sec5-temporal_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from util.plotting import *\n",
    "from util.util_data import *\n",
    "from datetime import date, datetime, time, timedelta\n",
    "import json\n",
    "import seaborn as sns\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.lines import Line2D\n",
    "import warnings\n",
    "from scipy.stats import gmean\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = 'libertine'\n",
    "plt.rcParams['font.weight'] = 'bold'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## figs. day-of-week(1x3), and hour-of-day(1x3) incident count distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load incident data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 0, 0, 0]\n"
     ]
    },
    {
     "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>incident_id</th>\n",
       "      <th>Incident_Title</th>\n",
       "      <th>incident_impact_level</th>\n",
       "      <th>Incident_color</th>\n",
       "      <th>provider</th>\n",
       "      <th>Playground</th>\n",
       "      <th>API-OpenAI</th>\n",
       "      <th>DALL·E</th>\n",
       "      <th>ChatGPT</th>\n",
       "      <th>API-Anthropic</th>\n",
       "      <th>Claude</th>\n",
       "      <th>Console</th>\n",
       "      <th>Character.AI</th>\n",
       "      <th>investigating_flag</th>\n",
       "      <th>investigating_timestamp</th>\n",
       "      <th>investigating_description</th>\n",
       "      <th>identified_flag</th>\n",
       "      <th>identified_timestamp</th>\n",
       "      <th>identified_description</th>\n",
       "      <th>monitoring_flag</th>\n",
       "      <th>monitoring_timestamp</th>\n",
       "      <th>monitoring_description</th>\n",
       "      <th>resolved_flag</th>\n",
       "      <th>resolved_timestamp</th>\n",
       "      <th>resolved_description</th>\n",
       "      <th>postmortem_flag</th>\n",
       "      <th>postmortem_timestamp</th>\n",
       "      <th>postmortem_description</th>\n",
       "      <th>start_timestamp</th>\n",
       "      <th>close_timestamp</th>\n",
       "      <th>time_span</th>\n",
       "      <th>over_one_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qv89mzdjvtrs</td>\n",
       "      <td>Elevated Errors</td>\n",
       "      <td>1</td>\n",
       "      <td>#f1c40f</td>\n",
       "      <td>anthropic</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-26 09:09:00</td>\n",
       "      <td>We're experiencing an elevated level of API er...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-26 09:37:00</td>\n",
       "      <td>The issue has been identified and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-26 09:41:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-26 10:21:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-26 09:09:00</td>\n",
       "      <td>2024-02-26 18:21:00+00:00</td>\n",
       "      <td>0 days 01:12:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tmsczhzhjd63</td>\n",
       "      <td>Elevated error rate on claude.ai</td>\n",
       "      <td>0</td>\n",
       "      <td>#333333</td>\n",
       "      <td>anthropic</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-16 12:35:00</td>\n",
       "      <td>We experienced an elevated level of errors on ...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-16 12:35:00</td>\n",
       "      <td>2024-02-16 20:35:00+00:00</td>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t6v85cs8j6jb</td>\n",
       "      <td>Elevated error rate on Claude-2.0, Claude-2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>#333333</td>\n",
       "      <td>anthropic</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 19:59:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 20:21:00</td>\n",
       "      <td>The issue has been identified and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 20:30:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 20:36:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-12 19:59:00</td>\n",
       "      <td>2024-02-13 04:36:00+00:00</td>\n",
       "      <td>0 days 00:37:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x5f4s8nvkyqq</td>\n",
       "      <td>Elevated error rate on Claude-2.0, Claude-2.1</td>\n",
       "      <td>0</td>\n",
       "      <td>#333333</td>\n",
       "      <td>anthropic</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 16:21:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 16:50:00</td>\n",
       "      <td>The issue has been identified and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 16:59:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 17:26:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-12 16:21:00</td>\n",
       "      <td>2024-02-13 01:26:00+00:00</td>\n",
       "      <td>0 days 01:05:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gr7nb0dwyydw</td>\n",
       "      <td>elevated error rates on claude-instant-1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>#333333</td>\n",
       "      <td>anthropic</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-05 15:07:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-05 15:58:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-05 17:55:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-05 15:07:00</td>\n",
       "      <td>2024-02-06 01:55:00+00:00</td>\n",
       "      <td>0 days 02:48:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    incident_id                                 Incident_Title  \\\n",
       "0  qv89mzdjvtrs                                Elevated Errors   \n",
       "1  tmsczhzhjd63               Elevated error rate on claude.ai   \n",
       "2  t6v85cs8j6jb  Elevated error rate on Claude-2.0, Claude-2.1   \n",
       "3  x5f4s8nvkyqq  Elevated error rate on Claude-2.0, Claude-2.1   \n",
       "4  gr7nb0dwyydw     elevated error rates on claude-instant-1.2   \n",
       "\n",
       "   incident_impact_level Incident_color   provider  Playground  API-OpenAI  \\\n",
       "0                      1        #f1c40f  anthropic           0           0   \n",
       "1                      0        #333333  anthropic           0           0   \n",
       "2                      0        #333333  anthropic           0           0   \n",
       "3                      0        #333333  anthropic           0           0   \n",
       "4                      0        #333333  anthropic           0           0   \n",
       "\n",
       "   DALL·E  ChatGPT  API-Anthropic  Claude  Console  Character.AI  \\\n",
       "0       0        0              1       1        1             0   \n",
       "1       0        0              0       0        0             0   \n",
       "2       0        0              1       1        1             0   \n",
