diff --git a/Red Hat NLP/Project Proposal.pdf b/Red Hat NLP/Project Proposal.pdf
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diff --git a/Red Hat NLP/notebooks/README.md b/Red Hat NLP/notebooks/README.md
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+### Notebooks
+
+This folder contains various reproducible and interactive Jupyter notebooks created for the project. These notebooks are also available for viewing on our public [JupyterHub instance](https://jupyterhub-opf-jupyterhub.apps.cnv.massopen.cloud/hub/login).
+The notebook organizational structure is described in detail below.
+
+#### Data Sources
+
+The notebooks defined in the [`data-sources`](data-sources) folder, explore and analyze the [Sippy](https://github.com/openshift/sippy) and [TestGrid](https://github.com/GoogleCloudPlatform/testgrid) data sources.
+
+1. **Sippy** - A Continuous Integration Private Investigator tool to process the job results from https://testgrid.k8s.io/. It reports on which tests fail most frequently along different dimensions such as by job, by platform, by sig etc.
+2. **TestGrid** -  A highly-configurable, interactive dashboard for viewing your test results in a grid.
+
+This folder contains:
+
+* a [`Sippy`](data-sources/Sippy) folder, which consists of:
+  1. [`sippy_failure_correlation.ipynb`](data-sources/Sippy/sippy_failure_correlation.ipynb) - Notebook which analyzes the available Sippy CI data and determine which test failures appear to be correlated with each other
+  2. a [`stage`](data-sources/Sippy/stage) folder which contains some new features and exploratory work we are trying out. It consists of:
+      1. [`sippy_EDA.ipynb`](data-sources/Sippy/stage/sippy_EDA.ipynb) - Notebook which uncovers and understands the Sippy data set
+      2. [`sippy-analysis.ipynb`](data-sources/Sippy/stage/sippy-analysis.ipynb) - Notebook analyzing the OpenShift CI test/job data from the [testgrid dashboards](https://testgrid.k8s.io/redhat-openshift-informing) that Sippy provides
+
+* a [`TestGrid`](data-sources/TestGrid) folder which consists of:
+  1. [`testgrid_EDA.ipynb`](data-sources/TestGrid/testgrid_EDA.ipynb) - Notebook which explores the existing TestGrid data at testgrid.k8s.io, giving specific attention to [Red Hat's OpenShift CI dashboards](https://testgrid.k8s.io/redhat-openshift-informing)
+  2. [`testgrid_indepth_EDA.ipynb`](data-sources/TestGrid/testgrid_indepth_EDA.ipynb) - Notebook which follows up on the above notebook and provides additional insights to the testgrid data
+  3. a [`background`](data-sources/TestGrid/background) folder which contains:
+      1. [`testgrid_feature_confirmation.ipynb`](data-sources/TestGrid/background/testgrid_feature_confirmation.ipynb) - Notebook determining if the testgrid features analzed in the [testgrid_EDA.ipynb](https://github.com/aicoe-aiops/ocp-ci-analysis/blob/master/notebooks/data-sources/TestGrid/testgrid_EDA.ipynb) notebook are uniform across grids
+
+#### Failure Type Classification
+
+The notebooks defined in the [`failure-type-classification`](failure-type-classification) folder focuses on addressing the problem of automating the task of test failure classification with TestGrid data. Failures which occur in a test can be legitimate or due to some other issues like an infrastructure flake, install flake, flaky test, or some other type of failure. Unsupervised machine learning methods and heuristics are explored in these notebooks to classify the test failures. The notebooks are organized into:
+
+* a [`background`](failure-type-classification/background) folder which consists of:
+  1. [`testgrid_flakiness_detection.ipynb`](failure-type-classification/background/testgrid_flakiness_detection.ipynb) - Notebook which tries to detect one of the test failure types such as a Flaky test
+
+* a [`stage`](failure-type-classification/stage) folder which consists of:
+  1. [`failure_type_classifier.ipynb`](failure-type-classification/stage/failure_type_classifier.ipynb) - Notebook for analyzing testgrids and generating a report that will identify the tests and dates where 4 different types of failures may have occurred
diff --git a/Red Hat NLP/notebooks/data-sources/BUSpark-gcs-build-logs.ipynb b/Red Hat NLP/notebooks/data-sources/BUSpark-gcs-build-logs.ipynb
new file mode 100644
index 00000000..6e59f455
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+++ b/Red Hat NLP/notebooks/data-sources/BUSpark-gcs-build-logs.ipynb	
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-efc1c5a624f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbs4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# from google.colab import files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
+     ]
+    }
+   ],
+   "source": [
+    "# Web scraping (Kyle, due 02/26/2021)\n",
+    "#     BeautifulSoup for web scraping\n",
+    "\n",
+    "from urllib.request import urlopen\n",
+    "from bs4 import BeautifulSoup\n",
+    "# from google.colab import files\n",
+    "import pandas as pd\n",
+    "import io\n",
+    "import numpy as np\n",
+    "import dateutil\n",
+    "from dateutil import parser\n",
+    "import textblob\n",
+    "from textblob import TextBlob\n",
+    "import nltk\n",
+    "import requests\n",
+    "from textblob import Word\n",
+    "import matplotlib.pyplot as plt\n",
+    "from nltk.stem.snowball import SnowballStemmer\n",
+    "nltk.download('punkt')\n",
+    "\n",
+    "# upload file\n",
+    "#uploaded = files.upload()\n",
+    "#read array\n",
+    "#df2 = pd.read_csv(io.BytesIO(uploaded['logs - Sheet1.csv']))\n",
+    "#turn to flat list\n",
+    "#array_pages = df2.to_numpy()\n",
+    "#array_pages = np.ndarray.tolist(array_pages)\n",
+    "#array_pages = [item for sublist in array_pages for item in sublist]\n",
+    "\n",
+    "#url core needed to pull\n",
+    "website = \"https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com\"\n",
+    "base = \"https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/logs/canary-release-openshift-origin-installer-e2e-aws-4.5-cnv/\"\n",
+    "ending = \"build-log.txt\"\n",
+    "url = \"https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/logs/canary-release-openshift-origin-installer-e2e-aws-4.5-cnv/1300557127638585344/build-log.txt\"\n",
+    "page = requests.get(base)    \n",
+    "data = page.text\n",
+    "soup = BeautifulSoup(data)\n",
+    "links = []\n",
+    "for link in soup.find_all('a'):\n",
+    "    links.append(link.get('href'))\n",
+    "links = links[1:-1]\n",
+    "\n",
+    "final_array = []\n",
+    "# create array of urls\n",
+    "for x in range(len(links)):\n",
+    "  final_array.append(str(website) + str(links[x]) + str(ending))\n",
+    "\n",
+    "\n",
+    "# pull all urls logs and store in 2-d array where array_of_logs[x] is a build-log file and \n",
+    "# array_of_logs[x][y] is an individual log line split by new line\n",
+    "array_of_logs = []\n",
+    "for x in range(len(final_array)):\n",
+    "  page = urlopen(final_array[x])\n",
+    "  html_bytes = page.read()\n",
+    "  array_of_logs.append(str(html_bytes).split('\\\\n'))\n",
+    "  \n",
+    "# first log\n",
+    "print(array_of_logs[0])\n",
+    "\n",
+    "\n",
+    "# Analysis on the log data. Trying to find a framework. API (Ningxiao, Parker, Tianze, Hong)\n",
+    "#     Identify limitations with data and potential risks of achieving project goals.\n",
+    "\n",
+    "\n",
+    "# ******* Tianze *******\n",
+    "#ignore useless \"Waiting for setup to finish...\"\n",
+    "def ignoreWaiting(logs):\n",
+    "  for i in range(len(logs)):\n",
+    "    if \"Waiting for setup to finish...\" in logs[i]:\n",
+    "      logs[i]=\"\"\n",
+    "  return logs\n",
+    "# ******* Tianze *******\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'requests' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-ca951e4e65cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbase2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/logs/release-openshift-ocp-installer-e2e-aws-serial-4.1/\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mending2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"build-log.txt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpage2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdata2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpage2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0msoup2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'requests' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "#url core needed to pull\n",
+    "website2 = \"https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com\"\n",
+    "base2 = \"https://gcsweb-ci.apps.ci.l2s4.p1.openshiftapps.com/gcs/origin-ci-test/logs/release-openshift-ocp-installer-e2e-aws-serial-4.1/\"\n",
+    "ending2 = \"build-log.txt\"\n",
+    "page2 = requests.get(base2)    \n",
+    "data2 = page2.text\n",
+    "soup2 = BeautifulSoup(data2)\n",
+    "links2 = []\n",
+    "for link2 in soup2.find_all('a'):\n",
+    "    links2.append(link2.get('href'))\n",
+    "links2 = links2[1:-1]\n",
+    "\n",
+    "final_array2 = []\n",
+    "# create array of urls\n",
+    "for x in range(len(links2)):\n",
+    "  final_array2.append(str(website2) + str(links2[x]) + str(ending2))\n",
+    "\n",
+    "\n",
+    "# pull all urls logs and store in 2-d array where array_of_logs[x] is a build-log file and \n",
+    "# array_of_logs[x][y] is an individual log line split by new line\n",
+    "array_of_logs2 = []\n",
+    "for x in range(len(final_array2)):\n",
+    "  page2 = urlopen(final_array2[x])\n",
+    "  html_bytes2 = page2.read()\n",
+    "  array_of_logs2.append(str(html_bytes2).split('\\\\n'))\n",
+    "  \n",
+    "# first log\n",
+    "print(array_of_logs2[0])\n",
+    "\n",
+    "\n",
+    "# Analysis on the log data. Trying to find a framework. API (Ningxiao, Parker, Tianze, Hong)\n",
+    "#     Identify limitations with data and potential risks of achieving project goals.\n",
+    "\n",
+    "\n",
+    "# ******* Tianze *******\n",
+    "#ignore useless \"Waiting for setup to finish...\"\n",
+    "def ignoreWaiting(logs):\n",
+    "  for i in range(len(logs)):\n",
+    "    if \"Waiting for setup to finish...\" in logs[i]:\n",
+    "      logs[i]=\"\"\n",
+    "  return logs\n",
+    "# ******* Tianze *******\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ******* PARKER *******\n",
+    "#  helper function detecting if a string is a date / timestamp\n",
+    "def is_date(str):\n",
+    "  try:\n",
+    "    dateutil.parser.parse(str)\n",
+    "    return True\n",
+    "  except:\n",
+    "    return False\n",
+    "\n",
+    "# logs variable contains all parsed logs\n",
+    "logs = []\n",
+    "for i in range(len(array_of_logs)):\n",
+    "  #  removing newline characters\n",
+    "  array_of_logs[i] = array_of_logs[i].replace('\\\\n', ' ')\n",
+    "  array_of_logs[i] = array_of_logs[i].splitlines()\n",
+    "\n",
+    "# removes leading 'b from log\n",
+    "  array_of_logs[i] = array_of_logs[i][0][2:]\n",
+    "# splitting each section as its own index (for parsing)\n",
+    "  array_of_logs[i] = array_of_logs[i].split(' ')\n",
+    "\n",
+    "#  tmp is log without timestamps\n",
+    "  tmp = []\n",
+    "  for j in range(len(array_of_logs[i])):\n",
+    "    if is_date(array_of_logs[i][j]) == False:\n",
+    "      tmp.append(array_of_logs[i][j])\n",
+    "    else:\n",
+    "      continue\n",
+    "  logs.append(tmp)\n",
+    "\n",
+    "# removes whitespace characters and keeps root words\n",
+    "stemmer = SnowballStemmer(\"english\")\n",
+    "for log in logs:\n",
+    "  log[:] = [stemmer.stem(x) for x in log if x != '']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This cell removes dates and timestamps, whitespace and keeps the stems of words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ******* PARKER *******\n",
+    "failures = []\n",
+    "successes = []\n",
+    "# word order for matrix: 0: fail, 1: error, 2: success, 3: run, 4: crashloopbackoff\n",
+    "word_matrix = []\n",
+    "for log in logs:\n",
+    "  tmp = ' '.join(log)\n",
+    "  tmp = TextBlob(tmp)\n",
+    "  fail = tmp.word_counts['fail']\n",
+    "  error = tmp.word_counts['error']\n",
+    "  success = tmp.word_counts['success']\n",
+    "  run = tmp.word_counts['run']\n",
+    "  crash = tmp.word_counts['crashloopbackoff']\n",
+    "  word_matrix.append([fail, error, success, run, crash])\n",
+    "\n",
+    "# remove noise\n",
+    "word_matrix = word_matrix[2:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This cell creates a matrix of keywords. each row is a single log entry and each column is a specific keyword"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ******* PARKER *******\n",
+    "import math\n",
+    "# Latent Semantic Analysis approach\n",
+    "\n",
+    "num_of_docs = [0 for i in range(len(word_matrix[0]))]\n",
+    "for i in range(len(word_matrix)):\n",
+    "  for j in range(len(word_matrix[i])):\n",
+    "    if word_matrix[i][j] != 0:\n",
+    "      num_of_docs[j] += 1\n",
+    "for i in range(len(word_matrix)):\n",
+    "  for j in range(len(word_matrix[i])):\n",
+    "    tf = word_matrix[i][j]\n",
+    "    idf = math.log(len(word_matrix) / num_of_docs[j])\n",
+    "    word_matrix[i][j] = tf * idf\n",
+    "\n",
+    "# TODO: try to implement anomaly detection with word matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ******* NINGXIAO *******\n",
+    "# find events in log, display frequecy of each event in a bar chart\n",
+    "import spacy\n",
+    "s = \"\"\n",
+    "for log in logs:\n",
+    "    temp = \"\"\n",
+    "    temp = temp.join(log)\n",
+    "    s += temp\n",
+    "# Error Message: Text of length 5496377 exceeds maximum of 1000000.\n",
+    "s = s[0:100000]\n",
+    "nlp = spacy.load(\"en_core_web_sm\")\n",
+    "doc = nlp(s)\n",
+    "dictionary = {};\n",
+    "for token in doc:\n",
+    "    if token.pos_ == 'VERB':\n",
+    "        if token.text not in dictionary:\n",
+    "            dictionary[token.text] = 1\n",
+    "        else:\n",
+    "            dictionary[token.text] += 1\n",
+    "    #print(token.text, toekn.pos_)\n",
+    "\n",
+    "#print(dictionary)\n",
+    "\n",
+    "keys = list(dictionary.keys())\n",
+    "values = list(dictionary.values())\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9.2, 7))  \n",
+    "ax.barh(keys,values)\n",
+    "plt.title('Verbs in logs')\n",
+    "plt.ylabel('Verbs')\n",
+    "plt.xlabel('Frequency')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The image above shows the verbs and their frequency appear in the tokenized log text. It indicates what event and how often it happens in the log."
+   ]
+  }
+ ],
+ "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/Sippy/sippy_failure_correlation.ipynb b/Red Hat NLP/notebooks/data-sources/Sippy/sippy_failure_correlation.ipynb
new file mode 100644
index 00000000..768b7bac
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/Sippy/sippy_failure_correlation.ipynb	
@@ -0,0 +1,1637 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Initial EDA \n",
+    "\n",
+    "This is a short notebook to look at the available data provided and to see if we can determine which test failures appear to be correlated with each other. \n",
+    "\n",
+    "In this notebook we will:\n",
+    "\n",
+    "1. Load and rearrange our dataset\n",
+    "2. Encode our data with a binary encoding scheme\n",
+    "3. Analyze data looking for highly correlated failure sets\n",
+    "4. Example: Given a test failure of interest return other test failures that are highly correlated.\n",
+    "5. Output a csv file that contains a list of highly correlated test sets and their associated job run urls\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load and arrange dataset   \n",
+    "\n",
+    "This data is a nested json, where we are mainly interested in the field \"failureGroups\", so we will slice to only that row, which is a dictionary, then unroll that into its own dataframe for analysis. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mcliffor/anaconda3/envs/devenv/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+      "  import pandas.util.testing as tm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sn\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>release</th>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>all</th>\n",
+       "      <td>{'all': {'successes': 477806, 'failures': 8619...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ByPlatform</th>\n",
+       "      <td>{'aws': {'successes': 196148, 'failures': 3249...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ByJob</th>\n",
+       "      <td>{'canary-release-openshift-origin-installer-e2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BySig</th>\n",
+       "      <td>{'sig-api-machinery': {'successes': 45866, 'fa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>failureGroups</th>\n",
+       "      <td>[{'job': 'release-openshift-origin-installer-e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jobPassRate</th>\n",
+       "      <td>[{'name': 'release-openshift-ocp-installer-e2e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <td>2020-08-10T12:10:38.344952931-04:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>topFailingTestsWithBug</th>\n",
+       "      <td>[{'name': '[sig-arch][Feature:ClusterUpgrade] ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>topFailingTestsWithoutBug</th>\n",
+       "      <td>[{'name': '[sig-network] network isolation whe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bugsByFailureCount</th>\n",
+       "      <td>[{'id': 1856189, 'status': 'ON_QA', 'last_chan...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                        data\n",
+       "release                                                                     \n",
+       "all                        {'all': {'successes': 477806, 'failures': 8619...\n",
+       "ByPlatform                 {'aws': {'successes': 196148, 'failures': 3249...\n",
+       "ByJob                      {'canary-release-openshift-origin-installer-e2...\n",
+       "BySig                      {'sig-api-machinery': {'successes': 45866, 'fa...\n",
+       "failureGroups              [{'job': 'release-openshift-origin-installer-e...\n",
+       "jobPassRate                [{'name': 'release-openshift-ocp-installer-e2e...\n",
+       "timestamp                                2020-08-10T12:10:38.344952931-04:00\n",
+       "topFailingTestsWithBug     [{'name': '[sig-arch][Feature:ClusterUpgrade] ...\n",
+       "topFailingTestsWithoutBug  [{'name': '[sig-network] network isolation whe...\n",
+       "bugsByFailureCount         [{'id': 1856189, 'status': 'ON_QA', 'last_chan..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = pd.read_json(\"../../../data/external/sippydata.json\", orient=\"index\")\n",
+    "d.columns = [\"data\"]\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>job</th>\n",
+       "      <th>url</th>\n",
+       "      <th>testGridJobUrl</th>\n",
+       "      <th>testFailures</th>\n",
+       "      <th>failedTestNames</th>\n",
+       "      <th>failed</th>\n",
+       "      <th>hasUnknownFailures</th>\n",
+       "      <th>succeeded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>release-openshift-origin-installer-e2e-azure-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/ori...</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-r...</td>\n",
+       "      <td>371</td>\n",
+       "      <td>[Overall, [sig-apps][Feature:DeploymentConfig]...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>release-openshift-ocp-installer-e2e-aws-ovn-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/ori...</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-r...</td>\n",
+       "      <td>352</td>\n",
+       "      <td>[Overall, [sig-arch] Managed cluster should ha...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>release-openshift-ocp-installer-e2e-azure-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/ori...</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-r...</td>\n",
+       "      <td>294</td>\n",
+       "      <td>[Overall, [k8s.io] Container Runtime blackbox ...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>release-openshift-ocp-installer-e2e-gcp-ovn-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/ori...</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-r...</td>\n",
+       "      <td>264</td>\n",
+       "      <td>[Overall, operator.Run template e2e-gcp - e2e-...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>release-openshift-origin-installer-e2e-azure-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/ori...</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-r...</td>\n",
+       "      <td>224</td>\n",
+       "      <td>[Overall, [sig-apps][Feature:DeploymentConfig]...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                job  \\\n",
+       "0  release-openshift-origin-installer-e2e-azure-4.6   \n",
+       "1   release-openshift-ocp-installer-e2e-aws-ovn-4.6   \n",
+       "2     release-openshift-ocp-installer-e2e-azure-4.6   \n",
+       "3   release-openshift-ocp-installer-e2e-gcp-ovn-4.6   \n",
+       "4  release-openshift-origin-installer-e2e-azure-4.6   \n",
+       "\n",
+       "                                                 url  \\\n",
+       "0  https://prow.svc.ci.openshift.org/view/gcs/ori...   \n",
+       "1  https://prow.svc.ci.openshift.org/view/gcs/ori...   \n",
+       "2  https://prow.svc.ci.openshift.org/view/gcs/ori...   \n",
+       "3  https://prow.svc.ci.openshift.org/view/gcs/ori...   \n",
+       "4  https://prow.svc.ci.openshift.org/view/gcs/ori...   \n",
+       "\n",
+       "                                      testGridJobUrl  testFailures  \\\n",
+       "0  https://testgrid.k8s.io/redhat-openshift-ocp-r...           371   \n",
+       "1  https://testgrid.k8s.io/redhat-openshift-ocp-r...           352   \n",
+       "2  https://testgrid.k8s.io/redhat-openshift-ocp-r...           294   \n",
+       "3  https://testgrid.k8s.io/redhat-openshift-ocp-r...           264   \n",
+       "4  https://testgrid.k8s.io/redhat-openshift-ocp-r...           224   \n",
+       "\n",
+       "                                     failedTestNames  failed  \\\n",
+       "0  [Overall, [sig-apps][Feature:DeploymentConfig]...    True   \n",
+       "1  [Overall, [sig-arch] Managed cluster should ha...    True   \n",
+       "2  [Overall, [k8s.io] Container Runtime blackbox ...    True   \n",
+       "3  [Overall, operator.Run template e2e-gcp - e2e-...    True   \n",
+       "4  [Overall, [sig-apps][Feature:DeploymentConfig]...    True   \n",
+       "\n",
+       "   hasUnknownFailures  succeeded  \n",
+       "0                True      False  \n",
+       "1                True      False  \n",
+       "2                True      False  \n",
+       "3                True      False  \n",
+       "4                True      False  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# unroll the failuregroups data\n",
+    "\n",
+    "failure_groups = pd.DataFrame(d.loc[\"failureGroups\"][0])\n",
+    "failure_groups.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(102, 8)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failure_groups.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Grab only the failed test names columns to see if there are any correlations.\n",
+    "failed_test_names = failure_groups.failedTestNames.apply(lambda x: x[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      [[sig-apps][Feature:DeploymentConfig] deployme...\n",
+       "1      [[sig-arch] Managed cluster should have no cra...\n",
+       "2      [[k8s.io] Container Runtime blackbox test when...\n",
+       "3      [operator.Run template e2e-gcp - e2e-gcp-ovn c...\n",
+       "4      [[sig-apps][Feature:DeploymentConfig] deployme...\n",
+       "                             ...                        \n",
+       "97     [[sig-arch] Managed cluster should have no cra...\n",
+       "98     [operator.Run template e2e-azure - e2e-azure-o...\n",
+       "99     [operator.Run template e2e-aws-serial - e2e-aw...\n",
+       "100    [operator.Run template e2e-azure - e2e-azure-o...\n",
+       "101    [operator.Run template e2e-azure - e2e-azure-o...\n",
+       "Name: failedTestNames, Length: 102, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failed_test_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is this data?\n",
+    "\n",
+    "It looks like we have 102 ci job runs in our dataset. And for each job run we have list of tests that failed. Our goal here is to develop a method to determine which tests are highly correlated with each other, or in other words, are likely to cooccur. \n",
+    "\n",
+    "So we want to evaluate the occurrence of these tests over all 102 jobs available to us in this dataset. In order to do this, we will encode each job as a vector, where each position represents 1 of the possible tests. Positions with 1 represent the test failed, otherwise it will be 0. This binary encoding scheme should simplify our job later on.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Encoding\n",
+    "\n",
+    "For our initial approach we are going to consider each test for each job as a feature that is either present (1) or absent (0). This should make comparing job results and test cooccurrence rather straightforward.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build vocab\n",
+    "First thing we need to do is determine how long our feature vector is going to be by collecting the unique test names in our dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5841\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1455"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# build a vocabulary of unique test names across the entire data set.\n",
+    "vocab = []\n",
+    "count = 0\n",
+    "for i in failed_test_names:\n",
+    "    for j in i:\n",
+    "        count += 1\n",
+    "        if j not in vocab:\n",
+    "            vocab.append(j)\n",
+    "\n",
+    "print(count)\n",
+    "len(vocab)\n",
+    "# the vocabulary represents about 1/4 of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1455"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# confirm that there are no duplicates in the vocab\n",
+    "len(pd.Series(vocab).unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply encoding scheme "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_tests(job):\n",
+    "    encoded = []\n",
+    "    for v in vocab:\n",
+    "        if v in job:\n",
+    "            encoded.extend([1])\n",
+    "        else:\n",
+    "            encoded.extend([0])\n",
+    "    return encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded = failed_test_names.apply(encode_tests)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
+       "1      [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, ...\n",
+       "2      [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, ...\n",
+       "3      [1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, ...\n",
+       "4      [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...\n",
+       "                             ...                        \n",
+       "97     [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ...\n",
+       "98     [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, ...\n",
+       "99     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
+       "100    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, ...\n",
+       "101    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
+       "Name: failedTestNames, Length: 102, dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>[sig-apps][Feature:DeploymentConfig] deploymentconfigs adoption will orphan all RCs and adopt them back when recreated</th>\n",
+       "      <th>[sig-network][Feature:Router] The HAProxy router should expose prometheus metrics for a route</th>\n",
+       "      <th>[sig-builds][Feature:Builds][webhook] TestWebhookGitHubPing</th>\n",
+       "      <th>operator.Run template e2e-azure - e2e-azure container test</th>\n",
+       "      <th>[sig-builds][Feature:Builds] oc new-app  should succeed with an imagestream</th>\n",
+       "      <th>[sig-instrumentation][Late] Alerts should have a Watchdog alert in firing state the entire cluster run</th>\n",
+       "      <th>[sig-instrumentation][Late] Alerts shouldn't report any alerts in firing state apart from Watchdog and AlertmanagerReceiversNotConfigured</th>\n",
+       "      <th>[sig-cli] oc adm must-gather runs successfully</th>\n",
+       "      <th>[sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit setting</th>\n",
+       "      <th>[sig-instrumentation] Prometheus when installed on the cluster shouldn't report any alerts in firing state apart from Watchdog and AlertmanagerReceiversNotConfigured [Early]</th>\n",
+       "      <th>...</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: emptydir] [Testpattern: Dynamic PV (block volmode)] volume-expand should not allow expansion of pvcs without AllowVolumeExpansion property</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: local][LocalVolumeType: dir-link-bindmounted] [Testpattern: Pre-provisioned PV (ntfs)][sig-windows] volumes should allow exec of files on the volume</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: emptydir] [Testpattern: Pre-provisioned PV (default fs)] subPath should support non-existent path</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: gcepd] [Testpattern: Dynamic PV (default fs)] provisioning should provision storage with snapshot data source [Feature:VolumeSnapshotDataSource]</th>\n",
+       "      <th>operator.Run template e2e-aws-scaleup-rhel7 - e2e-aws-scaleup-rhel7 container test</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: aws] [Testpattern: Dynamic PV (ext4)] volumes should allow exec of files on the volume</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: cinder] [Testpattern: Dynamic PV (block volmode)] volume-expand should not allow expansion of pvcs without AllowVolumeExpansion property</th>\n",
+       "      <th>operator.Run template e2e-aws-serial - e2e-aws-serial container test</th>\n",
+       "      <th>[sig-storage] CSI Volumes [Driver: csi-hostpath] [Testpattern: Dynamic PV (filesystem volmode)] volumeLimits should support volume limits [Serial]</th>\n",
+       "      <th>[sig-storage] In-tree Volumes [Driver: azure-disk] [Testpattern: Inline-volume (default fs)] subPath should be able to unmount after the subpath directory is deleted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\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",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\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",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\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",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1455 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   [sig-apps][Feature:DeploymentConfig] deploymentconfigs adoption will orphan all RCs and adopt them back when recreated  \\\n",
+       "0                                                  1                                                                        \n",
+       "1                                                  1                                                                        \n",
+       "2                                                  1                                                                        \n",
+       "3                                                  1                                                                        \n",
+       "4                                                  1                                                                        \n",
+       "\n",
+       "   [sig-network][Feature:Router] The HAProxy router should expose prometheus metrics for a route  \\\n",
+       "0                                                  1                                               \n",
+       "1                                                  0                                               \n",
+       "2                                                  1                                               \n",
+       "3                                                  1                                               \n",
+       "4                                                  1                                               \n",
+       "\n",
+       "   [sig-builds][Feature:Builds][webhook] TestWebhookGitHubPing  \\\n",
+       "0                                                  1             \n",
+       "1                                                  1             \n",
+       "2                                                  1             \n",
+       "3                                                  0             \n",
+       "4                                                  1             \n",
+       "\n",
+       "   operator.Run template e2e-azure - e2e-azure container test  \\\n",
+       "0                                                  1            \n",
+       "1                                                  0            \n",
+       "2                                                  1            \n",
+       "3                                                  0            \n",
+       "4                                                  1            \n",
+       "\n",
+       "   [sig-builds][Feature:Builds] oc new-app  should succeed with an imagestream  \\\n",
+       "0                                                  1                             \n",
+       "1                                                  0                             \n",
+       "2                                                  1                             \n",
+       "3                                                  0                             \n",
+       "4                                                  1                             \n",
+       "\n",
+       "   [sig-instrumentation][Late] Alerts should have a Watchdog alert in firing state the entire cluster run  \\\n",
+       "0                                                  1                                                        \n",
+       "1                                                  0                                                        \n",
+       "2                                                  1                                                        \n",
+       "3                                                  1                                                        \n",
+       "4                                                  1                                                        \n",
+       "\n",
+       "   [sig-instrumentation][Late] Alerts shouldn't report any alerts in firing state apart from Watchdog and AlertmanagerReceiversNotConfigured  \\\n",
+       "0                                                  1                                                                                           \n",
+       "1                                                  0                                                                                           \n",
+       "2                                                  1                                                                                           \n",
+       "3                                                  1                                                                                           \n",
+       "4                                                  1                                                                                           \n",
+       "\n",
+       "   [sig-cli] oc adm must-gather runs successfully  \\\n",
+       "0                                               1   \n",
+       "1                                               1   \n",
+       "2                                               1   \n",
+       "3                                               0   \n",
+       "4                                               1   \n",
+       "\n",
+       "   [sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit setting  \\\n",
+       "0                                                  1                                                                                                           \n",
+       "1                                                  0                                                                                                           \n",
+       "2                                                  1                                                                                                           \n",
+       "3                                                  0                                                                                                           \n",
+       "4                                                  1                                                                                                           \n",
+       "\n",
+       "   [sig-instrumentation] Prometheus when installed on the cluster shouldn't report any alerts in firing state apart from Watchdog and AlertmanagerReceiversNotConfigured [Early]  \\\n",
+       "0                                                  1                                                                                                                               \n",
+       "1                                                  0                                                                                                                               \n",
+       "2                                                  0                                                                                                                               \n",
+       "3                                                  1                                                                                                                               \n",
+       "4                                                  0                                                                                                                               \n",
+       "\n",
+       "   ...  \\\n",
+       "0  ...   \n",
+       "1  ...   \n",
+       "2  ...   \n",
+       "3  ...   \n",
+       "4  ...   \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: emptydir] [Testpattern: Dynamic PV (block volmode)] volume-expand should not allow expansion of pvcs without AllowVolumeExpansion property  \\\n",
+       "0                                                  0                                                                                                                                   \n",
+       "1                                                  0                                                                                                                                   \n",
+       "2                                                  0                                                                                                                                   \n",
+       "3                                                  0                                                                                                                                   \n",
+       "4                                                  0                                                                                                                                   \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: local][LocalVolumeType: dir-link-bindmounted] [Testpattern: Pre-provisioned PV (ntfs)][sig-windows] volumes should allow exec of files on the volume  \\\n",
+       "0                                                  0                                                                                                                                             \n",
+       "1                                                  0                                                                                                                                             \n",
+       "2                                                  0                                                                                                                                             \n",
+       "3                                                  0                                                                                                                                             \n",
+       "4                                                  0                                                                                                                                             \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: emptydir] [Testpattern: Pre-provisioned PV (default fs)] subPath should support non-existent path  \\\n",
+       "0                                                  0                                                                                          \n",
+       "1                                                  0                                                                                          \n",
+       "2                                                  0                                                                                          \n",
+       "3                                                  0                                                                                          \n",
+       "4                                                  0                                                                                          \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: gcepd] [Testpattern: Dynamic PV (default fs)] provisioning should provision storage with snapshot data source [Feature:VolumeSnapshotDataSource]  \\\n",
+       "0                                                  0                                                                                                                                         \n",
+       "1                                                  0                                                                                                                                         \n",
+       "2                                                  0                                                                                                                                         \n",
+       "3                                                  0                                                                                                                                         \n",
+       "4                                                  0                                                                                                                                         \n",
+       "\n",
+       "   operator.Run template e2e-aws-scaleup-rhel7 - e2e-aws-scaleup-rhel7 container test  \\\n",
+       "0                                                  0                                    \n",
+       "1                                                  0                                    \n",
+       "2                                                  0                                    \n",
+       "3                                                  0                                    \n",
+       "4                                                  0                                    \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: aws] [Testpattern: Dynamic PV (ext4)] volumes should allow exec of files on the volume  \\\n",
+       "0                                                  0                                                                               \n",
+       "1                                                  0                                                                               \n",
+       "2                                                  0                                                                               \n",
+       "3                                                  0                                                                               \n",
+       "4                                                  0                                                                               \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: cinder] [Testpattern: Dynamic PV (block volmode)] volume-expand should not allow expansion of pvcs without AllowVolumeExpansion property  \\\n",
+       "0                                                  0                                                                                                                                 \n",
+       "1                                                  0                                                                                                                                 \n",
+       "2                                                  0                                                                                                                                 \n",
+       "3                                                  0                                                                                                                                 \n",
+       "4                                                  0                                                                                                                                 \n",
+       "\n",
+       "   operator.Run template e2e-aws-serial - e2e-aws-serial container test  \\\n",
+       "0                                                  0                      \n",
+       "1                                                  0                      \n",
+       "2                                                  0                      \n",
+       "3                                                  0                      \n",
+       "4                                                  0                      \n",
+       "\n",
+       "   [sig-storage] CSI Volumes [Driver: csi-hostpath] [Testpattern: Dynamic PV (filesystem volmode)] volumeLimits should support volume limits [Serial]  \\\n",
+       "0                                                  0                                                                                                    \n",
+       "1                                                  0                                                                                                    \n",
+       "2                                                  0                                                                                                    \n",
+       "3                                                  0                                                                                                    \n",
+       "4                                                  0                                                                                                    \n",
+       "\n",
+       "   [sig-storage] In-tree Volumes [Driver: azure-disk] [Testpattern: Inline-volume (default fs)] subPath should be able to unmount after the subpath directory is deleted  \n",
+       "0                                                  0                                                                                                                      \n",
+       "1                                                  0                                                                                                                      \n",
+       "2                                                  0                                                                                                                      \n",
+       "3                                                  0                                                                                                                      \n",
+       "4                                                  0                                                                                                                      \n",
+       "\n",
+       "[5 rows x 1455 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_encoded = pd.DataFrame(encoded.array, columns=vocab)\n",
+    "df_encoded.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Confirm no duplicates \n",
+    "\n",
+    "We can see by the empty output below that each job's set of failed tests is unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets confirm that each job is unique in its set of test failures\n",
+    "matches = []\n",
+    "for i, k in enumerate(encoded):\n",
+    "    for j, g in enumerate(encoded):\n",
+    "        if k == g and i != j:\n",
+    "            matches.append((i, j))\n",
+    "print(matches)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Remove noise/ unique tests\n",
+    "\n",
+    "We want to make sure that our correlations are not just due to unique failed test sets present in our small dataset, and want to make sure our tests impact multiple jobs. For example, if we had a unique failed test set that only occurred in a single example, and shared no other failed tests among the vocabulary, then all of the tests would appear to be 100% correlated with each other, when in fact that is merely a consequence of insufficient data.     \n",
+    "\n",
+    "In order to prevent that, lets ignore any tests that occur only in a single job.\n",
+    "\n",
+    "In order to do that we will use `occurrence_count` to create a filter vector for any test that occurs only once. Then filter them out of our working DF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[sig-apps][Feature:DeploymentConfig] deploymentconfigs adoption will orphan all RCs and adopt them back when recreated                                                                  0.362745\n",
+       "[sig-arch] Managed cluster should have no crashlooping pods in core namespaces over four minutes                                                                                        0.362745\n",
+       "[sig-apps][Feature:DeploymentConfig] deploymentconfigs with revision history limits should never persist more old deployments than acceptable after being observed by the controller    0.313725\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# percent that each test is present in the jobs data\n",
+    "perc_present = df_encoded.sum() / len(df_encoded)\n",
+    "perc_present.sort_values(ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[sig-apps][Feature:DeploymentConfig] deploymentconfigs adoption will orphan all RCs and adopt them back when recreated                                                                  37\n",
+       "[sig-arch] Managed cluster should have no crashlooping pods in core namespaces over four minutes                                                                                        37\n",
+       "[sig-apps][Feature:DeploymentConfig] deploymentconfigs with revision history limits should never persist more old deployments than acceptable after being observed by the controller    32\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occurrence_count = df_encoded.sum()\n",
+    "occurrence_count.sort_values(ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_unique = list(occurrence_count[occurrence_count.values <= 1].index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_encoded = df_encoded.drop(filter_unique, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(102, 976)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_encoded.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After Filtering \n",
+    "We are left with 102 jobs with 976 possible test names. We can see here that we've dropped nearly 500 test names from our dataset, meaning that there were 500 that occurred in only 1 job, and correlation could not be well established.    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build Correlation Matrix \n",
+    "\n",
+    "Here we will use pandas built in `df.corr()` function to get a correlation matrix between each of our tests failures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = df_encoded.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since there are 900 features, its not practical to visualize the entire correlation matrix, but to get a sense of the data lets look at a subset of our our data and see if any test pairs jump out as being highly correlated.\n",
+    "\n",
+    "The best way to interpret the graph below is, white squares represent highly correlated failure pairs and black squares represent independent failure pairs. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sn.heatmap(corr_matrix.iloc[0:25, 0:25])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find highly correlated failure sets\n",
+    "\n",
+    "Going thorough a 900 X 900 heatmap is probably not the most efficient way to find failure groups. Let's iterate through the correlation matrix and for each test, collect those tests that have a correlation coefficient of 0.9 or higher. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "976"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For each feature, find the other features that are correlated by more than 0.9\n",
+    "top_correlation = {}\n",
+    "\n",
+    "for c in corr_matrix.columns:\n",
+    "    top_correlation[c] = []\n",
+    "    series = corr_matrix.loc[c]\n",
+    "\n",
+    "    for i, s in enumerate(series):\n",
+    "        if s > 0.90 and series.index[i] != c:\n",
+    "            top_correlation[c].append((series.index[i], s))\n",
+    "\n",
+    "len(top_correlation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Examine Correlation Sets\n",
+    "Then to make things easier, lets ignore all the tests for which there are no highly correlated tests. \n",
+    "\n",
+    "Finally lets look at the output of this and examine one of our sets of highly correlated test failures. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "186 sets of correlated tests \n",
+      "\n",
+      "Feature of interest: [sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit setting\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>test_name</th>\n",
+       "      <th>correlation coefficient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify buil...</td>\n",
+       "      <td>0.908153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[sig-auth][Feature:RoleBindingRestrictions] RoleBindingRestrictions should be functional  Rolebinding restrictions tests single project should suc...</td>\n",
+       "      <td>0.908153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[sig-apps][Feature:OpenShiftControllerManager] TestTriggers_configChange</td>\n",
+       "      <td>0.908153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-auth][Feature:OpenShiftAuthorization] authorization  TestClusterReaderCoverage should succeed</td>\n",
+       "      <td>0.908153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[sig-auth][Feature:OpenShiftAuthorization] self-SAR compatibility  TestSelfSubjectAccessReviewsNonExistingNamespace should succeed</td>\n",
+       "      <td>0.908153</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                                                               test_name  \\\n",
+       "0  [sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify buil...   \n",
+       "1  [sig-auth][Feature:RoleBindingRestrictions] RoleBindingRestrictions should be functional  Rolebinding restrictions tests single project should suc...   \n",
+       "2                                                                               [sig-apps][Feature:OpenShiftControllerManager] TestTriggers_configChange   \n",
+       "3                                                     [sig-auth][Feature:OpenShiftAuthorization] authorization  TestClusterReaderCoverage should succeed   \n",
+       "4                     [sig-auth][Feature:OpenShiftAuthorization] self-SAR compatibility  TestSelfSubjectAccessReviewsNonExistingNamespace should succeed   \n",
+       "\n",
+       "   correlation coefficient  \n",
+       "0                 0.908153  \n",
+       "1                 0.908153  \n",
+       "2                 0.908153  \n",
+       "3                 0.908153  \n",
+       "4                 0.908153  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.set_option(\"display.max_colwidth\", 150)\n",
+    "# top_correlation has a number of empty rows as not all tests have high correlations with others,\n",
+    "# lets grab only the sets that do\n",
+    "corr_sets = []\n",
+    "for i in top_correlation.items():\n",
+    "    if len(i[1]) >= 1:\n",
+    "        corr_sets.append(i)\n",
+    "print(f\"{len(corr_sets)} sets of correlated tests \\n\")\n",
+    "print(f\"Feature of interest: {corr_sets[1][0]}\")\n",
+    "pd.DataFrame(corr_sets[1][1], columns=[\"test_name\", \"correlation coefficient\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Validate correlation set\n",
+    "\n",
+    "We can see from the output above that the test `[sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit setting` is highly correlated with these 5 other tests. They appear to cooccur almost all of the time (0.9). However, lets just double check and make sure this isn't a one-off occurrence.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 : the number of times this test failed in our data set\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_name = (\n",
+    "    \"[sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  \"\n",
+    "    \"should prune errored builds based on the failedBuildsHistoryLimit setting\"\n",
+    ")\n",
+    "num = occurrence_count.loc[test_name]\n",
+    "print(f\"{num} : the number of times this test failed in our data set\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>test_name</th>\n",
+       "      <th>num_occurrences</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify buil...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[sig-auth][Feature:RoleBindingRestrictions] RoleBindingRestrictions should be functional  Rolebinding restrictions tests single project should suc...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[sig-apps][Feature:OpenShiftControllerManager] TestTriggers_configChange</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-auth][Feature:OpenShiftAuthorization] authorization  TestClusterReaderCoverage should succeed</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[sig-auth][Feature:OpenShiftAuthorization] self-SAR compatibility  TestSelfSubjectAccessReviewsNonExistingNamespace should succeed</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                                                               test_name  \\\n",
+       "0  [sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify buil...   \n",
+       "1  [sig-auth][Feature:RoleBindingRestrictions] RoleBindingRestrictions should be functional  Rolebinding restrictions tests single project should suc...   \n",
+       "2                                                                               [sig-apps][Feature:OpenShiftControllerManager] TestTriggers_configChange   \n",
+       "3                                                     [sig-auth][Feature:OpenShiftAuthorization] authorization  TestClusterReaderCoverage should succeed   \n",
+       "4                     [sig-auth][Feature:OpenShiftAuthorization] self-SAR compatibility  TestSelfSubjectAccessReviewsNonExistingNamespace should succeed   \n",
+       "\n",
+       "   num_occurrences  \n",
+       "0                5  \n",
+       "1                5  \n",
+       "2                5  \n",
+       "3                5  \n",
+       "4                5  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "focus = corr_sets[1][1]\n",
+    "for j in focus:\n",
+    "    lst.append((j[0], occurrence_count.loc[j[0]]))\n",
+    "\n",
+    "pd.DataFrame(lst, columns=[\"test_name\", \"num_occurrences\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Here we see that each test failure occurs in 5 different jobs, of which we know that no 2 that are exactly the same. These 5 test almost always fail in conjunction with \"prune builds...\".** \n",
+    "\n",
+    "Since we see that all of these failures occur in multiple jobs across the dataset, it provides some validity to the claim that these tests are related by the same underlying cause.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add job run urls "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to help the end-user diagnose the underlying cause driving this set of test failures we would also like to add links to the specific job runs where these failure sets are occurring. \n",
+    "\n",
+    "In order to do this, we need to return to our original dataframe to find the url column associated with our failedTestNames. And then match the urls for cases in which all of our test failure sets fail on the same run. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>job</th>\n",
+       "      <th>url</th>\n",
+       "      <th>testGridJobUrl</th>\n",
+       "      <th>testFailures</th>\n",
+       "      <th>failedTestNames</th>\n",
+       "      <th>failed</th>\n",
+       "      <th>hasUnknownFailures</th>\n",
+       "      <th>succeeded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>release-openshift-origin-installer-e2e-azure-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-release-4.6-blocking#release-openshift-origin-installer-e2e-azure-4.6</td>\n",
+       "      <td>371</td>\n",
+       "      <td>[Overall, [sig-apps][Feature:DeploymentConfig] deploymentconfigs adoption will orphan all RCs and adopt them back when recreated, [sig-network][Fe...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>release-openshift-ocp-installer-e2e-aws-ovn-4.6</td>\n",
+       "      <td>https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-ocp-installer-e2e-aws-ovn-4.6/1291592782258900992</td>\n",
+       "      <td>https://testgrid.k8s.io/redhat-openshift-ocp-release-4.6-informing#release-openshift-ocp-installer-e2e-aws-ovn-4.6</td>\n",
+       "      <td>352</td>\n",
+       "      <td>[Overall, [sig-arch] Managed cluster should have no crashlooping pods in core namespaces over four minutes, [sig-arch][Early] Managed cluster shou...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                job  \\\n",
+       "0  release-openshift-origin-installer-e2e-azure-4.6   \n",
+       "1   release-openshift-ocp-installer-e2e-aws-ovn-4.6   \n",
+       "\n",
+       "                                                                                                                                   url  \\\n",
+       "0  https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400   \n",
+       "1   https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-ocp-installer-e2e-aws-ovn-4.6/1291592782258900992   \n",
+       "\n",
+       "                                                                                                       testGridJobUrl  \\\n",
+       "0  https://testgrid.k8s.io/redhat-openshift-ocp-release-4.6-blocking#release-openshift-origin-installer-e2e-azure-4.6   \n",
+       "1  https://testgrid.k8s.io/redhat-openshift-ocp-release-4.6-informing#release-openshift-ocp-installer-e2e-aws-ovn-4.6   \n",
+       "\n",
+       "   testFailures  \\\n",
+       "0           371   \n",
+       "1           352   \n",
+       "\n",
+       "                                                                                                                                         failedTestNames  \\\n",
+       "0  [Overall, [sig-apps][Feature:DeploymentConfig] deploymentconfigs adoption will orphan all RCs and adopt them back when recreated, [sig-network][Fe...   \n",
+       "1  [Overall, [sig-arch] Managed cluster should have no crashlooping pods in core namespaces over four minutes, [sig-arch][Early] Managed cluster shou...   \n",
+       "\n",
+       "   failed  hasUnknownFailures  succeeded  \n",
+       "0    True                True      False  \n",
+       "1    True                True      False  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failure_groups.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit setting\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400\n",
+       "1       https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-ocp-installer-e2e-azure-4.6/1291835183837745152\n",
+       "2    https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291480293290545152\n",
+       "3       https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-ocp-installer-e2e-azure-4.6/1290553685616627712\n",
+       "4    https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1290697393284583424\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_urls = []\n",
+    "\n",
+    "for failure_group in corr_sets:\n",
+    "    urls = []\n",
+    "    group = failure_group[1]\n",
+    "    group = [x[0] for x in group]\n",
+    "    group.extend([failure_group[0]])\n",
+    "    sets = set(group)\n",
+    "\n",
+    "    for k, g in enumerate(failure_groups.failedTestNames):\n",
+    "        g = set(g)\n",
+    "        if sets.intersection(g) == sets:\n",
+    "            urls.append(failure_groups.url[k])\n",
+    "\n",
+    "    all_urls.append(urls)\n",
+    "print(corr_sets[1][0])\n",
+    "pd.Series(all_urls[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Data To CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>test_name</th>\n",
+       "      <th>correlated_tests</th>\n",
+       "      <th>urls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-builds][Feature:Builds] oc new-app  should succeed with an imagestream</td>\n",
+       "      <td>[([sig-apps][Feature:OpenShiftControllerManager] TestTriggers_MultipleICTs, 0.920985497016262)]</td>\n",
+       "      <td>[https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit ...</td>\n",
+       "      <td>[([sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify bu...</td>\n",
+       "      <td>[https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify buil...</td>\n",
+       "      <td>[([sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimi...</td>\n",
+       "      <td>[https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-auth][Feature:UserAPI] groups should work</td>\n",
+       "      <td>[([sig-auth][Feature:RoleBindingRestrictions] RoleBindingRestrictions should be functional  Create a rolebinding that also contains system:non-exi...</td>\n",
+       "      <td>[https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[sig-auth][Feature:OpenShiftAuthorization] RBAC proxy for openshift authz  RunLegacyClusterRoleEndpoint should succeed</td>\n",
+       "      <td>[([sig-auth][Feature:OpenShiftAuthorization] scopes TestUnknownScopes should succeed, 0.9081532183730032)]</td>\n",
+       "      <td>[https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                                                               test_name  \\\n",
+       "0                                                                            [sig-builds][Feature:Builds] oc new-app  should succeed with an imagestream   \n",
+       "1  [sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimit ...   \n",
+       "2  [sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify buil...   \n",
+       "3                                                                                                         [sig-auth][Feature:UserAPI] groups should work   \n",
+       "4                                 [sig-auth][Feature:OpenShiftAuthorization] RBAC proxy for openshift authz  RunLegacyClusterRoleEndpoint should succeed   \n",
+       "\n",
+       "                                                                                                                                        correlated_tests  \\\n",
+       "0                                                        [([sig-apps][Feature:OpenShiftControllerManager] TestTriggers_MultipleICTs, 0.920985497016262)]   \n",
+       "1  [([sig-instrumentation][sig-builds][Feature:Builds] Prometheus when installed on the cluster should start and expose a secured proxy and verify bu...   \n",
+       "2  [([sig-builds][Feature:Builds] prune builds based on settings in the buildconfig  should prune errored builds based on the failedBuildsHistoryLimi...   \n",
+       "3  [([sig-auth][Feature:RoleBindingRestrictions] RoleBindingRestrictions should be functional  Create a rolebinding that also contains system:non-exi...   \n",
+       "4                                             [([sig-auth][Feature:OpenShiftAuthorization] scopes TestUnknownScopes should succeed, 0.9081532183730032)]   \n",
+       "\n",
+       "                                                                                                                                                    urls  \n",
+       "0  [https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...  \n",
+       "1  [https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...  \n",
+       "2  [https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...  \n",
+       "3  [https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...  \n",
+       "4  [https://prow.svc.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-azure-4.6/1291269757735014400, https://prow...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "save = pd.DataFrame(corr_sets, columns=[\"test_name\", \"correlated_tests\"])\n",
+    "save[\"urls\"] = all_urls\n",
+    "save.to_csv(\"../../../data/interim/correlation_sets.csv\")\n",
+    "save.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "\n",
+    "The above notebook outlines to take a sippydata.json file for a specific time period and identify correlated failures along with the the urls for the specific instances where the failures all occurred together."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Next steps:\n",
+    "\n",
+    "This is a rather small dataset (~100 jobs) we may be able to get a better signal on test failure correlation if we had many more examples to use. \n",
+    "\n",
+    "We could also start to look at how stable these failure set are, and monitor how they drift overtime.  "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "devenv",
+   "language": "python",
+   "name": "devenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/Sippy/stage/sippy-analysis.ipynb b/Red Hat NLP/notebooks/data-sources/Sippy/stage/sippy-analysis.ipynb
new file mode 100644
index 00000000..8a98d697
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/Sippy/stage/sippy-analysis.ipynb	
@@ -0,0 +1,2675 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIPPY \n",
+    "A tool to process the job results from https://testgrid.k8s.io/\n",
+    "\n",
+    "Sippy provides dashboards  for the openshift CI test/job data.\n",
+    "It contains the health summary for CI’s latest release.\n",
+    "Reports on which tests fail most frequently along different dimensions:\n",
+    "* overall\n",
+    "* by job\n",
+    "* by platform (e.g. aws, gcp, etc)\n",
+    "* by sig (sig ownership of the test)\n",
+    "* Job runs that had large groups of test failures in a single run (generally indicative of a fundamental issue rather than a test problem)\n",
+    "* Job pass rates (which jobs are failing frequently, which are not, in sorted order)\n",
+    "\n",
+    "In this notebook we will be looking at the existing testgrid data at testgrid.k8s.io, giving specific attention to [Red Hat's CI dashboards](https://testgrid.k8s.io/redhat-openshift-informing).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Getting the data :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T19:16:52.567408Z",
+     "start_time": "2021-01-26T19:16:52.529237Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "from bs4 import BeautifulSoup\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:30:20.703916Z",
+     "start_time": "2021-01-26T17:30:20.691421Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# adding path to notebook consisting of modules to be imported\n",
+    "module_path = os.path.abspath(os.path.join(\"../../TestGrid\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:30:21.532518Z",
+     "start_time": "2021-01-26T17:30:21.521171Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length  # noqa: E402"
+   ]
+  },
+  {
+   "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 scrapping the html of any one of the dashboards. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.get(\n",
+    "    \"https://testgrid.k8s.io/redhat-openshift-informing?id=dashboard-group-bar\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\"redhat-openshift-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-4.6-blocking\"',\n",
+       " '\"redhat-openshift-okd-release-4.6-informing\"',\n",
+       " '\"redhat-openshift-okd-release-4.7-blocking\"',\n",
+       " '\"redhat-openshift-okd-release-4.7-informing\"',\n",
+       " '\"redhat-openshift-presubmit-master-gcp\"',\n",
+       " '\"redhat-osd\"']"
+      ]
+     },
+     "execution_count": 6,
+     "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 at 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. Let's take a look at a specific dashboard and see what kind of info its summary holds. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dashboard = \"redhat-openshift-ocp-release-4.6-informing\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.get(f\"https://testgrid.k8s.io/{dashboard}/summary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = response.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['canary-release-openshift-origin-installer-e2e-aws-4.6-cnv',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-proxy',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-workers-rhel7',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-assisted',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-assisted-onprem',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-compact',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-ipv4',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-upgrade',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-virtualmedia',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere-serial',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere-upi',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere-upi-serial',\n",
+       "       'periodic-ci-openshift-release-master-ocp-4.6-openshift-ipi-azure-arcconformance',\n",
+       "       'periodic-ci-openshift-release-master-origin-4.5-stable-to-4.6-ci-e2e-aws-upgrade',\n",
+       "       'periodic-ci-openshift-release-master-origin-4.5-stable-to-4.6-ci-e2e-azure-upgrade',\n",
+       "       'periodic-ci-openshift-release-master-origin-4.5-stable-to-4.6-ci-e2e-gcp-upgrade',\n",
+       "       'periodic-ci-openshift-release-master-origin-4.6-e2e-azure-upgrade',\n",
+       "       'periodic-ci-openshift-release-master-origin-4.6-e2e-gcp-upgrade',\n",
+       "       'promote-release-openshift-machine-os-content-e2e-aws-4.6',\n",
+       "       'promote-release-openshift-machine-os-content-e2e-aws-4.6-ppc64le',\n",
+       "       'promote-release-openshift-machine-os-content-e2e-aws-4.6-s390x',\n",
+       "       'release-openshift-ocp-installer-console-aws-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-aws-csi-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-aws-fips-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-aws-fips-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-aws-mirrors-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-aws-ovn-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-aws-upi-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-azure-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-azure-fips-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-azure-fips-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-azure-ovn-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-azure-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-gcp-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-gcp-fips-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-gcp-fips-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-gcp-ovn-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-gcp-rt-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-gcp-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-metal-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-metal-compact-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-metal-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-openstack-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-openstack-serial-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-ovirt-4.6',\n",
+       "       'release-openshift-ocp-installer-e2e-ovirt-upgrade-4.5-stable-to-4.6-ci',\n",
+       "       'release-openshift-origin-installer-e2e-aws-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-calico-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-compact-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-disruptive-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-sdn-network-stress-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-shared-vpc-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-upgrade-4.3-to-4.4-to-4.5-to-4.6-ci',\n",
+       "       'release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.5-to-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-azure-compact-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-azure-shared-vpc-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-gcp-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-gcp-compact-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-gcp-serial-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-gcp-shared-vpc-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-remote-libvirt-ppc64le-4.6',\n",
+       "       'release-openshift-origin-installer-e2e-remote-libvirt-s390x-4.6',\n",
+       "       'release-openshift-origin-installer-old-rhcos-e2e-aws-4.6'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ocp46 = pd.DataFrame(data)\n",
+    "ocp46.columns"
+   ]
+  },
+  {
+   "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_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-proxy\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'display_name': 'Overall',\n",
+       "  'test_name': 'Overall',\n",
+       "  'fail_count': 52,\n",
+       "  'fail_timestamp': 1610323253,\n",
+       "  'pass_timestamp': 0,\n",
+       "  'build_link': '',\n",
+       "  'build_url_text': '',\n",
+       "  'build_link_text': '',\n",
+       "  'failure_message': '',\n",
+       "  'linked_bugs': [],\n",
+       "  'fail_test_link': '2dbbe062d2460b6ad5f5116b89e31745 Overall'},\n",
+       " {'display_name': '[sig-builds][Feature:Builds] build have source revision metadata  started build should contain source revision information [Suite:openshift/conformance/parallel]',\n",
+       "  'test_name': '[sig-builds][Feature:Builds] build have source revision metadata  started build should contain source revision information [Suite:openshift/conformance/parallel]',\n",
+       "  'fail_count': 18,\n",
+       "  'fail_timestamp': 1611014468,\n",
+       "  'pass_timestamp': 0,\n",
+       "  'build_link': '',\n",
+       "  'build_url_text': '',\n",
+       "  'build_link_text': '',\n",
+       "  'failure_message': '',\n",
+       "  'linked_bugs': [],\n",
+       "  'fail_test_link': '8533187410fa701427c7751c7469d090 [sig-builds][Feature:Builds] build have source revision metadata  started build should contain source revision information [Suite:openshift/conformance/parallel]'},\n",
+       " {'display_name': 'operator.Run multi-stage test e2e-aws-proxy - e2e-aws-proxy-openshift-e2e-test container test',\n",
+       "  'test_name': 'operator',\n",
+       "  'fail_count': 45,\n",
+       "  'fail_timestamp': 1610323253,\n",
+       "  'pass_timestamp': 0,\n",
+       "  'build_link': '',\n",
+       "  'build_url_text': '',\n",
+       "  'build_link_text': '',\n",
+       "  'failure_message': '',\n",
+       "  'linked_bugs': [],\n",
+       "  'fail_test_link': '1dbe44c2df9ee1624c8bb346c872b049 operator'}]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(ocp46.columns[1])\n",
+    "ocp46.loc[\"tests\", ocp46.columns[1]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a detailed analysis, we will be looking into the data for a particular job from the \n",
+    "* dashboard : `redhat-openshift-ocp-release-4.6-informing`  \n",
+    "* job : `https://testgrid.k8s.io/redhat-openshift-ocp-release-4.2-informing/table?&show-stale-tests=&tab=release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.5-to-4.6`   \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.get(\n",
+    "    \"https://testgrid.k8s.io/redhat-openshift-ocp-release-4.6-informing/table? \\\n",
+    "&show-stale-tests=&tab=release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.5-to-4.6\"\n",
+    ")  # noqa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "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": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'original-name', 'alert', 'linked_bugs', 'messages',\n",
+       "       'short_texts', 'statuses', 'target', 'user_property'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "details = pd.DataFrame(response.json()[\"tests\"])\n",
+    "details.columns"
+   ]
+  },
+  {
+   "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>original-name</th>\n",
+       "      <th>alert</th>\n",
+       "      <th>linked_bugs</th>\n",
+       "      <th>messages</th>\n",
+       "      <th>short_texts</th>\n",
+       "      <th>statuses</th>\n",
+       "      <th>target</th>\n",
+       "      <th>user_property</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[API \"openshift-api-available\" was unreachable...</td>\n",
+       "      <td>[F, F, , , F, , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[Service was unreachable during disruption for...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 8, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[99 error level events were detected during th...</td>\n",
+       "      <td>[F, F, F, F, F, F, F, F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[14 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[12 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  \\\n",
+       "0  Cluster upgrade.[sig-api-machinery] OpenShift ...   \n",
+       "1  Cluster upgrade.[sig-network-edge] Application...   \n",
+       "2  openshift-tests.[sig-arch] Monitor cluster whi...   \n",
+       "3  openshift-tests.[sig-network] pods should succ...   \n",
+       "4  openshift-tests.[sig-network] pods should succ...   \n",
+       "\n",
+       "                                       original-name  \\\n",
+       "0  Cluster upgrade.[sig-api-machinery] OpenShift ...   \n",
+       "1  Cluster upgrade.[sig-network-edge] Application...   \n",
+       "2  openshift-tests.[sig-arch] Monitor cluster whi...   \n",
+       "3  openshift-tests.[sig-network] pods should succ...   \n",
+       "4  openshift-tests.[sig-network] pods should succ...   \n",
+       "\n",
+       "                                               alert linked_bugs  \\\n",
+       "0                                               None          []   \n",
+       "1  {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "2  {'fail-cl': '1', 'fail-count': 8, 'fail-time':...          []   \n",
+       "3  {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "4  {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "\n",
+       "                                            messages  \\\n",
+       "0  [API \"openshift-api-available\" was unreachable...   \n",
+       "1  [Service was unreachable during disruption for...   \n",
+       "2  [99 error level events were detected during th...   \n",
+       "3  [14 failures to create the sandbox\\n\\nns/opens...   \n",
+       "4  [12 failures to create the sandbox\\n\\nns/opens...   \n",
+       "\n",
+       "                short_texts  \\\n",
+       "0       [F, F, , , F, , , ]   \n",
+       "1   [F, F, F, F, F, F, , F]   \n",
+       "2  [F, F, F, F, F, F, F, F]   \n",
+       "3   [F, F, F, F, F, F, , F]   \n",
+       "4   [F, F, F, F, F, F, , F]   \n",
+       "\n",
+       "                                            statuses  \\\n",
+       "0  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "1  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "2  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "3  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "4  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "\n",
+       "                                              target user_property  \n",
+       "0  Cluster upgrade.[sig-api-machinery] OpenShift ...          None  \n",
+       "1  Cluster upgrade.[sig-network-edge] Application...          None  \n",
+       "2  openshift-tests.[sig-arch] Monitor cluster whi...          None  \n",
+       "3  openshift-tests.[sig-network] pods should succ...          None  \n",
+       "4  openshift-tests.[sig-network] pods should succ...          None  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "details.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = details.target.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>messages</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[API \"openshift-api-available\" was unreachable...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[Service was unreachable during disruption for...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[99 error level events were detected during th...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[14 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[12 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>[, , , , , , , , , ]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>84 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             messages\n",
+       "0   [API \"openshift-api-available\" was unreachable...\n",
+       "1   [Service was unreachable during disruption for...\n",
+       "2   [99 error level events were detected during th...\n",
+       "3   [14 failures to create the sandbox\\n\\nns/opens...\n",
+       "4   [12 failures to create the sandbox\\n\\nns/opens...\n",
+       "..                                                ...\n",
+       "79                                   [, , , , , , , ]\n",
+       "80                                   [, , , , , , , ]\n",
+       "81                   [, , , , , , , , , , , , , , , ]\n",
+       "82                   [, , , , , , , , , , , , , , , ]\n",
+       "83                               [, , , , , , , , , ]\n",
+       "\n",
+       "[84 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = details.messages\n",
+    "messages.to_frame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Test Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should never t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] Kubernetes...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OAuth APIs...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Overall</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>operator.Run cluster install and upgrade e2e-a...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Cluster fro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>openshift-tests.[sig-node] kubelet terminates ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>openshift-tests.[sig-arch][Feature:ClusterUpgr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade authentication</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade machine-config</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade openshift-api...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Cluster upgrade.[sig-cluster-lifecycle] Cluste...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Cluster upgrade.[sig-cluster-lifecycle] Cluste...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade cloud-credential</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade cluster-autos...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade config-operator</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade console</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade csi-snapshot-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade dns</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade etcd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade image-registry</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade ingress</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade insights</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade kube-apiserver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade kube-controll...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade kube-scheduler</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade kube-storage-...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade machine-api</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade machine-approver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade marketplace</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade monitoring</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade network</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade node-tuning</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade openshift-con...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade openshift-sam...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade operator-life...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade operator-life...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade operator-life...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade service-ca</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Cluster upgrade.Operator upgrade storage</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] Kubernetes...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OAuth APIs...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] daemonset-upgrade</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] deployment-upgrade</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] job-upgrade</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] replicaset-upgrade</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] statefulset-upgrade</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Cluster upgrade.[sig-arch] Check if critical a...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>Cluster upgrade.[sig-cluster-lifecycle] cluste...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>Cluster upgrade.[sig-mco] Machine config pools...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Cluster fro...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>Cluster upgrade.[sig-storage] [sig-api-machine...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>Cluster upgrade.[sig-storage] [sig-api-machine...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>Cluster upgrade.upgrade</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>Symptom Detection.Bug 1812261: iptables is seg...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>Symptom Detection.Infrastructure - AWS simulat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>Symptom Detection.Infrastructure - GCP quota e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>Symptom Detection.Node process segfaulted</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>Symptom Detection.Undiagnosed panic detected i...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>77</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should never t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should not fai...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            Test Name\n",
+       "0   Cluster upgrade.[sig-api-machinery] OpenShift ...\n",
+       "1   Cluster upgrade.[sig-network-edge] Application...\n",
+       "2   openshift-tests.[sig-arch] Monitor cluster whi...\n",
+       "3   openshift-tests.[sig-network] pods should succ...\n",
+       "4   openshift-tests.[sig-network] pods should succ...\n",
+       "5   openshift-tests.[sig-node] pods should never t...\n",
+       "6   Cluster upgrade.[sig-api-machinery] Kubernetes...\n",
+       "7   Cluster upgrade.[sig-api-machinery] OAuth APIs...\n",
+       "8   openshift-tests.[sig-network] pods should succ...\n",
+       "9                                             Overall\n",
+       "10  operator.Run template e2e-aws-upgrade - e2e-aw...\n",
+       "11  operator.Run cluster install and upgrade e2e-a...\n",
+       "12  Cluster upgrade.[sig-network-edge] Cluster fro...\n",
+       "13  openshift-tests.[sig-node] kubelet terminates ...\n",
+       "14  operator.Import the release payload \"latest\" f...\n",
+       "15  operator.Import the release payload \"initial\" ...\n",
+       "16  openshift-tests.[sig-arch][Feature:ClusterUpgr...\n",
+       "17  operator.Run template e2e-aws-upgrade - e2e-aw...\n",
+       "18    Cluster upgrade.Operator upgrade authentication\n",
+       "19    Cluster upgrade.Operator upgrade machine-config\n",
+       "20  Cluster upgrade.Operator upgrade openshift-api...\n",
+       "21  Cluster upgrade.[sig-cluster-lifecycle] Cluste...\n",
+       "22  Cluster upgrade.[sig-cluster-lifecycle] Cluste...\n",
+       "23  openshift-tests.[sig-network] pods should succ...\n",
+       "24  Cluster upgrade.Operator upgrade cloud-credential\n",
+       "25  Cluster upgrade.Operator upgrade cluster-autos...\n",
+       "26   Cluster upgrade.Operator upgrade config-operator\n",
+       "27           Cluster upgrade.Operator upgrade console\n",
+       "28  Cluster upgrade.Operator upgrade csi-snapshot-...\n",
+       "29               Cluster upgrade.Operator upgrade dns\n",
+       "30              Cluster upgrade.Operator upgrade etcd\n",
+       "31    Cluster upgrade.Operator upgrade image-registry\n",
+       "32           Cluster upgrade.Operator upgrade ingress\n",
+       "33          Cluster upgrade.Operator upgrade insights\n",
+       "34    Cluster upgrade.Operator upgrade kube-apiserver\n",
+       "35  Cluster upgrade.Operator upgrade kube-controll...\n",
+       "36    Cluster upgrade.Operator upgrade kube-scheduler\n",
+       "37  Cluster upgrade.Operator upgrade kube-storage-...\n",
+       "38       Cluster upgrade.Operator upgrade machine-api\n",
+       "39  Cluster upgrade.Operator upgrade machine-approver\n",
+       "40       Cluster upgrade.Operator upgrade marketplace\n",
+       "41        Cluster upgrade.Operator upgrade monitoring\n",
+       "42           Cluster upgrade.Operator upgrade network\n",
+       "43       Cluster upgrade.Operator upgrade node-tuning\n",
+       "44  Cluster upgrade.Operator upgrade openshift-con...\n",
+       "45  Cluster upgrade.Operator upgrade openshift-sam...\n",
+       "46  Cluster upgrade.Operator upgrade operator-life...\n",
+       "47  Cluster upgrade.Operator upgrade operator-life...\n",
+       "48  Cluster upgrade.Operator upgrade operator-life...\n",
+       "49        Cluster upgrade.Operator upgrade service-ca\n",
+       "50           Cluster upgrade.Operator upgrade storage\n",
+       "51  Cluster upgrade.[sig-api-machinery] Kubernetes...\n",
+       "52  Cluster upgrade.[sig-api-machinery] OAuth APIs...\n",
+       "53  Cluster upgrade.[sig-api-machinery] OpenShift ...\n",
+       "54       Cluster upgrade.[sig-apps] daemonset-upgrade\n",
+       "55      Cluster upgrade.[sig-apps] deployment-upgrade\n",
+       "56             Cluster upgrade.[sig-apps] job-upgrade\n",
+       "57      Cluster upgrade.[sig-apps] replicaset-upgrade\n",
+       "58     Cluster upgrade.[sig-apps] statefulset-upgrade\n",
+       "59  Cluster upgrade.[sig-arch] Check if critical a...\n",
+       "60  Cluster upgrade.[sig-cluster-lifecycle] cluste...\n",
+       "61  Cluster upgrade.[sig-mco] Machine config pools...\n",
+       "62  Cluster upgrade.[sig-network-edge] Application...\n",
+       "63  Cluster upgrade.[sig-network-edge] Cluster fro...\n",
+       "64  Cluster upgrade.[sig-storage] [sig-api-machine...\n",
+       "65  Cluster upgrade.[sig-storage] [sig-api-machine...\n",
+       "66                            Cluster upgrade.upgrade\n",
+       "67  Symptom Detection.Bug 1812261: iptables is seg...\n",
+       "68  Symptom Detection.Infrastructure - AWS simulat...\n",
+       "69  Symptom Detection.Infrastructure - GCP quota e...\n",
+       "70          Symptom Detection.Node process segfaulted\n",
+       "71  Symptom Detection.Undiagnosed panic detected i...\n",
+       "72  openshift-tests.[sig-arch] Monitor cluster whi...\n",
+       "73  openshift-tests.[sig-network] pods should succ...\n",
+       "74  openshift-tests.[sig-network] pods should succ...\n",
+       "75  openshift-tests.[sig-network] pods should succ...\n",
+       "76  openshift-tests.[sig-network] pods should succ...\n",
+       "77  openshift-tests.[sig-network] pods should succ...\n",
+       "78  openshift-tests.[sig-network] pods should succ...\n",
+       "79  openshift-tests.[sig-node] pods should never t...\n",
+       "80  openshift-tests.[sig-node] pods should not fai...\n",
+       "81  operator.All images are built and tagged into ...\n",
+       "82  operator.Find all of the input images from ocp...\n",
+       "83  operator.Run template e2e-aws-upgrade - e2e-aw..."
