spike_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import log10\n",
    "from math import log\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alxau\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (1,4,44) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "DATASET_PATH = 'data/'\n",
    "AUTH_FILTERED_PATH = 'data/authority_filtered/'\n",
    "SPIKES_PATH = 'data/spikes/'\n",
    "\n",
    "DATASET = pd.read_csv(DATASET_PATH + 'ferrero_corporate.csv')\n",
    "AUTH_FILTERED_DATASET = pd.read_csv(AUTH_FILTERED_PATH + 'ferrero_corp_auth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_user_scale():\n",
    "    s = pd.Series(np.exp(np.linspace(1, 10, 101)))\n",
    "    return (s -s.min())/ s.max() * 50 + 1\n",
    "\n",
    "\n",
    "def get_text_weight(df):\n",
    "    df = df.loc[df['score_engagement'].dropna().index]\n",
    "    df['percentile'] = get_user_percentile(df['score_engagement'])\n",
    "    scale = get_user_scale()\n",
    "    df['text_weighted'] = df['percentile'].apply(lambda x: scale[round(x)])\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_user_percentile(series):\n",
    "    return series.apply(lambda x: stats.percentileofscore(series, x))\n",
    "\n",
    "\n",
    "def get_days(irisdataset, thresh):\n",
    "    return irisdataset[irisdataset['mIndex'] > thresh][['mIndex', 'sign']]\n",
    "\n",
    "\n",
    "# Same as get_days, but on the left or right side of a merged dataset\n",
    "def get_days_left(irisdataset, thresh):\n",
    "    return irisdataset[irisdataset['mIndex_x'] > thresh][['mIndex_x', 'sign_x']]\n",
    "\n",
    "\n",
    "def get_days_right(irisdataset, thresh):\n",
    "    return irisdataset[irisdataset['mIndex_y'] > thresh][['mIndex_y', 'sign_y']]\n",
    "\n",
    "\n",
    "def get_polarity(df):\n",
    "    positive_emotions = ['emotion_joy', 'emotion_admiration']\n",
    "    negative_emotions = ['emotion_fear', 'emotion_anger', 'emotion_disapproval']\n",
    "    \n",
    "    positive = df['emotion_joy'] + df['emotion_admiration']\n",
    "    positive = positive.fillna(positive.mean())\n",
    "    \n",
    "    negative = df['emotion_fear'] + df['emotion_anger'] + df['emotion_disapproval']\n",
    "    negative = negative.fillna(negative.mean())\n",
    "\n",
    "    total = positive + negative\n",
    "    df['sentiment_value'] = positive / total\n",
    "    return df\n",
    "\n",
    "\n",
    "def convert_datetime_col_to_iso_format(df, col=None):\n",
    "    df = df.copy()\n",
    "    if (not col):\n",
    "        df = df.reset_index()\n",
    "        df['date_index'] = df['date_index'].apply(lambda x: pd.to_datetime(x, format='%Y/%m/%d', errors='coerce').date())\n",
    "        df = df.set_index(keys=['date_index'])\n",
    "        return df\n",
    "    else:\n",
    "        return df['date_index'].apply(lambda x: pd.to_datetime(x, format='%Y/%m/%d', errors='coerce').date())\n",
    "\n",
    "\n",
    "def iris(dataf, weight_by_user=False):\n",
    "    dataset = dataf.copy()\n",
    "                       \n",
    "    if weight_by_user:\n",
    "        dataset = get_text_weight(dataset)\n",
    "        \n",
    "    dataset = get_polarity(dataset)\n",
    "        \n",
    "    if weight_by_user:\n",
    "        dataset = dataset[['date_created_at', 'sentiment_value', 'text_weighted']]\n",
    "    else:\n",
    "        dataset = dataset[['date_created_at', 'sentiment_value']]\n",
    "\n",
    "#     dataset['date_index'] = pd.DatetimeIndex(dataset.date_created_at)\n",
    "    dataset['date_index'] =  pd.to_datetime(dataset.date_created_at,  errors='coerce')\n",
    "    dataset = dataset.loc[dataset['date_index'].dropna().index]\n",
    "    \n",
    "    volumes = dataset.groupby(pd.Grouper(key='date_index', freq='d')).count()['sentiment_value']\n",
    "    # print(volumes.head())\n",
    "    mean_sent = dataset.groupby(pd.Grouper(key='date_index', freq='d')).mean()['sentiment_value']\n",
    "        \n",
    "    if weight_by_user:\n",
    "        tmp = dataset[['date_index', 'text_weighted']]\n",
    "        weighted_volumes = tmp.groupby(pd.Grouper(key='date_index', freq='d')).sum()['text_weighted']\n",
    "        volume_medians = weighted_volumes.rolling(window=14).median()\n",
