{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Purpose**: Analyze the feedback that are embeded at the end of the tutorials in Galaxy Training Material"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from pprint import pprint\n",
    "import datetime\n",
    "import emoji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://docs.google.com/spreadsheets/d/1NfZhi5Jav7kl9zFCkeb7rIC2F8xW1isruv1TeO4WpNI/export?format=tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-07-08 09:01:07.895695'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the feedback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = (pd.read_csv(url, sep='\\t')\n",
    "    # remove last column\n",
    "      .drop(['Make feedback confidential?'], axis=1)\n",
    "    # rename column\n",
    "      .rename(columns = {'Timestamp': 'timestamp',\n",
    "                         'How much did you like this tutorial?': 'note',\n",
    "                         'What did you like?': 'pro',\n",
    "                         'What could be improved?': 'con',\n",
    "                         'Tutorial': 'tutorial_topic'}))\n",
    "# extract topic from tutorial name\n",
    "new = df.tutorial_topic.str[::-1].str.split('(', n = 1, expand = True)\n",
    "df[\"tutorial\"]= new[1].str[::-1].str[:-1]\n",
    "df[\"topic\"]= new[0].str[::-1].str[:-1]\n",
    "df = (df.drop(columns =[\"tutorial_topic\"])\n",
    "    # remove rows with NaN on note, pro and con\n",
    "      .dropna(subset=['note', 'pro', 'con'], how='all')\n",
    "    # replace NaN in note by 0\n",
    "      .fillna(value={'note': 0}))\n",
    "# format note to integer\n",
    "df.note = df.note.astype(int)\n",
    "# format pro and con to string\n",
    "df.pro = df.pro.astype(str)\n",
    "df.con = df.con.astype(str)\n",
    "# format timestamp to remove hour and use datetime\n",
    "df.timestamp = pd.to_datetime(\n",
    "    df.timestamp.str.split(' ', n = 1, expand = True)[0],\n",
    "    dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change topic for some tutorials\n",
    "df.loc[df.tutorial == 'Formation of the Super-Structures on the Inactive X', 'topic'] = 'Epigenetics'\n",
    "df.loc[df.tutorial == 'Identification of the binding sites of the Estrogen receptor', 'topic'] = 'Epigenetics'\n",
    "df.loc[df.tutorial == 'Identification of the binding sites of the T-cell acute lymphocytic leukemia protein 1 (TAL1)', 'topic'] = 'Epigenetics'\n",
    "df.loc[df.tutorial == 'RAD-Seq Reference-based data analysis', 'topic'] = 'Ecology'\n",
    "df.loc[df.tutorial == 'RAD-Seq de-novo data analysis', 'topic'] = 'Ecology'\n",
    "df.loc[df.tutorial == 'RAD-Seq to construct genetic maps', 'topic'] = 'Ecology'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aggregate the feedbacks and notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_notes(df, name):\n",
    "    '''Aggregage the notes\n",
    "    \n",
    "    :df: dataframe with all feedbacks\n",
    "    \n",
    "    :return: dataframe object with aggregated notes\n",
    "    '''\n",
    "    return (df.note\n",
    "        .value_counts(sort=False)\n",
    "        .to_frame()\n",
    "        .rename(columns= {'note': name}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "notes = get_notes(df, 'All topics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topic_df(grouped_by_topic, topic, notes):\n",
    "    '''Extract the dataframe for a topic and plot note histogram\n",
    "    \n",
    "    :grouped_by_topic: groupby object grouping by topic\n",
    "    :topic: topic to extract\n",
    "    \n",
    "    :return: dataframe object for the topic\n",
    "    '''\n",
    "    topic_df = (grouped_by_topic\n",
    "        .get_group(topic)\n",
    "        .drop('topic', 1))\n",
    "    \n",
    "    notes = pd.concat([notes, get_notes(topic_df, topic)], axis=1)\n",
    "    return topic_df, notes\n",
    "\n",
    "\n",
    "def extract_tutorial_feedbacks(topic_df, topic_name):\n",
    "    '''Extract pro/con per tutorial for a topic and \n",
    "    write them in a file\n",
    "    \n",
    "    :topic_df: dataframe object for the topic\n",
    "    :topic_name: name for the topic, name for the file\n",
