notebooks/data-sources/TestGrid/testgrid_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TestGrid: initial EDA and data collection \n",
    "\n",
    "\n",
    "_Note: This notebook can be run and experimented with directly using the \"ocp-ci-analysis\" image at our [jupyterhub instance](https://jupyterhub-opf-jupyterhub.apps.cnv.massopen.cloud/hub/login) hosted on the MOC._  \n",
    "\n",
    "This notebook explores how to access the existing TestGrid data at [testgrid.k8s.io](testgrid.k8s.io), giving specific attention to [Red Hat's OpenShift CI dashboards](https://testgrid.k8s.io/redhat-openshift-informing). Running the entire notebook will result in downloading and saving an up to date TestGrid dataset in json format.  \n",
    "\n",
    "To start, we will rely on some of the work already established by the sippy team [here](https://github.com/openshift/sippy/blob/46ad8d209ed1f4b8d5e5e6fa8033088a593d695c/main.go#L49-L249) to access the data aggregated by TestGrid and convert it into a format that can be directly analyzed in a notebook.      \n",
    "\n",
    "For this initial stage, we will be making the assumption that the grid format for visualizing CI data, with columns representing days and rows representing tests with the value of each being either passed, failed, or not run, as a meaningful representation for further analysis and possible machine learning application. We may update this assumption as we learn more about the data.     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is Testgrid?** According to the project's [readme](https://github.com/GoogleCloudPlatform/testgrid) it is a, \"highly configurable, interactive dashboard for viewing your test results in a grid!\" In other words, its an aggregation and visualization platform for CI data. Our hope is that this aggregation encodes some of the subject matter experts' knowledge, and will provide better initial features than going straight to the more complex underlying CI data artifacts [here](https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/logs/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../docs/assets/images/testgrid_0.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all the available dashboards\n",
    "\n",
    "We need a programmatic way to access all the available Red Hat Dashboards on TestGrid. This can be done by scraping the html of any one of the dashboards. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(\n",
    "    \"https://testgrid.k8s.io/redhat-openshift-informing?id=dashboard-group-bar\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\"redhat-openshift-informing\"',\n",
       " '\"redhat-openshift-ocp-release-3.11-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.1-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.1-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.2-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.2-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.3-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.3-broken\"',\n",
       " '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.4-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.4-broken\"',\n",
       " '\"redhat-openshift-ocp-release-4.4-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.5-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.5-broken\"',\n",
       " '\"redhat-openshift-ocp-release-4.5-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.6-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.6-broken\"',\n",
       " '\"redhat-openshift-ocp-release-4.6-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.7-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.7-broken\"',\n",
       " '\"redhat-openshift-ocp-release-4.7-informing\"',\n",
       " '\"redhat-openshift-ocp-release-4.8-blocking\"',\n",
       " '\"redhat-openshift-ocp-release-4.8-informing\"',\n",
       " '\"redhat-openshift-okd-release-4.3-informing\"',\n",
       " '\"redhat-openshift-okd-release-4.4-informing\"',\n",
       " '\"redhat-openshift-okd-release-4.5-blocking\"',\n",
       " '\"redhat-openshift-okd-release-4.5-informing\"',\n",
       " '\"redhat-openshift-okd-release-4.6-blocking\"',\n",
       " '\"redhat-openshift-okd-release-4.6-informing\"',\n",
       " '\"redhat-openshift-okd-release-4.7-informing\"',\n",
       " '\"redhat-openshift-presubmit-master-gcp\"',\n",
       " '\"redhat-osde2e-int-aws\"',\n",
       " '\"redhat-osde2e-int-gcp\"',\n",
       " '\"redhat-osde2e-int-moa\"',\n",
       " '\"redhat-osde2e-prod-aws\"',\n",
       " '\"redhat-osde2e-prod-gcp\"',\n",
       " '\"redhat-osde2e-prod-moa\"',\n",
       " '\"redhat-osde2e-stage-aws\"',\n",
       " '\"redhat-osde2e-stage-gcp\"',\n",
       " '\"redhat-osde2e-stage-moa\"']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html = BeautifulSoup(response.content)\n",
    "testgrid_script = html.findAll(\"script\")[3]\n",
    "testgrid_script = testgrid_script.text.split()[5].split(\",\")\n",
    "dashboard_names = [x.split(\":\")[1] for x in testgrid_script if \"name\" in x]\n",
    "dashboard_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect a specific release summary dashboard\n",
    "\n",
    "Now that we have a programmatic way of collecting all the dashboard names we can use this later on to collect all the available data provided by TestGrid. For now, let's take a look at a specific dashboard and see what kind of information it's summary holds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard = \"redhat-openshift-ocp-release-4.2-informing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(f\"https://testgrid.k8s.io/{dashboard}/summary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>promote-release-openshift-machine-os-content-e2e-aws-4.1</th>\n",
       "      <th>promote-release-openshift-machine-os-content-e2e-aws-4.2</th>\n",
       "      <th>promote-release-openshift-machine-os-content-e2e-aws-4.2-s390x</th>\n",
       "      <th>release-openshift-ocp-installer-console-aws-4.2</th>\n",
       "      <th>release-openshift-ocp-installer-e2e-aws-fips-4.2</th>\n",
       "      <th>release-openshift-ocp-installer-e2e-aws-fips-serial-4.2</th>\n",
       "      <th>release-openshift-ocp-installer-e2e-aws-mirrors-4.2</th>\n",
       "      <th>release-openshift-ocp-installer-e2e-aws-proxy-4.2</th>\n",
       "      <th>release-openshift-ocp-installer-e2e-aws-upi-4.2</th>\n",
       "      <th>release-openshift-ocp-installer-e2e-azure-4.2</th>\n",
       "      <th>...</th>\n",
       "      <th>release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.2</th>\n",
       "      <th>release-openshift-origin-installer-e2e-aws-upgrade-4.1-stable-to-4.2-ci</th>\n",
       "      <th>release-openshift-origin-installer-e2e-aws-upgrade-4.2-stable-to-4.2-nightly</th>\n",
       "      <th>release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2</th>\n",
       "      <th>release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.2</th>\n",
       "      <th>release-openshift-origin-installer-e2e-azure-upgrade-4.2</th>\n",
       "      <th>release-openshift-origin-installer-e2e-gcp-4.2</th>\n",
       "      <th>release-openshift-origin-installer-e2e-gcp-serial-4.2</th>\n",
       "      <th>release-openshift-origin-installer-e2e-gcp-upgrade-4.2</th>\n",
       "      <th>release-openshift-origin-installer-old-rhcos-e2e-aws-4.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alert</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_run_timestamp</th>\n",
       "      <td>1605727873000</td>\n",
       "      <td>1605729490000</td>\n",
       "      <td>1605711969000</td>\n",