    "    else:\n",
    "        volume_medians = volumes.rolling(window=14).median()\n",
    "\n",
    "    mean_sent_abs = abs(mean_sent - 0.5) + 1\n",
    "\n",
    "    if weight_by_user:\n",
    "        index = (weighted_volumes / volume_medians - 1) * mean_sent_abs\n",
    "        index.replace(to_replace=[np.inf, np.nan], value=0, inplace=True)   # Needed to handle limit cases\n",
    "    else:\n",
    "        index = (volumes / volume_medians - 1) * mean_sent_abs\n",
    "        index.replace(to_replace=[np.inf, np.nan], value=0, inplace=True)   # Needed to handle limit cases\n",
    "\n",
    "    return pd.DataFrame({'mIndex': index, \n",
    "                         'volume': volumes, \n",
    "                         'sentiment': mean_sent, \n",
    "                         'sign': (mean_sent - 0.5) > 0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_iris(iris, threshold):\n",
    "\n",
    "    fig = plt.figure(figsize=(10, 8), constrained_layout=True)\n",
    "    axs = fig.subplots(2,1)\n",
    "\n",
    "    plt.sca(axs[0])\n",
    "    plt.xticks(rotation=45)\n",
    "\n",
    "    axs[0].set_title('Volumes')\n",
    "    axs[0].grid(True)\n",
    "    axs[0].plot(iris.index.values, iris['volume'])\n",
    "\n",
    "    plt.sca(axs[1])\n",
    "    plt.xticks(rotation=45)\n",
    "\n",
    "    axs[1].set_title('Alert Spectrum')\n",
    "    axs[1].grid(True)\n",
    "    axs[1].plot(iris.index.values, iris['mIndex'])\n",
    "\n",
    "\n",
    "    print('####  Peaks found ####')\n",
    "    print(get_days(iris, threshold))\n",
    "\n",
    "    print('\\n####  Charts ####')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# Needed in case we decide to allow infinites in the relevance index\n",
    "def no_inf_max(values):\n",
    "    max = 0\n",
    "    for i in values:\n",
    "        if not math.isinf(i):\n",
    "            if i > max:\n",
    "                max = i\n",
    "    return max \n",
    "\n",
    "def plot_auth_spikes(df, threshold):\n",
    "    \n",
    "    fig = plt.figure(figsize=(10, 8), constrained_layout=True)\n",
    "    \n",
    "    ax = fig.subplots(2,1)\n",
    "    plt.sca(ax[0])\n",
    "    plt.xticks(rotation=45)\n",
    "    \n",
    "    ax0_auth = ax[0].twinx()\n",
    "\n",
    "    plt.sca(ax[1])\n",
    "    plt.xticks(rotation=45)\n",
    "\n",
    "    ax1_auth = ax[1].twinx()\n",
    "\n",
    "    color = 'tab:blue'\n",
    "    max_value_vol = df['volume_y'].max()\n",
    "    min_value_vol = df['volume_y'].min()\n",
    "\n",
    "    # Since the relevance index can be negative, we need to shift its values. We also need to watch out for empty values while computing its max\n",
    "    df_copy = df.copy()\n",
    "\n",
    "    min_value_mIndex = df['mIndex_x'].min()\n",
    "    min_value_mIndex = df['mIndex_y'].min()\n",
    "    if (min_value_mIndex < 0):\n",
    "        df_copy['mIndex_y'] =  df_copy['mIndex_y'] + abs(min_value_mIndex)\n",
    "        min_value_mIndex = 0\n",
    "    max_value_mIndex = no_inf_max(df_copy['mIndex_y'])\n",
    "\n",
    "    ax[0].set_title('Volumes')\n",
    "    ax[0].grid(True)\n",
    "    ax[0].plot(df.index.values, (df['volume_y'] - min_value_vol) / (max_value_vol - min_value_vol), color=color)\n",
    "    ax[0].tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "    ax[1].set_title('Alert Spectrum')\n",
    "    ax[1].grid(True)\n",
    "    ax[1].plot(df.index.values, (df['mIndex_y'] - min_value_mIndex) / (max_value_mIndex - min_value_mIndex), color=color)\n",
    "    ax[1].tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "    color = 'tab:orange'\n",
    "    max_value_vol = df['volume_x'].max()\n",
    "    min_value_vol = df['volume_x'].min()\n",
    "    \n",
    "    # Since the relevance index can be negative, we need to shift its values. We also need to watch out for empty values while computing its max\n",
    "    min_value_mIndex = df['mIndex_x'].min()\n",
    "    if (min_value_mIndex < 0):\n",
    "        df_copy['mIndex_x'] = df_copy['mIndex_x'] + abs(min_value_mIndex)\n",
    "        min_value_mIndex = 0\n",
    "    max_value_mIndex = no_inf_max(df_copy['mIndex_x'])\n",
    "\n",
    "\n",
