>\n",
              "      <td>4381700.50</td>\n",
              "      <td>4567426.5</td>\n",
              "      <td>4753152.50</td>\n",
              "      <td>4938878.5</td>\n",
              "      <td>5124604.50</td>\n",
              "      <td>5310330.5</td>\n",
              "      <td>5496056.50</td>\n",
              "      <td>5681782.5</td>\n",
              "      <td>5867508.50</td>\n",
              "      <td>6053234.5</td>\n",
              "      <td>6238960.50</td>\n",
              "      <td>6424686.5</td>\n",
              "      <td>6610412.50</td>\n",
              "      <td>6796138.5</td>\n",
              "      <td>6981864.50</td>\n",
              "      <td>7167590.5</td>\n",
              "      <td>7353316.50</td>\n",
              "      <td>7539042.5</td>\n",
              "      <td>7724768.50</td>\n",
              "      <td>7910494.5</td>\n",
              "      <td>8096220.50</td>\n",
              "      <td>8281946.5</td>\n",
              "      <td>8467672.00</td>\n",
              "      <td>8653398.0</td>\n",
              "      <td>8839124.00</td>\n",
              "      <td>9024850.0</td>\n",
              "      <td>9210576.00</td>\n",
              "      <td>9396302.0</td>\n",
              "      <td>9582028.00</td>\n",
              "      <td>9767754.0</td>\n",
              "      <td>9953480.0</td>\n",
              "      <td>10139206.0</td>\n",
              "      <td>10324932.0</td>\n",
              "      <td>10510658.0</td>\n",
              "      <td>10696384.0</td>\n",
              "      <td>10882110.0</td>\n",
              "      <td>11067836.0</td>\n",
              "      <td>11253562.0</td>\n",
              "      <td>11439288.0</td>\n",
              "      <td>11625014.0</td>\n",
              "      <td>11810740.0</td>\n",
              "      <td>11996466.0</td>\n",
              "      <td>12182192.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>3316038.50</td>\n",
              "      <td>3550457.0</td>\n",
              "      <td>3784875.50</td>\n",
              "      <td>4019294.0</td>\n",
              "      <td>4253712.50</td>\n",
              "      <td>4488131.0</td>\n",
              "      <td>4722549.50</td>\n",
              "      <td>4956968.0</td>\n",
              "      <td>5191386.50</td>\n",
              "      <td>5425805.0</td>\n",
              "      <td>5660223.50</td>\n",
              "      <td>5894642.0</td>\n",
              "      <td>6129060.50</td>\n",
              "      <td>6363479.0</td>\n",
              "      <td>6597897.50</td>\n",
              "      <td>6832316.0</td>\n",
              "      <td>7066734.50</td>\n",
              "      <td>7301153.0</td>\n",
              "      <td>7535571.50</td>\n",
              "      <td>7769990.0</td>\n",
              "      <td>8004408.50</td>\n",
              "      <td>8238827.0</td>\n",
              "      <td>8473246.00</td>\n",
              "      <td>8707665.0</td>\n",
              "      <td>8942084.00</td>\n",
              "      <td>9176503.0</td>\n",
              "      <td>9410922.00</td>\n",
              "      <td>9645341.0</td>\n",
              "      <td>9879760.00</td>\n",
              "      <td>10114179.0</td>\n",
              "      <td>10348598.00</td>\n",
              "      <td>10583017.0</td>\n",
              "      <td>10817436.00</td>\n",
              "      <td>11051855.0</td>\n",
              "      <td>11286274.00</td>\n",
              "      <td>11520693.0</td>\n",
              "      <td>11755112.0</td>\n",
              "      <td>11989531.0</td>\n",
              "      <td>12223950.0</td>\n",
              "      <td>12458369.0</td>\n",
              "      <td>12692788.0</td>\n",
              "      <td>12927207.0</td>\n",
              "      <td>13161626.0</td>\n",
              "      <td>13396045.0</td>\n",
              "      <td>13630464.0</td>\n",
              "      <td>13864883.0</td>\n",
              "      <td>14099302.0</td>\n",
              "      <td>14333721.0</td>\n",
              "      <td>14568140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>3137795.50</td>\n",
              "      <td>3193971.0</td>\n",
              "      <td>3250146.50</td>\n",
              "      <td>3306322.0</td>\n",
              "      <td>3362497.50</td>\n",
              "      <td>3418673.0</td>\n",
              "      <td>3474848.50</td>\n",
              "      <td>3531024.0</td>\n",
              "      <td>3587199.50</td>\n",
              "      <td>3643375.0</td>\n",
              "      <td>3699550.50</td>\n",
              "      <td>3755726.0</td>\n",
              "      <td>3811901.50</td>\n",
              "      <td>3868077.0</td>\n",
              "      <td>3924252.50</td>\n",
              "      <td>3980428.0</td>\n",
              "      <td>4036603.50</td>\n",
              "      <td>4092779.0</td>\n",
              "      <td>4148954.50</td>\n",
              "      <td>4205130.0</td>\n",
              "      <td>4261305.50</td>\n",
              "      <td>4317481.0</td>\n",
              "      <td>4373656.50</td>\n",
              "      <td>4429832.0</td>\n",
              "      <td>4486007.50</td>\n",
              "      <td>4542183.0</td>\n",
              "      <td>4598358.50</td>\n",
              "      <td>4654534.0</td>\n",
              "      <td>4710709.50</td>\n",
              "      <td>4766885.0</td>\n",
              "      <td>4823060.50</td>\n",
              "      <td>4879236.0</td>\n",
              "      <td>4935411.50</td>\n",
              "      <td>4991587.0</td>\n",
              "      <td>5047762.50</td>\n",
              "      <td>5103938.0</td>\n",
              "      <td>5160113.5</td>\n",
              "      <td>5216289.0</td>\n",
              "      <td>5272464.5</td>\n",
              "      <td>5328640.0</td>\n",
              "      <td>5384815.5</td>\n",
              "      <td>5440991.0</td>\n",
              "      <td>5497166.5</td>\n",
              "      <td>5553342.0</td>\n",
              "      <td>5609517.5</td>\n",
              "      <td>5665693.0</td>\n",
              "      <td>5721868.5</td>\n",
              "      <td>5778044.0</td>\n",
              "      <td>5834219.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>4040169.00</td>\n",
              "      <td>4998718.0</td>\n",
              "      <td>5957267.00</td>\n",
              "      <td>6915816.0</td>\n",
              "      <td>7874365.00</td>\n",
              "      <td>8832914.0</td>\n",
              "      <td>9791463.00</td>\n",
              "      <td>10750012.0</td>\n",
              "      <td>11708561.00</td>\n",
              "      <td>12667110.0</td>\n",
              "      <td>13625659.00</td>\n",
              "      <td>14584208.0</td>\n",
              "      <td>15542757.00</td>\n",
              "      <td>16501306.0</td>\n",
              "      <td>17459854.00</td>\n",
              "      <td>18418402.0</td>\n",
              "      <td>19376950.00</td>\n",
              "      <td>20335498.0</td>\n",
              "      <td>21294046.00</td>\n",
              "      <td>22252594.0</td>\n",
              "      <td>23211142.00</td>\n",
              "      <td>24169690.0</td>\n",
              "      <td>25128238.00</td>\n",
              "      <td>26086786.0</td>\n",
              "      <td>27045334.00</td>\n",
              "      <td>28003882.0</td>\n",
              "      <td>28962430.00</td>\n",
              "      <td>29920978.0</td>\n",
              "      <td>30879526.00</td>\n",
              "      <td>31838074.0</td>\n",
              "      <td>32796622.00</td>\n",
              "      <td>33755172.0</td>\n",
              "      <td>34713720.00</td>\n",
              "      <td>35672268.0</td>\n",
              "      <td>36630816.00</td>\n",
              "      <td>37589364.0</td>\n",
              "      <td>38547912.0</td>\n",
              "      <td>39506460.0</td>\n",
              "      <td>40465008.0</td>\n",
              "      <td>41423556.0</td>\n",
              "      <td>42382104.0</td>\n",
              "      <td>43340652.0</td>\n",
              "      <td>44299200.0</td>\n",
              "      <td>45257748.0</td>\n",
              "      <td>46216296.0</td>\n",
              "      <td>47174844.0</td>\n",
              "      <td>48133392.0</td>\n",
              "      <td>49091940.0</td>\n",
              "      <td>50050488.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>3244427.25</td>\n",
              "      <td>3407234.5</td>\n",
              "      <td>3570041.75</td>\n",
              "      <td>3732849.0</td>\n",
              "      <td>3895656.25</td>\n",
              "      <td>4058463.5</td>\n",
              "      <td>4221271.00</td>\n",
              "      <td>4384078.5</td>\n",
              "      <td>4546886.00</td>\n",
              "      <td>4709693.5</td>\n",
              "      <td>4872501.00</td>\n",
              "      <td>5035308.5</td>\n",
              "      <td>5198116.00</td>\n",
              "      <td>5360923.5</td>\n",
              "      <td>5523731.00</td>\n",
              "      <td>5686538.5</td>\n",
              "      <td>5849346.00</td>\n",
              "      <td>6012153.5</td>\n",
              "      <td>6174961.00</td>\n",
              "      <td>6337768.5</td>\n",
              "      <td>6500576.00</td>\n",
              "      <td>6663383.5</td>\n",
              "      <td>6826191.00</td>\n",
              "      <td>6988998.5</td>\n",
              "      <td>7151806.00</td>\n",
              "      <td>7314613.5</td>\n",
              "      <td>7477421.00</td>\n",