       "      <td>1605708248000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1605714979000</td>\n",
       "      <td>1605718868000</td>\n",
       "      <td>1605708247000</td>\n",
       "      <td>1605708247000</td>\n",
       "      <td>...</td>\n",
       "      <td>1605718391000</td>\n",
       "      <td>1605722228000</td>\n",
       "      <td>1605729194000</td>\n",
       "      <td>1605716352000</td>\n",
       "      <td>1605700568000</td>\n",
       "      <td>1605714980000</td>\n",
       "      <td>1605714981000</td>\n",
       "      <td>1605714980000</td>\n",
       "      <td>1605714979000</td>\n",
       "      <td>1605700809000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_update_timestamp</th>\n",
       "      <td>1605732527</td>\n",
       "      <td>1605732527</td>\n",
       "      <td>1605732524</td>\n",
       "      <td>1605730359</td>\n",
       "      <td>1605730375</td>\n",
       "      <td>1605730380</td>\n",
       "      <td>1605730401</td>\n",
       "      <td>1605730415</td>\n",
       "      <td>1605730436</td>\n",
       "      <td>1605730445</td>\n",
       "      <td>...</td>\n",
       "      <td>1605730599</td>\n",
       "      <td>1605730619</td>\n",
       "      <td>1605730603</td>\n",
       "      <td>1605730621</td>\n",
       "      <td>1605730621</td>\n",
       "      <td>1605730646</td>\n",
       "      <td>1605730669</td>\n",
       "      <td>1605730682</td>\n",
       "      <td>1605730695</td>\n",
       "      <td>1605730714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latest_green</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>no recent greens</td>\n",
       "      <td>no recent greens</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no recent greens</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overall_status</th>\n",
       "      <td>PASSING</td>\n",
       "      <td>PASSING</td>\n",
       "      <td>PASSING</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>...</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>PASSING</td>\n",
       "      <td>FAILING</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>PASSING</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "      <td>FLAKY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overall_status_icon</th>\n",
       "      <td>done</td>\n",
       "      <td>done</td>\n",
       "      <td>done</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>...</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>done</td>\n",
       "      <td>warning</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>done</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "      <td>remove_circle_outline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>10 of 10 (100.0%) recent columns passed (10 of...</td>\n",
       "      <td>10 of 10 (100.0%) recent columns passed (10 of...</td>\n",
       "      <td>10 of 10 (100.0%) recent columns passed (10 of...</td>\n",
       "      <td>8 of 10 (80.0%) recent columns passed (3213 of...</td>\n",
       "      <td>no completed results</td>\n",
       "      <td>no completed results</td>\n",
       "      <td>9 of 10 (90.0%) recent columns passed (9140 of...</td>\n",
       "      <td>6 of 10 (60.0%) recent columns passed (5470 of...</td>\n",
       "      <td>6 of 10 (60.0%) recent columns passed (8182 of...</td>\n",
       "      <td>6 of 10 (60.0%) recent columns passed (9032 of...</td>\n",
       "      <td>...</td>\n",
       "      <td>6 of 10 (60.0%) recent columns passed (9138 of...</td>\n",
       "      <td>9 of 10 (90.0%) recent columns passed (194 of ...</td>\n",
       "      <td>9 of 9 (100.0%) recent columns passed (189 of ...</td>\n",
       "      <td>0 of 10 (0.0%) recent columns passed (110 of 1...</td>\n",
       "      <td>9 of 10 (90.0%) recent columns passed (209 of ...</td>\n",
       "      <td>10 of 10 (100.0%) recent columns passed (210 o...</td>\n",
       "      <td>7 of 10 (70.0%) recent columns passed (8688 of...</td>\n",
       "      <td>8 of 10 (80.0%) recent columns passed (790 of ...</td>\n",
       "      <td>8 of 10 (80.0%) recent columns passed (193 of ...</td>\n",
       "      <td>4 of 10 (40.0%) recent columns passed (4148 of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tests</th>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'display_name': 'Overall', 'test_name': 'Ove...</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>dashboard_name</th>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>...</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "      <td>redhat-openshift-ocp-release-4.2-informing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>healthiness</th>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "      <td>{'tests': [], 'previousFlakiness': -1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      promote-release-openshift-machine-os-content-e2e-aws-4.1  \\\n",
       "alert                                                                            \n",
       "last_run_timestamp                                         1605727873000         \n",
       "last_update_timestamp                                         1605732527         \n",
       "latest_green                                                                     \n",
       "overall_status                                                   PASSING         \n",
       "overall_status_icon                                                 done         \n",
       "status                 10 of 10 (100.0%) recent columns passed (10 of...         \n",
       "tests                                                                 []         \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing         \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}         \n",
       "\n",
       "                      promote-release-openshift-machine-os-content-e2e-aws-4.2  \\\n",
       "alert                                                                            \n",
       "last_run_timestamp                                         1605729490000         \n",
       "last_update_timestamp                                         1605732527         \n",
       "latest_green                                                                     \n",
       "overall_status                                                   PASSING         \n",
       "overall_status_icon                                                 done         \n",
       "status                 10 of 10 (100.0%) recent columns passed (10 of...         \n",
       "tests                                                                 []         \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing         \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}         \n",
       "\n",
       "                      promote-release-openshift-machine-os-content-e2e-aws-4.2-s390x  \\\n",
       "alert                                                                                  \n",
       "last_run_timestamp                                         1605711969000               \n",
       "last_update_timestamp                                         1605732524               \n",
       "latest_green                                                                           \n",
       "overall_status                                                   PASSING               \n",
       "overall_status_icon                                                 done               \n",
       "status                 10 of 10 (100.0%) recent columns passed (10 of...               \n",