    "    ax0_auth.plot(df.index.values, (df['volume_x'] - min_value_vol) / (max_value_vol - min_value_vol), color=color)\n",
    "    ax0_auth.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "    ax1_auth.plot(df.index.values, (df['mIndex_x'] - min_value_mIndex) / (max_value_mIndex - min_value_mIndex), color=color)\n",
    "    ax1_auth.tick_params(axis='y', labelcolor=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####  Peaks found ####\n",
      "               mIndex   sign\n",
      "date_index                  \n",
      "2019-08-20   2.602240  False\n",
      "2019-08-21  13.554951  False\n",
      "2019-08-22   5.825910  False\n",
      "2019-08-23   2.485722  False\n",
      "2019-08-24   1.006199  False\n",
      "...               ...    ...\n",
      "2021-08-19   0.774941   True\n",
      "2021-08-20   0.858849  False\n",
      "2021-08-23   2.040529  False\n",
      "2021-08-24   1.287879  False\n",
      "2021-08-25   0.679956  False\n",
      "\n",
      "[165 rows x 2 columns]\n",
      "\n",
      "####  Charts ####\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "irisres = iris(DATASET)\n",
    "irisres = convert_datetime_col_to_iso_format(irisres)\n",
    "plot_iris(irisres, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>mIndex</th>\n",
       "      <th>volume</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sign</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>53</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>2.040529</td>\n",
       "      <td>67</td>\n",
       "      <td>0.468272</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-24</th>\n",
       "      <td>1.287879</td>\n",
       "      <td>49</td>\n",
       "      <td>0.360722</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>0.679956</td>\n",
       "      <td>41</td>\n",
       "      <td>0.437569</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-26</th>\n",
       "      <td>0.429460</td>\n",
       "      <td>39</td>\n",
       "      <td>0.406829</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>-0.171611</td>\n",
       "      <td>24</td>\n",
       "      <td>0.298725</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>758 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              mIndex  volume  sentiment   sign\n",
       "date_index                                    \n",
       "2019-08-01  0.000000      21   0.373657  False\n",
       "2019-08-02  0.000000      40   0.373657  False\n",
       "2019-08-03  0.000000      53   0.373657  False\n",
       "2019-08-04  0.000000      31   0.373657  False\n",
       "2019-08-05  0.000000      31   0.373657  False\n",
       "...              ...     ...        ...    ...\n",
       "2021-08-23  2.040529      67   0.468272  False\n",
       "2021-08-24  1.287879      49   0.360722  False\n",
       "2021-08-25  0.679956      41   0.437569  False\n",
       "2021-08-26  0.429460      39   0.406829  False\n",
       "2021-08-27 -0.171611      24   0.298725  False\n",
       "\n",
       "[758 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irisres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "irisres.to_csv(DATASET_PATH + 'irisres.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>mIndex</th>\n",
       "      <th>volume</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sign</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-20</th>\n",
       "      <td>2.602240</td>\n",
       "      <td>96</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-21</th>\n",
       "      <td>13.554951</td>\n",
       "      <td>378</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-22</th>\n",
       "      <td>5.825910</td>\n",
       "      <td>179</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-23</th>\n",
       "      <td>2.485722</td>\n",
       "      <td>93</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-24</th>\n",
       "      <td>1.006199</td>\n",
       "      <td>71</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-19</th>\n",
       "      <td>0.774941</td>\n",
       "      <td>36</td>\n",
       "      <td>0.524922</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-20</th>\n",
       "      <td>0.858849</td>\n",
       "      <td>38</td>\n",