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_names = details[\"name\"].unique()\n",
+    "test_names = pd.DataFrame(test_names)\n",
+    "test_names = test_names.rename(columns={0: \"Test Name\"})\n",
+    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
+    "    display(test_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Cluster upgrade.[sig-api-machinery] OpenShift ...\n",
+       "1     Cluster upgrade.[sig-network-edge] Application...\n",
+       "2     openshift-tests.[sig-arch] Monitor cluster whi...\n",
+       "3     openshift-tests.[sig-network] pods should succ...\n",
+       "4     openshift-tests.[sig-network] pods should succ...\n",
+       "                            ...                        \n",
+       "79    openshift-tests.[sig-node] pods should never t...\n",
+       "80    openshift-tests.[sig-node] pods should not fai...\n",
+       "81    operator.All images are built and tagged into ...\n",
+       "82    operator.Find all of the input images from ocp...\n",
+       "83    operator.Run template e2e-aws-upgrade - e2e-aw...\n",
+       "Name: name, Length: 84, dtype: object"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "details.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Split up the value at name to get exact test name. And store these values in two separate columns : Type (first bit) and Test Name (second bit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>original-name</th>\n",
+       "      <th>alert</th>\n",
+       "      <th>linked_bugs</th>\n",
+       "      <th>messages</th>\n",
+       "      <th>short_texts</th>\n",
+       "      <th>statuses</th>\n",
+       "      <th>target</th>\n",
+       "      <th>user_property</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Test Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[API \"openshift-api-available\" was unreachable...</td>\n",
+       "      <td>[F, F, , , F, , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Cluster upgrade</td>\n",
+       "      <td>[sig-api-machinery] OpenShift APIs remain avai...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[Service was unreachable during disruption for...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Cluster upgrade</td>\n",
+       "      <td>[sig-network-edge] Application behind service ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 8, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[99 error level events were detected during th...</td>\n",
+       "      <td>[F, F, F, F, F, F, F, F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[14 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-network] pods should successfully create ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[12 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-network] pods should successfully create ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should never t...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 1}, {'count': 2, 'value...</td>\n",
+       "      <td>openshift-tests.[sig-node] pods should never t...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-node] pods should never transition back t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should not fai...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 1}, {'count': 2, 'value...</td>\n",
+       "      <td>openshift-tests.[sig-node] pods should not fai...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-node] pods should not fail on systemd tim...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[{'count': 16, 'value': 1}]</td>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>operator</td>\n",
+       "      <td>All images are built and tagged into stable</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[{'count': 16, 'value': 1}]</td>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>operator</td>\n",
+       "      <td>Find all of the input images from ocp/4.5:${co...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , , , ]</td>\n",
+       "      <td>[{'count': 3, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>operator</td>\n",
+       "      <td>Run template e2e-aws-upgrade - e2e-aws-upgrade...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>84 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        original-name  \\\n",
+       "0   Cluster upgrade.[sig-api-machinery] OpenShift ...   \n",
+       "1   Cluster upgrade.[sig-network-edge] Application...   \n",
+       "2   openshift-tests.[sig-arch] Monitor cluster whi...   \n",
+       "3   openshift-tests.[sig-network] pods should succ...   \n",
+       "4   openshift-tests.[sig-network] pods should succ...   \n",
+       "..                                                ...   \n",
+       "79  openshift-tests.[sig-node] pods should never t...   \n",
+       "80  openshift-tests.[sig-node] pods should not fai...   \n",
+       "81  operator.All images are built and tagged into ...   \n",
+       "82  operator.Find all of the input images from ocp...   \n",
+       "83  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "\n",
+       "                                                alert linked_bugs  \\\n",
+       "0                                                None          []   \n",
+       "1   {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "2   {'fail-cl': '1', 'fail-count': 8, 'fail-time':...          []   \n",
+       "3   {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "4   {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "..                                                ...         ...   \n",
+       "79                                               None          []   \n",
+       "80                                               None          []   \n",
+       "81                                               None          []   \n",
+       "82                                               None          []   \n",
+       "83                                               None          []   \n",
+       "\n",
+       "                                             messages  \\\n",
+       "0   [API \"openshift-api-available\" was unreachable...   \n",
+       "1   [Service was unreachable during disruption for...   \n",
+       "2   [99 error level events were detected during th...   \n",
+       "3   [14 failures to create the sandbox\\n\\nns/opens...   \n",
+       "4   [12 failures to create the sandbox\\n\\nns/opens...   \n",
+       "..                                                ...   \n",
+       "79                                   [, , , , , , , ]   \n",
+       "80                                   [, , , , , , , ]   \n",
+       "81                   [, , , , , , , , , , , , , , , ]   \n",
+       "82                   [, , , , , , , , , , , , , , , ]   \n",
+       "83                               [, , , , , , , , , ]   \n",
+       "\n",
+       "                         short_texts  \\\n",
+       "0                [F, F, , , F, , , ]   \n",
+       "1            [F, F, F, F, F, F, , F]   \n",
+       "2           [F, F, F, F, F, F, F, F]   \n",
+       "3            [F, F, F, F, F, F, , F]   \n",
+       "4            [F, F, F, F, F, F, , F]   \n",
+       "..                               ...   \n",
+       "79                  [, , , , , , , ]   \n",
+       "80                  [, , , , , , , ]   \n",
+       "81  [, , , , , , , , , , , , , , , ]   \n",
+       "82  [, , , , , , , , , , , , , , , ]   \n",
+       "83              [, , , , , , , , , ]   \n",
+       "\n",
+       "                                             statuses  \\\n",
+       "0   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "1   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "2   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "3   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "4   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "..                                                ...   \n",
+       "79  [{'count': 2, 'value': 1}, {'count': 2, 'value...   \n",
+       "80  [{'count': 2, 'value': 1}, {'count': 2, 'value...   \n",
+       "81                        [{'count': 16, 'value': 1}]   \n",
+       "82                        [{'count': 16, 'value': 1}]   \n",
+       "83  [{'count': 3, 'value': 1}, {'count': 1, 'value...   \n",
+       "\n",
+       "                                               target user_property  \\\n",
+       "0   Cluster upgrade.[sig-api-machinery] OpenShift ...          None   \n",
+       "1   Cluster upgrade.[sig-network-edge] Application...          None   \n",
+       "2   openshift-tests.[sig-arch] Monitor cluster whi...          None   \n",
+       "3   openshift-tests.[sig-network] pods should succ...          None   \n",
+       "4   openshift-tests.[sig-network] pods should succ...          None   \n",
+       "..                                                ...           ...   \n",
+       "79  openshift-tests.[sig-node] pods should never t...          None   \n",
+       "80  openshift-tests.[sig-node] pods should not fai...          None   \n",
+       "81  operator.All images are built and tagged into ...          None   \n",
+       "82  operator.Find all of the input images from ocp...          None   \n",
+       "83  operator.Run template e2e-aws-upgrade - e2e-aw...          None   \n",
+       "\n",
+       "               Type                                          Test Name  \n",
+       "0   Cluster upgrade  [sig-api-machinery] OpenShift APIs remain avai...  \n",
+       "1   Cluster upgrade  [sig-network-edge] Application behind service ...  \n",
+       "2   openshift-tests     [sig-arch] Monitor cluster while tests execute  \n",
+       "3   openshift-tests  [sig-network] pods should successfully create ...  \n",
+       "4   openshift-tests  [sig-network] pods should successfully create ...  \n",
+       "..              ...                                                ...  \n",
+       "79  openshift-tests  [sig-node] pods should never transition back t...  \n",
+       "80  openshift-tests  [sig-node] pods should not fail on systemd tim...  \n",
+       "81         operator        All images are built and tagged into stable  \n",
+       "82         operator  Find all of the input images from ocp/4.5:${co...  \n",
+       "83         operator  Run template e2e-aws-upgrade - e2e-aws-upgrade...  \n",
+       "\n",
+       "[84 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new = details[\"name\"].str.split(\".\", n=1, expand=True)\n",
+    "details[\"Type\"] = new[0]\n",
+    "details[\"Test Name\"] = new[1]\n",
+    "details.drop(columns=[\"name\"], inplace=True)\n",
+    "details"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "details[\"values\"] = details[\"statuses\"].apply(decode_run_length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'count': 2, 'value': 12},\n",
+       " {'count': 2, 'value': 0},\n",
+       " {'count': 2, 'value': 1},\n",
+       " {'count': 2, 'value': 0},\n",
+       " {'count': 1, 'value': 12},\n",
+       " {'count': 4, 'value': 0},\n",
+       " {'count': 3, 'value': 1}]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "details[\"statuses\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "details.alert[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Cluster upgrade', 'openshift-tests', 'Overall', 'operator',\n",
+       "       'Symptom Detection'], dtype=object)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "details.Type.unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sippy provides [dashboards](https://sippy.ci.openshift.org/?release=4.6) for openshift CI test/job data. This dashboard contains the health summary for CI's latest release. \n",
+    "\n",
+    "The various categories are as follow :\n",
+    "* Job Pass Rates By Variant     \n",
+    "* Curated TRT Tests     \n",
+    "* Top Failing Tests Without a Bug \n",
+    "* Top Failing Tests With a Bug \n",
+    "* Job Pass Rates By Job Name \n",
+    "* Job Pass Rates By Most Reduced Pass Rate \n",
+    "* Infrequent Job Pass Rates By Job Name \n",
+    "* Canary Test Failures \n",
+    "* Job Runs With Failure Groups \n",
+    "* Test Impacting Bugs \n",
+    "* Test Impacting Components \n",
+    "* Job Impacting BZ Components"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analyzing [Sippy](https://github.com/openshift/sippy) Code to reproduce their approach"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test names can be deciphered using this [bit](https://github.com/openshift/sippy/blob/811be6ff0d094fb1bd172b5d68775d0f14464c90/pkg/testgridanalysis/testgridanalysisapi/types.go#L62) performed by sippy team."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### By Variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_openshift_variants = [\n",
+    "    \"aws\",\n",
+    "    \"azure\",\n",
+    "    \"fips\",\n",
+    "    \"gcp\",\n",
+    "    \"metal-assisted\",\n",
+    "    \"metal-upi\",\n",
+    "    \"metal-ipi\",\n",
+    "    \"never-stable\",\n",
+    "    \"openstack\",\n",
+    "    \"osd\",\n",
+    "    \"ovirt\",\n",
+    "    \"ovn\",\n",
+    "    \"ppc64le\",\n",
+    "    \"promote\",\n",
+    "    \"proxy\",\n",
+    "    \"realtime\",\n",
+    "    \"s390x\",\n",
+    "    \"serial\",\n",
+    "    \"upgrade\",\n",
+    "    \"vsphere-ipi\",\n",
+    "    \"vsphere-upi\",\n",
+    "]\n",
+    "details = details.fillna(\"NA\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0            ['upgrade']\n",
+       "1            ['upgrade']\n",
+       "2                     []\n",
+       "3                     []\n",
+       "4                     []\n",
+       "             ...        \n",
+       "79                    []\n",
+       "80                    []\n",
+       "81                    []\n",
+       "82                    []\n",
+       "83    ['aws', 'upgrade']\n",
+       "Name: variant_type, Length: 84, dtype: object"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = details.copy()\n",
+    "df[\"variant_type\"] = details[\"original-name\"].apply(\n",
+    "    lambda x: [m for m in all_openshift_variants if m in x]\n",
+    ")\n",
+    "df[\"variant_type\"] = df[\"variant_type\"].map(str)\n",
+    "df[\"variant_type\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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": [
+    "df[\"variant_type\"].value_counts().plot()\n",
+    "plt.xticks(rotation=80)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above cell, we can see that the variant : aws and upgrade have been marked in the column variant_type.\n",
+    "This is how sippy looks up for some keywords that comprise of these variants and further catergorie the tests accordingly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Curated TRT Tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## curated test substrings\n",
+    "curated_test = [\n",
+    "    \"[Feature:SCC][Early] should not have pod creation failures during install\",\n",
+    "    \"infrastructure should work\",\n",
+    "    \"install should work\",\n",
+    "    \"Kubernetes APIs remain available\",\n",
+    "    \"OAuth APIs remain available\",\n",
+    "    \"OpenShift APIs remain available\",\n",
+    "    \"Pod Container Status should never report success for a pending container\",\n",
+    "    \"pods should never transition back to pending\",\n",
+    "    \"pods should successfully create sandboxes\",\n",
+    "    \"upgrade should work\",\n",
+    "    \"Cluster completes upgrade\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Curated TRT\"] = details[\"original-name\"].apply(\n",
+    "    lambda x: [m for m in curated_test if m in x]\n",
+    ")\n",
+    "df[\"Curated TRT\"] = df[\"Curated TRT\"].map(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0              ['OpenShift APIs remain available']\n",
+       "1                                               []\n",
+       "2                                               []\n",
+       "3    ['pods should successfully create sandboxes']\n",
+       "4    ['pods should successfully create sandboxes']\n",
+       "Name: Curated TRT, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Curated TRT\"].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([\"['OpenShift APIs remain available']\", '[]',\n",
+       "       \"['pods should successfully create sandboxes']\",\n",
+       "       \"['pods should never transition back to pending']\",\n",
+       "       \"['Kubernetes APIs remain available']\",\n",
+       "       \"['OAuth APIs remain available']\", \"['Cluster completes upgrade']\"],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Curated TRT\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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": [
+    "df[\"Curated TRT\"].value_counts().plot()\n",
+    "plt.xticks(rotation=80)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Custom Job Setup Containers\n",
+    "custom_job = [\n",
+    "    \"e2e-aws-upgrade-ipi-install-install-stableinitial\",\n",
+    "    \"e2e-aws-upgrade-rollback-ipi-install-install-stableinitial\",\n",
+    "    \"e2e-aws-proxy-ipi-install-install\",\n",
+    "    \"e2e-aws-workers-rhel7-ipi-install-install\",\n",
+    "    \"e2e-azure-upgrade-ipi-conf-azure\",\n",
+    "    \"e2e-gcp-upgrade-ipi-install-install-stableinitial\",\n",
+    "    \"e2e-metal-ipi-baremetalds-devscripts-setup\",\n",
+    "    \"e2e-metal-ipi-ovn-ipv6-baremetalds-devscripts-setup\",\n",
+    "    \"e2e-metal-ipi-ovn-dualstack-baremetalds-devscripts-setup\",\n",
+    "    \"e2e-vsphere-ipi-install-vsphere\",\n",
+    "    \"e2e-vsphere-upi-upi-install-vsphere\",\n",
+    "    \"e2e-vsphere-upi-serial-upi-install-vsphere\",\n",
+    "    \"e2e-vsphere-serial-ipi-install-vsphere\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>original-name</th>\n",
+       "      <th>alert</th>\n",
+       "      <th>linked_bugs</th>\n",
+       "      <th>messages</th>\n",
+       "      <th>short_texts</th>\n",
+       "      <th>statuses</th>\n",
+       "      <th>target</th>\n",
+       "      <th>user_property</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Test Name</th>\n",
+       "      <th>values</th>\n",
+       "      <th>variant_type</th>\n",
+       "      <th>Curated TRT</th>\n",
+       "      <th>customJob</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[API \"openshift-api-available\" was unreachable...</td>\n",
+       "      <td>[F, F, , , F, , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>Cluster upgrade</td>\n",
+       "      <td>[sig-api-machinery] OpenShift APIs remain avai...</td>\n",
+       "      <td>[12, 12, 0, 0, 1, 1, 0, 0, 12, 0, 0, 0, 0, 1, ...</td>\n",
+       "      <td>['upgrade']</td>\n",
+       "      <td>['OpenShift APIs remain available']</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[Service was unreachable during disruption for...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>Cluster upgrade</td>\n",
+       "      <td>[sig-network-edge] Application behind service ...</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>['upgrade']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 8, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[99 error level events were detected during th...</td>\n",
+       "      <td>[F, F, F, F, F, F, F, F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[14 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-network] pods should successfully create ...</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>['pods should successfully create sandboxes']</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[12 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-network] pods should successfully create ...</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>['pods should successfully create sandboxes']</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       original-name  \\\n",
+       "0  Cluster upgrade.[sig-api-machinery] OpenShift ...   \n",
+       "1  Cluster upgrade.[sig-network-edge] Application...   \n",
+       "2  openshift-tests.[sig-arch] Monitor cluster whi...   \n",
+       "3  openshift-tests.[sig-network] pods should succ...   \n",
+       "4  openshift-tests.[sig-network] pods should succ...   \n",
+       "\n",
+       "                                               alert linked_bugs  \\\n",
+       "0                                                 NA          []   \n",
+       "1  {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "2  {'fail-cl': '1', 'fail-count': 8, 'fail-time':...          []   \n",
+       "3  {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "4  {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "\n",
+       "                                            messages  \\\n",
+       "0  [API \"openshift-api-available\" was unreachable...   \n",
+       "1  [Service was unreachable during disruption for...   \n",
+       "2  [99 error level events were detected during th...   \n",
+       "3  [14 failures to create the sandbox\\n\\nns/opens...   \n",
+       "4  [12 failures to create the sandbox\\n\\nns/opens...   \n",
+       "\n",
+       "                short_texts  \\\n",
+       "0       [F, F, , , F, , , ]   \n",
+       "1   [F, F, F, F, F, F, , F]   \n",
+       "2  [F, F, F, F, F, F, F, F]   \n",
+       "3   [F, F, F, F, F, F, , F]   \n",
+       "4   [F, F, F, F, F, F, , F]   \n",
+       "\n",
+       "                                            statuses  \\\n",
+       "0  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "1  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "2  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "3  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "4  [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "\n",
+       "                                              target user_property  \\\n",
+       "0  Cluster upgrade.[sig-api-machinery] OpenShift ...            NA   \n",
+       "1  Cluster upgrade.[sig-network-edge] Application...            NA   \n",
+       "2  openshift-tests.[sig-arch] Monitor cluster whi...            NA   \n",
+       "3  openshift-tests.[sig-network] pods should succ...            NA   \n",
+       "4  openshift-tests.[sig-network] pods should succ...            NA   \n",
+       "\n",
+       "              Type                                          Test Name  \\\n",
+       "0  Cluster upgrade  [sig-api-machinery] OpenShift APIs remain avai...   \n",
+       "1  Cluster upgrade  [sig-network-edge] Application behind service ...   \n",
+       "2  openshift-tests     [sig-arch] Monitor cluster while tests execute   \n",
+       "3  openshift-tests  [sig-network] pods should successfully create ...   \n",
+       "4  openshift-tests  [sig-network] pods should successfully create ...   \n",
+       "\n",
+       "                                              values variant_type  \\\n",
+       "0  [12, 12, 0, 0, 1, 1, 0, 0, 12, 0, 0, 0, 0, 1, ...  ['upgrade']   \n",
+       "1  [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...  ['upgrade']   \n",
+       "2  [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...           []   \n",
+       "3  [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...           []   \n",
+       "4  [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...           []   \n",
+       "\n",
+       "                                     Curated TRT customJob  \n",
+       "0            ['OpenShift APIs remain available']        []  \n",
+       "1                                             []        []  \n",
+       "2                                             []        []  \n",
+       "3  ['pods should successfully create sandboxes']        []  \n",
+       "4  ['pods should successfully create sandboxes']        []  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"customJob\"] = details[\"original-name\"].apply(\n",
+    "    lambda x: [m for m in custom_job if m in x]\n",
+    ")\n",
+    "df[\"customJob\"] = df[\"customJob\"].map(str)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Valid Buzilla Components\n",
+    "ValidBugzillaComponents = [\n",
+    "    \"apiserver-auth\",\n",
+    "    \"assisted-installer\",\n",
+    "    \"Bare Metal Hardware Provisioning\",\n",
+    "    \"Build\",\n",
+    "    \"Cloud Compute\",\n",
+    "    \"Cloud Credential Operator\",\n",
+    "    \"Cluster Loader\",\n",
+    "    \"Cluster Version Operator\",\n",
+    "    \"CNF Variant Validation\",\n",
+    "    \"Compliance Operator\",\n",
+    "    \"config-operator\",\n",
+    "    \"Console Kubevirt Plugin\",\n",
+    "    \"Console Metal3 Plugin\",\n",
+    "    \"Console Storage Plugin\",\n",
+    "    \"Containers\",\n",
+    "    \"crc\",\n",
+    "    \"Dev Console\",\n",
+    "    \"DNS\",\n",
+    "    \"Documentation\",\n",
+    "    \"Etcd\",\n",
+    "    \"Federation\",\n",
+    "    \"File Integrity Operator\",\n",
+    "    \"Fuse\",\n",
+    "    \"Hawkular\",\n",
+    "    \"ibm-roks-toolkit\",\n",
+    "    \"Image\",\n",
+    "    \"Image Registry\",\n",
+    "    \"Insights Operator\",\n",
+    "    \"Installer\",\n",
+    "    \"ISV Operators\",\n",
+    "    \"Jenkins\",\n",
+    "    \"kata-containers\",\n",
+    "    \"kube-apiserver\",\n",
+    "    \"kube-controller-manager\",\n",
+    "    \"kube-scheduler\",\n",
+    "    \"kube-storage-version-migrator\",\n",
+    "    \"Logging\",\n",
+    "    \"Machine Config Operator\",\n",
+    "    \"Management Console\",\n",
+    "    \"Metering Operator\",\n",
+    "    \"Migration Tooling\",\n",
+    "    \"Monitoring\",\n",
+    "    \"Multi-Arch\",\n",
+    "    \"Multi-cluster-management\",\n",
+    "    \"Networking\",\n",
+    "    \"Node\",\n",
+    "    \"Node Feature Discovery Operator\",\n",
+    "    \"Node Tuning Operator\",\n",
+    "    \"oauth-apiserver\",\n",
+    "    \"oauth-proxy\",\n",
+    "    \"oc\",\n",
+    "    \"OLM\",\n",
+    "    \"openshift-apiserver\",\n",
+    "    \"openshift-controller-manager\",\n",
+    "    \"Operator SDK\",\n",
+    "    \"Performance Addon Operator\",\n",
+    "    \"Reference Architecture\",\n",
+    "    \"Registry Console\",\n",
+    "    \"Release\",\n",
+    "    \"RHCOS\",\n",
+    "    \"RHMI Monitoring\",\n",
+    "    \"Routing\",\n",
+    "    \"Samples\",\n",
+    "    \"Security\",\n",
+    "    \"Service Broker\",\n",
+    "    \"Service Catalog\",\n",
+    "    \"service-ca\",\n",
+    "    \"Special Resources Operator\",\n",
+    "    \"Storage\",\n",
+    "    \"Templates\",\n",
+    "    \"Test Infrastructure\",\n",
+    "    \"Unknown\",\n",
+    "    \"Windows Containers\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Valid Bugzilla Components\"] = details[\"original-name\"].apply(\n",
+    "    lambda x: [m for m in ValidBugzillaComponents if m in x]\n",
+    ")\n",
+    "df[\"Valid Bugzilla Components\"] = df[\"Valid Bugzilla Components\"].map(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>original-name</th>\n",
+       "      <th>alert</th>\n",
+       "      <th>linked_bugs</th>\n",
+       "      <th>messages</th>\n",
+       "      <th>short_texts</th>\n",
+       "      <th>statuses</th>\n",
+       "      <th>target</th>\n",
+       "      <th>user_property</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Test Name</th>\n",
+       "      <th>values</th>\n",
+       "      <th>variant_type</th>\n",
+       "      <th>Curated TRT</th>\n",
+       "      <th>customJob</th>\n",
+       "      <th>Valid Bugzilla Components</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[API \"openshift-api-available\" was unreachable...</td>\n",
+       "      <td>[F, F, , , F, , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-api-machinery] OpenShift ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>Cluster upgrade</td>\n",
+       "      <td>[sig-api-machinery] OpenShift APIs remain avai...</td>\n",
+       "      <td>[12, 12, 0, 0, 1, 1, 0, 0, 12, 0, 0, 0, 0, 1, ...</td>\n",
+       "      <td>['upgrade']</td>\n",
+       "      <td>['OpenShift APIs remain available']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[Service was unreachable during disruption for...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>Cluster upgrade.[sig-network-edge] Application...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>Cluster upgrade</td>\n",
+       "      <td>[sig-network-edge] Application behind service ...</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>['upgrade']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 8, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[99 error level events were detected during th...</td>\n",
+       "      <td>[F, F, F, F, F, F, F, F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-arch] Monitor cluster whi...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[14 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-network] pods should successfully create ...</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>['pods should successfully create sandboxes']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 6, 'fail-time':...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[12 failures to create the sandbox\\n\\nns/opens...</td>\n",
+       "      <td>[F, F, F, F, F, F, , F]</td>\n",
+       "      <td>[{'count': 2, 'value': 12}, {'count': 2, 'valu...</td>\n",
+       "      <td>openshift-tests.[sig-network] pods should succ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-network] pods should successfully create ...</td>\n",
+       "      <td>[12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>['pods should successfully create sandboxes']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <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>79</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should never t...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 1}, {'count': 2, 'value...</td>\n",
+       "      <td>openshift-tests.[sig-node] pods should never t...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-node] pods should never transition back t...</td>\n",
+       "      <td>[1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>['pods should never transition back to pending']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>80</th>\n",
+       "      <td>openshift-tests.[sig-node] pods should not fai...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , ]</td>\n",
+       "      <td>[{'count': 2, 'value': 1}, {'count': 2, 'value...</td>\n",
+       "      <td>openshift-tests.[sig-node] pods should not fai...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>openshift-tests</td>\n",
+       "      <td>[sig-node] pods should not fail on systemd tim...</td>\n",
+       "      <td>[1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>81</th>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[{'count': 16, 'value': 1}]</td>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>operator</td>\n",
+       "      <td>All images are built and tagged into stable</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , , , , , , , , , ]</td>\n",
+       "      <td>[{'count': 16, 'value': 1}]</td>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>operator</td>\n",
+       "      <td>Find all of the input images from ocp/4.5:${co...</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>['oc']</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[, , , , , , , , , ]</td>\n",
+       "      <td>[, , , , , , , , , ]</td>\n",
+       "      <td>[{'count': 3, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>operator</td>\n",
+       "      <td>Run template e2e-aws-upgrade - e2e-aws-upgrade...</td>\n",
+       "      <td>[1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1]</td>\n",
+       "      <td>['aws', 'upgrade']</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>84 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        original-name  \\\n",
+       "0   Cluster upgrade.[sig-api-machinery] OpenShift ...   \n",
+       "1   Cluster upgrade.[sig-network-edge] Application...   \n",
+       "2   openshift-tests.[sig-arch] Monitor cluster whi...   \n",
+       "3   openshift-tests.[sig-network] pods should succ...   \n",
+       "4   openshift-tests.[sig-network] pods should succ...   \n",
+       "..                                                ...   \n",
+       "79  openshift-tests.[sig-node] pods should never t...   \n",
+       "80  openshift-tests.[sig-node] pods should not fai...   \n",
+       "81  operator.All images are built and tagged into ...   \n",
+       "82  operator.Find all of the input images from ocp...   \n",
+       "83  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "\n",
+       "                                                alert linked_bugs  \\\n",
+       "0                                                  NA          []   \n",
+       "1   {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "2   {'fail-cl': '1', 'fail-count': 8, 'fail-time':...          []   \n",
+       "3   {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "4   {'fail-cl': '1', 'fail-count': 6, 'fail-time':...          []   \n",
+       "..                                                ...         ...   \n",
+       "79                                                 NA          []   \n",
+       "80                                                 NA          []   \n",
+       "81                                                 NA          []   \n",
+       "82                                                 NA          []   \n",
+       "83                                                 NA          []   \n",
+       "\n",
+       "                                             messages  \\\n",
+       "0   [API \"openshift-api-available\" was unreachable...   \n",
+       "1   [Service was unreachable during disruption for...   \n",
+       "2   [99 error level events were detected during th...   \n",
+       "3   [14 failures to create the sandbox\\n\\nns/opens...   \n",
+       "4   [12 failures to create the sandbox\\n\\nns/opens...   \n",
+       "..                                                ...   \n",
+       "79                                   [, , , , , , , ]   \n",
+       "80                                   [, , , , , , , ]   \n",
+       "81                   [, , , , , , , , , , , , , , , ]   \n",
+       "82                   [, , , , , , , , , , , , , , , ]   \n",
+       "83                               [, , , , , , , , , ]   \n",
+       "\n",
+       "                         short_texts  \\\n",
+       "0                [F, F, , , F, , , ]   \n",
+       "1            [F, F, F, F, F, F, , F]   \n",
+       "2           [F, F, F, F, F, F, F, F]   \n",
+       "3            [F, F, F, F, F, F, , F]   \n",
+       "4            [F, F, F, F, F, F, , F]   \n",
+       "..                               ...   \n",
+       "79                  [, , , , , , , ]   \n",
+       "80                  [, , , , , , , ]   \n",
+       "81  [, , , , , , , , , , , , , , , ]   \n",
+       "82  [, , , , , , , , , , , , , , , ]   \n",
+       "83              [, , , , , , , , , ]   \n",
+       "\n",
+       "                                             statuses  \\\n",
+       "0   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "1   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "2   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "3   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "4   [{'count': 2, 'value': 12}, {'count': 2, 'valu...   \n",
+       "..                                                ...   \n",
+       "79  [{'count': 2, 'value': 1}, {'count': 2, 'value...   \n",
+       "80  [{'count': 2, 'value': 1}, {'count': 2, 'value...   \n",
+       "81                        [{'count': 16, 'value': 1}]   \n",
+       "82                        [{'count': 16, 'value': 1}]   \n",
+       "83  [{'count': 3, 'value': 1}, {'count': 1, 'value...   \n",
+       "\n",
+       "                                               target user_property  \\\n",
+       "0   Cluster upgrade.[sig-api-machinery] OpenShift ...            NA   \n",
+       "1   Cluster upgrade.[sig-network-edge] Application...            NA   \n",
+       "2   openshift-tests.[sig-arch] Monitor cluster whi...            NA   \n",
+       "3   openshift-tests.[sig-network] pods should succ...            NA   \n",
+       "4   openshift-tests.[sig-network] pods should succ...            NA   \n",
+       "..                                                ...           ...   \n",
+       "79  openshift-tests.[sig-node] pods should never t...            NA   \n",
+       "80  openshift-tests.[sig-node] pods should not fai...            NA   \n",
+       "81  operator.All images are built and tagged into ...            NA   \n",
+       "82  operator.Find all of the input images from ocp...            NA   \n",
+       "83  operator.Run template e2e-aws-upgrade - e2e-aw...            NA   \n",
+       "\n",
+       "               Type                                          Test Name  \\\n",
+       "0   Cluster upgrade  [sig-api-machinery] OpenShift APIs remain avai...   \n",
+       "1   Cluster upgrade  [sig-network-edge] Application behind service ...   \n",
+       "2   openshift-tests     [sig-arch] Monitor cluster while tests execute   \n",
+       "3   openshift-tests  [sig-network] pods should successfully create ...   \n",
+       "4   openshift-tests  [sig-network] pods should successfully create ...   \n",
+       "..              ...                                                ...   \n",
+       "79  openshift-tests  [sig-node] pods should never transition back t...   \n",
+       "80  openshift-tests  [sig-node] pods should not fail on systemd tim...   \n",
+       "81         operator        All images are built and tagged into stable   \n",
+       "82         operator  Find all of the input images from ocp/4.5:${co...   \n",
+       "83         operator  Run template e2e-aws-upgrade - e2e-aws-upgrade...   \n",
+       "\n",
+       "                                               values        variant_type  \\\n",
+       "0   [12, 12, 0, 0, 1, 1, 0, 0, 12, 0, 0, 0, 0, 1, ...         ['upgrade']   \n",
+       "1   [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...         ['upgrade']   \n",
+       "2   [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...                  []   \n",
+       "3   [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...                  []   \n",
+       "4   [12, 12, 0, 0, 12, 12, 0, 0, 12, 0, 0, 0, 0, 1...                  []   \n",
+       "..                                                ...                 ...   \n",
+       "79   [1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1]                  []   \n",
+       "80   [1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1]                  []   \n",
+       "81   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]                  []   \n",
+       "82   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]                  []   \n",
+       "83   [1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1]  ['aws', 'upgrade']   \n",
+       "\n",
+       "                                         Curated TRT customJob  \\\n",
+       "0                ['OpenShift APIs remain available']        []   \n",
+       "1                                                 []        []   \n",
+       "2                                                 []        []   \n",
+       "3      ['pods should successfully create sandboxes']        []   \n",
+       "4      ['pods should successfully create sandboxes']        []   \n",
+       "..                                               ...       ...   \n",
+       "79  ['pods should never transition back to pending']        []   \n",
+       "80                                                []        []   \n",
+       "81                                                []        []   \n",
+       "82                                                []        []   \n",
+       "83                                                []        []   \n",
+       "\n",
+       "   Valid Bugzilla Components  \n",
+       "0                         []  \n",
+       "1                         []  \n",
+       "2                         []  \n",
+       "3                         []  \n",
+       "4                         []  \n",
+       "..                       ...  \n",
+       "79                        []  \n",
+       "80                        []  \n",
+       "81                        []  \n",
+       "82                    ['oc']  \n",
+       "83                        []  \n",
+       "\n",
+       "[84 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['[]', \"['kube-apiserver']\", \"['openshift-apiserver']\",\n",
+       "       \"['config-operator']\", \"['kube-controller-manager']\",\n",
+       "       \"['kube-scheduler']\", \"['kube-storage-version-migrator']\",\n",
+       "       \"['openshift-controller-manager']\", \"['service-ca']\",\n",
+       "       \"['Node', 'oc']\", \"['oc']\"], dtype=object)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Valid Bugzilla Components\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "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": [
+    "df[\"Valid Bugzilla Components\"].value_counts().plot()\n",
+    "plt.xticks(rotation=80)\n",
+    "plt.ylim(0, 10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we have seen a few categories like Curated Jobs, classifying the jobs based on Variants and Bugzilla Components that can be seen on Sippy Dashboard. We can also try out more dashboards to see better results. This notebook takes in account job: https://testgrid.k8s.io/redhat-openshift-ocp-release-4.6-informing/table?&show-stale-tests=&tab=release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.5-to-4.6 case. But we can change this link in cell[10] and see various different dashboards and perform this analysis sippy uses."
+   ]
+  }
+ ],
+ "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Red Hat NLP/notebooks/data-sources/Sippy/stage/sippy_EDA.ipynb b/Red Hat NLP/notebooks/data-sources/Sippy/stage/sippy_EDA.ipynb
new file mode 100644
index 00000000..328fe353
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/Sippy/stage/sippy_EDA.ipynb	
@@ -0,0 +1,15822 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sippy Export of OpenShift Data - EDA\n",
+    "\n",
+    "\n",
+    "In this notebook we will take a look at some of the openshift CI data distilled by the Sippy project with the following **goals** in mind. \n",
+    "\n",
+    "1. Uncover the structure and contents of the dataset\n",
+    "2. Present some basic visualizations and  statistics on the provided data\n",
+    "3. Identify potential (or missing) features for further analysis or ML work\n",
+    "4. Capture questions/ points of clarification for the sippy team on the data. \n",
+    "\n",
+    "\n",
+    "In this notebook we will review a small sample dataset (sippydata.json) from August 10th, 2020 that has the following structure.   \n",
+    "\n",
+    "\n",
+    "* **All**:  map of all the tests that were run in any job, single key is \"all\", value is a struct of pass count, fail count, pass percentage for tests run in \"All jobs\", plus an array of test results sorted by pass rate, test result struct includes the name of the test, its pass count, fail count, pass percentage. \n",
+    "\n",
+    "* **ByPlatform**: same data as above, except the map has a key for each platform we support (e.g. aws, gcp, etc), and the test results are only for jobs associated w/ those platforms.\n",
+    "\n",
+    "* **ByJob**: same but sliced by the specific test job name.\n",
+    "\n",
+    "* **BySig**: same but sliced by the \"special interest group\"(team) that is responsible for the tests\n",
+    "\n",
+    "* **FailureGroups**:  job runs that had a large number of failing tests(usually indicates a fundamental problem in the product that impacted many tests), with metadata about the job run.\n",
+    "\n",
+    "* **JobPassRate**: data for each job(not job run) sorted by the rate at which runs for that job as a whole passed(had no failing tests)\n",
+    "\n",
+    "* **TopFaliingTestsWithBug**: tests which fail the most often (sorted by percentage, not raw count) across all job runs, and for which we have a known BZ\n",
+    "\n",
+    "* **TopFailingTestsWithoutBug**: same as above, but for which we have no known BZ open\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:35.278526Z",
+     "start_time": "2021-01-22T16:58:33.519439Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas_profiling import ProfileReport\n",
+    "\n",
+    "\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "sns.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:35.507628Z",
+     "start_time": "2021-01-22T16:58:35.279944Z"
+    }
+   },
+   "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>data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>release</th>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>all</th>\n",
+       "      <td>{'all': {'successes': 477806, 'failures': 8619...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ByPlatform</th>\n",
+       "      <td>{'aws': {'successes': 196148, 'failures': 3249...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ByJob</th>\n",
+       "      <td>{'canary-release-openshift-origin-installer-e2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BySig</th>\n",
+       "      <td>{'sig-api-machinery': {'successes': 45866, 'fa...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>failureGroups</th>\n",
+       "      <td>[{'job': 'release-openshift-origin-installer-e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>jobPassRate</th>\n",
+       "      <td>[{'name': 'release-openshift-ocp-installer-e2e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <td>2020-08-10T12:10:38.344952931-04:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>topFailingTestsWithBug</th>\n",
+       "      <td>[{'name': '[sig-arch][Feature:ClusterUpgrade] ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>topFailingTestsWithoutBug</th>\n",
+       "      <td>[{'name': '[sig-network] network isolation whe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bugsByFailureCount</th>\n",
+       "      <td>[{'id': 1856189, 'status': 'ON_QA', 'last_chan...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                        data\n",
+       "release                                                                     \n",
+       "all                        {'all': {'successes': 477806, 'failures': 8619...\n",
+       "ByPlatform                 {'aws': {'successes': 196148, 'failures': 3249...\n",
+       "ByJob                      {'canary-release-openshift-origin-installer-e2...\n",
+       "BySig                      {'sig-api-machinery': {'successes': 45866, 'fa...\n",
+       "failureGroups              [{'job': 'release-openshift-origin-installer-e...\n",
+       "jobPassRate                [{'name': 'release-openshift-ocp-installer-e2e...\n",
+       "timestamp                                2020-08-10T12:10:38.344952931-04:00\n",
+       "topFailingTestsWithBug     [{'name': '[sig-arch][Feature:ClusterUpgrade] ...\n",
+       "topFailingTestsWithoutBug  [{'name': '[sig-network] network isolation whe...\n",
+       "bugsByFailureCount         [{'id': 1856189, 'status': 'ON_QA', 'last_chan..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = pd.read_json(\"../../../../data/external/sippydata.json\", orient=\"index\")\n",
+    "d.columns = [\"data\"]\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time of data sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:36.713149Z",
+     "start_time": "2021-01-22T16:58:36.710022Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2020-08-10T12:10:38.344952931-04:00'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.loc[\"timestamp\"].values[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### All\n",
+    "\n",
+    "map of all the tests that were run in any job, single key is \"all\", value is a struct of pass count, fail count, pass percentage for tests run in \"All jobs\", plus an array of test results sorted by pass rate, test result struct includes the name of the test, its pass count, fail count, pass percentage. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:38.131354Z",
+     "start_time": "2021-01-22T16:58:38.103403Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>failures</th>\n",
+       "      <td>8619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>results</th>\n",
+       "      <td>[{'name': '[sig-network] network isolation whe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>successes</th>\n",
+       "      <td>477806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>testPassPercentage</th>\n",
+       "      <td>98.2281</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                  all\n",
+       "failures                                                         8619\n",
+       "results             [{'name': '[sig-network] network isolation whe...\n",
+       "successes                                                      477806\n",
+       "testPassPercentage                                            98.2281"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# unroll the all data\n",
+    "\n",
+    "_all = pd.DataFrame(d.loc[\"all\"][0])\n",
+    "_all.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:39.363790Z",
+     "start_time": "2021-01-22T16:58:39.253563Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc={\"figure.figsize\": (7, 7)})\n",
+    "plt.bar(\n",
+    "    [\"failures\", \"successes\"],\n",
+    "    [_all.loc[\"failures\"].values[0], _all.loc[\"successes\"].values[0]],\n",
+    ")\n",
+    "plt.title(\"Failures vs Successes: ALL\", fontsize=20)\n",
+    "plt.ylabel(\"Total Count\", fontsize=15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:40.618837Z",
+     "start_time": "2021-01-22T16:58:40.599390Z"
+    }
+   },
+   "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "      <th>BugList</th>\n",
+       "      <th>searchLink</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-network] network isolation when using a p...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-aws-upi - e2e-aws-up...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-gcp - e2e-gcp-rt con...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Run template e2e-metal-serial - e2e-m...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run template e2e-ovirt - e2e-ovirt co...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1074</th>\n",
+       "      <td>[k8s.io] Lease lease API should be available [...</td>\n",
+       "      <td>474</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99.789474</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1075</th>\n",
+       "      <td>[sig-network] Proxy version v1 should proxy lo...</td>\n",
+       "      <td>474</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99.789474</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1076</th>\n",
+       "      <td>[sig-api-machinery] Garbage collector should n...</td>\n",
+       "      <td>474</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99.789474</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1077</th>\n",
+       "      <td>[sig-api-machinery] CustomResourceDefinition r...</td>\n",
+       "      <td>474</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99.789474</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1078</th>\n",
+       "      <td>[sig-api-machinery] Servers with support for T...</td>\n",
+       "      <td>474</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>99.789474</td>\n",
+       "      <td>None</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1079 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   name  successes  failures  \\\n",
+       "0     [sig-network] network isolation when using a p...          0        10   \n",
+       "1     operator.Run template e2e-aws-upi - e2e-aws-up...          0        48   \n",
+       "2     operator.Run template e2e-gcp - e2e-gcp-rt con...          0        43   \n",
+       "3     operator.Run template e2e-metal-serial - e2e-m...          0        47   \n",
+       "4     operator.Run template e2e-ovirt - e2e-ovirt co...          0        48   \n",
+       "...                                                 ...        ...       ...   \n",
+       "1074  [k8s.io] Lease lease API should be available [...        474         1   \n",
+       "1075  [sig-network] Proxy version v1 should proxy lo...        474         1   \n",
+       "1076  [sig-api-machinery] Garbage collector should n...        474         1   \n",
+       "1077  [sig-api-machinery] CustomResourceDefinition r...        474         1   \n",
+       "1078  [sig-api-machinery] Servers with support for T...        474         1   \n",
+       "\n",
+       "      flakes  passPercentage BugList searchLink  \n",
+       "0          0        0.000000    None             \n",
+       "1          0        0.000000    None             \n",
+       "2          0        0.000000    None             \n",
+       "3          0        0.000000    None             \n",
+       "4          0        0.000000    None             \n",
+       "...      ...             ...     ...        ...  \n",
+       "1074       0       99.789474    None             \n",
+       "1075       0       99.789474    None             \n",
+       "1076       0       99.789474    None             \n",
+       "1077       0       99.789474    None             \n",
+       "1078       0       99.789474    None             \n",
+       "\n",
+       "[1079 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_results = pd.DataFrame(_all.loc[\"results\"][0])\n",
+    "all_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:41.843185Z",
+     "start_time": "2021-01-22T16:58:41.839380Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Are all the names unique?\n",
+    "len(all_results[\"name\"].unique()) == all_results[\"name\"].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:42.696053Z",
+     "start_time": "2021-01-22T16:58:42.691511Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series([], Name: BugList, dtype: int64)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Does BugList have any information?\n",