       "tests                                                                 []               \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing               \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}               \n",
       "\n",
       "                         release-openshift-ocp-installer-console-aws-4.2  \\\n",
       "alert                                                                      \n",
       "last_run_timestamp                                         1605708248000   \n",
       "last_update_timestamp                                         1605730359   \n",
       "latest_green                                                           1   \n",
       "overall_status                                                     FLAKY   \n",
       "overall_status_icon                                remove_circle_outline   \n",
       "status                 8 of 10 (80.0%) recent columns passed (3213 of...   \n",
       "tests                                                                 []   \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing   \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}   \n",
       "\n",
       "                      release-openshift-ocp-installer-e2e-aws-fips-4.2  \\\n",
       "alert                                                                    \n",
       "last_run_timestamp                                                   0   \n",
       "last_update_timestamp                                       1605730375   \n",
       "latest_green                                          no recent greens   \n",
       "overall_status                                                           \n",
       "overall_status_icon                                                      \n",
       "status                                            no completed results   \n",
       "tests                                                               []   \n",
       "dashboard_name              redhat-openshift-ocp-release-4.2-informing   \n",
       "healthiness                     {'tests': [], 'previousFlakiness': -1}   \n",
       "\n",
       "                      release-openshift-ocp-installer-e2e-aws-fips-serial-4.2  \\\n",
       "alert                                                                           \n",
       "last_run_timestamp                                                     0        \n",
       "last_update_timestamp                                         1605730380        \n",
       "latest_green                                            no recent greens        \n",
       "overall_status                                                                  \n",
       "overall_status_icon                                                             \n",
       "status                                              no completed results        \n",
       "tests                                                                 []        \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing        \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}        \n",
       "\n",
       "                      release-openshift-ocp-installer-e2e-aws-mirrors-4.2  \\\n",
       "alert                                                                       \n",
       "last_run_timestamp                                         1605714979000    \n",
       "last_update_timestamp                                         1605730401    \n",
       "latest_green                                                           1    \n",
       "overall_status                                                     FLAKY    \n",
       "overall_status_icon                                remove_circle_outline    \n",
       "status                 9 of 10 (90.0%) recent columns passed (9140 of...    \n",
       "tests                                                                 []    \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing    \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}    \n",
       "\n",
       "                       release-openshift-ocp-installer-e2e-aws-proxy-4.2  \\\n",
       "alert                                                                      \n",
       "last_run_timestamp                                         1605718868000   \n",
       "last_update_timestamp                                         1605730415   \n",
       "latest_green                                                           1   \n",
       "overall_status                                                     FLAKY   \n",
       "overall_status_icon                                remove_circle_outline   \n",
       "status                 6 of 10 (60.0%) recent columns passed (5470 of...   \n",
       "tests                                                                 []   \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing   \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}   \n",
       "\n",
       "                         release-openshift-ocp-installer-e2e-aws-upi-4.2  \\\n",
       "alert                                                                      \n",
       "last_run_timestamp                                         1605708247000   \n",
       "last_update_timestamp                                         1605730436   \n",
       "latest_green                                                           1   \n",
       "overall_status                                                     FLAKY   \n",
       "overall_status_icon                                remove_circle_outline   \n",
       "status                 6 of 10 (60.0%) recent columns passed (8182 of...   \n",
       "tests                                                                 []   \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing   \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}   \n",
       "\n",
       "                           release-openshift-ocp-installer-e2e-azure-4.2  ...  \\\n",
       "alert                                                                     ...   \n",
       "last_run_timestamp                                         1605708247000  ...   \n",
       "last_update_timestamp                                         1605730445  ...   \n",
       "latest_green                                                           1  ...   \n",
       "overall_status                                                     FLAKY  ...   \n",
       "overall_status_icon                                remove_circle_outline  ...   \n",
       "status                 6 of 10 (60.0%) recent columns passed (9032 of...  ...   \n",
       "tests                                                                 []  ...   \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing  ...   \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}  ...   \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.2  \\\n",
       "alert                                                                                  \n",
       "last_run_timestamp                                         1605718391000               \n",
       "last_update_timestamp                                         1605730599               \n",
       "latest_green                                                           1               \n",
       "overall_status                                                     FLAKY               \n",
       "overall_status_icon                                remove_circle_outline               \n",
       "status                 6 of 10 (60.0%) recent columns passed (9138 of...               \n",