              "      <td>7640228.5</td>\n",
              "      <td>7803036.00</td>\n",
              "      <td>7965843.5</td>\n",
              "      <td>8128651.00</td>\n",
              "      <td>8291458.5</td>\n",
              "      <td>8454266.00</td>\n",
              "      <td>8617073.0</td>\n",
              "      <td>8779880.00</td>\n",
              "      <td>8942687.0</td>\n",
              "      <td>9105494.0</td>\n",
              "      <td>9268301.0</td>\n",
              "      <td>9431108.0</td>\n",
              "      <td>9593915.0</td>\n",
              "      <td>9756722.0</td>\n",
              "      <td>9919529.0</td>\n",
              "      <td>10082336.0</td>\n",
              "      <td>10245143.0</td>\n",
              "      <td>10407950.0</td>\n",
              "      <td>10570757.0</td>\n",
              "      <td>10733564.0</td>\n",
              "      <td>10896371.0</td>\n",
              "      <td>11059178.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>3927902.25</td>\n",
              "      <td>4774184.5</td>\n",
              "      <td>5620466.50</td>\n",
              "      <td>6466748.5</td>\n",
              "      <td>7313030.50</td>\n",
              "      <td>8159312.5</td>\n",
              "      <td>9005595.00</td>\n",
              "      <td>9851877.0</td>\n",
              "      <td>10698159.00</td>\n",
              "      <td>11544441.0</td>\n",
              "      <td>12390723.00</td>\n",
              "      <td>13237005.0</td>\n",
              "      <td>14083287.00</td>\n",
              "      <td>14929569.0</td>\n",
              "      <td>15775851.00</td>\n",
              "      <td>16622133.0</td>\n",
              "      <td>17468416.00</td>\n",
              "      <td>18314698.0</td>\n",
              "      <td>19160980.00</td>\n",
              "      <td>20007262.0</td>\n",
              "      <td>20853544.00</td>\n",
              "      <td>21699826.0</td>\n",
              "      <td>22546108.00</td>\n",
              "      <td>23392390.0</td>\n",
              "      <td>24238672.00</td>\n",
              "      <td>25084954.0</td>\n",
              "      <td>25931236.00</td>\n",
              "      <td>26777518.0</td>\n",
              "      <td>27623800.00</td>\n",
              "      <td>28470082.0</td>\n",
              "      <td>29316364.00</td>\n",
              "      <td>30162646.0</td>\n",
              "      <td>31008928.00</td>\n",
              "      <td>31855210.0</td>\n",
              "      <td>32701492.00</td>\n",
              "      <td>33547774.0</td>\n",
              "      <td>34394056.0</td>\n",
              "      <td>35240340.0</td>\n",
              "      <td>36086624.0</td>\n",
              "      <td>36932908.0</td>\n",
              "      <td>37779192.0</td>\n",
              "      <td>38625476.0</td>\n",
              "      <td>39471760.0</td>\n",
              "      <td>40318044.0</td>\n",
              "      <td>41164328.0</td>\n",
              "      <td>42010612.0</td>\n",
              "      <td>42856896.0</td>\n",
              "      <td>43703180.0</td>\n",
              "      <td>44549464.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 49 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     2020/04/14  2020/04/15  2020/04/16  ...  2020/05/30  2020/05/31  2020/06/01\n",
              "0    3109747.50   3137875.0  3166002.50  ...   4403612.5   4431740.0   4459867.5\n",
              "1    3113232.75   3144845.5  3176458.25  ...   4567416.5   4599029.0   4630641.5\n",
              "2    3157299.50   3232979.0  3308658.50  ...   6638556.5   6714236.0   6789915.5\n",
              "3    3484130.25   3886640.5  4289151.00  ...  21999596.0  22402106.0  22804616.0\n",
              "4    3267345.75   3453071.5  3638797.25  ...  11810740.0  11996466.0  12182192.0\n",
              "..          ...         ...         ...  ...         ...         ...         ...\n",
              "995  3316038.50   3550457.0  3784875.50  ...  14099302.0  14333721.0  14568140.0\n",
              "996  3137795.50   3193971.0  3250146.50  ...   5721868.5   5778044.0   5834219.5\n",
              "997  4040169.00   4998718.0  5957267.00  ...  48133392.0  49091940.0  50050488.0\n",
              "998  3244427.25   3407234.5  3570041.75  ...  10733564.0  10896371.0  11059178.0\n",
              "999  3927902.25   4774184.5  5620466.50  ...  42856896.0  43703180.0  44549464.0\n",
              "\n",
              "[1000 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyCBL6mi6unG",
        "colab_type": "code",
        "outputId": "f75eadc3-028e-4579-9df0-0d137b1fc069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "questions[5].submit_from_samples(test_samples[\"2020/06/01\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/jax/lib/xla_bridge.py:123: UserWarning: No GPU/TPU found, falling back to CPU.\n",
            "  warnings.warn('No GPU/TPU found, falling back to CPU.')\n",
            "  3%|▎         | 165/5000 [00:02<01:15, 64.43it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Encoutered nan gradient, stopping early\n",
            "[[        nan         nan -222.83087 ]\n",
            " [ 224.76326   261.89325   147.95656 ]\n",
            " [ 494.42953   993.1275     74.874306]]\n",
            "[[ 0.517827    0.04312615 -2.975864  ]\n",
            " [ 0.68932223  0.24348636 -3.018332  ]\n",
            " [ 0.6501296   0.15358527 -3.0058026 ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Response [202]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHkA25lxd0pe",
        "colab_type": "text"
      },
      "source": [
        "#### Apr 4 updates\n",
        "* ~~increase number of samples to run to 1000, 100 might have been too few~~\n",
        "* My prediction distribution is way too narrow:\n",
        "  * I think so because:\n",
        "    * A priori -- there's no way I'm that confident\n",
        "    * compared to the community prediction\n",
        "  * What I'm going to do:\n",
        "    * ~~instead of sampling tests_today every day, sample tests_per_day for the whole duration. I had already identified this as a way to make the model make more sense, and it will also lead to a wider distribution~~\n",
        "    * TODO: do this in some more sophisticated way where I can more clearly express my guesses for how many tests will be administered over the whole interval, compared to now. Seems good to do, planning to come back to this\n",
        "* tests per day looks slightly less constant now\n",
        "  * TODO: consider modeling test_per_day based on something other than average tests over the past few days -- fit a quadratic or do a linear regression or something. Will skip this for now but maybe come back to it later\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAPfX-a5F0v1",
        "colab_type": "text"
      },
      "source": [
        "### 6. [short fuse] How many total confirmed deaths of novel coronavirus will be reported in the state of New York by April 2nd?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgaOLz4YHU8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from this metaculus comment: https://pandemic.metaculus.com/questions/3934/short-fuse-how-many-total-confirmed-deaths-of-novel-coronavirus-will-be-reported-in-the-state-of-new-york-by-april-2nd/#comment-25503\n",
        "supposed_covid_timeseries = pandas.read_csv(\"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\")\n",
        "ny = supposed_covid_timeseries[supposed_covid_timeseries[\"state\"].str.contains(\"New York\")]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bz6aplwmvEbi",
        "outputId": "f9257544-3f67-4ed1-9525-db7fd56376be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "ny"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 246,\n            'f': \"246\",\n        },\n\"2020-03-01\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 1,\n            'f': \"1\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 261,\n            'f': \"261\",\n        },\n\"2020-03-02\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 1,\n            'f': \"1\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 276,\n            'f': \"276\",\n        },\n\"2020-03-03\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 2,\n            'f': \"2\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 293,\n            'f': \"293\",\n        },\n\"2020-03-04\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 11,\n            'f': \"11\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 313,\n            'f': \"313\",\n        },\n\"2020-03-05\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 22,\n            'f': \"22\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 338,\n            'f': \"338\",\n        },\n\"2020-03-06\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 44,\n            'f': \"44\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 369,\n            'f': \"369\",\n        },\n\"2020-03-07\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 89,\n            'f': \"89\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 404,\n            'f': \"404\",\n        },\n\"2020-03-08\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 106,\n            'f': \"106\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 440,\n            'f': \"440\",\n        },\n\"2020-03-09\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 142,\n            'f': \"142\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 478,\n            'f': \"478\",\n        },\n\"2020-03-10\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 173,\n           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     