    "    '''\n",
    "    grouped_by_tuto = topic_df.groupby(by=\"tutorial\")\n",
    "    with open('../results/%s.md' % topic_name, 'w') as f:\n",
    "        for tuto, group in grouped_by_tuto:\n",
    "            # get groups\n",
    "            tuto_df = grouped_by_tuto.get_group(tuto)\n",
    "            pros = []\n",
    "            cons = []\n",
    "            # get pros/cons\n",
    "            for index, row in tuto_df.iterrows():\n",
    "                if row['pro'] != 'nan':\n",
    "                    pros.append(\"%s (*%s*)\" % (row['pro'], row['timestamp']))\n",
    "                if row['con'] != 'nan':\n",
    "                    cons.append(\"%s (*%s*)\" % (row['con'], row['timestamp']))\n",
    "            # write in report file\n",
    "            f.write(\"- **%s**\\n\" % tuto)\n",
    "            if len(pros) > 0:\n",
    "                f.write(\"  - Pro:\\n    - \")\n",
    "                f.write(\"\\n    - \".join(pros))\n",
    "            if len(cons) > 0:\n",
    "                f.write(\"\\n  - Con:\\n    - \")\n",
    "                f.write(\"\\n    - \".join(cons))\n",
    "            f.write(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assembly\n",
      "Computational chemistry\n",
      "Contributing to the Galaxy Training Material\n",
      "Data Manipulation\n",
      "Development in Galaxy\n",
      "Ecology\n",
      "Epigenetics\n",
      "Galaxy Server administration\n",
      "Genome Annotation\n",
      "Imaging\n",
      "Introduction to Galaxy Analyses\n",
      "Metabolomics\n",
      "Metagenomics\n",
      "Proteomics\n",
      "Sequence analysis\n",
      "Statistics and machine learning\n",
      "Teaching and Hosting Galaxy training\n",
      "Transcriptomics\n",
      "User Interface and Features\n",
      "Variant Analysis\n",
      "Visualisation\n"
     ]
    }
   ],
   "source": [
    "grouped_by_topic = df.groupby(by=\"topic\")\n",
    "for topic in grouped_by_topic.groups:\n",
    "    print(topic)\n",
    "    topic_df, notes = get_topic_df(grouped_by_topic, topic, notes)\n",
    "    topic_name = topic.lower().replace(' ', '-')\n",
    "    extract_tutorial_feedbacks(topic_df, topic_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Details (pros/cons) for each tutorials are available: https://github.com/bebatut/galaxy-training-material-stats/tree/master/results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General stats about feedback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feedback number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1248"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feedback number over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2018-09     52\n",
       "2018-10     63\n",
       "2018-11     39\n",
       "2018-12     28\n",
       "2019-01     37\n",
       "2019-02     41\n",
       "2019-03     37\n",
       "2019-04     61\n",
       "2019-05     42\n",
       "2019-06     43\n",
       "2019-07     62\n",
       "2019-08     62\n",
       "2019-09     74\n",
       "2019-10    111\n",
       "2019-11     51\n",
       "2019-12     46\n",
       "2020-01     52\n",
       "2020-02     62\n",
       "2020-03     70\n",
       "2020-04     69\n",
       "2020-05     58\n",
       "2020-06     76\n",
       "2020-07     12\n",
       "Freq: M, Name: timestamp, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = df.timestamp.dt.to_period(\"M\")\n",
    "nb_per_months = (df\n",
    "                 .groupby(months)\n",
    "                 .count()\n",
    "                 .timestamp)\n",
    "nb_per_months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "(nb_per_months\n",
    "    .cumsum()\n",
    "    .plot())\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Cumulative number of feedback')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feedback number per topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "topic\n",
       "Introduction to Galaxy Analyses                 549\n",
       "Transcriptomics                                 198\n",
       "Sequence analysis                               124\n",
       "Metagenomics                                     67\n",