       "tests                                                                 []               \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing               \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}               \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-aws-upgrade-4.1-stable-to-4.2-ci  \\\n",
       "alert                                                                                           \n",
       "last_run_timestamp                                         1605722228000                        \n",
       "last_update_timestamp                                         1605730619                        \n",
       "latest_green                                                           1                        \n",
       "overall_status                                                     FLAKY                        \n",
       "overall_status_icon                                remove_circle_outline                        \n",
       "status                 9 of 10 (90.0%) recent columns passed (194 of ...                        \n",
       "tests                                                                 []                        \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing                        \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}                        \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-aws-upgrade-4.2-stable-to-4.2-nightly  \\\n",
       "alert                                                                                                \n",
       "last_run_timestamp                                         1605729194000                             \n",
       "last_update_timestamp                                         1605730603                             \n",
       "latest_green                                                           1                             \n",
       "overall_status                                                   PASSING                             \n",
       "overall_status_icon                                                 done                             \n",
       "status                 9 of 9 (100.0%) recent columns passed (189 of ...                             \n",
       "tests                                                                 []                             \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing                             \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}                             \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2  \\\n",
       "alert                                                                                          \n",
       "last_run_timestamp                                         1605716352000                       \n",
       "last_update_timestamp                                         1605730621                       \n",
       "latest_green                                            no recent greens                       \n",
       "overall_status                                                   FAILING                       \n",
       "overall_status_icon                                              warning                       \n",
       "status                 0 of 10 (0.0%) recent columns passed (110 of 1...                       \n",
       "tests                  [{'display_name': 'Overall', 'test_name': 'Ove...                       \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing                       \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}                       \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.2  \\\n",
       "alert                                                                                   \n",
       "last_run_timestamp                                         1605700568000                \n",
       "last_update_timestamp                                         1605730621                \n",
       "latest_green                                                           1                \n",
       "overall_status                                                     FLAKY                \n",
       "overall_status_icon                                remove_circle_outline                \n",
       "status                 9 of 10 (90.0%) recent columns passed (209 of ...                \n",
       "tests                                                                 []                \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing                \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}                \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-azure-upgrade-4.2  \\\n",
       "alert                                                                            \n",
       "last_run_timestamp                                         1605714980000         \n",
       "last_update_timestamp                                         1605730646         \n",
       "latest_green                                                           1         \n",
       "overall_status                                                   PASSING         \n",
       "overall_status_icon                                                 done         \n",
       "status                 10 of 10 (100.0%) recent columns passed (210 o...         \n",
       "tests                                                                 []         \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing         \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}         \n",
       "\n",
       "                          release-openshift-origin-installer-e2e-gcp-4.2  \\\n",
       "alert                                                                      \n",
       "last_run_timestamp                                         1605714981000   \n",
       "last_update_timestamp                                         1605730669   \n",
       "latest_green                                                           1   \n",
       "overall_status                                                     FLAKY   \n",
       "overall_status_icon                                remove_circle_outline   \n",
       "status                 7 of 10 (70.0%) recent columns passed (8688 of...   \n",
       "tests                                                                 []   \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing   \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}   \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-gcp-serial-4.2  \\\n",
       "alert                                                                         \n",
       "last_run_timestamp                                         1605714980000      \n",
       "last_update_timestamp                                         1605730682      \n",
       "latest_green                                                           1      \n",
       "overall_status                                                     FLAKY      \n",
       "overall_status_icon                                remove_circle_outline      \n",
       "status                 8 of 10 (80.0%) recent columns passed (790 of ...      \n",
       "tests                                                                 []      \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing      \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}      \n",
       "\n",
       "                      release-openshift-origin-installer-e2e-gcp-upgrade-4.2  \\\n",
       "alert                                                                          \n",
