Timeseries_EDA.ipynb 3.01 MB
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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {},
   "outputs": [],
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   "source": [
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from tools import preprocessor, inputter\n",
    "\n",
    "pd.reset_option('^display.', silent=True)\n",
    "\n",
    "def make_citizen_training(df):\n",
    "    df['NumberWeeksSum'] = get_col_cumsum(df, 'NumberWeeks')\n",
    "    df['NumberTrainingSum'] = get_col_cumsum(df, 'NumberTraining')\n",
    "    df['NeedsBaseline'] = get_col_first(df, 'Needs')\n",
    "    df['MeanEvaluationMean'] = get_col_mean(df, 'MeanEvaluation')\n",
    "    df['StdEvaluationMean'] = get_col_mean(df, 'StdEvaluation')\n",
    "    df['NumberTrainingWeekMean'] = get_col_mean(df, 'NumberTrainingWeek')\n",
    "    df['MeanTimeBetweenTrainingMean'] = get_col_mean(df, 'MeanTimeBetweenTraining')\n",
    "    df['NumberCancelsSum'] = get_col_cumsum(df, 'NumberCancels')\n",
    "    df['MeanTimeBetweenCancelsMean'] = get_col_mean(df, 'MeanTimeBetweenCancels')\n",
    "    df['MeanNumberCancelsWeekMean'] = get_col_mean(df, 'MeanNumberCancelsWeek')\n",
    "    df['NeedsMean'] = get_col_mean(df, 'Needs')\n",
    "    df['PhysicsMean'] = get_col_mean(df, 'Physics')\n",
    "    df['NumberExercisesMean'] = get_col_mean(df, 'NumberExercises')\n",
    "    return df\n",
    "\n",
    "def make_citizen_ats(df):\n",
    "    df['NumberWeeksSum'] = get_col_cumsum(df, 'NumberWeeks')\n",
    "    df['NumberTrainingSum'] = get_col_cumsum(df, 'NumberTraining')\n",
    "    df['NumberAtsMean'] = get_col_mean(df, 'NumberAts')\n",
    "    return df\n",
    "\n",
    "def get_col_cumsum(df, col):\n",
    "    return np.around(df.groupby(['CitizenId', 'HasCompletedSession'])[col].transform(pd.Series.cumsum), decimals=2)\n",
    "\n",
    "def get_col_mean(df, col):\n",
    "    return np.around(df.groupby(['CitizenId', 'HasCompletedSession'])[col].transform(pd.Series.mean), decimals=2)\n",
    "\n",
    "def get_col_max(df, col):\n",
    "    return df.groupby(['CitizenId', 'HasCompletedSession'])[col].transform(pd.Series.max)\n",
    "\n",
    "def get_col_first(df, col):\n",
    "    return df.groupby(['CitizenId', 'HasCompletedSession'])[col].transform('first')\n",
    "\n",
    "df = pd.read_csv('../data/interim/screenings.csv', converters={'CitizenId': str})\n",
    "\n",
    "print(f\"Number of screenings: {len(df)}\")\n",
    "print(f\"Number of citizens: {df.CitizenId.nunique()}\")\n",
    "\n",
    "df = fm.make_complete_feature(df)\n",
    "df = make_citizen_training(df)\n",
    "df = make_citizen_ats(df)\n",
    "df = preprocessor.replace_ats_strings(df)\n",
    "\n",
    "df_comp = df.loc[df['Complete'] == 1]\n",
    "print(f\"Number of citizens that completed: {len(df_comp)}\")\n",
    "\n",
    "df_fail = df.loc[df['Complete'] == 0]\n",
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    "print(f\"Number of citizens that failed: {len(df_fail)}\")"
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   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>CitizenId</th>\n",
       "      <th>Gender</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>NumberSplit</th>\n",
       "      <th>NumberScreening</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
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       "      <th>...</th>\n",
       "      <th>StdEvaluationMean</th>\n",
       "      <th>NumberTrainingWeekMean</th>\n",
       "      <th>MeanTimeBetweenTrainingMean</th>\n",
       "      <th>NumberCancelsSum</th>\n",
       "      <th>MeanTimeBetweenCancelsMean</th>\n",
       "      <th>MeanNumberCancelsWeekMean</th>\n",
       "      <th>NeedsMean</th>\n",
       "      <th>PhysicsMean</th>\n",
       "      <th>NumberExercisesMean</th>\n",
       "      <th>NumberAtsMean</th>\n",
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       "      <td>25-06-2020</td>\n",