+    "all_results[\"BugList\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:44.214437Z",
+     "start_time": "2021-01-22T16:58:44.209530Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "    1079\n",
+       "Name: searchLink, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Does searchLink have any information?\n",
+    "all_results[\"searchLink\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that **'searchLink'** and **'BugList'** don't actually have any info and **'name'** is all unique values, so we will remove them from our exploratory analysis.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:58:45.781835Z",
+     "start_time": "2021-01-22T16:58:45.747730Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "profile = ProfileReport(\n",
+    "    all_results.drop([\"name\", \"BugList\", \"searchLink\"], axis=1),\n",
+    "    title=\"All results\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:04.054127Z",
+     "start_time": "2021-01-22T16:58:53.209818Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0aaa1349a5794527b3fe911240c6193b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Summarize dataset:   0%|          | 0/18 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78d2ed90ebd64edc97a76ae426bf590f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11ea8930f94140d3ac2cbb8c535c027c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<iframe width=\"100%\" height=\"800px\" srcdoc=\"&lt;!doctype html&gt;&lt;html lang=en&gt;&lt;head&gt;&lt;meta charset=utf-8&gt;&lt;meta name=viewport content=&quot;width=device-width, initial-scale=1, shrink-to-fit=no&quot;&gt;&lt;meta name=description content=&quot;Profile report generated with the `pandas-profiling` Python package&quot;&gt;&lt;meta name=author content=&quot;Simon Brugman and the open source community.&quot;&gt;&lt;meta name=generator content=&quot;Pandas Profiling v2.9.0&quot;&gt;&lt;meta name=url content=https://github.com/pandas-profiling/pandas-profiling&gt;&lt;meta name=date content=&quot;2021-01-22 16:58:53.214983&quot;&gt;&lt;title&gt;All results&lt;/title&gt;&lt;style&gt;\n",
+       "/*!\n",
+       " * Bootstrap v3.3.7 (http://getbootstrap.com)\n",
+       " * Copyright 2011-2016 Twitter, Inc.\n",
+       " * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n",
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bottom,from(#3c3c3c),to(#222));background-image:linear-gradient(to bottom,#3c3c3c 0,#222 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff3c3c3c&#x27;, endColorstr=&#x27;#ff222222&#x27;, GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-radius:4px}.navbar-inverse .navbar-nav&gt;.active&gt;a,.navbar-inverse .navbar-nav&gt;.open&gt;a{background-image:-webkit-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-o-linear-gradient(top,#080808 0,#0f0f0f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#080808),to(#0f0f0f));background-image:linear-gradient(to bottom,#080808 0,#0f0f0f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff080808&#x27;, endColorstr=&#x27;#ff0f0f0f&#x27;, GradientType=0);background-repeat:repeat-x;-webkit-box-shadow:inset 0 3px 9px rgba(0,0,0,.25);box-shadow:inset 0 3px 9px rgba(0,0,0,.25)}.navbar-inverse .navbar-brand,.navbar-inverse .navbar-nav&gt;li&gt;a{text-shadow:0 -1px 0 rgba(0,0,0,.25)}.navbar-fixed-bottom,.navbar-fixed-top,.navbar-static-top{border-radius:0}@media (max-width:767px){.navbar .navbar-nav .open .dropdown-menu&gt;.active&gt;a,.navbar .navbar-nav .open .dropdown-menu&gt;.active&gt;a:focus,.navbar .navbar-nav .open .dropdown-menu&gt;.active&gt;a:hover{color:#fff;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2e6da4&#x27;, GradientType=0);background-repeat:repeat-x}}.alert{text-shadow:0 1px 0 rgba(255,255,255,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05)}.alert-success{background-image:-webkit-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#c8e5bc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#c8e5bc));background-image:linear-gradient(to bottom,#dff0d8 0,#c8e5bc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffdff0d8&#x27;, endColorstr=&#x27;#ffc8e5bc&#x27;, GradientType=0);background-repeat:repeat-x;border-color:#b2dba1}.alert-info{background-image:-webkit-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#b9def0 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#b9def0));background-image:linear-gradient(to bottom,#d9edf7 0,#b9def0 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffd9edf7&#x27;, endColorstr=&#x27;#ffb9def0&#x27;, 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GradientType=0);background-repeat:repeat-x;border-color:#dca7a7}.progress{background-image:-webkit-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#ebebeb 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#ebebeb),to(#f5f5f5));background-image:linear-gradient(to bottom,#ebebeb 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffebebeb&#x27;, endColorstr=&#x27;#fff5f5f5&#x27;, GradientType=0);background-repeat:repeat-x}.progress-bar{background-image:-webkit-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-o-linear-gradient(top,#337ab7 0,#286090 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#286090));background-image:linear-gradient(to bottom,#337ab7 0,#286090 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff286090&#x27;, GradientType=0);background-repeat:repeat-x}.progress-bar-success{background-image:-webkit-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-o-linear-gradient(top,#5cb85c 0,#449d44 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5cb85c),to(#449d44));background-image:linear-gradient(to bottom,#5cb85c 0,#449d44 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff5cb85c&#x27;, endColorstr=&#x27;#ff449d44&#x27;, GradientType=0);background-repeat:repeat-x}.progress-bar-info{background-image:-webkit-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-o-linear-gradient(top,#5bc0de 0,#31b0d5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#5bc0de),to(#31b0d5));background-image:linear-gradient(to bottom,#5bc0de 0,#31b0d5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff5bc0de&#x27;, endColorstr=&#x27;#ff31b0d5&#x27;, GradientType=0);background-repeat:repeat-x}.progress-bar-warning{background-image:-webkit-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-o-linear-gradient(top,#f0ad4e 0,#ec971f 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f0ad4e),to(#ec971f));background-image:linear-gradient(to bottom,#f0ad4e 0,#ec971f 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fff0ad4e&#x27;, endColorstr=&#x27;#ffec971f&#x27;, GradientType=0);background-repeat:repeat-x}.progress-bar-danger{background-image:-webkit-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-o-linear-gradient(top,#d9534f 0,#c9302c 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9534f),to(#c9302c));background-image:linear-gradient(to bottom,#d9534f 0,#c9302c 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffd9534f&#x27;, endColorstr=&#x27;#ffc9302c&#x27;, GradientType=0);background-repeat:repeat-x}.progress-bar-striped{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.list-group{border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,.075);box-shadow:0 1px 2px rgba(0,0,0,.075)}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{text-shadow:0 -1px 0 #286090;background-image:-webkit-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2b669a 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2b669a));background-image:linear-gradient(to bottom,#337ab7 0,#2b669a 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2b669a&#x27;, GradientType=0);background-repeat:repeat-x;border-color:#2b669a}.list-group-item.active .badge,.list-group-item.active:focus .badge,.list-group-item.active:hover .badge{text-shadow:none}.panel{-webkit-box-shadow:0 1px 2px rgba(0,0,0,.05);box-shadow:0 1px 2px rgba(0,0,0,.05)}.panel-default&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-o-linear-gradient(top,#f5f5f5 0,#e8e8e8 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f5f5f5),to(#e8e8e8));background-image:linear-gradient(to bottom,#f5f5f5 0,#e8e8e8 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fff5f5f5&#x27;, endColorstr=&#x27;#ffe8e8e8&#x27;, GradientType=0);background-repeat:repeat-x}.panel-primary&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2e6da4&#x27;, GradientType=0);background-repeat:repeat-x}.panel-success&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#d0e9c6));background-image:linear-gradient(to bottom,#dff0d8 0,#d0e9c6 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffdff0d8&#x27;, endColorstr=&#x27;#ffd0e9c6&#x27;, GradientType=0);background-repeat:repeat-x}.panel-info&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#c4e3f3));background-image:linear-gradient(to bottom,#d9edf7 0,#c4e3f3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffd9edf7&#x27;, endColorstr=&#x27;#ffc4e3f3&#x27;, GradientType=0);background-repeat:repeat-x}.panel-warning&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#faf2cc));background-image:linear-gradient(to bottom,#fcf8e3 0,#faf2cc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fffcf8e3&#x27;, endColorstr=&#x27;#fffaf2cc&#x27;, GradientType=0);background-repeat:repeat-x}.panel-danger&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-o-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#ebcccc));background-image:linear-gradient(to bottom,#f2dede 0,#ebcccc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fff2dede&#x27;, endColorstr=&#x27;#ffebcccc&#x27;, GradientType=0);background-repeat:repeat-x}.well{background-image:-webkit-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#e8e8e8),to(#f5f5f5));background-image:linear-gradient(to bottom,#e8e8e8 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffe8e8e8&#x27;, endColorstr=&#x27;#fff5f5f5&#x27;, GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)}\n",
+       "/*# sourceMappingURL=bootstrap-theme.min.css.map */        &lt;/style&gt;&lt;style&gt;body {\n",
+       "  padding-top: 80px;\n",
+       "}\n",
+       ".content .container-fluid{\n",
+       "    margin-left: 20px !important;\n",
+       "    margin-right: 20px !important;\n",
+       "    margin-bottom: 20px;\n",
+       "}\n",
+       "\n",
+       ".page-header{\n",
+       "    border:0 !important;\n",
+       "}\n",
+       "\n",
+       ".row.variable, .section-items &gt; .row {\n",
+       "    border: 1px solid #e1e1e8;\n",
+       "    border-top: hidden;\n",
+       "}\n",
+       "\n",
+       ".row.spacing{\n",
+       "    padding: 2em 1em;\n",
+       "}\n",
+       "\n",
+       ".row.header {\n",
+       "    border-bottom: 1px solid #e1e1e8;\n",
+       "    /*background-color: #f5f5f5;*/\n",
+       "    padding-left: 2em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       ".dl-horizontal dt {\n",
+       "    text-align: left;\n",
+       "    padding-right: 1em;\n",
+       "    white-space: normal;\n",
+       "}\n",
+       "\n",
+       ".dl-horizontal dd {\n",
+       "    margin-left: 0;\n",
+       "}\n",
+       "\n",
+       ".col-md-12 {\n",
+       "    padding-left: 2em;\n",
+       "}\n",
+       "\n",
+       ".indent {\n",
+       "    margin-left: 1em;\n",
+       "}\n",
+       "\n",
+       ".center-img {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       "/* Table example_values */\n",
+       "table.example_values {\n",
+       "    border: 0;\n",
+       "}\n",
+       "\n",
+       ".example_values th {\n",
+       "    border: 0;\n",
+       "    padding: 0;\n",
+       "    color: #555;\n",
+       "    font-weight: 600;\n",
+       "}\n",
+       "\n",
+       ".example_values tr, .example_values td {\n",
+       "    border: 0;\n",
+       "    padding: 0;\n",
+       "    color: #555;\n",
+       "}\n",
+       "\n",
+       "/* STATS */\n",
+       "table.stats, table.sample, table.duplicate{\n",
+       "    border: 0;\n",
+       "}\n",
+       "\n",
+       ".stats tr, .sample tr, .duplicate tr {\n",
+       "    border: 0;\n",
+       "}\n",
+       "\n",
+       ".stats th, .stats td{\n",
+       "    color: #555;\n",
+       "    border: 0;\n",
+       "}\n",
+       "\n",
+       ".stats th {\n",
+       "    padding: 0 2em 0 0;\n",
+       "    font-weight: 600;\n",
+       "}\n",
+       "\n",
+       ".stats td {\n",
+       "    padding: 1px;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Sample table */\n",
+       "table.sample, table.duplicate{\n",
+       "    margin-bottom: 2em;\n",
+       "    margin-left: 1em;\n",
+       "}\n",
+       "\n",
+       ".sample td, .sample th, .duplicate td, .duplicate th {\n",
+       "    padding: 0.5em;\n",
+       "    white-space: nowrap;\n",
+       "    border: 0;\n",
+       "\n",
+       "}\n",
+       "\n",
+       ".sample thead, .duplicate thead {\n",
+       "    border-top: 0;\n",
+       "    border-bottom: 2px solid #ddd;\n",
+       "}\n",
+       "\n",
+       ".sample td, .duplicate td {\n",
+       "    width: 100%;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* There is no good solution available to make the divs equal height and then center ... */\n",
+       ".histogram {\n",
+       "    margin-top: 3em;\n",
+       "}\n",
+       "\n",
+       "/* Freq table */\n",
+       "table.freq {\n",
+       "    margin-bottom: 2em;\n",
+       "    border: 0;\n",
+       "}\n",
+       "\n",
+       "table.freq th, table.freq tr, table.freq td {\n",
+       "    border: 0;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".freq thead {\n",
+       "    font-weight: 600;\n",
+       "    white-space: nowrap;\n",
+       "    overflow: hidden;\n",
+       "    text-overflow: ellipsis;\n",
+       "\n",
+       "}\n",
+       "\n",
+       "/* Freq mini */\n",
+       ".freq.mini td {\n",
+       "    width: 50%;\n",
+       "    padding: 1px;\n",
+       "    font-size: 12px;\n",
+       "\n",
+       "}\n",
+       "\n",
+       "table.freq.mini {\n",
+       "    width: 100%;\n",
+       "}\n",
+       "\n",
+       ".freq.mini th {\n",
+       "    overflow: hidden;\n",
+       "    text-overflow: ellipsis;\n",
+       "    white-space: nowrap;\n",
+       "    max-width: 5em;\n",
+       "    font-weight: 400;\n",
+       "    text-align: right;\n",
+       "    padding-right: 0.5em;\n",
+       "}\n",
+       "\n",
+       "/* Message classes */\n",
+       ".missing {\n",
+       "    color: #a94442;\n",
+       "}\n",
+       "\n",
+       ".alert, .alert &gt; th, .alert &gt; td {\n",
+       "    color: #a94442;\n",
+       "}\n",
+       "\n",
+       ".ignore {\n",
+       "    opacity: 0.4;\n",
+       "}\n",
+       "\n",
+       "/* Bars in tables */\n",
+       ".freq.table{\n",
+       "    table-layout: fixed;\n",
+       "}\n",
+       "\n",
+       ".freq:not(.mini) tr td:nth-child(1), .freq:not(.mini) tr th:nth-child(1){\n",
+       "    width: auto;\n",
+       "    max-width: none;\n",
+       "\n",
+       "    white-space: nowrap;\n",
+       "    overflow: hidden;\n",
+       "    text-overflow: ellipsis;\n",
+       "}\n",
+       "\n",
+       ".freq:not(.mini) tr td:nth-child(2), .freq:not(.mini) tr td:nth-child(3), .freq:not(.mini) tr th:nth-child(2), .freq:not(.mini) tr th:nth-child(3){\n",
+       "    width: 100px;\n",
+       "    text-align: right;\n",
+       "}\n",
+       ".freq:not(.mini) tr td:nth-child(4), .freq:not(.mini) tr th:nth-child(4){\n",
+       "    width:200px;\n",
+       "}\n",
+       "\n",
+       ".freq .bar {\n",
+       "    float: left;\n",
+       "    width: 0;\n",
+       "    height: 100%;\n",
+       "    line-height: 20px;\n",
+       "    color: #fff;\n",
+       "    text-align: center;\n",
+       "    background-color: #337ab7;\n",
+       "    border-radius: 3px;\n",
+       "    margin-right: 4px;\n",
+       "}\n",
+       "\n",
+       ".other .bar {\n",
+       "    background-color: #999;\n",
+       "}\n",
+       "\n",
+       ".missing .bar {\n",
+       "    background-color: #a94442;\n",
+       "}\n",
+       "\n",
+       ".tooltip-inner {\n",
+       "    width: 100%;\n",
+       "    white-space: nowrap;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".extrapadding {\n",
+       "    padding: 2em;\n",
+       "}\n",
+       "\n",
+       ".variable .h4 {\n",
+       "    text-overflow: ellipsis;\n",
+       "    display:inline-block;\n",
+       "    width: calc(90%); /* The trick is here! */\n",
+       "    overflow:hidden;\n",
+       "}\n",
+       "\n",
+       ".variable.ignore .h4{\n",
+       "    text-decoration: line-through;\n",
+       "}\n",
+       "\n",
+       ".table-responsive{\n",
+       "    overflow: scroll;\n",
+       "    width: 100%;\n",
+       "    overflow-y: hidden;\n",
+       "}\n",
+       ".img-responsive{\n",
+       "    max-width: 99%;\n",
+       "}\n",
+       ".footer-text{\n",
+       "    padding:20px;\n",
+       "}\n",
+       "\n",
+       "table.list-warnings td{\n",
+       "    padding-right:10px;\n",
+       "}\n",
+       "\n",
+       "a.anchor-pos {\n",
+       "    display: block;\n",
+       "    position: relative;\n",
+       "    top: -70px;\n",
+       "    visibility: hidden;\n",
+       "}\n",
+       "\n",
+       "a.anchor-pos-variable{\n",
+       "    /*top: -70px;*/\n",
+       "}\n",
+       "\n",
+       "#sample-container, #duplicate-container{\n",
+       "    overflow: auto;\n",
+       "    width: 100%;\n",
+       "    overflow-y: hidden;\n",
+       "}\n",
+       "\n",
+       "#overview-content td, #overview-content th{\n",
+       "    border-top: 0;\n",
+       "    line-height: 1;\n",
+       "}\n",
+       "\n",
+       ".variable-description{\n",
+       "    color: #777;\n",
+       "    font-size: 10pt;\n",
+       "    margin-top: 10px;\n",
+       "    font-style: italic;\n",
+       "}\n",
+       "\n",
+       "select.multiple{\n",
+       "    width: 180px;\n",
+       "    height: 500px;\n",
+       "    margin: 10px 0;\n",
+       "}\n",
+       "\n",
+       ".named-list-item{\n",
+       "    padding: 1em;\n",
+       "}\n",
+       "\n",
+       "/* not printing tabs */\n",
+       "@media print {\n",
+       "    .tab-content &gt; .tab-pane, .collapse {\n",
+       "        display: block !important;\n",
+       "        opacity: 1 !important;\n",
+       "        visibility: visible !important;\n",
+       "        /*page-break-after: always;*/\n",
+       "        page-break-after: right;\n",
+       "        page-break-before: avoid;\n",
+       "    }\n",
+       "\n",
+       "    .nav-pills, .nav-tabs, button[data-toggle=&quot;collapse&quot;], .mini, .col-sm-3 img {\n",
+       "        display:none !important;\n",
+       "    }\n",
+       "\n",
+       "    a[download=&quot;config.yml&quot;]:after {\n",
+       "        content: none !important;\n",
+       "    }\n",
+       "\n",
+       "    .row {\n",
+       "        border: 0 !important;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       ".text-placeholder {\n",
+       "    display: inline-block;\n",
+       "    background-color: #444;\n",
+       "    height: 12px;\n",
+       "    border-radius: 100px;\n",
+       "    margin: 5px 0;\n",
+       "    min-width: 200px;\n",
+       "    opacity: .1;\n",
+       "}&lt;/style&gt;&lt;/head&gt;&lt;body&gt;&lt;a class=anchor-pos id=top&gt;&lt;/a&gt;&lt;nav class=&quot;navbar navbar-default navbar-fixed-top&quot;&gt;&lt;div class=container-fluid&gt;&lt;div class=navbar-header&gt;&lt;button type=button class=&quot;navbar-toggle collapsed&quot; data-toggle=collapse data-target=#navbar aria-expanded=false aria-controls=navbar&gt;&lt;span class=sr-only&gt;Toggle navigation&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;/button&gt;&lt;a class=&quot;navbar-brand anchor&quot; href=#top&gt;All results&lt;/a&gt;&lt;/div&gt;&lt;div id=navbar class=&quot;navbar-collapse collapse&quot;&gt;&lt;ul class=&quot;nav navbar-nav navbar-right&quot;&gt;&lt;li&gt;&lt;a class=anchor href=#overview&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#variables&gt;Variables&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#interactions&gt;Interactions&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#correlations&gt;Correlations&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#missing&gt;Missing values&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#sample&gt;Sample&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#duplicate&gt;Duplicate rows&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;/nav&gt;&lt;div class=content&gt;&lt;div class=container&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=overview&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Overview&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-pills&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#overview-dataset_overview aria-controls=overview-dataset_overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-warnings aria-controls=overview-warnings role=tab data-toggle=tab&gt;Warnings &lt;span class=badge&gt;6&lt;/span&gt;&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-reproduction aria-controls=overview-reproduction role=tab data-toggle=tab&gt;Reproduction&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content style=&quot;padding-top: 10px;&quot;&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=overview-dataset_overview&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Dataset statistics&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Number of variables&lt;/th&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Number of observations&lt;/th&gt;&lt;td&gt;1079&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows&lt;/th&gt;&lt;td&gt;810&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows (%)&lt;/th&gt;&lt;td&gt;75.1%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Total size in memory&lt;/th&gt;&lt;td&gt;33.8 KiB&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Average record size in memory&lt;/th&gt;&lt;td&gt;32.1 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Variable types&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;NUM&lt;/th&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-warnings&gt;&lt;table class=&quot;table table-condensed list-warnings&quot;&gt;&lt;p class=h4&gt;Warnings&lt;/p&gt;&lt;tr style=border-top:0&gt;&lt;td&gt; Dataset has 810 (75.1%) duplicate rows &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-default&quot;&gt;Duplicates&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_2216627888843619604&gt;&lt;code&gt;flakes&lt;/code&gt;&lt;/a&gt; is highly correlated with &lt;code&gt;failures&lt;/code&gt;&lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-default&quot;&gt;High correlation&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_2638975615588016290&gt;&lt;code&gt;failures&lt;/code&gt;&lt;/a&gt; is highly correlated with &lt;code&gt;flakes&lt;/code&gt;&lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-default&quot;&gt;High correlation&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_2638975615588016290&gt;&lt;code&gt;failures&lt;/code&gt;&lt;/a&gt; is highly skewed (γ1 = 31.17063275) &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-info&quot;&gt;Skewed&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_2216627888843619604&gt;&lt;code&gt;flakes&lt;/code&gt;&lt;/a&gt; is highly skewed (γ1 = 29.6873798) &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-info&quot;&gt;Skewed&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_2216627888843619604&gt;&lt;code&gt;flakes&lt;/code&gt;&lt;/a&gt; has 635 (58.9%) zeros &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-info&quot;&gt;Zeros&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-reproduction&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4&gt;Reproduction&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Analysis started&lt;/th&gt;&lt;td&gt;2021-01-22 16:58:53.214983&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Analysis finished&lt;/th&gt;&lt;td&gt;2021-01-22 16:59:01.877572&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duration&lt;/th&gt;&lt;td&gt;8.66 seconds&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Software version&lt;/th&gt;&lt;td&gt;&lt;a href=https://github.com/pandas-profiling/pandas-profiling&gt;pandas-profiling v2.9.0&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Download configuration&lt;/th&gt;&lt;td&gt;&lt;a download=config.yaml 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class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=variables&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Variables&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-8534385824264128662&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=successes&gt;&lt;a href=#pp_var_-8534385824264128662&gt;successes&lt;/a&gt;&lt;br&gt;&lt;small&gt;Real number (&amp;Ropf;&lt;sub&gt;&amp;ge;0&lt;/sub&gt;)&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;124&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;11.5%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing 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id=2638975615588016290extreme_values-2638975615588016290lastn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;td&gt;&amp;nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=1280&gt;1280&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=80&gt;80&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=55&gt;55&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=53&gt;53&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:50.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=50&gt;50&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;0.2%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; 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id=-1293813235776926393bottom--1293813235776926393extreme_values&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-1293813235776926393extreme_values--1293813235776926393firstn aria-controls=-1293813235776926393extreme_values--1293813235776926393firstn role=tab data-toggle=tab&gt;Minimum 5 values&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-1293813235776926393extreme_values--1293813235776926393lastn aria-controls=-1293813235776926393extreme_values--1293813235776926393lastn role=tab data-toggle=tab&gt;Maximum 5 values&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-1293813235776926393extreme_values--1293813235776926393firstn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;td&gt;&amp;nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=0&gt;0&lt;/td&gt;&lt;td&gt;6&lt;/td&gt;&lt;td&gt;0.6%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=2.083333333&gt;2.083333333&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:16.7%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=4.166666667&gt;4.166666667&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:16.7%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=6.976744186&gt;6.976744186&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:16.7%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=14.51612903&gt;14.51612903&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:16.7%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-1293813235776926393extreme_values--1293813235776926393lastn&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;td&gt;&amp;nbsp;&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=99.78947368&gt;99.78947368&lt;/td&gt;&lt;td&gt;14&lt;/td&gt;&lt;td&gt;1.3%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:13.9%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=99.77973568&gt;99.77973568&lt;/td&gt;&lt;td&gt;67&lt;/td&gt;&lt;td&gt;6.2%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:66.3%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=99.70414201&gt;99.70414201&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=99.57894737&gt;99.57894737&lt;/td&gt;&lt;td&gt;9&lt;/td&gt;&lt;td&gt;0.8%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:8.9%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=99.55947137&gt;99.55947137&lt;/td&gt;&lt;td&gt;101&lt;/td&gt;&lt;td&gt;9.4%&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt;&amp;nbsp;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=interactions&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Interactions&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; 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class=container-fluid&gt;&lt;div class=&quot;row center-block footer-text&quot;&gt;&lt;p class=&quot;text-muted text-center&quot;&gt;Report generated with &lt;a href=https://github.com/pandas-profiling/pandas-profiling&gt;pandas-profiling&lt;/a&gt;.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/footer&gt;&lt;script&gt;\n",
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+       "$(function () {\n",
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+       "});\n",
+       "\n",
+       "$(&quot;a[href^=&#x27;#&#x27;].anchor&quot;).on(&#x27;click&#x27;, function (e) {\n",
+       "\n",
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+       "    });\n",
+       "\n",
+       "});        &lt;/script&gt;&lt;/body&gt;&lt;/html&gt;\" frameborder=\"0\" allowfullscreen></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We cab see from this report that ? ? ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ByPlatform\n",
+    "\n",
+    "Same data as above, except the map has a key for each platform we support (e.g. aws, gcp, etc), and the test results are only for jobs associated w/ those platforms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:13.182354Z",
+     "start_time": "2021-01-22T16:59:13.054461Z"
+    }
+   },
+   "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>aws</th>\n",
+       "      <th>azure</th>\n",
+       "      <th>gcp</th>\n",
+       "      <th>metal</th>\n",
+       "      <th>metal-ipi</th>\n",
+       "      <th>openstack</th>\n",
+       "      <th>ovirt</th>\n",
+       "      <th>serial</th>\n",
+       "      <th>upgrade</th>\n",
+       "      <th>vsphere</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>successes</th>\n",
+       "      <td>196148</td>\n",
+       "      <td>164100</td>\n",
+       "      <td>92958</td>\n",
+       "      <td>8082</td>\n",
+       "      <td>1523</td>\n",
+       "      <td>14173</td>\n",
+       "      <td>283</td>\n",
+       "      <td>15321</td>\n",
+       "      <td>1695</td>\n",
+       "      <td>539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>failures</th>\n",
+       "      <td>3249</td>\n",
+       "      <td>2692</td>\n",
+       "      <td>1647</td>\n",
+       "      <td>214</td>\n",
+       "      <td>83</td>\n",
+       "      <td>332</td>\n",
+       "      <td>159</td>\n",
+       "      <td>831</td>\n",
+       "      <td>329</td>\n",
+       "      <td>243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>testPassPercentage</th>\n",
+       "      <td>98.3706</td>\n",
+       "      <td>98.386</td>\n",
+       "      <td>98.2591</td>\n",
+       "      <td>97.4204</td>\n",
+       "      <td>94.8319</td>\n",
+       "      <td>97.7111</td>\n",
+       "      <td>64.0271</td>\n",
+       "      <td>94.8551</td>\n",
+       "      <td>83.7451</td>\n",
+       "      <td>68.9258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>results</th>\n",
+       "      <td>[{'name': 'operator.Run template e2e-aws-upi -...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 119, 'failur...</td>\n",
+       "      <td>[{'name': 'operator.Run template e2e-gcp - e2e...</td>\n",
+       "      <td>[{'name': 'operator.Run template e2e-metal-ser...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 16, 'failure...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 19, 'failure...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 0, 'failures...</td>\n",
+       "      <td>[{'name': 'operator.Run template e2e-metal-ser...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 45, 'failure...</td>\n",
+       "      <td>[{'name': 'operator.Run template e2e-vsphere-u...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                  aws  \\\n",
+       "successes                                                      196148   \n",
+       "failures                                                         3249   \n",
+       "testPassPercentage                                            98.3706   \n",
+       "results             [{'name': 'operator.Run template e2e-aws-upi -...   \n",
+       "\n",
+       "                                                                azure  \\\n",
+       "successes                                                      164100   \n",
+       "failures                                                         2692   \n",
+       "testPassPercentage                                             98.386   \n",
+       "results             [{'name': 'Overall', 'successes': 119, 'failur...   \n",
+       "\n",
+       "                                                                  gcp  \\\n",
+       "successes                                                       92958   \n",
+       "failures                                                         1647   \n",
+       "testPassPercentage                                            98.2591   \n",
+       "results             [{'name': 'operator.Run template e2e-gcp - e2e...   \n",
+       "\n",
+       "                                                                metal  \\\n",
+       "successes                                                        8082   \n",
+       "failures                                                          214   \n",
+       "testPassPercentage                                            97.4204   \n",
+       "results             [{'name': 'operator.Run template e2e-metal-ser...   \n",
+       "\n",
+       "                                                            metal-ipi  \\\n",
+       "successes                                                        1523   \n",
+       "failures                                                           83   \n",
+       "testPassPercentage                                            94.8319   \n",
+       "results             [{'name': 'Overall', 'successes': 16, 'failure...   \n",
+       "\n",
+       "                                                            openstack  \\\n",
+       "successes                                                       14173   \n",
+       "failures                                                          332   \n",
+       "testPassPercentage                                            97.7111   \n",
+       "results             [{'name': 'Overall', 'successes': 19, 'failure...   \n",
+       "\n",
+       "                                                                ovirt  \\\n",
+       "successes                                                         283   \n",
+       "failures                                                          159   \n",
+       "testPassPercentage                                            64.0271   \n",
+       "results             [{'name': 'Overall', 'successes': 0, 'failures...   \n",
+       "\n",
+       "                                                               serial  \\\n",
+       "successes                                                       15321   \n",
+       "failures                                                          831   \n",
+       "testPassPercentage                                            94.8551   \n",
+       "results             [{'name': 'operator.Run template e2e-metal-ser...   \n",
+       "\n",
+       "                                                              upgrade  \\\n",
+       "successes                                                        1695   \n",
+       "failures                                                          329   \n",
+       "testPassPercentage                                            83.7451   \n",
+       "results             [{'name': 'Overall', 'successes': 45, 'failure...   \n",
+       "\n",
+       "                                                              vsphere  \n",
+       "successes                                                         539  \n",
+       "failures                                                          243  \n",
+       "testPassPercentage                                            68.9258  \n",
+       "results             [{'name': 'operator.Run template e2e-vsphere-u...  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_pltf = pd.DataFrame(d.loc[\"ByPlatform\"][0])\n",
+    "by_pltf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:15.256608Z",
+     "start_time": "2021-01-22T16:59:15.251984Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "barplot_s = by_pltf.loc[[\"successes\"], :]\n",
+    "barplot_f = by_pltf.loc[[\"failures\"], :]\n",
+    "barplot_p = by_pltf.loc[[\"testPassPercentage\"], :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:16.015322Z",
+     "start_time": "2021-01-22T16:59:15.789574Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc={\"figure.figsize\": (10, 10)})\n",
+    "barplot_s.plot(kind=\"bar\")\n",
+    "plt.ylabel(\"Total Count\", fontsize=15)\n",
+    "plt.title(\"Successes: by platform\", fontsize=20)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:16.558797Z",
+     "start_time": "2021-01-22T16:59:16.352136Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Jri4l33F391ShQsFyc8t6TCPQAQCANGJjL8rb20cFCxaXyWRydTn5hs1m07Vr8YqNvagiRUKyvB9LrgAAII3U1GQVLOhPmHMyk8mkggX973pmlEAHAADSRZhzjeycdwIdAACAwXENHQAAyJSffwF5ezk+NiQmpSoh/obD+81vCHQAACBT3l7uajNotcP7XTv5aSU4vNf8hyVXAACQ67377nC99FIXde3aXkOHDlZ8fLxGjnxH27b9V5K0ePECNWv2qCwWiySpc+cXdPr0KZ0+fVKvvNJD3bp1VJcu7fT555+58jByDDN0AAAg1xs4cLACAwMlSbNmTdPixQsUHl5He/f+rMaNm2rv3t0qV66CfvvtsIoXD9H169dVunQZffTRJDVo0EhduvSQJMXHx7vwKHIOgQ4AAOR6mzat05Ytm5SamqIbNxJVqlRpPf30s1q0aL6Sk5MVHR2tTp26aM+eXSpePES1a4dLkmrWrKVp0z5RYmKiatcOt3+f17DkCgAAcrUDB/Zr1aoVmjx5ihYuXKaXX+6r5OQklSgRKqvVpq+/3qLq1WvowQcf0t69u7V37249+OBDkqTHHmuiadPmKDS0pBYtmq8xYyJdfDQ5g0AHAABytYSEBBUs6KuAgAAlJydr/fo19rYHHwzXp5/OVHh4HRUrVlzx8Vf0888/KTy8jiTp77//UuHCQWrZso169HhZhw//6qrDyFEsuQIAgFytbt162rJlozp2fFYBAYGqWbOWPZg9+OBDWr9+jX1GrkaNmtq792cFBxeVJG3btlVbtmySh4e7TCaTBg4c5LLjyEkmm81mc3URrhYTc1VWa74/DQAcKDjYTxcv8jAGGNf586dUvHgZ+2eeQ+dc/z7/kmQ2mxQU5Jvu9szQAQCATCXE3+B5cbkY19ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMFxlysAAMhUoQBPuXt6Obzf1OQkxV5Jdni/+Q2BDsiinHoGU0Z4NhOA3MTd00snxj3n8H7LD1shiUB3rwh0QBZ5e7mrzaDVThtv7eSneeYTAOSg1NRUubvnjSiUN44CAADkae++O1ynT59SSkqyQkNLaejQSK1YsUzbt38jSUpNTdHJk39q06Zv9fHHk1SlSpiee669JGncuFH2z+PGjZKbm5tOnz6l69eva/78z7Vx4zqtXLlcFotFvr6+Gjw4QqVLl3Xh0d49Ah0AAMj1Bg4crMDAQEnSrFnTtHjxAvXtO0A9erwsSRo9eoTq1KkrX9/0X431T8eO/a6oqFkqUKCADhzYr23btmrq1Nny9PTUjz/u1PjxozV9+tycPByHI9ABAIBcb9OmddqyZZNSU1N040aiSpUqbW+bPXu6EhMT1b//G1nq67HHmqhAgQKSpJ07v9MffxxT797dJUk2m00JCfEOrz+nEegAAECuduDAfq1atULTp89VoUKFtGXLJq1Zs1KStG7dau3evUtTpsyQ2XzzaWxubm6yWm32/ZOTk27rz8engP3XNpvUqtVT6tWrjxOOJOfwHDoAAJCrJSQkqGBBXwUEBCg5OVnr16+RJO3evUuLFy/QhAkfysvL2759aGgpHTnyqyTp0qVL2rdvb4Z916/fUJs2rVd09AVJksVi0ZEjv+Xg0eQMZugAAECmUpOT/u8RI47vNzN169bTli0b1bHjswoICFTNmrV0+PCvWrhwrm7cuKE33uhv33batNl66qm2Gj78bXXu/IJKlSqtqlWrZdh3zZq11bt3P0VEvCmLxarU1BQ9/nhTVakS5pDjcxaTzWazZb5Z3hYTc/W2qVkgPcHBfk5/bMnFizy4xKiCg/34/YOhnT9/SsWLl3F1GflWeuffbDYpKCj9mz5YcgUAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGBzPoQMAAJnyC/SSt4enw/tNTElWQlzmz6LDnRHoAABAprw9PNVuWV+H9/tF++lKEIHuXhHoAABArvftt19r1qxp8vLy0uOPN9WsWdO0Zct3OnHiD02d+rGuX78uSXr11YGqU6eunn++jZo0eVK7d+/StWtX1a5dRz33XHsXH0XOIdABAIBc7fLlGE2c+J5mzpynUqVKa9myxZKk+Ph4vfPOEI0bN1E1ajwgi8Wia9eu2feLjb2suXMX6fLlGPXo8aIeeKC2Klas5KrDyFHcFAEAAHK1w4cP6b77KqtUqdKSpFatnpYkHTt2RGXLllONGg9Iktzc3OTv72/fr3Xrm9sVLhykevUaaP/+vU6u3HkIdAAAAAZHoAMAALla1arV9fvvR3XmzN+SpI0b10mSKlWqopMn/9ShQ/+TJFksFsXHx9v3u7VdbGysfvxxp2rXDndy5c7DNXQAACBXK1w4SIMHD9Xgwa/J29tb9eo1lLu7u4oWLapx4yZqypT/KDHxhkwms159daAeeuhhSVJAQKB69uysa9euqkuX7qpQoaKLjyTnEOgAAECmElOS9UX76TnSb1bUrfuIGjduKklav36NwsKqyWw2q0aNBzRz5rx093nyyebq06e/w2rNzQh0AAAgUwlxSS59Xtzy5Uv1zTdfy2JJlb9/gN5+e7jLasmNCHQAACDX69btJXXr9lKWt//yy7U5WE3uw00RAAAABkegAwAAMDgCHQAAgMER6AAAAAyOmyIAAECmCvl5yt3by+H9piYmKTYha48uQcYIdAAAIFPu3l7a+fRzDu+3/uoVkpMC3bFjR3X69Gk1afJEptvu27dHU6d+rE8//cwJld07llwBAEC+cOzY7/rmm62uLiNHMEMHAAByvQYNwvXyy331/ffbdeXKFb399jDt2fOzdu36QampqRozZoLKli0n6eY7XFeuXC6LxSJfX18NHhyhgIBAzZkzQ9evX1P37p1Us2Ytvf76EL377nCdPn1KKSnJCg0tpaFDI+Xv7+/io717zNABAABD8PX105w5C9W37wANHTpINWo8oHnzPlfz5q20cOFcSdKBA/u1bdtWTZ06W3PnLlLHjl00fvxoBQQEqlevPgoPr6P58z/X668PkSQNHDhYn376mRYuXKZy5cpr8eIFrjzEbGOGDgAAGEKTJk9KkipXriLJpPr1G/7f5zBt3/6NJGnnzu/0xx/H1Lt3d0mSzWZTQkJ8hn1u2rROW7ZsUmpqim7cSFSpUqVz9BhyitMDXb9+/fT333/LbDbLx8dHI0aMUFhYmP78809FREQoLi5OgYGBmjBhgsqWLStJ2W4DAAB5h6enpyTJbDbL09PD/r3ZbJbFYpEk2WxSq1ZPqVevPpn2d+DAfq1atULTp89VoUKFtGXLJq1ZszJnis9hTl9ynTBhgtasWaNVq1apZ8+eeueddyRJI0eOVKdOnbR582Z16tRJkZGR9n2y2wYAAPKX+vUbatOm9YqOviBJslgsOnLkN0lSwYIFdfXqVfu2CQkJKljQVwEBAUpOTtb69WtcUrMjOH2Gzs/Pz/7rq1evymQyKSYmRocPH9a8efMkSa1bt9aYMWN0+fJl2Wy2bLUVLlzY2YcGAECelZqYdPMRIznQryPVrFlbvXv3U0TEm7JYrEpNTdHjjzdVlSphevDBOlqyZJG6deuoWrVqq3//N7Rly0Z17PisAgICVbNmLR0+/KtD63EWl1xDN2zYMO3cuVM2m01z5szRuXPnVKxYMbm5uUmS3NzcVLRoUZ07d042my1bbXcT6IKCfB1/kIADBAf7Zb4Rci1+/2Bk0dFmubv//4W8hBup0o3UHBnrn+Nk5Kef9tl/XapUSW3e/I39c506dVSnzmL755YtW6lly1Zp+ggM9NecOfNv++699yakO96/+3Q2s9l8V3+HuCTQjRs3TpK0atUqTZw4UQMHDnRFGXYxMVdltdpcWgNyP1f8cL54McHpY8IxgoP9+P2DoVmtVqWmWl1dRr5ltVrT/B1iNpsynIRy6WNL2rZtq127dql48eK6cOGC/YJGi8Wi6OhohYSEKCQkJFttAAAA+YVTA921a9d07tw5++dt27YpICBAQUFBCgsL07p16yRJ69atU1hYmAoXLpztNgAAgPzCqUuuN27c0MCBA3Xjxg2ZzWYFBARoxowZMplMGjVqlCIiIjRt2jT5+/trwoT/v6ad3TYAAID8wGSz2fL9xWNcQ4esCA72U5tBq5023trJT3MNloFxDR2M7vz5UypevIyry8i30jv/ufYaOgAAANw7Xv0FAAAyFeBfQJ5ejo8NyUmpuhJ/w+H9pufYsaM6ffq0mjR5ItNt9+3bo6lTP9ann36Wpu3SpYt6993hmjJlZqb9dO/eSTNnzpWXl3e2as4qAh0AAMiUp5e7Rg9a5/B+Iye3dnifGTl27Hf98MP3WQp0d1KkSHCWwpwkzZ//+T2NlVUEOgAAkOs1aBCul1/uq++/364rV67o7beHac+en7Vr1w9KTU3VmDETVLZsOUnSxo3rtHLlclksFvn6+mrw4AgFBARqzpwZun79mrp376SaNWvp9deH6N13h+v06VNKSUlWaGgpDR0aKX9//zvWcu7cWfXq1UXr139tr61Hj5f1/ffblZSUqFdeeVWPPdbE3rZly3fy8fHJ0fPDNXQAAMAQfH39NGfOQvXtO0BDhw5SjRoPaN68z9W8eSstXDhXknTgwH5t27ZVU6fO1ty5i9SxYxeNHz9aAQGB6tWrj8LD62j+/M/1+utDJEkDBw7Wp59+poULl6lcufJavHhBtmozm82aP/9zTZjwoSZOfE+xsZcddtxZwQwdAAAwhCZNnpQkVa5cRZJJ9es3/L/PYdq+/earwHbu/E5//HFMvXt3lyTZbDYlJMRn2OemTeu0Zcsmpaam6MaNRJUqVTpbtbVu/bQkqXTpsrrvvsr69deDatDg0Wz1lR0EOgAAYAienp6Sbs6GeXp62L83m832t0bZbFKrVk+pV68+mfZ34MB+rVq1QtOnz1WhQoW0ZcsmrVmzMs12CxZ8qm++ubm8+tprbyokpIQjDsehWHIFAAB5Rv36DbVp03pFR1+QdPO1oEeO/CZJKliwoK5evWrfNiEhQQUL+iogIEDJyclav35Nun126/aS5s//XPPnf67atcPT3ebWvn/9dVrHjh1VtWo1HHlYmWKGDgAA5Bk1a9ZW7979FBHxpiwWq1JTU/T4401VpUqYHnywjpYsWaRu3TqqVq3a6t//DW3ZslEdOz6rgIBA1axZS4cP/5qtcS0Wi3r06KTExEQNGfKOChVy7mtIeVOEeFMEsoY3ReBu8KYIGN2/31SQF55Dl1Ny4k7Wu31TBDN0AAAgU0YPXXkdgQ4AAOAe7Nixx9UlcFMEAACA0RHoAAAADI5ABwAAYHAEOgAAAIPjpggAAJCpAH9PeXp5Obzf5KQkXYlPdni/+Q2BDgAAZMrTy0tRQ3s4vN/+4+dJyv2B7rvvvlWRIkVUtWr1bPexYcNa/fDD9xo7dqIDK7uJJVcAAIBMfP/9t/rtt+y9RcIZmKEDAAC53k8//aCZM6NktVoVGFhIQ4a8o+joC/r448mqWLGSjh49ogIFvPXOO6NUrlx5SdLGjeu0cuVyWSwW+fr6avDgCJUuXVYbNqzV1q2b5OfnrxMnjsvPz1djx05UUFARHTx4QP/5z0RZrTalpqaqW7ee8vcP0I4d32nPnp+1du1qtW/fSXXq1NWoUcN07do1JScnq169+urXb6AkKSUlRTNnTtWuXT/IbHZTiRKhGj9+0m3Hc+HCeb3zzhB16tRVTZo8cc/nh0AHAABytdjYyxo7NlJTpsxSuXLltW7dKr377nD17TtAx48f0+uvD9aIEaO1ceM6jR07Up9++pkOHNivbdu2aurU2fL09NSPP+7U+PGjNX36XEnSb78d1oIFS1SsWHFNmDBWX365TK+88qoWL16gjh276Iknmstms+nq1avy8/NTgwaNVKVKmJ57rr0kKSkpSRMm/Ec+Pj5KTU3Vm2/2108//aC6devps8/m6ezZM5o7d7E8PDwUFxd32/EcO/a7xowZoUGDIvTAA7Ucco4IdAAAIFf79ddDqlDhPvvMW8uWT2ny5Am6fv26SpYspVq1HpQkNWvWUhMnjtO1a1e1c+d3+uOPY+rdu7skyWazKSEh3t7n/fc/oGLFikuSqlWrrt27d0mSatcO14IFc3XmzN966KG6qlYt/WvmrFarpk37WAcP/k+STTExMTp27HfVrVtPP/ywQ/37vy4PDw9JUmBgoH2/P/74Q8OGDdHEiR+pbNlyDjtHBDoAAJDn2GxSq1ZPqVevPum2e3p62n9tNrvJYrFIktq166T69Rtp9+5d+uijiXroobrq3btfmv2XLVushIR4zZo1X15eXpowYZySk5Myrato0aK6fv269u/f69BAx00RAAAgV6tWrYaOH/9dp06dlHTz2rhKlSrLx8dHZ878rQMH9kuStm7dpPLlK6pgQV/Vr99QmzatV3T0BUmSxWLRkSO/ZTrW6dOnFBpaUm3bPqcXXuhovxGiYMGCunr1qn27hIQEBQUVkZeXly5ejNaOHdvtbfXqNdAXXyxRSkqKJN225Orv76+PPpqmzZs3aOnSRfd0Xv6JGToAAJCp5KSk/3vEiOP7zUyhQoU0fPhovfvuMFksFgUGFlJk5BhFR19Q+fIVtXbtKk2aNF7e3t4aPvxdSVLNmrXVu3c/RUS8KYvFqtTUFD3+eFNVqRJ2x7G+/HKp9u3bKw8Pd3l4eOqNN4ZIurmcO27cu/rmm6/Vvn0nvfBCB40Y8ba6dGmn4OBievDBh+x9dO7cXTNnRqlHj05yd/dQyZIlb3tUia+vrz78MEpvvfW6EhMT1b17r+ycutuYbDab7Z57MbiYmKuyWvP9aUAmgoP91GbQaqeNt3by07p4McFp48GxgoP9+P2DoZ0/f0rFi5dxdRl3tG/fHk2d+rE+/fQzV5ficOmdf7PZpKAg33S3Z8kVAADA4Ah0AADAkGrXDs+Ts3PZQaADAAAwOAIdAACAwRHoAAAADI5ABwAAYHA8hw4AAGSqUEABuXs6PjakJqcq9soNh/f7/vtj1KJF6wzflfrppzPVtWtP++u5jI5ABwAAMuXu6a5jk3Y4vN9Kgxs4vE9JiogYke73qampcnd317x5s9WxYxcCHQAAgLP89NMPmjkzSlarVYGBhTRkyDv64IPxev75dmrY8DFJ0s6d32vp0kWaMmWm+vfvrY4du6h+/YYaN26U3NzcdPr0KV2/fl01ajwgSerbt6dMJrOmTJkpPz8/Fx7dvSPQAQCAXC029rLGjo3UlCmzVK5cea1bt0rvvjtczz/fXhs3rrcHug0b1qhlyzbp9nHs2O+KipqlAgUKSJK++mq5pk+fKx8fH2cdRo7ipggAAJCr/frrIVWocJ/KlSsvSWrZ8in98cfvql+/kf73v/26ciVOV67E6Zdf9umxx5qk28djjzWxh7m8iBk6AABgSGazSQ0aPKqtWzdJkho0eDTD0Objk3fDnMQMHQAAyOWqVauh48d/16lTJyVJGzeuU6VKleXjU1AtWrTRhg3rtGHDOrVs+VSW+/TxKahr167mUMXOxwwdAADI1QoVKqThw0fr3XeHyWKxKDCwkCIjx0iSHnigpq5fv2b/dVZ16PCiXnutj7y8vPPETREmm81mc3URrhYTc1VWa74/DchEcLCf2gxa7bTx1k5+WhcvJjhtPDhWcLAfv38wtPPnT6l48TL2z0Z7Dp3R/fv8SzeXmIOCfNPdnhk6AACQKUJX7sY1dAAAAAbHDB2QS1lTkxUc7LxrOlKTkxR7Jdlp4wEAHIdAB+RSZndPnRj3nNPGKz9shSQCHQAYEUuuAAAABkegAwAAMDiWXAEAQKYCArzl6enh8H6Tk1N05Uqiw/vNbwh0AAAgU56eHpo8ebLD+x00aJCknA90778/Ri1atNYDD9S643bPP99GEyf+R+XLV8zxmhyJQAcAAPI0i8WiiIgRri4jRxHoAABArpaYmKixY0fq5MkTcnNzV+nSZTRmzPvauHGdVq5cLovFIl9fXw0eHKHSpctqw4a12rx5o3x8fPT336cVGTlGH388WR07dlH9+g21ZcsmLV++RKmpKZKkV199XeHhdVx8lPeGQAcAAHK1Xbt+1PXr17Ro0XJJUnx8vA4c2K9t27Zq6tTZ8vT01I8/7tT48aM1ffpcSdLhwwc1f/4ShYaWTNPfww/X1RNPNJPJZNLp0yc1cGA/ffXVBqcek6MR6AAAQK5WsWIlnTz5pyZPnqBatR5UvXoNtHPnd/rjj2Pq3bu7JMlmsykhId6+T40aNdMNc5J05szfGjVqmC5evCh3d3ddvhyjmJhLCgoq4ozDyREEOgAAkKuFhpbUokVfaM+e3frpp52aNWuqGjZ8TK1aPaVevfqku4+PT4EM+xs1apj6939DjRo9JqvVqqZNGyg52dgPVuc5dAAAIFeLjr4gs9lNjRo9ptdeG6S4uFjVr99QmzatV3T0BUk3b3w4cuS3LPV39epVhYSUkCStX7/G8GFOYoYOAABkQXJyyv89YsTx/Wbm+PE/NGNGlCTJarWoc+fuqlmztnr37qeIiDdlsViVmpqixx9vqipVwjLt77XX3tQ77wyWn5+fHn64ngICAu75OFzNZLPZbK4uwtViYq7Kas33pwGZCA72U5tBq5023trJTzv9Xa4XLyY4bby8LjjYj/MJQzt//pSKFy/j6jLyrfTOv9lsUlCQb7rbs+QKAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4nkMHAAAyVSjQS+4eng7vNzUlWbFxSQ7vN6f1799bHTt2Uf36DV1diiQCHQAAyAJ3D0/t3TLE4f0++OQHklwf6FJTU+XubtxYZNzKAQBAvnHu3Fn16tVF69d/fdvnOXM+U69eXdS8eWvt2bNLNptNgwZF6IEHakmSVqxYpuXLl8rX10+PPFJfK1d+ofXrv7bv36JFG+3bt1tPPfWMSpYsrdmzpys5OUkWi0Vdu/ZU06bNJEl//nlC7733rm7cuKEKFSrc9rqwS5cu6aOPJurChfNKSkpS06bN1LVrT6eeHwIdAAAwtCtXrqhixUoaMOAN7du3R6NGDdOyZat0+vQpffbZfM2b97kKFSqkjz6alGa/sLCq6t//dUlSfHy8pk2bIzc3N12+HKOXXuqiOnUekb+/v8aMidQLL3RQixatdejQQfXr95K9n7FjI9W9ey/VrFlbKSkpGjiwr8LCquqhh+o67RwQ6AAAgKF5eHioWbOWkqTatcPl5eWl06dPaf/+vXrkkfoqVKiQJKlVq6e0detG+36enl5q3PgJ++e4uFiNHz9af/99Wm5u7oqPv6LTp0+pXLly+vPP4/YxqlevofLlK0qSbty4of379youLs7ez/Xr13Ty5EkCHQAAwD+5ubnd9t71fy55ZleBAt4ymUz2z5Mnv6/69Rvpvfc+kMlkUocOzyo5+c7X99lsVplMJs2Zs9Cl1+Dx2BIAAJDrFS4cpNTUVP3991+SpK1bN9nbUlJS7J8PHNivpKQklSlTVjVr1tZPP/1gnz3btGndHcdISEhQSEiITCaTdu/+SWfO3ByrYEFflS9f0T7G4cOHdOLEH5IkH5+CeuCBWlq0aL69nwsXzism5pJDjjurnBolY2Nj9dZbb+n06dPy9PRUmTJlNHr0aBUuXFiVK1fWfffdJ7P5ZsacOHGiKleuLEnatm2bJk6cKIvFomrVqmn8+PEqUKBApm0AACBvcHd318CBg/TGG68qMDBQjzzSwN4WEBCgY8d+1+efL5TNZtOoUePk4eGhSpXuU6dOXdWnTw/5+BRUePhDKljQN8Mx+vbtr8mTJ+jTT2cpLKyqKlSoZG8bPvxdvffeu1q0aL7Kl6+oKlWq2tsiI8fok08+VNeu7SXdDHlDh0YqKKhIDpyJ9JlsNpst880cIy4uTkePHtXDDz8sSZowYYKuXLmi9957T5UrV9a+fftUsGDB2/a5du2annzySS1evFhly5bVsGHDFBISov79+9+x7W7ExFy9bRoXSE9wsJ/aDFrttPHWTn5aJ8Y957Txyg9boYsXE5w2Xl4XHOzH+YShnT9/SsWLl7F/zq3Pofv33a//dv36Nfn43MwWn346U2fO/K3IyDHZHs9Z/n3+JclsNikoKP1A6tQZusDAQHuYk6SaNWtqyZIld9znu+++U/Xq1VW2bFlJUocOHRQREaH+/fvfsQ0AADjOzdDl+ufF3a3p06N08OABpaamqESJUL311jBXl5QjXHb1ntVq1ZIlS9S4cWP7d126dJHFYlGjRo00YMAAeXp66ty5cypRooR9mxIlSujcuXOSdMc2AACQ94WElMhwdk6SBg1624nVuI7LAt2YMWPk4+Ojzp07S5K+/fZbhYSE6OrVqxoyZIimTp2qN954wym1ZDR9CeQ3wcF+ri4hT+F8wsiio81yd+feSVcxm8139XeISwLdhAkTdOrUKc2YMcN+E0RISIgkydfXVy+88ILmzZtn/37Xrl32fc+ePWvf9k5td4Nr6JAV+eGHM9d8OQ7X0MHorFarUlOtri4j37JarWn+DrnTNXROj94ffvihDh06pKlTp8rT8+bFlVeuXFFiYqKkm+9S27x5s8LCwiRJDRs21MGDB3Xy5ElJ0tKlS9WiRYtM2wAAAPILp87QHTt2TDNnzlTZsmXVoUMHSVLJkiXVq1cvRUZGymQyKTU1VbVq1dLAgQMl3ZyxGz16tF555RVZrVaFhYVp2LBhmbYBAADkF04NdJUqVdLRo0fTbVu7dm2G+zVt2lRNmza96zYAAOAY/oE+8vJwc3i/SSkWxcddd3i/+Q2v/gIAAJny8nDTyxv2Obzf2S1rO7zPO+nfv7c6duyi+vUbOnXcnMbtKwAAAHchNTXV1SWkwQwdAADI1ebPn6P4+Ct67bVBkqQrV+LUqdNziogYodmzp8tsdpPFkqo33nhLtWuHq3//3qpUqbIOHTqg+Ph4NW78hF555VV7f7/8sk+LFs3XpUuX1LhxU/XtO0CSdOnSJX300URduHBeSUlJatq0mbp27SlJev75NmrS5Ent27db5ctX1ODBQzVr1jT98steJSenqGLFiho0aKh8fHycf4JEoAMAALlc8+at9cor3dSv30C5u7tr69ZNql+/kebMmam33hqm6tXvl8ViUWLiDfs+J0+e0PTpc5WcnKw+fXqoevX77cusFy6c19Sps3X9+nW1b/+0Wrd+WqVKldbYsZHq3r2XatasrZSUFA0c2FdhYVX10EN1Jd18Hens2Qsl3QyZBQsWtH+eNu0TffbZvNuCozMR6AAAQK5WvHhxlS1bQT/9tFMNGjyqDRvW6bXX3pSvr68++eRDPfZYY9WtW0/ly1e079OiRWu5u7vL3d3dPrN2K9A9/ngTmc1m+fr6qkyZcjpz5m8VKRKs/fv3Ki4uzt7H9evXdPLkSXuga968lb1t587vdO3aNX377TZJUkpKsipWrOSEs5E+Ah0AAMj1WrZsrY0b1ykkJFTXrl3VAw/UUs2atXX8+B/au3e3RoyIUPv2L+qpp57JtC9PTy/7r81msywWi2w2q0wmk+bMWSh39/TjkY9PAfuvbTZp0KAIPfjgQ/d+cA7ATREAACDXe/TRxjpwYL+WLl2kFi1ay2Qy6fTpk6pQoaLateuoJ59sod9+O2zffvPmjUpNTdWNGze0bdt/Vbv2nYOXj09BPfBALS1aNN/+3YUL5xUTcynd7Rs0aKRlyxYrKenmixFuzub9ee8Hmk3M0AEAgEwlpVhy5BEjSSmWLG3n7e39f8uta/XFF2skSdOnR+nvv0/Lzc1dvr6+Gjo00r59mTJl1LdvT/tNEVl5TElk5Bh98smH6tq1vaSbIW/o0EgFBRVJs23nzt316acz1atX1/97jalJPXu+rLJly2XpeBzNZLPZ8v1LTHmXK7IiONhPbQatdtp4ayc/rRPjnnPaeOWHreDdow7Eu1xhdOfPn1Lx4mVcXUa25IVnzaV3/nPVu1wBAADgWCy5AgCAPCUqaparS3A6ZugAAEC6uCrLNbJz3gl0AAAgjVtvX4DzWSypMpvd7mofAh0AAEijQAFfJSTEyWazurqUfMVmsyohIVYFCqR/80NGuIYOAACk4esboNjYi7pw4W9JLL06j0ment7y9Q24q70IdAAAIA2TyaTChYu6ugxkEUuuAAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYnFMDXWxsrF5++WU1a9ZMbdq0Uf/+/XX58mVJ0i+//KKnnnpKzZo1U8+ePRUTE2PfL7ttAAAA+YFTA53JZFKvXr20efNmrV27VqVKldKkSZNktVo1ZMgQRUZGavPmzQoPD9ekSZMkKdttAAAA+YVTA11gYKAefvhh++eaNWvq7NmzOnTokLy8vBQeHi5J6tChgzZt2iRJ2W4DAADIL1x2DZ3VatWSJUvUuHFjnTt3TiVKlLC3FS5cWFarVXFxcdluAwAAyC/cXTXwmDFj5OPjo86dO2vr1q2uKkOSFBTk69LxgdwiONjP1SXkKZxPAM7ikkA3YcIEnTp1SjNmzJDZbFZISIjOnj1rb798+bLMZrMCAwOz3XY3YmKuymq13fNxIW/LDz+cL15McHUJeUZwsB/nE4BDmc2mDCehnL7k+uGHH+rQoUOaOnWqPD09JUnVq1dXYmKi9uzZI0launSpmjdvfk9tAAAA+YVTZ+iOHTummTNnqmzZsurQoYMkqWTJkpo6daomTpyokSNHKikpSaGhofrggw8kSWazOVttAAAA+YXJZrPl+7VGllyRFcHBfmozaLXTxls7+WmdGPec08YrP2wFS4QOxJIrAEe75yXXqKgoXbhwId226OhoRUVFZb86AAAA3JMsBbqpU6feMdBNnTrVoUUBAAAg67IU6O60Knv+/Hn5+/s7rCAAAADcnQxvivjqq6/01VdfSbr5yq5Ro0bJ1/f2ddvk5GQdPXpUDRo0yNkqAQAAkKEMA523t7f9eW42m01+fn4KCAi4bRsPDw81bNhQnTp1ytEiAQAAkLEMA12LFi3UokULSdLQoUPVr18/lSpVymmFAQAAIGuy9By68ePH53QdAAAAyKYsP1j44MGD2rp1q86fP6+kpKQ07R9//LFDCwMAAEDWZCnQff755xo9erQKFSqkMmXKyMPDI6frAgAAQBZlKdDNnTtXzz33nN599125uzv1bWEAAADIRJaeQxcTE6PWrVsT5gAAAHKhLAW6Ro0a6cCBAzldCwAAALIhS1NuL774okaMGKHU1FTVq1cv3TdDVKxY0eHFAQAAIHNZCnRdu3aVJEVFRaV5b6vNZpPJZNJvv/3m+OoAAACQqSwFuoULF+Z0HQAAAMimLAW6OnXq5HQdAAAAyKYsBbobN25kuk2BAgXuuRgAAADcvSwFulq1aslkMt1xG66hAwAAcI0sBbr33nsvTaC7cuWKduzYoePHj6tfv345UhwAAAAyl6VA9+yzz6b7fffu3TVy5EgdO3bMoUUBAAAg67L0YOE7adasmVavXu2IWgAAAJAN9xzoDh48KA8PD0fUAgAAgGzI0pLrxIkT03yXkpKiEydO6Mcff1S3bt0cXhgAAACyJkuBbtOmTWm+8/LyUvHixTVs2DC1b9/e4YUBAAAga7IU6LZt25bTdQAAACCbsnUNXUpKiqPrAAAAQDZlaYZOkvbt26dp06Zp7969SkxMlLe3t8LDw9WvXz/VqlUrJ2sEAADAHWQp0O3cuVOvvPKKypUrp5deeklFihTRpUuXtHnzZnXp0kWzZs1SvXr1crpWAAAApCNLge4///mPGjdurI8//vi2N0b0799fAwYM0IcffkigAwAAcJEsXUP3+++/64UXXkj3fa7t2rXT77//7vDCAAAAkDVZCnT+/v46ffp0um2nT5+Wv7+/Q4sCAABA1mUp0DVv3lwffvihVq9eraSkJElSUlKSVq9erf/85z9q3rx5jhYJAACAjGXpGrrBgwcrNjZWERERioiIkI+Pj65fvy5JatWqlYYMGZKjRQIAACBjWQp03t7emjx5svr166eDBw/q4sWLCg4OVo0aNVShQoWcrhEAAAB3kOXn0ElShQoVCHAAAAC5TIbX0P3555969tlntX379gx33r59u5599ln99ddfOVIcAAAAMpdhoJs3b558fHz06KOPZrjzo48+qoIFC2rOnDk5UhwAAAAyl2Gg27Fjh5577rlMO3juuee0Y8cOhxYFAACArMsw0EVHR6tUqVKZdlCyZElFR0c7tCgAAABkXYaBztfXV7GxsZl2EBcXp4IFCzq0KAAAAGRdhoGuZs2a2rBhQ6YdrF+/XjVr1nRkTQAAALgLGQa6Hj16aNOmTYqKipLFYknTbrVaFRUVpc2bN6tnz545WiQAAAAyluFz6B5++GENHTpUEyZM0NKlS/XII4+oRIkSkqRz587pxx9/VGxsrIYOHao6deo4rWAAAADc7o4PFu7atavCw8P12Wefac+ePdq8ebMkqVixYmrUqJE6d+6ssLAwpxQKAACA9GX6poiqVatq/PjxzqgFAAAA2ZDhNXQAAAAwBgIdAACAwRHoAAAADI5ABwAAYHAEOgAAAIMj0AEAABhcho8tqVu3rkwmU5Y7+vHHHx1SEADXSLakKDjYz2njJaYkKyEuyWnjAUBelmGge/HFF+8q0AEwNk83D7Vb1tdp433RfroSRKADAEfIMNANGDDAmXUAAAAgm7iGDgAAwOAyffXXLfv379eXX36pkydPKikp7TLJl19+6dDCAAAAkDVZmqHbuXOnOnfurPPnz2vv3r0qXLiwfHx8dOTIEcXFxalSpUo5XScAAAAykKVA98knn6hr166aNWuWJGngwIFauHChNm/eLHd3dz388MM5WiQAAAAylqVA98cff6hRo0Yym80ymUy6ceOGJCk0NFQDBgzQ9OnTc7RIAAAAZCxLgc7Ly0tWq1Umk0nBwcE6ffq0vc3X11cXLlzIsQIBAABwZ1m6KaJKlSr6888/Vb9+fT3yyCOaOXOmihUrJg8PD3388ce67777crpOAAAAZCBLM3TdunWzP2T4zTfflI+Pj1566SV17dpVly9fVmRkZI4WCQAAgIxlaYbu0Ucftf+6WLFiWrlypU6dOqXExESVL19enp6eOVYgAAAA7ixLM3RRUVG3XSdnMplUtmxZValSRXFxcYqKisqxAgEAAHBnWQp0U6dOzfDGh+joaE2dOtWhRQEAACDrshTobDZbhm3nz5+Xv7+/wwoCAADA3cnwGrqvvvpKX331laSbS6yjRo2Sr6/vbdskJyfr6NGjatCgQc5WCQAAgAxlGOi8vb0VGBgo6eYMnZ+fnwICAm7bxsPDQw0bNlSnTp1ytEgAAABkLMNA16JFC7Vo0UKSNHToUPXr10+lSpVyWmEAAADImiw9tmT8+PH2X8fGxurKlSsKCAhQoUKFcqwwAAAAZE2WAp0kbdiwQVOmTNHJkyft35UtW1avvfaafSYPAAAAzpelQLdu3ToNHjxYjRo10iuvvKKgoCDFxMRow4YNevPNN2W1WtWqVassDThhwgRt3rxZZ86c0dq1a+2vDWvcuLE8PT3l5eUlSRo8eLAaNmwoSfrll18UGRmppKQkhYaG6oMPPlBQUFCmbQAAAPlBlh5bMmPGDLVr106zZs1S27Zt1bBhQ7Vt21azZs3SCy+8oOnTp2d5wCZNmmjx4sUKDQ1N0/bJJ59o9erVWr16tT3MWa1WDRkyRJGRkdq8ebPCw8M1adKkTNsAAADyiywFulOnTqlZs2bptjVr1kynTp3K8oDh4eEKCQnJ8vaHDh2Sl5eXwsPDJUkdOnTQpk2bMm0DAADIL7K05FqkSBEdOnRI9evXT9N26NAhFSlSxCHFDB48WDabTQ8++KDefPNN+fv769y5cypRooR9m8KFC8tqtSouLu6ObbceuZIVQUG+mW8EwOGCg/1cXUKOyuvHByD3yDDQ7d69W1WrVlXBggX17LPPasqUKbJYLGrWrJmKFCmimJgYbdq0SdOnT9crr7xyz4UsXrxYISEhSk5O1rhx4zR69GinLZ/GxFyV1Zrx2zAAiR/OOeHixQRXl5BjgoP98vTxAXA+s9mU4SRUhoGua9euWrZsme6//369+uqrSk1N1ezZszVlyhT7Nt7e3urZs6deffXVey7y1jKsp6enOnXqpL59+9q/P3v2rH27y5cvy2w2KzAw8I5tAAAA+UWGge6f7281m81644031LNnTx07dkzR0dEqWrSoKlWqlObtEdlx/fp1WSwW+fn5yWazacOGDQoLC5MkVa9eXYmJidqzZ4/Cw8O1dOlSNW/ePNM2AACA/CLLz6GTpICAAPsNCNk1duxYbdmyRZcuXVKPHj0UGBioGTNmaMCAAbJYLLJarapQoYJGjhwp6WaYnDhxokaOHHnbo0kyawMAAMgv7hjotm/frhMnTmSpo7Zt22Zpu+HDh2v48OFpvl+1alWG+9SuXVtr16696zYAAID84I6BburUqVnqxGQyZTnQAQAAwLHuGOgWLlyo6tWrO6sWAAAAZMMdA523t7d8fHycVQsAAACyIUtvigAAAEDuRaADAAAwuAyXXI8cOeLMOgAAAJBNzNABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDc2qgmzBhgho3bqzKlSvr999/t3//559/qn379mrWrJnat2+vkydP3nMbAABAfuHUQNekSRMtXrxYoaGht30/cuRIderUSZs3b1anTp0UGRl5z20AAAD5hVMDXXh4uEJCQm77LiYmRocPH1br1q0lSa1bt9bhw4d1+fLlbLcBAADkJ+6uLuDcuXMqVqyY3NzcJElubm4qWrSozp07J5vNlq22woULu+x4AAAAnM3lgS43CArydXUJQL4UHOzn6hJyVF4/PgC5h8sDXUhIiC5cuCCLxSI3NzdZLBZFR0crJCRENpstW213KybmqqxWWw4cHfISfjg73sWLCa4uIccEB/vl6eMD4HxmsynDSSiXP7YkKChIYWFhWrdunSRp3bp1CgsLU+HChbPdBgAAkJ84dYZu7Nix2rJliy5duqQePXooMDBQ69ev16hRoxQREaFp06bJ399fEyZMsO+T3TYAAID8wqmBbvjw4Ro+fHia7ytUqKDly5enu0922wAAAPILly+5AgAA4N4Q6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4NxdXQAAwHgCArzl6enhlLGSk1N05UqiU8YCjIpABwC4a56eHpo8ebJTxho0aJAkAh1wJyy5AgAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcO6uLuCfGjduLE9PT3l5eUmSBg8erIYNG+qXX35RZGSkkpKSFBoaqg8++EBBQUGSdMc2AACA/CDXzdB98sknWr16tVavXq2GDRvKarVqyJAhioyM1ObNmxUeHq5JkyZJ0h3bAAAA8otcF+j+7dChQ/Ly8lJ4eLgkqUOHDtq0aVOmbQAAAPlFrlpylW4us9psNj344IN68803de7cOZUoUcLeXrhwYVmtVsXFxd2xLTAw0AXVAwAAOF+uCnSLFy9WSEiIkpOTNW7cOI0ePVpPPPFEjo8bFOSb42MASCs42M/VJeSovH58zsS5BO4sVwW6kJAQSZKnp6c6deqkvn37qmvXrjp79qx9m8uXL8tsNiswMFAhISEZtt2NmJirslptDjkG5F38QHG8ixcTXF1CjgkO9svzx+dMeflcAlllNpsynITKNdfQXb9+XQkJN/+Dtdls2rBhg8LCwlS9enUlJiZqz549kqSlS5eqefPmknTHNgAAgPwi18zQxcTEaMCAAbJYLLJarapQoYJGjhwps9msiRMnauTIkbc9mkTSHdsAAADyi1wT6EqVKqVVq1al21a7dm2tXbv2rtsAAADyg1yz5AoAAIDsIdABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDh3VxcAAHlRakqKgoP9nDZeclKSrsQnO208ALkLgQ4AcoC7h4eihvZw2nj9x8+TlDcDndXi3HCcmpKs2Lgkp40HOAKBDgCQq5ndPLR3yxCnjffgkx9IItDBWLiGDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAgyPQAQAAGByBDgAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBubu6AAAAcpMUi1XBwX5OGy8pxaL4uOtOGw95E4EOAIB/8HAz6+UN+5w23uyWtZ02FvIullwBAAAMjkAHAABgcCy5AnAJa3KyU69TSk1MUmxCstPGAwBnItABcAmzp6d2Pv2c08arv3qFRKADkEex5AoAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMHxHDoAyAOsqc59oTyA3IVABwB5gNndrGOTdjhtvEqDGzhtLACZY8kVAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6AAAAAyOQAcAAGBwBDoAAACDI9ABAAAYHIEOAADA4Ah0AAAABservwDkC6kpFt51CiDPItAByBfcPdw0etA6p40XObm108YCAJZcAQAADI5ABwAAYHAEOgAAAIMj0AEAABgcgQ4AAMDgCHQAAAAGR6ADAAAwOAIdAACAweWJQPfnn3+qffv2atasmdq3b6+TJ0+6uiQAAACnyROBbuTIkerUqZM2b96sTp06KTIy0tUlAQAAOI3hX/0VExOjw4cPa968eZKk1q1ba8yYMbp8+bIKFy6cpT7MZlNOlog8pGihAk4dzz0g2KnjBftk7b8ZR/Eq6tzjC3Dy759fYJBTx3P393LqeP7+/k4by9O7kNPGkqSgAp5OHY+fQ8iKO/05MdlsNpsTa3G4Q4cO6e2339b69evt37Vs2VIffPCBqlWr5sLKAAAAnCNPLLkCAADkZ4YPdCEhIbpw4YIsFoskyWKxKDo6WiEhIS6uDAAAwDkMH+iCgoIUFhamdevWSZLWrVunsLCwLF8/BwAAYHSGv4ZOko4fP66IiAjFx8fL399fEyZMUPny5V1dFgAAgFPkiUAHAACQnxl+yRUAACC/I9ABAAAYHIEOAADA4Ah0AAAABkegAwAAMDgCHQAAgMER6ADABf79xCieIAXgXhDoAMDJbDabTCaTJGnv3r2SZP8MANlBoAMAJ7sV3hYvXqxRo0bpzJkzLq4IgNER6ADABVatWqW1a9dq3rx5Cg0N1Z9//qmkpCRXlwXAoAh0AOBEVqtVNptNhw4dUrNmzRQbG6uoqCj17NlTPXr00IULF1xdIgADItABQA779w0PJpNJjz32mObNm6f3339fZcuW1dq1a1WoUCEdO3bMRVUCMDJ3VxcAAHnZP2+AWLVqlU6ePKlSpUqpVq1aWrVqlXx9feXp6anvvvtOf/31lypUqODiigEYETN0AJCDboW5zz//XMuXL1eNGjU0ceJEffvttypcuLDMZrOWL1+uCRMmaNKkSQoJCXFxxQCMiEAHADngn8usf//9t7777jvNnDlTly9fVvXq1dWtWzdJ0pUrV1SyZElNnTpV9913n6vKBWBwJhtPswSAHHPkyBEVKFBAX375paxWq37//XdNmTJF3t7eWrBggcqWLatHH33U1WUCMDhm6ADAgf75b+Q1a9ZoypQpKliwoHbt2qUVK1Zo9uzZ8vb21oYNG/Tll1+qdOnSLqwWQF7BDB0A5IBVq1bp4MGD6tixoypWrKizZ8+qffv2qlGjhgICAnT06FFNmDBBlSpVcnWpAPIAAh0AOMCtu1lv/f+rr76q7du3a+3atSpXrpwkKS4uTtu2bZO/v78qV66sUqVKubhqAHkFgQ4AHOjHH3/UI488Iknq06ePLly4oGXLlsnT09PFlQHIy7iGDgAc5OrVqxo2bJgGDx4sSZoxY4ZCQkLUtWtXJScnu7g6AHkZgQ4Asslqtd722dfXVwsWLNDx48cVEREhSZo2bZq8vLz08ssvu6JEAPkES64AcI/Wrl2rhx9+WEWLFpUknTt3Tr1791adOnU0YsQISdL58+dVvHhxV5YJIA9jhg4A7tKBAwf0yiuv2D9/88036tChgy5evChJCgkJUefOnbV48WKNHz9ekghzAHIUgQ4A7lKZMmUUHR2tV199VZL04YcfqkGDBuratas91Hl7e2vw4MHq1KmTK0sFkE+w5AoAWbRv3z6dP39eLVu2VGxsrPr27avAwEDNmDFDkjR8+HBt3bpVzz//vDZs2KDPPvtMJUuWdHHVAPIDAh0AZIHNZtNPP/2kcuXKKSUlRaVKlVJcXJz69OlzW6hbunSpUlJSVK9ePVWoUMHFVQPILwh0AHAXoqOj1aVLF3Xs2FHdu3e3h7pChQpp+vTpkv7/Q4YBwFm4hg4A7uDf/+b19/fXwIEDtXHjRi1atEiBgYGaOXOmTp8+rTfeeEOSCHMAnM7d1QUAQG71z5m2//3vfypSpIjc3d3VsmVLmc1mzZkzRyaTSS+++KKWLFmi+Ph4F1cMIL8i0AFAOs6fP6+iRYvKZDJp/vz52rhxo6pWrapLly7p1VdfVfPmzWU2mzVp0iR5eHioXbt28vf3d3XZAPIpllwB4F/++9//6rXXXpPZbNbq1au1bds2ff7550pKStKZM2c0btw4HTlyRE8++aTeeust+7tbAcBVCHQA8A/Xrl3TmjVr9NJLL2nnzp366aef9P777+uzzz7T2bNnNXHiRJnNZkVEROjw4cNq2rSpSpUq5eqyAeRzLLkCwD94eHioaNGi+uKLLxQbG6upU6fK29tbO3fu1OzZs+Xh4aHy5cvL09NTAQEBri4XACQxQwcAt/H09FS5cuV06NAhVatWTQEBATKbzbpy5YrmzZunDRs26H//+59eeuklhYaGurpcAJDEc+gAII1z587p/PnzmjVrlkqXLq127drpyJEjWr9+vWJiYvTuu++qSpUqri4TAOwIdACQgRMnTmjcuHG6//771apVK5UvX14JCQkstQLIdVhyBYAMlC9fXiNGjNAPP/ygtWvXShJhDkCuxAwdAGTir7/+kpubm0qUKOHqUgAgXQQ6AAAAg2PJFQAAwOAIdAAAAAZHoAMAADA4Ah0AAIDBEegAAAAMjkAHAABgcAQ6AAAAg/t/pjrl0+NcCjIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(rc={\"figure.figsize\": (10, 10)})\n",