       "last_run_timestamp                                         1605714979000       \n",
       "last_update_timestamp                                         1605730695       \n",
       "latest_green                                                           1       \n",
       "overall_status                                                     FLAKY       \n",
       "overall_status_icon                                remove_circle_outline       \n",
       "status                 8 of 10 (80.0%) recent columns passed (193 of ...       \n",
       "tests                                                                 []       \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing       \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}       \n",
       "\n",
       "                      release-openshift-origin-installer-old-rhcos-e2e-aws-4.2  \n",
       "alert                                                                           \n",
       "last_run_timestamp                                         1605700809000        \n",
       "last_update_timestamp                                         1605730714        \n",
       "latest_green                                                           1        \n",
       "overall_status                                                     FLAKY        \n",
       "overall_status_icon                                remove_circle_outline        \n",
       "status                 4 of 10 (40.0%) recent columns passed (4148 of...        \n",
       "tests                                                                 []        \n",
       "dashboard_name                redhat-openshift-ocp-release-4.2-informing        \n",
       "healthiness                       {'tests': [], 'previousFlakiness': -1}        \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ocp42 = pd.DataFrame(data)\n",
    "ocp42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each dashboard there are a number of jobs associated with it (We will use these column names later to get the access the detailed data). And for each job we have a number of features, some which contain additional data. \n",
    "\n",
    "* last run\n",
    "* last update\n",
    "* latest_green\n",
    "* overall_status\n",
    "* overall_status_icon\n",
    "* status**\n",
    "* tests**\n",
    "* dashboard_name\n",
    "* healthiness**\n",
    "\n",
    "_** features that have additional data_\n",
    "\n",
    "Let's take a look the additional data stored in status and tests for a job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "release-openshift-origin-installer-old-rhcos-e2e-aws-4.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'4 of 10 (40.0%) recent columns passed (4148 of 4158 or 99.8% cells)'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(ocp42.columns[-1])\n",
    "ocp42.loc[\"status\", ocp42.columns[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Status is a string value constructed by some other underlying data not present in the summary. I believe this output stats, for the last 10 runs, how many were completely successful and in this case is equivalent to +4,000 individual test passing.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>display_name</th>\n",
       "      <th>test_name</th>\n",
       "      <th>fail_count</th>\n",
       "      <th>fail_timestamp</th>\n",
       "      <th>pass_timestamp</th>\n",
       "      <th>build_link</th>\n",
       "      <th>build_url_text</th>\n",
       "      <th>build_link_text</th>\n",
       "      <th>failure_message</th>\n",
       "      <th>linked_bugs</th>\n",
       "      <th>fail_test_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Overall</td>\n",
       "      <td>Overall</td>\n",
       "      <td>97</td>\n",
       "      <td>1601434408</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>00adf7d0a5b40d453760b3e7bded5761 Overall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[Disruptive] Cluster upgrade should maintain a...</td>\n",
       "      <td>[Disruptive] Cluster upgrade should maintain a...</td>\n",
       "      <td>83</td>\n",
       "      <td>1601434408</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>[]</td>\n",
       "      <td>6865b6db39b77edc7fb8fd514c2df483 [Disruptive] ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        display_name  \\\n",
       "0                                            Overall   \n",
       "1  [Disruptive] Cluster upgrade should maintain a...   \n",
       "\n",
       "                                           test_name  fail_count  \\\n",
       "0                                            Overall          97   \n",
       "1  [Disruptive] Cluster upgrade should maintain a...          83   \n",
       "\n",
       "   fail_timestamp  pass_timestamp build_link build_url_text build_link_text  \\\n",
       "0      1601434408               0                                             \n",
       "1      1601434408               0                                             \n",
       "\n",
       "  failure_message linked_bugs  \\\n",
       "0                          []   \n",
       "1                          []   \n",
       "\n",
       "                                      fail_test_link  \n",
       "0           00adf7d0a5b40d453760b3e7bded5761 Overall  \n",
       "1  6865b6db39b77edc7fb8fd514c2df483 [Disruptive] ...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    ocp42.loc[\n",
    "        \"tests\",\n",
    "        \"release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\",\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These tests are ones that have been selected as \"alerts\" to display on the summary page by testgrid for a specific job on the dashboard. Its unclear here what logic is used to raise tests to \"alert\" status but it's likely the number or percent of failures(?). We also get the number of failures, as well as the time of the first failure and the time of the last time the test passed.  \n",
    "\n",
    "**Possible features**\n",
    "\n",
    "If we were limited to the data provided by a summary we could use some of the following things as vector feature to encode a dashboard:\n",
    "\n",
    "* percent PASSING\n",
    "* percent FLAKY\n",
    "* percent FAILING\n",
    "* percent STALE\n",
    "* percent of recent columns passed\n",
    "* percent cells passed\n",
    "* average number of alerts in \"tests\"\n",
    "* average fail_count across all \"tests\" \n",
    "* average seconds between first fail and last pass\n",
    "\n",
    "However, there is a lot of summarizing going on here and it does not take into account the _grids_ in the TestGrid tool. Let's move on the more detailed data and see what we have access to there. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detailed View\n",
    "\n",
    "Lets dig into the time series data in the detailed test grids.\n",
    "\n",
    "To pull the desired json we can follow this syntax: \n",
    "\n",
    "`https://testgrid.k8s.io/{dashboard_name}/table?&show-stale-tests=&tab={job_name}`\n",
    "\n",
    "From the work above we know how to get the list of all the dashboards and associated jobs. Here we will get the data for the job \"release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\" as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(\n",
    "    \"https://testgrid.k8s.io/redhat-openshift-ocp-release-4.2-informing/table? \\\n",