       "3       0        0              1       1        1             0   \n",
       "4       0        0              1       1        1             0   \n",
       "\n",
       "   investigating_flag investigating_timestamp  \\\n",
       "0                   1     2024-02-26 09:09:00   \n",
       "1                   0                     NaT   \n",
       "2                   1     2024-02-12 19:59:00   \n",
       "3                   1     2024-02-12 16:21:00   \n",
       "4                   1     2024-02-05 15:07:00   \n",
       "\n",
       "                           investigating_description  identified_flag  \\\n",
       "0  We're experiencing an elevated level of API er...                1   \n",
       "1                                                NaN                0   \n",
       "2         We are currently investigating this issue.                1   \n",
       "3         We are currently investigating this issue.                1   \n",
       "4         We are currently investigating this issue.                0   \n",
       "\n",
       "  identified_timestamp                             identified_description  \\\n",
       "0  2024-02-26 09:37:00  The issue has been identified and a fix is bei...   \n",
       "1                  NaT                                                NaN   \n",
       "2  2024-02-12 20:21:00  The issue has been identified and a fix is bei...   \n",
       "3  2024-02-12 16:50:00  The issue has been identified and a fix is bei...   \n",
       "4                  NaT                                                NaN   \n",
       "\n",
       "   monitoring_flag monitoring_timestamp  \\\n",
       "0                1  2024-02-26 09:41:00   \n",
       "1                0                  NaT   \n",
       "2                1  2024-02-12 20:30:00   \n",
       "3                1  2024-02-12 16:59:00   \n",
       "4                1  2024-02-05 15:58:00   \n",
       "\n",
       "                              monitoring_description  resolved_flag  \\\n",
       "0  A fix has been implemented and we are monitori...              1   \n",
       "1                                                NaN              1   \n",
       "2  A fix has been implemented and we are monitori...              1   \n",
       "3  A fix has been implemented and we are monitori...              1   \n",
       "4  A fix has been implemented and we are monitori...              1   \n",
       "\n",
       "   resolved_timestamp                               resolved_description  \\\n",
       "0 2024-02-26 10:21:00                   This incident has been resolved.   \n",
       "1 2024-02-16 12:35:00  We experienced an elevated level of errors on ...   \n",
       "2 2024-02-12 20:36:00                   This incident has been resolved.   \n",
       "3 2024-02-12 17:26:00                   This incident has been resolved.   \n",
       "4 2024-02-05 17:55:00                   This incident has been resolved.   \n",
       "\n",
       "   postmortem_flag postmortem_timestamp postmortem_description  \\\n",
       "0                0                  NaT                    NaN   \n",
       "1                0                  NaT                    NaN   \n",
       "2                0                  NaT                    NaN   \n",
       "3                0                  NaT                    NaN   \n",
       "4                0                  NaT                    NaN   \n",
       "\n",
       "      start_timestamp            close_timestamp        time_span  \\\n",
       "0 2024-02-26 09:09:00  2024-02-26 18:21:00+00:00  0 days 01:12:00   \n",
       "1 2024-02-16 12:35:00  2024-02-16 20:35:00+00:00  0 days 00:00:00   \n",
       "2 2024-02-12 19:59:00  2024-02-13 04:36:00+00:00  0 days 00:37:00   \n",
       "3 2024-02-12 16:21:00  2024-02-13 01:26:00+00:00  0 days 01:05:00   \n",
       "4 2024-02-05 15:07:00  2024-02-06 01:55:00+00:00  0 days 02:48:00   \n",
       "\n",
       "   over_one_day  \n",
       "0         False  \n",
       "1         False  \n",
       "2         False  \n",
       "3         False  \n",
       "4         False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load incident data\n",
    "path = f'data/clean/incident/2024-08-31/incident_stages.csv'\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "# status_lst = ['investigating', 'identified', 'monitoring', 'resolved', 'postmortem', 'start']\n",
    "# for status in status_lst:\n",
    "#     df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp'])\n",
    "\n",
    "status_lst = ['investigating', 'identified', 'monitoring', 'resolved', 'postmortem', 'start']\n",
    "for status in status_lst:\n",
    "    df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp'], errors='coerce')\n",
    "    # convert to PDT time\n",
    "    df[f'{status}_timestamp'] = df[f'{status}_timestamp'].dt.tz_convert('US/Pacific')\n",
    "    # remove timezone information\n",
    "    df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp']).dt.tz_localize(None)\n",
    "\n",
    "# filter out df corner cases\n",
    "count_lst = 5 * [0]\n",
    "corner_cases = pd.DataFrame()\n",
    "\n",
    "for index, record in df.iterrows():\n",
    "    if record['investigating_timestamp'] > record['identified_timestamp']:\n",
    "        count_lst[0] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "    if record['identified_timestamp'] > record['monitoring_timestamp']:\n",
    "        count_lst[1] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "    if record['monitoring_timestamp'] > record['resolved_timestamp']:\n",
    "        count_lst[2] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "    if record['resolved_timestamp'] > record['postmortem_timestamp']:\n",
    "        count_lst[3] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "print(count_lst)\n",
    "\n",
    "# transposing corner cases\n",
    "corner_cases = corner_cases.T\n",
    "# display(corner_cases)\n",
    "\n",
    "# filter out df corner cases\n",
    "df = df.drop(corner_cases.index)\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "df = df.rename(columns={'API': 'API-OpenAI', 'ChatGPT': 'ChatGPT', 'Labs': 'DALL·E', 'Playground': 'Playground', 'api.anthropic.com': 'API-Anthropic', 'claude.ai': 'Claude', 'console.anthropic.com': 'Console', 'Character.AI': 'Character.AI'})\n",
    "service_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "\n",
    "dfs = {}\n",
    "for service in service_lst:\n",
    "    dfs[service] = df[df[service] == 1]\n",
    "    dfs[service].reset_index(drop=True, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['incident_id', 'Incident_Title', 'incident_impact_level',\n",
       "       'Incident_color', 'provider', 'Playground', 'API-OpenAI', 'DALL·E',\n",