+    "barplot_f.plot(kind=\"bar\")\n",
+    "plt.ylabel(\"Total Count\", fontsize=15)\n",
+    "plt.title(\"Failures: by platform\", fontsize=20)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:19.346953Z",
+     "start_time": "2021-01-22T16:59:19.168059Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "by_pltf.loc[\"testPassPercentage\"].sort_values(ascending=False).apply(\n",
+    "    lambda x: x / 100\n",
+    ").plot(kind=\"bar\")\n",
+    "sns.set(rc={\"figure.figsize\": (10, 10)})\n",
+    "plt.ylabel(\"Pass Percent\", fontsize=15)\n",
+    "plt.title(\"Test Pass Percentage: by platform\", fontsize=20)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now lets dig into the results by platform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:22.867004Z",
+     "start_time": "2021-01-22T16:59:22.810254Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "aws          [{'name': 'operator.Run template e2e-aws-upi -...\n",
+       "azure        [{'name': 'Overall', 'successes': 119, 'failur...\n",
+       "gcp          [{'name': 'operator.Run template e2e-gcp - e2e...\n",
+       "metal        [{'name': 'operator.Run template e2e-metal-ser...\n",
+       "metal-ipi    [{'name': 'Overall', 'successes': 16, 'failure...\n",
+       "openstack    [{'name': 'Overall', 'successes': 19, 'failure...\n",
+       "ovirt        [{'name': 'Overall', 'successes': 0, 'failures...\n",
+       "serial       [{'name': 'operator.Run template e2e-metal-ser...\n",
+       "upgrade      [{'name': 'Overall', 'successes': 45, 'failure...\n",
+       "vsphere      [{'name': 'operator.Run template e2e-vsphere-u...\n",
+       "Name: results, dtype: object"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_pltf.loc[\"results\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:27.374812Z",
+     "start_time": "2021-01-22T16:59:24.402253Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>operator.Run template e2e-aws-upi - e2e-aws-up...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-aws - e2e-aws-ovn co...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22.727273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-arch][Feature:ClusterUpgrade] Cluster sho...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.809524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run template e2e-aws-fips - e2e-aws-f...</td>\n",
+       "      <td>13</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.952381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Run template e2e-aws - e2e-aws contai...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61.956522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>operator.Run template e2e-aws-scaleup-rhel7 - ...</td>\n",
+       "      <td>13</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76.470588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>operator.Run template e2e-aws-serial - e2e-aws...</td>\n",
+       "      <td>89</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77.391304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[sig-apps] Deployment should not disrupt a clo...</td>\n",
+       "      <td>142</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.225989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>[sig-arch] Managed cluster should have no cras...</td>\n",
+       "      <td>150</td>\n",
+       "      <td>28</td>\n",
+       "      <td>27</td>\n",
+       "      <td>84.269663</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "0   operator.Run template e2e-aws-upi - e2e-aws-up...          0        48   \n",
+       "1   operator.Run template e2e-aws - e2e-aws-ovn co...          7        35   \n",
+       "2   operator.Run template e2e-aws-upgrade - e2e-aw...          5        17   \n",
+       "3   [sig-arch][Feature:ClusterUpgrade] Cluster sho...          5        16   \n",
+       "4   operator.Run template e2e-aws-fips - e2e-aws-f...         13        29   \n",
+       "5   operator.Run template e2e-aws - e2e-aws contai...         57        35   \n",
+       "7   operator.Run template e2e-aws-scaleup-rhel7 - ...         13         4   \n",
+       "8   operator.Run template e2e-aws-serial - e2e-aws...         89        26   \n",
+       "9   [sig-apps] Deployment should not disrupt a clo...        142        35   \n",
+       "10  [sig-arch] Managed cluster should have no cras...        150        28   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "0        0        0.000000  \n",
+       "1        0       16.666667  \n",
+       "2        0       22.727273  \n",
+       "3        0       23.809524  \n",
+       "4        0       30.952381  \n",
+       "5        0       61.956522  \n",
+       "7        0       76.470588  \n",
+       "8        0       77.391304  \n",
+       "9        0       80.225989  \n",
+       "10      27       84.269663  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-azure - e2e-azure-ov...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[sig-arch][Feature:ClusterUpgrade] Cluster sho...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47.368421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-api-machinery] Kubernetes APIs remain ava...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>57.894737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run template e2e-azure-upgrade - e2e-...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Run template e2e-azure-serial - e2e-a...</td>\n",
+       "      <td>29</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67.441860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>operator.Run template e2e-azure - e2e-azure co...</td>\n",
+       "      <td>119</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72.121212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[Serial] [sig-auth][Feature:OAuthServer] [Requ...</td>\n",
+       "      <td>25</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>75.757576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
+       "      <td>29</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76.315789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>operator.Run template e2e-azure-serial - e2e-a...</td>\n",
+       "      <td>33</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76.744186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-azure - e2e-azure co...</td>\n",
+       "      <td>129</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78.181818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "1   operator.Run template e2e-azure - e2e-azure-ov...         14        28   \n",
+       "2   [sig-arch][Feature:ClusterUpgrade] Cluster sho...          9        10   \n",
+       "3   [sig-api-machinery] Kubernetes APIs remain ava...         11         8   \n",
+       "4   operator.Run template e2e-azure-upgrade - e2e-...         15        10   \n",
+       "5   operator.Run template e2e-azure-serial - e2e-a...         29        14   \n",
+       "6   operator.Run template e2e-azure - e2e-azure co...        119        46   \n",
+       "7   [Serial] [sig-auth][Feature:OAuthServer] [Requ...         25         8   \n",
+       "8   operator.Import the release payload \"initial\" ...         29         9   \n",
+       "9   operator.Run template e2e-azure-serial - e2e-a...         33        10   \n",
+       "10  operator.Run template e2e-azure - e2e-azure co...        129        36   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "1        0       33.333333  \n",
+       "2        0       47.368421  \n",
+       "3       11       57.894737  \n",
+       "4        0       60.000000  \n",
+       "5        0       67.441860  \n",
+       "6        0       72.121212  \n",
+       "7        0       75.757576  \n",
+       "8        0       76.315789  \n",
+       "9        0       76.744186  \n",
+       "10       0       78.181818  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>operator.Run template e2e-gcp - e2e-gcp-rt con...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-gcp - e2e-gcp-ovn co...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.976744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
+       "      <td>50</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run template e2e-gcp - e2e-gcp contai...</td>\n",
+       "      <td>37</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72.549020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[sig-arch][Feature:ClusterUpgrade] Cluster sho...</td>\n",
+       "      <td>31</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81.578947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[sig-instrumentation] Prometheus when installe...</td>\n",
+       "      <td>74</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>82.222222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>operator.Run template e2e-gcp-upgrade - e2e-gc...</td>\n",
+       "      <td>38</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[sig-arch] Managed cluster should have no cras...</td>\n",
+       "      <td>77</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>85.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[sig-instrumentation] Prometheus when installe...</td>\n",
+       "      <td>78</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-gcp-upgrade - e2e-gc...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88.888889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "0   operator.Run template e2e-gcp - e2e-gcp-rt con...          0        43   \n",
+       "1   operator.Run template e2e-gcp - e2e-gcp-ovn co...          3        40   \n",
+       "3   operator.Import the release payload \"initial\" ...         50        25   \n",
+       "4   operator.Run template e2e-gcp - e2e-gcp contai...         37        14   \n",
+       "5   [sig-arch][Feature:ClusterUpgrade] Cluster sho...         31         7   \n",
+       "6   [sig-instrumentation] Prometheus when installe...         74        16   \n",
+       "7   operator.Run template e2e-gcp-upgrade - e2e-gc...         38         7   \n",
+       "8   [sig-arch] Managed cluster should have no cras...         77        13   \n",
+       "9   [sig-instrumentation] Prometheus when installe...         78        12   \n",
+       "10  operator.Run template e2e-gcp-upgrade - e2e-gc...         40         5   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "0        0        0.000000  \n",
+       "1        0        6.976744  \n",
+       "3        0       66.666667  \n",
+       "4        0       72.549020  \n",
+       "5        0       81.578947  \n",
+       "6        4       82.222222  \n",
+       "7        0       84.444444  \n",
+       "8        8       85.555556  \n",
+       "9        0       86.666667  \n",
+       "10       0       88.888889  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>operator.Run template e2e-metal-serial - e2e-m...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-metal - e2e-metal co...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>53</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.516129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>109</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>96.460177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "0  operator.Run template e2e-metal-serial - e2e-m...          0        47   \n",
+       "2  operator.Run template e2e-metal - e2e-metal co...          9        53   \n",
+       "3  operator.Import the release payload \"latest\" f...        109         4   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "0       0        0.000000  \n",
+       "2       0       14.516129  \n",
+       "3       0       96.460177  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run multi-stage test e2e-metal-ipi - ...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.526316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run multi-stage test e2e-metal-ipi - ...</td>\n",
+       "      <td>16</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76.190476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-api-machinery] Watchers should observe an...</td>\n",
+       "      <td>18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>85.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[sig-api-machinery] CustomResourcePublishOpenA...</td>\n",
+       "      <td>18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>85.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[sig-api-machinery] Watchers should observe ad...</td>\n",
+       "      <td>18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>85.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[sig-api-machinery] CustomResourcePublishOpenA...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90.476190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[sig-api-machinery] Watchers should receive ev...</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90.476190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>41</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>operator.Run multi-stage test e2e-metal-ipi - ...</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>92.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>[sig-api-machinery] CustomResourcePublishOpenA...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>95.238095</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "1   operator.Run multi-stage test e2e-metal-ipi - ...         23        15   \n",
+       "2   operator.Run multi-stage test e2e-metal-ipi - ...         16         5   \n",
+       "3   [sig-api-machinery] Watchers should observe an...         18         3   \n",
+       "4   [sig-api-machinery] CustomResourcePublishOpenA...         18         3   \n",
+       "5   [sig-api-machinery] Watchers should observe ad...         18         3   \n",
+       "6   [sig-api-machinery] CustomResourcePublishOpenA...         19         2   \n",
+       "7   [sig-api-machinery] Watchers should receive ev...         19         2   \n",
+       "8   operator.Import the release payload \"latest\" f...         41         4   \n",
+       "9   operator.Run multi-stage test e2e-metal-ipi - ...         37         3   \n",
+       "10  [sig-api-machinery] CustomResourcePublishOpenA...         20         1   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "1        0       60.526316  \n",
+       "2        0       76.190476  \n",
+       "3        0       85.714286  \n",
+       "4        0       85.714286  \n",
+       "5        0       85.714286  \n",
+       "6        0       90.476190  \n",
+       "7        0       90.476190  \n",
+       "8        0       91.111111  \n",
+       "9        0       92.500000  \n",
+       "10       0       95.238095  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-openstack - e2e-open...</td>\n",
+       "      <td>13</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.634921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-openstack-serial - e...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22.580645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-api-machinery] API data in etcd should be...</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.428571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[sig-storage] CSI Volumes [Driver: csi-hostpat...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.615385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[sig-apps][Feature:DeploymentConfig] deploymen...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.615385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[sig-storage] CSI Volumes [Driver: csi-hostpat...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.615385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[sig-apps][Feature:DeploymentConfig] deploymen...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.615385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[sig-network][Feature:Router] The HAProxy rout...</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.615385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[sig-cluster-lifecycle][Feature:Machines][Seri...</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>85.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-openstack - e2e-open...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90.476190</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "1   operator.Run template e2e-openstack - e2e-open...         13        50   \n",
+       "2   operator.Run template e2e-openstack-serial - e...         14        48   \n",
+       "3   [sig-api-machinery] API data in etcd should be...         10         4   \n",
+       "4   [sig-storage] CSI Volumes [Driver: csi-hostpat...         11         2   \n",
+       "5   [sig-apps][Feature:DeploymentConfig] deploymen...         11         2   \n",
+       "6   [sig-storage] CSI Volumes [Driver: csi-hostpat...         11         2   \n",
+       "7   [sig-apps][Feature:DeploymentConfig] deploymen...         11         2   \n",
+       "8   [sig-network][Feature:Router] The HAProxy rout...         11         2   \n",
+       "9   [sig-cluster-lifecycle][Feature:Machines][Seri...         12         2   \n",
+       "10  operator.Run template e2e-openstack - e2e-open...         57         6   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "1        0       20.634921  \n",
+       "2        0       22.580645  \n",
+       "3        0       71.428571  \n",
+       "4        0       84.615385  \n",
+       "5        0       84.615385  \n",
+       "6        0       84.615385  \n",
+       "7        0       84.615385  \n",
+       "8        0       84.615385  \n",
+       "9        0       85.714286  \n",
+       "10       0       90.476190  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-ovirt - e2e-ovirt co...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-ovirt - e2e-ovirt co...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Create the release image \"initial\" co...</td>\n",
+       "      <td>46</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.872340</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "1  operator.Run template e2e-ovirt - e2e-ovirt co...          0        48   \n",
+       "2  operator.Run template e2e-ovirt - e2e-ovirt co...          2        46   \n",
+       "3  operator.Create the release image \"initial\" co...         46         1   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "1       0        0.000000  \n",
+       "2       0        4.166667  \n",
+       "3       0       97.872340  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>operator.Run template e2e-metal-serial - e2e-m...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-vsphere-upi-serial -...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.083333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-openstack-serial - e...</td>\n",
+       "      <td>14</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22.580645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run template e2e-azure-serial - e2e-a...</td>\n",
+       "      <td>29</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67.441860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Run template e2e-azure-serial - e2e-a...</td>\n",
+       "      <td>33</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76.744186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>operator.Run template e2e-aws-serial - e2e-aws...</td>\n",
+       "      <td>89</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77.391304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>operator.Run template e2e-gcp-serial - e2e-gcp...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88.888889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>operator.Run template e2e-openstack-serial - e...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>91.935484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[Serial] [sig-auth][Feature:OAuthServer] [Requ...</td>\n",
+       "      <td>187</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>92.574257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-gcp-serial - e2e-gcp...</td>\n",
+       "      <td>42</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>93.333333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "0   operator.Run template e2e-metal-serial - e2e-m...          0        47   \n",
+       "1   operator.Run template e2e-vsphere-upi-serial -...          1        47   \n",
+       "2   operator.Run template e2e-openstack-serial - e...         14        48   \n",
+       "4   operator.Run template e2e-azure-serial - e2e-a...         29        14   \n",
+       "5   operator.Run template e2e-azure-serial - e2e-a...         33        10   \n",
+       "6   operator.Run template e2e-aws-serial - e2e-aws...         89        26   \n",
+       "7   operator.Run template e2e-gcp-serial - e2e-gcp...         40         5   \n",
+       "8   operator.Run template e2e-openstack-serial - e...         57         5   \n",
+       "9   [Serial] [sig-auth][Feature:OAuthServer] [Requ...        187        15   \n",
+       "10  operator.Run template e2e-gcp-serial - e2e-gcp...         42         3   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "0        0        0.000000  \n",
+       "1        0        2.083333  \n",
+       "2        0       22.580645  \n",
+       "4        0       67.441860  \n",
+       "5        0       76.744186  \n",
+       "6        0       77.391304  \n",
+       "7        0       88.888889  \n",
+       "8        0       91.935484  \n",
+       "9        0       92.574257  \n",
+       "10       0       93.333333  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23.809524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[sig-arch][Feature:ClusterUpgrade] Cluster sho...</td>\n",
+       "      <td>45</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.962025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Run template e2e-azure-upgrade - e2e-...</td>\n",
+       "      <td>15</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>105</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.945946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
+       "      <td>109</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73.648649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>operator.Run template e2e-azure-upgrade - e2e-...</td>\n",
+       "      <td>20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>operator.Run template e2e-gcp-upgrade - e2e-gc...</td>\n",
+       "      <td>38</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.444444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>operator.Run template e2e-gcp-upgrade - e2e-gc...</td>\n",
+       "      <td>40</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88.888889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[sig-api-machinery] Kubernetes APIs remain ava...</td>\n",
+       "      <td>71</td>\n",
+       "      <td>8</td>\n",
+       "      <td>36</td>\n",
+       "      <td>89.873418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  successes  failures  \\\n",
+       "1   operator.Run template e2e-aws-upgrade - e2e-aw...          5        16   \n",
+       "2   [sig-arch][Feature:ClusterUpgrade] Cluster sho...         45        34   \n",
+       "3   operator.Run template e2e-azure-upgrade - e2e-...         15        10   \n",
+       "4   operator.Import the release payload \"latest\" f...        105        43   \n",
+       "5   operator.Import the release payload \"initial\" ...        109        39   \n",
+       "6   operator.Run template e2e-azure-upgrade - e2e-...         20         5   \n",
+       "7   operator.Run template e2e-gcp-upgrade - e2e-gc...         38         7   \n",
+       "8   operator.Run template e2e-gcp-upgrade - e2e-gc...         40         5   \n",
+       "9   [sig-api-machinery] Kubernetes APIs remain ava...         71         8   \n",
+       "10  operator.Find all of the input images from ocp...         78         2   \n",
+       "\n",
+       "    flakes  passPercentage  \n",
+       "1        0       23.809524  \n",
+       "2        0       56.962025  \n",
+       "3        0       60.000000  \n",
+       "4        0       70.945946  \n",
+       "5        0       73.648649  \n",
+       "6        0       80.000000  \n",
+       "7        0       84.444444  \n",
+       "8        0       88.888889  \n",
+       "9       36       89.873418  \n",
+       "10       0       97.500000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>operator.Run template e2e-vsphere-upi - e2e-vs...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-vsphere-upi-serial -...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.083333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Run template e2e-vsphere-upi-serial -...</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>98.989899</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "0  operator.Run template e2e-vsphere-upi - e2e-vs...          0        50   \n",
+       "2  operator.Run template e2e-vsphere-upi-serial -...          1        47   \n",
+       "3  operator.Run template e2e-vsphere-upi-serial -...         47         1   \n",
+       "4  operator.Import the release payload \"latest\" f...         98         1   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "0       0        0.000000  \n",
+       "2       0        2.083333  \n",
+       "3       0       97.916667  \n",
+       "4       0       98.989899  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for p in by_pltf.loc[\"results\"].index:\n",
+    "    platform = pd.DataFrame(by_pltf.loc[\"results\"][p])\n",
+    "    platform = platform[platform[\"name\"] != \"Overall\"]\n",
+    "    top_pass_percentage = (\n",
+    "        platform.sort_values(by=\"passPercentage\")\n",
+    "        .head(10)\n",
+    "        .drop([\"BugList\", \"searchLink\"], axis=1)\n",
+    "    )\n",
+    "    to_plot = (\n",
+    "        top_pass_percentage.set_index(\"name\")\n",
+    "        .drop([\"successes\", \"failures\", \"flakes\"], axis=1)\n",
+    "        .apply(lambda x: x / 100)\n",
+    "    )\n",
+    "    to_plot.plot(kind=\"bar\")\n",
+    "    plt.ylabel(\"Pass Percent\", fontsize=15)\n",
+    "    plt.title(f\"Test Pass Percentage: {p}\", fontsize=20)\n",
+    "    plt.xticks(rotation=85)\n",
+    "    plt.show()\n",
+    "    display(top_pass_percentage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ByJob\n",
+    "same but sliced by the specific test job name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:29.164909Z",
+     "start_time": "2021-01-22T16:59:29.072062Z"
+    }
+   },
+   "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>canary-release-openshift-origin-installer-e2e-aws-4.6-cnv</th>\n",
+       "      <th>periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi</th>\n",
+       "      <th>promote-release-openshift-machine-os-content-e2e-aws-4.6</th>\n",
+       "      <th>promote-release-openshift-machine-os-content-e2e-aws-4.6-ppc64le</th>\n",
+       "      <th>promote-release-openshift-machine-os-content-e2e-aws-4.6-s390x</th>\n",
+       "      <th>release-openshift-ocp-e2e-aws-scaleup-rhel7-4.6</th>\n",
+       "      <th>release-openshift-ocp-installer-console-aws-4.6</th>\n",
+       "      <th>release-openshift-ocp-installer-e2e-aws-4.6</th>\n",
+       "      <th>release-openshift-ocp-installer-e2e-aws-csi-4.6</th>\n",
+       "      <th>release-openshift-ocp-installer-e2e-aws-fips-4.6</th>\n",
+       "      <th>...</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-azure-shared-vpc-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-azure-upgrade-4.5-stable-to-4.6-ci</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-azure-upgrade-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-gcp-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-gcp-compact-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-gcp-serial-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-gcp-shared-vpc-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-gcp-upgrade-4.5-stable-to-4.6-ci</th>\n",
+       "      <th>release-openshift-origin-installer-e2e-gcp-upgrade-4.6</th>\n",
+       "      <th>release-openshift-origin-installer-old-rhcos-e2e-aws-4.6</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>successes</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1523</td>\n",
+       "      <td>10529</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>16189</td>\n",
+       "      <td>11251</td>\n",
+       "      <td>67741</td>\n",
+       "      <td>161.000000</td>\n",
+       "      <td>35120</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2982.000000</td>\n",
+       "      <td>240</td>\n",
+       "      <td>172</td>\n",
+       "      <td>1008.000000</td>\n",
+       "      <td>998.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>4029.000000</td>\n",
+       "      <td>241</td>\n",
+       "      <td>615</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>failures</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>83</td>\n",
+       "      <td>39</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>24</td>\n",
+       "      <td>325</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>867</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>49</td>\n",
+       "      <td>48</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>41</td>\n",
+       "      <td>92</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>testPassPercentage</th>\n",
+       "      <td>70.0</td>\n",
+       "      <td>94.8319</td>\n",
+       "      <td>99.631</td>\n",
+       "      <td>57.142857</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>99.6798</td>\n",
+       "      <td>99.7871</td>\n",
+       "      <td>99.5225</td>\n",
+       "      <td>88.461538</td>\n",
+       "      <td>97.5908</td>\n",
+       "      <td>...</td>\n",
+       "      <td>99.765808</td>\n",
+       "      <td>83.045</td>\n",
+       "      <td>78.1818</td>\n",
+       "      <td>99.506417</td>\n",
+       "      <td>99.501496</td>\n",
+       "      <td>97.315436</td>\n",
+       "      <td>99.752414</td>\n",
+       "      <td>85.461</td>\n",
+       "      <td>86.9873</td>\n",
+       "      <td>68.965517</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>results</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 16, 'failure...</td>\n",
+       "      <td>[{'name': 'operator.Run template e2e-aws - e2e...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 13, 'failure...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 37, 'failure...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 39, 'failure...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 7, 'failures...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 5, 'failures...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 4, 'failures...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 6, 'failures...</td>\n",
+       "      <td>[{'name': 'Overall', 'successes': 25, 'failure...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    canary-release-openshift-origin-installer-e2e-aws-4.6-cnv  \\\n",
+       "successes                                                         7.0           \n",
+       "failures                                                          3.0           \n",
+       "testPassPercentage                                               70.0           \n",
+       "results                                                           NaN           \n",
+       "\n",
+       "                   periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi  \\\n",
+       "successes                                                        1523           \n",
+       "failures                                                           83           \n",
+       "testPassPercentage                                            94.8319           \n",
+       "results             [{'name': 'Overall', 'successes': 16, 'failure...           \n",
+       "\n",
+       "                   promote-release-openshift-machine-os-content-e2e-aws-4.6  \\\n",
+       "successes                                                       10529         \n",
+       "failures                                                           39         \n",
+       "testPassPercentage                                             99.631         \n",
+       "results             [{'name': 'operator.Run template e2e-aws - e2e...         \n",
+       "\n",
+       "                    promote-release-openshift-machine-os-content-e2e-aws-4.6-ppc64le  \\\n",
+       "successes                                                    4.000000                  \n",
+       "failures                                                     3.000000                  \n",
+       "testPassPercentage                                          57.142857                  \n",
+       "results                                                           NaN                  \n",
+       "\n",
+       "                    promote-release-openshift-machine-os-content-e2e-aws-4.6-s390x  \\\n",
+       "successes                                                         7.0                \n",
+       "failures                                                          0.0                \n",
+       "testPassPercentage                                              100.0                \n",
+       "results                                                           NaN                \n",
+       "\n",
+       "                      release-openshift-ocp-e2e-aws-scaleup-rhel7-4.6  \\\n",
+       "successes                                                       16189   \n",
+       "failures                                                           52   \n",
+       "testPassPercentage                                            99.6798   \n",
+       "results             [{'name': 'Overall', 'successes': 13, 'failure...   \n",
+       "\n",
+       "                      release-openshift-ocp-installer-console-aws-4.6  \\\n",
+       "successes                                                       11251   \n",
+       "failures                                                           24   \n",
+       "testPassPercentage                                            99.7871   \n",
+       "results             [{'name': 'Overall', 'successes': 37, 'failure...   \n",
+       "\n",
+       "                          release-openshift-ocp-installer-e2e-aws-4.6  \\\n",
+       "successes                                                       67741   \n",
+       "failures                                                          325   \n",
+       "testPassPercentage                                            99.5225   \n",
+       "results             [{'name': 'Overall', 'successes': 39, 'failure...   \n",
+       "\n",
+       "                    release-openshift-ocp-installer-e2e-aws-csi-4.6  \\\n",
+       "successes                                                161.000000   \n",
+       "failures                                                  21.000000   \n",
+       "testPassPercentage                                        88.461538   \n",
+       "results                                                         NaN   \n",
+       "\n",
+       "                     release-openshift-ocp-installer-e2e-aws-fips-4.6  ...  \\\n",
+       "successes                                                       35120  ...   \n",
+       "failures                                                          867  ...   \n",
+       "testPassPercentage                                            97.5908  ...   \n",
+       "results             [{'name': 'Overall', 'successes': 7, 'failures...  ...   \n",
+       "\n",
+       "                    release-openshift-origin-installer-e2e-azure-shared-vpc-4.6  \\\n",
+       "successes                                                 2982.000000             \n",
+       "failures                                                     7.000000             \n",
+       "testPassPercentage                                          99.765808             \n",
+       "results                                                           NaN             \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-azure-upgrade-4.5-stable-to-4.6-ci  \\\n",
+       "successes                                                         240                          \n",
+       "failures                                                           49                          \n",
+       "testPassPercentage                                             83.045                          \n",
+       "results             [{'name': 'Overall', 'successes': 5, 'failures...                          \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-azure-upgrade-4.6  \\\n",
+       "successes                                                         172         \n",
+       "failures                                                           48         \n",
+       "testPassPercentage                                            78.1818         \n",
+       "results             [{'name': 'Overall', 'successes': 4, 'failures...         \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-gcp-4.6  \\\n",
+       "successes                                             1008.000000   \n",
+       "failures                                                 5.000000   \n",
+       "testPassPercentage                                      99.506417   \n",
+       "results                                                       NaN   \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-gcp-compact-4.6  \\\n",
+       "successes                                                  998.000000       \n",
+       "failures                                                     5.000000       \n",
+       "testPassPercentage                                          99.501496       \n",
+       "results                                                           NaN       \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-gcp-serial-4.6  \\\n",
+       "successes                                                  145.000000      \n",
+       "failures                                                     4.000000      \n",
+       "testPassPercentage                                          97.315436      \n",
+       "results                                                           NaN      \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-gcp-shared-vpc-4.6  \\\n",
+       "successes                                                 4029.000000          \n",
+       "failures                                                    10.000000          \n",
+       "testPassPercentage                                          99.752414          \n",
+       "results                                                           NaN          \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-gcp-upgrade-4.5-stable-to-4.6-ci  \\\n",
+       "successes                                                         241                        \n",
+       "failures                                                           41                        \n",
+       "testPassPercentage                                             85.461                        \n",
+       "results             [{'name': 'Overall', 'successes': 6, 'failures...                        \n",
+       "\n",
+       "                   release-openshift-origin-installer-e2e-gcp-upgrade-4.6  \\\n",
+       "successes                                                         615       \n",
+       "failures                                                           92       \n",
+       "testPassPercentage                                            86.9873       \n",
+       "results             [{'name': 'Overall', 'successes': 25, 'failure...       \n",
+       "\n",
+       "                   release-openshift-origin-installer-old-rhcos-e2e-aws-4.6  \n",
+       "successes                                                   20.000000        \n",
+       "failures                                                     9.000000        \n",
+       "testPassPercentage                                          68.965517        \n",
+       "results                                                           NaN        \n",
+       "\n",
+       "[4 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "by_job = pd.DataFrame(d.loc[\"ByJob\"][0])\n",
+    "by_job.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:32.143495Z",
+     "start_time": "2021-01-22T16:59:32.136174Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "barplot_s = by_job.loc[[\"successes\"]].T.sort_values(by=\"successes\")\n",
+    "barplot_f = by_job.loc[[\"failures\"]].T.sort_values(by=\"failures\")\n",
+    "barplot_p = by_job.loc[[\"testPassPercentage\"]].T.sort_values(by=\"testPassPercentage\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:36.476435Z",
+     "start_time": "2021-01-22T16:59:36.250302Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sns.set(rc = {\"figure.figsize\":(10,10)})\n",
+    "barplot_s.tail(10).plot.barh()\n",
+    "plt.title(\" Top 10 Successes: by job\", fontsize=20)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:42.405591Z",
+     "start_time": "2021-01-22T16:59:42.160177Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "barplot_s.head(10).plot.barh()\n",
+    "plt.title(\" Bottom 10 Successes: by job\", fontsize=20)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:48.852377Z",
+     "start_time": "2021-01-22T16:59:48.600201Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "barplot_p.apply(lambda x: x / 100).head(10).plot.barh()\n",
+    "plt.title(\" Bottom test pass percent: by job\", fontsize=20)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "now lets look at the results\n",
+    "\n",
+    "since there are about 50 different jobs here, lets focus on displaying results just for those that lowest 5 test passing percentage and also have results.\n",
+    "\n",
+    "-- question for sippy team. Why do some not have any results? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:50.427872Z",
+     "start_time": "2021-01-22T16:59:50.420181Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "by_job_t = by_job.T.sort_values(by=\"testPassPercentage\")\n",
+    "by_job_t = by_job_t.dropna()\n",
+    "by_job = by_job_t.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T16:59:53.582574Z",
+     "start_time": "2021-01-22T16:59:52.750453Z"
+    },
+    "scrolled": false
+   },
+   "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>operator.Run template e2e-ovirt - e2e-ovirt co...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-ovirt - e2e-ovirt co...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Create the release image \"initial\" co...</td>\n",
+       "      <td>46</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.87234</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "0  operator.Run template e2e-ovirt - e2e-ovirt co...          0        46   \n",
+       "1  operator.Run template e2e-ovirt - e2e-ovirt co...          0        46   \n",
+       "3  operator.Create the release image \"initial\" co...         46         1   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "0       0         0.00000  \n",
+       "1       0         0.00000  \n",
+       "3       0        97.87234  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-vsphere-upi-serial -...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.083333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run template e2e-vsphere-upi-serial -...</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.916667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>48</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.959184</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "1  operator.Run template e2e-vsphere-upi-serial -...          1        47   \n",
+       "2  operator.Run template e2e-vsphere-upi-serial -...         47         1   \n",
+       "3  operator.Import the release payload \"latest\" f...         48         1   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "1       0        2.083333  \n",
+       "2       0       97.916667  \n",
+       "3       0       97.959184  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-gcp - e2e-gcp-rt con...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>43</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.727273</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "1  operator.Run template e2e-gcp - e2e-gcp-rt con...          0        43   \n",
+       "2  operator.Import the release payload \"latest\" f...         43         1   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "1       0        0.000000  \n",
+       "2       0       97.727273  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-metal-serial - e2e-m...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>97.916667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "1  operator.Run template e2e-metal-serial - e2e-m...          0        47   \n",
+       "2  operator.Import the release payload \"latest\" f...         47         1   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "1       0        0.000000  \n",