       "Galaxy Server administration                     52\n",
       "Epigenetics                                      43\n",
       "Variant Analysis                                 37\n",
       "Statistics and machine learning                  31\n",
       "Assembly                                         30\n",
       "Genome Annotation                                24\n",
       "Proteomics                                       23\n",
       "User Interface and Features                      15\n",
       "Contributing to the Galaxy Training Material     14\n",
       "Data Manipulation                                11\n",
       "Imaging                                           6\n",
       "Computational chemistry                           6\n",
       "Metabolomics                                      4\n",
       "Teaching and Hosting Galaxy training              3\n",
       "Ecology                                           1\n",
       "Development in Galaxy                             1\n",
       "Visualisation                                     1\n",
       "Name: timestamp, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grouped_by_topic\n",
    "     .count()\n",
    "     .sort_values('timestamp', ascending=False)\n",
    "     .timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 tutorials with feedbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tutorial\n",
       "A short introduction to Galaxy                        306\n",
       "Galaxy 101                                            103\n",
       "Quality Control                                        93\n",
       "Reference-based RNA-Seq data analysis                  65\n",
       "From peaks to genes                                    64\n",
       "Visualization of RNA-Seq results with Volcano Plot     31\n",
       "Mapping                                                31\n",
       "RNA-Seq reads to counts                                25\n",
       "NGS data logistics                                     24\n",
       "16S Microbial Analysis with mothur (extended)          24\n",
       "Name: timestamp, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df\n",
    "    .groupby(by=\"tutorial\")\n",
    "    .count()\n",
    "    .sort_values('timestamp', ascending=False)\n",
    "    .timestamp\n",
    "    .head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_note_histogram(s, title):\n",
    "    plt.figure()\n",
    "    s.plot(kind='barh', color='k', ylim=(0,5), xlim=(0,1), title=title)\n",
    "    plt.xlabel('Proportion of feedback')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>All topics</th>\n",
       "      <th>Assembly</th>\n",
       "      <th>Computational chemistry</th>\n",
       "      <th>Contributing to the Galaxy Training Material</th>\n",
       "      <th>Data Manipulation</th>\n",
       "      <th>Development in Galaxy</th>\n",
       "      <th>Ecology</th>\n",
       "      <th>Epigenetics</th>\n",
       "      <th>Galaxy Server administration</th>\n",
       "      <th>Genome Annotation</th>\n",
       "      <th>...</th>\n",
       "      <th>Metabolomics</th>\n",
       "      <th>Metagenomics</th>\n",
       "      <th>Proteomics</th>\n",
       "      <th>Sequence analysis</th>\n",
       "      <th>Statistics and machine learning</th>\n",
       "      <th>Teaching and Hosting Galaxy training</th>\n",
       "      <th>Transcriptomics</th>\n",
       "      <th>User Interface and Features</th>\n",
       "      <th>Variant Analysis</th>\n",
       "      <th>Visualisation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No value</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>246</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>852</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>70</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          All topics  Assembly  Computational chemistry  \\\n",
       "No value           9         1                        0   \n",
       "1                 51         1                        0   \n",
       "2                 23         2                        0   \n",
       "3                 67         2                        1   \n",