       "       'ChatGPT', 'API-Anthropic', 'Claude', 'Console', 'Character.AI',\n",
       "       'investigating_flag', 'investigating_timestamp',\n",
       "       'investigating_description', 'identified_flag', 'identified_timestamp',\n",
       "       'identified_description', 'monitoring_flag', 'monitoring_timestamp',\n",
       "       'monitoring_description', 'resolved_flag', 'resolved_timestamp',\n",
       "       'resolved_description', 'postmortem_flag', 'postmortem_timestamp',\n",
       "       'postmortem_description', 'start_timestamp', 'close_timestamp',\n",
       "       'time_span', 'over_one_day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### temporal distribution of incidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Day\n",
       "Monday       30\n",
       "Tuesday      40\n",
       "Wednesday    57\n",
       "Thursday     48\n",
       "Friday       38\n",
       "Saturday      8\n",
       "Sunday        6\n",
       "Name: resolved_flag, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## data preparation\n",
    "openai_services = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground']\n",
    "anthropic_services = ['API-Anthropic', 'Claude', 'Console']\n",
    "character_ai_services = ['Character.AI']\n",
    "\n",
    "openai_day_of_week_dfs = {}\n",
    "anthropic_day_of_week_dfs = {}\n",
    "character_ai_day_of_week_dfs = {}\n",
    "for provider, services in zip(['OpenAI', 'Anthropic', 'Character.AI'], [openai_services, anthropic_services, character_ai_services]):\n",
    "    for service in services:\n",
    "        df = dfs[service]\n",
    "        df['Day'] = df['start_timestamp'].dt.day_name()\n",
    "        df['Day'] = pd.Categorical(df['Day'], categories=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], ordered=True)\n",
    "        day_of_week = df.groupby('Day')['resolved_flag'].sum()\n",
    "        if provider == 'OpenAI':\n",
    "            openai_day_of_week_dfs[service] = day_of_week\n",
    "        elif provider == 'Anthropic':\n",
    "            anthropic_day_of_week_dfs[service] = day_of_week\n",
    "        elif provider == 'Character.AI':\n",
    "            character_ai_day_of_week_dfs[service] = day_of_week\n",
    "\n",
    "openai_day_of_week_dfs['API-OpenAI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incident Count by Day of Week\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# incidetn count by day of week, 3 subplots in one row for each provider, aggregated by service\n",
    "print('Incident Count by Day of Week')\n",
    "\n",
    "set_half_column_figure_font(height_multiplier=1.0)\n",
    "fig, axs = plt.subplots(1, 3, figsize=(30, 6))\n",
    "fig.subplots_adjust(wspace=0.0)\n",
    "\n",
    "# OpenAI\n",
    "ax = axs[0]\n",
    "colors = sns.color_palette('Blues', 4)\n",
    "# colors = sns.color_palette('Set2', 4)\n",
    "colors = colors[::-1]\n",
    "# hatches = ['o', 'x', '.', '+']\n",
    "hatches = ['', '', '', '']\n",
    "for service in openai_services:\n",
    "    playground = ax.bar(openai_day_of_week_dfs[service].index, openai_day_of_week_dfs[service], color=colors[3], width=0.6, hatch=hatches[3])\n",
    "    dalle = ax.bar(openai_day_of_week_dfs[service].index, openai_day_of_week_dfs[service], color=colors[2], width=0.6, hatch=hatches[2], bottom=openai_day_of_week_dfs['Playground'])\n",
    "    chatgpt = ax.bar(openai_day_of_week_dfs[service].index, openai_day_of_week_dfs[service], color=colors[1], width=0.6, hatch=hatches[1], bottom=openai_day_of_week_dfs['DALL·E'] + openai_day_of_week_dfs['Playground'])\n",
    "    api = ax.bar(openai_day_of_week_dfs[service].index, openai_day_of_week_dfs[service], color=colors[0], width=0.6, hatch=hatches[0], bottom=openai_day_of_week_dfs['ChatGPT'] + openai_day_of_week_dfs['DALL·E'] + openai_day_of_week_dfs['Playground'])\n",
    "\n",
    "    ax.set_title(r'\\textbf{OpenAI}', fontsize=41)\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend([api, chatgpt, dalle, playground], [r'\\textbf{API}', r'\\textbf{ChatGPT}', r'\\textbf{DALL·E}', r'\\textbf{Playground}'], loc='upper right', fontsize=30)\n",
    "    ax.set_ylabel(r'\\textbf{Incident Count}', fontsize=41)\n",
    "    labels = [r\"\\textbf{Mon.}\", r\"\\textbf{Tue.}\", r\"\\textbf{Wed.}\", r\"\\textbf{Thu.}\", r\"\\textbf{Fri.}\", r\"\\textbf{Sat.}\", r\"\\textbf{Sun.}\"]\n",
    "    ax.set_xticklabels(labels, rotation=0, fontsize=41)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "    ax.xaxis.set_tick_params(labelsize=41)\n",
    "    ax.yaxis.set_tick_params(labelsize=41)\n",
    "\n",
    "# Anthropic\n",
    "ax = axs[1]\n",
    "colors = sns.color_palette('Oranges', 4)\n",
    "colors = colors[::-1]\n",
    "# hatches = ['o', 'x', '.', '+']\n",
    "hatches = ['', '', '', '']\n",
    "for service in anthropic_services:\n",
    "    console = ax.bar(anthropic_day_of_week_dfs[service].index, anthropic_day_of_week_dfs[service], width=0.6, color=colors[2], hatch=hatches[2])\n",
    "    claude = ax.bar(anthropic_day_of_week_dfs[service].index, anthropic_day_of_week_dfs[service], width=0.6, color=colors[1], hatch=hatches[1], bottom=anthropic_day_of_week_dfs['Console'])\n",
    "    api_anthropic = ax.bar(anthropic_day_of_week_dfs[service].index, anthropic_day_of_week_dfs[service], width=0.6, color=colors[0], hatch=hatches[0], bottom=anthropic_day_of_week_dfs['Claude'] + anthropic_day_of_week_dfs['Console'])\n",
    "    ax.set_title(r'\\textbf{Anthropic}', fontsize=41)\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend([api_anthropic, claude, console], [r'\\textbf{API}', r'\\textbf{Claude}', r'\\textbf{Console}'], loc='upper right', fontsize=32)\n",
    "    # ax.set_ylabel(r'\\textbf{Outage Count}')\n",
    "    labels = [r\"\\textbf{Mon.}\", r\"\\textbf{Tue.}\", r\"\\textbf{Wed.}\", r\"\\textbf{Thu.}\", r\"\\textbf{Fri.}\", r\"\\textbf{Sat.}\", r\"\\textbf{Sun.}\"]\n",
    "    ax.set_xticklabels(labels, rotation=0, fontsize=41)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "    ax.xaxis.set_tick_params(labelsize=41)\n",
    "    ax.yaxis.set_tick_params(labelsize=41)\n",
    "\n",
    "# Character.AI\n",
    "ax = axs[2]\n",
    "colors = sns.color_palette('Greens', 4)\n",
    "# hatches = ['o', 'x', '.', '+']\n",
    "hatches = ['', '', '', '']\n",
    "for service in character_ai_services:\n",
    "    character_ai_day_of_week_dfs[service].plot(kind='bar', ax=ax, color=colors[3], hatch=hatches[0], width=0.6)\n",
    "    ax.set_title(r'\\textbf{Character.AI}', fontsize=41)\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend([r'\\textbf{Character.AI}'], loc='upper left', fontsize=32)\n",
    "    # ax.set_ylabel(r'\\textbf{Outage Count}')\n",
    "    labels = [r\"\\textbf{Mon.}\", r\"\\textbf{Tue.}\", r\"\\textbf{Wed.}\", r\"\\textbf{Thu.}\", r\"\\textbf{Fri.}\", r\"\\textbf{Sat.}\", r\"\\textbf{Sun.}\"]\n",