+       "2       0       97.916667  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+//133XnnnR5bxczQ51lo3rx5uXlFmzdvlo+Pj5YsWaIlS5aUu01JSYkyMjKUmZmpiIiICu+3sLBQu3fvVkREhKZPn17hMb6+vmUaGEnKzMzUhx9+qJUrVyoxMVEFBQVlrk9JSXH+/2WXXabY2FhNmTJFO3bsULdu3dS2bVslJCSUa1TcfU7z5s1zzqU68fEc8xd27twpLy+vCj/g27dvL7PZrF27dpW7Li4urtxcC8d9O+73TGt2CAkJUcOGDcsd7wgeJ+5X1q9fP7322mu69tpr9a9//UuXXXaZ2rZte8oPxosvvrjC5yUdHxo8Vyr7Pdm8ebMkqWvXri7X4Or7l5OTU+Hv/a233urS3KWWLVtW+NhS2de4Z8+eeuutt/TCCy9o1apV6ty5s9q2basLL7zwjFaOrVixQp999pm2b9+uzMxMlZaWlrk+MzPzjBaBOF5rx5yzf0pPT5fVatWff/5Z7verVatWZX5et26dc76dQ926dTVo0KAzbnf+aenSpXrllVcUHR2tSZMmlZvbU9ntiUNF77vj9XYMVZ7IEeqOHTtWpjZJ2r17tyZNmlTuNn/++aek43PYXA0GJSUlmjNnjhYvXqz9+/crNzdXNpvNeb1j/tapnOo1OJdtuYOfn5/bc7/c+Tw6mYreg4EDB6pevXrKycnRuHHj1Lp167NerGK32yVJVqtVb731li655BJJx+davv766/rjjz+0fft2LV26VH379j2rxzoTBLWzUKtWrXKXZWVlqbS0VJMnTz7lbQsKCk4a1HJycmS325WRkXHa+znxNtdff70SExPVqlUr9e/fX2FhYfL29lZOTo5mzJhR5ttAcHCwPv/8c02cOFE//vij81tdRESEhg4dqnvuucfZwLr7nL766qtyHwb33Xefs2HNzc1VWFhYuZArHe+hcsz1+qeKXu8TL8/NzXVedqbvw8mCgKPXzGq1Oi+77bbbFBERoU8++UQzZ87U9OnTZTKZ1L59e40dO7bCD46K7t/RmJ7YeFe2yn5PHK/1P3t4TsXV9y8nJ6fC923gwIEuBbUT5wc5VPQa161bV1988YUmTZqkn3/+WUuXLpV0PNTdfvvtzm/+rpg+fbpeeeUVhYWFqVOnToqLi1NAQIBznt/u3bvP+Nt4VlaWJOnDDz885XH//CCUjq/KPNH69evLvbaXXXaZBg0adEbtzj8tW7ZM//d//6fIyEjNmDGjwmBe2e2JQ0U9mo6/21P9TpwYqB2v9eeff37a2lz18MMP6/vvv1f9+vV15ZVXqlatWs6/s+nTp7vU03qq1+BctuUOUVFRbn15cffz6GQqqvOyyy5TvXr19OqrryorK0sff/xxhT2W7nD8fkRHRztDmoPJZNKVV16p7du3a+vWrQS16qaiX9zg4GDZ7fZyf1TucDQ4F110UZnVfKcyd+5cJSYm6r777tP9999f5rpNmzZpxowZ5W5Tu3ZtvfLKK7Lb7dq3b5/Wrl2r2bNn691335XNZtNDDz10Rs/pdKsMQ0JClJ2drZKSknLftktLS5WZmVlho5uWllbh/TkuP7Exroz3wRUDBgzQgAEDlJOTo02bNun777/Xl19+qVGjRunbb781xLJ1qfLfE8drnZyc7PI3bVffv3r16mnPnj0u3efZatKkid5++22VlpZq9+7d+uWXX5x7KQUEBDiHTE/F8cEXHR2tefPmles1c/TSnCnH675x40a3J53/s426//77y7UP/3wcd9qdEzkWhtSqVUvTp09Xo0aNTvo4ldmeVCbH7+CCBQvOavGHw7Zt2/T999+rU6dOmjp1aplpEjabTR988IFL93O61+BcteUO7vYwn8nnUUVO1Q7s3LlTRUVFuuaaayq8fuHChVq4cKGaN29+2mkVjRs3llRxoJfkXJlbVFTkStmVjjlqlaxNmzbKzs7W3r17z/g+goKC1LRpU+3du9f5De90Dh48KEm66qqryl3366+/nvK2JpNJTZs21S233KKPP/5Y0vFtBxwq4zmdKCEhQTabTRs2bKiwVqvVqosuuqjcdUlJSRXu/u1odE68TWXXfDqhoaHq1q2bXnrpJQ0cOFBZWVmnfd0rm2Pu14m9fq5y9z1p06aNpOMr5FzlzvtX1by9vXXxxRfrzjvv1FtvvSWp7N/AqV7bzMxM5eTk6JJLLikX0vLz87Vjx46zqs3xWlf03lSmM2l3HBxnJ4iJidGsWbNOGtKkqv/bdEfr1q0lya2Vg47fjYp6xA8dOiTp+DD7P+eybt26tdI/+Ku6LT+Zs/k8clXv3r11/fXXl/vXrVs3ScfnyF1//fXq3bv3ae+rTZs2CgwM1JEjRyrsLf39998l6bQrXc8VglolGzlypCTp6aefrnDuQUFBgUvfsEeOHKmSkhI98cQTFZ7HMTs7u8wHgOMX6J/flHbu3Kn//e9/5W6/d+/eCns4HJedODG1sp6Tg2Ny8ZtvvqnCwkLn5YWFhc4tIK6//vpyt7NarXrjjTfKNIiHDx/WzJkz5e3treuuu+6c1VyRtWvXOuc2nCgjI0NSxZN7zyXHIoWjR4+6fVt335MBAwYoODhYn332WYUN74nzfhzcef+qwvbt28sMlztU9DfgGAZKSkoqd3xUVJQCAgK0Y8cO5efnOy8vKSnRyy+/rMzMzLOqc9iwYfLx8dGrr75abjGLdHzrhMoKce62O9LxYbnHHntMcXFxmjVrVoXDnf98DOnc/m2eqUGDBik0NFSTJ0/W1q1by11vs9nKndPyVH93ju1x/tkup6en64UXXqiUmj3Zlp+Mu59HZ+K+++7Tyy+/XO7fHXfcIel46H755ZfLLfjbv39/uXmWAQEBGjx4sIqLi/X222+Xadf37Nmjr776St7e3rr66qsrpXZ3MfRZyTp27KgxY8borbfeUp8+fdS1a1fVq1dPBQUFOnr0qH799Ve1bdv2tPNNrr/+eu3YsUOffPKJevfurc6dOysuLk7Z2dlKTEzUr7/+qkGDBjn/2Pv3768PP/xQr7zyitatW6eGDRvq4MGDWrFihXr37q1vvvmmzP2vXr1aEyZMUJs2bdSoUSNFRUXp2LFj+uGHH+Tl5eX8Za/M5+TQr18//fDDD/r222917bXXqlevXs65PImJifrXv/5V4Yd2s2bNtHXrVg0aNEhXXHGFcx+unJwcPfroo2VWGVV2zRW57777FBgYqDZt2qhu3bqy2+3asGGDtm3bphYtWqhTp05nfN9nomPHjlqyZInuv/9+devWTX5+fqpTp45Lpz1x9z2JjIzUm2++qQceeEAjRoxQ165d1axZM+Xl5WnPnj1KSkpybvjp4M77VxUWLFigOXPm6NJLL1X9+vUVFhamQ4cOafny5fL19dWtt97qPLZNmzYKCAjQ9OnTlZWV5ZxXd8sttygkJES33HKLpkyZon79+unKK69USUmJ1q1bp+zsbOeqzzPVpEkTvfzyy3ryySfVt29fdenSRY0aNVJpaamOHj2qjRs3KiIiosLJ4e5yt91Zu3atnnjiCdlsNnXo0KHCc6GGhIQ4A4JUNX+bZyoiIkITJ07UvffeqxtuuEEdO3Z0Li45duyYNm3apKysrDKLOjp27KgPP/xQTz/9tK666ioFBQUpNDRUw4cPV8uWLdW2bVstXbpUN910k9q2bav09HT99NNPaty4caWcYcSTbfnJuPt5VJX+9a9/SSo/rPrQQw9pw4YNmj59ujZv3qy2bdsqLS1N33//vYqLi/XEE09UeRvlQFA7B+688061bdtWM2fO1MaNG/Xjjz8qODhYsbGxuuGGG1yejPjss8+qa9eu+uyzz/TLL784J3zHxcXpjjvuKPPBGRsbq9mzZ+uNN97Qxo0btWrVKl1wwQV69tln1bFjx3J/GF26dFFSUpJ+/fVX/fDDD8rLy1NMTIyuuOIKjRw5Um3btj0nz8nhrbfeUvv27fXll19qzpw5ko5/IN1+++26+eabK7xNWFiYpk6dqgkTJmjevHnKy8vThRdeqNtvv73Cne0ru+Z/GjNmjFatWqUdO3Zo5cqVzmD0yCOP6Oabb67ynayHDBmio0ePavHixfrggw9UWlqqyy67zOXz07n7nnTv3l1ffvmlcwf31atXKzQ0VBdccIHuuuuucse7+/6da3379pXFYtGmTZu0Y8cOFRUVKTY2Vtdee61uu+02xcfHl6l94sSJevfdd/XVV185h0euu+46hYSEOLfBmDt3rubMmaOQkBB16tRJDz30UIUr19zVv39/NW/eXB9//LHWrVunVatWKTAwUDExMerTp89J5+mcCXfanaNHjzp7SB1bvPxT3bp1ywQ16dz/bZ6Njh076uuvv9ZHH32kVatWacOGDfLx8VFMTIwuv/xy9enTp8zxXbp00bhx4/T55587FwfUrVtXw4cPl9ls1vvvv6+3335bP/30k2bOnKnY2FgNGTJE99xzj6699tqzrtfTbXlF3P08MoLg4GDNnj1bU6ZM0ZIlSzRr1iz5+/vr0ksv1e23367OnTt7rDaTvaKxG8BgKjplEKoP3j8AODPMUQMAADAoghoAAIBBEdQAAAAMijlqAAAABkWPGgAAgEER1AAAAAzqvN1HLTMzXzYbo7o4vaioYKWn53m6DADnGdoWuMLLy6SIiKCTXn/eBjWbzU5Qg8v4XQFwLtC24Gwx9AkAAGBQBDUAAACDIqgBAAAY1Hk7Rw0AACOyWkuVmZmq0lKLp0tBFfLyMisgIFjBwWEymUwu346gBgBAFcrMTJW/f6CCgmq79YGN6stut8tqLVVubpYyM1MVGRnj8m0Z+gQAoAqVlloUFBRKSKtBTCaTvL19FB4eJYulyK3bEtQAAKhihLSayWTykuTeli0MfQIA4GEhoQHy96v8j+Si4lLl5hRW+v266+WXn9OGDesVFhau4uIide3aQ/fcc79Ha0pKOqr169eqf/9BHq3jdKo0qI0fP17fffedjhw5ooULFyo+Pr7cMVarVS+99JJ+/vlnmUwm3XnnnRoyZEhVlgkAQJXy9/NWvzELKv1+F77ZX7mVfq9nZvjwWzV48I3Ky8vTbbcNVcuWrdS5czeXbltaWipv78qNLElJR/X1118R1E505ZVXasSIERo2bNhJj1m4cKEOHTqkpUuXKisrSwMGDFDHjh1Vr169KqwUAICaoXPndrrtttH6+eeVKi4u0l133avu3a+UJD3//FM6dOigSkosqlu3vh5//BmFhobq0KE/9fLLz6uoqEg2m1XXXNNPQ4feop9/XqGpU9+Xl5dZVmupHn54rNq2bVfm8YKDg9W8+UV/3W+Jpkx5T5s3b5TFUqILL7xQY8Y8rsDAQL388nMym806dOigCgoKNG3aJ1q0aIHmzv1MkuTj46PXX/+PIiOjtGbNKs2Y8ZGKiy3y8fHR/ff/ny6+uKV++22DJk58Sxdd1EI7dmyTZNLzz7+iRo0a6623XldS0hGNHDlU9erV00svva7Jk9/W5s2/qaSkROHh4Xr88WdUu3acJOnLL+do7tzPFBwcoo4dr9C8eZ9r8eIfJOmkj18ZqjSotWvX7rTHfPPNNxoyZIi8vLwUGRmpXr16acmSJRo1alQVVAgAQM3j5eWladM+0aFDf+ruu+9Q69aXKCIiUg8++IjCw8MlSVOmvKfZs6frnnvu17x5X6hz56665ZbbJEk5OTmSpA8++J/Gjn1SF1/cSlarVUVF5Ydd09JStW3bFl133UDNnj1dQUFBmjp1hiTpvfcmaubMj3XXXfdKkvbu/V2TJ09RQECAfvttg2bO/FjvvfeBoqJqqaCgQGazWUeOJGratA/11luTFBQUrD/+2K9HHnlA8+YtliQdOLBfTzzxjMaOfVLTp3+o6dM/1LPPvqT/+7+xevfdd/ThhzOdtQ0fPlL33feQJGnhwvl6//2Jev75V7Vv317NnDlNH3/8iSIiIvT22284b3O6xz9bhpujlpSUpDp16jh/jouL07FjxzxYEQAA57e+fftLkho0aKT4+GbasWObOnfupiVLFmnp0iUqLS1RYWGR6tdvIElq0+YSvffeRBUVFalt23bOXrNLL22niRPfUvfuPXX55Z10wQUXOh9j1qzpWrhwgcxms4YOHaH27TtoypR3lZ+frxUrfpQklZRYdOGFTZ236d79SgUEBEiS1qxZrauvvlZRUbUkSYGBgZKkdevW6MiRRN17753O21mtVmVkpP/1nBoqPr65JKlFi5Zavfrnk74Oa9eu1rx5c1VYWCCr1eq8fNOmjerY8QpFRERIkq699jp9//23p338yMgoF9+BkzNcUKssUVHBni4B1Uh0dIinSwBwHqqobUlJ8ZK3d9VtuuDKY5nNf9dkMpnk5eWl7ds3a/78LzV16jRFRETou+++1fz58+Tt7aVevXqrdes2WrdujWbPnq5vvvlazz//sv7v/x7Vvn17tXHjr3r66XG6+ebhGjBgkEwmk0aMGKkhQ24q99hjxz6udu0uK3e5yWRScHCgsy4vL8nLy1Tu+Xh5SR07dtKzz75Y7j4OHz4oPz8/5218fLxls1nl7e0ls9lLJtPfr09S0lFNmvQfffzxTNWpU1dbt27RM888IW9vL3l5mf7aYsPrhNfU9Nd1J3/8inh5ebn1mWO4oBYXF6ejR4+qVatWksr3sLkqPT1PNpt7S2BRM0VHhyg11SjTbQGcL07WtthsNpWW2qqsDlce6+uv52vkyFE6fPiQfv99txISWmjHju0KCgpWUFCICgqK9PXXC2S321VaalNi4mHVqVNXV1/dV3Xq1NMrr7yg0lKbDh36U40aNVGjRk2Ul5evHTt2qG/fAbLb7bLZ7OVqueKKrvrkk1lKSLhYfn7+KijIV0pKiho1alzuNh06XKHx419Sv34DFRkZ5Rz6vPTSDvrggyn6/fe9uuCCJpKkXbt2KCGhhaxWm+z2v1+DE3/29w9UXl6e87qcnDx5e3srLCxCFkupvvxyrvP1a9XqEs2aNV1paRkKDw/XokVfSzpe26kevyI2m63M74WXl+mUnUuGC2pXX3215s6dq6uuukpZWVlatmyZZs+e7emyAAA4b1mtVt1221AVFRXp0UefUEREpC6/vJOWLv1WN988SGFh4WrT5hLt3LlDkvTjj99r6dIl8vHxlslk0oMPjpEkvf/+ZCUmHpLZ7K3g4GA9/vgzp3zc4cNH6sMP/6dRo0bIy+t4L9Xtt49Wo0aNyx3btm073XLLSD300L9lMnnJ19dH48f/R/XrN9Azz7yo1157UcXFxSotLVHLlq1PGpQcmjS5UA0aNNQtt9yghg0b6aWXXlePHr00fPgNCgsLV8eOV2jLlk2SpKZN4zV06AjdffdtCgwMUrt27RUUdDxcnenju8pkt9urrNvppZde0tKlS5WWlqaIiAiFh4dr8eLFGj16tB544AG1bNlSVqtVL7zwglavXi1JGj16tG688Ua3H4seNbiKHjUA58LJ2pZjxw6qdu2GZS7z5D5qnTu309KlPznnfKFiBQX5CgwMkiR9+OH/dORIop55xrXhzhP98/0/XY9alQa1qkRQg6sIagDOBXeCmicR1Fzz5pvjtW3bFpWWlqhOnboaO/ZJ1aoV7fb9uBvUDDf0CQAAqs6qVRs8XUK1MGbMYx55XM71CQAAYFAENQAAqth5OusIp2G32ySZ3LoNQQ0AgCrk7e2r/PwcwloNcnxbkxJlZaXJ19ffrdsyRw0AgCoUERGtzMxU5eVleboUVCEvL7MCAoIVHBzm1u0IagAAVCGz2Vu1asV5ugxUEwQ1AMBZOVd7gJ0POD1dWa7s64ay+MsCAJwVfz9v9RuzwNNloBpY+GZ/sWule1hMAAAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFAENQAAAIMiqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwKIIaAACAQRHUAAAADIqgBgAAYFDeVf2ABw4c0Lhx45SVlaXw8HCNHz9ejRo1KnNMenq6Hn/8cSUlJam0tFQdOnTQU089JW/vKi8XAADAY6q8R+3ZZ5/V0KFD9d1332no0KF65plnyh3z3//+V02aNNHChQv19ddfa8eOHVq6dGlVlwoAAOBRVRrU0tPTtXPnTvXt21eS1LdvX+3cuVMZGRlljjOZTMrPz5fNZpPFYlFJSYliY2OrslQAAACPq9KglpSUpNjYWJnNZkmS2WxWTEyMkpKSyhz373//WwcOHFDnzp2d/y699NKqLBUAAMDjDDnpa8mSJWrWrJmmT5+u/Px8jR49WkuWLNHVV1/t8n1ERQWfwwpxvomODvF0CQBQI9DeuqdKg1pcXJySk5NltVplNptltVqVkpKiuLi4MsfNmjVLr7zyiry8vBQSEqKePXtq3bp1bgW19PQ82Wz2yn4KOA9FR4coNTXX02UA1RYfvHAH7W1ZXl6mU3YuVenQZ1RUlBISErRo0SJJ0qJFi5SQkKDIyMgyx9WrV08//fSTJMlisWjNmjVq2rRpVZYKAADgcVW+6vO5557TrFmz1KdPH82aNUvPP/+8JGn06NHatm2bJOmJJ57Qxo0b1a9fPw0YMECNGjXSDTfcUNWlAgAAeJTJbrefl+ODDH3CVQx9AmcnOjpE/cYs8HQZqAYWvtmf9vYfDDX0CQAAANcR1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEFVeVA7cOCAbrzxRvXp00c33nij/vzzzwqP++abb9SvXz/17dtX/fr1U1paWtUWCgAA4GHeVf2Azz77rIYOHar+/ftrwYIFeuaZZzRjxowyx2zbtk2TJ0/W9OnTFR0drdzcXPn6+lZ1qQAAAB7lUo/a/PnzlZmZWeF1WVlZmj9/vksPlp6erp07d6pv376SpL59+2rnzp3KyMgoc9y0adN0++23Kzo6WpIUEhIiPz8/lx4DAADgfOFSUHv88cd1+PDhCq9LTEzU448/7tKDJSUlKTY2VmazWZJkNpsVExOjpKSkMsft379fhw8f1rBhwzRw4EC99957stvtLj0GAADA+cKloc9ThaSsrCwFBwdXWkGSZLVatWfPHn388ceyWCwaNWqU6tSpowEDBrh8H1FRlVsTzm/R0SGeLgEAagTaW/ecNKgtW7ZMP/zwg/Pn9957TxEREWWOsVgs2rBhg1q2bOnSg8XFxSk5OVlWq1Vms1lWq1UpKSmKi4src1ydOnV09dVXy9fXV76+vrryyiu1detWt4JaenqebDZ64XB60dEhSk3N9XQZQLXFBy/cQXtblpeX6ZSdSycNahkZGfr999+dPx86dEipqalljvHx8dEVV1yhe+65x6VioqKilJCQoEWLFql///5atGiREhISFBkZWea4vn37auXKlerfv79KS0u1du1a9enTx6XHAAAAOF+Y7C5M/rrlllv03HPPqUmTJmf9gPv379e4ceOUk5Oj0NBQjR8/XhdccIFGjx6tBx54QC1btpTNZtP48eP1008/ycvLS507d9Zjjz0mLy/XdxOhRw2uokcNODvR0SHqN2aBp8tANbDwzf60t/9wuh41l4JadURQg6sIasDZIajBVQS18s546POfkpOTtWLFCh07dkzFxcVlrjOZTHr00UfPvEoAAACU41JQ+/777/V///d/stlsioyMlI+PT5nrCWoAAACVz6Wg9tZbb+mKK67Qa6+9pvDw8HNcEgAAACQXN7w9duyYRowYQUgDAACoQi4FtUsuuUQHDhw417UAAADgBC4NfY4bN06PPPKIAgMD1alTJ4WGhpY7JiAgoNKLAwAAqMlcCmrXXXedpOPn/DSZTBUes2vXrsqrCgAAAK4FtVdeeeWkAQ0AAADnhktBbdCgQee6DgAAAPyDyxveStK+ffu0fft2HTt2TIMHD1Z0dLQOHjyoqKgoBQeffFddAAAAuM+loJafn68nnnhC3333nby9vWW1WtWlSxdFR0frrbfeUp06dfTYY4+d61oBAABqFJe253jttde0adMmTZs2Tb/99ptOPD1ot27d9PPPP5+zAgEAAGoql4La0qVL9cgjj+jyyy+X2Wwuc12dOnV05MiRc1IcAABATeZSUCsuLj7pWQny8/PLhTcAAACcPZeCWsuWLbVgwYIKr/vuu+90ySWXVGpRAAAAcHExwYMPPqjbbrtNI0eO1NVXXy2TyaSVK1dq2rRp+u677zRr1qxzXScAAECN41KPWrt27TRt2jRZLBa9+OKLstvtmjRpkg4fPqyPP/5YrVq1Otd1AgAA1Dgu76N26aWX6pNPPlFRUZGys7MVGhrK+T0BAADOIZeCWl5engoKChQTEyN/f3/5+/s7r0tJSVFQUJCCgoLOWZEAAAA1kUtDn08++aQmTpxY4XWTJ0/WU089ValFAQAAwMWgtmHDBnXv3r3C67p27apff/21MmsCAACAXAxqubm5ZYY7T+Tn56ecnJxKLQoAAAAuBrWGDRtqxYoVFV63cuVKNWjQoDJrAgAAgFxcTHDLLbfo2WeflY+PjwYNGqTo6Gilpqbqq6++0ieffKLnnnvuHJcJAABQ87gU1G644QalpaVpypQpmjZtmvNyPz8/PfTQQ7rhhhvOVX0AAAA11mmDms1mU2pqqm699Vbdcsst2rRpk7KyshQeHq5LLrlEISEhVVEnAABAjeNSUOvZs6fef/99de3aVV27dq2KugAAAGq80y4m8Pb2Vp06dVRUVFQV9QAAAOAvLq36HD16tP773/8qIyPjXNcDAACAv7i0mGDVqlVKSUlRz5491aJFC0VFRclkMjmvN5lMevvtt89VjQAAADWSS0EtMzNTjRs3LvMzAAAAzi2XgtrMmTPPdR0AAAD4B5fmqJ3IbrcrOTlZpaWl56IeAAAA/MXloLZy5UoNGTJELVu2VPfu3bVnzx5J0lNPPaUFCxacswIBAABqKpeC2vz583XPPffoggsu0Isvvii73e68rlGjRvriiy/OWYEAAAA1lUtB7f3339cdd9yh8ePH67rrritzXdOmTbV///5zUhwAAEBN5lJQO3r0qDp16lThdb6+vsrLy6vUogAAAOBiUIuLi9OuXbsqvG779u1q2LBhpRYFAAAAF4Pa9ddfr8mTJ2vBggXOU0nZ7XatWbNGH3zwgYYMGXJOiwQAAKiJXNpHbfTo0UpKStK4ceNkNpslSTfddJNsNptuvPFGjRgx4pwWCQAAUBO5FNRMJpOeffZZ3Xbbbfrll1+UlZWlsLAwXX755WXOWAAAAIDKc8qglpqaqoULF+rIkSOKjo5Wjx49dNNNN1VVbQAAADXaSYPazp07deuttyovL0+RkZHKzs7W5MmT9corr5TbogMAAACV76SLCd58803Vr19fy5cv1+rVq7V27Vr16tVLr732WlXWBwAAUGOdNKjt2rVL//73v1W7dm1JUnBwsB577DFlZGQoKSmpygoEAACoqU4a1DIyMhQTE1PmMsfPmZmZ57YqAAAAnHoxQXFxsQoLC50/W61WSVJRUVGZyyUpICDgHJQHAABQc50yqJ1sf7Rhw4aVu+xkZy4AAADAmTlpUHv11Versg4AAAD8w0mD2sCBA6uyDgAAAPyDS+f6BAAAQNUjqAEAABgUQQ0AAMCgCGoAAAAGRVADAAAwqFPuo+aQnp6ugoIC1a9fX5Jkt9v1+eefa9++ferYsaN69ux5TosEAACoiVzqURs3bpymT5/u/Pmdd97R888/r59//ln33Xef5s2bd84KBAAAqKlcCmo7d+7U5ZdfLkmy2Wz67LPP9PDDD2vJkiW6++67y4Q4AAAAVA6Xglpubq7Cw8MlSdu3b1d2drauu+46SdLll1+uQ4cOnbMCAQAAaiqXglrt2rW1b98+SdLKlSt1wQUXKDY2VtLxEOfr63vuKgQAAKihXFpMMHjwYE2YMEG//PKLVq5cqf/7v/9zXrdlyxY1adLknBUIAABQU7kU1O666y7FxsZq27Zteuqpp3T99dc7r8vKytKQIUPOWYEAAAA1lUtBTZIGDBigAQMGlLv8hRdeqMx6AAAA8BeXgtr+/fuVm5urNm3aSJIKCwv1/vvvO/dRu+WWW85ljQAAADWSS4sJnnvuOS1fvtz584QJEzRjxgwVFxfrjTfe0AcffHDOCgQAAKipXApqe/fudfamlZSUaMGCBXriiSf04Ycf6uGHH9aXX355LmsEAACokVwKaoWFhQoODpZ0fJVnYWGhevfuLUlq0aKFjh49eu4qBAAAqKFcCmr16tXT5s2bJUnff/+9EhISFBERIUnKzMxUUFDQOSsQAACgpnJpMcHIkSP1/PPPa8mSJdq1a5deeeUV53Xr169Xs2bNzlmBAAAANZVLQW3IkCFq1KiRtm3bpkceeUQdO3Z0XhcWFqZbb731nBUIAABQU7m8j1r79u3Vvn37cpfff//9lVoQAAAAjnM5qEnSsWPHdODAAVkslnLXdevWrdKKAgAAgItBLS8vTw899JBWr14tSbLb7ZIkk8nkPGbXrl3noDwAAICay6VVn2+99ZaSkpI0e/Zs2e12TZ48WTNnztT111+vevXqac6cOee6TgAAgBrHpaC2cuVK3X333WrdurUkKSYmRu3bt9eLL76oK6+8Uh9++OE5LRIAAKAmcimopaenKy4uTmazWQEBAcrOznZe161bN+eQKAAAACqPS0Gtdu3ayszMlCQ1atRIK1ascF63ZcsW+fn5nZPiAAAAajKXFhN06tRJv/zyi3r37q1bb71V48aN044dO+Tj46MNGzbotttuO9d1AgAA1DguBbVHH31UhYWFkqQBAwYoKChIS5YsUXFxsZ5++mnddNNN57RIAACAmuiUQa2oqEgrV67UkSNHVKtWLXXq1Em1atVS7969nSdlBwAAwLlx0qB2+PBhjRw5UkeOHHFeFhwcrLfffludO3eukuIAAABqspMuJpgwYYK8vLw0e/ZsbdmyRYsXL1ZCQoKeeeaZqqwPAACgxjppUNu0aZMeeughXXrppfLz81OTJk30wgsvKCkpSSkpKVVZIwAAQI100qCWmpqq+vXrl7msQYMGstvtSktLO+eFAQAA1HQu7aMGAACAqnfKVZ+jRo2S2Wwud/nIkSPLXb5mzZrKrQwAAKCGO2lQu++++6qyDgAAAPwDQQ0AAMCgmKMGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMqsqD2oEDB3TjjTeqT58+uvHGG/Xnn3+e9Ng//vhDrVu31vjx46uuQAAAAIOo8qD27LPPaujQofruu+80dOhQPfPMMxUeZ7Va9eyzz6pXr15VXCEAAIAxVGlQS09P186dO9W3b19JUt++fbVz505lZGSUO3bKlCnq3r27GjVqVJUlAgAAGEaVBrWkpCTFxsbKbDZLksxms2JiYpSUlFTmuN27d2vVqlUaOXJkVZYHAABgKCc9KbunlJSU6Omnn9arr77qDHRnIioquBKrwvkuOjrE0yUAQI1Ae+ueKg1qcXFxSk5OltVqldlsltVqVUpKiuLi4pzHpKam6tChQ7rzzjslSTk5ObLb7crLy9OLL77o8mOlp+fJZrNX+nPA+Sc6OkSpqbmeLgOotvjghTtob8vy8jKdsnOpSoNaVFSUEhIStGjRIvXv31+LFi1SQkKCIiMjncfUqVNH69atc/48adIkFRQU6LHHHqvKUgEAADyuyld9Pvfcc5o1a5b69OmjWbNm6fnnn5ckjR49Wtu2bavqcgAAAAzLZLfbz8vxQYY+4SqGPoGzEx0don5jFni6DFQDC9/sT3v7D6cb+uTMBAAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAZFUAMAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAQAAGBRBDQAAwKAIagAAAAblXdUPeODAAY0bN05ZWVkKDw/X+PHj1ahRozLHvPvuu/rmm2/k5eUlHx8fPfzww+rSpUtVlwoAAOBRVR7Unn32WQ0dOlT9+/fXggUL9Mwzz2jGjBlljmnVqpVuv/12BQQEaPfu3Ro+fLhWrVolf3//qi4XAADAY6p06DM9PV07d+5U3759JUl9+/bVzp07lZGRUea4Ll26KCAgQJLUrFkz2e12ZWVlVWWpAAAAHlelQS0pKUmxsbEym82SJLPZrJiYGCUlJZ30NvPnz1eDBg1Uu3btqioTAADAEKp86NMd69ev1zvvvKOPPvrI7dtGRQWfg4pwvoqODvF0CQBQI9DeuqdKg1pcXJySk5NltVplNptltVqVkpKiuLi4csdu2rRJjz76qN577z1dcMEFbj9WenqebDZ7ZZSN81x0dIhSU3M9XQZQbfHBC3fQ3pbl5WU6ZedSlQ59RkVFKSEhQYsWLZIkLVq0SAkJCYqMjCxz3NatW/Xwww9r4sSJatGiRVWWCAAAYBhVvo/ac889p1mzZqlPnz6aNWuWnn/+eUnS6NGjtW3bNknS888/r6KiIj3zzDPq37+/+vfvrz179lR1qQAAAB5lstvt5+X4IEOfcBVDn8DZiY4OUb8xCzxdBqqBhW/2p739B0MNfQIAAMB1BDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQBDUAAACDIqgBAAAYFEENAADAoAhqAAAABkVQAwAAMCiCGgAAgEER1AAAAAyKoAYAAGBQVR7UDhw4oBtvvFF9+vTRjTfeqD///LPcMVarVc8//7x69eql3r17a+7cuVVdJgAAgMdVeVB79tlnNXToUH333XcaOnSonnnmmXLHLFy4UIcOHdLSpUs1Z84cTZo0SYmJiVVdKgAAgEdVaVBLT0/Xzp071bdvX0lS3759tXPnTmVkZJQ57ptvvtGQIUPk5eWlyMhI9erVS0uWLKnKUgEAADzOuyofLCkpSbGxsTKbzZIks9msmJgYJSUlKTIyssxxderUcf4cFxenY8eOufVYXl6myikaNQK/L8DZiYkI8HQJqCZob8s63etRpUGtKkVEBHm6BFQjUVHBni4BqNY+fOoqT5eAaoL21j1VOvQZFxen5ORkWa1WSccXDaSkpCguLq7ccUePHnX+nJSUpNq1a1dlqQAAAB5XpUEtKipKCQkJWrRokSRp0aJFSkhIKDPsKUlXX3215s6dK5vNpoyMDC1btkx9+vSpylIBAAA8zmS32+1V+YD79+/XuHHjlJOTo9DQUI0fP14XXHCBRo8erQceeEAtW7aU1WrVCy+8oNWrV0uSRo8erRtvvLEqywQAAPC4Kg9qAAAAcA1nJgAAADAoghoAAIBBEdQAAAAMiqAGAABgUAQ1AAAAgyKoAWfhn4umWUQN4GwcPnxYKSkpni4DBnLenkIKONeSk5O1fPly7du3TzExMbrmmmtUv359T5cFoJpauXKlZs6cqXXr1mnt2rVKTU3VihUrNHToUPn6+nq6PHgIPWqAGxw9Zvv379cTTzyhpUuXqnHjxjpw4IAef/xxbd682bMFAqiW7Ha73n77bT3yyCNq2rSpgoKC1KBBA3311Vfy8fHxdHnwIIIa4AZHUNu4caNq1aqljz76SDfccINeffVVdezYUQsWLJAklZaWerJMANVMZmam/P391bx5c+dleXl58vLykslk8mBl8DSCGnAGzGazYmNjJcnZiNaqVcv5zdfbm1kFAFxXXFysuLg4bdy40TnM+ccff8jPz0+SZLPZPFkePIhPE+AMhISEaPXq1SoqKlLPnj21YcMGHTx4UD169FB6err8/PwUHBzs6TIBVBNRUVHq0aOHXnvtNZlMJr3wwgvasGGDxowZI0n0qtVgnOsTcIPdbpfJZNLs2bO1cuVKSVJhYaHy8/Nlt9sVHR2tgoICPfzww7r00ks9XC2A6sDRrkjS+vXrtWrVKkVGRuqKK65Q06ZNPVwdPI2gBpwlq9WqwsJC5eTkKC0tTYmJiWrXrp1iYmI8XRoAg3OEtIMHD8pkMqlBgwYnPQY1E0ENcIOjwfziiy9ktVoVERGhkJAQhYeHy9vbm2+/ANxitVplNps1fvx4+fr66uGHH1Zpaam8vb313nvvqXbt2ho0aBBhrQZjjhrgBkdDuXnzZiUnJ6u0tFSFhYX6/fff1bRpU33wwQcKCQnxcJUAqpusrCx1795d0t+ry48dO6batWs7LyOo1UwENeAMvPTSS87/t1gs+vXXXzV37lwFBQV5sCoA1Y0jfPn4+Ojo0aMqKSlxXpeamqrIyMgyx6HmYXsO4AwUFRWptLRUdrtdvr6+6tSpk9LS0uTlxZ8UANc52oxRo0Zp1apVevvtt7VkyRLdcccdCgsL08UXXyyJoFaT0aMGuCknJ0dz5sxRTEyMgoOD5ePjo19//VUFBQWSju93RGAD4I6SkhI9+OCDWrhwoZYvX66ePXvquuuuYyoFCGqAu3Jzc7Vx40aZTCYVFBTIy8tLzZo108svvyxJhDQAbnvggQe0ePFitWrVytOlwGAIaoCbAgMDNX78eIWFhUk6vqO4zWZTQEAAE34BuM1isahx48Y6fPiwYmJi5OPjwxc+OBHUADctXrxY+/bt03PPPSeLxSI/Pz998803Onr0qEaNGkVYA+CWwsJCFRYWasyYMRowYIDCw8MVGBioiIgItW7d2tPlwcMIaoCLEhMTtXv3bq1du1YxMTHKy8uTxWJRZGSktm3b5vwGbLPZZDabPVwtgOqipKREjRs3lpeXl9auXavCwkJlZ2erSZMmat26NfNeazg2vAVctGfPHn388cdaunSpzGazLBaLcyl9mzZtNHbsWLVp04ZGFcBZs9vtKi4ulr+/v6dLgYcR1AA3rVixQq1bt1ZERISk46tAS0pKFB4eTk8aAJc5vtT99ttvWrRokerWravg4GCFhIQoICBATZo0qfCUUqhZGPoEXGS321VaWqru3bvrt99+07Jly3T11VcrNDTU06UBqIYcPe+lpaXKz8/Xvn37lJubq99//13Hjh3TCy+8oAYNGtBLX8PRowa46YMPPtDKlSu1fft2LVu2TN7e3nrxxRf1xBNPOHcRBwBXnGzx0X/+8x+1adNGPXr0IKjVcLzzgJu+/PJLzZw5UxdeeKGCgoIUFhamXbt2KSAgwNOlAahmTCaT8vLylJubq6KiIhUVFUk6fp7P0tJS5zGouRj6BNxQXFyssLAw5zCoY6Kvt7c3QQ2AWxw9ZV988YXS09MVFRWlsLAwZWVlaceOHRo2bJinS4QBENQAN1gsFjVr1kzLli1zLhz44osvVKtWLUknH8YAgH9yDGdarVbl5+crMzNTOTk5CgoK0ksvvcR5PiGJOWqA23bv3q1HH31UaWlp8vX1Vf369fXyyy+rYcOGni4NQDWWn58vPz8/eXvTh4K/EdSAM/Tnn3/KbDYrNjZWvr6+ni4HQDW1bds2zZ07V8nJyTKZTOrcubOGDx/u6bJgECwmAFzg+D6zf/9+rVu3TpIUFBSkNWvWaM2aNZ4sDUA1lpOTo//85z8KDg7WXXfdpYEDB2rZsmWaMmWKp0uDQdC/CrjAcVqoxYsXq7CwUB06dNBHH32ktWvXqmHDhiopKVGvXr2YowbALUlJSSosLNTYsWOdl8XHx+upp57SnXfeydYcoEcNcEdGRoZatWqlvXv3ymq16v3331fDhg119OhRSX/3vAHAqTjaCovFIj8/Px09elQFBQWSjm/N4VhRTpsCetQAFzh6yQICApSUlKT169erTp06ql27tjIyMhQfH+/hCgFUJ442pWHDhmrVqpVeeOEF9ejRQwcOHNCOHTs0ePDgMseh5qJHDXCBY+hh2LBhWrFihfbu3atrrrlGknTo0CHFxsZKolEF4DqbzabQ0FA9+OCD6tq1qzZv3ix/f3+NGzdOAwYMkCSGPcGqT+Bs2Gw2paenKywsjJWfANz2559/KjAwUDExMZKkvLw8BQUF8aUPTkR1wA1Wq1VWq9U5b8TLy0vR0dGENABusVqtkqQ5c+Zo/fr1kqTx48erXbt2evzxx5WTk+PJ8mAgBDXADWazWWazmW+7ACrFnj17FB8frwMHDigtLU3Lly9XQUGBduzYIYnFBCCoAS6zWCzau3evp8sAcB5wfNkrLi5WcnKyZs2apUsvvVRxcXHKz89XREREmeNQcxHUABcdPHhQL7zwgqfLAHAecCwSuPbaa7V48WJt2rRJXbp0kSRlZmYqKirKk+XBQNieA3CRzWZTTEyMLBaLTCaTfHx8PF0SgGpu6NChatSokZo0aaLY2FglJSVp2LBhBDU4seoTOA3H2Qa2bduml156SZGRkfrXv/6lwMBA+fn5qV69emrUqJGnywQAnIfoUQNOwzFHxGKxqHHjxvL19dWiRYtUUlKi1NRUDR48WCNHjuRULwDcZrfbZbfbaTtwUvSoAS6wWq0ym82SVOZ8noWFhbLb7QoMDPRkeQCA8xQRHnCB2WxWRkaGvvzyS917773asmWLrFarDhw4wPJ5AG4pLCzUkSNHlJGRodzcXBUUFMhisTj3VgNOxNAncBqOIc0pU6ZIkvbt26eioiKZzWb997//1S233KL27duX6WkDgJNZt26dPv74Y9WrV08BAQEKCQlRSEiIQkND5ePjo4suukhNmzb1dJkwCIIa4KJNmzZp9uzZOnr0qIKCgiQdX0bv+H8AcEWTJk00cOBAFRYWKjU1VTk5OUpKSlJBQYESExM1dOhQNW3alHmvkERQA07L0UsWEBCgjIwMpaenO8/Ll5+fr8jIyDLHAcCp1K9fX/Xr1z/tcYQ0SAQ14LQcAaxPnz6aN2+ekpOTlZycrClTpqhx48bsdwTALY55rfn5+frxxx+VkpIiPz8/hYSEKDg4WO3bt1dYWJiHq4RRsOoTcMOkSZO0YsUKZWRkqGvXrnr44YcVHh7u6bIAVCOOIc3JkyfrwIED+u6779SiRQsdPHhQVqtVn332mZo0acK8V0giqAEAUKUcAaxfv376+uuvNWrUKP33v/9Vfn6+JkyYoDFjxjinVAAMfQIusFgs2rhxo3799Vd5eXkpICBAvr6+qlu3rnr27Onp8gBUIyf2ktlsNmVmZio5OVn16tXTjh075Ovr68HqYDQENeAUHN98t2zZoldeeUXNmjVTdHS0UlNTlZmZqcLCQvXs2ZPVWQDcYrVadcUVV6ikpEQdOnTQrFmz1LhxY1ksFgUHB3u6PBgIQ5/AKTgC2KeffqpDhw7pscceK3NdSUmJ/Pz8PFghgOrIbrcrLS1N0dHRys3N1XPPPaeCggLdd999atGihafLg4HQowa4ICQkRNLxfdN8fX3l7+8vs9lMSANwRkwmk6Kjo3XkyBF5eXnpjTfeYOEAKkRQA07B0eFstVq1cuVKHT58WK1atZKvr6/sdru6d++uxo0be7hKANVNRkaGvvrqK+3evVu+vr4ymUyyWq26+OKLNWzYMFZ8womgBpyC40TsMTExuuGGG1RUVKS0tDRZLBYdO3ZMl156qSTRqAJwiWM6xS+//KLFixfr5ptvlr+/v/Lz85WZmano6GhJtCn4G3PUgFPIzMxUWFhYhQsFbDabTCYTjSkAlzmC2rRp02QymXTrrbd6uiQYHD1qwClMmzZN9957rz788EMVFBQoMjJSISEhCgsLk5+fny6//HKW0gNwW2BgoDZs2KANGzYoNjZWQUFB8vf3V2BgoKdLg8EQ1IBTuPTSS53z0bKzs5WUlKS8vDxZLBbl5OSoXbt2BDUALnP0wAcFBWnfvn167733VK9ePXl5eSk/P1+33367EhISPFwljIShT+AMlZSUyMfHx9NlAKhGHHPPZsyYoZiYGAUEBCgzM1NFRUVKTU3VoEGDVLduXU+XCQOhRw04BUejmpiYqA0bNqikpESBgYEKCwtTREQE+x0BOCOrVq3SW2+9xea2OC2CGnAKJpNJqamp+t///qdNmzbpyJEjatCggfbs2aM+ffronXfeYXUWAJc52oo6depo27Ztuuyyy5yry4GKENSAk3AEsD179ig/P19TpkzR66+/rrffflufffaZ0tLSyhwHAK76/ffftWbNGl199dWqV6+eIiMjFRYWpnbt2nm6NBgMQQ04CUcAS0tLU2xsrI4ePSqLxSLp+JkKtm7d6uEKAVRHJSUl6ty5s0pLS3X48GFt3rxZWVlZMplMmj9/vqfLg8EQ1ICTcPSSBQcHq2nTpgoNDZWfn58WLFigdevWOZfRsx4HgDt8fHz073//29NloJpg1SdwGjk5OSosLFRsbKzmz5+vqVOnqmfPnrr11ltVq1Ythj4BuKW4uFgrV67U4sWL1bRpU913333as2ePvL291aRJE0+XB4Mpv906AEl/95StWrVKhYWFkqQBAwaUOe2LJEIaAJfYbDZJ0saNG7V06VKZTCb9+eefkqTNmzdr6tSpZY4DJIIacFJWq1WS9NlnnykpKUmSnHPUXn31VW3fvl0SQ58A3PP777+rZcuWGj58uAICApyXM50CFWGOGnASa9as0YoVK/THH39o+fLlOnbsmIKCghQTE6NDhw4pPDzc0yUCqIYKCwtVq1Yt7dixQ82aNZMkHTp0SLVq1fJwZTAighpwEnFxcapfv74iIiKUmJiovXv3Kjs7W6WlpRo4cKAuuOACSQx9AnCNl9fxQaxu3bpp+fLlWrx4sTp27KjPPvtMf/75p/ME7bQpOBGLCYDT2Llzpy666CJPlwHgPGC1WmU2m7VmzRp9/fXX2r17t4KCgvT4449zphNUiKAGnILValVqaqqWLVsmPz8/RUREKDw8XJGRkc4eNQBwhSOkzZ49W0OGDJGvr6/zury8PE4nhQoR1IBTSE9P19ixY5WVlaXAwEDl5eUpOztb0dHRmjNnDltzAHDboEGD9Nlnn5UJakOGDNHEiRMVFxfnwcpgRMxRAyrgCGAHDhxQXl6evvzyy3LXS8wlAeC6+fPn6/vvv9exY8f07rvvql69eqpVq5ZiYmJUVFSksLAwT5cIAyKoARVwBLDAwEC1bdtWFotFZrPZefJkAhoAdzVr1kxHjhzRpk2blJiYqC1btigrK0tZWVkaMGCAc3sO4EQENaACNptNXl5eSk1N1cqVK5WcnKzu3bsrLCxMwcHBatiwIUvpAbglISFBCQkJ6tixo9q2bevpclBNsOEtUAHHMnp/f3+1bt1aPj4+mjdvniZOnKg777xTCxculPT3prgA4IqDBw9q+fLlZc4+kJiYqHXr1nmwKhgZiwmAk3D0qlWERQQAzsTRo0f1/PPPq2XLlrrxxhv1xx9/aNq0afLz89Pbb7/tXBkKOBDUgFPIy8vT/PnzdfDgQZlMJjVv3lxXXXUVy+gBuM3xBS8lJUVPP/20Dh48qKioKA0ePFiDBg3ydHkwKIY+gQo4hiWmTZumFStWKDAwUHFxcfroo4/07rvvOk/SDgCuMplMysnJ0YoVKxQeHi673a4WLVqob9++ni4NBkaPGlABxzffvn37as6cOQoKCnJeN3jwYE2cOFF169b1YIUAqhPHkOZHH32k1atX69Zbb9Ull1yiF198UUVFRXr++ecVERHh6TJhQPSoARU4cXuO/fv3Oy+32+2y2WzsdwTALY55Z61bt9bkyZPVtWtXhYSE6PXXX5e/v7/Wr18vSWUWGQASPWrAKc2aNUvr169X165dFRkZqV9++UVpaWl6++23PV0agGrG0VO/ZMkSJSUlafDgwQoICFBpaan8/PxOungJNRu/FcApDB8+XC1bttQXX3yhd955R35+fnrttdc8XRaAasYR0qZMmaJff/1VEyZMUHZ2tgoLC/Xqq68qPT3d0yXCoNjwFjiJgoICHT16VKNHj9bo0aM9XQ6AaswxneLbb7/V//73P+3cuVPBwcEKDQ3Vxo0b5e3NxzEqRo8a8A+O2QC7d+/W7NmzJUklJSWSpN9++01z5871WG0Aqi/HHNeYmBhZLBbn4gGz2azw8HDPFgfDIqgB/+AIagcOHFBpaakkyWKxSJKOHTum3377TRJnJQDgHovFonbt2umjjz5y9tgvWLBAUVFRbKCNkyKoASdRVFTkDG2O7TmOHj3KN18AZ8TPz09Dhw7V3r17FRYWpokTJ+rTTz/V2LFjPV0aDIxVn8BJ7N+/X1OmTFF0dLR69OihP/74QytXrtR1112nq6666pSnmAKAk0lJSdGWLVvk5eWlDh06cKYTnBJBDTiFX375RR9//LH279+viIgIPfzww+rcubOnywIA1BAENeAkOPE6gHPBZrM52xd65XE6BDXgFP65SziNKoCzwRdAuIuNW4B/+Gc4czSqNK4AzpbJZFJRUZEsFot8fHxkNpvl6+vr6bJgYPSoAQBwDjl60Xbu3KlPP/1UxcXFioiIUGhoqEwmkwYPHqzY2FhPlwmDokcNOEFxcbHeffdd1apVS6Ghoc7GNCwsTKGhoQoODpa/v7+nywRQjTiC2sSJExUbG6tmzZopPz9fmZmZSkxMVL9+/TxdIgyMoAacoLCwULm5uSooKFB+fr6KiopUUlIim80mi8WiuLg4vfjii8wzAeAyR1tx5MgR/fe///VwNahuGPoETmCz2ZSVlaWSkhIVFRXJy8tLRUVFKigoUFZWlnx9fdWxY0eCGgC3vfbaa4qPj1fHjh0VEBAgb29veXt700uPUyKoAadw+PBh2e12BQYGKjAwUP7+/qz8BOA2i8WiESNGKDs7W23atFFYWJiCg4MVERGhYcOGebo8GBhDn8AJHD1lhw8f1vfff69ffvlFdrtd3t7eys/P18CBAzV48GDOSgDALSaTSffcc4+KioqUnJys7OxspaSkKD09XRLbduDkCGrACWw2m8xmsxYtWqSsrCwFBwcrJCREDRo00A8//KA6dep4ukQA1ZCPj4+6det20usJaTgZghpQgUOHDunmm2/W6tWrFRcXpwEDBigtLU3JycmeLg1ANVRSUqLvvvtOS5YsUXh4uHx9fRUcHKwOHTroiiuu8HR5MDDGboAK5Obmqri4WEVFRTp8+LBKSkq0f/9+Tp4MwC2ODbSXL1+utWvXymKxaPv27crOztbMmTO1adMmSZLVavVkmTAwetSAEziGH4YMGaK4uDj16tVL7777ru644w75+PioadOmZY4DgFNxrNfbtWuX2rRpo4CAAKWmpmrkyJGaNm2aIiIiJNGm4OQIasAJHAsEHHNJ6tWrp+eee0579uxRx44dnad6oVEF4I709HS1aNFChw4dUmpqqiRp7969zi9/wMkw9Amc4M0335QkvfPOO5o9e7a++eYbJScnq0mTJsrJyfFwdQCqG7PZLEnq2LGj6tatq3bt2ik1NVUvvfSSjh07pgsvvNDDFcLo6FED/mK1WtWsWTNJx/c82rx5s3Jzc5Wfn6/CwkJ5eXnp888/93CVAKqjK6+8UlarVQEBAbryyiu1YsUK3X777erUqZMksd0PTooNb4F/yM3N1YoVK9SzZ0/nKaSKiopUWlrKt18AQJWiRw34h8OHD+ubb75Rv379FBQU5OlyAJxnbDab7Ha7c1gUOBX6WoG/ODqXfXx8FBsbq6SkJA9XBOB8YrVandtwMNQJV9GjBvzFcQqXxMRELVmyRLt371aXLl0UGRmpoKAgtWjRQk2aNPF0mQCqKXrQcCYIasBfHN9w69Wrp1GjRqm0tFQHDhzQli1bdODAAd14441q0qSJrFYrDS4Al9lsNq1du1Zz5sxRYGCgYmJiVKtWLTVt2lSXX365p8uDwbGYADjBiSdbLy4ulp+fn4crAlBdOXrpt2zZotdff12XXHKJ8vPzlZKSoj///FNNmjTRxIkT+fKHU6JHDTiBl5eX/vzzTy1YsEAbNmzQo48+qosuuki//vqrmjdv7txFHABOxxHUDhw4oJiYGD3yyCMVHkNIw6kwmxH4i+OcfP/73/8UFBSk5ORklZaWytvbW++//77++OMPSX8vOgCAU3H0zjdr1kytWrVSUlKSrFarbDabs73hLCc4HXrUgH/Yu3evXn31VW3cuFGBgYGSjq/WCg8Pl0TDCsA1jh614OBgrVq1Sp988ok6duyo8PBw+fr6qk+fPpxCCqdFjxrwF8e334CAAGVkZCgnJ0fNmzdXaWmpCgoKFBUV5eEKAVQnjl6zadOmKS4uTiNHjlRkZKSysrK0evVq5xZAjuOAitCjBvzDiBEj9M4772j79u36/PPPtXHjRl166aXOHjUAcIWj9/3gwYMaM2aMEhISKjyOPdVwKqz6BCowe/Zsbd++XYcPH1a7du10//33M+EXgFscQ59Tp05VcXGx+vTpo/DwcHl7e8tsNis0NNTTJaIaIKgBJygtLVVOTo4iIyMlSSUlJbJYLJxKCoDbHEHt7rvv1r59+3ThhRcqMjJSoaGh8vHx0b///W8FBAR4ukwYHEOfgP5uUPfs2aOFCxdq3Lhx2r59u9577z3VqlVLd955p+rVq+fpMgFUI46hzwcffFDFxcXKzs5WWlqakpKSlJKSIl9fXw9XiOqAoAao7H5HmZmZkqQffvhBYWFhio6O1pQpU/TCCy+U2RAXAFyRkJCgrKws7du3T/Xq1dPgwYM9XRKqEYIaoL/3Rjt27JhiY2O1adMmpaWl6YUXXtD333+v7OzsMscBgCssFou+/PJLLViwQBdeeKEKCwsVFxenUaNGsUAJLqFrANDfq67atGmjI0eO6D//+Y8aNWokHx8f7dixg605ALjF8aVu3759WrJkie69917deuut6tevnw4ePKjPP/+8zHHAydCjBujvuSSXXnqp8vPzdezYMQ0cOFCSFB4erosvvrjMcQBwKo7pFIcPH1Z0dLS6dOkiSWratKnCw8P17rvv6s4775TNZmNFOU6JoAacwGQyqVu3bpKk3377TRdeeKFGjx7tvJ75aQBc4fhS5+fnp7S0NG3cuFGtWrVSTk6OfvjhB11wwQUerhDVBUEN+AeLxSJfX1+9/vrrevbZZxUaGur8dgwArnC0F927d1dqaqomT56s2rVrq6CgQEFBQbrpppsk8eUPp0dQA/7imCviGIbIy8tTRESEJIY8AbjH8eXum2++Ub169XTPPfdox44dCgoKUo8ePRQdHc0XQLiEoAb8xdFgOoLawIEDVbt2bU+WBKCacsw9W7JkiUaOHKm2bdvqsssuK3MMIQ2u4MwEqPGsVqu+++47BQcHKyAgwPmvTp06ys/Pl7+/v4KDgz1dJoBqxNFbNnPmTBUWFmrAgAGKioqSl5cXAQ1uoUcNNV5OTo4WLFjgDGNeXl7Oc/H5+/srJiZGd955p4erBFAdLVy4UPv27dNPP/2kuLg4RUZGKiAgQKNHj+bUdHAJPWqo8ex2u1JSUpSfn6+srCwVFBSooKBAubm5Sk1NVXBwsIYPH858EgAu27Rpk1q3bq2jR4/q8OHDOnLkiFJSUpSenq4jR47o7bfflr+/v6fLRDVAUAMqUFBQIH9/f1ZkAXCbxWLRiBEj9Nlnn+mJJ57QK6+84umSUI0x9An8JSMjQ19//bVWrVqlsLAwmc1mmc1mXX311erWrRs9agBckpOTo+zsbH366adatWqVNm7cKJvNpsDAQPn7+ysoKIiFSnAZQQ01ntVqldls1qJFi5SYmCiz2azc3Fw1aNBAy5YtU4cOHSSJoAbAJWFhYfq///s/zZkzRxaLRXPnzlVJSYl8fX0lSQ0aNNA999xDmwKXENSAv+zbt0/XXHONdu3apejoaPXr109hYWHOxhUAXOHj46PevXsrKipKP/30kwYPHqzExETl5+crLS1NoaGhni4R1QhBDfhLVlaWfHx8lJ2drdzcXEnS1q1b1aRJEw9XBqA6atu2rRISEhQQEKD69euXu57eNLiCmdKo8Rwb3A4ePFhxcXG68sordfjwYT3++OOS5AxqNKoA3BUQECCbzSa73S6bzSabzebpklDNsOoT+MuhQ4dUq1YtBQYGasWKFdq6dav69OmjZs2aebo0ANUQc9BQGehRA/7y1ltvOfc16t69ux544AFt27ZNFovFw5UBqI4IaagMzFFDjZecnKy0tDTt2LFDqampzm05rFarJk6cqOuvv97TJQKoRv773//qjz/+UEhIiO68807FxsZ6uiRUYwQ11GglJSVaunSpFi5cqJSUFL388svy9fVVWFiYTCaTLrroIk+XCKCa6dSpk1q1aiWr1aqwsDBPl4NqjqCGGs3Hx0c33HCDmjdvru+//15XXXWVEhMTdfToUQUGBuqOO+7wdIkAqpn58+eruLhYwcHBzn0YgTPFYgLgL7m5uUpKSlJYWBhDFQDO2Pr161VYWCiTyaSuXbt6uhxUcywmQI3m+J6SmJio2bNna9y4cfr0008lScuXL9fq1as9WR6Aauiyyy7T5Zdf7mxfHP+1Wq1KS0vzZGmohghqqNEcDeiPP/6o3NxcdejQQcXFxZKkHTt2aMWKFZKON7AA4KrMzEznlz7H6s/i4mLNmDHDk2WhGmKOGmo0R1A7evSoOnbsqIyMDB05ckTS8XAWHR3tyfIAVDOFhYXasWOHVq5cqZSUFG3dulUFBQWKjo7Wb7/9pjVr1kj6+xzDwOkQ1FCjOb7pBgQEKDMzUxs2bFCvXr0kHT/3Z58+fcocBwCnUlRUpD179mj16tVKSUnRp59+qqKiInl5eclut6tv376SaFPgOhYTAJLS09P16aefaubMmWrTpo38/PxUu3Zt3XHHHSwsAOC2nTt3KjU1VU2aNFF6erokKSQkRI0aNZKXF7OO4Dp61ABJUVFRuvbaa9WkSRPt3r1bMTExuvHGG+XtzZ8IANc5Thvl4+Oj2NhY1atXT+Hh4fr555914YUXEtLgNj6FgL/ExcUpMDBQXbp0kclkUmZmpmrVqsUQBQCX2Ww2mc1mffLJJ2rTpo2aN2+uiRMn6vfff1eDBg00atQoNWjQwNNlohohqAGSpk6dqp9++knR0dHy9vaWv7+/fH199cQTTxDUALgtJSVFjRo10r59+5Sbm6tx48Zp8uTJ2rdvnxo0aCCbzUbvGlxCUEONl5+fr3nz5mns2LEymUzKy8tTdna2CgsLaUgBuMXxxS4vL09JSUlauHChevTooebNm8tisahWrVpljgNOh6CGGi8rK0uRkZHq0aOHp0sBUM05vtzdf//9+uijj2Q2m9WlSxeVlpYqLy/PueUPQQ2uYtUnajybzaZvv/1W27ZtU/v27RUcHKyAgADVqlVLderU8XR5AKqp3NxchYSESDrec79lyxZ16NCB/dPgFnrUUGM5Vmft379fCxYs0NGjR1VQUKCSkhIVFBTokksu0ciRI5lLAsBtycnJmjBhggIDA/XCCy8oLy9PDRs2JKTBbQQ11FiOoLZ+/Xr5+vpqypQpOnbsmIqLi5WRkeHsTWOIAoA70tPT9fLLL6tZs2ZavHixpOPBberUqZo0aZKHq0N1Q1BDjWcymdStWzfVqVOnwqFOghoAd6SkpMhqtWrAgAFat26dJMnf318pKSmSRC893EJQQ41ntVo1c+ZM7d27V/Hx8QoLC1NAQIAuvvhihYeHe7o8ANWEo5c+LS3NuQdjaGiopOO9bI72hKnhcAdBDTWWo6csJiZGbdq0UV5enn777TeVlpYqOTlZY8aMUXh4uLPxBYBTcbQTjRo1UmBgoB555BGFh4frm2++0bfffqtOnTqVOQ5wBas+UeMVFRWptLTUuW9aTk6OsrKyFB8fr6CgIE+XB6AaOnz4sGbNmqXExEQFBQWpdevWuummm1hMALcR1IATzJkzR4MGDZKPj4+nSwFQTW3atEmRkZGKiYnRwYMHFRkZKV9fX/n7+8vf39/T5aGaYTYjoL/njPzvf/9TUVGRh6sBUB2VlJRIkj7//HMdPnxYAQEBat68uWJiYjRv3jxt377dwxWiOmKOGvAXi8WiiIgIhYSEMC8NgNsOHjyovXv3avv27apfv77MZrNsNptiYmL0xRdfKCEhQZJoX+AWghqg45N7fX19dc899zh/BgB3ZGVladOmTUpKStLmzZu1f/9+Wa1Wmc1mtW3bVk2bNpVE+wL3MEcNNd7+/fv1zjvvqF27dhoxYoQOHz4sHx8f1a5d29OlAaiGvvvuO7Vu3Vo2m015eXkqKCjQRRddJF9fX0+XhmqIOWqokRzfT7Zv367p06fL19dXK1eulCRt2bJF06dPL3McALiqT58+CgsLU2FhoXx9fdWwYUMlJyd7uixUUwx9okZyzBHZt2+fQkJC1Lt3b33zzTeSjm+Ae+IO4iynB+CqkpISffbZZ/rss8/k5eWl6OhoJScnq1u3bho7dqyny0M1RI8aarSUlBQ1atRIVqtVtWrVkiRlZGQoJibGw5UBqI4OHz6s77//Xs8++6wk6d///reaN2+uzMxMSfTSw30ENdRIjvPstW3bVvv27dOkSZNUWlqq77//Xr/99ptatmwpiUm/AFzjCGBpaWmKi4tT/fr1Vbt2bbVr106jR492Xk9Qg7sY+kSN1q5dO5WUlCg1NVVHjhzRoUOH1LVrV/Xq1UuSOHEyAJc4plNkZ2crNDRU+fn5slgs2rFjh37++WdlZ2c7jwPcwapPQNLRo0d19OhR1atXj9WeAM5YWlqa0tLS1KhRI3366adatmyZateurauuukp9+vSRzWbjCyDcQlBDjTV//nzt2bNHvXv3Vtu2bZ2X7969WxdccAFL6QG4zGKxyGQylTn9nM1m07Zt21S7dm3FxsZ6sDpUZwQ11Egvvviijhw5ombNmunnn3/WoEGDNHz4cEnSnXfeqddff13h4eGeLRJAtfHFF19o06ZNqlWrlnx9fRUYGKjw8HDFxsaqpKREF198saKiojxdJqoh5qihRlq/fr0+++wzSVLnzp31n//8R+Hh4erbt6+OHTumkJAQD1cIoDoJDw9XXFycc5PbzMxMHThwQFarVRkZGXrooYcUFRXF6aPgNoIaahzHpN6goCBJUvv27XX33Xdr6tSpCggIUGBgIHunAXBL9+7dnYuQiouLlZ+fr6KiIhUWFiozM1P16tWTxEpyuI+ghhqnoKBAtWrVUmpqqqKjoyVJXbt2VXZ2tt58801nQ8qkXwCu+vDDD1VYWKigoCCNHDlSkZGRni4J5wmCGmqcWrVq6cknn3T+7Dhpcr9+/XTw4EHt27dPEsvoAbjOsVjAx8dH3t5/f7Q62hF60nCmCGqocXx8fHThhRc6fzabzcrNzVVISIjuu+8+5eXlOS8HgNMpKSnRgQMH5OPjo/Dw8DKhjICGs8W4DmqswsJCTZkyRT179tQPP/wgSdqwYYPS09M9XBmA6sRms6lp06bOsxEAlYkeNdQ4jqHOmTNnymq1qmXLljp27Jik43ur1a9fX3fddZfzOAA4FT8/P/Xt21cSUyZQ+QhqqLEOHTqkfv36yWQyObfj8PPzc+6fxpAFAHfRbqCyMfSJGsfRkBYWFio8PFzHjh1TixYtJElHjhxRTEyMJ8sDAMCJHjXUOI4tN0aPHq33339fK1askMlk0ieffKLIyEglJCSUOQ4AAE/hFFKo0Xbu3KmVK1cqMzNTkjRixAjnxpQA4IqdO3cqMzNT0dHRCgoKUmBgoPz9/eXr6yuTycSXPpwVghpqPIvFouLiYk4bBeCMvPzyy/r2228VFBSk9PR0mc1mhYWFKSgoSOHh4Xr88ccVHx/v6TJRTRHUgL9wDj4A7jqx3ZgyZYqKi4vVrVs3mc1mLVq0SL/88osmTJig+Ph42hicEeaoAZJKS0vL7CYOAK4wmUwqKSmRj4+PfvjhB7355pvO6RMtWrTQY489prCwMOexgLsYOAckQhqAM+bj4yPp+BkKHHsyOqxZs0b+/v6eKAvnCT6dUGM5hiH27t2rrVu3avDgwZ4uCUA1Nm7cOD300EOqW7eugoODlZOTo8GDBzt71IAzwRw11Fg2m01eXl766quvtH79er366qucjQDAWTl69Kg2b97sPH9wr1695Ovr6+myUI3Ro4Yay/EdJSkpiW+8ACpFnTp1VLt2bbbkQKWhRw013h9//CG73a4mTZqwKgvAGSkqKtLcuXM1ceJEhYeHO09N17t3b9199920LThjRH7UeBdccIGaNGkiiVVZANxjs9kkScuWLdPevXv1wAMPKD4+XmPGjJF0fEX5iccB7mLoEzUe33QBnK3k5GRdeOGFioqKUuPGjdWnTx+lp6c7V4QyFIozRVBDjWS3253/TsRCAgBnIicnR82aNVNxcbFyc3MlSfv27XOe8cRms9G+4IwQ8VGj5OTkSJLz/Htms7nMPwBwh6OnrEuXLmrQoIE6deqk9PR0de7cWYmJierevbskplXgzLGYADWG3W7X5ZdfLi8vL/n4+CgwMFBhYWGKiIhQTEyMatWqpQceeMDTZQKo5vLy8rRt2zZFRkaqadOmDHvirDD0iRrDZDJpyZIlys/PV1ZWltLT05WSkqLU1FSlpKQoMTHR0yUCqMaWL1+un376SWazWX369FGzZs08XRLOA/SoAQBwlt555x2tX79e3bt3l5eXl1asWKH+/ftr4MCBTKvAWaFHDTXStm3bNG3aNNlsNvn5+Sk6OlrNmzfXtdde6+nSAFRD33//vT755BMFBATIYrGobdu2ev3119W/f3+CGs4KA+eoMRz7GK1evVozZsyQj4+PkpOT5evrq7lz52rv3r2SJKvV6skyAVQz+fn5MpvNCg0NlY+Pj4KCgnTJJZcoIyPDuT0HcKboUUONs3fvXjVt2lR16tTRli1b9OSTTyomJkaNGjWSxH5HANzXqlUrPfDAAxo+fLgKCwu1dOlStWzZUhJ7NeLsENRQYzimY2ZkZKh58+bKyclRUFCQpOMLDX7//XdJ7HcEwD1BQUF69NFHNXXqVE2ZMkW+vr5q2rSphgwZIomtOXB2WEyAGsNms8nLy0uzZ89WvXr1FB4errfeekuhoaFKS0vTrbfeqquvvtp5HACcSmFhofLz8xUYGKiAgACZTCbl5+erpKRE4eHhkkR7grNGUEONY7fbVVJSIrPZrOXLl2v16tVq1aqVevfureDgYE+XB6CaWLt2rT799FNFR0fL29tbAQEBCgkJUUhIiOx2uxISEpzDn8CZIqihxjh8+LDq16/PN1wAlWL37t1as2aNzGaz8vLyVFRUpKKiIlmtVqWnp+vaa69V7969aXNwVghqqDFuvvlmTZ8+XVdddZWSk5MVGRmp0NBQxcbGyt/fX2+88QY9agDclpeXJz8/P2dgcwyJRkREKCIiwtPloZojqKHGsVqtOnbsmFJTU3X06FEdO3ZMiYmJevzxx1lKD8BlJSUl8vHx0UMPPaSUlBQ99dRTuuiiizxdFs4zBDXUKHl5ebrzzjv1ySefeLoUAOeR+fPna+rUqYqMjNTo0aPVtWtXT5eE8wSD5qhR8vPz5e3NrjQAKteAAQM0d+5c3XHHHZo+fbruvfderVy50tNl4TzAJxZqHH9/f40ZM0bXXHONgoKC5Ofnp8jISOeGtwDgjuLiYkl/n/Xk0KFD6tChg77++mulpqZq0KBBLCbAGWPoEzXKnj17NGHCBPn4+MjX11fe3t4qLCxU27ZtNWrUKHYQB+AWm82mJ598Uj///LO6du2qPn36qH79+oqOjta2bdv04IMP6tdff/V0majGCGqocUpKSuTl5aWMjAylp6crOztbYWFhat68OUENgFsKCws1f/58DRgwQHa73bnxrcPkyZN13333ebBCVHcENdQ4W7Zs0YIFCzRgwAC1atVKGRkZCgkJYcUngErF/mmoDPwGoUaw2WySpDlz5mjBggXasGGD9u/fL0maPn26Vq1aJenv84ECgCsKCwv15ZdfqmfPnurQoYO6dOmivn37avz48fLy8nK2PcCZIqihRnAEsNWrV6tfv35q3bq1/P39JUn79+9XYWFhmeMA4FQcAWzZsmXauHGjbr75ZnXu3Fnjxo2Tr6+vYmNjJdGm4Oyx6hM1gmPOSFFRkRo0aKDCwkJFR0c7L4uJiSlzHACciiOAJSUl6eKLL1ZISIgyMjJ07bXXKi0tTUFBQZLE0CfOGr9BqBEcjWXbtm21cOFC/f777/L19dXnn38uq9WqevXqSSKoAXCNo63Iy8tTVFSUioqKnO3MkSNHlJiYKEkMfeKsEdRQo9x9990qKiqSxWLRSy+9pOXLl+vuu+92DlMAgCscoaxdu3aKjY1V586dtWXLFrVs2VK7du1St27dJPHlD2ePVZ+ocSwWizIyMlRUVKQ6derI19fX0yUBOA/k5eVp165dCgsLU5MmTWQ2mz1dEs4D9KihxigoKNC0adNkNptVu3ZtLV26VK+//royMjI8XRqAaqq0tFRr1qzRpEmT9PHHHysgIEDx8fGENFQaghpqjCNHjuiHH36Q2WzWN998o5UrV8rX11czZszwdGkAqqm3335br776qiwWi+x2u55++ml9+umnslqtni4N5wlWfeK85zjbQFpammrVqiVJ2r59u2677TbVrl1bb775piTJarXyLRiAW7799lstWbJEZrNZRUVF6t69u5577jkNGTLE06XhPEGPGmqMkpIS5efn66WXXtIff/yhXr166dixYwoPD/d0aQCqofz8fNWrV08+Pj7y8vJSYGCgWrVqpcLCQnl70w+CysFvEs57jlVXnTt3VlpamrZv36677rpLknTs2DG1bNnSk+UBqKZKS0sVHh6uu+++W8OGDVNBQYFWr16t3r17O+e+RkZGerhKVHes+kSNYrfbVVpaqtLSUueJ2X18fJxDogDgqoMHD+rJJ5+Uv7+/ioqKVFpaqpycHMXFxcnb21stWrTQAw884OkyUc0R1FCjOE6S/J///Eft2rVTly5dPF0SgPNAcXGx8vLylJeXp9TUVB07dkwhISHO/dSAM8XQJ2oUx/eSXbt2qV27dpL+Dm8A4C7HYiU/Pz/5+fkpKipKDRs2LHc9cKb4dEKN4mgwo6KiVKdOnTKXAYC7Kmo/7Ha789RRtC84Wwx94rx39OhRBQYGKjg42LkSq6SkRD4+Ph6uDMD5hh40VDaCGs579957r2w2m7y9veXr6ys/Pz+Fh4crICBAwcHBGjlyJA0rgDNSWFiowsJChYaGsiUHzgl+q3Bes9lsuummm2SxWFRQUKCCggLl5eUpPz9feXl5ys7OJqQBOCNbt27VhAkT9Ouvv2rJkiUKCwvTV199pf79+ysqKsrT5eE8QVDDec3Ly+ukKzutVqtKS0uruCIA1Z1jePOFF17QhAkT9Pjjj8vPz08RERFavHixrrnmGk+XiPMIQQ01RnJysr799ltlZWXJ399f4eHhql27trp37+7p0gBUI45eeJvNpsaNG6ukpEShoaGSjn8BDAsL82R5OM8Q1HBec3zz3bJliz766CNlZmbKYrGobt26+vXXXzVq1ChJbNEBwD0lJSWKjo5WUlKSbDabgoKClJaWJqvVqsDAQE+Xh/MIQQ3nNUdQ27p1q+Lj4xUfH68ff/xRr776qsaPH+/p8gBUUz4+Pho2bJjuuusu5eXl6cknn9Svv/6q+++/39Ol4TxDUMN5zbGoOTs7W3Xq1FFOTo5iYmIkSbVr11Z6enqZ4wDAVV27dlX9+vW1fPly+fr6avDgwWrTpo2ny8J5hqCG85pjONPX11f+/v4KDQ3V4sWLNWHCBK1fv1433HCDJDalBOCevLw8WSwWNW7cWA0bNlRqaqr8/Pzk5eXFXmqoVOyjhhrDMQ/t22+/1erVq9WhQwd1795dISEhni4NQDXhaEe+/PJLrV27VhMmTNBXX32lF198Ue3bt9e9996rVq1aebpMnEeYPY0a4YMPPlBycrIkqWfPnho3bpxiY2NlNps9XBmA6sTRt5GSkqKLLrpIBQUF2rlzp9588021adNGP/30k6Tjqz+BykBQw3ktLy9PNptNS5YscQ5F+Pn5KTg4WLNmzVJ2draHKwRQHeXk5MjHx0crVqyQj4+PevToIZvN5lzxydAnKgtz1HBe27hxo3766ScdPnxYH330kUJCQuTn5ydvb29t3LiRYU8AbnH0wnft2lXvvfeedu/erVdeeUWStGvXLvXp08eT5eE8RFDDeS0mJkYNGzZUWFiYwsLClJ+fr9zcXJlMJo0dO1bBwcGeLhFANdSxY0dddNFFMplMCg0NVVFRkQYNGqQWLVpIEvsyotKwmAA1QmJiourVqyfp+JCFl5cXIQ3AGbNarcrOzpbFYpG3t7f8/f3l4+MjPz8/T5eG8ww9ajjvWa1WZWRk6KmnnlKTJk309NNPa/fu3SotLdXFF1/s6fIAVEPfffedJk2apAMHDsjPz0++vr4KDw/XV199xZdAVCr6ZnHecnQWb9myRR9++KFatmyp1NRUScd72ObMmSPp+HJ7AHCV3W7XxIkT9cknn+iiiy7SmjVrdOutt+qyyy4jpKHSEdRw3nIEtYMHD6px48bq0qWLc1giPz9fhYWFZY4DAFdkZ2crPDxcISEhKikpUWBgoIYNG6aMjAxPl4bzEEOfOO/l5uaqVq1aSk1NVa1atSRJGRkZioyM9HBlAKqjvLw81alTRxaLRVFRUdq9e7cKCwu1b98+SX9vigtUBn6TcN5yNJSXXnqpkpKS9MEHH8hkMmnq1KlatWqVunTpIon9jgC4JywsTMOGDZO/v7+uv/563XjjjXrmmWecJ2SnTUFlYtUnzmuOc+7t2bNHs2fP1sGDB1W/fn3961//0uWXX863XgBnLD8/X0FBQcrPz5fFYlFERISnS8J5iKCGGqWkpEQ+Pj7Onzl5MgB3FRQUaPXq1frkk0/Ur18/DRo0SFu3blWtWrVUp04dT5eH8wxz1HDeO3bsmL7++mv98ccf8vX1VXBwsKxWq+655x6Fh4d7ujwA1YTVapXZbNbnn3+uw4cPy2QyKTc3V5K0dOlS1alTR0OHDmWOGioVv0k4bzk6i++//34dOnRILVu21AUXXKDg4GDZbLYyPWsA4KodO3aof//+atu2rQICAiQdX6BEm4JzgR41nLccQ5pWq1UvvfSS7Ha7LBaLLBaLiouLFRQU5OEKAVQnjjbF0VuWmJjo3DQ7KyvLuaqc6RSoTPSo4bxmsVjUoUMH7d69WyaTSX5+fgoJCXE2qADgKkcAu/LKK7VixQpt2bJFBQUFGj9+vLy8vNSsWbMyxwGVgR41nNe8vb3VokULXX/99UpISFBQUJB8fX3VpEkTPfbYY54uD0A14ghgV111laxWq3bu3KmpU6eqdevWeuyxx1hIgHOCVZ84LzlWc27cuFFjx47VI488oqCgIOXm5iotLU3/3969B0dd3nscf4fd3O8hIQkBBCJBLgYlQAOKKGADWsSDrcownHKsFRykWmudnjKnPXKq9oICFadiubXUS72hIKjkgCAUWhDUIBACJIYkJNlcNrfd7C275w9n19KenhMo4cdv83nNOBN2N84nM5kn39/zPN/niYuL41vf+pa6PkWk2yorK0OXr6empobGDo0j0pM0oyZhzeFwcOONNzJz5ky8Xi8RERHn3e2pwVVEusPj8fDCCy8QHx8fuoQ9NjaW+Ph44uPjiY2NpaioyOiYEoZUqElYCj7hRkVFERkZSXl5OUOHDj3vM2qhF5HuCgQCXHfddbjdbtra2nC73dTW1uJyuXA6nSQnJ6tQkx6hpU8JS8EibNOmTTz55JPExMQQHx9PYmIibrebRx55hNmzZ2vJQkQuyl8fnu1yufB4PCQlJRmcSsKRCjUJaz6fD4fDgcvloqWlhZaWFs6ePcuYMWPIy8tToSYi3RIcKxwOB4cOHWLTpk1cf/31PPTQQ5SUlJCYmMiQIUOMjilhSEufEtasVitJSUkkJyfT3t7Otddey9e+9rXQ+yrSRKQ7goXaRx99xO7du0lLS6O1tRWAo0ePUlNTw+OPP64tFXLJ6bdJwl6weWD58uWcPXvW4DQiYkbBxaeTJ09yww03MHXqVKKjowFobW0NLYNqkUouNRVqEvaCs2YNDQ0kJycbnEZEzCg4jvj9fiwWC2VlZfTr1w+ApqYmUlNTz/ucyKWiQk3CXnAZYvLkyfTt29fgNCJiRsFxZPr06Zw9e5Zdu3bh9/tZvXo1NTU1TJgwAVChJpeemgkkrHm9Xurq6mhvbycvLw+rVdsyReTiNTc38+mnn7Jt2zYqKioYMWIE9913H7m5uUZHkzClQk3Cltvt5pVXXuH5559n8ODBvP766xw4cIDDhw/z0EMPGR1PREwk2Ezwne98h1WrVpGQkADoPEbpefrtkrATfPY4deoU+/fv58033yQmJgaA6Oho9u3bB3DeDQUiIv+X4JJmfHw8zc3NoddVpElP0zqQhK2mpiays7PxeDzEx8cDX14DE9z0q8lkEbkQgUAAi8XCE088wdy5c8nKyiIpKYm4uDjS09ONjidhSoWahC2LxUJqaiqfffYZWVlZAHz66adkZGQYnExEzKizs5OOjg4AVq1aFbqRIC0tjc2bNxucTsKV9qhJ2AoEAmzYsIE1a9aE2uiHDBnCY489xqBBg3QrgYj801wuF+3t7XoAlB6jQk3CXkVFBcePHyc9PZ0hQ4aEijYRkQvV1dVFRERE6D+RnqZCTcJSZWUl7e3t9O/fn7S0NKPjiIiIXBS1q0jY+ctf/sKzzz7L448/zo9+9CPKyspC7/385z/n1KlTBqYTERHpPhVqEnZWr17NlClTePXVV5kwYQLf//73Q8XZ4cOH1U4vIhetrq6OM2fO4PF4jI4ivYT+YknYaW1tZc6cOSQlJXH//ffzne98h0cffRSPx4PVag0dzyEi0h3BHUJHjx5l7dq13H777ezZs4dAIMDGjRs5ffq0wQklnOl4Dgkrfr8fn89HfX09mZmZBAIB5syZQ0tLCwsWLKCtrU2FmohckOBF7L/5zW+45557sNlsxMfHExERwYEDB8jLy+Pqq69WJ7n0CM2oSVjx+XzMmzePuro64KvTxOfPn8+1117LmTNnNJCKyAUJjhnNzc1MnDgRr9cbalJyOp2hrzW2SE/QjJqElaioKObNmxf6d/AevsjISB5++GFmzpxpYDoRMaPgvtacnByOHz9OfX09ycnJeL1eHA6HOsulR2lGTcJOcD9JVVUVmzZtYtu2bcCXB1OOHj3ayGgiYmL33Xcf69evp6WlhU2bNjFjxgzmzJmjw26lR+kcNQkrwT0iR48e5fe//z0lJSUUFhbyxBNP8MYbb1BbW8uSJUu0l0RELorNZqO4uJj29na+9rWvkZ+fj8ViMTqWhDEtfUpYCRZgf/7znxk1ahTf+MY32LNnDwAdHR20tLQAX20OFhHpLo/Hg8vl4rbbbiM+Pp6oqCijI0kvoEJNwkpwgtjpdJKbm8vZs2dJT08HoL29PfS1iMiFsNvtvPbaa+zfvx+Hw0EgEMDj8TB69GiefvppzdJLj1GhJmEluOl36NChtLa2sn//fu68804qKir47LPPmDt3LqDuLBHpnq6uLiwWCwcPHuTdd9/lySefJC4uDqfTSVtbG3FxcUZHlDCnQk3CSkREBIFAgFmzZvHiiy9y/Phxdu/eTf/+/Xn00Ue56aabAHQ7gYh0S3CLRFRUFDNnziQ/P/+894Oz+Hr4k56iZgIJS3+9DOF0OvH7/aEDKkVEumvfvn2cPHkSu91ObW0tc+bMIS8vj4SEBGJjYwG07Ck9SoWahC0NniLyz9q2bRvbtm0jNTWVsrIy7HY7KSkp9OnTh6amJv793/+d6dOnh85sFLnUVKiJiIj8A263m66uLqqrq4mPjwfA4XDgcrmw2+2MHDlS56hJj1L5L72GnklE5EJFR0cTFxfHf//3fxMVFUVOTg55eXnk5+fj8/l0zI/0OBVqEpZcLhfl5eVUV1eHXtMyqIhcqIaGBurr63n33Xepra3F5XLhcDgA2LhxI06n0+CEEu7U9Slhp62tjT/+8Y8899xzjB07lo0bN1JcXExdXR3z5883Op6ImMjnn3/Ohx9+SF1dHe+88w4JCQnExMTQp08f6urqSElJMTqihDnNqEnYCC5tfvzxx5w5c4Z169aRlJQEELqtAL48F0lEpDtyc3OZMGEC48aNIzs7G4CmpiZaWlr45S9/SUJCgsEJJdxpRk3CRrDL02azMWTIEHw+H4mJicCXs2xpaWkGJxQRsxk0aBCDBg1iwoQJ9OvXz+g40gtpRk3CRnAPWmxsLBaLhcOHD4e6sU6fPq3ro0TkggUCAQKBAP369cPv96spSS47FWoSNoKF2u23347b7ea1117j2LFj3HHHHdhsNu6++25AtxKISPdFRESExpY+ffqoKUkuO52jJmHHbreTkJDAp59+yokTJxg2bBhjxozRnXwiclGam5vxer1kZmYaHUV6IU0tSNhZt24ddXV1jB8/nn/9139l4sSJ7N69G5vNZnQ0ETGhP//5z+zdu/e81w4ePMiePXsMSiS9iQo1CRt1dXVUVVXx0Ucf0dzcjMfjwe12A/Dmm2/icrkMTigiZtLU1MTp06d5//33qaiowOFwUF9fD8DevXupqKgAwO/3GxlTwpy6PiVsnDx5kp07d1JVVcXLL79MXFwckZGRBAIBKioqdN6RiFyQxsZGiouLOXLkCM3NzSxfvhyfz4fVauXjjz/me9/7ntERpRdQoSZh4+qrr6arq4tz584xYcIEnE4nTqeTiIgIVq5cGTpTTUSkO/r378/MmTOpr69n2LBhZGRkYLfb8Xg8/Mu//AsjRowA1KAkPUvNBBJ2vF4vkZGRRscQkTDxzDPPsHjxYmJiYoyOIr2QZtQkrPh8PioqKvjwww+x2+3ExsYSExNDRkYGc+bMMTqeiJhI8BDtyspKmpqayMnJMTqS9EKar5WwEJwYPnXqFCtWrODAgQPs27ePc+fOsX79epqbmwFt+hWR7guemdbe3s6zzz7L/v37qaiooK2tzeBk0ptoRk3CQvDJt7S0lKuvvpqZM2fyu9/9jl/84he8/PLLoU4tEZEL4fF4SEhI4IsvvuA//uM/8Pv9OJ1O4uPj2bVrl9HxpBdQoSZhxW63k5SURHV1NVbrl7/egUAgdEyHtmSKyIWIioriueeeC/3b6/XS2dmJw+EwMJX0JirUJCwEC7CMjAxiY2PJzs6mpaWFn/3sZ5w8eZJbb73V4IQiYlbnzp3jT3/6E4FAgP79+zN27Fiys7ONjiW9hLo+xfSCy55/6+DBg7z77rtMmjSJm266SVdIiUi3BceVL774grVr13L8+HEyMjIoLy9nyJAhLF++XEf+yGWhQk3Cwp49e3C5XPTt25eUlBQSEhJITU0lOjra6GgiYkJ+v58+ffrw0ksvcezYMZ566qnQe7/61a+Iiori4YcfDn1OpKdo6VPCwpEjR6ioqCAiIgKPxxN6Go6JicHlcvGf//mfulBZRLotOIdhs9n+bpkzPj4+9L7mOqSnqVCTsHD33XfjdDpxuVyh5oG2tjba29tDDQYiIt0VnCUbO3Ysmzdv5q233iI/P5/29nZKS0uZPn06wP+67ULkUtLSp4Sd2tpaurq6QkugXV1dWCwWo2OJiEmtW7eOXbt2ha6oW7x4MXfddVeos1ykJ6lQk7Dh8/n44IMPePXVV6moqMDn8zF58mSWLVtGbGys0fFExMSam5ux2+3k5uYaHUV6GRVqEjZqamq47777+OlPf8qkSZNobGzkt7/9LW1tbTz99NNGxxMRE6qrq+P111/H4XAQGRnJqFGjmDp1KlFRUUZHk15CrSpiesFnDbvdzvDhw5k0aRIA6enp3HHHHZw9exb48qBKEZEL8atf/Ypjx47Rt29fYmJiWLFiBevXr9d1dHLZaIFdwobX68XhcHDgwAGGDx9OTEwMFRUV5OfnAxAZGfkPz1wTEflbbrebjz/+mD179oReW7hwITNnzmTRokUGJpPeRIWahA2/309tbS1Lly4lLy+PiooKnE4n48aNY+nSpUyePJkZM2YYHVNETMLj8XDDDTdQWVlJTk4OVquV+vp6MjIyjI4mvYgKNTG94AzZgAED+PnPf47VaqW5uRmn00lraysej4e6ujoSEhIMTioiZhCcebfb7Rw5coRHH32UO++8k87OTg4fPszUqVOpr68nMjKStLQ0o+NKmFMzgYSF4MDqdrtpaWkhPj6emJiYUPt8cCOwNgCLSHedO3eODRs24Pf7OXv2LA6HA5fLhcPhoKWlhRtvvJFnnnnG6JgS5lSoiekFi7TDhw9TXFxMVVUVfr+fyMhIHA4HP/zhD7nmmmuMjikiJvL/7Wd1Op243W5SU1MvYyrpjbT0Kabn9/uxWCysXLmSESNGcPfdd9PV1YXL5aKpqYm+ffsaHVFETCYiIoKOjg527txJZWUlqamp9O3bl/j4eEaMGEG/fv2Ii4szOqb0AirUxPSCV7306dOH733ve+ftRVOXp4hcqOBtJm+//TYffPABcXFxdHV10dHRQUVFBT/84Q/55je/qVtP5LLQ0qeEja1bt2Kz2bjhhhtIT08nKSlJe9JE5IIFC7D777+fefPmccsttxgdSXoxzahJWAgEAnR0dLB27Vq2bdtGREQEgUCAqKgoXn31VaPjiYiJBGfJZs+ejcvlwm63ExkZSWRkJBaLRXd8ymWl3zYJCw0NDaxcuZL169cTGxuLx+PB4XDg8XiMjiYiJpWdnc1PfvITPvroI3Jzc0lISCAyMpKZM2dqf5pcNirUJCx4PB5mzJjBqFGjjI4iImHiueeeY9y4ccTGxlJeXo7D4aCpqYmioiKjo0kvokJNTC3YLNDW1kZZWRlr1qxh2rRpJCYm0rdvXy1RiMhFs9ls/O53vzM6hvRy+ismphbs6PR6vbjdbt555x1effVV/H4/NpuNb33rWyxbtkzdWSJyQTo7Oxk5ciTFxcVcc801xMXFER0dTXR0NJGRkUbHk15EXZ8SdjweDx6Ph/b2diwWC/369TM6koiYTENDA48++iitra2MHz8+dNNJ//79ueeee4yOJ72IZtQkbBw8eJCTJ08yefJkBg8eTGxsrGbRROSiJCUlsWDBAgKBALW1tXR0dNDU1ERbWxvw5UHbwTMcRXqSZtTE1IKD5ZYtW9i7dy9bt25l9erVTJ8+nccff5w777yTSZMm6eBbEemWjo4O6uvrSU5OJiUlRftcxXD6DRRTCz5nvPHGGzz11FNER0cTExMDgN1uNzKaiJjQn/70J9atW0f//v2JiooiPj6exMREkpOTsVqtjBs3Tt3lclmpUBNT++tmgrS0NBobGxk4cCDw5WbgjIyM8z4nIvJ/KSgoICEhAYfDQWNjI3a7nZaWFhobG0N3fo4aNUpLn3LZqFATUwsOlIWFhWzfvp3y8nJcLhcHDx4EID093ch4ImIy6enp3Ro3VKTJ5aI9ahIWfD4fP/7xjzl58iSJiYnU1NSwYsUKrrvuOqOjiYjJ/PVxPj6fDwCr1UpDQwN9+/ZVkSaXlQo1MT2Px8Pp06cZOXIkJ0+epKuri2uuuUaDqYhctJ07dzJ+/HiSkpJChdt3v/tdvv/97zNy5Eg1KMllo79kYnput5sXXngBgOHDhzNy5EhaWlpYvny5wclExGyCcxdr1qwhKioK+OqS9ueff54RI0YA2vcql4/2qIlpOZ1OSkpKOHDgAOfOnaOiogKv10tOTg7l5eWcOHEC0HlHItJ9wQJs9OjRHD16lIKCgtD4ESzcRC4nFWpiWh6Ph5qaGvbt20ddXR2/+c1v6OjowO/309DQwPjx44GvnpBFRLrD4/Hw8ccfs2/fPqZNm0ZWVhZpaWmkp6czceJEo+NJL6M9amJaXV1duFwuDhw4QE1NDTfeeCM2mw2Px0NWVhbDhg3TTJqIXDC3280rr7yCx+Ph3LlzNDY2UltbS2xsLH/4wx+0P00uKxVqIiIi/4DL5Qodoi1iBC19iun97bOGnnRF5J/h8/nYsWMHxcXFJCYmEh8fz5QpUygsLDQ6mvRCWhcS04uIiAgtRahIE5GL5ff7Adi/fz9bt25lyJAhFBYW4nA4WLVqFSUlJQYnlN5IS58iIiJ8ddDtypUriYuL44EHHgi9t2bNGjo6OvjBD35w3oG4Ij1NS59ienV1dezcuRO/309ycjJ9+/YlISGB0aNHazAVkQsWERGBzWY777Xa2lqGDRsWel/kctGMmpjeI488QkNDAzExMTgcDhwOB62trezYsUObgEXkgp06dYq1a9disVgYNWoU1dXV1NTUcP/995Ofn6+uT7msVKiJqbndbr7+9a+zZ88eo6OISBgIFmElJSVs2bKFiooKMjMzeeihh+jfv7/R8aQX0tKnmJrH42Hq1KnU1dWRkZGhpU4RuWjBW0xWrFjB4sWLyc/PD71XXl6Oz+fDatWfTbm8NKMmphR86j179iyLFy8mNTWVoqIiUlNTSU5OJisri9zcXKNjiogJ3XbbbWzfvv2816ZPn86bb75JcnKyQamkt9KjgZhScH9IZGQkN9xwA16vl927d9Pe3k5dXR0FBQU888wz6s4SkW578cUXeeutt7DZbDz22GNkZmaSlZVFSkoKSUlJKtLEEJpRExER4cvOzn379vHyyy8zbtw4qquraWhoIBAI8O1vf5s77rjD6IjSC6lQE1MKzpTt2LGD7du3M3ToUFJSUkhLSyMxMZGRI0eSkZFhdEwRMaHKykquuuoqo2OIAFr6FJMKLmcmJCSQmJhITU0Nn332GadPn6a+vp6nnnqKOXPmhDYHi4j8f4J7X4MNBampqSQmJpKTk8NVV11Fdna20RGlF9KMmpjWPzrLaPXq1UyZMoVrr71W5x2JSLcEx4ojR46wdetWTpw4QXl5OampqVRWVlJUVMSqVavU+SmXnX7bxLQiIiJCd/NFRETQ1dWF1Wrlk08+YcKECQanExEz+etCLSMjgzFjxlBXV8eiRYtYvXo148aNA1Bzklx2KtTElIKD6lNPPYXX6yUrK4v09HQcDgdNTU1kZmYCuupFRLonuLjU2NjIddddR2NjI52dnQB4vV4OHz5MYWGhZunlstPmHTGl4KA6ZMgQLBYLZWVlvPfee3z++ef87Gc/00ZgEbkgwb2smZmZJCcnM3DgQKqqqti8eTPHjx9nwIABBieU3kozamJKffr0weVyce+99563FOHxePjkk08MTCYiZhScJZs6dSpxcXGkpKRw5MgRVq9ezezZs7nlllsA1Jwkl52aCcR0urq62Lp1Ky+99BL9+vWjoKCAGTNm8P777/PBBx9gt9vZsWOH0TFFxESCHeLLli1j4cKFoe0TIkbTjJqYTnV1NX/4wx+YPXs2AAcPHuTQoUOcOXOGhx9+mNtvv93ghCJiNsGZstOnTxMbG2twGpGvqFAT06mvryc9PZ358+cDcPXVV7N06VJ27dplcDIRMbvo6GiWLl3KtGnTyMzMJCkpiaSkJAYOHGh0NOmlVKiJ6TQ2NtKnTx86OzsJBALYbDYKCgoA6Ozs1NOwiFwUr9fL4MGDKSsrY9euXVgsFtxuN1arlV//+tdGx5NeSoWamE5nZyfHjx/nJz/5CbGxsZw9exaXy8Xrr7+O0+lk3LhxjBo1yuiYImIyVquVBx98kLS0NOx2O7W1tXi93tB5jSJGUKEmpjN16lQGDBhAS0sLjY2NDBo0iJaWFo4cOUJVVRWpqamMGjVK10eJyAWJiIjA7Xbz7LPPUlBQwJQpU+jo6CAyMtLoaNKLqetTRER6teAhtseOHePtt9/m888/Jzk5mRdeeIHXX3+dpqYmFi1apMNuxRCabhBT6urqCn3t8/nw+XwANDQ0aJlCRC5IcL7i6NGjxMXFsWDBArKysoAvj+2orKwMfS1yualQE1OyWCzs3LmTtrY2rFZr6Cn3xz/+MaWlpcBXg6+IyP8lOFbU1tYyfPhw/H4/6enpADQ3N5OdnW1kPOnlVKiJ6QQH1TVr1hAVFQV8dVHy888/z4gRIwDd8yki3RPcy5qfn09paSkvvfQSACUlJZSWljJ06FBAY4oYQ4WamE5wsBw9ejRHjx49bzkiKipKg6mIXJDgmDFlyhRyc3OJiIigrKyMX/ziF4wZM4Zbb70V0PVRYgw1E4gpeTwevvnNb+JyuZg2bRpZWVmkpaWRnp7OxIkTjY4nIibW1NREeXk5eXl5JCcnGx1HejkdzyGmFAgEmDNnDh6Ph3PnznHo0CFqa2uJjY1l4sSJ6s4SkQvm8/nYuHEjVVVV9OvXj7a2NiZPnhzaYiFiBM2oiem5XC5iYmKMjiEiJvfTn/6Ujo4O0tLSaGxspKamhvnz5zNr1iyjo0kvphk1MSWfz8eOHTsoLi4mMTGR+Ph4pkyZQmFhodHRRMSEvF4ve/fuZcuWLSQkJABQWlrKkiVLVKiJobQzUkwl2Diwf/9+tm7dypAhQygsLMThcLBq1SpKSkoMTigiZuR0OikoKAgVaQADBgwgJyfHwFQimlETkwmu1B85coTrr7+eBx54AIDbbruNNWvWUFxcTH5+Pl1dXaEjO0RE/j9tbW0cPHiQefPmcfPNNxMVFUVZWRk5OTm0tLQQGRlJfHy80TGlF1KhJqYUERGBzWY777Xa2lqGDRsWel9EpLtiYmKYPXs2NpuN0tJS6uvraW1tJTs7m7lz5zJ27FiefPJJo2NKL6RmAjGlU6dOsXbtWiwWC6NGjaK6upqamhruv/9+8vPz1fUpIhfE7/f/3TlpTqeTxsbGUMPSoEGDDEonvZkKNTGdYBFWUlLCli1bqKioIDMzk4ceeoj+/fsbHU9ETOrAgQNs3rwZq9VKXl4eRUVFuj5KDKdCTUwl+NS7YsUKFi9efN75RuXl5QwaNAirVSv6ItI9wTFl27ZtFBcXk5GRgcVi4dixY4wYMYIHH3yQ1NRUo2NKL6auTzGV4NJEcXHx3x1C+cADD+BwOIyIJSImFZyr2Lt3L4WFhSxdupQf/ehHbNq0iS+++IJDhw4BnHdVncjlpKkHMZUXX3yRt956C5vNxmOPPUZmZiZZWVmkpKSQlJSk615E5KLYbDYGDx583msWiyXU6ak9r2IUFWpiKrNmzSI1NZWXX36Z1NRUysvL+ctf/kIgEGDBggVGxxMRkwke4zN37lxWrlxJYWEhubm5VFdXEwgEQg0EKtTEKNqjJqZUWVnJVVddZXQMEQkTXV1dvPPOOxw7dizU7blkyRLy8/ONjia9nAo1MZVgx2dVVRVvvPEGqampJCYmkpOTw1VXXaUOLRH5p3g8Hjwez3k3FIgYSYWamEawSDty5Ahbt27lxIkTlJeXk5qaSmVlJUVFRaxatQqfz6fOTxERCQv6ayam8deFWkZGBmPGjKGuro5FixaxevVqxo0bB6Cro0REJGzoeA4xjeDkb2NjI0OHDqWjo4POzk4AvF4vhw8fPu9zIiIiZqdCTUwjeIZaZmYmycnJDBw4kKqqKjZv3szx48cZMGCAwQlFREQuLS19imkE2+OnTp1KXFwcKSkpHDlyhNWrVzN79mxuueUWgL+7r09ERMSs1EwgphG86mXZsmUsXLiQzMxMoyOJiIj0KE09iGkEZ8pOnz5NbGyswWlERER6nmbUxHS++93vEhMTw7Rp08jMzCQpKYmkpCQGDhxodDQREZFLSnvUxFS8Xi+DBw+mrKyMXbt2YbFYcLvdWK1Wfv3rXxsdT0RE5JLSjJqYSiAQwG63k5aWht1up7a2Fq/Xi9/v5/rrrzc6noiIyCWlGTUxlYiICNxuN88++ywFBQVMmTKFjo4OIiMjjY4mIiJyyamZQEwhOPF77Ngx1q9fz6FDh3jllVcAeO+999iwYcN5nxMREQkHKtTEFIIF2NGjR4mLi2PBggVkZWUBXx7bUVlZGfpaREQkXKhQE1MIFmq1tbUMHz4cv99Peno6AM3NzWRnZxsZT0REpEeoUBNTCJ6hlp+fT2lpKS+99BIAJSUllJaWMnToUOCr2wtERETCgZoJxBSCBViweeCTTz6hrKyMAwcOMG3aNG699VZA10eJiEh40fEcYkpNTU2Ul5eTl5dHcnKy0XFERER6hGbUxFR8Ph8bN26kqqqKfv360dbWxuTJk4mKijI6moiIyCWnQk1M5b/+67/o6OggLS2N06dPs2fPHpxOJ7NmzTI6moiIyCWnQk1Mw+v1snfvXrZs2UJCQgIApaWlLFmyRIWaiIiEJe28FtNwOp0UFBSEijSAAQMGkJOTY2AqERGRnqMZNTGNtrY2Dh48yLx587j55puJioqirKyMnJwcWlpaiIyMJD4+3uiYIiIil4y6PsU0Ghoa2LRpEzabDa/XS319Pa2trWRnZ1NVVcXYsWN58sknjY4pIiJyyahQE9Pw+/1/d06a0+mksbERl8tFTEwMgwYNMiidiIjIpadCTUzlwIEDbN68GavVSl5eHkVFRbo+SkREwpaaCeSKF7xofdu2bfzxj38kOTmZpKQkdu7cyYYNG7Db7QYnFBER6RlqJpArXnDSd+/evRQWFnLvvfeG3nvggQc4dOgQX//61//XpVEREREz0181MQ2bzcbgwYPPe81isYQ6PXUhu4iIhBvNqMkVz2KxADB37lxWrlxJYWEhubm5VFdXEwgEQg0EKtRERCTcqJlATKOrq4t33nmHY8eOhbo9lyxZQn5+vtHRREREeoQKNTEdj8eDx+M574YCERGRcKRCTUREROQKpWYCERERkSuUCjURERGRK5QKNREREZErlAo1ERERkSuUCjURERGRK5QKNRHpNaZOncq6deuYNWsWBQUFPPLII7jdblpbW1m4cCGFhYWMHz+ehQsXUldXF/q++fPns2LFCu69916uv/56Fi1ahN1u5wc/+AFjx47lrrvuorq6OvT5M2fO8G//9m9MmDCBoqIitm/fbsSPKyJhQIWaiPQq7733HmvXrmXnzp2cPHmSt956C7/fz5w5c/jwww/58MMPiY6OZtmyZed93/bt2/nlL3/JRx99xNmzZ7n33nu56667OHjwILm5uTz//PMAOJ1O7rvvPr7xjW+wf/9+VqxYwRNPPMHp06eN+HFFxORUqIlIrzJ//nwyMzNJSUnhlltu4cSJE6SmplJUVERsbCwJCQk8+OCDHDp06LzvmzNnDoMGDSIxMZGbbrqJgQMHMmnSJKxWKzNmzOD48eMA7N69m5ycHO666y6sVisjR46kqKiI999/34gfV0RMTnd9ikivkpGREfo6NjYWm81GZ2cnTz/9NHv37qW1tRUAh8NBV1dX6K7Z9PT00PdFR0ef9++YmBicTicANTU1lJSUMG7cuND7XV1d3HHHHT36c4lIeFKhJiK93vr166moqOC1114jIyODEydOcOedd3IxF7dkZ2czfvx4NmzY0ANJRaS30dKniPR6DoeD6OhokpKSaGlpYfXq1Rf9/7r55pv54osvePvtt/F6vXi9XkpKSjhz5swlTCwivYUKNRHp9b797W/jdrspLCzknnvuYfLkyRf9/0pISGDdunVs376dyZMnc+ONN7J8+XI8Hs8lTCwivYUuZRcRERG5QmlGTUREROQKpUJNRERE5AqlQk1ERETkCqVCTUREROQKpUJNRERE5AqlQk1ERETkCqVCTUREROQKpUJNRERE5AqlQk1ERETkCvU/xACKB3dGmwoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>successes</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>flakes</th>\n",
+       "      <th>passPercentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run template e2e-aws-upi - e2e-aws-up...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  successes  failures  \\\n",
+       "1  operator.Run template e2e-aws-upi - e2e-aws-up...          0        48   \n",
+       "\n",
+       "   flakes  passPercentage  \n",
+       "1       0               0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = by_job.columns[:5]\n",
+    "for i in c:\n",
+    "    job = pd.DataFrame(by_job[i].results).drop([\"BugList\", \"searchLink\"], axis=1)\n",
+    "    job = job[job[\"name\"] != \"Overall\"]\n",
+    "\n",
+    "    print(i)\n",
+    "    print(f\"Job pass percentage is {int(by_job[i].testPassPercentage)}%\")\n",
+    "    display(pd.DataFrame(job))\n",
+    "    print(\"\\n\")\n",
+    "\n",
+    "    to_plot = (\n",
+    "        job.set_index(\"name\")\n",
+    "        .drop([\"successes\", \"failures\", \"flakes\"], axis=1)\n",
+    "        .apply(lambda x: x / 100)\n",
+    "    )\n",
+    "    to_plot.plot(kind=\"bar\")\n",
+    "    plt.ylabel(\"Pass Percent\", fontsize=15)\n",
+    "    plt.title(f\"{i}\", fontsize=20)\n",
+    "    plt.xticks(rotation=85)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The connection to \"results\" is a little unclear here. let's take the chart above for example: it shows that the job \"release-openshift-ocp-installer-e2e-aws-upi-4.6\" has a test pass percentage of 71%, but the results show only a single test with a pass percentage of 0. Which leads me to believe that the test pass percentage for the job is not a function purely of the results shown here.\n",
+    "\n",
+    "We should follow up with sippy team for clarification."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BySig \n",
+    "\n",
+    "same as above but sliced by the \"special interest group\"(team) that is responsible for the tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-22T17:00:08.758763Z",
+     "start_time": "2021-01-22T17:00:08.553260Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1611334803093,
+   "trusted": false
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/background/testgrid_feature_confirmation.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/background/testgrid_feature_confirmation.ipynb
new file mode 100644
index 00000000..16abb4ce
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/background/testgrid_feature_confirmation.ipynb	
@@ -0,0 +1,1873 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TestGrid Additional Features - uniform or unique?\n",
+    "\n",
+    "As can be seen in an [earlier notebook](../testgrid_EDA.ipynb), TestGrids have a more metadata (features) than just the test values we've been focused on. \n",
+    "\n",
+    "In this notebook we are going to take a closer look at these other metadata fields for both Openshift and Kubernetes and determine if they are uniform across grids and worth taking a closer look at our are distinct by grid. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# access the testgrid.k8s.io to get the dashboards for Red Hat\n",
+    "response = requests.get(\n",
+    "    \"https://testgrid.k8s.io/redhat-openshift-informing?id=dashboard-group-bar\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "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]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "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": [
+    "# Print all the feature names for one grid\n",
+    "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",
+    ")\n",
+    "for i in response.json().keys():\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a list for one specific dashboard, we need to check and make sure that this is standard across all Red Hat and Kubernetes grids before moving forward. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": [
+    "# Iterate through each board and collect\n",
+    "download = True\n",
+    "if download is True:\n",
+    "    available_features = {}\n",
+    "\n",
+    "    for dashboard in dashboard_names:\n",
+    "        response_1 = requests.get(f\"https://testgrid.k8s.io/{dashboard}/summary\")\n",
+    "        jobs = response_1.json().keys()\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",
+    "            available_features[f\"{dashboard}_{job}\"] = response_2.json().keys()\n",
+    "        print(f\"{dashboard} downloaded \")\n",
+    "else:\n",