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       "      <td>31-08-2020</td>\n",
       "      <td>31-08-2020</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>47.0</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>10-09-2020</td>\n",
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       "      <td>07-08-2020</td>\n",
       "      <td>07-08-2020</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4.00</td>\n",
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       "<p>601 rows × 56 columns</p>\n",
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      ],
      "text/plain": [
       "      index   CitizenId  Gender  BirthYear  NumberSplit  NumberScreening  \\\n",
       "0         0  3810622973       0         31            0                0   \n",
       "1         1  5806703169       0         35            0                0   \n",
       "5         8  4420982563       1         49            0                0   \n",
       "8        13  3806883741       0         44            0                0   \n",
       "15       23  3610642969       0         32            2                0   \n",
       "...     ...         ...     ...        ...          ...              ...   \n",
       "2122   3180  3010883085       0         44            0                0   \n",
       "2130   3193  3003042889       0         52            0                0   \n",
       "2141   3211  5403004571       1         50            0                0   \n",
       "2142   3212  4212803493       0         40            0                0   \n",
       "2143   3214  4208665171       1         33            0                0   \n",
       "\n",
       "       StartDate     EndDate  NumberWeeks  MeanEvaluation  ...  \\\n",
       "0     01-06-2016  01-06-2016        14.43             0.0  ...   \n",
       "1     25-06-2020  25-06-2020         2.00             4.0  ...   \n",
       "5     31-08-2020  31-08-2020         0.00             3.0  ...   \n",
       "8     10-09-2020  10-09-2020         0.00             6.0  ...   \n",
       "15    28-06-2018  28-06-2018         0.00             5.0  ...   \n",
       "...          ...         ...          ...             ...  ...   \n",
       "2122  16-04-2019  16-04-2019         0.57             2.0  ...   \n",
       "2130  19-03-2018  19-03-2018         0.86             0.0  ...   \n",
       "2141  10-10-2016  10-10-2016         0.00             0.0  ...   \n",
       "2142  07-08-2020  07-08-2020         0.00             0.0  ...   \n",
       "2143  06-04-2016  06-04-2016         0.00             0.0  ...   \n",
       "\n",
       "      StdEvaluationMean  NumberTrainingWeekMean  MeanTimeBetweenTrainingMean  \\\n",
       "0                   0.0                     0.0                          0.0   \n",
       "1                   0.0                     0.0                          0.0   \n",
       "5                   0.0                     0.0                          0.0   \n",
       "8                   0.0                     0.0                          0.0   \n",
       "15                  0.0                     0.0                          0.0   \n",
       "...                 ...                     ...                          ...   \n",
       "2122                0.0                     0.0                          0.0   \n",
       "2130                0.0                     0.0                          0.0   \n",
       "2141                0.0                     0.0                          0.0   \n",
       "2142                0.0                     0.0                          0.0   \n",
       "2143                0.0                     0.0                          0.0   \n",
       "\n",
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       "   index   CitizenId  Gender  BirthYear  NumberSplit  NumberScreening  \\\n",
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       "    index   CitizenId  Gender  BirthYear  NumberSplit  NumberScreening  \\\n",