    "    &show-stale-tests=&tab=release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we want to do is see what what keys we have in our detailed job data. Below we can see that there are a number of fields in this json, but the one we are going to focus on for now is \"tests\" as it contains the information we need to recreate the testgrids as dataframes or arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test-group-name\n",
      "query\n",
      "status\n",
      "phase-timer\n",
      "cached\n",
      "summary\n",
      "bugs\n",
      "changelists\n",
      "column_ids\n",
      "custom-columns\n",
      "column-header-names\n",
      "groups\n",
      "metrics\n",
      "tests\n",
      "row_ids\n",
      "timestamps\n",
      "clusters\n",
      "test_id_map\n",
      "test-metadata\n",
      "stale-test-threshold\n",
      "num-stale-tests\n",
      "add-tabular-names-option\n",
      "show-tabular-names\n",
      "description\n",
      "bug-component\n",
      "code-search-path\n",
      "open-test-template\n",
      "file-bug-template\n",
      "attach-bug-template\n",
      "results-url-template\n",
      "code-search-url-template\n",
      "about-dashboard-url\n",
      "open-bug-template\n",
      "context-menu-template\n",
      "results-text\n",
      "latest-green\n",
      "triage-enabled\n",
      "notifications\n",
      "overall-status\n"
     ]
    }
   ],
   "source": [
    "for i in response.json().keys():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the complete json output for tests, looks like there are a number of fields that currently aren't populated or have duplicate data, so lets go ahead and drop them. Specifically, \"linked_bugs\", \"message\", \"short_texts\", user_property\", \"target\", and \"original-name\".   "
   ]
  },
  {
   "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>name</th>\n",
       "      <th>alert</th>\n",
       "      <th>statuses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Overall</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 97, 'fail-time'...</td>\n",
       "      <td>[{'count': 97, 'value': 12}]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>openshift-tests.Monitor cluster while tests ex...</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 83, 'fail-time'...</td>\n",
       "      <td>[{'count': 40, 'value': 12}, {'count': 1, 'val...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>openshift-tests.[Disruptive] Cluster upgrade s...</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 83, 'fail-time'...</td>\n",
       "      <td>[{'count': 40, 'value': 12}, {'count': 1, 'val...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 40, 'fail-time'...</td>\n",
       "      <td>[{'count': 40, 'value': 12}, {'count': 1, 'val...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 50, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 60, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Symptom Detection.Bug 1812261: iptables is seg...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Symptom Detection.Infrastructure - AWS simulat...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Symptom Detection.Infrastructure - GCP quota e...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Symptom Detection.Node process segfaulted</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Symptom Detection.Undiagnosed panic detected i...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>operator.All images are built and tagged into ...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 60, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>operator.Find all of the input images from ocp...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 60, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 50, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 name  \\\n",
       "0                                             Overall   \n",
       "1   openshift-tests.Monitor cluster while tests ex...   \n",
       "2   openshift-tests.[Disruptive] Cluster upgrade s...   \n",
       "3   operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
       "4   operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
       "5   operator.Import the release payload \"latest\" f...   \n",
       "6   operator.Import the release payload \"initial\" ...   \n",
       "7   Symptom Detection.Bug 1812261: iptables is seg...   \n",
       "8   Symptom Detection.Infrastructure - AWS simulat...   \n",
       "9   Symptom Detection.Infrastructure - GCP quota e...   \n",
       "10          Symptom Detection.Node process segfaulted   \n",
       "11  Symptom Detection.Undiagnosed panic detected i...   \n",
       "12  operator.All images are built and tagged into ...   \n",
       "13  operator.Find all of the input images from ocp...   \n",
       "14  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
       "\n",
       "                                                alert  \\\n",
       "0   {'fail-cl': '1', 'fail-count': 97, 'fail-time'...   \n",
       "1   {'fail-cl': '1', 'fail-count': 83, 'fail-time'...   \n",
       "2   {'fail-cl': '1', 'fail-count': 83, 'fail-time'...   \n",
       "3   {'fail-cl': '1', 'fail-count': 40, 'fail-time'...   \n",
       "4                                                None   \n",
       "5                                                None   \n",
       "6                                                None   \n",
       "7                                                None   \n",
       "8                                                None   \n",
       "9                                                None   \n",
       "10                                               None   \n",
       "11                                               None   \n",
       "12                                               None   \n",
       "13                                               None   \n",
       "14                                               None   \n",
       "\n",
       "                                             statuses  \n",
       "0                        [{'count': 97, 'value': 12}]  \n",
       "1   [{'count': 40, 'value': 12}, {'count': 1, 'val...  \n",
       "2   [{'count': 40, 'value': 12}, {'count': 1, 'val...  \n",
       "3   [{'count': 40, 'value': 12}, {'count': 1, 'val...  \n",
       "4   [{'count': 40, 'value': 1}, {'count': 1, 'valu...  \n",
       "5   [{'count': 50, 'value': 1}, {'count': 1, 'valu...  \n",
       "6   [{'count': 60, 'value': 1}, {'count': 1, 'valu...  \n",
       "7   [{'count': 40, 'value': 1}, {'count': 1, 'valu...  \n",
       "8   [{'count': 40, 'value': 1}, {'count': 1, 'valu...  \n",
       "9   [{'count': 40, 'value': 1}, {'count': 1, 'valu...  \n",
       "10  [{'count': 40, 'value': 1}, {'count': 1, 'valu...  \n",
       "11  [{'count': 40, 'value': 1}, {'count': 1, 'valu...  \n",
       "12  [{'count': 60, 'value': 1}, {'count': 1, 'valu...  \n",
       "13  [{'count': 60, 'value': 1}, {'count': 1, 'valu...  \n",
       "14  [{'count': 50, 'value': 1}, {'count': 1, 'valu...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details = pd.DataFrame(response.json()[\"tests\"]).drop(\n",
    "    [\n",
    "        \"linked_bugs\",\n",
    "        \"messages\",\n",
    "        \"short_texts\",\n",
    "        \"user_property\",\n",
    "        \"target\",\n",
    "        \"original-name\",\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the column \"statuses\" above we can see that the time series data is [run length encoded](https://en.wikipedia.org/wiki/Run-length_encoding). Let's add a decoded column so we can get the data in an array format we could use for machine learning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_run_length(x):\n",