+    "    print(\"Not Downloading\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"redhat-openshift-informing\"_release-openshift-okd-installer-e2e-aws-upgrade\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-informing\"_release-openshift-origin-installer-e2e-aws-upgrade\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-3.11-informing\"_periodic-ci-openshift-origin-release-3.11-e2e-gcp\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.1-blocking\"_release-openshift-ocp-installer-e2e-aws-4.1\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.1-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.1\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.1-blocking\"_release-openshift-origin-installer-e2e-aws-4.1\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.1-blocking\"_release-openshift-origin-installer-e2e-aws-serial-4.1\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.1-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.1-stable-to-4.1-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.1-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-blocking\"_release-openshift-ocp-installer-e2e-aws-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-blocking\"_release-openshift-origin-installer-e2e-aws-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-blocking\"_release-openshift-origin-installer-e2e-aws-serial-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.1\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.2-s390x\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-console-aws-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-aws-fips-4.2\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-aws-fips-serial-4.2\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-aws-mirrors-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-aws-proxy-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-aws-upi-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-azure-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-azure-fips-4.2\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-azure-fips-serial-4.2\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-azure-serial-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-gcp-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-4.2\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-serial-4.2\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-gcp-serial-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-metal-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-ocp-installer-e2e-metal-serial-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.1-stable-to-4.2-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.2-stable-to-4.2-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-gcp-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-gcp-serial-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.2-informing\"_release-openshift-origin-installer-old-rhcos-e2e-aws-4.2\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-blocking\"_release-openshift-ocp-installer-e2e-aws-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-blocking\"_release-openshift-origin-installer-e2e-gcp-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-blocking\"_release-openshift-origin-installer-e2e-gcp-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-broken\"_release-openshift-ocp-installer-e2e-azure-ovn-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-broken\"_release-openshift-ocp-installer-e2e-gcp-ovn-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.3-ppc64le\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.3-s390x\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-e2e-aws-scaleup-rhel7-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-console-aws-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-aws-fips-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-aws-fips-serial-4.3\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-aws-mirrors-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-aws-ovn-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-aws-proxy-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-aws-upi-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-azure-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-azure-fips-4.3\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-azure-fips-serial-4.3\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-azure-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-gcp-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-4.3\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-serial-4.3\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-gcp-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-metal-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-metal-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-openstack-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-openstack-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-vsphere-upi-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-ocp-installer-e2e-vsphere-upi-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-4.3-cnv\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-calico-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-compact-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-disruptive-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-serial-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-shared-vpc-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.1-to-4.2-to-4.3-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.2-nightly-to-4.3-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.2-to-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-fips-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.2-to-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-azure-compact-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-azure-shared-vpc-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-gcp-compact-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-gcp-shared-vpc-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-ppc64le-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-s390x-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.3-informing\"_release-openshift-origin-installer-old-rhcos-e2e-aws-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-blocking\"_release-openshift-ocp-installer-e2e-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-blocking\"_release-openshift-origin-installer-e2e-gcp-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-blocking\"_release-openshift-origin-installer-e2e-gcp-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-broken\"_release-openshift-ocp-installer-e2e-azure-ovn-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-broken\"_release-openshift-ocp-installer-e2e-gcp-ovn-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-broken\"_release-openshift-origin-installer-e2e-azure-compact-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_periodic-ci-openshift-release-master-ocp-4.4-e2e-aws-proxy\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_periodic-ci-openshift-release-master-ocp-4.4-e2e-metal-ipi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_periodic-ci-openshift-release-master-ocp-4.4-e2e-metal-ipi-compact\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_periodic-ci-openshift-release-master-ocp-4.4-e2e-metal-ipi-ipv4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_periodic-ci-openshift-release-master-ocp-4.4-e2e-vsphere-upi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_periodic-ci-openshift-release-master-ocp-4.4-e2e-vsphere-upi-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.4-ppc64le\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.4-s390x\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-e2e-aws-scaleup-rhel7-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-console-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-aws-fips-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-aws-fips-serial-4.4\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-aws-mirrors-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-aws-ovn-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-aws-upi-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-azure-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-azure-fips-4.4\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-azure-fips-serial-4.4\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-azure-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-gcp-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-4.4\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-serial-4.4\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-gcp-rt-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-gcp-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-metal-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-metal-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-openstack-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-openstack-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-ocp-installer-e2e-ovirt-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-4.4-cnv\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-calico-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-compact-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-disruptive-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-ovn-network-stress-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-sdn-network-stress-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-serial-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-shared-vpc-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.1-to-4.2-to-4.3-to-4.4-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.4-nightly-to-4.4-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.4-stable-to-4.4-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-fips-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.4-to-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-azure-shared-vpc-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-gcp-compact-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-gcp-shared-vpc-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-ppc64le-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-s390x-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.4-informing\"_release-openshift-origin-installer-old-rhcos-e2e-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-blocking\"_release-openshift-ocp-installer-e2e-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-blocking\"_release-openshift-origin-installer-e2e-gcp-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-blocking\"_release-openshift-origin-installer-e2e-gcp-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-broken\"_release-openshift-ocp-installer-cluster-logging-operator-e2e-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-broken\"_release-openshift-ocp-installer-e2e-azure-ovn-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-broken\"_release-openshift-ocp-installer-e2e-gcp-ovn-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-broken\"_release-openshift-ocp-installer-elasticsearch-operator-e2e-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_canary-release-openshift-origin-installer-e2e-aws-4.5-cnv\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-ci-e2e-44-stable-to-45-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-aws-proxy\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-metal-ipi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-metal-ipi-compact\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-metal-ipi-ipv4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-vsphere\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-vsphere-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-vsphere-upi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_periodic-ci-openshift-release-master-ocp-4.5-e2e-vsphere-upi-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.5-ppc64le\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.5-s390x\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-e2e-aws-scaleup-rhel7-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-console-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-aws-csi-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-aws-fips-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-aws-fips-serial-4.5\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-aws-mirrors-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-aws-ovn-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-aws-upi-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-azure-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-azure-fips-4.5\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-azure-fips-serial-4.5\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-azure-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-gcp-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-4.5\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-serial-4.5\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-gcp-rt-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-gcp-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-metal-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-metal-compact-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-metal-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-openstack-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-openstack-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-ovirt-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-ocp-installer-e2e-ovirt-upgrade-4.4-stable-to-4.5-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-calico-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-compact-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-disruptive-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-ovn-network-stress-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-sdn-network-stress-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-serial-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-shared-vpc-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.2-to-4.3-to-4.4-to-4.5-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.4-stable-to-4.5-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.4-to-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-azure-compact-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-azure-shared-vpc-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.4-stable-to-4.5-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-gcp-compact-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-gcp-shared-vpc-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.4-stable-to-4.5-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-ppc64le-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-s390x-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.5-informing\"_release-openshift-origin-installer-old-rhcos-e2e-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-blocking\"_release-openshift-ocp-installer-e2e-aws-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-blocking\"_release-openshift-origin-installer-e2e-aws-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-blocking\"_release-openshift-origin-installer-e2e-azure-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-broken\"_release-openshift-ocp-installer-cluster-logging-operator-e2e-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-broken\"_release-openshift-ocp-installer-elasticsearch-operator-e2e-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_canary-release-openshift-origin-installer-e2e-aws-4.6-cnv\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-proxy\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-workers-rhel7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-compact\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-ipv4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-metal-ipi-virtualmedia\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere-upi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_periodic-ci-openshift-release-master-ocp-4.6-e2e-vsphere-upi-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.6-ppc64le\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.6-s390x\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-console-aws-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-aws-csi-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-aws-fips-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-aws-fips-serial-4.6\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-aws-mirrors-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-aws-ovn-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-aws-upi-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-azure-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-azure-fips-4.6\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-azure-fips-serial-4.6\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-azure-ovn-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-azure-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-gcp-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-4.6\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-serial-4.6\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-gcp-ovn-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-gcp-rt-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-gcp-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-metal-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-metal-compact-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-metal-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-openstack-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-openstack-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-ovirt-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-ocp-installer-e2e-ovirt-upgrade-4.5-stable-to-4.6-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-compact-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-disruptive-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-shared-vpc-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.3-to-4.4-to-4.5-to-4.6-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.5-stable-to-4.6-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.5-to-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-azure-compact-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-azure-shared-vpc-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.5-stable-to-4.6-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-gcp-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-gcp-compact-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-gcp-serial-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-gcp-shared-vpc-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.5-stable-to-4.6-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-ppc64le-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-e2e-remote-libvirt-s390x-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.6-informing\"_release-openshift-origin-installer-old-rhcos-e2e-aws-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-blocking\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-metal-ipi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-blocking\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-metal-ipi-ovn-dualstack\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-blocking\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-metal-ipi-ovn-ipv6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-blocking\"_release-openshift-ocp-installer-e2e-aws-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-blocking\"_release-openshift-ocp-installer-e2e-aws-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-blocking\"_release-openshift-origin-installer-e2e-aws-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-broken\"_release-openshift-ocp-installer-cluster-logging-operator-e2e-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-broken\"_release-openshift-ocp-installer-elasticsearch-operator-e2e-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_canary-release-openshift-origin-installer-e2e-aws-4.7-cnv\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-aws-proxy\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-vsphere\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-vsphere-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-vsphere-upi\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_periodic-ci-openshift-release-master-ocp-4.7-e2e-vsphere-upi-serial\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.7-ppc64le\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_promote-release-openshift-machine-os-content-e2e-aws-4.7-s390x\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-console-aws-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-aws-csi-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-aws-fips-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-aws-fips-serial-4.7\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-aws-mirrors-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-aws-ovn-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-aws-upi-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-azure-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-azure-fips-4.7\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-azure-fips-serial-4.7\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-azure-ovn-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-azure-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-gcp-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-4.7\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-gcp-fips-serial-4.7\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-gcp-ovn-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-gcp-rt-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-gcp-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-metal-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-metal-compact-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-metal-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-openstack-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-openstack-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-ocp-installer-e2e-ovirt-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-compact-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-disruptive-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-sdn-multitenant-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-shared-vpc-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.3-to-4.4-to-4.6-to-4.7-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-4.6-stable-to-4.7-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.6-to-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-azure-compact-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-azure-shared-vpc-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.6-stable-to-4.7-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-azure-upgrade-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-gcp-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-gcp-compact-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-gcp-serial-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-gcp-shared-vpc-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.6-stable-to-4.7-ci\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-e2e-gcp-upgrade-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.7-informing\"_release-openshift-origin-installer-old-rhcos-e2e-aws-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.8-blocking\"_periodic-ci-openshift-release-master-ocp-4.8-e2e-metal-ipi\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.8-blocking\"_periodic-ci-openshift-release-master-ocp-4.8-e2e-metal-ipi-ovn-dualstack\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.8-blocking\"_periodic-ci-openshift-release-master-ocp-4.8-e2e-metal-ipi-ovn-ipv6\n",
+      "set()\n",
+      "\"redhat-openshift-ocp-release-4.8-blocking\"_release-openshift-ocp-installer-e2e-aws-4.8\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.8-blocking\"_release-openshift-origin-installer-e2e-gcp-4.8\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-ocp-release-4.8-informing\"_periodic-ci-openshift-release-master-ocp-4.8-e2e-aws-proxy\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.3-informing\"_release-openshift-okd-installer-e2e-aws-4.3\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.4-informing\"_promote-release-openshift-okd-machine-os-content-e2e-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.4-informing\"_release-openshift-okd-installer-e2e-aws-4.4\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.5-blocking\"_release-openshift-okd-installer-e2e-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.5-informing\"_promote-release-openshift-okd-machine-os-content-e2e-aws-4.5\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.6-blocking\"_release-openshift-okd-installer-e2e-aws-4.6\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-okd-release-4.6-informing\"_promote-release-openshift-okd-machine-os-content-e2e-aws-4.6\n",
+      "set()\n",
+      "\"redhat-openshift-okd-release-4.7-informing\"_release-openshift-okd-installer-e2e-aws-4.7\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-presubmit-master-gcp\"_pull-ci-openshift-cluster-etcd-operator-master-e2e-gcp\n",
+      "set()\n",
+      "\"redhat-openshift-presubmit-master-gcp\"_pull-ci-openshift-kubernetes-master-e2e-gcp\n",
+      "{'alerts'}\n",
+      "\"redhat-openshift-presubmit-master-gcp\"_pull-ci-openshift-origin-master-e2e-gcp\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-aws\"_osde2e-int-aws-e2e-osd-default-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-aws\"_osde2e-int-aws-e2e-osd-default-plus-one-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-aws\"_osde2e-int-aws-e2e-osd-default-plus-two-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-aws\"_osde2e-int-aws-e2e-upgrade-to-osd-default-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-aws\"_osde2e-int-aws-e2e-upgrade-to-osd-default-plus-one-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-gcp\"_osde2e-int-gcp-e2e-osd-default-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-gcp\"_osde2e-int-gcp-e2e-osd-default-plus-two-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-gcp\"_osde2e-int-gcp-e2e-upgrade-to-osd-default-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-gcp\"_osde2e-int-gcp-e2e-upgrade-to-osd-default-plus-one-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-gcp\"_osde2e-int-gcp-osd-default-plus-one-nightly\n",
+      "set()\n",
+      "\"redhat-osde2e-int-moa\"_osde2e-int-moa-e2e-osd-default-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-moa\"_osde2e-int-moa-e2e-osd-default-plus-one-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-moa\"_osde2e-int-moa-e2e-osd-default-plus-two-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-moa\"_osde2e-int-moa-e2e-upgrade-to-osd-default-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-int-moa\"_osde2e-int-moa-e2e-upgrade-to-osd-default-plus-one-nightly\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-e2e-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-e2e-middle-imageset\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-e2e-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-e2e-oldest-imageset\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-e2e-upgrade-default-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-informing-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-aws\"_osde2e-prod-aws-informing-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-gcp\"_osde2e-prod-gcp-e2e-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-gcp\"_osde2e-prod-gcp-e2e-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-gcp\"_osde2e-prod-gcp-e2e-upgrade-default-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-moa\"_osde2e-prod-moa-e2e-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-moa\"_osde2e-prod-moa-e2e-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-prod-moa\"_osde2e-prod-moa-e2e-upgrade-default-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-aws\"_osde2e-stage-aws-e2e-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-aws\"_osde2e-stage-aws-e2e-middle-imageset\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-aws\"_osde2e-stage-aws-e2e-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-aws\"_osde2e-stage-aws-e2e-oldest-imageset\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-aws\"_osde2e-stage-aws-e2e-upgrade-default-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-aws\"_osde2e-stage-aws-e2e-upgrade-default-next-z\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-gcp\"_osde2e-stage-gcp-e2e-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-gcp\"_osde2e-stage-gcp-e2e-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-gcp\"_osde2e-stage-gcp-e2e-upgrade-default-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-moa\"_osde2e-stage-moa-e2e-default\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-moa\"_osde2e-stage-moa-e2e-next\n",
+      "{'alerts'}\n",
+      "\"redhat-osde2e-stage-moa\"_osde2e-stage-moa-e2e-upgrade-default-next\n",
+      "{'alerts'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "### for each grid this will print out any features they are missing from a master list\n",
+    "all_features = []\n",
+    "for i in available_features.keys():\n",
+    "    all_features.extend(list(available_features[i]))\n",
+    "\n",
+    "uniqe_list_redhat = set(all_features)\n",
+    "\n",
+    "for i in available_features.keys():\n",
+    "    print(i)\n",
+    "    print(uniqe_list_redhat.difference(set(available_features[i])))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see above that all the features are the same for each grid expect most do not include the feature \"alerts\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kubernetes Feature Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dashboard = '\"google-aws\"'\n",
+    "job = \"kops-aws-cni-amazon-vpc\"\n",
+    "response = requests.get(\"https://testgrid.k8s.io/google-aws?id=dashboard-group-bar\")\n",
+    "response_2 = requests.get(\n",
+    "    f\"https://testgrid.k8s.io/{dashboard}/table?&show-stale-tests=&tab={job}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\"google-aws\"',\n",
+       " '\"google-cel\"',\n",
+       " '\"google-gce\"',\n",
+       " '\"google-gce-compute-image-tools\"',\n",
+       " '\"google-gcp-guest\"',\n",
+       " '\"google-gci\"',\n",
+       " '\"google-gke\"',\n",
+       " '\"google-gce-upgrade\"',\n",
+       " '\"google-kops-gce\"',\n",
+       " '\"google-osconfig\"',\n",
+       " '\"google-rules_k8s\"',\n",
+       " '\"google-soak\"',\n",
+       " '\"google-unit\"',\n",
+       " '\"google-windows\"']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get the grid names for google-aws\n",
+    "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": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "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": [
+    "# Print all the feature names for one grid\n",
+    "k8s_features = list(response_2.json().keys())\n",
+    "for i in response_2.json().keys():\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'alerts'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# compare this grid with the Red Hat master list of features\n",
+    "uniqe_list_redhat.difference(set(k8s_features))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "again, we can see that in this case the Kubernetes and Red Hat grids appear to be the same except for \"alerts\" in some cases.\n",
+    "\n",
+    "Let's double check an iterate through all of the google grids. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"google-aws\" downloaded \n",
+      "\"google-cel\" downloaded \n",
+      "\"google-gce\" downloaded \n",
+      "\"google-gce-compute-image-tools\" downloaded \n",
+      "\"google-gcp-guest\" downloaded \n",
+      "\"google-gci\" downloaded \n",
+      "\"google-gke\" downloaded \n",
+      "\"google-gce-upgrade\" downloaded \n",
+      "\"google-kops-gce\" downloaded \n",
+      "\"google-osconfig\" downloaded \n",
+      "\"google-rules_k8s\" downloaded \n",
+      "\"google-soak\" downloaded \n",
+      "\"google-unit\" downloaded \n",
+      "\"google-windows\" downloaded \n"
+     ]
+    }
+   ],
+   "source": [
+    "download = True\n",
+    "if download is True:\n",
+    "    available_features = {}\n",
+    "\n",
+    "    for dashboard in dashboard_names:\n",
+    "        response_1 = requests.get(f\"https://testgrid.k8s.io/{dashboard}/summary\")\n",
+    "        jobs = response_1.json().keys()\n",
+    "        for job in jobs:\n",
+    "            response_2 = requests.get(\n",
+    "                f\"https://testgrid.k8s.io/{dashboard}/table?&show-stale-tests=&tab={job}\"\n",
+    "            )\n",
+    "            if response_2.status_code != 200:\n",
+    "                continue\n",
+    "\n",
+    "            available_features[f\"{dashboard}_{job}\"] = response_2.json().keys()\n",
+    "\n",
+    "        print(f\"{dashboard} downloaded \")\n",
+    "else:\n",
+    "    print(\"Not Downloading\")\n",
+    "\n",
+    "all_features = []\n",
+    "for i in available_features.keys():\n",
+    "    all_features.extend(list(available_features[i]))\n",
+    "\n",
+    "uniqe_list_k8s = set(all_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "set()\n",
+      "set()\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n",
+      "{'alerts'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in available_features.keys():\n",
+    "    print(uniqe_list_k8s.difference(set(available_features[i])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "uniqe_list_k8s.difference(uniqe_list_redhat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great so, we can see from the above that not only do we have the same situation for the google-aws set of grids, but there is also no difference between the full Red Hat and Kubernetes lists of features.\n",
+    "\n",
+    "Therefore we know that we have a reliable set of features that we can look at beyond the grid tests themselves. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "devenv",
+   "language": "python",
+   "name": "devenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/blocked_timed_out.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/blocked_timed_out.ipynb
new file mode 100644
index 00000000..cc51672c
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/blocked_timed_out.ipynb	
@@ -0,0 +1,514 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "brown-check",
+   "metadata": {},
+   "source": [
+    "# Tests Blocked or Timed Out\n",
+    "\n",
+    "This notebook is an extension to the [number_of_flakes](number_of_flakes.ipynb) notebook. In this notebook, the key perfomance indicators that we would like to create greater visbility into and track over time is the percent of tests that got blocked or were timed out. By observing the above metrics and tracking them wrt time, we will be able to quantify the efficiency of our testing platforms.\n",
+    "\n",
+    "* number and percent of tests blocked\n",
+    "* number and percent of tests timed out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "brilliant-expense",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:17:45.512006Z",
+     "start_time": "2021-03-04T21:17:44.690478Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "\n",
+    "sys.path.append(\"../../..\")\n",
+    "\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../../data-sources/TestGrid\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "\n",
+    "from ipynb.fs.defs.number_of_flakes import (  # noqa: E402\n",
+    "    testgrid_labelwise_encoding,\n",
+    ")  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "boxed-enough",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:17:52.593985Z",
+     "start_time": "2021-03-04T21:17:46.403143Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load test file\n",
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    testgrid_data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "standard-parade",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:13.571985Z",
+     "start_time": "2021-03-04T21:17:53.328200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "blocked_tests_list = testgrid_labelwise_encoding(testgrid_data, 8)[0:1000000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "worst-egypt",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:13.579468Z",
+     "start_time": "2021-03-04T21:18:13.574707Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(blocked_tests_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "burning-ecology",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:13.593463Z",
+     "start_time": "2021-03-04T21:18:13.582117Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(datetime.datetime(2020, 10, 8, 20, 48, 5),\n",
+       " '\"redhat-openshift-informing\"',\n",
+       " 'release-openshift-okd-installer-e2e-aws-upgrade',\n",
+       " 'Application behind service load balancer with PDB is not disrupted',\n",
+       " False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "blocked_tests_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "handled-metabolism",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:14.099459Z",
+     "start_time": "2021-03-04T21:18:13.595956Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test_blocked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  test_blocked  \n",
+       "0  Application behind service load balancer with ...         False  \n",
+       "1  Application behind service load balancer with ...         False  \n",
+       "2  Application behind service load balancer with ...         False  \n",
+       "3  Application behind service load balancer with ...         False  \n",
+       "4  Application behind service load balancer with ...         False  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "blocked_tests_df = pd.DataFrame(\n",
+    "    blocked_tests_list, columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"test_blocked\"]\n",
+    ")\n",
+    "blocked_tests_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "excessive-listening",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:14.171760Z",
+     "start_time": "2021-03-04T21:18:14.101620Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1000000 out of 19 million rows due to pvc limits.\n",
+    "# 250mb = 1 million --> 4750 mb = 19 million\n",
+    "blocked_tests_df.head(1000).to_csv(\n",
+    "    \"../../../../data/processed/blocked_tests.csv\",\n",
+    "    header=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "confident-simon",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:40.785714Z",
+     "start_time": "2021-03-04T21:18:19.671742Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "timed_out_tests_list = testgrid_labelwise_encoding(testgrid_data, 9)[0:1000000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "professional-territory",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:44.685279Z",
+     "start_time": "2021-03-04T21:18:44.204603Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test_timed_out</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  test_timed_out  \n",
+       "0  Application behind service load balancer with ...           False  \n",
+       "1  Application behind service load balancer with ...           False  \n",
+       "2  Application behind service load balancer with ...           False  \n",
+       "3  Application behind service load balancer with ...           False  \n",
+       "4  Application behind service load balancer with ...           False  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "timed_out_tests_df = pd.DataFrame(\n",
+    "    timed_out_tests_list, columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"test_timed_out\"]\n",
+    ")\n",
+    "timed_out_tests_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "perfect-packing",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:18:56.583623Z",
+     "start_time": "2021-03-04T21:18:56.564500Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1000000 out of 19 million rows due to pvc limits.\n",
+    "# 250mb = 1 million --> 4750 mb = 19 million\n",
+    "timed_out_tests_df.head(1000).to_csv(\n",
+    "    \"../../../../data/processed/timed_out_tests.csv\",\n",
+    "    header=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "sublime-timing",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-04T21:25:43.028630Z",
+     "start_time": "2021-03-04T21:25:42.863987Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of tests: 1000000\n",
+      "Total number of tests blocked: 0\n",
+      "Tests blocked percentage: 0.000000\n",
+      "Total number of timed out tests: 0\n",
+      "Test timed out percentage: 0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Metrics\n",
+    "no_tests = blocked_tests_df.head(1000000).test.count()\n",
+    "print(\"Total number of tests: %i\" % (no_tests))\n",
+    "no_blocked = blocked_tests_df.head(1000000).test_blocked.sum()\n",
+    "print(\"Total number of tests blocked: %i\" % (no_blocked))\n",
+    "test_blocked_percentage = (\n",
+    "    (\n",
+    "        blocked_tests_df.head(1000000).test_blocked.sum()\n",
+    "        / blocked_tests_df.head(1000000).test.count()\n",
+    "    )\n",
+    ") * 100\n",
+    "print(\"Tests blocked percentage: %f\" % (test_blocked_percentage))\n",
+    "no_timed_out = timed_out_tests_df.head(1000000).test_timed_out.sum()\n",
+    "print(\"Total number of timed out tests: %i\" % (no_timed_out))\n",
+    "test_timed_out_percentage = (\n",
+    "    (\n",
+    "        timed_out_tests_df.head(1000000).test_timed_out.sum()\n",
+    "        / timed_out_tests_df.head(1000000).test.count()\n",
+    "    )\n",
+    ") * 100\n",
+    "print(\"Test timed out percentage: %f\" % (test_timed_out_percentage))"
+   ]
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1612822647266,
+   "trusted": true
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/build_pass_failure.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/build_pass_failure.ipynb
new file mode 100644
index 00000000..41b6e271
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/build_pass_failure.ipynb	
@@ -0,0 +1,1366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "transsexual-smile",
+   "metadata": {},
+   "source": [
+    "# Quantify Builds\n",
+    "\n",
+    "In this notebook, the key perfomance indicators that we would like to create greater visbility into and track over time is the percent of builds that passed/failed. This can be used to capture the build success rate ie. number of successful builds / deployments relative to the total number of builds / deployments.\n",
+    "\n",
+    "* number and percent of builds passed and failed\n",
+    "* plot of build pass and failure over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "global-afghanistan",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:30:23.180307Z",
+     "start_time": "2021-03-18T16:30:23.173817Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sys.path.append(\"../../..\")\n",
+    "\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../../data-sources/TestGrid\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "\n",
+    "from ipynb.fs.defs.number_of_flakes import (  # noqa: E402\n",
+    "    testgrid_labelwise_encoding,\n",
+    ")  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "moved-france",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:30:31.200927Z",
+     "start_time": "2021-03-18T16:30:24.128277Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load test file\n",
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    testgrid_data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "injured-appliance",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:30:51.478504Z",
+     "start_time": "2021-03-18T16:30:32.082552Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "build_failures_list = testgrid_labelwise_encoding(testgrid_data, 12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "monetary-adolescent",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:30:51.486077Z",
+     "start_time": "2021-03-18T16:30:51.481411Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19483548"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(build_failures_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "intended-visibility",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:30:51.501715Z",
+     "start_time": "2021-03-18T16:30:51.488800Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(datetime.datetime(2020, 10, 8, 20, 48, 5),\n",
+       " '\"redhat-openshift-informing\"',\n",
+       " 'release-openshift-okd-installer-e2e-aws-upgrade',\n",
+       " 'Application behind service load balancer with PDB is not disrupted',\n",
+       " False)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_failures_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "colonial-directory",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:00.127169Z",
+     "start_time": "2021-03-18T16:30:51.503799Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>build_failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  build_failure  \n",
+       "0  Application behind service load balancer with ...          False  \n",
+       "1  Application behind service load balancer with ...           True  \n",
+       "2  Application behind service load balancer with ...          False  \n",
+       "3  Application behind service load balancer with ...          False  \n",
+       "4  Application behind service load balancer with ...          False  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "build_failures_df = pd.DataFrame(\n",
+    "    build_failures_list, columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"build_failure\"]\n",
+    ")\n",
+    "build_failures_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "egyptian-understanding",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:01.387304Z",
+     "start_time": "2021-03-18T16:31:00.129150Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "build_failures_df = build_failures_df.loc[build_failures_df['test'] == \"Overall\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "intensive-chorus",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:01.402932Z",
+     "start_time": "2021-03-18T16:31:01.389662Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>build_failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp                           tab  \\\n",
+       "590 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "591 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "592 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "593 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "594 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                                 job     test  build_failure  \n",
+       "590  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "591  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "592  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "593  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "594  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_failures_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "respiratory-humor",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:01.647668Z",
+     "start_time": "2021-03-18T16:31:01.405890Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1000000 out of 19 million rows due to pvc limits.\n",
+    "# 250mb = 1 million --> 4750 mb = 19 million\n",
+    "build_failures_df.to_csv(\n",
+    "    \"../../../../data/processed/build_failures.csv\",\n",
+    "    header=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "verified-likelihood",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:22.363880Z",
+     "start_time": "2021-03-18T16:31:01.650260Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "build_passing_list = testgrid_labelwise_encoding(testgrid_data, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "herbal-subject",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:30.948513Z",
+     "start_time": "2021-03-18T16:31:22.366958Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>build_passing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  build_passing  \n",
+       "0  Application behind service load balancer with ...          False  \n",
+       "1  Application behind service load balancer with ...          False  \n",
+       "2  Application behind service load balancer with ...          False  \n",
+       "3  Application behind service load balancer with ...          False  \n",
+       "4  Application behind service load balancer with ...          False  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "build_passing_df = pd.DataFrame(\n",
+    "    build_passing_list, columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"build_passing\"]\n",
+    ")\n",
+    "build_passing_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "variable-citizenship",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:32.165018Z",
+     "start_time": "2021-03-18T16:31:30.950983Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "build_passing_df = build_passing_df.loc[build_passing_df['test'] == \"Overall\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fossil-receipt",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:32.183635Z",
+     "start_time": "2021-03-18T16:31:32.167240Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>build_passing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              timestamp                           tab  \\\n",
+       "590 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "591 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "592 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "593 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "594 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                                 job     test  build_passing  \n",
+       "590  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "591  release-openshift-okd-installer-e2e-aws-upgrade  Overall           True  \n",
+       "592  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "593  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  \n",
+       "594  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "build_passing_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "helpful-modern",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:32.463925Z",
+     "start_time": "2021-03-18T16:31:32.186140Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1000000 out of 19 million rows due to pvc limits.\n",
+    "# 250mb = 1 million --> 4750 mb = 19 million\n",
+    "build_passing_df.to_csv(\n",
+    "    \"../../../../data/processed/build_pass.csv\",\n",
+    "    header=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "pretty-athletics",
+   "metadata": {},
+   "source": [
+    "## Number and percent of builds passed and failed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "proud-latvia",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:35.950915Z",
+     "start_time": "2021-03-18T16:31:35.929594Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of Builds: 32734\n",
+      "Total number of failing builds: 13928\n",
+      "Build failure percentage: 42.549032\n",
+      "Total number of passing builds: 13449\n",
+      "Build pass percentage: 41.085721\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Metrics\n",
+    "no_tests = build_passing_df.test.count()\n",
+    "print(\"Total number of Builds: %i\" % (no_tests))\n",
+    "no_failures = build_failures_df.build_failure.sum()\n",
+    "print(\"Total number of failing builds: %i\" % (no_failures))\n",
+    "build_failures_percentage = (\n",
+    "    (\n",
+    "        build_failures_df.build_failure.sum()\n",
+    "        / build_failures_df.test.count()\n",
+    "    )\n",
+    ") * 100\n",
+    "print(\"Build failure percentage: %f\" % (build_failures_percentage))\n",
+    "no_pass = build_passing_df.build_passing.sum()\n",
+    "print(\"Total number of passing builds: %i\" % (no_pass))\n",
+    "build_pass_percentage = (\n",
+    "    (\n",
+    "        build_passing_df.build_passing.sum()\n",
+    "        / build_passing_df.test.count()\n",
+    "    )\n",
+    ") * 100\n",
+    "print(\"Build pass percentage: %f\" % (build_pass_percentage))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lasting-terrorism",
+   "metadata": {},
+   "source": [
+    "## Plot of build pass and failure over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "seventh-norman",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:39.162850Z",
+     "start_time": "2021-03-18T16:31:39.156204Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot_builds_tab_grid(tab, job, df):\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    Takes in input as tab and grid and plots change in \n",
+    "    build pass/fail over time\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    df = df[(df[\"tab\"] == tab) | (df[\"job\"] == job)]\n",
+    "    \n",
+    "    sns.set(rc={\"figure.figsize\": (15, 5)})\n",
+    "    sns.lineplot(x=\"timestamp\", y=\"build_status\", data=df)\n",
+    "    plt.xlabel(\"Timestamps\")\n",
+    "    plt.ylabel(\"Build Pass or Fail\")\n",
+    "    plt.title(\"Change in Build Pass or Failure over time\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "requested-north",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:40.607250Z",
+     "start_time": "2021-03-18T16:31:40.575479Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "combined = pd.merge(build_failures_df,build_passing_df,on=['timestamp', 'tab', 'job', 'test'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "guided-change",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:41.883166Z",
+     "start_time": "2021-03-18T16:31:41.449823Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>build_failure</th>\n",
+       "      <th>build_passing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32871</th>\n",
+       "      <td>2020-09-25 08:01:10</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32872</th>\n",
+       "      <td>2020-09-25 00:01:27</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32873</th>\n",
+       "      <td>2020-09-24 16:00:30</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32874</th>\n",
+       "      <td>2020-09-24 08:00:46</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32875</th>\n",
+       "      <td>2020-09-24 00:02:12</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>32876 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                timestamp                           tab  \\\n",
+       "0     2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1     2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2     2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3     2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4     2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "...                   ...                           ...   \n",
+       "32871 2020-09-25 08:01:10     \"redhat-osde2e-stage-moa\"   \n",
+       "32872 2020-09-25 00:01:27     \"redhat-osde2e-stage-moa\"   \n",
+       "32873 2020-09-24 16:00:30     \"redhat-osde2e-stage-moa\"   \n",
+       "32874 2020-09-24 08:00:46     \"redhat-osde2e-stage-moa\"   \n",
+       "32875 2020-09-24 00:02:12     \"redhat-osde2e-stage-moa\"   \n",
+       "\n",
+       "                                                   job     test  \\\n",
+       "0      release-openshift-okd-installer-e2e-aws-upgrade  Overall   \n",
+       "1      release-openshift-okd-installer-e2e-aws-upgrade  Overall   \n",
+       "2      release-openshift-okd-installer-e2e-aws-upgrade  Overall   \n",
+       "3      release-openshift-okd-installer-e2e-aws-upgrade  Overall   \n",
+       "4      release-openshift-okd-installer-e2e-aws-upgrade  Overall   \n",
+       "...                                                ...      ...   \n",
+       "32871        osde2e-stage-moa-e2e-upgrade-default-next  Overall   \n",
+       "32872        osde2e-stage-moa-e2e-upgrade-default-next  Overall   \n",
+       "32873        osde2e-stage-moa-e2e-upgrade-default-next  Overall   \n",
+       "32874        osde2e-stage-moa-e2e-upgrade-default-next  Overall   \n",
+       "32875        osde2e-stage-moa-e2e-upgrade-default-next  Overall   \n",
+       "\n",
+       "       build_failure  build_passing  \n",
+       "0              False          False  \n",
+       "1              False           True  \n",
+       "2              False          False  \n",
+       "3              False          False  \n",
+       "4              False          False  \n",
+       "...              ...            ...  \n",
+       "32871          False           True  \n",
+       "32872          False           True  \n",
+       "32873          False           True  \n",
+       "32874          False           True  \n",
+       "32875          False           True  \n",
+       "\n",
+       "[32876 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "trying-belly",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:43.430028Z",
+     "start_time": "2021-03-18T16:31:43.425742Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def label_race (row):\n",
+    "   if row['build_failure']:\n",
+    "      return 'Fail'\n",
+    "\n",
+    "   if row['build_passing']:\n",
+    "      return 'Pass'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "vietnamese-timer",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:45.471745Z",
+     "start_time": "2021-03-18T16:31:44.183035Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "combined['build_status']=combined.apply (lambda row: label_race(row), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "willing-basin",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:45.489310Z",
+     "start_time": "2021-03-18T16:31:45.474411Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>build_failure</th>\n",
+       "      <th>build_passing</th>\n",
+       "      <th>build_status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Pass</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job     test  build_failure  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade  Overall          False   \n",
+       "\n",
+       "   build_passing build_status  \n",
+       "0          False         None  \n",
+       "1           True         Pass  \n",
+       "2          False         None  \n",
+       "3          False         None  \n",
+       "4          False         None  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "bearing-beast",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:45.500197Z",
+     "start_time": "2021-03-18T16:31:45.491531Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32876"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(combined)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "rough-fitness",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:46.997893Z",
+     "start_time": "2021-03-18T16:31:46.972248Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# since we are only interested in success and failure statuses\n",
+    "combined = combined.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "accepted-radar",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:47.632680Z",
+     "start_time": "2021-03-18T16:31:47.627805Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27489"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(combined)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "collective-bookmark",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:49.382725Z",
+     "start_time": "2021-03-18T16:31:48.952586Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_builds_tab_grid(\"redhat-openshift-informing\", \"release-openshift-okd-installer-e2e-aws-upgrade\", combined)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "alive-success",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:50.670316Z",
+     "start_time": "2021-03-18T16:31:50.274952Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_builds_tab_grid(\"redhat-openshift-ocp-release-4.2-informing\", \"release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\", combined)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "cloudy-syracuse",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:51.095981Z",
+     "start_time": "2021-03-18T16:31:50.672730Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_builds_tab_grid(\"redhat-osde2e-stage-moa\", \"osde2e-stage-moa-e2e-upgrade-default-next\", combined)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "cleared-wrapping",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-18T16:31:51.492505Z",
+     "start_time": "2021-03-18T16:31:51.098351Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_builds_tab_grid(\"redhat-openshift-ocp-release-4.5-blocking\",\"release-openshift-origin-installer-e2e-gcp-serial-4.5\",combined)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "expressed-manitoba",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "\n",
+    "In this Notebook, we use the \"Overall\" as a proxy for a build. Testgrid refers an aggregate of multiple tests performed at a certain timestamp within a Job as a Build and each build has a unique Build ID. In this notebook, we went ahead and used the labels provided by testgrid which classify a test overall as Pass or Fail to indicate build success and failures and thus calculate the percent of success and failures."