       "4                246         9                        1   \n",
       "5                852        15                        4   \n",
       "\n",
       "          Contributing to the Galaxy Training Material  Data Manipulation  \\\n",
       "No value                                             0                  0   \n",
       "1                                                    2                  1   \n",
       "2                                                    1                  1   \n",
       "3                                                    1                  0   \n",
       "4                                                    0                  8   \n",
       "5                                                   10                  1   \n",
       "\n",
       "          Development in Galaxy  Ecology  Epigenetics  \\\n",
       "No value                      0        0            1   \n",
       "1                             0        0            2   \n",
       "2                             0        0            3   \n",
       "3                             0        0            2   \n",
       "4                             0        0           12   \n",
       "5                             1        1           23   \n",
       "\n",
       "          Galaxy Server administration  Genome Annotation  ...  Metabolomics  \\\n",
       "No value                             1                  2  ...             0   \n",
       "1                                    1                  5  ...             0   \n",
       "2                                    0                  2  ...             0   \n",
       "3                                    3                  1  ...             0   \n",
       "4                                    7                  5  ...             0   \n",
       "5                                   40                  9  ...             4   \n",
       "\n",
       "          Metagenomics  Proteomics  Sequence analysis  \\\n",
       "No value             0           0                  0   \n",
       "1                    2           1                  6   \n",
       "2                    1           0                  3   \n",
       "3                    7           0                 10   \n",
       "4                   16           5                 35   \n",
       "5                   41          17                 70   \n",
       "\n",
       "          Statistics and machine learning  \\\n",
       "No value                                0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       2   \n",
       "4                                       8   \n",
       "5                                      21   \n",
       "\n",
       "          Teaching and Hosting Galaxy training  Transcriptomics  \\\n",
       "No value                                     0                3   \n",
       "1                                            0                6   \n",
       "2                                            0                0   \n",
       "3                                            0               11   \n",
       "4                                            1               40   \n",
       "5                                            2              138   \n",
       "\n",
       "          User Interface and Features  Variant Analysis  Visualisation  \n",
       "No value                            0                 0              0  \n",
       "1                                   0                 8              0  \n",
       "2                                   1                 2              0  \n",
       "3                                   2                 2              0  \n",
       "4                                   3                 4              0  \n",
       "5                                   9                21              1  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes = (notes\n",
    "         .fillna(0.0)\n",
    "         .astype(int)\n",
    "         .rename(index = {0: 'No value'}))\n",
    "# 1: emoji.emojize(':-1:', use_aliases=True)\n",
    "# 5: emoji.emojize(':heart:', use_aliases=True)\n",