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       "0   01-06-2016  01-06-2016        14.43             0.0  ...   \n",
       "1   25-06-2020  25-06-2020         2.00             4.0  ...   \n",
       "5   31-08-2020  31-08-2020         0.00             3.0  ...   \n",
       "8   10-09-2020  10-09-2020         0.00             6.0  ...   \n",
       "15  28-06-2018  28-06-2018         0.00             5.0  ...   \n",
       "\n",
       "    StdEvaluationMean  NumberTrainingWeekMean  MeanTimeBetweenTrainingMean  \\\n",
       "0                 0.0                     0.0                          0.0   \n",
       "1                 0.0                     0.0                          0.0   \n",
       "5                 0.0                     0.0                          0.0   \n",
       "8                 0.0                     0.0                          0.0   \n",
       "15                0.0                     0.0                          0.0   \n",
       "\n",
       "    NumberCancelsSum  MeanTimeBetweenCancelsMean  MeanNumberCancelsWeekMean  \\\n",
       "0                  0                         0.0                        0.0   \n",
       "1                  0                         0.0                        0.0   \n",
       "5                  0                         0.0                        0.0   \n",
       "8                  0                         0.0                        0.0   \n",
       "15                 0                         0.0                        0.0   \n",
       "\n",
       "    NeedsMean  PhysicsMean  NumberExercisesMean  NumberAtsMean  \n",
       "0        29.0         13.0                  4.0          12.00  \n",
       "1        19.0         26.0                  5.0           9.00  \n",
       "5        47.0         27.0                  3.0          18.00  \n",
       "8        12.0         41.0                  9.0          28.00  \n",
       "15        7.0         64.0                  8.0           6.67  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
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     },
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     "execution_count": 4,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
   ],
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   "source": [
    "df_fail.head()"
   ]
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  },
  {
   "cell_type": "code",
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "text/plain": [
       "2       0.0\n",
       "3       3.0\n",
       "4       6.0\n",
       "6       8.0\n",
       "7       9.0\n",
       "       ... \n",
       "2136    8.0\n",
       "2137    8.0\n",
       "2138    8.0\n",
       "2139    8.0\n",
       "2140    8.0\n",
       "Name: NumberExercisesMean, Length: 1543, dtype: float64"
      ]
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     "execution_count": 5,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
   ],
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   "source": [
    "df_comp.NumberExercisesMean"
   ]
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  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
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      "image/png": 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4DLi/b/3+VpMkLQCDnlmcC1yZ5NNt/QR+Ol+TJOlhbtCnoU5P8jngua302qr66vDakiTNJ4NehoLe7LB3V9VZ9KbeWD6kniRJ88ygEwmeBrwNOKWVHgn81bCakiTNL4OeWfxz4KXADwGq6jvAPxlWU5Kk+WXQsLi/Td5XAEl+bngtSZLmm0HD4oIkf0FvGvHfB77AHPwQkiRp79D5NFSbRvwTwFOAu4EnA++sqg1D7k2SNE90hkVVVZKLq+qXAQNCkhagQS9DXZPkmUPtRJI0bw36De5nAa9Ocgu9J6JC76Tj6cNqTJI0f+wyLJIcXlXfBl44on4kSfNQ15nFZ+jNNntrkr+uqn8xgp4kSfNM1z2L9C3/wlwdNMlBSS5M8o0kNyZ5dpJDkmxIclN7P7iNTZL3J5lKcu1OvwcuSRqBrrCoWZb31FnA/6yqpwBHAjcCa4BLq2oFcGlbBzgeWNFeq4EPzWEfkqQBdIXFkUnuTnIP8PS2fHeSe5LcvTsHTHIg8GvA2QBVdX9V3Qms5KfTnp9Dbxp0Wv3c6rmc3hcDH787x5Yk7Z5d3rOoqn2GcMzlwDTwl0mOBK4G3gQcVlW3tTG389MfV1oMbOr7/OZWu62vRpLV9M48OPzww/eowWVrPrtHn9fD1y1nvHjcLUhj8VCmKJ8ri4CjgA9V1a/SexR3Tf+A/nmoBlVVa6tqsqomJyYm5qxZSdJ4wmIzsLmqrmjrF9ILj+/uuLzU3re27VuApX2fX9JqkqQRGXlYVNXtwKYkT26lY4EbgPXAqlZbBVzUltcDr2lPRR0D3NV3uUqSNAKDfoN7rr0R+HiSfYGbgdfSC64LkpwE3Aq8so29GHgRMAXc28ZKkkZoLGFRVV8DJmfYdOwMYws4edg9SZJmN457FpKkvYxhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6jS2sEiyT5KvJvkfbX15kiuSTCX5RPt9bpLs19an2vZl4+pZkhaqcZ5ZvAm4sW/9PcCZVfUk4A7gpFY/Cbij1c9s4yRJIzSWsEiyBHgx8JG2HuA3gAvbkHOAE9ryyrZO235sGy9JGpFxnVm8D3gr8JO2/hjgzqra3tY3A4vb8mJgE0Dbflcb/48kWZ1kY5KN09PTQ2xdkhaekYdFkpcAW6vq6rncb1WtrarJqpqcmJiYy11L0oK3aAzHfA7w0iQvAvYHfh44CzgoyaJ29rAE2NLGbwGWApuTLAIOBL4/+rYlaeEa+ZlFVZ1SVUuqahlwIvDFqvpd4EvAy9uwVcBFbXl9W6dt/2JV1QhblqQFbz59z+JtwFuSTNG7J3F2q58NPKbV3wKsGVN/krRgjeMy1IOq6m+Bv23LNwNHzzDmH4BXjLQxSdI/Mp/OLCRJ85RhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6jTysEiyNMmXktyQ5Pokb2r1Q5JsSHJTez+41ZPk/Ummklyb5KhR9yxJC904ziy2A39UVUcAxwAnJzkCWANcWlUrgEvbOsDxwIr2Wg18aPQtS9LCNvKwqKrbquqatnwPcCOwGFgJnNOGnQOc0JZXAudWz+XAQUkeP9quJWlhG+s9iyTLgF8FrgAOq6rb2qbbgcPa8mJgU9/HNrfazvtanWRjko3T09PDa1qSFqCxhUWSRwN/Dby5qu7u31ZVBdRD2V9Vra2qyaqanJiYmMNOJUljCYskj6QXFB+vqk+18nd3XF5q71tbfQuwtO/jS1pNkjQi43gaKsDZwI1V9d6+TeuBVW15FXBRX/017amoY4C7+i5XSZJGYNEYjvkc4PeAryf5WqudCpwBXJDkJOBW4JVt28XAi4Ap4F7gtSPtVpI0+rCoqv8NZJbNx84wvoCTh9qUJGmX/Aa3JKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSeq014RFkuOSfDPJVJI14+5HkhaSvSIskuwDfAA4HjgCeFWSI8bblSQtHHtFWABHA1NVdXNV3Q+cD6wcc0+StGAsGncDA1oMbOpb3ww8q39AktXA6rb6gyTfHFFvD3eHAt8bdxPzRd4z7g40A/+N9tnDf6NPnG3D3hIWnapqLbB23H083CTZWFWT4+5Dmo3/Rkdjb7kMtQVY2re+pNUkSSOwt4TFVcCKJMuT7AucCKwfc0+StGDsFZehqmp7kjcAlwD7AOuq6voxt7VQeGlP853/RkcgVTXuHiRJ89zechlKkjRGhoUkqZNhoVk5xYrmsyTrkmxNct24e1kIDAvNyClWtBf4KHDcuJtYKAwLzcYpVjSvVdVlwLZx97FQGBaazUxTrCweUy+SxsywkCR1Miw0G6dYkfQgw0KzcYoVSQ8yLDSjqtoO7Jhi5UbgAqdY0XyS5DzgK8CTk2xOctK4e3o4c7oPSVInzywkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLU6f8DnbY9wNa82DsAAAAASUVORK5CYII=",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     },
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     "metadata": {
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      "needs_background": "light",
      "transient": {}
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     },
     "output_type": "display_data"
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    },
    {
     "data": {
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      "image/png": 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",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     },
     "metadata": {
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      "needs_background": "light",
      "transient": {}
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     },
     "output_type": "display_data"
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    },
    {
     "data": {
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      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     },
     "metadata": {
Christian Marius Lillelund's avatar
Christian Marius Lillelund committed
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      "needs_background": "light",
      "transient": {}
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     },
     "output_type": "display_data"
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    },
    {
     "data": {
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thecml committed
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      "image/png": 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",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     },
     "metadata": {
Christian Marius Lillelund's avatar
Christian Marius Lillelund committed
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      "needs_background": "light",
      "transient": {}
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     },
     "output_type": "display_data"
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    },
    {
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