       "      <td>0.493919</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>2.040529</td>\n",
       "      <td>67</td>\n",
       "      <td>0.468272</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-24</th>\n",
       "      <td>1.287879</td>\n",
       "      <td>49</td>\n",
       "      <td>0.360722</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>0.679956</td>\n",
       "      <td>41</td>\n",
       "      <td>0.437569</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mIndex  volume  sentiment   sign\n",
       "date_index                                     \n",
       "2019-08-20   2.602240      96   0.373657  False\n",
       "2019-08-21  13.554951     378   0.373657  False\n",
       "2019-08-22   5.825910     179   0.373657  False\n",
       "2019-08-23   2.485722      93   0.373657  False\n",
       "2019-08-24   1.006199      71   0.373657  False\n",
       "...               ...     ...        ...    ...\n",
       "2021-08-19   0.774941      36   0.524922   True\n",
       "2021-08-20   0.858849      38   0.493919  False\n",
       "2021-08-23   2.040529      67   0.468272  False\n",
       "2021-08-24   1.287879      49   0.360722  False\n",
       "2021-08-25   0.679956      41   0.437569  False\n",
       "\n",
       "[165 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_spikes = irisres.loc[irisres['mIndex'] >= 0.5]\n",
    "iris_spikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####  Peaks found ####\n",
      "              mIndex   sign\n",
      "date_index                 \n",
      "2019-10-17  1.100317   True\n",
      "2019-10-23  1.411402  False\n",
      "2019-10-24  5.524376  False\n",
      "2019-10-25  1.182024  False\n",
      "2019-10-30  1.289454   True\n",
      "2019-10-31  3.668820  False\n",
      "2019-11-08  2.393225   True\n",
      "2019-12-01  3.318031  False\n",
      "2019-12-06  5.530052  False\n",
      "2019-12-10  2.212021  False\n",
      "2019-12-13  1.106010  False\n",
      "2019-12-24  1.036259  False\n",
      "2020-02-08  1.496028  False\n",
      "2020-02-10  1.017680  False\n",
      "2020-02-13  1.463476   True\n",
      "2020-02-18  1.106010  False\n",
      "2020-02-19  3.318031  False\n",
      "2020-02-21  1.106010  False\n",
      "2020-05-09  1.380941   True\n",
      "2020-05-14  1.447140  False\n",
      "2020-10-08  3.318031  False\n",
      "2021-03-27  1.106010  False\n",
      "2021-04-16  1.106010  False\n",
      "2021-04-19  1.106010  False\n",
      "2021-06-04  1.475816  False\n",
      "\n",
      "####  Charts ####\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "irisres_auth = iris(AUTH_FILTERED_DATASET)\n",
    "irisres_auth = convert_datetime_col_to_iso_format(irisres_auth)\n",
    "plot_iris(irisres_auth, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>mIndex</th>\n",
       "      <th>volume</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sign</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.39399</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            mIndex  volume  sentiment   sign\n",
       "date_index                                  \n",
       "2019-08-02     0.0       1    0.39399  False\n",
       "2019-08-03     0.0       0        NaN  False\n",
       "2019-08-04     0.0       0        NaN  False\n",
       "2019-08-05     0.0       0        NaN  False\n",
       "2019-08-06     0.0       0        NaN  False\n",
       "...            ...     ...        ...    ...\n",
       "2021-08-22     0.0       0        NaN  False\n",
       "2021-08-23     0.0       0        NaN  False\n",
       "2021-08-24     0.0       0        NaN  False\n",
       "2021-08-25     0.0       0        NaN  False\n",
       "2021-08-26     0.0       1    0.00000  False\n",
       "\n",
       "[756 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irisres_auth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mIndex</th>\n",
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       "      <th>2019-10-17</th>\n",
       "      <td>1.100317</td>\n",
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       "      <th>2019-12-10</th>\n",
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       "      <th>2019-12-24</th>\n",
       "      <td>1.036259</td>\n",
       "      <td>2</td>\n",