    "    ax.set_xticklabels(labels, rotation=0, fontsize=41)\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "    ax.xaxis.set_tick_params(labelsize=41)\n",
    "    ax.yaxis.set_tick_params(labelsize=41)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plot/sec5-temporal_analysis/incident_count_day_of_week_by_provider.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec5-temporal_analysis/incident_count_day_of_week_by_provider.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preparation\n",
    "openai_services = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground']\n",
    "anthropic_services = ['API-Anthropic', 'Claude', 'Console']\n",
    "character_ai_services = ['Character.AI']\n",
    "\n",
    "openai_hour_of_day_dfs = {}\n",
    "anthropic_hour_of_day_dfs = {}\n",
    "character_ai_hour_of_day_dfs = {}\n",
    "for provider, services in zip(['OpenAI', 'Anthropic', 'Character.AI'], [openai_services, anthropic_services, character_ai_services]):\n",
    "    for service in services:\n",
    "        df = dfs[service]\n",
    "        # df['Day'] = df['start_timestamp'].dt.day_name()\n",
    "        # df['Day'] = pd.Categorical(df['Day'], categories=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], ordered=True)\n",
    "        # day_of_week = df.groupby('Day')['resolved_flag'].sum()\n",
    "        df['Hour'] = df['start_timestamp'].dt.hour\n",
    "        df['Hour'] = pd.Categorical(df['Hour'], categories=range(24), ordered=True)\n",
    "        hour_of_day = df.groupby('Hour')['resolved_flag'].sum()\n",
    "        if provider == 'OpenAI':\n",
    "            openai_hour_of_day_dfs[service] = hour_of_day\n",
    "        elif provider == 'Anthropic':\n",
    "            anthropic_hour_of_day_dfs[service] = hour_of_day\n",
    "        elif provider == 'Character.AI':\n",
    "            character_ai_hour_of_day_dfs[service] = hour_of_day\n",
    "\n",
    "# openai_hour_of_day_dfs['API-OpenAI']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incident Count by Hour of Day\n"
     ]
    },
    {
     "data": {
      "image/png": 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ljrsHXvb73729vcj+je/7arVacl23Z2LfNE2VSiUyfQMAAADAgvm+37NIVzoJ1J52fzSfzw+1YxcAAAAAAADOppkFfqfTadXrdVUqFeXzeX32s5+dSrlvvvmmXNdVIpFQOp2eSpnSSRbxYAs+y7J0eHiow8PDkcuI0mg0Igf2crlc1/f9Atn7BXGPatmDXgAAAFbFsgcZ+74vx3F09+7d2OecvgcdlDXsdGaxuHtY0zR7gsr39vZmHqQct7PNsgfp1+v1vo8bhiHTNGWapizLGpiZHQAAAAAwP/v7+5HHpzmPFbBte+plnhYEst+5c6dnIXLQP93e3lY2m51LfVaV67pyHEe3b9+W7/vh+2gYhra3t2VZlvL5/NQTXQEAAAAAgLNtZoHfnQM92WxWjUZDr7zyykRlHhwc6Itf/GLkOSbVGQjium4YBD4NjuP0ZEaURgv8HidQJe4129vbI5cFAACAXnH3b8sUZJzL5foGop9+bFDgd61W0/Xr1+X7vkzTVKVSiX2uZVld98Gu6w5V50nEXZN79+7N/NyTKJVKPZnTAQAAAACrIW7nrFks2LUsS6ZpTjVhUKdyuRxmFA92CstkMrpy5YqOjo50eHgo13Xluq6q1aoMw9CtW7f6LvT2fV87OzvhrlbBsVarJd/3Zdt25HvoOI4qlUrXcyWF/69UKj1zbf34vq/9/X01Gg25rhuWEwSz37x5c+A4yjBc11WxWOwaEwnex+DxYO6wXC4rl8v1HV8BAAAAAADoNLPA783NTb322mv6zne+o7feekumaapcLmtnZ0eXLl0aqaw333xTpVJJjuOo3W5LOgnoePXVV2dQ88WyLCsyIGacQJW4QT8yAwIAAExHXHbpZQoyHpRd7MqVKyOVZ1mWjo+Ph3puVNnBhOqsTCMY33Vd7ezsSIq/xoZhRJZZKpW43wYAAACAcyZuF6dZZXI+OjqaSbnVajUM+pZO6l+r1XqeV6/Xw0Bu3/eVzWb7LmhutVoDd7qK4nneWK+LUiwWVS6Xw++DoPbgGgXB2sViUaVSaezz1Ot1ZbPZrvMcHBz0jBW4rhsurK9Wq3IcZ2bXFQAAAAAAnC0zC/yWToIevvjFLyqRSKjdbqtQKKhQKIRbmBmGEQZSdAZOBAHLnud1BUEHQd+JRKJr4GkaLMsKyx+H53na2tqKfGyU7H22bUcGfk8z4/csthYEAAA4j+ICfAdlzZ6n87ZdcJCl6/Q1ODw8HKmccbOTzyrjGgAAAABg9axan/x0kHeQmfr0DryZTEatVqtr99xisSjLsiJ36zVNU41GQ77v6/bt20MHc9+4cUOmacr3fRWLxbHGWzzPUzqd7nptoVCIDO4OMpOPOwcZFfTdbDYjPweWZeng4ECpVCqsZzabjQy0BwAAAAAA6DTTwG/btsOs30HwtyQdHx93bW8W53QgdlCGYRj68pe/PJM6L9rNmze7Mg4Exh3MOi3IYAAAAIDpsG2759523KDhfvL5vPb394fOth2Iy1g9LfV6XXfu3JHruvI8r2vr5UXJZDI999SjXJO4RaHVarVrUjtwfHw80yzmAAAAAIDltUyLv+cpl8upWCx2jQHk8/nYrNXB3JRlWUMHfnfOaZ0ONB+G7/tKpVJddYwL+g7OV6vVerKDDyMI3O5UKpX6Bv9blqVMJhO+H/V6Xa7rspMYAAAAAADoa23WJ6jVarp8+bKkk8Dt4Kvdbg/8inpNIpHQwcHBrKu9MJZlRQ4CjTpwGBdsc+PGjXGqBQAAgBinJ/Wkk3u3aQc/O46z8IDqgOu6ymazSiQSymazKpfLOjw8lGVZunXrlprNpo6PjyfaGnkScRPBwyw+BQAAAABgFMvSV5+G02Mcg5IJbW9vd33ved7A+axxF6iP87rr1693XR/LsoYaqyiVSiMv8I4qd5g5uZs3b3Z9X6lURjovAAAAAAA4f2Ye+G2apvb39yOzdw/zdfp1b7zxhl599dVZV3uhogaHRg0eigtqGXd7OgAAAETL5XKRC/f29/endg7f9+V53lJkfMpms0qlUmE2KtM0VavVdHx8rFqtpkwmI8uyZBjGzLJg+74vx3FiJ5NN01Qul+s5vqhAdAAAAAAAVkEul1OlUpFt28pkMmo2m32fP41ERrMSZM/uNErG8FEDzavVatf3pmkONS5yOrB+muNJAAAAAADgbJp54Ld0Mmjx1ltv6erVqz2B3P2yfUvqyg5eq9W0s7MzjyovVBAsc9oogz23b9/uOVYoFPpuKQcAAIDxRGVjmuaCu2Dy8HQWqHlLp9Nd2zGbpqlms6lMJjPXeniep3Q63TcLVlSQt+M4PZO+AAAAAABM4qzNu+RyOTUaDdVqtYFtm9WC72mIGjPol718ElHJmIZ9b04/z/f9M5VFHgAAAAAATN9cAr+lk4Ev13X1la98pSvAOy7Tt/RhULht2zo6OtKXvvSleVV34W7dutVzrFgsDjXY4zhOV0COdPL+k+EQAABgNmzbVqFQ6Drm+37PFsnj8DxPxWJRhmH0nGOe6vV6z0RmpVLpO5G5yIlKwzDUaDR6jp/e5nkUTLwCAAAAAE5b1n7xrC17gHJUMPaoWbyHFTX+MMq5TgfYL0vWdAAAAAAAsJzW53myy5cvq1Qq6Vd/9VdVrVbVaDQiB16kk0GOTCajmzdv6rXXXptnNfvyPC+yzkdHR7GvaTQakQN/UdvPByzLUqPRUDqdDo/5vq9UKqVGoxGbZcFxnK7XSB9mYgQAAMDslEoleZ7XtQCvXq8rn8/3zUzdj+/74b1drVabSj3HdefOnZ5j29vbfV/T7x45zumJ0Var1fOcYAJ0a2urb1m2batSqXRt5ez7vjY3N9VsNkfOyjZOewAAAAAAZ59pmpHBuq1Wa6mzYg/i+7729/fVbDZ1eHgoz/OWOtg7ELfb16yuRdR74jhOmOgKAAAAAABgmuYa+B24fPmyvvKVr+grX/mKJOn+/ftqtVryfV+GYWhzc3MR1RqK67pdgSPDcBwnMli8X+C3dBKocjr42/M8bW1tqVKpdL3e8zyVSiVVq9WuMoKg71UeWAQAAFgVtVpNxWJR5XI5PFatVuU4jmq1mizLGrosx3GUzWbl+75qtdrMtiMe1pUrV3qOeZ4X26ZgcnhUlmV13TtHTZwHQeiDAs+lk3tu0zTD9zKoW9Q9dT+u6/bcawMAAAAAIJ3M50T1GV3XHXnR8TDy+bwODw9nlvSnXq9rb2+vJ4Datm1ls1mZpinTNFWpVLrGQJZF1CLyebMsa6ydeJPJ5EjjRwAAAAAA4PxZW3QFpJNA8M3NTb322mtLHfR9mmEYY32NwrZtHR8fK5PJdB3P5/NKJBLa2NhQIpHQ1tZW16CiYRgqlUo6Ojoi6BsAAGCOSqVSTzZpz/OUSqWUSqVUrVZjt+z1PE/ValWpVErpdFrJZFLNZrPnXnARcrlcz33lzs5O5HODnWpyuVzPZOWg4Ol8Pt91Ht/3u7Ko+76varUqy7KGngi1bVt3797tCZ7P5/Pa2NhQsViM3YnI8zwVi0WlUilJUqVSWXgQPgAAAABguWSz2cjjUbtnTcP+/n5sVutJBOMX2Ww2LN8wDFUqFR0fH6vRaCiXy8m27ZkEtK+qqHk40zRl2/bIXwR9AwAAAACAQRaS8XuVZTIZtdvtuZ7TMAzVarUwa2Kj0ZDrumGW9OA5pmlqe3tb6XR6KYKDAAAAzivLsnR0dCTHcVQqlcKg4tO7x5imKcMw5Pt+VzB4sIivUCgMPJfrumEAdtTWwtevXw//H2QCcxxHxWJRUnRG7SDIOZlMqtFohHW6e/eustlsV3s2NjaUy+W0tbUl3/d1584d1et1FQqFcEeazjYXi8Xw3FFtNE1TBwcHYR2kkwn0YPIzCBy/devWwPemk2EYajQaYSB3EEzu+77K5XKYocwwDCWTyZ73JshkZtu2crlcT7viBFnSBtnb29Pt27e7zler1UZq4yiC7HCnxS1K6LwenWq1GpP950y9XtedO3fkOE7XNu/Bz45lWUqn0zMNBKlWq2o2m3Ich34xAAAAloJt22H/vlPcIuNJ+L4f7qA7TUHQd2cbVnVX2aBfPy9bW1s9x6LGaAAAAAAAAKaBwO8VYhiGcrnc0NvRAwCA5fMv8z+/6CpgjoJsTb7vy3Ec3blzR67ryvM8tVqtMMjWNE1ZliXbtsOAyWG1Wq2uLFynJ2M7AzMDvu8P9ZrTQZtB8LTrurp9+3YY+BkETVuWpe3tbR0dHYWvzeVyYRBz0G7pZBI2LijUsiwdHx+rWq3q9u3b8jwvPNeNGzdUKpXGnnTuDKh2HCdsT1C3zgn0oD1B4HmnXC6nGzduaH9/v29dOq9P8B7GPb/zWs16gvjw8LAnO1xQr6j6BZ/VqM8SzodisahqtRp7zYOfHc/zwoUVlmVpd3d3KgHYvu+HdTgtCLAJfre5rqtqtSrDMLS7uzvUIhoAAABgUqVSqWeBcHB/Os0szkEw+bR3o8pmsz33+8sU9B2MJwxj3guUo65F1GJrAAAAAACAaSDwGwAAAJgxwzCUyWRmkn3Wtu2Rd6SZdBcby7JGmrQOAuBHYRiGCoXCTAM2x6lXp2BhZj/LunAzyP4ODOI4Tk8ASJCB/8qVK7p3715XsHcn13WVzWZlWdZE2eFd19X169d7Mg8Wi8Wuny/P81SpVMLFKEGw+O3bt3VwcLA0ASsAAAA4m3K5nEqlUs9OSnt7e1Pd0alSqUjSULtQDSsIUO+UyWSW6h766Oho6OcGi7lPt2naQfgB0zRlmmbXtQ8Wxo7SDwoWvi/jOAIAAAAAAFgeAwO/33zzzdjtvgdJJpP6pV/6paGf/+abb0o6GSC5evXqWOcEAAAAAACTq1arXcEkhUJBu7u7scEf5XJZxWKx57jrutra2lKj0Rh5sUVU0Ldt22o0Gj3PNU0zzLLYuUV9UAbB3wAAAJi1Wq2mVCrVdaxer6ter09lMbjjOHIcR6ZpTjXjd1R26mvXrg183bjzh+M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",