'v': 1374,\n            'f': \"1374\",\n        },\n{\n            'v': 17,\n            'f': \"17\",\n        }],\n [{\n            'v': 882,\n            'f': \"882\",\n        },\n\"2020-03-18\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 2382,\n            'f': \"2382\",\n        },\n{\n            'v': 27,\n            'f': \"27\",\n        }],\n [{\n            'v': 936,\n            'f': \"936\",\n        },\n\"2020-03-19\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 4152,\n            'f': \"4152\",\n        },\n{\n            'v': 30,\n            'f': \"30\",\n        }],\n [{\n            'v': 990,\n            'f': \"990\",\n        },\n\"2020-03-20\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 7102,\n            'f': \"7102\",\n        },\n{\n            'v': 57,\n            'f': \"57\",\n        }],\n 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\"44635\",\n        },\n{\n            'v': 535,\n            'f': \"535\",\n        }],\n [{\n            'v': 1422,\n            'f': \"1422\",\n        },\n\"2020-03-28\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 53363,\n            'f': \"53363\",\n        },\n{\n            'v': 782,\n            'f': \"782\",\n        }],\n [{\n            'v': 1477,\n            'f': \"1477\",\n        },\n\"2020-03-29\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 59568,\n            'f': \"59568\",\n        },\n{\n            'v': 965,\n            'f': \"965\",\n        }],\n [{\n            'v': 1532,\n            'f': \"1532\",\n        },\n\"2020-03-30\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 67174,\n            'f': \"67174\",\n        },\n{\n            'v': 1224,\n            'f': \"1224\",\n        }],\n [{\n            'v': 1587,\n            'f': \"1587\",\n        },\n\"2020-03-31\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 75832,\n            'f': \"75832\",\n        },\n{\n            'v': 1550,\n            'f': \"1550\",\n        }],\n [{\n            'v': 1642,\n            'f': \"1642\",\n        },\n\"2020-04-01\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 83889,\n            'f': \"83889\",\n        },\n{\n            'v': 1941,\n            'f': \"1941\",\n        }],\n [{\n            'v': 1697,\n            'f': \"1697\",\n        },\n\"2020-04-02\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 92770,\n            'f': \"92770\",\n        },\n{\n            'v': 2653,\n            'f': \"2653\",\n        }],\n [{\n            'v': 1752,\n            'f': \"1752\",\n        },\n\"2020-04-03\",\n\"New York\",\n{\n      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   'f': \"130703\",\n        },\n{\n            'v': 4758,\n            'f': \"4758\",\n        }],\n [{\n            'v': 1972,\n            'f': \"1972\",\n        },\n\"2020-04-07\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 140081,\n            'f': \"140081\",\n        },\n{\n            'v': 5563,\n            'f': \"5563\",\n        }],\n [{\n            'v': 2027,\n            'f': \"2027\",\n        },\n\"2020-04-08\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 149401,\n            'f': \"149401\",\n        },\n{\n            'v': 6268,\n            'f': \"6268\",\n        }],\n [{\n            'v': 2083,\n            'f': \"2083\",\n        },\n\"2020-04-09\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 159937,\n            'f': \"159937\",\n        },\n{\n            'v': 7067,\n            'f': \"7067\",\n        }],\n [{\n            'v': 2139,\n            'f': \"2139\",\n        },\n\"2020-04-10\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 170512,\n            'f': \"170512\",\n        },\n{\n            'v': 7844,\n            'f': \"7844\",\n        }],\n [{\n            'v': 2195,\n            'f': \"2195\",\n        },\n\"2020-04-11\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 180458,\n            'f': \"180458\",\n        },\n{\n            'v': 8627,\n            'f': \"8627\",\n        }],\n [{\n            'v': 2251,\n            'f': \"2251\",\n        },\n\"2020-04-12\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 188694,\n            'f': \"188694\",\n        },\n{\n            'v': 9385,\n            'f': \"9385\",\n        }],\n [{\n            'v': 2307,\n            'f': \"2307\",\n        },\n\"2020-04-13\",\n\"New York\",\n{\n            'v': 36,\n            'f': \"36\",\n        },\n{\n            'v': 195031,\n            'f': \"195031\",\n        },\n{\n            'v': 10056,\n            'f': \"10056\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"date\"], [\"string\", \"state\"], [\"number\", \"fips\"], [\"number\", \"cases\"], [\"number\", \"deaths\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>313</th>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>2020-03-06</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>2020-03-07</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>2020-03-08</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>440</th>\n",
              "      <td>2020-03-09</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>142</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>2020-03-10</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>521</th>\n",
              "      <td>2020-03-11</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>217</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>326</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>618</th>\n",
              "      <td>2020-03-13</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>421</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>669</th>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>610</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>722</th>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>732</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>775</th>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>950</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>828</th>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>1374</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>882</th>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>2382</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>936</th>\n",
              "      <td>2020-03-19</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>4152</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990</th>\n",
              "      <td>2020-03-20</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>7102</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1044</th>\n",
              "      <td>2020-03-21</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>10356</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1098</th>\n",
              "      <td>2020-03-22</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>15168</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1152</th>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>20875</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1206</th>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>25665</td>\n",
              "      <td>218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1260</th>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>33066</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1314</th>\n",
              "      <td>2020-03-26</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>38987</td>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368</th>\n",
              "      <td>2020-03-27</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>44635</td>\n",
              "      <td>535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1422</th>\n",
              "      <td>2020-03-28</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>53363</td>\n",
              "      <td>782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1477</th>\n",
              "      <td>2020-03-29</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>59568</td>\n",
              "      <td>965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1532</th>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>67174</td>\n",
              "      <td>1224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1587</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>75832</td>\n",
              "      <td>1550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1642</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>83889</td>\n",
              "      <td>1941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1697</th>\n",
              "      <td>2020-04-02</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>92770</td>\n",
              "      <td>2653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1752</th>\n",
              "      <td>2020-04-03</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>102870</td>\n",
              "      <td>2935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1807</th>\n",
              "      <td>2020-04-04</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>114996</td>\n",
              "      <td>3568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1862</th>\n",
              "      <td>2020-04-05</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>122911</td>\n",
              "      <td>4161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1917</th>\n",
              "      <td>2020-04-06</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>130703</td>\n",
              "      <td>4758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>2020-04-07</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>140081</td>\n",
              "      <td>5563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2027</th>\n",