       "      <td>0.463741</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-08</th>\n",
       "      <td>1.496028</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003972</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>1.017680</td>\n",
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       "      <th>2020-02-13</th>\n",
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       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
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       "      <th>2020-02-19</th>\n",
       "      <td>3.318031</td>\n",
       "      <td>4</td>\n",
       "      <td>0.393990</td>\n",
       "      <td>False</td>\n",
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       "      <th>2020-02-21</th>\n",
       "      <td>1.106010</td>\n",
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       "      <td>0.393990</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.380941</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.052860</td>\n",
       "      <td>False</td>\n",
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       "      <th>2020-10-08</th>\n",
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       "      <th>2021-06-04</th>\n",
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      "text/plain": [
       "              mIndex  volume  sentiment   sign\n",
       "date_index                                    \n",
       "2019-10-17  1.100317       1   0.600317   True\n",
       "2019-10-23  1.411402       1   0.088598  False\n",
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       "2019-10-30  1.289454       1   0.789454   True\n",
       "2019-10-31  3.668820       2   0.277060  False\n",
       "2019-11-08  2.393225       3   0.696613   True\n",
       "2019-12-01  3.318031       2   0.393990  False\n",
       "2019-12-06  5.530052       6   0.393990  False\n",
       "2019-12-10  2.212021       3   0.393990  False\n",
       "2019-12-13  1.106010       2   0.393990  False\n",
       "2019-12-24  1.036259       2   0.463741  False\n",
       "2020-02-08  1.496028       1   0.003972  False\n",
       "2020-02-10  1.017680       1   0.482320  False\n",
       "2020-02-13  1.463476       1   0.963476   True\n",
       "2020-02-18  1.106010       2   0.393990  False\n",
       "2020-02-19  3.318031       4   0.393990  False\n",
       "2020-02-21  1.106010       1   0.393990  False\n",
       "2020-05-09  1.380941       1   0.880941   True\n",
       "2020-05-14  1.447140       1   0.052860  False\n",
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       "2021-04-19  1.106010       2   0.393990  False\n",
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      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_auth_spikes = irisres_auth.loc[irisres_auth['mIndex'] >= 0.5]\n",
    "iris_auth_spikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_join = irisres.join(irisres_auth, how='left', lsuffix='_x', rsuffix='_y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2021-08-24</th>\n",
       "      <td>1.287879</td>\n",
       "      <td>49</td>\n",
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      ],
      "text/plain": [
       "            mIndex_x  volume_x  sentiment_x  sign_x  mIndex_y  volume_y  \\\n",
       "date_index                                                                \n",
       "2019-08-01  0.000000        21     0.373657   False       NaN       NaN   \n",
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       "2019-08-05  0.000000        31     0.373657   False       0.0       0.0   \n",
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       "2021-08-24  1.287879        49     0.360722   False       0.0       0.0   \n",
       "2021-08-25  0.679956        41     0.437569   False       0.0       0.0   \n",
       "2021-08-26  0.429460        39     0.406829   False       0.0       1.0   \n",
       "2021-08-27 -0.171611        24     0.298725   False       NaN       NaN   \n",
       "\n",
       "            sentiment_y sign_y  \n",
       "date_index                      \n",
       "2019-08-01          NaN    NaN  \n",
       "2019-08-02      0.39399  False  \n",
       "2019-08-03          NaN  False  \n",
       "2019-08-04          NaN  False  \n",