      "text/plain": [
       "<Figure size 3000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# incidetn count by hour of day, 3 subplots in one row for each provider, aggregated by service\n",
    "print('Incident Count by Hour of Day')\n",
    "set_half_column_figure_font(height_multiplier=1.0)\n",
    "fig, axs = plt.subplots(1, 3, figsize=(30, 6))\n",
    "fig.subplots_adjust(wspace=0.0)\n",
    "\n",
    "# OpenAI\n",
    "ax = axs[0]\n",
    "colors = sns.color_palette('Blues', 4)\n",
    "# colors = sns.color_palette('Set2', 4)\n",
    "colors = colors[::-1]\n",
    "# hatches = ['o', 'x', '.', '+']\n",
    "hatches = ['', '', '', '']\n",
    "for service in openai_services:\n",
    "    playground = ax.bar(openai_hour_of_day_dfs[service].index, openai_hour_of_day_dfs[service], color=colors[3], width=0.6, hatch=hatches[3])\n",
    "    dalle = ax.bar(openai_hour_of_day_dfs[service].index, openai_hour_of_day_dfs[service], color=colors[2], width=0.6, hatch=hatches[2], bottom=openai_hour_of_day_dfs['Playground'])\n",
    "    chatgpt = ax.bar(openai_hour_of_day_dfs[service].index, openai_hour_of_day_dfs[service], color=colors[1], width=0.6, hatch=hatches[1], bottom=openai_hour_of_day_dfs['DALL·E'] + openai_hour_of_day_dfs['Playground'])\n",
    "    api = ax.bar(openai_hour_of_day_dfs[service].index, openai_hour_of_day_dfs[service], color=colors[0], width=0.6, hatch=hatches[0], bottom=openai_hour_of_day_dfs['ChatGPT'] + openai_hour_of_day_dfs['DALL·E'] + openai_hour_of_day_dfs['Playground'])\n",
    "\n",
    "    ax.set_title(r'\\textbf{OpenAI}', fontsize=41)\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend([api, chatgpt, dalle, playground], [r'\\textbf{API}', r'\\textbf{ChatGPT}', r'\\textbf{DALL·E}', r'\\textbf{Playground}'], loc='upper right', fontsize=30)\n",
    "    ax.set_ylabel(r'\\textbf{Incident Count}', fontsize=41)\n",
    "    ax.set_xticks([0, 5, 10, 15, 20])\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "    ax.xaxis.set_tick_params(labelsize=41, rotation=0)\n",
    "    ax.yaxis.set_tick_params(labelsize=41)\n",
    "\n",
    "# Anthropic\n",
    "ax = axs[1]\n",
    "colors = sns.color_palette('Oranges', 4)\n",
    "colors = colors[::-1]\n",
    "# hatches = ['o', 'x', '.', '+']\n",
    "hatches = ['', '', '', '']\n",
    "for service in anthropic_services:\n",
    "    console = ax.bar(anthropic_hour_of_day_dfs[service].index, anthropic_hour_of_day_dfs[service], width=0.6, color=colors[2], hatch=hatches[2])\n",
    "    claude = ax.bar(anthropic_hour_of_day_dfs[service].index, anthropic_hour_of_day_dfs[service], width=0.6, color=colors[1], hatch=hatches[1], bottom=anthropic_hour_of_day_dfs['Console'])\n",
    "    api_anthropic = ax.bar(anthropic_hour_of_day_dfs[service].index, anthropic_hour_of_day_dfs[service], width=0.6, color=colors[0], hatch=hatches[0], bottom=anthropic_hour_of_day_dfs['Claude'] + anthropic_hour_of_day_dfs['Console'])\n",
    "    ax.set_title(r'\\textbf{Anthropic}', fontsize=41)\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend([api_anthropic, claude, console], [r'\\textbf{API}', r'\\textbf{Claude}', r'\\textbf{Console}'], loc='upper right', fontsize=32)\n",
    "    # ax.set_ylabel(r'\\textbf{Outage Count}')\n",
    "    ax.set_xticks([0, 5, 10, 15, 20])\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "    ax.xaxis.set_tick_params(labelsize=41)\n",
    "    ax.yaxis.set_tick_params(labelsize=41)\n",
    "\n",
    "# Character.AI\n",
    "ax = axs[2]\n",
    "colors = sns.color_palette('Greens', 4)\n",
    "# hatches = ['o', 'x', '.', '+']\n",
    "hatches = ['', '', '', '']\n",
    "for service in character_ai_services:\n",
    "    # character_ai = character_ai_hour_of_day_dfs[service].plot(kind='bar', ax=ax, color=colors[3], hatch=hatches[0], width=0.6)\n",
    "    character_ai = ax.bar(character_ai_hour_of_day_dfs[service].index, character_ai_hour_of_day_dfs[service], color=colors[3], width=0.6, hatch=hatches[0])\n",
    "    ax.set_title(r'\\textbf{Character.AI}', fontsize=41)\n",
    "    ax.set_xlabel('')\n",
    "    ax.legend([r'\\textbf{Character.AI}'], loc='upper left', fontsize=32)\n",
    "    # ax.set_ylabel(r'\\textbf{Outage Count}')\n",
    "    # set x-axis labels, 0, 5, 10, 15, 20\n",
    "    ax.set_xticks([0, 5, 10, 15, 20])\n",
    "    ax.grid(axis='y', linestyle='--', alpha=0.8)\n",
    "    ax.xaxis.set_tick_params(labelsize=41, rotation=0)\n",
    "    ax.yaxis.set_tick_params(labelsize=41)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.0)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plot/sec5-temporal_analysis/incident_count_hour_of_day_by_provider.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec5-temporal_analysis/incident_count_hour_of_day_by_provider.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auto-correlation of incidents: by month, week, day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load incident data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 0, 0, 0]\n"
     ]
    },
    {
     "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>incident_id</th>\n",
       "      <th>Incident_Title</th>\n",
       "      <th>incident_impact_level</th>\n",
       "      <th>Incident_color</th>\n",
       "      <th>provider</th>\n",
       "      <th>Playground</th>\n",
       "      <th>API-OpenAI</th>\n",
       "      <th>DALL·E</th>\n",
       "      <th>ChatGPT</th>\n",
       "      <th>API-Anthropic</th>\n",
       "      <th>Claude</th>\n",
       "      <th>Console</th>\n",
       "      <th>Character.AI</th>\n",
       "      <th>investigating_flag</th>\n",
       "      <th>investigating_timestamp</th>\n",
       "      <th>investigating_description</th>\n",
       "      <th>identified_flag</th>\n",
       "      <th>identified_timestamp</th>\n",
       "      <th>identified_description</th>\n",
       "      <th>monitoring_flag</th>\n",
       "      <th>monitoring_timestamp</th>\n",
       "      <th>monitoring_description</th>\n",
       "      <th>resolved_flag</th>\n",
       "      <th>resolved_timestamp</th>\n",
       "      <th>resolved_description</th>\n",
       "      <th>postmortem_flag</th>\n",
       "      <th>postmortem_timestamp</th>\n",
       "      <th>postmortem_description</th>\n",
       "      <th>start_timestamp</th>\n",
       "      <th>close_timestamp</th>\n",
       "      <th>time_span</th>\n",