              "      <td>2020-04-08</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>149401</td>\n",
              "      <td>6268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2083</th>\n",
              "      <td>2020-04-09</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>159937</td>\n",
              "      <td>7067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2139</th>\n",
              "      <td>2020-04-10</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>170512</td>\n",
              "      <td>7844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2195</th>\n",
              "      <td>2020-04-11</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>180458</td>\n",
              "      <td>8627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2251</th>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>188694</td>\n",
              "      <td>9385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2307</th>\n",
              "      <td>2020-04-13</td>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>195031</td>\n",
              "      <td>10056</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            date     state  fips   cases  deaths\n",
              "246   2020-03-01  New York    36       1       0\n",
              "261   2020-03-02  New York    36       1       0\n",
              "276   2020-03-03  New York    36       2       0\n",
              "293   2020-03-04  New York    36      11       0\n",
              "313   2020-03-05  New York    36      22       0\n",
              "338   2020-03-06  New York    36      44       0\n",
              "369   2020-03-07  New York    36      89       0\n",
              "404   2020-03-08  New York    36     106       0\n",
              "440   2020-03-09  New York    36     142       0\n",
              "478   2020-03-10  New York    36     173       0\n",
              "521   2020-03-11  New York    36     217       0\n",
              "568   2020-03-12  New York    36     326       0\n",
              "618   2020-03-13  New York    36     421       0\n",
              "669   2020-03-14  New York    36     610       2\n",
              "722   2020-03-15  New York    36     732       6\n",
              "775   2020-03-16  New York    36     950      10\n",
              "828   2020-03-17  New York    36    1374      17\n",
              "882   2020-03-18  New York    36    2382      27\n",
              "936   2020-03-19  New York    36    4152      30\n",
              "990   2020-03-20  New York    36    7102      57\n",
              "1044  2020-03-21  New York    36   10356      80\n",
              "1098  2020-03-22  New York    36   15168     122\n",
              "1152  2020-03-23  New York    36   20875     159\n",
              "1206  2020-03-24  New York    36   25665     218\n",
              "1260  2020-03-25  New York    36   33066     325\n",
              "1314  2020-03-26  New York    36   38987     432\n",
              "1368  2020-03-27  New York    36   44635     535\n",
              "1422  2020-03-28  New York    36   53363     782\n",
              "1477  2020-03-29  New York    36   59568     965\n",
              "1532  2020-03-30  New York    36   67174    1224\n",
              "1587  2020-03-31  New York    36   75832    1550\n",
              "1642  2020-04-01  New York    36   83889    1941\n",
              "1697  2020-04-02  New York    36   92770    2653\n",
              "1752  2020-04-03  New York    36  102870    2935\n",
              "1807  2020-04-04  New York    36  114996    3568\n",
              "1862  2020-04-05  New York    36  122911    4161\n",
              "1917  2020-04-06  New York    36  130703    4758\n",
              "1972  2020-04-07  New York    36  140081    5563\n",
              "2027  2020-04-08  New York    36  149401    6268\n",
              "2083  2020-04-09  New York    36  159937    7067\n",
              "2139  2020-04-10  New York    36  170512    7844\n",
              "2195  2020-04-11  New York    36  180458    8627\n",
              "2251  2020-04-12  New York    36  188694    9385\n",
              "2307  2020-04-13  New York    36  195031   10056"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwLXLicqIuZ1",
        "colab_type": "code",
        "outputId": "06f9a14c-0f7a-4192-f7ac-7f234728607f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# manually fit an exponential curve\n",
        "seaborn.pointplot(ny[\"date\"], ny[\"deaths\"], color=\"red\")\n",
        "seaborn.pointplot(ny[\"date\"], [1.28**x for x in range(0,len(ny[\"date\"]))])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcfe32e1080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3hfoHqAQ-c9",
        "colab_type": "code",
        "outputId": "7192bd9c-297b-460d-ebd7-3da85c04f543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def death_model():\n",
        "  fit_base = 1.28\n",
        "  # mar 29 is day 29, so apr 2 is day 33\n",
        "  day_number = 33\n",
        "  fuzzed_base = ergo.lognormal_from_interval(fit_base - 0.01, fit_base + 0.01)\n",
        "  ergo.tag(fuzzed_base**day_number, \"ny_deaths\")\n",
        "\n",
        "death_samples = ergo.run(death_model, 1000)\n",
        "\n",
        "questions[6].samples = death_samples.ny_deaths\n",
        "# questions[6].show_submission(questions[6].samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:00<00:00, 3019.83it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVKbz0-4UiZh",
        "colab_type": "text"
      },
      "source": [
        "### 7. What will be the US unemployment rate for March 2020?\n",
        "https://pandemic.metaculus.com/questions/3922/what-will-be-the-us-unemployment-rate-for-march-2020/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12EysFj3U2Wy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "monthly_unemployment_wide = pandas.read_csv(\"https://gist.githubusercontent.com/brachbach/d966ef4221215bdd58a5067802ded0be/raw/4a944a28eb9f222023396020913aabd507538060/monthly_unemployment_wide.csv\")\n",
        "# monthly_unemployment_wide"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNgk-E_fWh90",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "months = [\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"]\n",
        "unemployment = pandas.melt(monthly_unemployment_wide, id_vars=[\"Year\"],\n",
        "                           value_vars=[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],\n",
        "                           var_name=\"month\",\n",
        "                           value_name=\"percent_unemployment\")\n",
        "\n",
        "unemployment[\"month\"] = unemployment[\"month\"].apply(lambda name: months.index(name) + 1)\n",
        "unemployment = unemployment.sort_values(by=[\"Year\", \"month\"])\n",
        "unemployment[\"monthly_diff\"] = unemployment[\"percent_unemployment\"].diff()\n",
        "# unemployment"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZgibkcAcKZX",
        "colab_type": "code",
        "outputId": "b5705cca-4fc1-4061-ad8e-a9312fe45d94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "biggest_monthly_increase = unemployment.nlargest(5, \"monthly_diff\")\n",
        "biggest_monthly_increase"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 658,\n            'f': \"658\",\n        },\n{\n            'v': 1949,\n            'f': \"1949\",\n        },\n{\n            'v': 10,\n            'f': \"10\",\n        },\n{\n            'v': 7.9,\n            'f': \"7.9\",\n        },\n{\n            'v': 1.3000000000000007,\n            'f': \"1.3000000000000007\",\n        }],\n [{\n            'v': 808,\n            'f': \"808\",\n        },\n{\n            'v': 1953,\n            'f': \"1953\",\n        },\n{\n            'v': 12,\n            'f': \"12\",\n        },\n{\n            'v': 4.5,\n            'f': \"4.5\",\n        },\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 27,\n            'f': \"27\",\n        },\n{\n            'v': 1975,\n            'f': \"1975\",\n        },\n{\n            'v': 1,\n            'f': \"1\",\n        },\n{\n            'v': 8.1,\n            'f': \"8.1\",\n        },\n{\n            'v': 0.8999999999999995,\n            'f': \"0.8999999999999995\",\n        }],\n [{\n            'v': 293,\n            'f': \"293\",\n        },\n{\n            'v': 1949,\n            'f': \"1949\",\n        },\n{\n            'v': 5,\n            'f': \"5\",\n        },\n{\n            'v': 6.1,\n            'f': \"6.1\",\n        },\n{\n            'v': 0.7999999999999998,\n            'f': \"0.7999999999999998\",\n        }],\n [{\n            'v': 229,\n            'f': \"229\",\n        },\n{\n            'v': 1958,\n            'f': \"1958\",\n        },\n{\n            'v': 4,\n            'f': \"4\",\n        },\n{\n            'v': 7.4,\n            'f': \"7.4\",\n        },\n{\n            'v': 0.7000000000000002,\n            'f': \"0.7000000000000002\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"number\", \"Year\"], [\"number\", \"month\"], [\"number\", \"percent_unemployment\"], [\"number\", \"monthly_diff\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>month</th>\n",
              "      <th>percent_unemployment</th>\n",
              "      <th>monthly_diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>658</th>\n",
              "      <td>1949</td>\n",
              "      <td>10</td>\n",
              "      <td>7.9</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>808</th>\n",
              "      <td>1953</td>\n",
              "      <td>12</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1975</td>\n",
              "      <td>1</td>\n",