       "2019-08-05          NaN  False  \n",
       "...                 ...    ...  \n",
       "2021-08-23          NaN  False  \n",
       "2021-08-24          NaN  False  \n",
       "2021-08-25          NaN  False  \n",
       "2021-08-26      0.00000  False  \n",
       "2021-08-27          NaN    NaN  \n",
       "\n",
       "[758 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_auth_spikes(iris_join, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-20</th>\n",
       "      <td>2.602240</td>\n",
       "      <td>96</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-21</th>\n",
       "      <td>13.554951</td>\n",
       "      <td>378</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39399</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-22</th>\n",
       "      <td>5.825910</td>\n",
       "      <td>179</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39399</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-23</th>\n",
       "      <td>2.485722</td>\n",
       "      <td>93</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39399</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-24</th>\n",
       "      <td>1.006199</td>\n",
       "      <td>71</td>\n",
       "      <td>0.373657</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-19</th>\n",
       "      <td>0.774941</td>\n",
       "      <td>36</td>\n",
       "      <td>0.524922</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-20</th>\n",
       "      <td>0.858849</td>\n",
       "      <td>38</td>\n",
       "      <td>0.493919</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>2.040529</td>\n",
       "      <td>67</td>\n",
       "      <td>0.468272</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-24</th>\n",
       "      <td>1.287879</td>\n",
       "      <td>49</td>\n",
       "      <td>0.360722</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>0.679956</td>\n",
       "      <td>41</td>\n",
       "      <td>0.437569</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             mIndex_x  volume_x  sentiment_x  sign_x  mIndex_y  volume_y  \\\n",
       "date_index                                                                 \n",
       "2019-08-20   2.602240        96     0.373657   False       0.0       0.0   \n",
       "2019-08-21  13.554951       378     0.373657   False       0.0       1.0   \n",
       "2019-08-22   5.825910       179     0.373657   False       0.0       1.0   \n",
       "2019-08-23   2.485722        93     0.373657   False       0.0       1.0   \n",
       "2019-08-24   1.006199        71     0.373657   False       0.0       0.0   \n",
       "...               ...       ...          ...     ...       ...       ...   \n",
       "2021-08-19   0.774941        36     0.524922    True       0.0       0.0   \n",
       "2021-08-20   0.858849        38     0.493919   False       0.0       0.0   \n",
       "2021-08-23   2.040529        67     0.468272   False       0.0       0.0   \n",
       "2021-08-24   1.287879        49     0.360722   False       0.0       0.0   \n",
       "2021-08-25   0.679956        41     0.437569   False       0.0       0.0   \n",
       "\n",
       "            sentiment_y sign_y  \n",
       "date_index                      \n",
       "2019-08-20          NaN  False  \n",
       "2019-08-21      0.39399  False  \n",
       "2019-08-22      0.39399  False  \n",
       "2019-08-23      0.39399  False  \n",
       "2019-08-24          NaN  False  \n",
       "...                 ...    ...  \n",
       "2021-08-19          NaN  False  \n",
       "2021-08-20          NaN  False  \n",
       "2021-08-23          NaN  False  \n",
       "2021-08-24          NaN  False  \n",
       "2021-08-25          NaN  False  \n",
       "\n",
       "[165 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_spikes_df = iris_join.loc[iris_join['mIndex_x'] >= 0.5 ]\n",
    "merged_spikes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_spikes.to_csv(SPIKES_PATH + 'ferrero_corp_spikes.csv')\n",
    "iris_auth_spikes.to_csv(SPIKES_PATH + 'ferrero_corp_auth_spikes.csv')\n",
    "merged_spikes_df.to_csv(SPIKES_PATH + 'ferrero_corp_merged.csv')"
   ]
  }
 ],
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