    "notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v6uctdJ3lYKHHeRLdXyjL0XbbIsl+wLHAWUOsaxQG+b14OvC4JFckWZ/k+KFVN1yDtMWZwM8A3wZuBk6pqoeGU96Ssqj3zeU2VEzmmTf3e9iDrLMcDHycSY6kC5jDm1Y0OoO0xRnA71TVg90fq8vWIG2xEpgC/iWwO3BNkmur6v+1Lm7IBmmLFwE3AkcBTwMuT3JVVf2odXFLzKLeN5dbwNwF7D/r+VPo/vJY6DrLwUDHmeRZwPuBl1TV94ZU27AN0hZrgfP7cFkNHJNkc1X9n+GUODSD/h+5t6oeAB5IciVwMLDcAmaQtng98D+qOxFxW5K/BZ4JfGk4JS4Zi3rfXG4fkX0ZOCjJU5M8Cng18Mk563wSOL7/VsRzgR9W1XeGXegQbLctkhwAXAS8bhn+dTrbdtuiqp5aVWuqag3wCeDfLcNwgcH+j1wCPD/JyiSPAZ4D3DrkOodhkLa4k64nR5J9gWcA3xhqlUvDot43l1UPpqo2J/kt4FK6b4h8sKpuSfLGfvlZdN8QOga4Dfg7ur9Qlp0B2+LtwBOA9/V/uW+uZTiC7IBtMREGaYuqujXJp4ENwEPA+6tq3q+vjrMBfy9+Dzgnyc10HxP9TlUtu2H8k3wUOAJYneQu4HeBXWHH3jcdKkaS1MRy+4hMkrREGDCSpCYMGElSEwaMJKkJA0aS1IQBo7HQj3B8Y5KNST7eX58xzNd/25znX2z8es/sj/eGJE+bs+zfJLk1yed2wuucnuS0fvqKJDv8NfUktydZvaP70fgzYDQu/r6qDqmqfw78I/DG2QuTrGjxov2FZbvQjar7E1X1vBavN8srgEuq6tCq+vqcZSfRXQh6ZOMapB1iwGgcXQUc2N+z5HNJzgNuTrJbkg8lubn/y/9IgCQnJrkkyaf7e3/87syOkvyHvle0Mcmp/bw1fQ/hfcD1wAeA3fsexbn9Opv6n0ny7n77m5O8qp9/RN8j+ESSryY5N/MMcpbkkCTXprvHxsVJHpfkGOBU4N/O7aUkeTvdmHFn9a+7ov/55X4fvzlr3bfMmv+OWfP/c98On6G7Mn2216a7N9DGJIf16x/Wz7uh//mMfv6KJP+zP+4NSU6eU+vufZv/xsD/slpeRn0fAh8+BnkAm/qfK+mGMnkT3ZXHDwBP7Zf9NvChfvqZdMN87AacCHyHbtSC3YGNdGOPTdGNkLsH3VDstwCHAmvormB/7tzXn6eefw1cTncl+L79az6pr+2HdGM27QJcAxw+z3FtAH6pn/5vwBn99OnAaVtpiyuAtf30OuC/9NOPBqaBpwJHA2fTXX2+C/Apunt+zBzzY4DH0l2Zfdqs/f5ZP/0C+nuD9Out7KdfCFzYT7+J7vYGM8se3/+8vW/DzwDHj/p3x8foHstqqBgta7snubGfvoquV/E84EtV9bf9/MOBPwGoqq8muYNu6HmAy6sfzDPJRf26BVxc3aCOM/OfTzfu0h3V3fdiew4HPlpVDwLfTXf3x58HftTXdle/7xvp3nSvntkwyV7A3lU1c8fIDwMfH7xJgC5InpUtd+DcCzion380cEM/f89+/qr+mP+ur2Hu2Fsfhe7+IEkem2TvfpsPJzmIrs127dd9IXBWVW3ut5l9P5FLgHdV1bkLPB4tIwaMxsXfV9Uhs2f0nzg9MHvWNrafOyZSbWf9B7ax7GFlbGPZj2dNP0ib/28BTq6qSx82M3kR8M6q+tM5809l28Osz9dOvwd8rqqOTbKGrqcz89pb29cXgJckOa+qHI9qQnkORsvJlcBxAEmeDhwAfK1f9stJHp9kd7oT6F/o139Fksck2YPuhmNXbWXf/5Rk13nmXwm8qj8fsQ/dR0sDDeVeVT8Evp/k+f2s1wGf38Ym87kUeNNMbUme3h/LpcAbkuzZz98vyRP7eo/tz4+sAl42Z38z55AOpxsx94d0vaJv9ctPnLXuZcAbk6zst3n8rGVvB74HvG+Bx6NlxIDRcvI+YEW6kW8vAE6sqplexNXAn9PdPOrCqpququuBc+gC4Tq6UYNveORuge58xoaZk/yzXEx3HuUm4K+Bt1bV3Quo+QTg3Uk2AIfQnYdZiPcDXwGuT7IR+FO6cyKXAefR3SzsZrpbEKzqj/kC+nbgkYH6/XRfwT6L7ttqAO8C3pnkC3Tnmma/9p107XIT3a2WZzsV2C3JuxZ4TFomHE1Zy16SE+lOiv/WqGuRJok9GElSE/ZgJElN2IORJDVhwEiSmjBgJElNGDCSpCYMGElSE/8f9oD/dGLTv+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZeneRSZKaMGAkSU0spoA5YdwFbEFsiw1siw1siw1siw0W3BaL5iC/JGm0FlMPRpI0QgaMJKmJqQuYJM9K8s0k1yT5n3NMT5J399PXJXniOOochSHa4vC+DdYl+WqSx4+jzlHYXFsMzPekJHcmecEo6xulYdoiyUFJLk1yZZIvj7rGURni/8jOST6d5LK+LY4cR52tJflAkhuSXLGR6Qv73KyqqXkAWwPfAn4T2Ba4DPjtWfMcCvwrEGB/4KJx1z3GtngKsEs/fMhibouB+b4IfA54wbjrHuPfxTLgG8Be/evdxl33GNvizcD/6YeXAz8Bth137Q3a4qnAE4ErNjJ9QZ+b09aD2Q+4pqq+XVW/Aj4CPHfWPM8FTqrOhcCyJA8ddaEjsNm2qKqvVtUt/csLgT1HXOOoDPN3AfBq4JPADaMsbsSGaYsXA6dV1fcAqmpa22OYtihgaZIAO9IFzPrRltleVZ1Lt20bs6DPzWkLmD2A7w+8vq4fN995psF8t/Moum8o02izbZFkD+D5wPEjrGschvm72AfYJck5SdYmednIqhutYdriPcBvAdcDlwOvraq7RlPeFmVBn5vTdqmYzDFu9u+wh5lnGgy9nUmeThcwBzStaHyGaYt3AW+qqju7L6tTa5i2WAKsBP4TsB1wQZILq+rfWhc3YsO0xe8BlwLPAB4JnJ3kvKr6WevitjAL+tyctoC5Dnj4wOs96b55zHeeaTDUdiZ5HPA+4JCqunlEtY3aMG2xCvhIHy67AocmWV9V/zKaEkdm2P8jN1XVHcAdSc4FHg9MW8AM0xZHAn9V3YGIa5J8B3gM8LXRlLjFWNDn5rTtIvs6sHeSRyTZFngR8KlZ83wKeFn/q4j9gVur6oejLnQENtsWSfYCTgNeOoXfTgdtti2q6hFVtaKqVgCfAP50CsMFhvs/cgZwYJIlSbYHngxcNeI6R2GYtvgeXU+OJLsDjwa+PdIqtwwL+tycqh5MVa1P8mfAmXS/EPlAVV2Z5JX99OPpfiF0KHAN8HO6byhTZ8i2eAvwYOC9/Tf39TWFV5Adsi0WhWHaoqquSvJ5YB1wF/C+qprz56uTbMi/i7cDJya5nG430Zuqauou45/kVOAgYNck1wFvBbaB+/a56aViJElNTNsuMknSFsKAkSQ1YcBIkpowYCRJTRgwkqQmDBhNhP4Kx5cmuSLJx/vzM0b5/m+e9fqrjd/vMf32XpLkkbOm/VGSq5J86X54n2OSHN0Pn5PkPv9MPcm1SXa9r+vR5DNgNCl+UVX7VtVjgV8BrxycmGTrFm/an1i2Fd1Vde9WVU9p8X4DngecUVVPqKpvzZp2FN2JoE9vXIN0nxgwmkTnAY/q71nypSSnAJcneWCSDya5vP/m/3SAJEckOSPJ5/t7f7x1ZkVJ/kffK7oiyev6cSv6HsJ7gYuB9wPb9T2Kk/t5bu+fk+Sv++UvT/LCfvxBfY/gE0muTnJy5rjIWZJ9k1yY7h4bpyfZJcmhwOuAP5ndS0nyFrprxh3fv+/W/fPX+3W8YmDeNwyMP3Zg/J/37fAFujPTB70k3b2BrkiyXz//fv24S/rnR/fjt07yN/12r0vy6lm1bte3+X8b+l9W02Xc9yHw4WOYB3B7/7yE7lImr6I78/gO4BH9tNcDH+yHH0N3mY8HAkcAP6S7asF2wBV01x5bSXeF3B3oLsV+JfAEYAXdGez7z37/Oer5Q+BsujPBd+/f86F9bbfSXbNpK+AC4IA5tmsd8LR++G3Au/rhY4CjN9IW5wCr+uHVwP/qhx8ArAEeARwMnEB39vlWwGfo7vkxs83bAzvRnZl99MB6/6kffir9vUH6+Zb0w88EPtkPv4ru9gYz0x7UP1/bt+EXgJeN+2/Hx/geU3WpGE217ZJc2g+fR9ereArwtar6Tj/+AODvAarq6iTfpbv0PMDZ1V/MM8lp/bwFnF7dRR1nxh9Id92l71Z334vNOQA4taruBH6c7u6PTwJ+1td2Xb/uS+k+dM+fWTDJzsCyqpq5Y+SHgI8P3yRAFySPy4Y7cO4M7N2PPxi4pB+/Yz9+ab/NP+9rmH3trVOhuz9Ikp2SLOuX+VCSvenabJt+3mcCx1fV+n6ZwfuJnAG8o6pOnuf2aIoYMJoUv6iqfQdH9Huc7hgctYnlZ18TqTYz/x2bmHaPMjYx7ZcDw3fS5v9bgFdX1Zn3GJn8HnBcVf3jrPGvY9OXWZ+rnd4OfKmqnp9kBV1PZ+a9N7aurwCHJDmlqrwe1SLlMRhNk3OBwwGS7APsBXyzn/afkzwoyXZ0B9C/0s//vCTbJ9mB7oZj521k3b9Oss0c488FXtgfj1hOt2tpqEu5V9WtwC1JDuxHvRT48iYWmcuZwKtmakuyT78tZwJ/nGTHfvweSXbr631+f3xkKXDYrPXNHEM6gO6KubfS9Yp+0E8/YmDes4BXJlnSL/OggWlvAW4G3jvP7dEUMWA0Td4LbJ3uyrcfBY6oqplexPnAh+luHvXJqlpTVRcDJ9IFwkV0Vw2+5N6rBbrjGetmDvIPOJ3uOMplwBeBN1bVj+ZR88uBv06yDtiX7jjMfLwP+AZwcZIrgH+kOyZyFnAK3c3CLqe7BcHSfps/St8O3DtQb0n3E+zj6X6tBvAO4LgkX6E71jT43t+ja5fL6G61POh1wAOTvGOe26Qp4dWUNfWSHEF3UPzPxl2LtJjYg5EkNWEPRpLUhD0YSVITBowkqQkDRpLUhAEjSWrCgJEkNfH/AfXyTNd0DG6bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YzDFv9uewh1lnEgx9nEleSRcwBzataHSGaYuPAr9fVQ93f6xOrGHaYhmwH/Cvge2Aq5JcXVX/0Lq4RTZMW7wauB44CHg2cHGSK6rqp62L28Is6H1z0gLmDuAZA893p/vLY77rTIKhjjPJ84FTgddW1T2LVNtiG6YtVgJn9+GyHDgkybqq+tzilLhohv0/cndVPQA8kORy4AXApAXMMG3xNuBPq7sQcWuS7wJ7A19fnBK3GAt635y0U2TfAJ6T5FlJngC8Bfj8rHU+DxzZfyriAODeqvrhYhe6CDbaFkn2AM4DjpjAv04HbbQtqupZVbWiqlYA5wD/fgLDBYb7P3IB8NIky5I8CXgRcPMi17kYhmmL79H15EiyK7AX8J1FrXLLsKD3zYnqwVTVuiS/A1xI9wmR06rqpiTv6pefTPcJoUOAW4F/pvsLZeIM2RYfAH4OOKn/y31dTeAIskO2xZIwTFtU1c1JvgSsBh4BTq2qOT++Os6G/L34EHB6khvpThP9flVN3DD+ST4NvAJYnuQO4I+AbWDT3jcdKkaS1MSknSKTJG0hDBhJUhMGjCSpCQNGktSEASNJasKA0VjoRzi+PsmaJJ/tv5+xmK//B7Oef63x6+3dH+91SZ49a9mbktyc5NLN8DrHJzmun74sySZ/TD3JbUmWb+p+NP4MGI2LB6tqn6r6ReBnwLsGFybZusWL9l8s24puVN3/r6pe0uL1BhwKXFBV+1bVt2ctezvdF0Ff2bgGaZMYMBpHVwB79vcsuTTJWcCNSbZN8jdJbuz/8n8lQJKjklyQ5Ev9vT/+aGZHSf5T3ytak+TYft6KvodwEnAt8Algu75HcWa/zv39zyT5SL/9jUne3M9/Rd8jOCfJt5KcmTkGOUuyT5Kr091j4/wkT0lyCHAs8I7ZvZQkH6AbM+7k/nW37n9+o9/Hbw+s+96B+R8cmP9f+na4hO6b6YPemu7eQGuS7N+vv38/77r+5179/K2T/Hl/3KuTHDOr1u36Nn/n0P+ymiyjvg+BDx/DPID7+5/L6IYyeTfdN48fAJ7VL/s94G/66b3phvnYFjgK+CHdqAXbAWvoxh7bj26E3O3phmK/CdgXWEH3DfYDZr/+HPX8KnAx3TfBd+1f82l9bffSjdm0FXAVcOAcx7UaeHk//d+Aj/bTxwPHbaAtLgNW9tNHA3/YTz8RmAaeBRwMnEL37fOtgC/S3fNj5pifBDyZ7pvZxw3s9+P99Mvo7w3Sr7esn34VcG4//W662xvMLHtq//O2vg0vAY4c9e+Oj9E9JmqoGE207ZJc309fQdereAnw9ar6bj//QOCvAKrqW0lupxt6HuDi6gfzTHJev24B51c3qOPM/JfSjbt0e3X3vdiYA4FPV9XDwD+mu/vjLwE/7Wu7o9/39XRvulfObJhkJ2Dnqpq5Y+QZwGeHbxKgC5LnZ/0dOHcCntPPPxi4rp+/Qz9/x/6Y/7mvYfbYW5+G7v4gSZ6cZOd+mzOSPIeuzbbp130VcHJVreu3GbyfyAXACVV15jyPRxPEgNG4eLCq9hmc0Z9xemBw1uNsP3tMpNrI+g88zrJHlfE4yx4amH6YNv/fAhxTVRc+ambyauDDVfXXs+Yfy+MPsz5XO30IuLSqDkuygq6nM/PaG9rXV4HXJjmrqhyPaonyGowmyeXA4QBJngvsAdzSL/s3SZ6aZDu6C+hf7dc/NMmTkmxPd8OxKzaw7/+bZJs55l8OvLm/HjFFd2ppqKHcq+pe4CdJXtrPOgL4yuNsMpcLgXfP1Jbkuf2xXAj8VpId+vm7Jdmlr/ew/vrIjsDrZu1v5hrSgXQj5t5L1yv6Qb/8qIF1LwLelWRZv81TB5Z9ALgHOGmex6MJYsBokpwEbJ1u5NvPAEdV1Uwv4krgU3Q3jzq3qqar6lrgdLpAuIZu1ODrHrtboLuesXrmIv+A8+muo9wAfBl4X1X9aB41/ybwkSSrgX3orsPMx6nAN4Frk6wB/prumshFwFl0Nwu7ke4WBDv2x/wZ+nbgsYH6k3QfwT6Z7tNqACcAH07yVbprTYOv/T26drmB7lbLg44Ftk1ywjyPSRPC0ZQ18ZIcRXdR/HdGXYu0lNiDkSQ1YQ9GktSEPRhJUhMGjCSpCQNGktSEASNJasKAkSQ18f8AoTFzMZbit/QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zyyQ3ApePu44dxEqWvoP1rLEtNrEtNrEtNnlgVS3rbvUrtnclO7DLl3tH0GmTZM626NgWm9gWm9gWmyRZ9m3o3UUmSWrCgJEkNTFLAXP8uAvYgdgWm9gWm9gWm9gWmyy7LWbmIL8kabRmqQcjSRohA0aS1MTUBUySpyS5PMkVSf7bItOT5J399PVJfmMcdY7CEG1xeN8G65N8McnDxlHnKGypLQbme3iSW5I8a5T1jdIwbZHkcUkuSnJJks+PusZRGeL/yB5J/neSr/Rt8eJx1NlakvcnuTbJhiWmL+97s6qm5gXsDHwd+FXgDsBXgF9fMM+hwKeBAI8Ezh933WNsi0cDd+uHnzrLbTEw378C/xd41rjrHuPvxZ7AV4F79+/3GnfdY2yL1wF/0w+vAq4H7jDu2hu0xW8BvwFsWGL6sr43p60HcxBwRVV9o6p+CXwEeMaCeZ4BnFCd84A9k9xz1IWOwBbboqq+WFU/6t+eB+w34hpHZZjfC4CjgU8A146yuBEbpi1+Dzilqr4FUFXT2h7DtEUBuycJsBtdwGwcbZntVdVZdNu2lGV9b05bwOwLfHvg/dX9uK2dZxps7XYeSfcXyjTaYlsk2Rd4JnDcCOsah2F+Lx4A3C3J55KsS/LCkVU3WsO0xbHArwHXABcDr6iqW0dT3g5lWd+b03armCwybuF52MPMMw2G3s4kj6cLmIObVjQ+w7TF24HXVtUt3R+rU2uYtlgBrAF+G9gVODfJeVX1/1oXN2LDtMWTgYuAJwD3A85IcnZV3dC6uB3Msr43py1grgbuNfB+P7q/PLZ2nmkw1HYmeSjwXuCpVfXDEdU2asO0xVrgI324rAQOTbKxqv7XaEocmWH/j1xXVTcDNyc5C3gYMG0BM0xbvBj46+oORFyR5JvAg4AvjabEHcayvjenbRfZl4H9k9w3yR2A5wL/vGCefwZe2J8V8UjgJ1X13VEXOgJbbIsk9wZOAV4whX+dDtpiW1TVfatqdVWtBj4O/OEUhgsM93/kk8BjkqxIcmfgEcClI65zFIZpi2/R9eRIsjfwQOAbI61yx7Cs782p6sFU1cYkfwScRneGyPur6pIkL+unH0d3htChwBXAT+n+Qpk6Q7bF64F7AO/q/3LfWFN4B9kh22ImDNMWVXVpks8A64FbgfdW1aKnr06yIX8v3gR8IMnFdLuJXltVU3cb/yQnAY8DVia5GvgLYBfYtu9NbxUjSWpi2naRSZJ2EAaMJKkJA0aS1IQBI0lqwoCRJDVhwGgi9Hc4vijJhiQf66/PGOX6X7fg/Rcbr+9B/fZemOR+C6Y9O8mlSc7cDut5Q5Jj+uHPJdnm09STXJlk5bZ+jiafAaNJ8bOqOqCq/gPwS+BlgxOT7Nxipf2FZTvR3VX331XVo1usb8BhwCer6sCq+vqCaUfSXQj6+MY1SNvEgNEkOhu4f//MkjOTfBi4OMmdkvxjkov7v/wfD5DkiCSfTPKZ/tkffzH/QUn+a98r2pDklf241X0P4V3ABcD7gF37HsWJ/Tw39T+T5G/75S9O8px+/OP6HsHHk1yW5MQscpOzJAckOS/dMzZOTXK3JIcCrwR+f2EvJcnr6e4Zd1y/3p37n1/uP+OlA/O+emD8GwfG/1nfDp+luzJ90PPTPRtoQ5KD+vkP6sdd2P98YD9+5yR/12/3+iRHL6h1177N/2Dof1lNl3E/h8CXr2FewE39zxV0tzJ5Od2VxzcD9+2n/Qnwj/3wg+hu83En4Ajgu3R3LdgV2EB377E1dHfIvQvdrdgvAQ4EVtNdwf7IhetfpJ7/DJxBdyX43v0679nX9hO6ezbtBJwLHLzIdq0HHtsP/yXw9n74DcAxS7TF54C1/fBRwJ/3w3cE5oD7AocAx9Ndfb4T8Cm6Z37Mb/OdgbvSXZl9zMDnvqcf/i36Z4P0863oh58IfKIffjnd4w3mp929/3ll34afBV447t8dX+N7TdWtYjTVdk1yUT98Nl2v4tHAl6rqm/34g4G/B6iqy5JcRXfreYAzqr+ZZ5JT+nkLOLW6mzrOj38M3X2XrqruuRdbcjBwUlXdAnw/3dMfHw7c0Nd2df/ZF9F96Z4zv2CSPYA9q2r+iZEfBD42fJMAXZA8NJuewLkHsH8//hDgwn78bv343ftt/mlfw8J7b50E3fNBktw1yZ79Mh9Msj9dm+3Sz/tE4Liq2tgvM/g8kU8Cb6mqE7dyezRFDBhNip9V1QGDI/o9TjcPjtrM8gvviVRbmP/mzUy7TRmbmfaLgeFbaPP/LcDRVXXabUYmTwbeXFXvXjD+lWz+NuuLtdObgDOr6plJVtP1dObXvdRnfQF4apIPV5X3o5pRHoPRNDkLOBwgyQOAewOX99OelOTuSXalO4D+hX7+w5LcOcld6B44dvYSn/1vSXZZZPxZwHP64xGr6HYtDXUr96r6CfCjJI/pR70A+PxmFlnMacDL52tL8oB+W04DXpJkt378vkn26ut9Zn98ZHfg6Qs+b/4Y0sF0d8z9CV2v6Dv99CMG5j0deFmSFf0ydx+Y9nrgh8C7tnJ7NEUMGE2TdwE7p7vz7cnAEVU134s4B/gQ3cOjPlFVc1V1AfABukA4n+6uwRfe/mOB7njG+vmD/ANOpTuO8hXgX4HXVNX3tqLmFwF/m2Q9cADdcZit8V7gq8AFSTYA76Y7JnI68GG6h4VdTPcIgt37bT6Zvh24faD+KN0p2MfRna0G8BbgzUm+QHesaXDd36Jrl6/QPWp50CuBOyV5y1Zuk6aEd1PW1EtyBN1B8T8ady3SLLEHI0lqwh6MJKkJezCSpCYMGElSEwaMJKkJA0aS1IQBI0lq4v8DgyDA/Lth+GEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "notes_prop = (notes/notes.sum().round(2))\n",
    "for col in notes_prop.columns:\n",
    "    plot_note_histogram(notes_prop[col], col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>All topics</th>\n",
       "      <th>Assembly</th>\n",
       "      <th>Computational chemistry</th>\n",
       "      <th>Contributing to the Galaxy Training Material</th>\n",
       "      <th>Data Manipulation</th>\n",
       "      <th>Development in Galaxy</th>\n",
       "      <th>Ecology</th>\n",
       "      <th>Epigenetics</th>\n",
       "      <th>Galaxy Server administration</th>\n",
       "      <th>Genome Annotation</th>\n",
       "      <th>...</th>\n",
       "      <th>Metabolomics</th>\n",
       "      <th>Metagenomics</th>\n",
       "      <th>Proteomics</th>\n",
       "      <th>Sequence analysis</th>\n",
       "      <th>Statistics and machine learning</th>\n",
       "      <th>Teaching and Hosting Galaxy training</th>\n",
       "      <th>Transcriptomics</th>\n",
       "      <th>User Interface and Features</th>\n",
       "      <th>Variant Analysis</th>\n",
       "      <th>Visualisation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No value</th>\n",
       "      <td>0.007212</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015152</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.040865</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.018429</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.053686</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080645</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.197115</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.238806</td>\n",
       "      <td>0.217391</td>\n",
       "      <td>0.282258</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.202020</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.682692</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.611940</td>\n",
       "      <td>0.739130</td>\n",
       "      <td>0.564516</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.567568</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          All topics  Assembly  Computational chemistry  \\\n",
       "No value    0.007212  0.033333                 0.000000   \n",
       "1           0.040865  0.033333                 0.000000   \n",
       "2           0.018429  0.066667                 0.000000   \n",