              "      <td>8.1</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>1949</td>\n",
              "      <td>5</td>\n",
              "      <td>6.1</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>1958</td>\n",
              "      <td>4</td>\n",
              "      <td>7.4</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year  month  percent_unemployment  monthly_diff\n",
              "658  1949     10                   7.9           1.3\n",
              "808  1953     12                   4.5           1.0\n",
              "27   1975      1                   8.1           0.9\n",
              "293  1949      5                   6.1           0.8\n",
              "229  1958      4                   7.4           0.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4rR6sfUcnOX",
        "colab_type": "code",
        "outputId": "4477e430-aa9f-462f-bf1b-39ce2abca1e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def unemployment_model():\n",
        "  proxy_delta = ergo.random_choice(biggest_monthly_increase[\"monthly_diff\"].to_list())\n",
        "  fuzzed_delta = ergo.normal_from_interval(proxy_delta * 0.5, proxy_delta * 2)\n",
        "  feb_rate = 3.5\n",
        "  ergo.tag(feb_rate + fuzzed_delta, \"mar_unemployment\")\n",
        "\n",
        "unemployment_samples = ergo.run(unemployment_model, 1000)\n",
        "\n",
        "questions[7].samples = unemployment_samples.mar_unemployment\n",
        "# questions[7].show_submission(questions[7].samples)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [00:00<00:00, 1863.45it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BI9tDJPe0EB",
        "colab_type": "text"
      },
      "source": [
        "## 8. How many days will the city of New Orleans spend under lockdown between 2020-03-25 and 2020-04-15?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3930/how-many-days-will-the-city-of-new-orleans-spend-under-lockdown-between-2020-03-25-and-2020-04-15/\n",
        "\n",
        "Predicting in the [lockdown model](https://colab.research.google.com/drive/1BRplIkEvySIWLDfL2m-I2-69ES725Hnv)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6ED8srHfwOm",
        "colab_type": "text"
      },
      "source": [
        "## 9. Will Florida go under lockdown between 2020-03-25 and 2020-04-25?\n",
        "\n",
        "https://pandemic.metaculus.com/questions/3926/will-florida-go-under-lockdown-between-2020-03-25-and-2020-04-25/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lySCkfKCSoF4",
        "colab_type": "text"
      },
      "source": [
        "#### Thinking through the question\n",
        "* Which other states are currently not under lockdown?\n",
        "  * https://www.usatoday.com/story/news/nation/2020/03/21/coronavirus-lockdown-orders-shelter-place-stay-home-state-list/2891193001/\n",
        "    * lists 34 states (`$('h2').length`)\n",
        "  * https://www.wsj.com/articles/a-state-by-state-guide-to-coronavirus-lockdowns-11584749351\n",
        "    * lists 31 states (`$(\"h6\").length`), but actually Florida is on there so not all are true lockdowns\n",
        "* Hmm, resolution may well be ambiguous. Florida is already doing some partial lockdown:\n",
        "  * https://www.wsj.com/articles/florida-unlike-other-hard-hit-states-avoids-broad-coronavirus-lockdown-11585560601\n",
        "  *[https://www.wsj.com/articles/a-state-by-state-guide-to-coronavirus-lockdowns-11584749351](reports that some parts of Florida have closed down business, but not all)\n",
        "  * what if e.g. the Governor orders the businesses that ordinarily serve most Floridians to shut down, but doesn't close rural ones, and doesn't order people to stay home?\n",
        "\n",
        "Doesn't seem crazy to me to think that the Governor will maintain the current not-quite a lockdown policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q04VW8EBj5mb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "questions[9].binary_prediction = 0.55"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQDqb5FKcIaj",
        "colab_type": "text"
      },
      "source": [
        "# Submit predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ov__zuhYvlS3",
        "colab_type": "text"
      },
      "source": [
        "Convert samples to Metaculus distributions and visualize:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiwHiT3WezEu",
        "colab_type": "code",
        "outputId": "359f663d-9198-494a-8c65-3fcf179c1433",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for question in questions:\n",
        "  if question.type == \"binary\":\n",
        "    print(f\"Prediction for {question}: {question.binary_prediction}\")\n",
        "  elif question.type == \"continuous\":\n",
        "    try:\n",
        "      question.show_submission(question.samples)\n",
        "    except:\n",
        "      print(f\"No submission or submission can't be shown for {question}\")\n",
        "    print(\"\\n\\n\")\n",
        "  else:\n",
        "    raise ValueError(\"Unknown question type!\")\n",
        "  print(\"\\n\\n\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 263.19it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8AdJvajBsMBvxbb5jDd+rbdov+FJxNL4NVX+Zg63rlDjr8Ev8zzmzgn32O+y+z8LQ95vBDOezSTSWyOk24ms6I5dXFHXxit4+PfifUHf7/IMbCqew+5en6JgGi/Hr+CYwC4Jt/r7+4gGcc4845z6Mrwl/C39sDLaOiUhkOwxU/mDbInr6s4CF+BqrUnZdqEfipsw2fLLS74Bhljcp6mbR/mNkI75WqCJim41xzh0SMe1gn8PenkMg/nkk0WWPlkTOG7FsBsrMrCRi2FRiV0bEMqzzp3PuZ865o/E1Z7PxTdLq8F8ADokos9T5m51xzrU4577unDsQXxl1mZmdmGC8e+M+4KPAGufchsgRCVwrYfd9og7/U/lA+1DCzOx8fBvjE4OKrn6vAwdGfa5HEHFTopmdEsT+cefcqxHTvQ3kmNmsePPiz4VNQY40mHjXOtjzujDQsb0lRlnxDHScDnSMDvd4iGnQxNc514FvQH4ZuxIJ8A3AL2Notawl+G8vzWY2CX9QRdoGDNbH31P4nfqp4P2TUe+HYxsw2cxyE5z+QXwN9DlmFgr+jjGzg6MnNLNxZnaVmc00s6yglvp8fNs28Dv8RWa2wLwiM/to8IEX4XeO2qCsL7ArYY4X92Db8gbgh8GJAjOrNLOFCa53pDuB/wjmr8C3l0ukC6nfAN83s1nB+h5uvg/KhLdpNDMrNLP34392W4q/qQUSW9fvBvMfgm8f94dgeAm+TWmrmR2Eb0s5mET244EM57PZBlSb2VD66L4d+LiZnWxm2WaWb74Lu8ga/s+Z2ZygNvZq4N6gpuxu4KNmdqL5rpW+jj9ZPrvHUnaPcaDtU4K/0DYFvypd0T/CzMab2UIzKwqW08qumtJYyxnKtkhkOwy0HoNti+jpS4Lx9fgk9T8TjDMRK4BFwfEzD38j6nBUAV8Lyvs0vv3nQ865LfgbBH9qZmOCc9uM4DhM1N6eQ8C3C/2QmX3GzHLMrNzM5g5h2aNlb84b/RUizwI/Cva/w/E3tSW6PYZz/jwmuP6E8F+cOoGwcy6Mvz5da2ZVwbSTzOzk4PXHguua4X+p6CP+MTlszvdo8UF8U8dog10ro8sKA78F/sfMJgbH/bttL7rDNLOz8cfwh11Ul23Oubfxx+QVwef6SeBwfBKPmX0Qvy+f7pxbGmN9/whcHeQFx+O/MEfW4j+HbwuciHjXut0kcGzfjT8nTDazcfiEP57fAN8ws6OD6/3M/usbA1wPRuB4iCnRC8JT+BNfZF+xi4NhQ0l8r8LfydeMb4/yx6jxP8KfAJvM7BvRM0fEUhKx3Oj3w/EE/lvUVjMbtPY4qH4/Cd991mZ8lftP8A3jo3Xja3Qex58QX8Nf9M4LylqGr0G/Dt/8Y3XEuDfwd50+h99JDiOofR8g7puAOcG2/FOMeP4Pf7PCo2bWgk/AFwy2zjH8AP/F6BX8zWovBcMG8z/4A+dR/Pa4Cd+WaCjbtN91wTpsw98gdB/+hsr+k28i6/oUfpv/HbjGOdf/AJRv4GvmWvAn/5gniShX4psCNZnZZxKYPtpwPpt7gv/1ZvZSIjMEJ5eF+FqpWvy382+y+/nhd/g2eVvxPw1+LZh3Jf6Xm5/ja08+jq+x6B5gkYMd5/+Lv7GiDr/uf4sYl4X/wr0Z3978/cRPKoa0LRLcDnHXI4FtEb3et+F/ttuEv8v5+T2WsPe+i69hacSfd+8YZnlL8Pd31OGbZ5zhdvUZ+3l8s6w3guXdi6+BStTenkMIavxOxX/J6L//4IghLHu07M15o9+Z+GvFZvzNRVc45x5PZMa9PH/2GxPE2ojfL+vxzRjAt39fDTxvZjvw17F3BeNmBe9b8deo651ziTYP2ivOuWXOuT1+Ok/gWhnLN/D73Qv4fegn7N3DvX6Av7nyBfN93rea2Q0R4xfhf6JvxLdPPsM5VxuM+y7+V5+HIuaNfF7CV/DnxO34L4pfds5F1vguYPf8bCDxrnWxDHRs/xrfdvdl/DEbnc/t5Jy7B3/euAN/TPwJf0McDH492OvjIZ7+O4BFMpKZVeO7ggol0DYqI5nZk/ibekbkqVUiIpJcutbtMuKPLBYRERER2Rcp8RURERGRjKCmDiIiIiKSEVTjKyIiIiIZIZEHWIjstyoqKlx1dXWqwxAR2a+8+OKLdc65ylTHITLSlPhKWquurmbZsmWpDkNEZL9iZgM9iUtkv6WmDjIoMzvFzFaa2Woz26OTajPLM7M/BOOXBN2m9I/7djB8ZX+H5wOVaWa/D4a/Zma/DTpTJ3iQQLOZrQj+vje6ay0iIiLpRomvDMjMsvGPVv4I/jGWZ5rZnKjJvgg0OudmAtfiOwAnmG4RcAhwCnB98GScgcr8PXAQvuPxAnZ/Os9i59zc4O/qkV9bERERSWdKfGUw84HVzrk1wROo7sI/3SrSQuDW4PW9wInB4ysXAnc557qcc2vxT4uZP1CZzrmHXAD/2OF4j4sVERERGRK18ZXBTMI/urVfDXs+PnfnNM65XjNrxj+6cRK7P4a1JhjGYGUGTRzOAf41YvC7zexl/KMLvxH1yMbIeS8ELgSYOnXqIKsnIumqp6eHmpoaOjs7Ux3KPis/P5/JkycTCoVSHYpIUijxlX3V9cDTzrnFwfuXgGnOuVYzOxX/rO9ZsWZ0zt0I3Agwb948dVQtkqFqamooKSmhuroa/yOURHLOUV9fT01NDdOnT091OCJJoaYOMphNwJSI95ODYTGnMbMcoBSoH2DeAcs0syuASuCy/mHOuR3Oudbg9UNAyMwqhrNiIpLeOjs7KS8vV9Ibh5lRXl6uGnHJKEp8ZTAvALPMbLqZ5eJvVnsgapoHgHOD12cATwRtdB8AFgW9PkzH19AuHahMM/sScDJwpnMu3L8AMzsgaDeMmc3H77v1o7LGIpI2lPQOTNtHMo2aOsiAgja7lwCPANnAb51zr5vZ1cAy59wDwE3A78xsNdCAT2QJprsbeAPoBS52zvUBxCozWOQNwHrgueCE/MegB4czgC+bWS/QASxyet62iIiIDIEpd5B0Nm/ePKcHWIhkpjfffJODDz545/s7lmwY0fLPWjD4zbNmxtlnn83tt98OQG9vLxMmTGDBggU8+OCDPPDAA7zxxhtcfvkeXaTvdMstt3DSSScxceLEEYs9UvR2CuJ+0Tk3b1QWKJJCqvEVkZSLl5AkkliI7MuKiop47bXX6OjooKCggMcee4xJkybtHH/aaadx2mmnDVjGLbfcwqGHHjqkxLe3t5ecHF3iRaKpja+IiMgoOvXUU/nrX/8KwJ133smZZ565c9wtt9zCJZdcAsDChQu57bbbAPjVr37F2Wefzb333suyZcs4++yzmTt3Lh0dHVRXV1NXVwfAsmXLOOGEEwC48sorOeecczj++OM555xzqK2t5fTTT+eYY47hmGOO4ZlnnkniWovsm/R1UEREZBQtWrSIq6++mo997GO88sornH/++SxevHiP6W688UaOP/54pk+fzk9/+lOef/55ysrKuO6667jmmmuYN2/wlgdvvPEG//znPykoKOCss87i0ksv5T3veQ8bNmzg5JNP5s033xyNVRTZbyjxFRERGUWHH34469at48477+TUU0+NO9348eO5+uqr+cAHPsD9999PWVnZkJd12mmnUVBQAMDjjz/OG2+8sXPcjh07aG1tpbi4eOgrIZImlPiKiIiMstNOO41vfOMbPPnkk9TXx++J8dVXX6W8vJzNmzfHnSYnJ4dw2Pf2GN0Hb1FR0c7X4XCY559/nvz8/GFGL5I+1MZXRERklJ1//vlcccUVHHbYYXGnWbp0KQ8//DDLly/nmmuuYe3atQCUlJTQ0tKyc7rq6mpefPFFAO6777645Z100kn8/Oc/3/l+xYoVw10Nkf2eanxFRCQjpLKXkMmTJ/O1r30t7viuri4uuOACbr75ZiZOnMhPf/pTzj//fJ544gnOO+88LrroIgoKCnjuuee44oor+OIXv8h3v/vdnTe2xfKzn/2Miy++mMMPP5ze3l7e9773ccMNN4zC2onsP9SPr6Q19eO77xlKX6rqzkyGI1b/tLIn9eMrmURNHUREREQkIyjxFREREZGMoMRXRERERDKCEl8RERERyQhKfEVEREQkIyjxFREREZGMoH58RUQkMyy7eWTLm/eFQSfJzs7msMMOo6enh5ycHD7/+c9z6aWXkpUVv95p3bp1PPvss5x11lkjGa2IoBpfERGRUVNQUMCKFSt4/fXXeeyxx3j44Ye56qqrBpxn3bp13HHHHUmKUCSzKPEVERFJgqqqKm688Uauu+46nHOsW7eO9773vRx11FEcddRRPPvsswBcfvnlLF68mLlz53LttdfGnU5Ehk5NHURERJLkwAMPpK+vj+3bt1NVVcVjjz1Gfn4+q1at4swzz2TZsmX8+Mc/5pprruHBBx8EoL29PeZ0IjJ0SnxFRERSoKenh0suuYQVK1aQnZ3N22+/PazpRGRwSnxFRESSZM2aNWRnZ1NVVcVVV13F+PHjefnllwmHw+Tn58ec59prr01oOhEZnNr4ioiIJEFtbS0XXXQRl1xyCWZGc3MzEyZMICsri9/97nf09fUBUFJSQktLy8754k0nIkOnGl8REckMCXQ/NtI6OjqYO3fuzu7MzjnnHC677DIAvvKVr3D66adz2223ccopp1BUVATA4YcfTnZ2NkcccQTnnXde3OlEZOjMOZfqGERGzbx585xuAtm33LFkQ8LTnrVg6ihGIunuzTff5OCDD051GPu8WNvJzF50zs1LUUgio0ZNHURkn/DapmbqWrpSHYaIiKQxJb4iknIvrGvgjqUbuO+lmlSHIiIiaUyJr4ik1KrtLfx5xSYKc7NZ39DO1ubOVIckaUTN+Qam7SOZRomviKRMU3s3dyzZQFVJPhefMJOcLGPpuvpUhyVpIj8/n/r6eiV3cTjnqK+vV/doklHUq4OIpMzb21rp6g3z2WOmMK4ol8MmlbJ8QxMnH3IAeTnZqQ5P9nOTJ0+mpqaG2traVIeyz8rPz2fy5MmpDkMkaZT4ikjKbGnuIC8ni6qSPADmTy9j+cYmXqlp5pjqshRHJ/u7UCjE9OnTUx2GiOxD1NRBRFJmS3MnE0rzMTMAppYVMn5MHkvXNqQ4MhERSUdKfEUkJcLOsbW5kwmlBTuHmRlHThnHpqYOWrt6UxidiIikIyW+IpISjW3ddPeFmVC6+401U8sKAdjY0J6KsEREJI0p8RWRlNgcdFsWWeMLMGlcAVkGG5T4iojICFPiKwkxs1PMbKWZrTazy2OMzzOzPwTjl5hZdcS4bwfDV5rZyYOVaWa/D4a/Zma/NbNQMNzM7GfB9K+Y2VGju9YymrY0d5BlUDUmb7fhoewsJpQWKPEVEZERp8RXBmVm2cAvgI8Ac4AzzWxO1GRfBBqdczOBa4GfBPPOARYBhwCnANebWfYgZf4eOAg4DCgAvhQM/wgwK/i7EPjlyK+tjLYZG+5hxoZ72LF1LRPzujho0307h/WbWlbIpsYOevvCKYxURETSjRJfScR8YLVzbo1zrhu4C1gYNc1C4Nbg9b3AieZv1V8I3OWc63LOrQVWB+XFLdM595ALAEuByRHLuC0Y9Tww1swmjNZKy+ha355HdWHsp7RNKSukuy/Mym0tSY5KRETSmRJfScQkYGPE+5pgWMxpnHO9QDNQPsC8g5YZNHE4B/jbEOKQ/UBLbxb1PSGqC7piju+/wW35hqZkhiUiImlOia/sy64HnnbOLR7KTGZ2oZktM7NlemLTvml9u+/JYVqcGt9xhSGK8nKU+IqIyIhS4iuJ2ARMiXg/ORgWcxozywFKgfoB5h2wTDO7AqgELhtiHDjnbnTOzXPOzausrExg9STZ1nX4G9qmxanxNTOmjitg+YbGZIYlIiJpTomvJOIFYJaZTTezXPzNag9ETfMAcG7w+gzgiaCN7gPAoqDXh+n4G9OWDlSmmX0JOBk40zkXjlrG54PeHY4Fmp1zW0ZjhWV0bejIZ1yoh9JQX9xpppQVsqaujca27iRGJiIi6Swn1QHIvs8512tmlwCPANnAb51zr5vZ1cAy59wDwE3A78xsNdCAT2QJprsbeAPoBS52zvUBxPrsVHkAACAASURBVCozWOQNwHrgueBRtn90zl0NPAScir9Brh34wuivvYyG7V0hxuf17DE8smeHzt5CHmUqK564hw9M6IZ5+rhFRGR4lPhKQpxzD+ETz8hh34t43Ql8Os68PwR+mEiZwfCY+2VQg3zxkAKXfVJ9dw6zijsGnGZGYQdZOFY0hHziKyIiMkxq6iAiSRV20NCTQ3mod8Dp8rMdM8f08Uqjvp+LiMjIUOIrIkm1ozebXpdFee6eTR1248IcNa6dlxtCOJec2EREJL2pKkVEkqqh2592ynNj1/hWNbzA5O1PEepr52gLUdb3SWpaj92tOw8REZG9ocRXRJKqvicEQHlozxrfCbX/ZOr2J2guqqalcBolfU18q+Fudix5Bo45DYrVPZ2IiOw9NXUQkaSqj1PjW9G4gqnbn6Cu9FBWTjubTVXvp+DYL/AvPV+noKsW7joLemI/8EJERCQRSnxFJKnqu0PkWJiSnF19+Fq4j8m1T9FaMIl3Jn0CZ9kA5GbBttLD+VnehVCzFP58MWrwKyIie0uJr4gkVX2379Ehy3YNq2h6mbyeZmoq3w+2+2npiHE93NRyLOEPfBdeuxdeuy/JEYuISLpQG18RSar6nhBlEc0czPUxse6ftBZMpLl4xh7TH1HWy63vZLG6p4LZpVPgwUuhdTuECnafUA+4EBGRQajGV0SSqr47Z7euzMqbXye/p4lNle8Dsz2mP6LMT7uiKQ8O+zR0tcDKh5MWr4iIpA8lviKSNOGwo6EntNvDK8qaX6czNJam4lkx55le3EdJTphXGnJg7FSYdjysWwwtW5MVtoiIpAklviKSNHVtXfQ521njm9XXTWnbGhpL3hWzthcgy2BuWS/L6nP9gNmnQHYIVj+WrLBFRCRNKPEVkaTZ0uS7I+vvyqy07R2yXB9NY2YPON9xVd281ZzD9s4syCv2tb6bXvJtfUVERBKkxFdEkmZLs098y4Ia33E7VtKblU9L4dQB53vv+G4AntnmH37BjA9CVo5qfUVEZEiU+IpI0mxp7gCgItQLLszY1lU0lcza2W9vPHPG9lKWG2bxtqC5Q14JTDsONr0I7fWjHbaIiKQJJb4ikjRbmzsJBQ+vKGnfSKivw7fvHUSWwfHju/nn9txdz6848AT/f/0zoxWuiIikGSW+IpI0m5s7KcvtxQxKW98hTBZNMfrujeW9Vd1s78zm7R1B7XDBOBh/GGx4Hvq6RzFqERFJF0p8RSRptjZ3UB7y7XuLO2pozx9PODsvoXnfE7Tz3dncAaD6PdDTDpuXj3isIiKSfpT4ikjSbG7q9D06uDDFHZtoLZyc8LwTC8McWNK7e+JbPhNKJsDaxexqAyEiIhKbEl8RSYq+sGPbjk7Kc3so6KolO9xDa0HiiS/45g5L6nLp7AsGmPla3x01vnszERGRASjxFZGkaGjrpjfsGBfqpaS9BoDWwklDKuOkiV109hkPbMjfNXDi0ZAVghW3j2S4IiKShpT4ikhS1Ld1AVAa6qO4o4ae7EK6QuOGVMZxVT3MGdvDDW8XEu5v2RDKhwlHwKv3QU/HCEctIiLpRImviCRFXYu/Oa00p5fi9qB9b5zHFMdjBv8yu501LTk8viWire+U+dDVzDMP3sIdSzbs/BMREYmkxFdEkqKu1df4Vma1UNBdR2vB0Jo59Pvo5C4mF/bxq5WFuwaWz6S1YBIH1vxpJEIVEZE0pcRXRJKiP/Gd3LseYMg3tvXLyYILZrfzYn0uz20PHmFsWayZtJAD6pdQ2LFlROIVEZH0o8RXRJKitrWL3OwsyrtqcEBrwcS9Lusz1R1MLOjjoudKWV6fA8C6iR/FcEzd8sgIRSwiIulGia+IJEVdSzflxbkUdm2nK7cs4QdXxFKQA3e9v5HSXMfZT4/liS25tBROob70UKZteXgEoxYRkXSSk+oARCQz1Ld1UVGcR2FLLe15lQnNs2Rtwx7DFkwvA2BqcZh7T2jknMVjOf+ZsVSVrCJ3zHv5dP0vKWlbT0vRtBGNX0RE9n+q8RWRpKhr7eKAIsjvrqcjr2pEyqwqCPPHDzbyo6N2kB/K5qeb5hB2RvtLf6CpvXtEliEiIulDNb4ikhR1Ld2cWNqI4WjPH5nEF6Aox3HmgZ0caC+x4YBc3l43g6N3/J1P/f1DHNfzHHPLemHeF0ZseSIisv9Sja+IjDrnHPVtXcy0jQAjVuMbbWpBN3njZzMzazOHZm1g0VPjeHxz7uAziohIRlDiKyKjrrmjh54+x9TedYQti868slFbVsOYg3EYP6h4lNljernouVJe29Q8assTEZH9h5o6iMioq2v17W3Hd66hM7cCZ9l7XVasG94i9eYU0Vx8IBNbXuW2Exo5+bFyLv3DCv7y1feQH9r75YqIyP5PNb4iMur6H14xtnX1iLbvjad+zCHk9zQxtn0d/zWvhVXbW/nvR1aO+nJFRGTfpsRXREZdXWsXxbST37aZjgS7MhuOxjEHEbZs2Lyc9x/QzTnHTuOmf67lhXUD1xaLiEh6U+IrgzKzU8xspZmtNrPLY4zPM7M/BOOXmFl1xLhvB8NXmtnJg5VpZpcEw5yZVUQMP8HMms1sRfD3vdFbYxlpdS1dzLYagKTU+PZl59NUPJPujS+yZE0dMyqLKcnP4d/ve4U7lmwY9eWLiMi+SYmvDMjMsoFfAB8B5gBnmtmcqMm+CDQ652YC1wI/CeadAywCDgFOAa43s+xBynwG+BCwPkY4i51zc4O/q0dyPWV01bV2c1DW6PboEK2+9FBye1spad9Abk4W751VyZraNtbXtyVl+SIisu9R4iuDmQ+sds6tcc51A3cBC6OmWQjcGry+FzjRzCwYfpdzrss5txZYHZQXt0zn3HLn3LrRXilJrrrWLg7N3QY5BXSFxiZlmU0ls+izHMqb3wBgfnUZhbnZPLmyNinLFxGRfY8SXxnMJGBjxPuaYFjMaZxzvUAzUD7AvImUGcu7zexlM3vYzA6JN5GZXWhmy8xsWW2tkpx9QV1rNzOyt0L5DDBLyjLDWbk0lcymbMebWLiX3Jws3jOzgpXbWtS9mYhIhlLiK/uLl4BpzrkjgJ8Df4o3oXPuRufcPOfcvMrK0b+RSgZX19rFVLZA2YFJXW7DmDmE+tqoanwRgGMPLCc/lMX1T65OahwiIrJvUOIrg9kETIl4PzkYFnMaM8sBSoH6AeZNpMzdOOd2OOdag9cPAaHIm99k39bY0kZV71Yon5nU5TaVzKIvK8TULY8AkB/KZt60Mh59fRsNbd1JjUVERFJPiW+GMbM/mtlHzSzRz/4FYJaZTTezXPzNag9ETfMAcG7w+gzgCeecC4YvCnp9mA7MApYmWGZ03AcE7YYxs/n4fbc+wXWQFHLOkd9WQzZ9vqlDEoWzQjQWz2bK1sexcC8Ac6eMpTfs+OurW5Iai4iIpJ4S38xzPXAWsMrMfmxm7xpo4qDN7iXAI8CbwN3OudfN7GozOy2Y7Cag3MxWA5cBlwfzvg7cDbwB/A242DnXF69MADP7mpnV4GuBXzGz3wTLOAN4zcxeBn4GLAqSa9nHtXX3MaFvs3+T5BpfgIbSQ8jvaWR8w1IAJpTm867xJdz/Uk3SYxERkdTSI4szjHPuceBxMysFzgxebwR+DdzunOuJMc9DwENRw74X8boT+HSc5f0Q+GEiZQbDf4ZPbKOHXwdcN+DKyT6pvrWLA22rf1M2A99jXfI0Fc+gJ7uQqVseZWvFcZgZ1RVFPPL6Vn7+91WUF+cBcNaCqUmNS0REkk81vhnIzMqB84AvAcuB/wOOAh5LYViSpupau6i2rfSGSqAo+c2yXVaImvEfYMq2x7Gw/153xORSDFhR05T0eEREJHWU+GYYM7sfWAwUAh93zp3mnPuDc+6rQHFqo5N0VNvSTbVtpbt0etK6Mou24YCTyetp5oD6JQCMLcxlekURKzY0oRYzIiKZQ4lv5vm1c26Oc+5Hzrkt4B85DOCcm5fa0CQd1bV2cWDWFqwi+e17+22pOJ7unOKdvTuAv8mtvq2bzc2dKYtLRESSS4lv5vlBjGHPJT0KyRiNO3YwkXpyq2alLIZwdi414z/IlG1/Jyto7vCuA0oAWLWtJWVxiYhIcinxzRBBd2BHAwVmdqSZHRX8nYBv9iAyKlz9GrLMkV2RusQXfHOH3N4WJtT6m+tK8kNMHJvPSiW+IiIZQ706ZI6T8Te0TQb+J2J4C/CdVAQkmSG3eZ1/UZ7cp7ZF21LxbjpDY6ne8lc2jT8BgNnjS3j67Vo6uvtSGpuIiCSHEt8M4Zy7FbjVzE53zt2X6ngkcxS1rvMvypL78IpoLivEhgknc2DNn8npbaM3p4jZVSU8ubKW1bWtKY1NRESSQ00dMoSZfS54WW1ml0X/pTQ4SWulnZtoyxoDBWNTHQrrJn6UnHAnk7c9AcCUskLyQ1lq5ysikiGU+GaOouB/MVAS409kVJT3bKUpf2KqwwCgbuwRtBZMpHrzXwHIzjJmVpXw9rYWdWsmIpIB1NQhQzjnfhX8vyrVsUjm6OzpY6LbSkfRYakOxbMs1k84lYPX3kx+Vx2deRXMrirmtU3NrNzWwkEHjEl1hCIiMopU45thzOy/zGyMmYXM7O9mVhvRDEJk+JbdvPOv/tnbmGR19ISzdg1PsbUTP0aW62PaZv/E7Nnj/Q8eT66sTWVYIiKSBKrxzTwnOee+ZWafBNYBnwKeBm5PaVSSlpau2swnrY/tfUXsWNuQsjhmbLhnt/etBRM5eO2t9Gbl8860z1BZkseSNfVc9P7U3oAnIiKjSzW+maf/y85HgXucc82pDEbSXKffvfrySlMcyO5qx86lsGs7hZ1bAaguL2LZ+kb6wmrnKyKSzpT4Zp4Hzewt4Gjg72ZWCeiZrTIqQt0+8bV9LPGtLz2EsGVT2bQCgOryQlo6e1m5Vb07iIikMyW+GcY5dzlwHDDPOdcDtAELUxuVpKv87ibCzggVFKc6lN30ZRfQWHIQ5c2vkdXXTXWF7/TkhXWpa44hIiKjT4lvZjoI+KyZfR44AzgpxfFImirua2Ab4wjlZKc6lD3UjjuCUF8Hk7c/wbjCXCaW5rM0he2QRURk9OnmtgxjZr8DZgArgP7ntDrgtpQFJWlrbF8DW6lIdRgxNRfNoDM0lpkb7mHDhFM4ZnoZz75Tj3MOM0t1eCIiMgqU+GaeecAcp976JQkqwnUszzqYfbJ3XDNqxx3FlO1PUNK6lvnTq/nzis2sr2/f2fRBRETSi5o6ZJ7XgANSHYRkgL5eymimKas81ZHEVTt2LmHLYebGe5lfXQbAUrXzFRFJW0p8M08F8IaZPWJmD/T/pTooSUMdDWThaAmVpTqSuHpCxWwc/0EO3PRnZpblMK4wxAtq5ysikrbU1CHzXJnqACQz9LbVkwN05IxNdSgDas8bT15PM+/cfCEzQh9l8WttLOFxABZ8+uspjk5EREaSanwzjHPuKfwT20LB6xeAl1IalKSljh2+5rQnb99OfHcUVdOeV8UB9Ut4V1E7W7tyae7Z93qhEBGR4VPim2HM7ALgXuBXwaBJwJ9SF5Gkq67WRrpcDlm5hakOZWBmbC1fQFHXNt6X8zoAq9oKUhyUiIiMBiW+medi4HhgB4BzbhVQldKIJC259no2uQpKc/f9DkTqSg+jJ7uQee1Pk41jVVt+qkMSEZFRoMQ383Q557r735hZDr4fX5ERldNZz0ZXxdic3lSHMiiXlcO2snmUt77NcQXrebtVNb4iIulIiW/mecrMvgMUmNmHgXuAv6Q4JklDBV31bHSVlIb6Bp94H7B93DzCls0Xc/7GO+0F9OnroIhI2lHim3kuB2qBV4F/AR4C/iOlEUn66ekgP9zGZirJzwqnOpqE9ISKqS89lON7nyMv3M6GjrxUhyQiIiNM3ZllGOdc2Mz+BPzJOVeb6ngkTbXXA9CQVc7+9PTfrWULqGx6mUXZ/+Dt1mNSHY6IiIww1fhmCPOuNLM6YCWw0sxqzex7qY5N0lC778psR/a++/CKWNoLDqC5cBpfyHmEd1pzUx2OiIiMMCW+meNSfG8OxzjnypxzZcAC4HgzuzS1oUna6fA1vm2hfbsP31i2lh/LBGtgcvvrqQ5FRERGmBLfzHEOcKZzbm3/AOfcGuBzwOdTFpWkp/YGWl0BOaH9r51sU8ls6rIq+bR7hPqWzlSHIyIiI0iJb+YIOefqogcG7XxDKYhH0li4rZ4NrpJxufvHjW27MWNV6XHMzVrD2pceT3U0IiIygpT4Zo7uvRwnMmThtqAP39C+34dvLOGqQ2hwxZSu+NXgE4uIyH5DiW/mOMLMdsT4awEOS3VwkkacI6uzgY2uknH7aeKbk5PDX+0EZjQuhvp3Uh2OiIiMECW+GcI5l+2cGxPjr8Q5N2hTBzM7xcxWmtlqM7s8xvg8M/tDMH6JmVVHjPt2MHylmZ08WJlmdkkwzJlZRcRwM7OfBeNeMbOjhrNNZJR0tZAV7tmva3wBXi06jl6XTfi5X6Q6FBERGSFKfGVQZpYN/AL4CDAHONPM5kRN9kWg0Tk3E7gW+Ekw7xxgEXAIcApwvZllD1LmM8CHgPVRy/gIMCv4uxD45Uiup4yQoEeHDa5qv63xBRg/Jpc/9R0Py3+/s3s2ERHZvynxlUTMB1Y759Y457qBu4CFUdMsBG4NXt8LnGhmFgy/yznXFfQosTooL26Zzrnlzrl1MeJYCNzmvOeBsWY2YUTXVIYvSBJrXAWl+3HiO7uog9/0nUpWXycsuynV4YiIyAhQ4iuJmARsjHhfEwyLOY1zrhdoBsoHmDeRMvcmDszsQjNbZmbLamv1cLqkC57a1p5bQc5+9NS2aJW5PTQUzeStovmw9NfQ25XqkEREZJiU+Eracc7d6Jyb55ybV1lZmepwMk97Pc02hpL8/fuJ6GZw1NSx/LrvVGjdBq/fn+qQRERkmJT4SiI2AVMi3k8OhsWcxsxygFKgfoB5Eylzb+KQVGtvYAuVVOXvh334Rjlq2jjua5pF37gZ8IKaO4iI7O+U+EoiXgBmmdl0M8vF36z2QNQ0DwDnBq/PAJ5wzrlg+KKg14fp+BvTliZYZrQHgM8HvTscCzQ757aMxArKCGqvZ4OrSovE98gpYwHjnamfgZqlsOWVVIckIiLDoMRXBhW02b0EeAR4E7jbOfe6mV1tZqcFk90ElJvZauAy4PJg3teBu4E3gL8BFzvn+uKVCWBmXzOzGnyN7itm9ptgGQ8Ba/A3yP0a+Moor7oMVV8vrrOJVb3pkfgePnksOVnGw6EPQk6BbnITEdnPma+UE0lP8+bNc8uWLUt1GJmjcR383xF8q+cC5hw2j4Oz9++WKAuml3Ha38eRn+24e9z1sHkFfOgqCBX4CeZ9IbUBiowSM3vROTcv1XGIjDTV+IrIyGn0XS9vTJOmDgALKnpY0RCia8p7oK8bavRFSkRkf6XEV0RGTuM6ADa69Li5DWBBZTfdYWN53wwonQLrnwH9UiYisl9S4isiI6dpPWGy2OLKqcrvS3U0I+KYih4Mx/O1Iah+D7RuhYZ3Uh2WiIjsBSW+IjJyGtfTmjOOPrKpKkiPGt/SXMecsb0sqc2FiUf69r3rn0l1WCIisheU+IrIyGlaT112JSWhMPnZqQ5m5Cyo6OGl+hBd5MLk+bDlZejckeqwRERkiJT4isjIaVyfNg+viHRsVTddYePlhhBMOw5cGGpeSHVYIiIyREp8RWRkdLdD23bWhysZn2aJ7/ygne+SuhAUj4eyA2Hj87rJTURkP6PEV0RGRpPvymxV7/i0ad/bb2yu46DSoJ0vwJQF0FYLG5ekNjARERkSJb4iMjKCPnzf6B6fNj06RFpQ2cOy+hBdfcCEuZCdB8t/l+qwRERkCJT4isjICGp83+kbnzZtfJesbdj5V+Ua6OwzbnutE3LyfA8Pr90PXa2pDlNERBKkxFdERkbjOsI5+dQxhso0SXwjHVLSRsjCLG8u9gOmLICeNnj9/tQGJiIiCVPiKyIjo2EtHUVTAWNCmrXxBcjPdhxS0r4r8R1XDeWzYPntKY1LREQSp8RXREZG41oa8icDMKko/dr4AhxV2sqWrlzWtGSDGRx1ju/doW5VqkMTEZEEKPEVkeELh6FhLduyJ5BtLu26M+t3ZGkbAE9sCXp3OHwRWLZuchMR2U8o8RWR4WvZAn1drAtXcUBBmJw0PbNU5fUwOb+Lf2zN8wNKxsPsk2HFndDXk9rgRERkUGl6eRKRpGpcC8Cb3ZVMKkzPZg79jixtZWltiJYeCwacA23bYfXjqQ1MREQGpcRXRIavYQ0Ar7SOY1JhejZz6HdUaSs9zli8LWjuMOvDUFSlm9xERPYDSnxFZPga1uKycni5tSTta3zfVdxBeV6YBzcGzR2yQ3DEInj7b9C6PbXBiYjIgJT4isjwNa6lb8wUusNZaZ/4Zht8fEonj2/Jo7kjaNd75DkQ7oWX70ptcCIiMiAlviIyfA1raCucAsDENG/qAPCpqZ10h42HXt3iB1TO9g+0WH47OJfa4EREJC4lviIyPM5BwzrqcycBpH2NL8Bh43qZUdLL/S9t2jXwyM9B3UqoWZa6wEREZEBKfEVkeDoaoauZzdkTgMxIfM3gU9M6WbqugY1P3QrLbobuNsjOhUf/w78XEZF9jhJfERmeoEeHNb1VlBflUpCT4niS5BNTOwG4f32+H5CTDxPnwubl0NuVwshERCQeJb4iMjwNvg/ft7rKmTi2IMXBJM+kwjDvruzmnvUF9PU3651yLPR1wZaXUxqbiIjEpsRXRIYneHjFiraxTMqgxBfg3JkdbGzL5pFNQddm46ZDUSVsfD61gYmISExKfEVkeOpX48ZMZm1TmEnjMivx/fDELqqLe/nVykLfmYOZ792hYQ3UrU51eCIiEkWJr4gMT93b9I6bSUdPX8bU+C5Z28CStQ0sW9fAiWV1vNwY4oW6kB85eT5YNrzwm9QGKSIie1DiKyJ7zzmoW0VzUTVAxtX4Ary/vJmSnF5ufLvQD8gfAxOP9H36du5IbXAiIrIbJb4isvdatkJ3K9vypgJkTI1vpLwsx8mVTTy+JY+3mrP9wOnvg+4WWPH71AYnIiK7UeIrInuv7m0A1ttkIDMTX4CPVDUwJhTmR68U+wFjp/oeHpbcAOH079dYRGR/ocRXRPZekPj+dXMRudlZPPTqlp3tX5esbUhxcMlTnBPmqwe38dS2PJ7emusHHvtlaFwHbz2Y0thERGQXJb4isvfqVkFuMW+3FVNRnIuZpTqilPn8jA6mFPXxn68W+359D/oYlM+Ep/4bwuFUhyciIijxFZHhqF8FFbOobe2makx+qqNJqbxs+PdDW3mrOYe71+ZDdg6871uw7VVY+ddUhyciIijxlQSZ2SlmttLMVpvZ5THG55nZH4LxS8ysOmLct4PhK83s5MHKNLPpQRmrgzJzg+HnmVmtma0I/r40umstg6pbRe+4mTR19FBZkpfqaFLuo5O7mF/RzX++WszW5k449HRf6/vkT1TrKyKyD1DiK4Mys2zgF8BHgDnAmWY2J2qyLwKNzrmZwLXAT4J55wCLgEOAU4DrzSx7kDJ/AlwblNUYlN3vD865ucGfOkpNpe42aN5IXf40ACqLMzvxXbK2gaXrGjh7/Ea6euHc3y7l9y9s2lXr+8b9qQ5RRCTjKfGVRMwHVjvn1jjnuoG7gIVR0ywEbg1e3wucaL7B50LgLudcl3NuLbA6KC9mmcE8HwzKICjzE6O4brK36v2TydZnTQSgSjW+AByQ38OZk2pZua2FlzY0wmFnwAGHwaPf9V8WREQkZZT4SiImARsj3tcEw2JO45zrBZqB8gHmjTe8HGgKyoi1rNPN7BUzu9fMpsQK1swuNLNlZrastrY28bWUoalbBcAb3QeQZVCe4TW+kU6paqS6vJAHX9nC6rp2OPUa2LEJFv801aGJiGQ0Jb6yP/kLUO2cOxx4jF01zLtxzt3onJvnnJtXWVmZ1AAzSt0qsCyW7RhHeVEe2VmZ26NDtCyDz8ybQk52Fhfc9iLNFUfDEWfCsz/f+YVBRESST4mvJGITEFm7OjkYFnMaM8sBSoH6AeaNN7weGBuUsduynHP1zrmuYPhvgKOHtVay95bdDCsfgsJy3qqppTqnjhkb7mHGhntSHdk+Y2xhLmfPn0pNYztfvWs5fSdeCblFcO/50Ns16PwiIjLylPhKIl4AZgW9LeTib1Z7IGqaB4Bzg9dnAE8451wwfFHQ68N0YBawNF6ZwTz/CMogKPPPAGY2IWJ5pwFvjvB6ylDs2Ey4ZCLrW7OZnK9ELpbqiiKuXngoT79dyzcf3krfab+Ara/A41emOjQRkYyUM/gkkumcc71mdgnwCJAN/NY597qZXQ0sc849ANwE/M7MVgMN+ESWYLq7gTeAXuBi51wfQKwyg0X+O3CXmf0AWB6UDfA1MzstKKcBOG+UV13i6e2E9joaKhfQ64xJ+d2pjmifE1n7/ZmJ5dy9HGrXN/NfZfOZ8Pz1MHme7+5MRESSRomvJMQ59xDwUNSw70W87gQ+HWfeHwI/TKTMYPgafK8P0cO/DXx7qLHLKNixBYD1WVMBmKQa3wGdPqEeA/6wuZJvjT2HW6dkkfXHf4HCcjjwhBRHJyKSOdTUQUSGbodv4v1Kn098J6rGd1CfmlDP5yZvZ3FTOed1XkZv2Uy462zY8HyqQxMRyRhKfEVk6HZshlABK9qrmFTYR362S3VE+4WPj2/g0gM38eymPk5tuJTGrDJ6bzmNp/98M3cs2ZDq8ERE0p6aOojI0LVshpKJrG7NYUZJ7+DTy07HjmuhLLSW/35nMh/rupz7i37Ee5f/GzVVH4Csj4NlwbwvpDpMEZG0pBpfERmacBh2bKavZCLvtOQwa0xfqiPa78wu7uSHB63HhQr4QMtVvJU/lynb/wFLb4Sub595YgAAFM1JREFUllSHJyKStpT4isjQNK6Fvm62hKbQ2WccWdaT6oj2S1V5PXz/XeuZXtzHR5q+yT2Fn8XVr4Ynfwyv3ANOzUdEREaaEl8RGZptrwGwvGcaAEeXK/HdW8U5Yb49cyMnlDfzzYaF/HjslYSLKuCPX4I7z9zZe4aIiIwMJb4iMjRbXwOMf7RNY0JBHxMKw6mOaL+WkwUXTdvKZyfW8qvNB3Je+Ht0nfh9WPMP+MUCePEW37xERESGTYmviAzNpmXw/9u78/ioynuP45/fzGQmK4EkrGENq1AKIpuKivvS1uW6W6/L1ba3L6l28b6u2t7W2+ttte1t63WtrbVVS12o7aW27nsREJRdtgCBAJEACYEsTGZ57h9z0AQzSZCECZnv+/V6XufMc57znOfhMMyPc55znrx+LKzKYZKu9nYIs8Trzn4yeS/zdmZy2buDqZr6Hcgpgr/eAvdNgtd/lJgqWkREPjMFviLSfvE4bF1MXV4J2+r9GubQwS4bup9fHV/D2r0BvrRgDB8e802YcBXUVsI7P4XVc6GxLtXNFBE5ainwFZH227kGwntZHxgBaHxvZzhjQCNzZlYTd3DxmwXM5SQ49Q4YOAU2vA4PTId1L6W6mSIiRyUFviLSfuULAXincRSZfsfYnnqHb0dauKmKhZuqqNtTyQ9GbmRQZgM3L8xn1gf9qR5zFRz/DcjIgtmXwezLYefaVDdZROSoogksRKT9yt+D7CJe3VPM53tFyNB/nTtNr4wYd47ewpLwQH75YQ7zdwa5+ZhMrpw8hGDZW1D6Cjw4HQYfD6POgVCeJr4QEWmDfrZEpP3KFxIbOJVVNRka5nAE+A0mZ27lrjFl9M1o4AdL8zjp70XM9n2B/Sd/D4acCFvmwxt3wdq/Q93uVDdZRKRLU+ArIu1TtwuqNrAtdzxRZwp8j6Ch2WG+P6qc20eUkxOIcccHPTjxtaH8jGuomHo7FI2B9S/DLz8HL94ONdtS3WQRkS5JQx1EpH22LgLgrYZhZJhjSpEC3yPJDCbm1zGhRx0uty+Prs/mwTXZ3L9mHDP6jOSG0RsZtG8pJQt/hVv4CGUDvsjqkuv54ukzU910EZEuQ4GviLTPlvk4X4DHNvXkxL4N5Ac1pW4qmMH0PhGO71PD9nofc8oyebosi+uXjSE7OI4z+1/Cdb7nGV8xl+Hb/gKVX4AZ34RBU1PddBGRlNNQBxFpn9LXqOszmY174pxXHE51awQYkB3n5rH1vHPubp44qZrhvXN5fksGF226kIuCD/NS0bW4ze/Co2fC4xfC9qWpbrKISErpiq+ItK1mK+xYyaLBswj4jLMU+KbUwk1Vn8oLAldOPZnacJSl5XtYXFbF17aeTe/gadw54D3O3vIYgUdOgUHT4JjzIZjTvAK9EUJE0oACXxFp2/pXAPjdztEMK8ph7Ta9PaCryg0FmDGiiBOHF1JeVU9VfSO3Lsvktsix3JH1HJeXv0i0YhW1Yy6l55AJ+OyTfWcv3NJinVdNG3yEWi8i0rkU+IpI29a/TGPuQN7aVcBFx+anujWSxPAtzzb7PMJbnjvOx/zqPJ6pvpA/1M7grvhvmLjyMeYsn8nD/i8TCgXJX7KAmoYIOcEA/fIzGdgri/75WfibRsYiIkc5Bb4i0rrIftj4JqsKzsNf5WNs/x6wI9WNkkOR7Y9zelENpxfVsD9mfFB3A/76d7h490uc4C/l3uDNbIj2Yee+MBv31/FeWWIoRY/MAMcNKWDm6N4M6JmV4l6IiBw+Bb4i0rrN/4BIPb/bNYoZI4rICemfjaNZpt8xrkeY+h5TWZtbyIjyP3FX3Q94a8wDVE6agnOOPQ0RtlTVs2RLNW+urWTmz97kppkj+NeZJYQC/lR3QUTkM9NbHUSkdWv+RtSXyYt1o/jaKSWpbo10oJrc4awsuYFoIIfTFn2VkZufwoBe2UEmDOzJdScM4ztnjWZMvzx+8eo6Tvjx69zzwpqkY4FFRLo6Bb4iklxjPW7Fs7zKNMYN7sPxJYWpbpF0sHCokFXDbqCi6ESmfPjfTFn1Q3zxTyYnKcgJcsWUwVx/wlBizvHI2xt5d8MunNN7nEXk6KPAV0SSW/VnLLyPRxtOYdZpIzDTg07dUcwf4u3j7mVVyY2MLJ/Dqe99hVC4+SvTRvbN4xunjmRk31yeX17BrNlL2Ldfs/eJyNFFga+IJOXef4zNvoHU9p3CqaP7pLo50omc+Vk2+hbmTbibwpqVnD3/KnruXdusTFbQz9XTh3D2uH68uOojzr9/Hms+2puiFouIHDoFviLSsh2rsK2LeDw8k1vOGKWrvd3c8C3PMnzLswSi9awZejXByF7Onn8lU1b8Z7NyPjNOGdWb2TdOozYc5cIH5jHn/a0parWIyKHR49ki0qLqN+4jxwXYN+YSzvlcv1Q3R46guqxiVpbcyMjyZxi5dQ6Yjw+O+Tdi/syPy0wrKeRvN8/glj8u5dZnl7FoUxV3nj+OrOAhvPVh8WPJt2kmORHpBLriKyKf0rh9BT3WPM1zvrO47Z9OTHVzJAUiGXmsHnYd2wtPYGT5M5w771J6Vy1uVqZPXiZP3jiNWaeO4OnF5Zzy0zd4YsFmGqPxFLVaRKR1pidzpTubPHmyW7x4cdsF5WPxWJwNPz+D3rVreGj8HIYMGvipMgfPECbdW232IKatvJPchm1sLD6f5SNnUZ/Vv1mZzbvrWLJlD++VVdG3R4jzJwzggonFjBvQI/kwGV3x7bLM7H3n3ORUt0Oko2mog4h8zDnH7Cd/xdV17zO78KYWg15JPzuKpvO3Gc8xvvRhRpc9yZCKF9kw8CLWDb6SvXnDARhSmMNt547hrXU7eXLBFh6bV8av39lEUW6QaSWFnDC8kONLChlWlIOF90Hlatg8Dxr2QGMduChYADKyIKsn9BwMA46F7IIU915EuhNd8ZVuTVd8268xGufBOS9y7eqvEM3uwysnPQv+YItldcU3fQUb91C8822KalbgczF25Y9nW59TqCw4jprcETQGewJQH46ytqKK+o9KyapezeBoGcdYOcf4yxlI5cf1OfNhGdngC4CLQWN9YnlA/mAYMAH6T4TiSYlgOKvXke522tEVX+mudMVXRNi4s5b/mP0Wd+3+FsFggJ43/glK9c+DfFpjsCebis+nvO/p9K5eSna4kgnr7/94e8wXJOrPwhePkBGr/zg/HvBTGRxIqY3mL42n80G4mDXxwXxEL4p90D8YY0B2nAGZEYaFahg6oA/969dSsOdDMj9agW/1Xz9pREEJ9BkLBcOg19BEyh8EOb0TQbHeQCIiSeiXTSSN7di7n0f/sYlF89/i5/57GRyoxn/N81BYAqWallaSiwZyqOh9IhsGX0oovJuCvWvIry0l1FhFRrSOuC+D3LpywsGe1If60BDqjfMFyACmAFcXF7K0KsDSqgY27Quwvd7Hol0ZfFQfIkYerAAY76XLGRDYx8mZG5gS2MDY2C76bVlFj3Uv4483Nm+YLwAZORDKg1Cut8yDoLccfzHk9IG8fpBdqCBZJM1oqIO0i5mdA9wL+IHfOOfuPmh7CHgcOA7YDVzunCvztt0O3ADEgJudcy+1VqeZDQOeAgqB94F/ds41tnaMZDTUoTnnHJX7wry9bievr6lk6ep1XGkv8fXAXwkHe7Fg4j1UFk5psx4NdZDOEnewJxJgT8RPTTTAnkiAmqifPZEAO8MZVDZmsCOSTTgax4jTmxqG2A5GZO5lZG4DQ0J19A9voshqyIvvJRTdi69xH8Rjnz5YIBN6FEP+wMQV43xvvUcx5PVPjDXOzIeM7LQLkDXUQborXfGVNpmZH3gAOBPYCiwys7nOuQ+bFLsBqHbOjTCzK4B7gMvNbCxwBTAOGAC8amajvH2S1XkP8Avn3FNm9rBX90PJjtG5vW+dc45Y3BGJORpjcSIHUtQRiSde6eQzw2fe0vfJuh3I87Zbk3Jx54jGEnVEY83XI7E40bgjGosTiTmiB+VHYonP9ZEYdeEo1fWN7K6pY1f1Hqp3bidv/3bGWhlfDq7jvuByAi7Clr5nsmjc9wiH9CCRpJbPoCAYpSAYBcItlikddAkNkRjVdRGq6xupqmuktK6RBbVhdu8Ks3d/tFn5woxGjs2vY3TmHgb1K6Kffx+9Yrvo0VhJVkMFmTXbCVWsJnP/ToxPXwyKWoBacqi1HBrIotEXImpBQlnZOH8Iy8jCH8wiEMoiI5RNMDOLUGY2WaEggUAGPn8G+PxeCoB5S9/BS2/d/ODPSATmGdmQ4S0PfPbrp1vks9K3R9pjKlDqnNsIYGZPARcATQPfC4A7vfU5wP2WeIfRBcBTzrkwsMnMSr36aKlOM1sNnAZc5ZX5vVfvQ8mO4TrhtsW3n1nKorIqYjFHzAtuo/HEsmmKxrvuHZO7A49wmX8xOYQJWeSTDd7zajXZQ9lQdBnrhlzBvpyhKWmjyGcxonxO84xMLxUmPu6PGR+Fg1TsD1LhLXfHc3lmVw92b/URJ4/E/8ObyyBKX6tmALvobTXkWx2FvloKrJaevnryXR3ZNBCIRwm6WmisJoMIIRchZBFCNJJJpPn3rRNE8NNIBnF8xPHhMBxGHD9xMwpzM/H5/HD9C9BzUKe2ReRoo8BX2qMYKG/yeSswLVkZ51zUzGpI/AwVAwsO2rfYW2+pzkJgj3Mu2kL5ZMfY1bQhZvZV4Kvex1ozW9vunrat6ODjdVVXtlliuZceamnjUdPPw5QO/UyHPkIH9bO0AxrSydrfz28PPpzjDDmcnUW6KgW+0u045x4BHumMus1scTqMe1M/u4906COonyLSPpqyWNpjG9D0ftlAL6/FMmYWAPJJPICWbN9k+buBnl4dBx8r2TFERERE2qTAV9pjETDSzIaZWZDEw2pzDyozF7jWW78EeN0bezsXuMLMQt7bGkYC7yWr09vnDa8OvDr/r41jiIiIiLRJQx2kTd542lnASyRePfZb59wqM/shsNg5Nxd4FHjCe3itikQgi1fuGRIPwkWBm5xLTMvUUp3eIf8deMrM7gKWeHWT7BhHWKcMoeiC1M/uIx36COqniLSD3uMrIiIiImlBQx1EREREJC0o8BURERGRtKDAV8RjZnea2TYzW+ql85psu93MSs1srZmd3ST/HC+v1Mxua5I/zMwWevlPew/wdXnJ+nM0MbMyM1vhncPFXl6Bmb1iZuu9ZS8v38zsf73+LjezSU3qudYrv97Mrk12vCPFzH5rZpVmtrJJXof1y8yO8/7cSr19UzJHb5J+dqvvppkNMrM3zOxDM1tlZrd4+d3ufIp0Oc45JSUl5yAxK9ytLeSPBZYBIWAYsIHEA3l+b72ExHxoy4Cx3j7PAFd46w8DX091/9rR/6T9OZoSUAYUHZT3E+A2b/024B5v/TzgBcCA6cBCL78A2Ogte3nrvVLcr5OBScDKzugXibetTPf2eQE4twv1s1t9N4H+wCRvPQ9Y5/Wl251PJaWulnTFV6RtH0+77JzbRGJyp6k0mcrZOdcIHJh22UhMu3xgXtXfAxemoN2HqsX+pLhNHeUCEucBmp+PC4DHXcICEu+Q7g+cDbzinKtyzlUDrwDnHOlGN+Wce5vE20ya6pB+edt6OOcWOOcc8Dgp+jubpJ/JHJXfTedchXPuA299H7CaxMyU3e58inQ1CnxFmpvl3Ur87YHbjLQ8ZXNxK/mtTbvclSXrz9HGAS+b2fuWmL4aoK9zrsJb/wjo660f6rntajqqX8Xe+sH5XUm3/G6a2VDgWGAh6XU+RVJCga+kFTN71cxWtpAuAB4ChgMTgQrgf1LaWPmsZjjnJgHnAjeZ2clNN3pXwLrdexy7a7883fK7aWa5wJ+Abzrn9jbd1s3Pp0jKaAILSSvOuTPaU87Mfg08731sbcrmVqdd9q4stTTFc1fUnqmpuzzn3DZvWWlmfyZx23uHmfV3zlV4t4ErveKtTak986D8Nzu56Z9FR/Vrm7d+cPkuwTm348B6d/lumlkGiaD3D86557zstDifIqmkK74iHu+H5oCLgANPlXfktMtdWXumpu7SzCzHzPIOrANnkTiPTae7Pnga7Gu8p+anAzXereaXgLPMrJd3W/0sL6+r6ZB+edv2mtl0bxzsNXShv7Pd7bvp/Rk/Cqx2zv28yaa0OJ8iKZXqp+uUlLpKAp4AVgDLSfzQ9G+y7bsknhJfS5Ono0k8bb3O2/bdJvklJH6AS4FngVCq+9fOP4MW+3O0JO/PfZmXVh3oA4mxna8B64FXgQIv34AHvP6uACY3qetfvPNXClzfBfr2RxK3+SMkxmze0JH9AiaTCCg3APfjzezZRfrZrb6bwAwSwxiWA0u9dF53PJ9KSl0tacpiEREREUkLGuogIiIiImlBga+IiIiIpAUFviIiIiKSFhT4ioiIiEhaUOArIiIiImlBga+IiIiIpAUFviIiKWJmQ83sqnaUm2hm57Wj3Ewze76tciIi6UqBr4hIJzOzZNPDDwXaDHyBiSQmOBARkcOgwFdEJAnviuwaM/udma0zsz+Y2RlmNs/M1pvZVC/NN7MlZvaumY329r3OzOaa2eskZuNqyd3ASWa21My+ZWaZZvaYma3w6jvVm3L3h8DlXrnLkx1TRERal+wqhIiIJIwALiUxNewiEldoZwDnA3cA1wAnOeeiZnYG8CPgYm/fScDnnXNVSeq+DbjVOfdFADP7DuCcc+PNbAzwMjAK+D6JaWpneeV6tHJMERFJQoGviEjrNjnnVgCY2SrgNeecM7MVJIYq5AO/N7ORgAMymuz7SitBb0tmAPcBOOfWmNlmEoHvwVo7poiIJKGhDiIirQs3WY83+RwncfHgv4A3nHOfA74EZDYpX9dJbWrtmCIikoQCXxGRw5MPbPPWrzvEffcBeU0+vwN8GcDMRgGDgbUtlDucY4qIpC0FviIih+cnwI/NbAmHPnxsORAzs2Vm9i3gQcDnDaN4GrjOORcG3gDGHni47TCPKSKStsw5l+o2iIiIiIh0Ol3xFREREZG0oFtkIiKdzMzGA08clB12zk1LRXtERNKVhjqIiIiISFrQUAcRERERSQsKfEVEREQkLSjwFREREZG0oMBXRERERNLC/wP6a/L3oWyaUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  1%|          | 26/5000 [00:00<00:19, 259.17it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 271.89it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 25/5000 [00:00<00:20, 247.73it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Prediction for Will the US federal government shut down all non-essential services by 2020-04-19?: 0.07133333333333333\n",