    "    lst = []\n",
    "    for run_length in x:\n",
    "        extension = [run_length[\"value\"]] * run_length[\"count\"]\n",
    "        lst.extend(extension)\n",
    "    return lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "details[\"values\"] = details[\"statuses\"].apply(decode_run_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>name</th>\n",
       "      <th>alert</th>\n",
       "      <th>statuses</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Overall</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 97, 'fail-time'...</td>\n",
       "      <td>[{'count': 97, 'value': 12}]</td>\n",
       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>openshift-tests.Monitor cluster while tests ex...</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 83, 'fail-time'...</td>\n",
       "      <td>[{'count': 40, 'value': 12}, {'count': 1, 'val...</td>\n",
       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>openshift-tests.[Disruptive] Cluster upgrade s...</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 83, 'fail-time'...</td>\n",
       "      <td>[{'count': 40, 'value': 12}, {'count': 1, 'val...</td>\n",
       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
       "      <td>{'fail-cl': '1', 'fail-count': 40, 'fail-time'...</td>\n",
       "      <td>[{'count': 40, 'value': 12}, {'count': 1, 'val...</td>\n",
       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 50, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 60, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Symptom Detection.Bug 1812261: iptables is seg...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Symptom Detection.Infrastructure - AWS simulat...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Symptom Detection.Infrastructure - GCP quota e...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Symptom Detection.Node process segfaulted</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Symptom Detection.Undiagnosed panic detected i...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 40, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>operator.All images are built and tagged into ...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 60, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>operator.Find all of the input images from ocp...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 60, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
       "      <td>None</td>\n",
       "      <td>[{'count': 50, 'value': 1}, {'count': 1, 'valu...</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 name  \\\n",
       "0                                             Overall   \n",
       "1   openshift-tests.Monitor cluster while tests ex...   \n",
       "2   openshift-tests.[Disruptive] Cluster upgrade s...   \n",
       "3   operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
       "4   operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
       "5   operator.Import the release payload \"latest\" f...   \n",
       "6   operator.Import the release payload \"initial\" ...   \n",
       "7   Symptom Detection.Bug 1812261: iptables is seg...   \n",
       "8   Symptom Detection.Infrastructure - AWS simulat...   \n",
       "9   Symptom Detection.Infrastructure - GCP quota e...   \n",
       "10          Symptom Detection.Node process segfaulted   \n",
       "11  Symptom Detection.Undiagnosed panic detected i...   \n",
       "12  operator.All images are built and tagged into ...   \n",
       "13  operator.Find all of the input images from ocp...   \n",
       "14  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
       "\n",
       "                                                alert  \\\n",
       "0   {'fail-cl': '1', 'fail-count': 97, 'fail-time'...   \n",
       "1   {'fail-cl': '1', 'fail-count': 83, 'fail-time'...   \n",
       "2   {'fail-cl': '1', 'fail-count': 83, 'fail-time'...   \n",
       "3   {'fail-cl': '1', 'fail-count': 40, 'fail-time'...   \n",
       "4                                                None   \n",
       "5                                                None   \n",
       "6                                                None   \n",
       "7                                                None   \n",
       "8                                                None   \n",
       "9                                                None   \n",
       "10                                               None   \n",
       "11                                               None   \n",
       "12                                               None   \n",
       "13                                               None   \n",
       "14                                               None   \n",
       "\n",
       "                                             statuses  \\\n",
       "0                        [{'count': 97, 'value': 12}]   \n",
       "1   [{'count': 40, 'value': 12}, {'count': 1, 'val...   \n",
       "2   [{'count': 40, 'value': 12}, {'count': 1, 'val...   \n",
       "3   [{'count': 40, 'value': 12}, {'count': 1, 'val...   \n",
       "4   [{'count': 40, 'value': 1}, {'count': 1, 'valu...   \n",
       "5   [{'count': 50, 'value': 1}, {'count': 1, 'valu...   \n",
       "6   [{'count': 60, 'value': 1}, {'count': 1, 'valu...   \n",
       "7   [{'count': 40, 'value': 1}, {'count': 1, 'valu...   \n",
       "8   [{'count': 40, 'value': 1}, {'count': 1, 'valu...   \n",
       "9   [{'count': 40, 'value': 1}, {'count': 1, 'valu...   \n",
       "10  [{'count': 40, 'value': 1}, {'count': 1, 'valu...   \n",
       "11  [{'count': 40, 'value': 1}, {'count': 1, 'valu...   \n",
       "12  [{'count': 60, 'value': 1}, {'count': 1, 'valu...   \n",
       "13  [{'count': 60, 'value': 1}, {'count': 1, 'valu...   \n",
       "14  [{'count': 50, 'value': 1}, {'count': 1, 'valu...   \n",
       "\n",
       "                                               values  \n",
       "0   [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...  \n",
       "1   [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...  \n",
       "2   [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...  \n",
       "3   [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...  \n",
       "4   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "5   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "6   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "7   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "8   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "9   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "10  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "11  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "12  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "13  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
       "14  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all our data unrolled, lets plot it and confirm it matches the testgrid images displayed on the website. \n",
    "\n",