       "      <th>over_one_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zm9ssldbs7ds</td>\n",
       "      <td>Increased queue wait times for GPT-3.5-Turbo f...</td>\n",
       "      <td>1</td>\n",
       "      <td>#f4ac36</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-29 12:41:00</td>\n",
       "      <td>We are seeing a spike in demand for 3.5 turbo ...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-29 18:16:00</td>\n",
       "      <td>The queue length is settling and wait times fo...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-29 20:13:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-29 12:41:00</td>\n",
       "      <td>2024-03-01 04:13:00+00:00</td>\n",
       "      <td>0 days 07:32:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62wkltqhbrmk</td>\n",
       "      <td>Elevated error rate impacting API services (in...</td>\n",
       "      <td>2</td>\n",
       "      <td>#e86c09</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 07:06:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 10:38:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 12:40:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-14 07:06:00</td>\n",
       "      <td>2024-02-14 20:40:00+00:00</td>\n",
       "      <td>0 days 05:34:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70p519qgngrw</td>\n",
       "      <td>Elevated errors on GPT-4V for ChatGPT and API</td>\n",
       "      <td>2</td>\n",
       "      <td>#e86c09</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 07:03:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 07:36:00</td>\n",
       "      <td>The issue has been identified and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 10:39:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-14 12:40:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-13 07:03:00</td>\n",
       "      <td>2024-02-14 20:40:00+00:00</td>\n",
       "      <td>1 days 05:37:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mswp9vt22gm2</td>\n",
       "      <td>Elevated errors across multiple API endpoints ...</td>\n",
       "      <td>2</td>\n",
       "      <td>#e86c09</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 12:11:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 12:38:00</td>\n",
       "      <td>We have identified this issue and a fix is bei...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 13:46:00</td>\n",
       "      <td>A fix has been implemented, and we are monitor...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-13 15:38:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-13 12:11:00</td>\n",
       "      <td>2024-02-13 23:38:00+00:00</td>\n",
       "      <td>0 days 03:27:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>shqybftjc7mc</td>\n",
       "      <td>Elevated errors on GPT-4V for ChatGPT and API</td>\n",
       "      <td>1</td>\n",
       "      <td>#f4ac36</td>\n",
       "      <td>openai</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 09:19:00</td>\n",
       "      <td>We are currently investigating this issue.</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 10:19:00</td>\n",
       "      <td>A fix has been implemented and we are monitori...</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-02-12 10:39:00</td>\n",
       "      <td>This incident has been resolved.</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-02-12 09:19:00</td>\n",
       "      <td>2024-02-12 18:39:00+00:00</td>\n",
       "      <td>0 days 01:20:00</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    incident_id                                     Incident_Title  \\\n",
       "0  zm9ssldbs7ds  Increased queue wait times for GPT-3.5-Turbo f...   \n",
       "1  62wkltqhbrmk  Elevated error rate impacting API services (in...   \n",
       "2  70p519qgngrw      Elevated errors on GPT-4V for ChatGPT and API   \n",
       "3  mswp9vt22gm2  Elevated errors across multiple API endpoints ...   \n",
       "4  shqybftjc7mc      Elevated errors on GPT-4V for ChatGPT and API   \n",
       "\n",
       "   incident_impact_level Incident_color provider  Playground  API-OpenAI  \\\n",
       "0                      1        #f4ac36   openai           0           1   \n",
       "1                      2        #e86c09   openai           0           1   \n",
       "2                      2        #e86c09   openai           0           1   \n",
       "3                      2        #e86c09   openai           0           1   \n",
       "4                      1        #f4ac36   openai           0           1   \n",
       "\n",
       "   DALL·E  ChatGPT  API-Anthropic  Claude  Console  Character.AI  \\\n",
       "0       0        0              0       0        0             0   \n",
       "1       0        0              0       0        0             0   \n",
       "2       0        1              0       0        0             0   \n",
       "3       0        1              0       0        0             0   \n",
       "4       0        1              0       0        0             0   \n",
       "\n",
       "   investigating_flag investigating_timestamp  \\\n",
       "0                   0                     NaT   \n",
       "1                   1     2024-02-14 07:06:00   \n",
       "2                   1     2024-02-13 07:03:00   \n",
       "3                   1     2024-02-13 12:11:00   \n",
       "4                   0                     NaT   \n",
       "\n",
       "                    investigating_description  identified_flag  \\\n",
       "0                                         NaN                1   \n",
       "1  We are currently investigating this issue.                0   \n",
       "2  We are currently investigating this issue.                1   \n",
       "3  We are currently investigating this issue.                1   \n",
       "4                                         NaN                1   \n",
       "\n",
       "  identified_timestamp                             identified_description  \\\n",
       "0  2024-02-29 12:41:00  We are seeing a spike in demand for 3.5 turbo ...   \n",
       "1                  NaT                                                NaN   \n",
       "2  2024-02-13 07:36:00  The issue has been identified and a fix is bei...   \n",
       "3  2024-02-13 12:38:00  We have identified this issue and a fix is bei...   \n",
       "4  2024-02-12 09:19:00         We are currently investigating this issue.   \n",
       "\n",
       "   monitoring_flag monitoring_timestamp  \\\n",
       "0                1  2024-02-29 18:16:00   \n",
       "1                1  2024-02-14 10:38:00   \n",
       "2                1  2024-02-14 10:39:00   \n",
       "3                1  2024-02-13 13:46:00   \n",
       "4                1  2024-02-12 10:19:00   \n",
       "\n",