            "\n",
            "\n",
            "\n",
            "Prediction for Will the Emergency Telework Act (S.3561) become law by 4/25/20?: 0.08809523809523809\n",
            "\n",
            "\n",
            "\n",
            "Prediction for By May 1 will there be an iOS or Android app that shares an individual's COVID-19 infection status with more than 1M other users?: 0.38846153846153847\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "  3%|▎         | 166/5000 [00:00<00:19, 247.60it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Encoutered nan gradient, stopping early\n",
            "[[       nan        nan -229.19742]\n",
            " [ 281.74265  327.9386   125.21753]\n",
            " [ 427.8719   745.5885   103.97989]]\n",
            "[[ 0.5213326   0.04481379 -2.9777741 ]\n",
            " [ 0.67209536  0.21730426 -3.0133    ]\n",
            " [ 0.6785902   0.17808057 -3.0089262 ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  1%|          | 28/5000 [00:00<00:17, 277.58it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 273.91it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 24/5000 [00:00<00:20, 238.12it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 267.88it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "No submission or submission can't be shown for How many days will the city of New Orleans spend under lockdown between 2020-03-25 and 2020-04-15?\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Prediction for Will Florida go under lockdown between 2020-03-25 and 2020-04-25?: 0.55\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iC_rceYye_lx",
        "colab_type": "text"
      },
      "source": [
        "If everything looks good, submit the predictions!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzwGTAFZcIhw",
        "colab_type": "code",
        "outputId": "ad583563-6ac4-4f43-ea36-7b736137b73b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "source": [
        "def submit_all():\n",
        "  for question in questions:\n",
        "    if question.type == \"binary\":\n",
        "      try:\n",
        "        r = question.submit(question.binary_prediction)\n",
        "        print(f\"Submitted for {question.name}\")\n",
        "        print(f\"https://pandemic.metaculus.com{question.page_url}\")\n",
        "      except requests.exceptions.HTTPError as e:\n",
        "        print(f\"Couldn't make prediction for {question.name} -- maybe this question is now closed? See error below.\")\n",
        "        print(e)\n",
        "    elif question.type == \"continuous\":\n",
        "      try:\n",
        "        question.samples\n",
        "      except:\n",
        "        print(f\"No predictions for {question}\")\n",
        "        continue\n",
        "\n",
        "      try:\n",
        "        r = question.submit_from_samples(question.samples)\n",
        "        print(f\"Submitted for {question.name}\")\n",
        "        print(f\"https://pandemic.metaculus.com{question.page_url}\")\n",
        "      except requests.exceptions.HTTPError as e:\n",
        "        print(f\"Couldn't make prediction for {question.name} -- maybe this question is now closed? See error below.\")\n",
        "        print(e)\n",
        "    else:\n",
        "      raise ValueError(\"Unknown question type!\")\n",
        "      \n",
        "\n",
        "submit_all()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 266.52it/s]\n",
            "  1%|          | 26/5000 [00:00<00:19, 255.36it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Submitted for What will the Seattle Police Department report as the total number of criminal offenses in March 2020?\n",
            "https://pandemic.metaculus.com/questions/3924/what-will-the-seattle-police-department-report-as-the-total-number-of-criminal-offenses-in-march-2020/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 269.27it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Submitted for What will Washington state’s Department of Revenue report as the 2020 Q1 gross business income?\n",
            "https://pandemic.metaculus.com/questions/3923/what-will-washington-states-department-of-revenue-report-as-the-2020-q1-gross-business-income/\n",
            "Couldn't make prediction for Will the US federal government shut down all non-essential services by 2020-04-19? -- maybe this question is now closed? See error below.\n",
            "(\"('400 Client Error: Bad Request for url: https://pandemic.metaculus.com/api2/questions/3921/predict/',)\", 'request body: {\"prediction\": 0.07133333333333333, \"void\": false}', 'response json: {\\'non_field_errors\\': [\"Cannot predict on a question that\\'s not open!\"]}')\n",
            "Submitted for Will the Emergency Telework Act (S.3561) become law by 4/25/20?\n",
            "https://pandemic.metaculus.com/questions/3918/will-the-emergency-telework-act-s3561-become-law-by-42520/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "  1%|          | 27/5000 [00:00<00:18, 264.02it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Submitted for By May 1 will there be an iOS or Android app that shares an individual's COVID-19 infection status with more than 1M other users?\n",
            "https://pandemic.metaculus.com/questions/3915/by-may-1-will-there-be-an-ios-or-android-app-that-shares-an-individuals-covid-19-infection-status-with-more-than-1m-other-users/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "  3%|▎         | 134/5000 [00:00<00:19, 247.00it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Encoutered nan gradient, stopping early\n",
            "[[  56.726654  149.32896   200.03879 ]\n",
            " [        nan         nan -404.89557 ]\n",
            " [  48.187782  117.946884  204.85678 ]]\n",
            "[[ 0.89044327  0.5763766  -3.0467222 ]\n",
            " [ 0.50971544  0.04337742 -2.8926947 ]\n",
            " [ 0.9081516   0.62669027 -3.0605824 ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "  1%|          | 28/5000 [00:00<00:18, 273.43it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Submitted for By June 1, how many tests for COVID-19 will have been administered in the US?\n",
            "https://pandemic.metaculus.com/questions/3916/by-june-1-how-many-tests-for-covid-19-will-have-been-administered-in-the-us/\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:19<00:00, 261.94it/s]\n",
            "  0%|          | 23/5000 [00:00<00:21, 229.43it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Couldn't make prediction for [short fuse] How many total confirmed deaths of novel coronavirus will be reported in the state of New York by April 2nd? -- maybe this question is now closed? See error below.\n",
            "(\"('400 Client Error: Bad Request for url: https://pandemic.metaculus.com/api2/questions/3934/predict/',)\", 'request body: {\"prediction\": {\"kind\": \"multi\", \"d\": [{\"kind\": \"logistic\", \"x0\": 0.5008589029312134, \"s\": 0.015188154764473438, \"w\": 0.19202777743339539, \"low\": 0.0, \"high\": 0.99}, {\"kind\": \"logistic\", \"x0\": 0.4805207848548889, \"s\": 0.016074342653155327, \"w\": 0.3177727460861206, \"low\": 0.0, \"high\": 0.99}, {\"kind\": \"logistic\", \"x0\": 0.45007002353668213, \"s\": 0.023274032399058342, \"w\": 0.490199476480484, \"low\": 0.0, \"high\": 0.99}]}, \"void\": false}', 'response json: {\\'non_field_errors\\': [\"Cannot predict on a question that\\'s not open!\"]}')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 5000/5000 [00:18<00:00, 266.35it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Couldn't make prediction for What will be the US unemployment rate for March 2020? -- maybe this question is now closed? See error below.\n",
            "(\"('400 Client Error: Bad Request for url: https://pandemic.metaculus.com/api2/questions/3922/predict/',)\", 'request body: {\"prediction\": {\"kind\": \"multi\", \"d\": [{\"kind\": \"logistic\", \"x0\": 0.2211281955242157, \"s\": 0.01, \"w\": 0.7507925033569336, \"low\": 0.0, \"high\": 0.99}, {\"kind\": \"logistic\", \"x0\": 0.6863842010498047, \"s\": 0.4577600359916687, \"w\": 0.10677008330821991, \"low\": 0.0, \"high\": 0.6648781532403318}, {\"kind\": \"logistic\", \"x0\": 0.2636052072048187, \"s\": 0.012183872982859612, \"w\": 0.1424374282360077, \"low\": 0.0, \"high\": 0.99}]}, \"void\": false}', 'response json: {\\'non_field_errors\\': [\"Cannot predict on a question that\\'s not open!\"]}')\n",
            "No predictions for How many days will the city of New Orleans spend under lockdown between 2020-03-25 and 2020-04-15?\n",
            "Couldn't make prediction for Will Florida go under lockdown between 2020-03-25 and 2020-04-25? -- maybe this question is now closed? See error below.\n",
            "(\"('400 Client Error: Bad Request for url: https://pandemic.metaculus.com/api2/questions/3926/predict/',)\", 'request body: {\"prediction\": 0.55, \"void\": false}', 'response json: {\\'non_field_errors\\': [\"Cannot predict on a question that\\'s not open!\"]}')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4zJaSMq_K0e",
        "colab_type": "text"
      },
      "source": [
        "# TODO\n",
        "1. consider automatically pulling all open questions and matching them to models"
      ]
    }
  ]
}