    "_note: these did match at 12:23  pm 11/19/2020 if they no longer appear to, the image needs update. Live rendering of the grid can be found here https://testgrid.k8s.io/redhat-openshift-ocp-release-4.2-informing#release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.imshow(np.array(list(details[\"values\"].values)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../docs/assets/images/testgrid_01.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent. As we can see from the images above, we now have a means of going from any testgrid dashboard of interest directly to a 2d numpy representation of our grids where we could apply some 2D signal processing, computer vision or even convolutional neural network methods (maybe). Maybe we could even consider collecting a number of these objects over multiple days and treat it as a 3D tensor of 2D frames? These are just some possible ideas, but the main point here is that we now have the  testgrid data represented in a more data science friendly format, opening many more possible avenues for analysis.      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(details[\"values\"][7])\n",
    "plt.title(details[\"name\"][7])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also break our data down to a single time series, if we want to directly analyze the behavior of a single test within this job. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Data\n",
    "\n",
    "Now that we have a fairly clear view of the data available to us via TestGrid and how to get it into a data science friendly format, the next thing we want to do is collect a large sample of this data and see how we can organize it in aggregate (across jobs). As this data appears to update at least daily, we also want to pull a large sample, so that we have a fixed data set to work with moving forward (one that is immutable (doesn't change on us every day), and available independent of the TestGrid service's availability).   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now go ahead and download the grid data similar to what is represented above in the data frame \"details\" for each job: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"redhat-openshift-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-3.11-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.1-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.1-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.2-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.2-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.3-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.3-broken\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.3-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.4-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.4-broken\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.4-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.5-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.5-broken\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.5-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.6-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.6-broken\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.6-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.7-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.7-broken\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.7-informing\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.8-blocking\" downloaded \n",
      "\"redhat-openshift-ocp-release-4.8-informing\" downloaded \n",
      "\"redhat-openshift-okd-release-4.3-informing\" downloaded \n",
      "\"redhat-openshift-okd-release-4.4-informing\" downloaded \n",
      "\"redhat-openshift-okd-release-4.5-blocking\" downloaded \n",
      "\"redhat-openshift-okd-release-4.5-informing\" downloaded \n",
      "\"redhat-openshift-okd-release-4.6-blocking\" downloaded \n",
      "\"redhat-openshift-okd-release-4.6-informing\" downloaded \n",
      "\"redhat-openshift-okd-release-4.7-informing\" downloaded \n",
      "\"redhat-openshift-presubmit-master-gcp\" downloaded \n",
      "\"redhat-osde2e-int-aws\" downloaded \n",
      "\"redhat-osde2e-int-gcp\" downloaded \n",
      "\"redhat-osde2e-int-moa\" downloaded \n",
      "\"redhat-osde2e-prod-aws\" downloaded \n",
      "\"redhat-osde2e-prod-gcp\" downloaded \n",
      "\"redhat-osde2e-prod-moa\" downloaded \n",
      "\"redhat-osde2e-stage-aws\" downloaded \n",
      "\"redhat-osde2e-stage-gcp\" downloaded \n",
      "\"redhat-osde2e-stage-moa\" downloaded \n"
     ]
    }
   ],
   "source": [
    "download = True\n",
    "if download:\n",
    "    data_set = {}\n",
    "\n",
    "    for dashboard in dashboard_names:\n",
    "        response_1 = requests.get(\n",
    "            f\"https://testgrid.k8s.io/{dashboard}/summary\"\n",
    "        )\n",
    "        jobs = response_1.json().keys()\n",
    "        dashboard_jobs = {}\n",
    "\n",
    "        for job in jobs:\n",
    "            response_2 = requests.get(\n",
    "                f\"https://testgrid.k8s.io/{dashboard}/table?&show-stale-tests=&tab={job}&grid=old\"\n",
    "            )\n",
    "            if response_2.status_code != 200:\n",
    "                continue\n",
    "\n",
    "            grid = [\n",
    "                {\"name\": x[\"name\"], \"statuses\": x[\"statuses\"]}\n",
    "                for x in response_2.json()[\"tests\"]\n",
    "            ]\n",
    "            time_stamps = response_2.json()[\"timestamps\"]\n",
    "\n",
    "            dashboard_jobs[job] = {\"grid\": grid, \"timestamps\": time_stamps}\n",
    "\n",
    "        data_set[dashboard] = dashboard_jobs\n",
    "        print(f\"{dashboard} downloaded \")\n",
    "else:\n",
    "    print(\"Not Downloading\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = datetime.datetime.today()\n",
    "file_path = f\"../data/raw/testgrid_{date.day}{date.month}.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(file_path, \"w\") as outfile:\n",
    "    json.dump(data_set, outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have our data collected in a dictionary and saved as a json file. And getting the specific test grid is as simple as referencing the dashboard and job of interest:  `data[\"dashboard_name][\"job_name]`\n",
    "\n",
    "Also, it appears that all the existing test grids taken together is only about 130 Mb, so we should be able to work with the whole set at once without too much trouble.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps:\n",
    "\n",
    "Now that we know what data we have access to, how to access it and how to collect the entire available dataset, we want to see how to work with the downloaded dataset and see how comparable each of the jobs and tests are. And like most initial data exploration, we're now left with a few more questions then we had at the beginning.   \n",
    "\n",
    "**What  are some of the questions we are still left with?**\n",
    "\n",
    "* How comparable are the testgrids?\n",
    "* How do we analyze them in aggregate to learn from their combined behavior?  \n",
    "* How many/ which tests do they all have in common?\n",
    "* Are their time series dates comparable? \n",
    "* Are there sub-groups that should only be compared with one another?\n",
    "* Is looking at the grid matrices independent of test names a valid approach for issue identification? \n",
    "* What is the expected behavior of a test over time across multiple jobs.\n",
    "* How does the entire test platform/specific tests perform on a given day?\n",
    "* How does the entire test platform behavior evolve over time. \n",
    "* Is there sufficient data here for useful ML approaches?\n",
    "* Can we develop some meaningful alerting/ problem identification with the results of the above questions? \n",
    "\n",
    "I think there are enough additional questions here that its growing beyond the scope of this notebook. Let's go ahead address these more complex data set questions in the next [EDA notebook](testgrid_indepth_EDA.ipynb). "
   ]
  }
 ],
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