       "                              monitoring_description  resolved_flag  \\\n",
       "0  The queue length is settling and wait times fo...              1   \n",
       "1  A fix has been implemented and we are monitori...              1   \n",
       "2  A fix has been implemented and we are monitori...              1   \n",
       "3  A fix has been implemented, and we are monitor...              1   \n",
       "4  A fix has been implemented and we are monitori...              1   \n",
       "\n",
       "   resolved_timestamp              resolved_description  postmortem_flag  \\\n",
       "0 2024-02-29 20:13:00  This incident has been resolved.                0   \n",
       "1 2024-02-14 12:40:00  This incident has been resolved.                0   \n",
       "2 2024-02-14 12:40:00  This incident has been resolved.                0   \n",
       "3 2024-02-13 15:38:00  This incident has been resolved.                0   \n",
       "4 2024-02-12 10:39:00  This incident has been resolved.                0   \n",
       "\n",
       "  postmortem_timestamp postmortem_description     start_timestamp  \\\n",
       "0                  NaT                    NaN 2024-02-29 12:41:00   \n",
       "1                  NaT                    NaN 2024-02-14 07:06:00   \n",
       "2                  NaT                    NaN 2024-02-13 07:03:00   \n",
       "3                  NaT                    NaN 2024-02-13 12:11:00   \n",
       "4                  NaT                    NaN 2024-02-12 09:19:00   \n",
       "\n",
       "             close_timestamp        time_span  over_one_day  \n",
       "0  2024-03-01 04:13:00+00:00  0 days 07:32:00         False  \n",
       "1  2024-02-14 20:40:00+00:00  0 days 05:34:00         False  \n",
       "2  2024-02-14 20:40:00+00:00  1 days 05:37:00          True  \n",
       "3  2024-02-13 23:38:00+00:00  0 days 03:27:00         False  \n",
       "4  2024-02-12 18:39:00+00:00  0 days 01:20:00         False  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load incident data\n",
    "path = f'data/clean/incident/2024-08-31/incident_stages.csv'\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "# status_lst = ['investigating', 'identified', 'monitoring', 'resolved', 'postmortem', 'start']\n",
    "# for status in status_lst:\n",
    "#     df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp'])\n",
    "\n",
    "status_lst = ['investigating', 'identified', 'monitoring', 'resolved', 'postmortem', 'start']\n",
    "for status in status_lst:\n",
    "    df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp'], errors='coerce')\n",
    "    # convert to PDT time\n",
    "    df[f'{status}_timestamp'] = df[f'{status}_timestamp'].dt.tz_convert('US/Pacific')\n",
    "    # remove timezone information\n",
    "    df[f'{status}_timestamp'] = pd.to_datetime(df[f'{status}_timestamp']).dt.tz_localize(None)\n",
    "\n",
    "# filter out df corner cases\n",
    "count_lst = 5 * [0]\n",
    "corner_cases = pd.DataFrame()\n",
    "\n",
    "for index, record in df.iterrows():\n",
    "    if record['investigating_timestamp'] > record['identified_timestamp']:\n",
    "        count_lst[0] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "    if record['identified_timestamp'] > record['monitoring_timestamp']:\n",
    "        count_lst[1] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "    if record['monitoring_timestamp'] > record['resolved_timestamp']:\n",
    "        count_lst[2] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "    if record['resolved_timestamp'] > record['postmortem_timestamp']:\n",
    "        count_lst[3] += 1\n",
    "        corner_cases = pd.concat([corner_cases, record], axis=0)\n",
    "print(count_lst)\n",
    "\n",
    "# transposing corner cases\n",
    "corner_cases = corner_cases.T\n",
    "# display(corner_cases)\n",
    "\n",
    "# filter out df corner cases\n",
    "df = df.drop(corner_cases.index)\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "df = df.rename(columns={'API': 'API-OpenAI', 'ChatGPT': 'ChatGPT', 'Labs': 'DALL·E', 'Playground': 'Playground', 'api.anthropic.com': 'API-Anthropic', 'claude.ai': 'Claude', 'console.anthropic.com': 'Console', 'Character.AI': 'Character.AI'})\n",
    "service_lst = ['API-OpenAI', 'ChatGPT', 'DALL·E', 'Playground', 'API-Anthropic', 'Claude', 'Console', 'Character.AI']\n",
    "\n",
    "dfs = {}\n",
    "for service in service_lst:\n",
    "    dfs[service] = df[df[service] == 1]\n",
    "    dfs[service].reset_index(drop=True, inplace=True)\n",
    "\n",
    "provider_lst = ['openai', 'anthropic', 'character']\n",
    "dfs_provider = {}\n",
    "for provider in provider_lst:\n",
    "    for service in service_lst:\n",
    "        if provider in dfs[service]['provider'].unique():\n",
    "            dfs_provider[provider] = dfs_provider.get(provider, pd.DataFrame())\n",
    "            dfs_provider[provider] = pd.concat([dfs_provider[provider], dfs[service][dfs[service]['provider'] == provider]], axis=0)\n",
    "\n",
    "display(dfs_provider['openai'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OpenAI autocorelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45 192 1337 32076\n"
     ]
    }
   ],
   "source": [
    "df = dfs_provider['openai']\n",
    "\n",
    "df['incident_start_time'] = df[['investigating_timestamp', 'identified_timestamp', 'monitoring_timestamp', 'resolved_timestamp']].min(axis=1)\n",
    "df = df[['resolved_flag', 'incident_start_time']]\n",
    "\n",
    "# add column 'year', 'month', 'week', 'day', 'hour'\n",
    "df['year'] = df['incident_start_time'].dt.year\n",
    "df['month_of_year'] = df['incident_start_time'].dt.month\n",
    "df['week_of_year'] = df['incident_start_time'].dt.isocalendar().week\n",
    "df['day_of_year'] = df['incident_start_time'].dt.dayofyear\n",
    "df['hour_of_day'] = df['incident_start_time'].dt.hour\n",
    "\n",
    "# add column 'year_num', 'month_num', 'week_num', 'day_num', 'hour_num'\n",
    "df['year_num'] = df['year'] - df['year'].min()\n",
    "df['month_num'] = df['year_num'] * 12 + df['month_of_year']\n",
    "df['week_num'] = df['year_num'] * 52 + df['week_of_year']\n",
    "df['day_num'] = df['year_num'] * 365 + df['day_of_year']\n",
    "df['hour_num'] = df['day_num'] * 24 + df['hour_of_day']\n",
    "\n",
    "# select and group columns for auto-correlation\n",
    "df_month = df[['month_num', 'resolved_flag']]\n",
    "group_by_month = df_month.groupby('month_num')['resolved_flag'].sum()\n",
    "df_month = pd.DataFrame({'month_num': range(max(group_by_month.index)+1), 'resolved_flag': 0})\n",
    "df_month = df_month.merge(group_by_month, how='left', on='month_num').fillna(0)\n",
    "df_month = df_month['resolved_flag_y']\n",
    "\n",
    "df_week = df[['week_num', 'resolved_flag']]\n",