+   ]
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1616085009075,
+   "trusted": false
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/correlated_failures.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/correlated_failures.ipynb
new file mode 100644
index 00000000..fa41ce5d
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/correlated_failures.ipynb	
@@ -0,0 +1,1683 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Correlated test failure sets per test and average size of correlation set\n",
+    "\n",
+    "This notebook outputs 2 artifacts: \n",
+    "\n",
+    "1. A CSV file that provides, for a given test, all of the other tests that are highly correlated (correlation coefficient of 0.9 or above). This CSV file ommits any tests that do not have any highly correlated tests. So, if a test is not present on the list, then it has no highly correlated tests associated with it and has been removed from the record. The calculation for correlation is performed on all available data exposed by the Red Hat test grid instance at the time the notebook is run.  \n",
+    "\n",
+    "2. A summary metric that can be easily tracked over time that represents the average size of correlated test sets in the above CSV. \n",
+    "\n",
+    "\n",
+    "__Note__: This notebook follows a very similar approach to an earlier [EDA notebook](https://github.com/aicoe-aiops/ocp-ci-analysis/blob/master/notebooks/data-sources/Sippy/sippy_failure_correlation.ipynb) where we correlated failures with a different dataset. For simplicity, much of the reasoning behind the decisions made in this notebook have been omitted here, but can be found in the above linked notebook.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "from pathlib import Path\n",
+    "\n",
+    "sys.path.append(\"../../..\")\n",
+    "\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../../data-sources/TestGrid\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "\n",
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load test file\n",
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    testgrid_data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "current_grid = testgrid_data['\"redhat-openshift-ocp-release-4.4-informing\"'][\n",
+    "    \"release-openshift-origin-installer-e2e-azure-shared-vpc-4.4\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tests = [current_grid[\"grid\"][i][\"name\"] for i in range(len(current_grid[\"grid\"]))]\n",
+    "# unroll the run-length encoding and set bool for flake or not (x==13)\n",
+    "decoded = [\n",
+    "    (np.array(decode_run_length(current_grid[\"grid\"][i][\"statuses\"])) == 12).tolist()\n",
+    "    for i in range(len(current_grid[\"grid\"]))\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>test</th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Monitor cluster while tests execute</td>\n",
+       "      <td>[True, True, True, True, True, True, True, Tru...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Overall</td>\n",
+       "      <td>[False, False, True, False, False, True, False...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[Conformance][Area:Networking][Feature:Router]...</td>\n",
+       "      <td>[False, False, True, False, False, False, Fals...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[Conformance][Area:Networking][Feature:Router]...</td>\n",
+       "      <td>[False, False, True, False, False, False, Fals...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[Conformance][Area:Networking][Feature:Router]...</td>\n",
+       "      <td>[False, False, True, False, False, False, Fals...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                test  \\\n",
+       "0                Monitor cluster while tests execute   \n",
+       "1                                            Overall   \n",
+       "2  [Conformance][Area:Networking][Feature:Router]...   \n",
+       "3  [Conformance][Area:Networking][Feature:Router]...   \n",
+       "4  [Conformance][Area:Networking][Feature:Router]...   \n",
+       "\n",
+       "                                              values  \n",
+       "0  [True, True, True, True, True, True, True, Tru...  \n",
+       "1  [False, False, True, False, False, True, False...  \n",
+       "2  [False, False, True, False, False, False, Fals...  \n",
+       "3  [False, False, True, False, False, False, Fals...  \n",
+       "4  [False, False, True, False, False, False, Fals...  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matrix = pd.DataFrame(zip(tests, decoded), columns=[\"test\", \"values\"])\n",
+    "matrix.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>50</th>\n",
+       "      <th>51</th>\n",
+       "      <th>52</th>\n",
+       "      <th>53</th>\n",
+       "      <th>54</th>\n",
+       "      <th>55</th>\n",
+       "      <th>56</th>\n",
+       "      <th>57</th>\n",
+       "      <th>58</th>\n",
+       "      <th>59</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Monitor cluster while tests execute</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Overall</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[Conformance][Area:Networking][Feature:Router] The HAProxy router [Top Level] [Conformance][Area:Networking][Feature:Router] The HAProxy router should enable openshift-monitoring to pull metrics [Skipped:ibmcloud] [Suite:openshift/conformance/parallel/minimal]</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[Conformance][Area:Networking][Feature:Router] The HAProxy router [Top Level] [Conformance][Area:Networking][Feature:Router] The HAProxy router should override the route host for overridden domains with a custom value [Suite:openshift/conformance/parallel/minimal]</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>[Conformance][Area:Networking][Feature:Router] The HAProxy router [Top Level] [Conformance][Area:Networking][Feature:Router] The HAProxy router should respond with 503 to unrecognized hosts [Suite:openshift/conformance/parallel/minimal]</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 60 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                       0      1     2      3   \\\n",
+       "test                                                                            \n",
+       "Monitor cluster while tests execute                  True   True  True   True   \n",
+       "Overall                                             False  False  True  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  True  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  True  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  True  False   \n",
+       "\n",
+       "                                                       4      5      6   \\\n",
+       "test                                                                      \n",
+       "Monitor cluster while tests execute                  True   True   True   \n",
+       "Overall                                             False   True  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "\n",
+       "                                                       7      8      9   ...  \\\n",
+       "test                                                                     ...   \n",
+       "Monitor cluster while tests execute                  True   True  False  ...   \n",
+       "Overall                                             False  False  False  ...   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False  ...   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False  ...   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False  ...   \n",
+       "\n",
+       "                                                       50     51     52  \\\n",
+       "test                                                                      \n",
+       "Monitor cluster while tests execute                  True   True   True   \n",
+       "Overall                                             False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "\n",
+       "                                                       53     54     55  \\\n",
+       "test                                                                      \n",
+       "Monitor cluster while tests execute                  True   True   True   \n",
+       "Overall                                             False   True  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False   \n",
+       "\n",
+       "                                                       56     57     58     59  \n",
+       "test                                                                            \n",
+       "Monitor cluster while tests execute                  True   True   True   True  \n",
+       "Overall                                             False  False  False  False  \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False  False  \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False  False  \n",
+       "[Conformance][Area:Networking][Feature:Router] ...  False  False  False  False  \n",
+       "\n",
+       "[5 rows x 60 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matrix = pd.DataFrame(matrix[\"values\"].to_list(), index=matrix[\"test\"])\n",
+    "matrix.head()"
+   ]
+  },
+  {
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>50</th>\n",
+       "      <th>51</th>\n",
+       "      <th>52</th>\n",
+       "      <th>53</th>\n",
+       "      <th>54</th>\n",
+       "      <th>55</th>\n",
+       "      <th>56</th>\n",
+       "      <th>57</th>\n",
+       "      <th>58</th>\n",
+       "      <th>59</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>operator install service-catalog-apiserver</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>operator install service-catalog-controller-manager</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>operator.All images are built and tagged into stable</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>operator.Find all of the input images from ocp/4.4:${component} and tag them into the output image stream</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>operator.Run template e2e-azure - e2e-azure container teardown</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 60 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                       0      1      2   \\\n",
+       "test                                                                      \n",
+       "operator install service-catalog-apiserver          False  False  False   \n",
+       "operator install service-catalog-controller-man...  False  False  False   \n",
+       "operator.All images are built and tagged into s...  False  False  False   \n",
+       "operator.Find all of the input images from ocp/...  False  False  False   \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  False  False   \n",
+       "\n",
+       "                                                       3      4      5   \\\n",
+       "test                                                                      \n",
+       "operator install service-catalog-apiserver          False  False  False   \n",
+       "operator install service-catalog-controller-man...  False  False  False   \n",
+       "operator.All images are built and tagged into s...  False  False  False   \n",
+       "operator.Find all of the input images from ocp/...  False  False  False   \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  False  False   \n",
+       "\n",
+       "                                                       6      7      8   \\\n",
+       "test                                                                      \n",
+       "operator install service-catalog-apiserver          False  False  False   \n",
+       "operator install service-catalog-controller-man...  False  False  False   \n",
+       "operator.All images are built and tagged into s...  False  False  False   \n",
+       "operator.Find all of the input images from ocp/...  False  False  False   \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  False  False   \n",
+       "\n",
+       "                                                       9   ...     50     51  \\\n",
+       "test                                                       ...                 \n",
+       "operator install service-catalog-apiserver          False  ...  False  False   \n",
+       "operator install service-catalog-controller-man...  False  ...  False  False   \n",
+       "operator.All images are built and tagged into s...  False  ...  False  False   \n",
+       "operator.Find all of the input images from ocp/...  False  ...  False  False   \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  ...  False  False   \n",
+       "\n",
+       "                                                       52     53     54  \\\n",
+       "test                                                                      \n",
+       "operator install service-catalog-apiserver          False  False  False   \n",
+       "operator install service-catalog-controller-man...  False  False  False   \n",
+       "operator.All images are built and tagged into s...  False  False  False   \n",
+       "operator.Find all of the input images from ocp/...  False  False  False   \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  False  False   \n",
+       "\n",
+       "                                                       55     56     57  \\\n",
+       "test                                                                      \n",
+       "operator install service-catalog-apiserver          False  False  False   \n",
+       "operator install service-catalog-controller-man...  False  False  False   \n",
+       "operator.All images are built and tagged into s...  False  False  False   \n",
+       "operator.Find all of the input images from ocp/...  False  False  False   \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  False  False   \n",
+       "\n",
+       "                                                       58     59  \n",
+       "test                                                              \n",
+       "operator install service-catalog-apiserver          False  False  \n",
+       "operator install service-catalog-controller-man...  False  False  \n",
+       "operator.All images are built and tagged into s...  False  False  \n",
+       "operator.Find all of the input images from ocp/...  False  False  \n",
+       "operator.Run template e2e-azure - e2e-azure con...  False  False  \n",
+       "\n",
+       "[5 rows x 60 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matrix.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here we iterate through each grid in our dataset and collect the the names of all tests that fail\n",
+    "# during a build. We will store this in the 'failure_groups' list.\n",
+    "\n",
+    "failure_groups = []\n",
+    "\n",
+    "for tab in testgrid_data.keys():\n",
+    "    for grid in testgrid_data[tab].keys():\n",
+    "        current_grid = testgrid_data[tab][grid]\n",
+    "\n",
+    "        tests = [\n",
+    "            current_grid[\"grid\"][i][\"name\"] for i in range(len(current_grid[\"grid\"]))\n",
+    "        ]\n",
+    "        # unroll the run-length encoding and set bool for flake or not (x==13)\n",
+    "        decoded = [\n",
+    "            (\n",
+    "                np.array(decode_run_length(current_grid[\"grid\"][i][\"statuses\"])) == 12\n",
+    "            ).tolist()\n",
+    "            for i in range(len(current_grid[\"grid\"]))\n",
+    "        ]\n",
+    "\n",
+    "        matrix = pd.DataFrame(zip(tests, decoded), columns=[\"test\", \"values\"])\n",
+    "        matrix = pd.DataFrame(matrix[\"values\"].to_list(), index=matrix[\"test\"])\n",
+    "\n",
+    "        for c, items in matrix.iteritems():\n",
+    "            if len(items[items].index) > 1:\n",
+    "                failure_groups.append(items[items].index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "failure_groups = pd.Series(failure_groups)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14491"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(failure_groups)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "115746\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "5065"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now we want a vocabulary for of all unique tests in our dataset so that we can encode our\n",
+    "# failure sets using a one-hot encoding scheme.\n",
+    "vocab = []\n",
+    "count = 0\n",
+    "for i in failure_groups:\n",
+    "    for j in i:\n",
+    "        count += 1\n",
+    "        if j not in vocab:\n",
+    "            vocab.append(j)\n",
+    "\n",
+    "print(count)\n",
+    "len(vocab)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5065"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# confirm that there are no duplicates in the vocab\n",
+    "len(pd.Series(vocab).unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_tests(job):\n",
+    "    encoded = []\n",
+    "    for v in vocab:\n",
+    "        if v in job:\n",
+    "            encoded.extend([1])\n",
+    "        else:\n",
+    "            encoded.extend([0])\n",
+    "    return encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded = failure_groups.apply(encode_tests)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
+       "1    [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
+       "2    [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
+       "3    [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n",
+       "4    [0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, ...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Application behind service load balancer with PDB is not disrupted</th>\n",
+       "      <th>Cluster frontend ingress remain available</th>\n",
+       "      <th>Kubernetes APIs remain available</th>\n",
+       "      <th>Monitor cluster while tests execute</th>\n",
+       "      <th>OpenShift APIs remain available</th>\n",
+       "      <th>Overall</th>\n",
+       "      <th>operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container setup</th>\n",
+       "      <th>operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container test</th>\n",
+       "      <th>[sig-arch][Feature:ClusterUpgrade] Cluster should remain functional during upgrade [Disruptive] [Serial] [Suite:openshift]</th>\n",
+       "      <th>Operator upgrade authentication</th>\n",
+       "      <th>...</th>\n",
+       "      <th>[Suite: e2e] Pods should be Running or Succeeded</th>\n",
+       "      <th>[install] [Suite: operators] [OSD] Managed Velero Operator velero Access should be allowed to edit Schedules</th>\n",
+       "      <th>[install] [Suite: operators] [OSD] Managed Velero Operator velero Access should be allowed to edit VolumeSnapshotLocations</th>\n",
+       "      <th>[install] [Suite: operators] [OSD] Configure AlertManager Operator clusterRoles should exist</th>\n",
+       "      <th>[install] [Suite: operators] [OSD] Configure AlertManager Operator configmaps should exist</th>\n",
+       "      <th>[install] [Suite: operators] [OSD] Configure AlertManager Operator deployment should exist</th>\n",
+       "      <th>[install] [Suite: operators] [OSD] Configure AlertManager Operator deployment should have all desired replicas ready</th>\n",
+       "      <th>[Suite: operators] AlertmanagerInhibitions inhibits ClusterOperatorDegraded</th>\n",
+       "      <th>[Suite: operators] [OSD] Splunk Forwarder Operator Operator Upgrade should upgrade from the replaced version</th>\n",
+       "      <th>[Suite: operators] [OSD] RBAC Operator Operator Upgrade should upgrade from the replaced version</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</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>...</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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</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>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</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>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 5065 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Application behind service load balancer with PDB is not disrupted  \\\n",
+       "0                                                  1                    \n",
+       "1                                                  0                    \n",
+       "2                                                  1                    \n",
+       "3                                                  1                    \n",
+       "4                                                  0                    \n",
+       "\n",
+       "   Cluster frontend ingress remain available  \\\n",
+       "0                                          1   \n",
+       "1                                          0   \n",
+       "2                                          1   \n",
+       "3                                          1   \n",
+       "4                                          1   \n",
+       "\n",
+       "   Kubernetes APIs remain available  Monitor cluster while tests execute  \\\n",
+       "0                                 1                                    1   \n",
+       "1                                 0                                    0   \n",
+       "2                                 0                                    1   \n",
+       "3                                 0                                    1   \n",
+       "4                                 1                                    1   \n",
+       "\n",
+       "   OpenShift APIs remain available  Overall  \\\n",
+       "0                                1        0   \n",
+       "1                                0        1   \n",
+       "2                                0        0   \n",
+       "3                                0        0   \n",
+       "4                                1        1   \n",
+       "\n",
+       "   operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container setup  \\\n",
+       "0                                                  0                         \n",
+       "1                                                  1                         \n",
+       "2                                                  0                         \n",
+       "3                                                  0                         \n",
+       "4                                                  0                         \n",
+       "\n",
+       "   operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container test  \\\n",
+       "0                                                  0                        \n",
+       "1                                                  0                        \n",
+       "2                                                  0                        \n",
+       "3                                                  0                        \n",
+       "4                                                  1                        \n",
+       "\n",
+       "   [sig-arch][Feature:ClusterUpgrade] Cluster should remain functional during upgrade [Disruptive] [Serial] [Suite:openshift]  \\\n",
+       "0                                                  0                                                                            \n",
+       "1                                                  0                                                                            \n",
+       "2                                                  0                                                                            \n",
+       "3                                                  0                                                                            \n",
+       "4                                                  1                                                                            \n",
+       "\n",
+       "   Operator upgrade authentication  ...  \\\n",
+       "0                                0  ...   \n",
+       "1                                0  ...   \n",
+       "2                                0  ...   \n",
+       "3                                0  ...   \n",
+       "4                                0  ...   \n",
+       "\n",
+       "   [Suite: e2e] Pods should be Running or Succeeded  \\\n",
+       "0                                                 0   \n",
+       "1                                                 0   \n",
+       "2                                                 0   \n",
+       "3                                                 0   \n",
+       "4                                                 0   \n",
+       "\n",
+       "   [install] [Suite: operators] [OSD] Managed Velero Operator velero Access should be allowed to edit Schedules  \\\n",
+       "0                                                  0                                                              \n",
+       "1                                                  0                                                              \n",
+       "2                                                  0                                                              \n",
+       "3                                                  0                                                              \n",
+       "4                                                  0                                                              \n",
+       "\n",
+       "   [install] [Suite: operators] [OSD] Managed Velero Operator velero Access should be allowed to edit VolumeSnapshotLocations  \\\n",
+       "0                                                  0                                                                            \n",
+       "1                                                  0                                                                            \n",
+       "2                                                  0                                                                            \n",
+       "3                                                  0                                                                            \n",
+       "4                                                  0                                                                            \n",
+       "\n",
+       "   [install] [Suite: operators] [OSD] Configure AlertManager Operator clusterRoles should exist  \\\n",
+       "0                                                  0                                              \n",
+       "1                                                  0                                              \n",
+       "2                                                  0                                              \n",
+       "3                                                  0                                              \n",
+       "4                                                  0                                              \n",
+       "\n",
+       "   [install] [Suite: operators] [OSD] Configure AlertManager Operator configmaps should exist  \\\n",
+       "0                                                  0                                            \n",
+       "1                                                  0                                            \n",
+       "2                                                  0                                            \n",
+       "3                                                  0                                            \n",
+       "4                                                  0                                            \n",
+       "\n",
+       "   [install] [Suite: operators] [OSD] Configure AlertManager Operator deployment should exist  \\\n",
+       "0                                                  0                                            \n",
+       "1                                                  0                                            \n",
+       "2                                                  0                                            \n",
+       "3                                                  0                                            \n",
+       "4                                                  0                                            \n",
+       "\n",
+       "   [install] [Suite: operators] [OSD] Configure AlertManager Operator deployment should have all desired replicas ready  \\\n",
+       "0                                                  0                                                                      \n",
+       "1                                                  0                                                                      \n",
+       "2                                                  0                                                                      \n",
+       "3                                                  0                                                                      \n",
+       "4                                                  0                                                                      \n",
+       "\n",
+       "   [Suite: operators] AlertmanagerInhibitions inhibits ClusterOperatorDegraded  \\\n",
+       "0                                                  0                             \n",
+       "1                                                  0                             \n",
+       "2                                                  0                             \n",
+       "3                                                  0                             \n",
+       "4                                                  0                             \n",
+       "\n",
+       "   [Suite: operators] [OSD] Splunk Forwarder Operator Operator Upgrade should upgrade from the replaced version  \\\n",
+       "0                                                  0                                                              \n",
+       "1                                                  0                                                              \n",
+       "2                                                  0                                                              \n",
+       "3                                                  0                                                              \n",
+       "4                                                  0                                                              \n",
+       "\n",
+       "   [Suite: operators] [OSD] RBAC Operator Operator Upgrade should upgrade from the replaced version  \n",
+       "0                                                  0                                                 \n",
+       "1                                                  0                                                 \n",
+       "2                                                  0                                                 \n",
+       "3                                                  0                                                 \n",
+       "4                                                  0                                                 \n",
+       "\n",
+       "[5 rows x 5065 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_encoded = pd.DataFrame(encoded.array, columns=vocab)\n",
+    "df_encoded.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Overall                                                                 0.788627\n",
+       "Monitor cluster while tests execute                                     0.340004\n",
+       "operator.Import the release payload \"latest\" from an external source    0.103582\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# percent that each test is present in the data\n",
+    "perc_present = df_encoded.sum() / len(df_encoded)\n",
+    "perc_present.sort_values(ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Overall                                                                 11428\n",
+       "Monitor cluster while tests execute                                      4927\n",
+       "operator.Import the release payload \"latest\" from an external source     1501\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occurrence_count = df_encoded.sum()\n",
+    "occurrence_count.sort_values(ascending=False).head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also want to make sure that our correlation values are not just due to unique failed test sets present in our dataset. We want to make sure our tests impact multiple jobs. For example, if we had a unique failed test set that only occurred in a single example, and shared no other failed tests among the vocabulary, then all of the tests would appear to be 100% correlated with each other, when in fact that is merely a consequence of insufficient data. In order to prevent that, we will ignore any tests that occur only in a single job. In order to do that we will use occurrence_count to create a filter vector for any test that occurs only once. Then filter them out of our working DF."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_unique = list(occurrence_count[occurrence_count.values <= 1].index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_encoded = df_encoded.drop(filter_unique, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(14491, 4267)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_encoded.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this takes time\n",
+    "corr_matrix = df_encoded.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4267"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# For each feature, find the other features that are correlated by more than 0.9\n",
+    "top_correlation = {}\n",
+    "\n",
+    "for c in corr_matrix.columns:\n",
+    "    top_correlation[c] = []\n",
+    "    series = corr_matrix.loc[c]\n",
+    "\n",
+    "    for i, s in enumerate(series):\n",
+    "        if s > 0.90 and series.index[i] != c:\n",
+    "            top_correlation[c].append((series.index[i], s))\n",
+    "\n",
+    "len(top_correlation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "614 sets of correlated tests \n",
+      "\n",
+      "Feature of interest: [sig-api-machinery] OpenShift APIs remain available\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>test_name</th>\n",
+       "      <th>correlation coefficient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-api-machinery] OAuth APIs remain available</td>\n",
+       "      <td>0.980253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         test_name  correlation coefficient\n",
+       "0  [sig-api-machinery] OAuth APIs remain available                 0.980253"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.set_option(\"display.max_colwidth\", 150)\n",
+    "# top_correlation has a number of empty rows as not all tests have high correlations with others,\n",
+    "# lets grab only the sets that have at least 1 highly correlated test\n",
+    "corr_sets = []\n",
+    "for i in top_correlation.items():\n",
+    "    if len(i[1]) >= 1:\n",
+    "        corr_sets.append(i)\n",
+    "print(f\"{len(corr_sets)} sets of correlated tests \\n\")\n",
+    "print(f\"Feature of interest: {corr_sets[1][0]}\")\n",
+    "pd.DataFrame(corr_sets[1][1], columns=[\"test_name\", \"correlation coefficient\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "102 : the number of times this test failed in our data set\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_name = \"[sig-api-machinery] OpenShift APIs remain available\"\n",
+    "num = occurrence_count.loc[test_name]\n",
+    "print(f\"{num} : the number of times this test failed in our data set\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>test_name</th>\n",
+       "      <th>num_occurrences</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-api-machinery] OAuth APIs remain available</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         test_name  num_occurrences\n",
+       "0  [sig-api-machinery] OAuth APIs remain available              102"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lst = []\n",
+    "focus = corr_sets[1][1]\n",
+    "for j in focus:\n",
+    "    lst.append((j[0], occurrence_count.loc[j[0]]))\n",
+    "\n",
+    "pd.DataFrame(lst, columns=[\"test_name\", \"num_occurrences\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>test_name</th>\n",
+       "      <th>correlated_tests</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sig-api-machinery] OAuth APIs remain available</td>\n",
+       "      <td>[([sig-api-machinery] OpenShift APIs remain available, 0.9802531617970734)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[sig-api-machinery] OpenShift APIs remain available</td>\n",
+       "      <td>[([sig-api-machinery] OAuth APIs remain available, 0.9802531617970734)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Operator upgrade kube-controller-manager</td>\n",
+       "      <td>[(Operator upgrade etcd, 1.0)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Operator upgrade etcd</td>\n",
+       "      <td>[(Operator upgrade kube-controller-manager, 1.0)]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator install kube-scheduler</td>\n",
+       "      <td>[(operator install kube-controller-manager, 0.9261749970073855)]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             test_name  \\\n",
+       "0      [sig-api-machinery] OAuth APIs remain available   \n",
+       "1  [sig-api-machinery] OpenShift APIs remain available   \n",
+       "2             Operator upgrade kube-controller-manager   \n",
+       "3                                Operator upgrade etcd   \n",
+       "4                      operator install kube-scheduler   \n",
+       "\n",
+       "                                                              correlated_tests  \n",
+       "0  [([sig-api-machinery] OpenShift APIs remain available, 0.9802531617970734)]  \n",
+       "1      [([sig-api-machinery] OAuth APIs remain available, 0.9802531617970734)]  \n",
+       "2                                               [(Operator upgrade etcd, 1.0)]  \n",
+       "3                            [(Operator upgrade kube-controller-manager, 1.0)]  \n",
+       "4             [(operator install kube-controller-manager, 0.9261749970073855)]  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save CSV\n",
+    "Path(\"../../../../data/processed/metrics/failure_correlation/\").mkdir(\n",
+    "    parents=True, exist_ok=True\n",
+    ")\n",
+    "save = pd.DataFrame(corr_sets, columns=[\"test_name\", \"correlated_tests\"])\n",
+    "save.to_csv(\n",
+    "    \"../../../../data/processed/metrics/failure_correlation/failure_correlation_sets.csv\"\n",
+    ")\n",
+    "save.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(614, 2)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "save.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timestamp = datetime.datetime.now()\n",
+    "average_corr = save[\"correlated_tests\"].apply(len).mean()\n",
+    "metric_to_save = pd.DataFrame(\n",
+    "    [[timestamp, average_corr]],\n",
+    "    columns=[\"timestamp\", \"average_number_of_correlated_failures\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save average size of correlation set\n",
+    "metric_to_save.to_csv(\n",
+    "    r\"../../../../data/processed/metrics/failure_correlation/{}-{}-{}-avg-corr.csv\".format(\n",
+    "        timestamp.year, timestamp.month, timestamp.day\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>timestamp</th>\n",
+       "      <th>average_number_of_correlated_failures</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-09 16:23:24.005437</td>\n",
+       "      <td>9.397394</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   timestamp  average_number_of_correlated_failures\n",
+       "0 2021-03-09 16:23:24.005437                               9.397394"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metric_to_save"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "devenv",
+   "language": "python",
+   "name": "devenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/metric_template.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/metric_template.ipynb
new file mode 100644
index 00000000..41bc24d4
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/metric_template.ipynb	
@@ -0,0 +1,1052 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# {Metric name}\n",
+    "- Write the name of the metric or the name of a group of metrics that will be implemented in this notebook.\n",
+    "\n",
+    "## Description:\n",
+    "The desciption of the metrics should explain their definition and purpose. It can include the following points:\n",
+    "\n",
+    "    - A list of all the metrics implemented in this notebook.\n",
+    "    - What are these metrics calculating? and how?\n",
+    "    - How to interpret the metrics? \n",
+    "    - Link to the ocp-ci-analysis repository issue.\n",
+    "    - Additional notes and information.\n",
+    "    \n",
+    "\n",
+    "\n",
+    "# Example: _Flake metrics_\n",
+    "\n",
+    "_One of the key perfomance indicators that we would like to create greater visbility into and track over time is overall number and percent of flakes that occur. Individual test runs are flagged a \"flake\" if they are run mulitple times with a mix of passes and failes without any changes to the code being tested. Although they occur for individual test runs, there are a number of aggregate views that developers may want to look at to assess the overall health of their project or testing platform. Through this notebook, we will be able to compute:_\n",
+    "* Percentage of flakes on platform each day\n",
+    "* Percentage of flakes by tab each week\n",
+    "* Percentage of flakes by grid each month\n",
+    "* Percentage of flakes by test overall (this can also be seen as a severity level = overall flake rate of test)\n",
+    "* Severity of flakes\n",
+    "\n",
+    "_In order to provide maximum flexibility for the end-user of this work, instead of creating a number of dataframes to answer each of these specifc questions, we will define a long and narrow data structure (a list of tuples saved as a csv for now) that contains only 5 columns (\"timestamp\", \"tab\",\"grid\",\"test\",\"flake\"). This allows superset (or pandas) to perform the last filter and/or aggreagtion of interest to an end user. Which is to say, there may appear to be a lot of repetion within the final dataset, but each row should be unique, and it should provide the simpelest useability for an end-user._  \n",
+    "\n",
+    "_Linked issues: [Issue 1](https://github.com/aicoe-aiops/ocp-ci-analysis/issues/104), [Issue 2](https://github.com/aicoe-aiops/ocp-ci-analysis/issues/138)_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Import libraries\n",
+    "from enum import Enum\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "import os\n",
+    "from io import BytesIO\n",
+    "import gzip\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "from dotenv import load_dotenv, find_dotenv\n",
+    "import boto3\n",
+    "\n",
+    "load_dotenv(find_dotenv())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Specify variables\n",
+    "\n",
+    "# Specify the path for input grid data\n",
+    "INPUT_DATA_PATH = \"../../../../data/raw/testgrid_810.json.gz\"\n",
+    "\n",
+    "# Specify the path for output metric data\n",
+    "OUTPUT_DATA_PATH = \"../../../../data/processed/metrics/\"\n",
+    "\n",
+    "## CEPH Bucket variables\n",
+    "## Create a .env file on your local with the correct configs\n",
+    "s3_endpoint_url = os.getenv(\"S3_ENDPOINT\")\n",
+    "s3_access_key = os.getenv(\"S3_ACCESS_KEY\")\n",
+    "s3_secret_key = os.getenv(\"S3_SECRET_KEY\")\n",
+    "s3_bucket = os.getenv(\"S3_BUCKET\")\n",
+    "s3_path = os.getenv(\"S3_PROJECT_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Import data\n",
+    "with gzip.open(INPUT_DATA_PATH, \"rb\") as read_file:\n",
+    "    testgrid_data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Helper classes and functions\n",
+    "## In your metric notebook, you could just\n",
+    "## load the template notebook functions\n",
+    "## If your metric requires specific helper\n",
+    "## functions, write them here, otherwise if these\n",
+    "## functions are general, update the template.\n",
+    "\n",
+    "\n",
+    "class TestStatus(Enum):\n",
+    "    \"\"\"\n",
+    "    Enum to encode what test status each value in testgrid corresponds to\n",
+    "\n",
+    "    Basically python equivalent of the enum here:\n",
+    "    https://github.com/GoogleCloudPlatform/testgrid/blob/a18fe953cf98174c215c43e0258b0515e37c283b/pb/test_status/test_status.proto#L3\n",
+    "    \"\"\"\n",
+    "\n",
+    "    NO_RESULT = 0\n",
+    "    PASS = 1\n",
+    "    PASS_WITH_ERRORS = 2\n",
+    "    PASS_WITH_SKIPS = 3\n",
+    "    RUNNING = 4\n",
+    "    CATEGORIZED_ABORT = 5\n",
+    "    UNKNOWN = 6\n",
+    "    CANCEL = 7\n",
+    "    BLOCKED = 8\n",
+    "    TIMED_OUT = 9\n",
+    "    CATEGORIZED_FAIL = 10\n",
+    "    BUILD_FAIL = 11\n",
+    "    FAIL = 12\n",
+    "    FLAKY = 13\n",
+    "    TOOL_FAIL = 14\n",
+    "    BUILD_PASSED = 15\n",
+    "\n",
+    "\n",
+    "def decode_run_length(x):\n",
+    "    \"\"\"\n",
+    "    Decodes the run length encoded data into an unrolled form.\n",
+    "    Returns a list of values.\n",
+    "\n",
+    "    E.g. takes in [{\"value\":12, \"count\":3}, {\"value\":1, \"count\":2}]\n",
+    "    and gives [12, 12, 12, 1, 1]\n",
+    "    \"\"\"\n",
+    "    lst = []\n",
+    "    for run_length in x:\n",
+    "        extension = [run_length[\"value\"]] * run_length[\"count\"]\n",
+    "        lst.extend(extension)\n",
+    "    return lst\n",
+    "\n",
+    "\n",
+    "def testgrid_labelwise_encoding(data, label):\n",
+    "    \"\"\"\n",
+    "    Run length encode the dataset and unroll the dataset into a list.\n",
+    "    Return flattened list after encoding specified value as\n",
+    "    True and rest as False\n",
+    "    \"\"\"\n",
+    "\n",
+    "    percent_label_by_grid_csv = []\n",
+    "\n",
+    "    for tab in data.keys():\n",
+    "\n",
+    "        for grid in data[tab].keys():\n",
+    "            current_grid = data[tab][grid]\n",
+    "            if len(current_grid[\"grid\"]) == 0:\n",
+    "                pass\n",
+    "            else:\n",
+    "                # get all timestamps for this grid (x-axis of grid)\n",
+    "                timestamps = [\n",
+    "                    datetime.datetime.fromtimestamp(x // 1000)\n",
+    "                    for x in current_grid[\"timestamps\"]\n",
+    "                ]\n",
+    "                # get all test names for this grid (y-axis of grid)\n",
+    "                tests = [\n",
+    "                    current_grid[\"grid\"][i][\"name\"]\n",
+    "                    for i in range(len(current_grid[\"grid\"]))\n",
+    "                ]\n",
+    "                # unroll the run-length encoding and set bool for flake or not (x==13)\n",
+    "                decoded = [\n",
+    "                    (\n",
+    "                        np.array(decode_run_length(current_grid[\"grid\"][i][\"statuses\"]))\n",
+    "                        == label\n",
+    "                    ).tolist()\n",
+    "                    for i in range(len(current_grid[\"grid\"]))\n",
+    "                ]\n",
+    "                # add the timestamp to bool value\n",
+    "                decoded = [list(zip(timestamps, g)) for g in decoded]\n",
+    "                # add the test, tab and grid name to each entry\n",
+    "                # TODO: any ideas for avoiding this quad-loop\n",
+    "                for i, d in enumerate(decoded):\n",
+    "                    for j, k in enumerate(d):\n",
+    "                        decoded[i][j] = (k[0], tab, grid, tests[i], k[1])\n",
+    "                # accumulate the results\n",
+    "                percent_label_by_grid_csv.append(decoded)\n",
+    "\n",
+    "    # output above leaves us with a doubly nested list. Flatten\n",
+    "    flat_list = [item for sublist in percent_label_by_grid_csv for item in sublist]\n",
+    "    flatter_list = [item for sublist in flat_list for item in sublist]\n",
+    "\n",
+    "    return flatter_list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculation\n",
+    "- In this section, calculate the metric values from the data.\n",
+    "- For example, we shall use flake metrics calculation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19483548\n"
+     ]
+    }
+   ],
+   "source": [
+    "unrolled_list = testgrid_labelwise_encoding(testgrid_data, 13)\n",
+    "print(len(unrolled_list))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>flake</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                              grid  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  flake  \n",
+       "0  Application behind service load balancer with ...  False  \n",
+       "1  Application behind service load balancer with ...  False  \n",
+       "2  Application behind service load balancer with ...  False  \n",
+       "3  Application behind service load balancer with ...  False  \n",
+       "4  Application behind service load balancer with ...  False  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Next, print the data frame head with the metric values.\n",
+    "df = pd.DataFrame(unrolled_list, columns=[\"timestamp\", \"tab\", \"grid\", \"test\", \"flake\"])\n",
+    "df.head()"
+   ]
+  },
+  {
+   "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>test</th>\n",
+       "      <th>flake_severity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Add Secret to Workloads.Add Secret to Workload...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Add Secret to Workloads.Add Secret to Workload...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alertmanager: Configuration.creates PagerDuty ...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alertmanager: Configuration.creates a receiver...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alertmanager: Configuration.deletes PagerDuty ...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7793</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7794</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7795</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7796</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7797</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7798 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   test  flake_severity\n",
+       "0     Add Secret to Workloads.Add Secret to Workload...             0.0\n",
+       "1     Add Secret to Workloads.Add Secret to Workload...             0.0\n",
+       "2     Alertmanager: Configuration.creates PagerDuty ...             0.0\n",
+       "3     Alertmanager: Configuration.creates a receiver...             0.0\n",
+       "4     Alertmanager: Configuration.deletes PagerDuty ...             0.0\n",
+       "...                                                 ...             ...\n",
+       "7793  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7794  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7795  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7796  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7797  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "\n",
+       "[7798 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Calculate flake severity or flake rate by tests\n",
+    "## Moving forward, this will be aggregated in Superset\n",
+    "## For the sake of completeness, it is implmented here\n",
+    "flake_severity = df.groupby(\"test\").flake.mean().reset_index()\n",
+    "flake_severity.rename(columns={\"flake\": \"flake_severity\"}, inplace=True)\n",
+    "flake_severity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization \n",
+    "- In this optional section, you can provide a quick visualization of the metric that you computed.\n",
+    "- Although, the main visualization will be done in Superset, the graphs helps to understand the metric better.\n",
+    "- The following plot shows the flake severity metric for a snapshot of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x720 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=((15, 10)))\n",
+    "flake_severity.hist(bins=50)\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Flake Severity\")\n",
+    "plt.ylim((0, 25))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save to Ceph\n",
+    "* Use the following helper function to save the data frame in a parquet format on the Ceph bucket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def upload_to_ceph(dataframe, filename):\n",
+    "    \"\"\"\n",
+    "    This helper function takes as input the data frame to be uploaded, and the output filename.\n",
+    "    It then saves the data frame in the defined ceph bucket.\n",
+    "    \"\"\"\n",
+    "    parquet_buffer = BytesIO()\n",
+    "    dataframe.to_parquet(parquet_buffer)\n",
+    "    s3_resource = boto3.resource(\n",
+    "        \"s3\",\n",
+    "        endpoint_url=s3_endpoint_url,\n",
+    "        aws_access_key_id=s3_access_key,\n",
+    "        aws_secret_access_key=s3_secret_key,\n",
+    "    )\n",
+    "    s3_obj = s3_resource.Object(s3_bucket, f\"{s3_path}/{filename}\")\n",
+    "    status = s3_obj.put(Body=parquet_buffer.getvalue())\n",
+    "    return status\n",
+    "\n",
+    "\n",
+    "def read_from_ceph(key):\n",
+    "    \"\"\"\n",
+    "    Helper function to read from ceph and see if the saved data is correct.\n",
+    "    \"\"\"\n",
+    "    buffer = BytesIO()\n",
+    "    s3 = boto3.resource(\n",
+    "        \"s3\",\n",
+    "        endpoint_url=s3_endpoint_url,\n",
+    "        aws_access_key_id=s3_access_key,\n",
+    "        aws_secret_access_key=s3_secret_key,\n",
+    "    )\n",
+    "\n",
+    "    s3_object = s3.Object(s3_bucket, f\"{s3_path}/{key}\")\n",
+    "    s3_object.download_fileobj(buffer)\n",
+    "    df_temp = pd.read_parquet(buffer)\n",
+    "    return df_temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ResponseMetadata': {'RequestId': 'kmdvrioj-5xh3ql-1e2x',\n",
+       "  'HostId': 'kmdvrioj-5xh3ql-1e2x',\n",
+       "  'HTTPStatusCode': 200,\n",
+       "  'HTTPHeaders': {'x-amz-request-id': 'kmdvrioj-5xh3ql-1e2x',\n",
+       "   'x-amz-id-2': 'kmdvrioj-5xh3ql-1e2x',\n",
+       "   'access-control-allow-origin': '*',\n",
+       "   'access-control-allow-credentials': 'true',\n",
+       "   'access-control-allow-methods': 'GET,POST,PUT,DELETE,OPTIONS',\n",
+       "   'access-control-allow-headers': 'Content-Type,Content-MD5,Authorization,X-Amz-User-Agent,X-Amz-Date,ETag,X-Amz-Content-Sha256',\n",
+       "   'access-control-expose-headers': 'ETag,X-Amz-Version-Id',\n",
+       "   'etag': '\"1c10ff80de3690398700987f9add64d4\"',\n",
+       "   'date': 'Wed, 17 Mar 2021 20:11:47 GMT',\n",
+       "   'content-length': '0',\n",
+       "   'set-cookie': '1a4aa612fe797ac8466d7ee00e5520d5=9fe18d066a3948e2fb125672aec1d05d; path=/; HttpOnly; Secure; SameSite=None'},\n",
+       "  'RetryAttempts': 0},\n",
+       " 'ETag': '\"1c10ff80de3690398700987f9add64d4\"'}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "upload_to_ceph(df, \"flake_severity/flake.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>flake</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483543</th>\n",
+       "      <td>2020-09-25 08:01:10</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483544</th>\n",
+       "      <td>2020-09-25 00:01:27</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483545</th>\n",
+       "      <td>2020-09-24 16:00:30</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483546</th>\n",
+       "      <td>2020-09-24 08:00:46</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483547</th>\n",
+       "      <td>2020-09-24 00:02:12</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19483548 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   timestamp                           tab  \\\n",
+       "0        2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1        2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2        2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3        2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4        2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "...                      ...                           ...   \n",
+       "19483543 2020-09-25 08:01:10     \"redhat-osde2e-stage-moa\"   \n",
+       "19483544 2020-09-25 00:01:27     \"redhat-osde2e-stage-moa\"   \n",
+       "19483545 2020-09-24 16:00:30     \"redhat-osde2e-stage-moa\"   \n",
+       "19483546 2020-09-24 08:00:46     \"redhat-osde2e-stage-moa\"   \n",
+       "19483547 2020-09-24 00:02:12     \"redhat-osde2e-stage-moa\"   \n",
+       "\n",
+       "                                                     grid  \\\n",
+       "0         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "...                                                   ...   \n",
+       "19483543        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483544        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483545        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483546        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483547        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "\n",
+       "                                                       test  flake  \n",
+       "0         Application behind service load balancer with ...  False  \n",
+       "1         Application behind service load balancer with ...  False  \n",
+       "2         Application behind service load balancer with ...  False  \n",
+       "3         Application behind service load balancer with ...  False  \n",
+       "4         Application behind service load balancer with ...  False  \n",
+       "...                                                     ...    ...  \n",
+       "19483543                                            Overall  False  \n",
+       "19483544                                            Overall  False  \n",
+       "19483545                                            Overall  False  \n",
+       "19483546                                            Overall  False  \n",
+       "19483547                                            Overall  False  \n",
+       "\n",
+       "[19483548 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Sanity check to see if the dataset is the same\n",
+    "read_from_ceph(\"flake_severity/flake.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save to disk\n",
+    "* Use the following helper function to save the data frame in the parquet format on the disk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_to_disk(dataframe, filename):\n",
+    "    \"\"\"\n",
+    "    Helper function to save the dataframe\n",
+    "    as a parquet file to disk.\n",
+    "    \"\"\"\n",
+    "    dataframe.to_parquet(f\"{OUTPUT_DATA_PATH}/{filename}\")\n",
+    "    return True\n",
+    "\n",
+    "\n",
+    "def read_from_disk(filename):\n",
+    "    \"\"\"\n",
+    "    Helper function to read from disk and see if the saved data is the same.\n",
+    "    \"\"\"\n",
+    "    return pd.read_parquet(f\"{OUTPUT_DATA_PATH}/{filename}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "save_to_disk(df, \"flake.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>flake</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483543</th>\n",
+       "      <td>2020-09-25 08:01:10</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483544</th>\n",
+       "      <td>2020-09-25 00:01:27</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483545</th>\n",
+       "      <td>2020-09-24 16:00:30</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483546</th>\n",
+       "      <td>2020-09-24 08:00:46</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19483547</th>\n",
+       "      <td>2020-09-24 00:02:12</td>\n",
+       "      <td>\"redhat-osde2e-stage-moa\"</td>\n",
+       "      <td>osde2e-stage-moa-e2e-upgrade-default-next</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19483548 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   timestamp                           tab  \\\n",
+       "0        2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1        2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2        2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3        2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4        2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "...                      ...                           ...   \n",
+       "19483543 2020-09-25 08:01:10     \"redhat-osde2e-stage-moa\"   \n",
+       "19483544 2020-09-25 00:01:27     \"redhat-osde2e-stage-moa\"   \n",
+       "19483545 2020-09-24 16:00:30     \"redhat-osde2e-stage-moa\"   \n",
+       "19483546 2020-09-24 08:00:46     \"redhat-osde2e-stage-moa\"   \n",
+       "19483547 2020-09-24 00:02:12     \"redhat-osde2e-stage-moa\"   \n",
+       "\n",
+       "                                                     grid  \\\n",
+       "0         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4         release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "...                                                   ...   \n",
+       "19483543        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483544        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483545        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483546        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "19483547        osde2e-stage-moa-e2e-upgrade-default-next   \n",
+       "\n",
+       "                                                       test  flake  \n",
+       "0         Application behind service load balancer with ...  False  \n",
+       "1         Application behind service load balancer with ...  False  \n",
+       "2         Application behind service load balancer with ...  False  \n",
+       "3         Application behind service load balancer with ...  False  \n",
+       "4         Application behind service load balancer with ...  False  \n",
+       "...                                                     ...    ...  \n",
+       "19483543                                            Overall  False  \n",
+       "19483544                                            Overall  False  \n",
+       "19483545                                            Overall  False  \n",
+       "19483546                                            Overall  False  \n",
+       "19483547                                            Overall  False  \n",
+       "\n",
+       "[19483548 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Sanity check to see if the dataset is the same\n",
+    "read_from_disk(\"flake.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "- Report the status of the calculation that you aimed to complete in this notebook.\n",
+    "- Highlight any difficulties in the calculation of the metric.\n",
+    "- Optional: Add any side notes, future efforts\n",
+    "\n",
+    "### _Example Conclusion_ \n",
+    "_This notebook computed number of flakes and the flake severity metric. The dataframe saved on ceph can be used to generate aggregated views and visualizations._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/number_of_flakes.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/number_of_flakes.ipynb
new file mode 100644
index 00000000..792824eb
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/number_of_flakes.ipynb	
@@ -0,0 +1,796 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quantify Flakes\n",
+    "\n",
+    "One of the key perfomance indicators that we would like to create greater visbility into and track over time is overall number and percent of flakes that occur. Individual test runs are flagged a \"flake\" if they are run mulitple times with a mix of passes and failes without any changes to the code being tested. Although they occur for individual test runs, there are a number of aggregate views that developers may want to look at to assess the overall health of thier project or testing platform. For Example:\n",
+    "\n",
+    "* percent flakes on platform each day\n",
+    "* percent flakes by tab each week\n",
+    "* percent flakes by grid each month\n",
+    "* percent flakes by test overall (this can also be seen as a severity level = overall flake rate of test)\n",
+    "\n",
+    "In order to provide maxium flexibility for the end-user of this work, instead of creating a number of dataframes to answer each of these specifc questions, we will define a long and narrow data structure (a list of tuples saved as a csv for now) that contains only 5 columns (\"timestamp\", \"tab\",\"grid\",\"test\",\"flake\"). This allows superset (or pandas) to perform the last filter and/or aggreagtion of interest to an end user. Which is to say, there may appear to be a lot of repetion within the final dataset, but each row should be unique, and it should provide the simpelest useability for an end-user.    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:18:51.978625Z",
+     "start_time": "2021-02-09T21:18:51.454578Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import matplotlib.pyplot as plt\n",
+    "sys.path.append(\"../../..\")\n",
+    "\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../../data-sources/TestGrid\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "\n",
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:18:56.350078Z",
+     "start_time": "2021-02-09T21:18:51.981071Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load test file\n",
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    testgrid_data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:18:56.365547Z",
+     "start_time": "2021-02-09T21:18:56.352827Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def testgrid_labelwise_encoding(data, label):\n",
+    "\n",
+    "    \"\"\"\n",
+    "    Run length encode the dataset and unroll the dataset into a list.\n",
+    "    Return flattened list after encoding specified value as\n",
+    "    True and rest as False\n",
+    "    \"\"\"\n",
+    "\n",
+    "    percent_label_by_grid_csv = []\n",
+    "\n",
+    "    for tab in data.keys():\n",
+    "\n",
+    "        for grid in data[tab].keys():\n",
+    "            current_grid = data[tab][grid]\n",
+    "            if len(current_grid[\"grid\"]) == 0:\n",
+    "                pass\n",
+    "            else:\n",
+    "                # get all timestamps for this grid (x-axis of grid)\n",
+    "                timestamps = [\n",
+    "                    datetime.datetime.fromtimestamp(x // 1000)\n",
+    "                    for x in current_grid[\"timestamps\"]\n",
+    "                ]\n",
+    "                # get all test names for this grid (y-axis of grid)\n",
+    "                tests = [\n",
+    "                    current_grid[\"grid\"][i][\"name\"]\n",
+    "                    for i in range(len(current_grid[\"grid\"]))\n",
+    "                ]\n",
+    "                # unroll the run-length encoding and set bool for flake or not (x==13)\n",
+    "                decoded = [\n",
+    "                    (\n",
+    "                        np.array(decode_run_length(current_grid[\"grid\"][i][\"statuses\"]))\n",
+    "                        == label\n",
+    "                    ).tolist()\n",
+    "                    for i in range(len(current_grid[\"grid\"]))\n",
+    "                ]\n",
+    "                # add the timestamp to bool value\n",
+    "                decoded = [list(zip(timestamps, g)) for g in decoded]\n",
+    "                # add the test, tab and grid name to each entry\n",
+    "                # TODO: any ideas for avoiding this quad-loop\n",
+    "                for i, d in enumerate(decoded):\n",
+    "                    for j, k in enumerate(d):\n",
+    "                        decoded[i][j] = (k[0], tab, grid, tests[i], k[1])\n",
+    "                # accumulate the results\n",
+    "                percent_label_by_grid_csv.append(decoded)\n",
+    "\n",
+    "    # output above leaves us with a doubly nested list. Flatten\n",
+    "    flat_list = [item for sublist in percent_label_by_grid_csv for item in sublist]\n",
+    "    flatter_list = [item for sublist in flat_list for item in sublist]\n",
+    "\n",
+    "    return flatter_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:14.516215Z",
+     "start_time": "2021-02-09T21:18:56.367489Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "unrolled_list = testgrid_labelwise_encoding(testgrid_data, 13)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:14.523760Z",
+     "start_time": "2021-02-09T21:19:14.518905Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(datetime.datetime(2020, 10, 8, 16, 48, 5),\n",
+       " '\"redhat-openshift-informing\"',\n",
+       " 'release-openshift-okd-installer-e2e-aws-upgrade',\n",
+       " 'Application behind service load balancer with PDB is not disrupted',\n",
+       " False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unrolled_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:14.572932Z",
+     "start_time": "2021-02-09T21:19:14.525319Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19483548"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(unrolled_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:22.561708Z",
+     "start_time": "2021-02-09T21:19:14.574520Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>flake</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 16:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 15:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 10:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 07:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 04:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 16:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 15:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 10:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 07:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 04:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                              grid  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  flake  \n",
+       "0  Application behind service load balancer with ...  False  \n",
+       "1  Application behind service load balancer with ...  False  \n",
+       "2  Application behind service load balancer with ...  False  \n",
+       "3  Application behind service load balancer with ...  False  \n",
+       "4  Application behind service load balancer with ...  False  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "df_csv = pd.DataFrame(\n",
+    "    unrolled_list, columns=[\"timestamp\", \"tab\", \"grid\", \"test\", \"flake\"]\n",
+    ")\n",
+    "df_csv.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Flake Severity Metric \n",
+    "Some tests would be flaky for a lot of builds and thus have a high flake rate or severity. This metric can be leveraged to help prioritize the work needed to resolve the flake issues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>test</th>\n",
+       "      <th>flake</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>19483548</td>\n",
+       "      <td>19483548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>7798</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Overall</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>32734</td>\n",
+       "      <td>19455237</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            test     flake\n",
+       "count   19483548  19483548\n",
+       "unique      7798         2\n",
+       "top      Overall     False\n",
+       "freq       32734  19455237"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flake_severity = df_csv[['test', 'flake']]\n",
+    "df_flake_severity.describe()"
+   ]
+  },
+  {
+   "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>test</th>\n",
+       "      <th>flake_severity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Add Secret to Workloads.Add Secret to Workload...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Add Secret to Workloads.Add Secret to Workload...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alertmanager: Configuration.creates PagerDuty ...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alertmanager: Configuration.creates a receiver...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alertmanager: Configuration.deletes PagerDuty ...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7793</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7794</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7795</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7796</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7797</th>\n",
+       "      <td>user.openshift.io~v1~Group.Kubernetes resource...</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7798 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   test  flake_severity\n",
+       "0     Add Secret to Workloads.Add Secret to Workload...             0.0\n",
+       "1     Add Secret to Workloads.Add Secret to Workload...             0.0\n",
+       "2     Alertmanager: Configuration.creates PagerDuty ...             0.0\n",
+       "3     Alertmanager: Configuration.creates a receiver...             0.0\n",
+       "4     Alertmanager: Configuration.deletes PagerDuty ...             0.0\n",
+       "...                                                 ...             ...\n",
+       "7793  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7794  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7795  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7796  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "7797  user.openshift.io~v1~Group.Kubernetes resource...             0.0\n",
+       "\n",
+       "[7798 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## The following metric implements flake severity\n",
+    "## or flake rate by tests\n",
+    "## Moving forward, this will be aggregated in Superset\n",
+    "## For the sake of completeness, it is implmented here\n",
+    "flake_severity = df_csv.groupby(\"test\").flake.mean().reset_index()\n",
+    "flake_severity.rename(columns={\"flake\": \"flake_severity\"}, inplace=True)\n",
+    "flake_severity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x720 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = ((15,10)))\n",
+    "flake_severity.hist()\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Flake Severity\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above graph we can conclude that most of the tests have very low(~0) flake severity. One might look at this graph and assume that almost all of the tests have flake severity as 0. So, to get more clarity we have plotted another graph and here we've gone from about 8,000 tests to investigate for probable flakiness to less than 100. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x720 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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lXlFVt46wfUlqkkM9ktSYVNXwG02uB24HCnhvVZ0xyzYrgZUAS5cuPWzVqlWLamvtbXdy8z39b3/Ivrstqp1BWr9+PUuWLBl1GVtl0vsw6fWDfRgXw+zDihUrLp9tKH1Uwb9PVd2YZG/gEuBVVXXpXNtPTU3VmjVrFtXW6edcwNuv7n9Eaxw/gWv16tUsX7581GVslUnvw6TXD/ZhXAyzD0lmDf6RDPVU1Y3d97XA+cDho6hDklo09OBPskuSXTc9Bo4Erhl2HZLUqlFc1bMUOD/JpvY/WlWfGUEdktSkoQd/VV0HPHbY7UqSerycU5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNGeVn7o6lA07+1KzLx/EDWiRpMTzil6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMV7Hfz8Yt2v/56pnLvdXnfO1e9Zzdrlf2pC09Tzil6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXGG7j6tKU3Rc33mrlumJpr+9cesoGTFtH+1hq3G9Pmc3/VOtd+WrwBbZLe/0k3X74M4uftEb8kNcbgl6TGGPyS1BiDX5IaY/BLUmNGEvxJnpPke0l+mOTkUdQgSa0aevAn2R74n8BzgYOA45McNOw6JKlVozjiPxz4YVVdV1W/AlYBR4+gDklqUqpquA0mxwLPqao/656fCDyxql45Y7uVwMru6SOB7y2yyT2BWxf52nFhH0Zv0usH+zAuhtmH/atqr5kLR3HnbmZZttlvn6o6AzhjqxtL1lTV1NbuZ5Tsw+hNev1gH8bFOPRhFEM9PwX2m/b8t4EbR1CHJDVpFMH/DeDAJL+TZCfgxcCFI6hDkpo09KGeqtqQ5JXAZ4HtgTOr6tsDbHKrh4vGgH0YvUmvH+zDuBh5H4Z+cleSNFreuStJjTH4Jakx20zwLzQNRHre1a2/KsnjR1HnfProw6OSXJbkl0leN4oa59NH/Sd0P/urknwlyWNHUed8+ujD0V39VyRZk+T3R1HnfPqdEiXJE5Js7O6tGSt9vA/Lk9zZvQ9XJHnDKOqcSz/vQdeHK5J8O8kXh1pgVU38F72TxP8GPAzYCbgSOGjGNkcBn6Z3H8ERwNdGXfci+rA38ATgzcDrRl3zIup/MrBH9/i5E/oeLOG+c2OPAb476rq3tA/Ttvs8cDFw7KjrXsT7sBy4aNS1bkX9uwPfAR7aPd97mDVuK0f8/UwDcTTwoer5KrB7kmXDLnQeC/ahqtZW1TeAX4+iwAX0U/9Xqur27ulX6d3DMU766cP66v6nArswy82HI9bvlCivAs4D1g6zuD5N+rQu/dT/EuATVfVj6P3fHmaB20rw7wv8ZNrzn3bLtnSbURr3+haypfW/nN5fYOOkrz4kOSbJd4FPAS8bUm39WrAPSfYFjgHeM8S6tkS//5aelOTKJJ9O8ujhlNaXfup/BLBHktVJLk/yp0Orjm3nw9b7mQair6kiRmjc61tI3/UnWUEv+MdtfLzf6UTOB85P8jTgVOBZgy5sC/TTh38A/qaqNiazbT5y/fThm/TmoVmf5Cjgk8CBA6+sP/3UvwNwGPBMYGfgsiRfrarvD7q4TY1vC/qZBmLcp4oY9/oW0lf9SR4DvB94blX9fEi19WuL3oOqujTJ7ybZs6rGZeKwfvowBazqQn9P4KgkG6rqk8MpcUEL9qGq1k17fHGSfxyj96HfPLq1qu4G7k5yKfBYYCjBP/ITIffTyZQdgOuA3+G+kymPnrHN8/jNk7tfH3XdW9qHaduewvid3O3nPXgo8EPgyaOudyv68HDuO7n7eOBnm56Pw9eW/Dvqtj+L8Tu528/78FvT3ofDgR+Py/vQZ/2/B3yu2/aBwDXAwcOqcZs44q85poFI8ufd+vfQu3rhKHrB8wvg34+q3tn004ckvwWsAR4E3Jvk1fSuFlg3546HpM/34A3AQ4B/7I42N9QYzbTYZx/+GPjTJL8G7gGOq+5/8jjosw9jrc8+HAv8RZIN9N6HF4/L+9BP/VV1bZLPAFcB9wLvr6prhlWjUzZIUmO2lat6JEl9MvglqTEGvyQ1xuCXpMYY/JLUGINfE6mbVfKKaV8HdLMdXrTA605K8u5FtnlEkq917V2b5JRFFd9/e/sk+Xj3+NDuDlVpq20T1/GrSfdU1aHTFyQ5YMBtng28qKquTLI98MhBNZRkh6q6kd716gCH0rvj9uJBtal2eMSvbVKSw7s5/7/Vfd8spJM8r/t8gz2THNk9/maSf06yZJbd7g3cBFBVG6vqO91+dklyZpJvdO0d3S3/2vTJw7oJuQ6bZ/uTurb/Bfjf3V8x1yTZCXgTcFz318ZxSX6QZK/uddt1877veT//GLWNMvg1qXaeNsxz/izrvws8raoeR++O4bdMX5nkGOBkendzA/wt8Kyqejy9u6NfM8s+3wl8L8n5SV6R5AHd8v8CfL6qngCsAP4+yS70puN9UdfeMmCfqrp8nu0BngS8tKqesanR6k3t+wbgn6rq0Kr6J+AjwAndJs8CrqzxmKdGE8ChHk2qzYZ6ZtgNODvJgfRmRtxx2roV9IZNjqyqdUn+HXAQ8OVuKomdgMtm7rCq3pTkHOBIevOpH0/vA0GOBP4o930q2gPozUv0MeAS4I30fgH8c7d+ru0BLqmq2/ro/5nABfRm2nwZ8ME+XiMBBr+2XacCX6iqY7qx/9XT1l1H79ORHkHv6D70Avf4hXZaVf8G/K8k7wNuSfKQ7vV/XFXfm7l9kp93M5IeB7xi0+LZtk/yRODufjpXVT9JcnOSZwBP5L6jf2lBDvVoW7UbvZkzAU6ase5HwAuAD3Vj8F8FnpLk4QBJHpjkETN32J0T2DTX+oHARuAOepNxvWrTuiSPm/ayVcBfA7tV1dXdsvm2n8tdwK4zlr2f3pDPx6pqYx/7kACDX9uu/w68NcmX6c2Q+Bu6o+0T6A2/PIjeL4dzk1xF7xfBo2bZ54n0xvivAD4MnNAF7qn0hpKuSnJN93yTjwMvpjfss8l828/lC8BBm07udssupPcZwA7zaIs4O6c0oZJMAe+sqqeOuhZNFsf4pQmU5GTgL3BsX4vgEb8kNcYxfklqjMEvSY0x+CWpMQa/JDXG4Jekxvw/+//iBCLvOAIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = ((15,10)))\n",
+    "flake_severity.hist(bins=50)\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Flake Severity\")\n",
+    "plt.ylim((0,25))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above graph we can see that there are other values of flake severity for some tests. However it is a small bunch of tests that show a different set of flake severity. Let's see some tests that have a high flake severity score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top 5 tests with the highest flake score :"
+   ]
+  },
+  {
+   "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>test</th>\n",
+       "      <th>flake_severity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3376</th>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute</td>\n",
+       "      <td>0.624231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3377</th>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute...</td>\n",
+       "      <td>0.433333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3819</th>\n",
+       "      <td>[sig-network-edge] Application behind service ...</td>\n",
+       "      <td>0.291732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3820</th>\n",
+       "      <td>[sig-network-edge] Cluster frontend ingress re...</td>\n",
+       "      <td>0.257874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3487</th>\n",
+       "      <td>[sig-auth][Feature:SCC][Early] should not have...</td>\n",
+       "      <td>0.188283</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   test  flake_severity\n",
+       "3376     [sig-arch] Monitor cluster while tests execute        0.624231\n",
+       "3377  [sig-arch] Monitor cluster while tests execute...        0.433333\n",
+       "3819  [sig-network-edge] Application behind service ...        0.291732\n",
+       "3820  [sig-network-edge] Cluster frontend ingress re...        0.257874\n",
+       "3487  [sig-auth][Feature:SCC][Early] should not have...        0.188283"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flake_severity.nlargest(5, 'flake_severity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:31.520216Z",
+     "start_time": "2021-02-09T21:19:22.564599Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1 million out of 30 million rows due to pvc limits. Expected data size is 7.5 GB\n",
+    "# 250mb = 1 million --> 7500 mb = 30 million\n",
+    "file = \"flakes.csv\"\n",
+    "folder = \"../../../../data/processed/metrics/percent_flake_by_test\"\n",
+    "if not os.path.exists(folder):\n",
+    "    os.makedirs(folder)\n",
+    "\n",
+    "fullpath = os.path.join(folder, file)\n",
+    "df_csv.head(1000000).to_csv(fullpath, header=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:31.524437Z",
+     "start_time": "2021-02-09T21:19:31.522190Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Send to Ceph - TODO!\n",
+    "# currently waiting on ceph bucket but it will look something like this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-09T21:19:31.548034Z",
+     "start_time": "2021-02-09T21:19:31.526038Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.003037"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Overall flake percentage\n",
+    "df_csv.head(1000000).flake.sum() / df_csv.head(1000000).flake.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1612822647050,
+   "trusted": true
+  },
+  "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": "3.7.0"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/pct_fixed_each_ts.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/pct_fixed_each_ts.ipynb
new file mode 100644
index 00000000..a90de65b
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/pct_fixed_each_ts.ipynb	
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "charming-pioneer",
+   "metadata": {},
+   "source": [
+    "# Percent of Failing Tests Fixed\n",
+    "\n",
+    "This notebook is an addition to the series of KPI notebook in which we calculate key performance indicators for CI processes. In this notebook, we will calculate the KPI \"Percent of failing tests fixed in each run/timestamp\". Essentially we will determine what percent of tests that were failing in the previous test run got fixed in the current test run.\n",
+    "\n",
+    "For OpenShift managers, this information can potentially help quantify the agility and efficiency of their team. If this number is high, it means they are able to quickly identify the root causes of all failing tests in the previous run and fix them. Conversely if this number is low, it means only a small percent of previously failing tests get fixed in each new run, which in turn implies that their CI process is likely not as efficient as it could be."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "textile-mortgage",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:14.897443Z",
+     "start_time": "2021-02-08T22:09:14.371522Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import gzip\n",
+    "import json\n",
+    "import datetime as dt\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "sys.path.append(\"../../..\")\n",
+    "\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../../data-sources/TestGrid\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "\n",
+    "from ipynb.fs.defs.number_of_flakes import (  # noqa: E402\n",
+    "    testgrid_labelwise_encoding,\n",
+    ")  # noqa: E402\n",
+    "\n",
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "realistic-struggle",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:19.031967Z",
+     "start_time": "2021-02-08T22:09:15.200904Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load test file\n",
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "needed-highlight",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"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-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\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NOTE: this for loop is a modified version of the testgrid_labelwise_encoding function\n",
+    "# We've adapted this loop to put a \"True\" if a test was fixed in the current run, and\n",
+    "# \"False\" otherwise. Basically instead of indicating \"is_flake\" or \"is_pass\" etc., it\n",
+    "# now indicates \"is passing now but was failing before\" aka \"is_flip\"\n",
+    "percent_label_by_grid_csv = []\n",
+    "\n",
+    "for tab in data.keys():\n",
+    "    print(tab)\n",
+    "\n",
+    "    for grid in data[tab].keys():\n",
+    "        current_grid = data[tab][grid]\n",
+    "\n",
+    "        # get all timestamps for this grid (x-axis of grid)\n",
+    "        timestamps = [\n",
+    "            dt.datetime.fromtimestamp(t // 1000) for t in current_grid[\"timestamps\"]\n",
+    "        ]\n",
+    "\n",
+    "        tests = []\n",
+    "        all_tests_did_get_fixed = []\n",
+    "\n",
+    "        # NOTE: this list of dicts goes from most recent to least recent\n",
+    "        for i, current_test in enumerate(current_grid[\"grid\"]):\n",
+    "            tests.append(current_test[\"name\"])\n",
+    "            statuses_decoded = decode_run_length(current_grid[\"grid\"][i][\"statuses\"])\n",
+    "\n",
+    "            did_get_fixed = []\n",
+    "            for status_i in range(0, len(statuses_decoded) - 1):\n",
+    "                did_get_fixed.append(\n",
+    "                    statuses_decoded[status_i] == 1\n",
+    "                    and statuses_decoded[status_i + 1] == 12\n",
+    "                )\n",
+    "\n",
+    "            # the least recent test cannot have \"True\", assuming it wasnt failing before\n",
+    "            did_get_fixed.append(False)\n",
+    "\n",
+    "            # add results for all timestamps for current test\n",
+    "            all_tests_did_get_fixed.append(np.array(did_get_fixed))\n",
+    "\n",
+    "        all_tests_did_get_fixed = [\n",
+    "            list(zip(timestamps, g)) for g in all_tests_did_get_fixed\n",