       "3           0.053686  0.066667                 0.166667   \n",
       "4           0.197115  0.300000                 0.166667   \n",
       "5           0.682692  0.500000                 0.666667   \n",
       "\n",
       "          Contributing to the Galaxy Training Material  Data Manipulation  \\\n",
       "No value                                      0.000000           0.000000   \n",
       "1                                             0.142857           0.090909   \n",
       "2                                             0.071429           0.090909   \n",
       "3                                             0.071429           0.000000   \n",
       "4                                             0.000000           0.727273   \n",
       "5                                             0.714286           0.090909   \n",
       "\n",
       "          Development in Galaxy  Ecology  Epigenetics  \\\n",
       "No value                    0.0      0.0     0.023256   \n",
       "1                           0.0      0.0     0.046512   \n",
       "2                           0.0      0.0     0.069767   \n",
       "3                           0.0      0.0     0.046512   \n",
       "4                           0.0      0.0     0.279070   \n",
       "5                           1.0      1.0     0.534884   \n",
       "\n",
       "          Galaxy Server administration  Genome Annotation  ...  Metabolomics  \\\n",
       "No value                      0.019231           0.083333  ...           0.0   \n",
       "1                             0.019231           0.208333  ...           0.0   \n",
       "2                             0.000000           0.083333  ...           0.0   \n",
       "3                             0.057692           0.041667  ...           0.0   \n",
       "4                             0.134615           0.208333  ...           0.0   \n",
       "5                             0.769231           0.375000  ...           1.0   \n",
       "\n",
       "          Metagenomics  Proteomics  Sequence analysis  \\\n",
       "No value      0.000000    0.000000           0.000000   \n",
       "1             0.029851    0.043478           0.048387   \n",
       "2             0.014925    0.000000           0.024194   \n",
       "3             0.104478    0.000000           0.080645   \n",
       "4             0.238806    0.217391           0.282258   \n",
       "5             0.611940    0.739130           0.564516   \n",
       "\n",
       "          Statistics and machine learning  \\\n",
       "No value                         0.000000   \n",
       "1                                0.000000   \n",
       "2                                0.000000   \n",
       "3                                0.064516   \n",
       "4                                0.258065   \n",
       "5                                0.677419   \n",
       "\n",
       "          Teaching and Hosting Galaxy training  Transcriptomics  \\\n",
       "No value                              0.000000         0.015152   \n",
       "1                                     0.000000         0.030303   \n",
       "2                                     0.000000         0.000000   \n",
       "3                                     0.000000         0.055556   \n",
       "4                                     0.333333         0.202020   \n",
       "5                                     0.666667         0.696970   \n",
       "\n",
       "          User Interface and Features  Variant Analysis  Visualisation  \n",
       "No value                     0.000000          0.000000            0.0  \n",
       "1                            0.000000          0.216216            0.0  \n",
       "2                            0.066667          0.054054            0.0  \n",
       "3                            0.133333          0.054054            0.0  \n",
       "4                            0.200000          0.108108            0.0  \n",
       "5                            0.600000          0.567568            1.0  \n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
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