    "group_by_week = df_week.groupby('week_num')['resolved_flag'].sum()\n",
    "df_week = pd.DataFrame({'week_num': range(max(group_by_week.index)+1), 'resolved_flag': 0})\n",
    "df_week = df_week.merge(group_by_week, how='left', on='week_num').fillna(0)\n",
    "df_week = df_week['resolved_flag_y']\n",
    "\n",
    "df_day = df[['day_num', 'resolved_flag']]\n",
    "group_by_day = df_day.groupby('day_num')['resolved_flag'].sum()\n",
    "df_day = pd.DataFrame({'day_num': range(max(group_by_day.index)+1), 'resolved_flag': 0})\n",
    "df_day = df_day.merge(group_by_day, how='left', on='day_num').fillna(0)\n",
    "df_day = df_day['resolved_flag_y']\n",
    "\n",
    "df_hour = df[['hour_num', 'resolved_flag']]\n",
    "group_by_hour = df_hour.groupby('hour_num')['resolved_flag'].sum()\n",
    "df_hour = pd.DataFrame({'hour_num': range(max(group_by_hour.index)+1), 'resolved_flag': 0})\n",
    "df_hour = df_hour.merge(group_by_hour, how='left', on='hour_num').fillna(0)\n",
    "df_hour = df_hour['resolved_flag_y']\n",
    "\n",
    "print(len(df_month), len(df_week), len(df_day), len(df_hour))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting auto-correlation for df_month, df_week, df_day\n",
    "set_half_column_figure_font(height_multiplier=1.0)\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "fig.subplots_adjust(wspace=0.0)\n",
    "\n",
    "# month\n",
    "ax = axs[0]\n",
    "plot_acf(df_month, lags=12, ax=ax)\n",
    "ax.set_title('')\n",
    "ax.set_xlabel(r'\\textbf{Month (Lags=12)}', fontsize=24.9)\n",
    "ax.xaxis.set_tick_params(labelsize=24.9)\n",
    "ax.yaxis.set_tick_params(labelsize=24.9)\n",
    "ax.set_ylim(bottom=0)\n",
    "\n",
    "# week\n",
    "ax = axs[1]\n",
    "plot_acf(df_week, lags=50, ax=ax)\n",
    "ax.set_title('')\n",
    "ax.set_xlabel(r'\\textbf{Week (Lags=50)}', fontsize=24.9)\n",
    "ax.xaxis.set_tick_params(labelsize=24.9)\n",
    "ax.yaxis.set_tick_params(labelsize=24.9)\n",
    "ax.set_ylim(bottom=0)\n",
    "\n",
    "# day\n",
    "ax = axs[2]\n",
    "plot_acf(df_day, lags=80, ax=ax)\n",
    "ax.set_title('')\n",
    "ax.set_xlabel(r'\\textbf{Day (Lags=80)}', fontsize=24.9)\n",
    "ax.xaxis.set_tick_params(labelsize=24.9)\n",
    "ax.yaxis.set_tick_params(labelsize=24.9)\n",
    "ax.set_ylim(bottom=0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('plot/sec5-temporal_analysis/auto_correlation_openai.pdf')\n",
    "plt.savefig('plot/sec5-temporal_analysis/auto_correlation_openai.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anthropic auto-correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 88 606 14539\n"
     ]
    }
   ],
   "source": [
    "df = dfs_provider['anthropic']\n",
    "\n",
    "df['incident_start_time'] = df[['investigating_timestamp', 'identified_timestamp', 'monitoring_timestamp', 'resolved_timestamp']].min(axis=1)\n",
    "df = df[['resolved_flag', 'incident_start_time']]\n",
    "\n",
    "# add column 'year', 'month', 'week', 'day', 'hour'\n",
    "df['year'] = df['incident_start_time'].dt.year\n",
    "df['month_of_year'] = df['incident_start_time'].dt.month\n",
    "df['week_of_year'] = df['incident_start_time'].dt.isocalendar().week\n",
    "df['day_of_year'] = df['incident_start_time'].dt.dayofyear\n",
    "df['hour_of_day'] = df['incident_start_time'].dt.hour\n",
    "\n",
    "# add column 'year_num', 'month_num', 'week_num', 'day_num', 'hour_num'\n",
    "df['year_num'] = df['year'] - df['year'].min()\n",
    "df['month_num'] = df['year_num'] * 12 + df['month_of_year']\n",
    "df['week_num'] = df['year_num'] * 52 + df['week_of_year']\n",
    "df['day_num'] = df['year_num'] * 365 + df['day_of_year']\n",
    "df['hour_num'] = df['day_num'] * 24 + df['hour_of_day']\n",
    "\n",
    "# select and group columns for auto-correlation\n",
    "df_month = df[['month_num', 'resolved_flag']]\n",
    "group_by_month = df_month.groupby('month_num')['resolved_flag'].sum()\n",
    "df_month = pd.DataFrame({'month_num': range(max(group_by_month.index)+1), 'resolved_flag': 0})\n",
    "df_month = df_month.merge(group_by_month, how='left', on='month_num').fillna(0)\n",
    "df_month = df_month['resolved_flag_y']\n",
    "\n",
    "df_week = df[['week_num', 'resolved_flag']]\n",
    "group_by_week = df_week.groupby('week_num')['resolved_flag'].sum()\n",
    "df_week = pd.DataFrame({'week_num': range(max(group_by_week.index)+1), 'resolved_flag': 0})\n",
    "df_week = df_week.merge(group_by_week, how='left', on='week_num').fillna(0)\n",
    "df_week = df_week['resolved_flag_y']\n",
    "\n",
    "df_day = df[['day_num', 'resolved_flag']]\n",
    "group_by_day = df_day.groupby('day_num')['resolved_flag'].sum()\n",
    "df_day = pd.DataFrame({'day_num': range(max(group_by_day.index)+1), 'resolved_flag': 0})\n",
    "df_day = df_day.merge(group_by_day, how='left', on='day_num').fillna(0)\n",
    "df_day = df_day['resolved_flag_y']\n",
    "\n",
    "df_hour = df[['hour_num', 'resolved_flag']]\n",
    "group_by_hour = df_hour.groupby('hour_num')['resolved_flag'].sum()\n",
    "df_hour = pd.DataFrame({'hour_num': range(max(group_by_hour.index)+1), 'resolved_flag': 0})\n",
    "df_hour = df_hour.merge(group_by_hour, how='left', on='hour_num').fillna(0)\n",
    "df_hour = df_hour['resolved_flag_y']\n",
    "\n",
    "print(len(df_month), len(df_week), len(df_day), len(df_hour))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting auto-correlation for df_month, df_week, df_day\n",
    "set_half_column_figure_font(height_multiplier=1.0)\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n",
    "fig.subplots_adjust(wspace=0.0)\n",
    "\n",
    "# month\n",
    "ax = axs[0]\n",
    "plot_acf(df_month, lags=12, ax=ax)\n",
    "ax.set_title('')\n",
    "ax.set_xlabel(r'\\textbf{Month (Lags=12)}', fontsize=24.9)\n",
    "ax.xaxis.set_tick_params(labelsize=24.9)\n",
    "ax.yaxis.set_tick_params(labelsize=24.9)\n",
    "ax.set_ylim(bottom=0)\n",
    "\n",
    "# week\n",
    "ax = axs[1]\n",
    "plot_acf(df_week, lags=50, ax=ax)\n",
    "ax.set_title('')\n",
    "ax.set_xlabel(r'\\textbf{Week (Lags=50)}', fontsize=24.9)\n",
    "ax.xaxis.set_tick_params(labelsize=24.9)\n",
    "ax.yaxis.set_tick_params(labelsize=24.9)\n",
    "ax.set_ylim(bottom=0)\n",
    "\n",
    "# day\n",
    "ax = axs[2]\n",
    "plot_acf(df_day, lags=80, ax=ax)\n",
    "ax.set_title('')\n",
    "ax.set_xlabel(r'\\textbf{Day (Lags=80)}', fontsize=24.9)\n",
    "ax.xaxis.set_tick_params(labelsize=24.9)\n",
    "ax.yaxis.set_tick_params(labelsize=24.9)\n",
    "ax.set_ylim(bottom=0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('plot/sec5-temporal_analysis/auto_correlation_anthropic.pdf', bbox_inches='tight')\n",
    "plt.savefig('plot/sec5-temporal_analysis/auto_correlation_by_anthropic.png', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}