+    "        ]\n",
+    "\n",
+    "        # add the test, tab and grid name to each entry\n",
+    "        # TODO: any ideas for avoiding this quad-loop\n",
+    "        for i, d in enumerate(all_tests_did_get_fixed):\n",
+    "            for j, k in enumerate(d):\n",
+    "                all_tests_did_get_fixed[i][j] = (k[0], tab, grid, tests[i], k[1])\n",
+    "\n",
+    "        # accumulate the results\n",
+    "        percent_label_by_grid_csv.append(all_tests_did_get_fixed)\n",
+    "\n",
+    "# output above leaves us with a doubly nested list. Flatten\n",
+    "flat_list = [item for sublist in percent_label_by_grid_csv for item in sublist]\n",
+    "flatter_list = [item for sublist in flat_list for item in sublist]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eastern-delicious",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(datetime.datetime(2020, 10, 8, 20, 48, 5),\n",
+       " '\"redhat-openshift-informing\"',\n",
+       " 'release-openshift-okd-installer-e2e-aws-upgrade',\n",
+       " 'Application behind service load balancer with PDB is not disrupted',\n",
+       " False)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flatter_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "legal-lease",
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>did_get_fixed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                              grid  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  did_get_fixed  \n",
+       "0  Application behind service load balancer with ...          False  \n",
+       "1  Application behind service load balancer with ...          False  \n",
+       "2  Application behind service load balancer with ...          False  \n",
+       "3  Application behind service load balancer with ...          False  \n",
+       "4  Application behind service load balancer with ...          False  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this df indicates whether a test was fixed or not at a given timestamp (as compared to previous one)\n",
+    "df_csv = pd.DataFrame(\n",
+    "    flatter_list, columns=[\"timestamp\", \"tab\", \"grid\", \"test\", \"did_get_fixed\"]\n",
+    ")\n",
+    "df_csv.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "operational-prairie",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tab                           grid                                             timestamp          \n",
+       "\"redhat-openshift-informing\"  release-openshift-okd-installer-e2e-aws-upgrade  2020-09-23 22:16:02    0\n",
+       "                                                                               2020-09-24 00:04:39    1\n",
+       "                                                                               2020-09-24 01:57:00    0\n",
+       "                                                                               2020-09-24 03:48:47    6\n",
+       "                                                                               2020-09-24 05:36:09    0\n",
+       "                                                                                                     ..\n",
+       "\"redhat-osde2e-stage-moa\"     osde2e-stage-moa-e2e-upgrade-default-next        2020-10-07 08:01:13    0\n",
+       "                                                                               2020-10-07 16:01:54    0\n",
+       "                                                                               2020-10-08 00:01:28    0\n",
+       "                                                                               2020-10-08 08:02:28    0\n",
+       "                                                                               2020-10-08 16:01:08    0\n",
+       "Name: did_get_fixed, Length: 32663, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# each element in this multiindexed series tells how many tests got fixed at each run/timestamp\n",
+    "num_fixed_per_ts = df_csv.groupby([\"tab\", \"grid\", \"timestamp\"]).did_get_fixed.sum()\n",
+    "num_fixed_per_ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "stainless-congress",
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 20:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 19:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 14:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 11:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 08:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 20:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 19:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 14:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 11:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 08:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  failure  \n",
+       "0  Application behind service load balancer with ...    False  \n",
+       "1  Application behind service load balancer with ...     True  \n",
+       "2  Application behind service load balancer with ...    False  \n",
+       "3  Application behind service load balancer with ...    False  \n",
+       "4  Application behind service load balancer with ...    False  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this df indicates whether a test was failing or not at a given timestamp\n",
+    "failures_df = pd.DataFrame(\n",
+    "    testgrid_labelwise_encoding(data, 12),\n",
+    "    columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"failure\"],\n",
+    ")\n",
+    "failures_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bronze-pittsburgh",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tab                           job                                              timestamp          \n",
+       "\"redhat-openshift-informing\"  release-openshift-okd-installer-e2e-aws-upgrade  2020-09-23 22:16:02    1\n",
+       "                                                                               2020-09-24 00:04:39    0\n",
+       "                                                                               2020-09-24 01:57:00    7\n",
+       "                                                                               2020-09-24 03:48:47    0\n",
+       "                                                                               2020-09-24 05:36:09    0\n",
+       "                                                                                                     ..\n",
+       "\"redhat-osde2e-stage-moa\"     osde2e-stage-moa-e2e-upgrade-default-next        2020-10-07 08:01:13    0\n",
+       "                                                                               2020-10-07 16:01:54    0\n",
+       "                                                                               2020-10-08 00:01:28    0\n",
+       "                                                                               2020-10-08 08:02:28    0\n",
+       "                                                                               2020-10-08 16:01:08    0\n",
+       "Name: failure, Length: 32663, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# each element in this multiindexed series tells how many tests failed at each run/timestamp\n",
+    "num_failures_per_ts = failures_df.groupby([\"tab\", \"job\", \"timestamp\"]).failure.sum()\n",
+    "num_failures_per_ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "humanitarian-london",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tab                           grid                                             timestamp          \n",
+       "\"redhat-openshift-informing\"  release-openshift-okd-installer-e2e-aws-upgrade  2020-09-23 22:16:02    0.000000\n",
+       "                                                                               2020-09-24 00:04:39    1.000000\n",
+       "                                                                               2020-09-24 01:57:00    0.000000\n",
+       "                                                                               2020-09-24 03:48:47    0.857143\n",
+       "                                                                               2020-09-24 05:36:09    0.000000\n",
+       "                                                                                                        ...   \n",
+       "\"redhat-osde2e-stage-moa\"     osde2e-stage-moa-e2e-upgrade-default-next        2020-10-07 08:01:13    0.000000\n",
+       "                                                                               2020-10-07 16:01:54    0.000000\n",
+       "                                                                               2020-10-08 00:01:28    0.000000\n",
+       "                                                                               2020-10-08 08:02:28    0.000000\n",
+       "                                                                               2020-10-08 16:01:08    0.000000\n",
+       "Length: 32663, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# dividing the above two df's tells us what percent of failing tests got fixed at each timestamp\n",
+    "pct_fixed_per_ts = (num_fixed_per_ts / num_failures_per_ts.shift()).fillna(0)\n",
+    "pct_fixed_per_ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "informational-growing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the metric (takes abot 67 MB in csv data)\n",
+    "file = \"pct_fixed_per_run.csv\"\n",
+    "folder = \"../../../../data/processed/metrics/percent_tests_fixed\"\n",
+    "if not os.path.exists(folder):\n",
+    "    os.makedirs(folder)\n",
+    "\n",
+    "fullpath = os.path.join(folder, file)\n",
+    "pct_fixed_per_ts.to_csv(fullpath, header=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1612822647266,
+   "trusted": true
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/persistent_failures_analysis.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/persistent_failures_analysis.ipynb
new file mode 100644
index 00000000..7e0d7225
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/persistent_failures_analysis.ipynb	
@@ -0,0 +1,641 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Persistent Failures Analysis\n",
+    "\n",
+    "Speed and quality of builds are some of the key performance indicators for the continuous integration process. That is, reduction in the number of failing builds, or the time taken to fix them, should generally indicate an improvement in the development process. In this notebook, we will analyze the data collected from [TestGrid](https://testgrid.k8s.io/redhat) to calculate metrics such as percent of failures that persist for long times, how long do such failures last for (i.e. how long does it take to fix them), etc. Our goal here is to provide engineers and managers with insights such as\n",
+    "- Which tests (e.g. network or storage) have the most \"long lasting\" failures\n",
+    "- Which platforms (e.g. AWS or bare metal) have the most \"long lasting\" failures\n",
+    "- How long does it take to get a failing test passing again\n",
+    "- How long does it take to get a failing build to build again\n",
+    "\n",
+    "In this notebook, we will follow the same convention as in [number_of_flakes.ipynb](number_of_flakes.ipynb), i.e., create a long dataframe and let the end user decide what level they want to create aggregate views at."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-10T22:26:16.410641Z",
+     "start_time": "2021-02-10T22:26:16.107076Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "from enum import Enum\n",
+    "\n",
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-10T22:26:06.954600Z",
+     "start_time": "2021-02-10T22:26:02.886547Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-10T22:26:06.962910Z",
+     "start_time": "2021-02-10T22:26:06.957130Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class TestStatus(Enum):\n",
+    "    \"\"\"Enum to encode what test status each value in testgrid corresponds to\n",
+    "\n",
+    "    Basically python equivalent of the enum here:\n",
+    "    https://github.com/GoogleCloudPlatform/testgrid/blob/a18fe953cf98174c215c43e0258b0515e37c283b/pb/test_status/test_status.proto#L3\n",
+    "    \"\"\"\n",
+    "\n",
+    "    NO_RESULT = 0\n",
+    "    PASS = 1\n",
+    "    PASS_WITH_ERRORS = 2\n",
+    "    PASS_WITH_SKIPS = 3\n",
+    "    RUNNING = 4\n",
+    "    CATEGORIZED_ABORT = 5\n",
+    "    UNKNOWN = 6\n",
+    "    CANCEL = 7\n",
+    "    BLOCKED = 8\n",
+    "    TIMED_OUT = 9\n",
+    "    CATEGORIZED_FAIL = 10\n",
+    "    BUILD_FAIL = 11\n",
+    "    FAIL = 12\n",
+    "    FLAKY = 13\n",
+    "    TOOL_FAIL = 14\n",
+    "    BUILD_PASSED = 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-10T22:26:14.128830Z",
+     "start_time": "2021-02-10T22:26:06.965061Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"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-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\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "177291"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# calculate consecutive failure stats\n",
+    "consec_fail_stats_tuples = []\n",
+    "\n",
+    "for tab in data.keys():\n",
+    "    print(tab)\n",
+    "\n",
+    "    for grid in data[tab].keys():\n",
+    "        current_grid = data[tab][grid]\n",
+    "\n",
+    "        ## Extract relevant info for each test\n",
+    "        for current_test in current_grid[\"grid\"]:\n",
+    "\n",
+    "            # number of failing cells\n",
+    "            n_failing_cells = 0\n",
+    "\n",
+    "            # total number of occurences of failures (consecutive or one-time)\n",
+    "            n_fail_instances = 0\n",
+    "\n",
+    "            # number of occurences of consecutive (not \"one-time\") failures\n",
+    "            n_consecutive_fail_instances = 0\n",
+    "\n",
+    "            # times spent fixing each occurence of failure\n",
+    "            times_spent = []\n",
+    "\n",
+    "            # helper variables for calculating time spent fixing\n",
+    "            prev_failing = False\n",
+    "            prev_passing = False\n",
+    "            curr_time_spent = 0\n",
+    "            prev_oldest_ts_idx = 0\n",
+    "\n",
+    "            # prev_not_passing = False\n",
+    "\n",
+    "            # total number of timestamps\n",
+    "            n_cells = 0\n",
+    "\n",
+    "            # total number of transitions form pass to fail\n",
+    "            # and fail to pass\n",
+    "            pass_to_fail = 0\n",
+    "            fail_to_pass = 0\n",
+    "\n",
+    "            for e, s in enumerate(current_test[\"statuses\"]):\n",
+    "                # oldest (least recent) timestamp in current rle encoded dict\n",
+    "                curr_oldest_ts_idx = prev_oldest_ts_idx + s[\"count\"]\n",
+    "\n",
+    "                # if the current status is not failing and the previous (i.e.\n",
+    "                # the \"newer\") status was failing, then this marks the start\n",
+    "                # point of failure. since end point would have already been\n",
+    "                # calculated in previous loop, we just need to save time spent\n",
+    "                if s[\"value\"] != TestStatus.FAIL.value:\n",
+    "                    if prev_failing:\n",
+    "                        times_spent.append(curr_time_spent)\n",
+    "                        curr_time_spent = 0\n",
+    "                        if s[\"value\"] == TestStatus.PASS.value:\n",
+    "                            pass_to_fail += (\n",
+    "                                1  # Inverted because the dictioary is from most recent\n",
+    "                            )\n",
+    "\n",
+    "                elif s[\"value\"] == TestStatus.FAIL.value:\n",
+    "                    n_fail_instances += 1\n",
+    "                    n_failing_cells += s[\"count\"]\n",
+    "                    if s[\"count\"] > 1:\n",
+    "                        n_consecutive_fail_instances += 1\n",
+    "                    if prev_passing:\n",
+    "                        fail_to_pass += (\n",
+    "                            1  # Inverted because the dictioary is from most recent\n",
+    "                        )\n",
+    "\n",
+    "                    # if previous (i.e. the \"newer\") status was not failing\n",
+    "                    # and now its failing, then time delta between the oldest\n",
+    "                    # ts from previous status and current one must have been\n",
+    "                    # spent fixing the failure\n",
+    "                    if not prev_failing:\n",
+    "                        curr_time_spent += (\n",
+    "                            current_grid[\"timestamps\"][max(0, prev_oldest_ts_idx - 1)]\n",
+    "                            - current_grid[\"timestamps\"][curr_oldest_ts_idx - 1]\n",
+    "                        )\n",
+    "\n",
+    "                # update helper variables\n",
+    "                prev_failing = s[\"value\"] == TestStatus.FAIL.value\n",
+    "                prev_passing = s[\"value\"] == TestStatus.PASS.value\n",
+    "                prev_oldest_ts_idx = curr_oldest_ts_idx\n",
+    "                n_cells += s[\"count\"]\n",
+    "\n",
+    "            # test never got to non-fail status again so time spent so far\n",
+    "            # wont have been added to times_spent yet\n",
+    "            if curr_time_spent != 0:\n",
+    "                times_spent.append(curr_time_spent)\n",
+    "\n",
+    "            ## Calculate stats for this test\n",
+    "\n",
+    "            # consecutive failure rate\n",
+    "            try:\n",
+    "                consec_fail_rate = n_consecutive_fail_instances / n_fail_instances\n",
+    "            except ZeroDivisionError:\n",
+    "                consec_fail_rate = 0\n",
+    "\n",
+    "            # mean length of failures\n",
+    "            try:\n",
+    "                mean_fail_len = n_failing_cells / n_fail_instances\n",
+    "            except ZeroDivisionError:\n",
+    "                mean_fail_len = 0\n",
+    "\n",
+    "            # mean time to fix\n",
+    "            try:\n",
+    "                mean_time_to_fix = sum(times_spent) / len(times_spent)\n",
+    "            except ZeroDivisionError:\n",
+    "                mean_time_to_fix = 0\n",
+    "\n",
+    "            pass_to_fail_rate = pass_to_fail / n_cells\n",
+    "            fail_to_pass_rate = fail_to_pass / n_cells\n",
+    "\n",
+    "            # save the results to list\n",
+    "            consec_fail_stats_tuples.append(\n",
+    "                [\n",
+    "                    tab,\n",
+    "                    grid,\n",
+    "                    current_test[\"name\"],\n",
+    "                    consec_fail_rate,\n",
+    "                    mean_fail_len,\n",
+    "                    mean_time_to_fix,\n",
+    "                    pass_to_fail_rate,\n",
+    "                    fail_to_pass_rate,\n",
+    "                ]\n",
+    "            )\n",
+    "\n",
+    "len(consec_fail_stats_tuples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-10T22:26:14.333834Z",
+     "start_time": "2021-02-10T22:26:14.131197Z"
+    }
+   },
+   "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>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>consec_fail_rate</th>\n",
+       "      <th>mean_fail_len</th>\n",
+       "      <th>mean_time_to_fix</th>\n",
+       "      <th>pass_to_fail_rate</th>\n",
+       "      <th>fail_to_pass_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>0.062500</td>\n",
+       "      <td>1.062500</td>\n",
+       "      <td>17479125.0</td>\n",
+       "      <td>0.008475</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Cluster frontend ingress remain available</td>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>1.142857</td>\n",
+       "      <td>19371000.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Kubernetes APIs remain available</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>26793750.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.008475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Monitor cluster while tests execute</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>1.125000</td>\n",
+       "      <td>17794562.5</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>OpenShift APIs remain available</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7584500.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.008475</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            tab  \\\n",
+       "0  \"redhat-openshift-informing\"   \n",
+       "1  \"redhat-openshift-informing\"   \n",
+       "2  \"redhat-openshift-informing\"   \n",
+       "3  \"redhat-openshift-informing\"   \n",
+       "4  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                              grid  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  consec_fail_rate  \\\n",
+       "0  Application behind service load balancer with ...          0.062500   \n",
+       "1          Cluster frontend ingress remain available          0.142857   \n",
+       "2                   Kubernetes APIs remain available          0.000000   \n",
+       "3                Monitor cluster while tests execute          0.125000   \n",
+       "4                    OpenShift APIs remain available          0.000000   \n",
+       "\n",
+       "   mean_fail_len  mean_time_to_fix  pass_to_fail_rate  fail_to_pass_rate  \n",
+       "0       1.062500        17479125.0           0.008475           0.000000  \n",
+       "1       1.142857        19371000.0           0.000000           0.000000  \n",
+       "2       1.000000        26793750.0           0.000000           0.008475  \n",
+       "3       1.125000        17794562.5           0.000000           0.000000  \n",
+       "4       1.000000         7584500.0           0.000000           0.008475  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# put results in a pretty dataframe\n",
+    "consec_fail_stats_df = pd.DataFrame(\n",
+    "    data=consec_fail_stats_tuples,\n",
+    "    columns=[\n",
+    "        \"tab\",\n",
+    "        \"grid\",\n",
+    "        \"test\",\n",
+    "        \"consec_fail_rate\",\n",
+    "        \"mean_fail_len\",\n",
+    "        \"mean_time_to_fix\",\n",
+    "        \"pass_to_fail_rate\",\n",
+    "        \"fail_to_pass_rate\",\n",
+    "    ],\n",
+    ")\n",
+    "consec_fail_stats_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-10T22:26:14.368194Z",
+     "start_time": "2021-02-10T22:26:14.335870Z"
+    }
+   },
+   "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>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>test</th>\n",
+       "      <th>consec_fail_rate</th>\n",
+       "      <th>mean_fail_len</th>\n",
+       "      <th>mean_time_to_fix</th>\n",
+       "      <th>pass_to_fail_rate</th>\n",
+       "      <th>fail_to_pass_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>19692</th>\n",
+       "      <td>\"redhat-openshift-ocp-release-4.3-broken\"</td>\n",
+       "      <td>release-openshift-ocp-installer-e2e-azure-ovn-4.3</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>5.130903e+09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35064</th>\n",
+       "      <td>\"redhat-openshift-ocp-release-4.3-informing\"</td>\n",
+       "      <td>release-openshift-origin-installer-e2e-aws-4.3...</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>5.100456e+09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48958</th>\n",
+       "      <td>\"redhat-openshift-ocp-release-4.4-broken\"</td>\n",
+       "      <td>release-openshift-origin-installer-e2e-azure-c...</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>5.100443e+09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69902</th>\n",
+       "      <td>\"redhat-openshift-ocp-release-4.4-informing\"</td>\n",
+       "      <td>release-openshift-origin-installer-e2e-aws-upg...</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>5.100441e+09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164990</th>\n",
+       "      <td>\"redhat-openshift-okd-release-4.4-informing\"</td>\n",
+       "      <td>promote-release-openshift-okd-machine-os-conte...</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>5.100395e+09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 tab  \\\n",
+       "19692      \"redhat-openshift-ocp-release-4.3-broken\"   \n",
+       "35064   \"redhat-openshift-ocp-release-4.3-informing\"   \n",
+       "48958      \"redhat-openshift-ocp-release-4.4-broken\"   \n",
+       "69902   \"redhat-openshift-ocp-release-4.4-informing\"   \n",
+       "164990  \"redhat-openshift-okd-release-4.4-informing\"   \n",
+       "\n",
+       "                                                     grid     test  \\\n",
+       "19692   release-openshift-ocp-installer-e2e-azure-ovn-4.3  Overall   \n",
+       "35064   release-openshift-origin-installer-e2e-aws-4.3...  Overall   \n",
+       "48958   release-openshift-origin-installer-e2e-azure-c...  Overall   \n",
+       "69902   release-openshift-origin-installer-e2e-aws-upg...  Overall   \n",
+       "164990  promote-release-openshift-okd-machine-os-conte...  Overall   \n",
+       "\n",
+       "        consec_fail_rate  mean_fail_len  mean_time_to_fix  pass_to_fail_rate  \\\n",
+       "19692                1.0           90.0      5.130903e+09                0.0   \n",
+       "35064                1.0           60.0      5.100456e+09                0.0   \n",
+       "48958                1.0           60.0      5.100443e+09                0.0   \n",
+       "69902                1.0           60.0      5.100441e+09                0.0   \n",
+       "164990               1.0           60.0      5.100395e+09                0.0   \n",
+       "\n",
+       "        fail_to_pass_rate  \n",
+       "19692                 0.0  \n",
+       "35064                 0.0  \n",
+       "48958                 0.0  \n",
+       "69902                 0.0  \n",
+       "164990                0.0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the output here shows what tabs and grids have overall the longest failures\n",
+    "res = consec_fail_stats_df[consec_fail_stats_df[\"test\"] == \"Overall\"]\n",
+    "res.sort_values(\"mean_time_to_fix\", ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Save the metric (takes abot 50 MB in csv data)\n",
+    "file = \"failure_analysis.csv\"\n",
+    "folder = \"../../../../data/processed/metrics/persistent_failure_analysis\"\n",
+    "if not os.path.exists(folder):\n",
+    "    os.makedirs(folder)\n",
+    "\n",
+    "fullpath = os.path.join(folder, file)\n",
+    "consec_fail_stats_df.to_csv(fullpath, header=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1612907227414,
+   "trusted": true
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/test_pass_failures.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/test_pass_failures.ipynb
new file mode 100644
index 00000000..d6a9a1b3
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/metrics/test_pass_failures.ipynb	
@@ -0,0 +1,536 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "brown-check",
+   "metadata": {},
+   "source": [
+    "# Quantify Tests\n",
+    "\n",
+    "This notebook is an extension to the [number_of_flakes](number_of_flakes.ipynb) notebook. In this notebook, the key perfomance indicators that we would like to create greater visbility into and track over time is the percent of tests that passed/failed. This can help track and measure the effectiveness and success of our testing process.\n",
+    "\n",
+    "By measuring this metric, we can observe the trend of test failures over time and a decrease in the percent of failures over time (or increase in percent of test passes), should correlate to improved test efficiencies, enhanced testing process and error free releases.  In this notebook, we derive the following metrics from the TestGrid dataset:\n",
+    "\n",
+    "* total number of test cases\n",
+    "* number of test cases passed\n",
+    "* number of test cases failed\n",
+    "* percent of tests that pass\n",
+    "* percent of tests that fail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "brilliant-expense",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:14.897443Z",
+     "start_time": "2021-02-08T22:09:14.371522Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "import pandas as pd\n",
+    "\n",
+    "sys.path.append(\"../../..\")\n",
+    "\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../../data-sources/TestGrid\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "\n",
+    "from ipynb.fs.defs.number_of_flakes import (  # noqa: E402\n",
+    "    testgrid_labelwise_encoding,\n",
+    ")  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "boxed-enough",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:19.031967Z",
+     "start_time": "2021-02-08T22:09:15.200904Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load test file\n",
+    "with gzip.open(\"../../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    testgrid_data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "standard-parade",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:34.731090Z",
+     "start_time": "2021-02-08T22:09:19.991258Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "failures_list = testgrid_labelwise_encoding(testgrid_data, 12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "worst-egypt",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:35.959527Z",
+     "start_time": "2021-02-08T22:09:35.954444Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19483548"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(failures_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "burning-ecology",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:09:37.225228Z",
+     "start_time": "2021-02-08T22:09:37.220095Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(datetime.datetime(2020, 10, 8, 16, 48, 5),\n",
+       " '\"redhat-openshift-informing\"',\n",
+       " 'release-openshift-okd-installer-e2e-aws-upgrade',\n",
+       " 'Application behind service load balancer with PDB is not disrupted',\n",
+       " False)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failures_list[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "handled-metabolism",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2021-02-08T22:11:18.046Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 16:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 15:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 10:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 07:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 04:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 16:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 15:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 10:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 07:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 04:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  failure  \n",
+       "0  Application behind service load balancer with ...    False  \n",
+       "1  Application behind service load balancer with ...     True  \n",
+       "2  Application behind service load balancer with ...    False  \n",
+       "3  Application behind service load balancer with ...    False  \n",
+       "4  Application behind service load balancer with ...    False  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "failures_df = pd.DataFrame(\n",
+    "    failures_list, columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"failure\"]\n",
+    ")\n",
+    "failures_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "excessive-listening",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:06:25.771549Z",
+     "start_time": "2021-02-08T22:06:14.236882Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1000000 out of 19 million rows due to pvc limits.\n",
+    "# 250mb = 1 million --> 4750 mb = 19 million\n",
+    "failures_df.head(1000000).to_csv(\n",
+    "    \"../../../../data/processed/failures.csv\",\n",
+    "    header=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "confident-simon",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:06:42.772105Z",
+     "start_time": "2021-02-08T22:06:25.774788Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "passing_list = testgrid_labelwise_encoding(testgrid_data, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "international-sculpture",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:06:42.779094Z",
+     "start_time": "2021-02-08T22:06:42.774821Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19483548"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(passing_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "professional-territory",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T22:06:54.956900Z",
+     "start_time": "2021-02-08T22:06:54.639105Z"
+    }
+   },
+   "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>timestamp</th>\n",
+       "      <th>tab</th>\n",
+       "      <th>job</th>\n",
+       "      <th>test</th>\n",
+       "      <th>passing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-10-08 16:48:05</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-10-08 15:12:01</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-10-08 10:18:13</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-10-08 07:15:28</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-10-08 04:27:53</td>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>Application behind service load balancer with ...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp                           tab  \\\n",
+       "0 2020-10-08 16:48:05  \"redhat-openshift-informing\"   \n",
+       "1 2020-10-08 15:12:01  \"redhat-openshift-informing\"   \n",
+       "2 2020-10-08 10:18:13  \"redhat-openshift-informing\"   \n",
+       "3 2020-10-08 07:15:28  \"redhat-openshift-informing\"   \n",
+       "4 2020-10-08 04:27:53  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                               job  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "2  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "3  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "4  release-openshift-okd-installer-e2e-aws-upgrade   \n",
+       "\n",
+       "                                                test  passing  \n",
+       "0  Application behind service load balancer with ...    False  \n",
+       "1  Application behind service load balancer with ...    False  \n",
+       "2  Application behind service load balancer with ...    False  \n",
+       "3  Application behind service load balancer with ...    False  \n",
+       "4  Application behind service load balancer with ...    False  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to dataframe\n",
+    "passing_df = pd.DataFrame(\n",
+    "    passing_list, columns=[\"timestamp\", \"tab\", \"job\", \"test\", \"passing\"]\n",
+    ")\n",
+    "passing_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "perfect-packing",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2021-02-08T22:07:26.466Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# saving only the first 1000000 out of 19 million rows due to pvc limits.\n",
+    "# 250mb = 1 million --> 4750 mb = 19 million\n",
+    "passing_df.head(1000000).to_csv(\n",
+    "    \"../../../../data/processed/pass.csv\",\n",
+    "    header=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "sublime-timing",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2021-02-08T22:07:26.468Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of tests: 1000000\n",
+      "Total number of failing tests: 3989\n",
+      "Test failure percentage: 0.398900\n",
+      "Total number of passing tests: 704558\n",
+      "Test pass percentage: 70.455800\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Metrics\n",
+    "no_tests = passing_df.head(1000000).test.count()\n",
+    "print(\"Total number of tests: %i\" % (no_tests))\n",
+    "no_failures = failures_df.head(1000000).failure.sum()\n",
+    "print(\"Total number of failing tests: %i\" % (no_failures))\n",
+    "test_failures_percentage = (\n",
+    "    (failures_df.head(1000000).failure.sum() / failures_df.head(1000000).test.count())\n",
+    ") * 100\n",
+    "print(\"Test failure percentage: %f\" % (test_failures_percentage))\n",
+    "no_pass = passing_df.head(1000000).passing.sum()\n",
+    "print(\"Total number of passing tests: %i\" % (no_pass))\n",
+    "test_pass_percentage = (\n",
+    "    (passing_df.head(1000000).passing.sum() / passing_df.head(1000000).passing.count())\n",
+    ") * 100\n",
+    "print(\"Test pass percentage: %f\" % (test_pass_percentage))"
+   ]
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1612822647266,
+   "trusted": true
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_EDA.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_EDA.ipynb
new file mode 100644
index 00000000..5fbbdb21
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_EDA.ipynb	
@@ -0,0 +1,2643 @@
+{
+ "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 assume that the grid format for visualizing CI data, with columns representing days, and rows representing tests with the value of each being 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": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:20.892789Z",
+     "start_time": "2021-01-18T14:14:20.423702Z"
+    }
+   },
+   "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"
+   ]
+  },
+  {
+   "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": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:22.266973Z",
+     "start_time": "2021-01-18T14:14:22.249001Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 2,
+     "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": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:26.141038Z",
+     "start_time": "2021-01-18T14:14:24.231381Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "response = requests.get(\n",
+    "    \"https://testgrid.k8s.io/redhat-openshift-informing?id=dashboard-group-bar\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:26.153117Z",
+     "start_time": "2021-01-18T14:14:26.143878Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\"redhat-openshift-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-4.6-blocking\"',\n",
+       " '\"redhat-openshift-okd-release-4.6-informing\"',\n",
+       " '\"redhat-openshift-okd-release-4.7-blocking\"',\n",
+       " '\"redhat-openshift-okd-release-4.7-informing\"',\n",
+       " '\"redhat-openshift-presubmit-master-gcp\"',\n",
+       " '\"redhat-osd\"']"
+      ]
+     },
+     "execution_count": 4,
+     "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": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:27.051339Z",
+     "start_time": "2021-01-18T14:14:27.048119Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dashboard = \"redhat-openshift-ocp-release-4.2-informing\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:28.027060Z",
+     "start_time": "2021-01-18T14:14:27.818307Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "response = requests.get(f\"https://testgrid.k8s.io/{dashboard}/summary\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:28.310396Z",
+     "start_time": "2021-01-18T14:14:28.307162Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "data = response.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:29.786625Z",
+     "start_time": "2021-01-18T14:14:29.728359Z"
+    }
+   },
+   "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>1613143335000</td>\n",
+       "      <td>1613145679000</td>\n",
+       "      <td>1613128026000</td>\n",
+       "      <td>1613136504000</td>\n",
+       "      <td>1609459236000</td>\n",
+       "      <td>1609459243000</td>\n",
+       "      <td>1613111955000</td>\n",
+       "      <td>1613115429000</td>\n",
+       "      <td>1613136512000</td>\n",
+       "      <td>1613136505000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1613115068000</td>\n",
+       "      <td>1613142729000</td>\n",
+       "      <td>1613086531000</td>\n",
+       "      <td>1613128535000</td>\n",
+       "      <td>1613128534000</td>\n",
+       "      <td>1613128651000</td>\n",
+       "      <td>1613111954000</td>\n",
+       "      <td>1613111955000</td>\n",
+       "      <td>1613128530000</td>\n",
+       "      <td>1613131206000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>last_update_timestamp</th>\n",
+       "      <td>1613150529</td>\n",
+       "      <td>1613150713</td>\n",
+       "      <td>1613150592</td>\n",
+       "      <td>1613150510</td>\n",
+       "      <td>1613150731</td>\n",
+       "      <td>1613150667</td>\n",
+       "      <td>1613150991</td>\n",
+       "      <td>1613150761</td>\n",
+       "      <td>1613150851</td>\n",
+       "      <td>1613150620</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1613150540</td>\n",
+       "      <td>1613150873</td>\n",
+       "      <td>1613151010</td>\n",
+       "      <td>1613151013</td>\n",
+       "      <td>1613150459</td>\n",
+       "      <td>1613150831</td>\n",
+       "      <td>1613150905</td>\n",
+       "      <td>1613150800</td>\n",
+       "      <td>1613150979</td>\n",
+       "      <td>1613150964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>latest_green</th>\n",
+       "      <td>missing</td>\n",
+       "      <td>no recent greens</td>\n",
+       "      <td>missing</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",
+       "      <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>FAILING</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>PASSING</td>\n",
+       "      <td>PASSING</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>FAILING</td>\n",
+       "      <td>...</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>PASSING</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>FAILING</td>\n",
+       "      <td>PASSING</td>\n",
+       "      <td>FLAKY</td>\n",
+       "      <td>FAILING</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>warning</td>\n",
+       "      <td>remove_circle_outline</td>\n",
+       "      <td>remove_circle_outline</td>\n",
+       "      <td>done</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>warning</td>\n",
+       "      <td>...</td>\n",
+       "      <td>remove_circle_outline</td>\n",
+       "      <td>done</td>\n",
+       "      <td>remove_circle_outline</td>\n",
+       "      <td>warning</td>\n",
+       "      <td>done</td>\n",
+       "      <td>remove_circle_outline</td>\n",
+       "      <td>warning</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>0 of 10 (0.0%) recent columns passed (213 of 2...</td>\n",
+       "      <td>8 of 10 (80.0%) recent columns passed (8 of 10...</td>\n",
+       "      <td>9 of 10 (90.0%) recent columns passed (3411 of...</td>\n",
+       "      <td>1 of 1 (100.0%) recent columns passed (914 of ...</td>\n",
+       "      <td>1 of 1 (100.0%) recent columns passed (87 of 8...</td>\n",
+       "      <td>7 of 10 (70.0%) recent columns passed (7321 of...</td>\n",
+       "      <td>4 of 10 (40.0%) recent columns passed (3652 of...</td>\n",
+       "      <td>5 of 10 (50.0%) recent columns passed (5465 of...</td>\n",
+       "      <td>6 of 10 (60.0%) recent columns passed (8123 of...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6 of 10 (60.0%) recent columns passed (5517 of...</td>\n",
+       "      <td>10 of 10 (100.0%) recent columns passed (490 o...</td>\n",
+       "      <td>9 of 10 (90.0%) recent columns passed (387 of ...</td>\n",
+       "      <td>0 of 10 (0.0%) recent columns passed (264 of 3...</td>\n",
+       "      <td>10 of 10 (100.0%) recent columns passed (490 o...</td>\n",
+       "      <td>8 of 10 (80.0%) recent columns passed (430 of ...</td>\n",
+       "      <td>5 of 10 (50.0%) recent columns passed (8684 of...</td>\n",
+       "      <td>8 of 10 (80.0%) recent columns passed (705 of ...</td>\n",
+       "      <td>8 of 10 (80.0%) recent columns passed (440 of ...</td>\n",
+       "      <td>2 of 10 (20.0%) recent columns passed (2402 of...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tests</th>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'display_name': 'Operator results.operator c...</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': 'openshift-tests.[Feature:Bu...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'display_name': 'Operator results.operator c...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>[{'display_name': 'openshift-tests.[sig-storag...</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                                         1613143335000         \n",
+       "last_update_timestamp                                         1613150529         \n",
+       "latest_green                                                     missing         \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                                         1613145679000         \n",
+       "last_update_timestamp                                         1613150713         \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 (213 of 2...         \n",
+       "tests                  [{'display_name': 'Operator results.operator c...         \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                                         1613128026000               \n",
+       "last_update_timestamp                                         1613150592               \n",
+       "latest_green                                                     missing               \n",
+       "overall_status                                                     FLAKY               \n",
+       "overall_status_icon                                remove_circle_outline               \n",
+       "status                 8 of 10 (80.0%) recent columns passed (8 of 10...               \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                                         1613136504000   \n",
+       "last_update_timestamp                                         1613150510   \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 (3411 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                                         1609459236000   \n",
+       "last_update_timestamp                                         1613150731   \n",
+       "latest_green                                                           1   \n",
+       "overall_status                                                   PASSING   \n",
+       "overall_status_icon                                                 done   \n",
+       "status                 1 of 1 (100.0%) recent columns passed (914 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-serial-4.2  \\\n",
+       "alert                                                                           \n",
+       "last_run_timestamp                                         1609459243000        \n",
+       "last_update_timestamp                                         1613150667        \n",
+       "latest_green                                                           1        \n",
+       "overall_status                                                   PASSING        \n",
+       "overall_status_icon                                                 done        \n",
+       "status                 1 of 1 (100.0%) recent columns passed (87 of 8...        \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                                         1613111955000    \n",
+       "last_update_timestamp                                         1613150991    \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 (7321 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                                         1613115429000   \n",
+       "last_update_timestamp                                         1613150761   \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 (3652 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                                         1613136512000   \n",
+       "last_update_timestamp                                         1613150851   \n",
+       "latest_green                                                           1   \n",
+       "overall_status                                                     FLAKY   \n",
+       "overall_status_icon                                remove_circle_outline   \n",
+       "status                 5 of 10 (50.0%) recent columns passed (5465 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                                         1613136505000  ...   \n",
+       "last_update_timestamp                                         1613150620  ...   \n",
+       "latest_green                                                           1  ...   \n",
+       "overall_status                                                   FAILING  ...   \n",
+       "overall_status_icon                                              warning  ...   \n",
+       "status                 6 of 10 (60.0%) recent columns passed (8123 of...  ...   \n",
+       "tests                  [{'display_name': 'openshift-tests.[Feature:Bu...  ...   \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                                         1613115068000               \n",
+       "last_update_timestamp                                         1613150540               \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 (5517 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                                         1613142729000                        \n",
+       "last_update_timestamp                                         1613150873                        \n",
+       "latest_green                                                           1                        \n",
+       "overall_status                                                   PASSING                        \n",
+       "overall_status_icon                                                 done                        \n",
+       "status                 10 of 10 (100.0%) recent columns passed (490 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-aws-upgrade-4.2-stable-to-4.2-nightly  \\\n",
+       "alert                                                                                                \n",
+       "last_run_timestamp                                         1613086531000                             \n",
+       "last_update_timestamp                                         1613151010                             \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 (387 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                                         1613128535000                       \n",
+       "last_update_timestamp                                         1613151013                       \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 (264 of 3...                       \n",
+       "tests                  [{'display_name': 'Operator results.operator c...                       \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                                         1613128534000                \n",
+       "last_update_timestamp                                         1613150459                \n",
+       "latest_green                                                           1                \n",
+       "overall_status                                                   PASSING                \n",
+       "overall_status_icon                                                 done                \n",
+       "status                 10 of 10 (100.0%) recent columns passed (490 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-azure-upgrade-4.2  \\\n",
+       "alert                                                                            \n",
+       "last_run_timestamp                                         1613128651000         \n",
+       "last_update_timestamp                                         1613150831         \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 (430 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-4.2  \\\n",
+       "alert                                                                      \n",
+       "last_run_timestamp                                         1613111954000   \n",
+       "last_update_timestamp                                         1613150905   \n",
+       "latest_green                                                           1   \n",
+       "overall_status                                                   FAILING   \n",
+       "overall_status_icon                                              warning   \n",
+       "status                 5 of 10 (50.0%) recent columns passed (8684 of...   \n",
+       "tests                  [{'display_name': 'openshift-tests.[sig-storag...   \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                                         1613111955000      \n",
+       "last_update_timestamp                                         1613150800      \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 (705 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                                         1613128530000       \n",
+       "last_update_timestamp                                         1613150979       \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 (440 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                                         1613131206000        \n",
+       "last_update_timestamp                                         1613150964        \n",
+       "latest_green                                                           1        \n",
+       "overall_status                                                     FLAKY        \n",
+       "overall_status_icon                                remove_circle_outline        \n",
+       "status                 2 of 10 (20.0%) recent columns passed (2402 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": 8,
+     "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. A job refers to a collection of tests that execute together. In the table above, the columns are different jobs in the dashboards (We will use these column names later to get access to 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 at the additional data stored in status and tests for a job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:14:35.926310Z",
+     "start_time": "2021-01-18T14:14:35.920274Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release-openshift-origin-installer-old-rhcos-e2e-aws-4.2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'2 of 10 (20.0%) recent columns passed (2402 of 2416 or 99.4% cells)'"
+      ]
+     },
+     "execution_count": 9,
+     "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 outputs states, 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": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-18T14:23:00.119458Z",
+     "start_time": "2021-01-18T14:23:00.099983Z"
+    }
+   },
+   "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>Operator results.operator conditions kube-cont...</td>\n",
+       "      <td>Operator results.operator conditions kube-cont...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>1612462063000</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Operator unavailable (AvailableZeroNodesActive...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Operator results.operator conditions kube-con...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Operator results.operator conditions kube-sche...</td>\n",
+       "      <td>Operator results.operator conditions kube-sche...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>1612462063000</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Operator unavailable (AvailableZeroNodesActive...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Operator results.operator conditions kube-sch...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>1612462063000</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Operator unavailable (NoData): null</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Operator results.operator conditions openshif...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>57</td>\n",
+       "      <td>1612462063000</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Operator unavailable (Available): null</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Operator results.operator conditions openshif...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Overall</td>\n",
+       "      <td>Overall</td>\n",
+       "      <td>96</td>\n",
+       "      <td>1612462063000</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>[]</td>\n",
+       "      <td>Overall</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws - e2e-aw...</td>\n",
+       "      <td>operator.Run multi-stage test e2e-aws - e2e-aw...</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1612462063000</td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>Installing from release registry.build01.ci.op...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>operator.Run multi-stage test e2e-aws - e2e-a...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        display_name  \\\n",
+       "0  Operator results.operator conditions kube-cont...   \n",
+       "1  Operator results.operator conditions kube-sche...   \n",
+       "2  Operator results.operator conditions openshift...   \n",
+       "3  Operator results.operator conditions openshift...   \n",
+       "4                                            Overall   \n",
+       "5  operator.Run multi-stage test e2e-aws - e2e-aw...   \n",
+       "\n",
+       "                                           test_name  fail_count  \\\n",
+       "0  Operator results.operator conditions kube-cont...          57   \n",
+       "1  Operator results.operator conditions kube-sche...          57   \n",
+       "2  Operator results.operator conditions openshift...          57   \n",
+       "3  Operator results.operator conditions openshift...          57   \n",
+       "4                                            Overall          96   \n",
+       "5  operator.Run multi-stage test e2e-aws - e2e-aw...          82   \n",
+       "\n",
+       "   fail_timestamp  pass_timestamp build_link build_url_text build_link_text  \\\n",
+       "0   1612462063000               0                                             \n",
+       "1   1612462063000               0                                             \n",
+       "2   1612462063000               0                                             \n",
+       "3   1612462063000               0                                             \n",
+       "4   1612462063000               0                                             \n",
+       "5   1612462063000               0                                             \n",
+       "\n",
+       "                                     failure_message linked_bugs  \\\n",
+       "0  Operator unavailable (AvailableZeroNodesActive...          []   \n",
+       "1  Operator unavailable (AvailableZeroNodesActive...          []   \n",
+       "2                Operator unavailable (NoData): null          []   \n",
+       "3             Operator unavailable (Available): null          []   \n",
+       "4                                                             []   \n",
+       "5  Installing from release registry.build01.ci.op...          []   \n",
+       "\n",
+       "                                      fail_test_link  \n",
+       "0   Operator results.operator conditions kube-con...  \n",
+       "1   Operator results.operator conditions kube-sch...  \n",
+       "2   Operator results.operator conditions openshif...  \n",
+       "3   Operator results.operator conditions openshif...  \n",
+       "4                                            Overall  \n",
+       "5   operator.Run multi-stage test e2e-aws - e2e-a...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for e, i in enumerate(ocp42.columns):\n",
+    "    temp = ocp42.loc[\"tests\", ocp42.columns[e]]\n",
+    "    if temp:\n",
+    "        temp = pd.DataFrame(temp)\n",
+    "        break\n",
+    "temp"
+   ]
+  },
+  {
+   "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": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.380005Z",
+     "start_time": "2021-01-15T12:55:24.102323Z"
+    }
+   },
+   "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": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.389300Z",
+     "start_time": "2021-01-15T12:55:24.382334Z"
+    }
+   },
+   "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": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.490597Z",
+     "start_time": "2021-01-15T12:55:24.392165Z"
+    }
+   },
+   "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': 12, 'fail-time'...</td>\n",
+       "      <td>[{'count': 12, 'value': 12}, {'count': 11, 'va...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Operator results.operator conditions authentic...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 5, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Operator results.operator conditions cloud-cre...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 5, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Operator results.operator conditions dns</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 4, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Operator results.operator conditions monitoring</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 10, 'fail-time'...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Operator results.operator conditions network</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 4, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 5, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Operator results.operator conditions service-ca</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>openshift-tests.Monitor cluster while tests ex...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 22, 'fail-time'...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>openshift-tests.[Disruptive] Cluster upgrade s...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 9, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 3, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Operator results.operator conditions image-reg...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Operator results.operator conditions console</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Operator results.operator conditions operator-...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Symptom Detection.Undiagnosed panic detected i...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] daemonset-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] deployment-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] job-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] replicaset-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] statefulset-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Cluster upgrade.[sig-storage] [sig-api-machine...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Cluster upgrade.[sig-storage] [sig-api-machine...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Cluster upgrade.service-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Cluster upgrade.upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Operator results.operator conditions cluster-a...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Operator results.operator conditions ingress</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Operator results.operator conditions insights</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Operator results.operator conditions kube-apis...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Operator results.operator conditions kube-cont...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Operator results.operator conditions kube-sche...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Operator results.operator conditions machine-api</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Operator results.operator conditions machine-c...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Operator results.operator conditions marketplace</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Operator results.operator conditions node-tuning</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Operator results.operator conditions operator-...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Operator results.operator conditions operator-...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Operator results.operator conditions service-c...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Operator results.operator conditions service-c...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Operator results.operator conditions storage</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Symptom Detection.Bug 1812261: iptables is seg...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Symptom Detection.Infrastructure - AWS simulat...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Symptom Detection.Infrastructure - GCP quota e...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Symptom Detection.Node process segfaulted</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name  \\\n",
+       "0                                             Overall   \n",
+       "1   Operator results.operator conditions authentic...   \n",
+       "2   Operator results.operator conditions cloud-cre...   \n",
+       "3            Operator results.operator conditions dns   \n",
+       "4     Operator results.operator conditions monitoring   \n",
+       "5        Operator results.operator conditions network   \n",
+       "6   Operator results.operator conditions openshift...   \n",
+       "7     Operator results.operator conditions service-ca   \n",
+       "8   openshift-tests.Monitor cluster while tests ex...   \n",
+       "9   openshift-tests.[Disruptive] Cluster upgrade s...   \n",
+       "10  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "11  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "12  Operator results.operator conditions image-reg...   \n",
+       "13       Operator results.operator conditions console   \n",
+       "14  Operator results.operator conditions operator-...   \n",
+       "15  Symptom Detection.Undiagnosed panic detected i...   \n",
+       "16  operator.Import the release payload \"initial\" ...   \n",
+       "17  operator.Import the release payload \"latest\" f...   \n",
+       "18       Cluster upgrade.[sig-apps] daemonset-upgrade   \n",
+       "19      Cluster upgrade.[sig-apps] deployment-upgrade   \n",
+       "20             Cluster upgrade.[sig-apps] job-upgrade   \n",
+       "21      Cluster upgrade.[sig-apps] replicaset-upgrade   \n",
+       "22     Cluster upgrade.[sig-apps] statefulset-upgrade   \n",
+       "23  Cluster upgrade.[sig-storage] [sig-api-machine...   \n",
+       "24  Cluster upgrade.[sig-storage] [sig-api-machine...   \n",
+       "25                    Cluster upgrade.service-upgrade   \n",
+       "26                            Cluster upgrade.upgrade   \n",
+       "27  Operator results.operator conditions cluster-a...   \n",
+       "28       Operator results.operator conditions ingress   \n",
+       "29      Operator results.operator conditions insights   \n",
+       "30  Operator results.operator conditions kube-apis...   \n",
+       "31  Operator results.operator conditions kube-cont...   \n",
+       "32  Operator results.operator conditions kube-sche...   \n",
+       "33   Operator results.operator conditions machine-api   \n",
+       "34  Operator results.operator conditions machine-c...   \n",
+       "35   Operator results.operator conditions marketplace   \n",
+       "36   Operator results.operator conditions node-tuning   \n",
+       "37  Operator results.operator conditions openshift...   \n",
+       "38  Operator results.operator conditions openshift...   \n",
+       "39  Operator results.operator conditions operator-...   \n",
+       "40  Operator results.operator conditions operator-...   \n",
+       "41  Operator results.operator conditions service-c...   \n",
+       "42  Operator results.operator conditions service-c...   \n",
+       "43       Operator results.operator conditions storage   \n",
+       "44  Symptom Detection.Bug 1812261: iptables is seg...   \n",
+       "45  Symptom Detection.Infrastructure - AWS simulat...   \n",
+       "46  Symptom Detection.Infrastructure - GCP quota e...   \n",
+       "47          Symptom Detection.Node process segfaulted   \n",
+       "48  operator.All images are built and tagged into ...   \n",
+       "49  operator.Find all of the input images from ocp...   \n",
+       "50  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "\n",
+       "                                                alert  \\\n",
+       "0   {'fail-cl': '1', 'fail-count': 12, 'fail-time'...   \n",
+       "1   {'fail-cl': '1', 'fail-count': 5, 'fail-time':...   \n",
+       "2   {'fail-cl': '1', 'fail-count': 5, 'fail-time':...   \n",
+       "3   {'fail-cl': '1', 'fail-count': 4, 'fail-time':...   \n",
+       "4   {'fail-cl': '1', 'fail-count': 10, 'fail-time'...   \n",
+       "5   {'fail-cl': '1', 'fail-count': 4, 'fail-time':...   \n",
+       "6   {'fail-cl': '1', 'fail-count': 5, 'fail-time':...   \n",
+       "7                                                None   \n",
+       "8   {'fail-cl': '1', 'fail-count': 22, 'fail-time'...   \n",
+       "9   {'fail-cl': '1', 'fail-count': 9, 'fail-time':...   \n",
+       "10  {'fail-cl': '1', 'fail-count': 3, 'fail-time':...   \n",
+       "11                                               None   \n",
+       "12                                               None   \n",
+       "13                                               None   \n",
+       "14                                               None   \n",
+       "15                                               None   \n",
+       "16                                               None   \n",
+       "17                                               None   \n",
+       "18                                               None   \n",
+       "19                                               None   \n",
+       "20                                               None   \n",
+       "21                                               None   \n",
+       "22                                               None   \n",
+       "23                                               None   \n",
+       "24                                               None   \n",
+       "25                                               None   \n",
+       "26                                               None   \n",
+       "27                                               None   \n",
+       "28                                               None   \n",
+       "29                                               None   \n",
+       "30                                               None   \n",
+       "31                                               None   \n",
+       "32                                               None   \n",
+       "33                                               None   \n",
+       "34                                               None   \n",
+       "35                                               None   \n",
+       "36                                               None   \n",
+       "37                                               None   \n",
+       "38                                               None   \n",
+       "39                                               None   \n",
+       "40                                               None   \n",
+       "41                                               None   \n",
+       "42                                               None   \n",
+       "43                                               None   \n",
+       "44                                               None   \n",
+       "45                                               None   \n",
+       "46                                               None   \n",
+       "47                                               None   \n",
+       "48                                               None   \n",
+       "49                                               None   \n",
+       "50                                               None   \n",
+       "\n",
+       "                                             statuses  \n",
+       "0   [{'count': 12, 'value': 12}, {'count': 11, 'va...  \n",
+       "1   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "2   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "3   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "4   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "5   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "6   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "7   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "8   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "9   [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "10  [{'count': 1, 'value': 12}, {'count': 1, 'valu...  \n",
+       "11  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "12  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "13  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "14  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "15  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "16  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "17  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "18  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "19  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "20  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "21  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "22  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "23  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "24  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "25  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "26  [{'count': 12, 'value': 0}, {'count': 11, 'val...  \n",
+       "27  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "28  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "29  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "30  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "31  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "32  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "33  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "34  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "35  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "36  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "37  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "38  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "39  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "40  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "41  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "42  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "43  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "44  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "45  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "46  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "47  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "48  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "49  [{'count': 1, 'value': 1}, {'count': 1, 'value...  \n",
+       "50  [{'count': 1, 'value': 1}, {'count': 1, 'value...  "
+      ]
+     },
+     "execution_count": 13,
+     "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": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.496020Z",
+     "start_time": "2021-01-15T12:55:24.492650Z"
+    }
+   },
+   "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": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.511066Z",
+     "start_time": "2021-01-15T12:55:24.497645Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "details[\"values\"] = details[\"statuses\"].apply(decode_run_length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.622617Z",
+     "start_time": "2021-01-15T12:55:24.512871Z"
+    }
+   },
+   "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': 12, 'fail-time'...</td>\n",
+       "      <td>[{'count': 12, 'value': 12}, {'count': 11, 'va...</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>Operator results.operator conditions authentic...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 5, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 12, 1, 12, 12, 12, 12, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Operator results.operator conditions cloud-cre...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 5, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 12, 1, 12, 12, 12, 12, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Operator results.operator conditions dns</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 4, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 1, 1, 12, 12, 12, 12, 0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Operator results.operator conditions monitoring</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 10, 'fail-time'...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 12, 12, 12, 12, 12, 12,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Operator results.operator conditions network</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 4, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 1, 1, 12, 12, 12, 12, 0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 5, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 12, 1, 12, 12, 12, 12, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Operator results.operator conditions service-ca</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 1, 0, 12, 1, 1, 1, 12, 1, 1, 0, 0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>openshift-tests.Monitor cluster while tests ex...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 22, 'fail-time'...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 12, 0, 12, 12, 12, 12, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>openshift-tests.[Disruptive] Cluster upgrade s...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 9, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 0, 12, 12, 0, 12, 12, 12, 12, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>{'fail-cl': '1', 'fail-count': 3, 'fail-time':...</td>\n",
+       "      <td>[{'count': 1, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 0, 12, 12, 1, 12, 12, 1, 12, 12, 12, 12, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 12, 1, 1, 12, 1, 1, 1, 1, 1, 1, 1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Operator results.operator conditions image-reg...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 12, 1, 1, 12, 12, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Operator results.operator conditions console</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 12, 1, 1, 0, 0, 0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Operator results.operator conditions operator-...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 12, 0, 0, 0,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Symptom Detection.Undiagnosed panic detected i...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>operator.Import the release payload \"initial\" ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>operator.Import the release payload \"latest\" f...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] daemonset-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] deployment-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] job-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] replicaset-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Cluster upgrade.[sig-apps] statefulset-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Cluster upgrade.[sig-storage] [sig-api-machine...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Cluster upgrade.[sig-storage] [sig-api-machine...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Cluster upgrade.service-upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Cluster upgrade.upgrade</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 12, 'value': 0}, {'count': 11, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Operator results.operator conditions cluster-a...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Operator results.operator conditions ingress</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Operator results.operator conditions insights</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Operator results.operator conditions kube-apis...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Operator results.operator conditions kube-cont...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Operator results.operator conditions kube-sche...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Operator results.operator conditions machine-api</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Operator results.operator conditions machine-c...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Operator results.operator conditions marketplace</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Operator results.operator conditions node-tuning</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Operator results.operator conditions openshift...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Operator results.operator conditions operator-...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Operator results.operator conditions operator-...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>Operator results.operator conditions service-c...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Operator results.operator conditions service-c...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Operator results.operator conditions storage</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Symptom Detection.Bug 1812261: iptables is seg...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Symptom Detection.Infrastructure - AWS simulat...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Symptom Detection.Infrastructure - GCP quota e...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Symptom Detection.Node process segfaulted</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>operator.All images are built and tagged into ...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>operator.Find all of the input images from ocp...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>operator.Run template e2e-aws-upgrade - e2e-aw...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>[{'count': 1, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 0, 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   Operator results.operator conditions authentic...   \n",
+       "2   Operator results.operator conditions cloud-cre...   \n",
+       "3            Operator results.operator conditions dns   \n",
+       "4     Operator results.operator conditions monitoring   \n",
+       "5        Operator results.operator conditions network   \n",
+       "6   Operator results.operator conditions openshift...   \n",
+       "7     Operator results.operator conditions service-ca   \n",
+       "8   openshift-tests.Monitor cluster while tests ex...   \n",
+       "9   openshift-tests.[Disruptive] Cluster upgrade s...   \n",
+       "10  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "11  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "12  Operator results.operator conditions image-reg...   \n",
+       "13       Operator results.operator conditions console   \n",
+       "14  Operator results.operator conditions operator-...   \n",
+       "15  Symptom Detection.Undiagnosed panic detected i...   \n",
+       "16  operator.Import the release payload \"initial\" ...   \n",
+       "17  operator.Import the release payload \"latest\" f...   \n",
+       "18       Cluster upgrade.[sig-apps] daemonset-upgrade   \n",
+       "19      Cluster upgrade.[sig-apps] deployment-upgrade   \n",
+       "20             Cluster upgrade.[sig-apps] job-upgrade   \n",
+       "21      Cluster upgrade.[sig-apps] replicaset-upgrade   \n",
+       "22     Cluster upgrade.[sig-apps] statefulset-upgrade   \n",
+       "23  Cluster upgrade.[sig-storage] [sig-api-machine...   \n",
+       "24  Cluster upgrade.[sig-storage] [sig-api-machine...   \n",
+       "25                    Cluster upgrade.service-upgrade   \n",
+       "26                            Cluster upgrade.upgrade   \n",
+       "27  Operator results.operator conditions cluster-a...   \n",
+       "28       Operator results.operator conditions ingress   \n",
+       "29      Operator results.operator conditions insights   \n",
+       "30  Operator results.operator conditions kube-apis...   \n",
+       "31  Operator results.operator conditions kube-cont...   \n",
+       "32  Operator results.operator conditions kube-sche...   \n",
+       "33   Operator results.operator conditions machine-api   \n",
+       "34  Operator results.operator conditions machine-c...   \n",
+       "35   Operator results.operator conditions marketplace   \n",
+       "36   Operator results.operator conditions node-tuning   \n",
+       "37  Operator results.operator conditions openshift...   \n",
+       "38  Operator results.operator conditions openshift...   \n",
+       "39  Operator results.operator conditions operator-...   \n",
+       "40  Operator results.operator conditions operator-...   \n",
+       "41  Operator results.operator conditions service-c...   \n",
+       "42  Operator results.operator conditions service-c...   \n",
+       "43       Operator results.operator conditions storage   \n",
+       "44  Symptom Detection.Bug 1812261: iptables is seg...   \n",
+       "45  Symptom Detection.Infrastructure - AWS simulat...   \n",
+       "46  Symptom Detection.Infrastructure - GCP quota e...   \n",
+       "47          Symptom Detection.Node process segfaulted   \n",
+       "48  operator.All images are built and tagged into ...   \n",
+       "49  operator.Find all of the input images from ocp...   \n",
+       "50  operator.Run template e2e-aws-upgrade - e2e-aw...   \n",
+       "\n",
+       "                                                alert  \\\n",
+       "0   {'fail-cl': '1', 'fail-count': 12, 'fail-time'...   \n",
+       "1   {'fail-cl': '1', 'fail-count': 5, 'fail-time':...   \n",
+       "2   {'fail-cl': '1', 'fail-count': 5, 'fail-time':...   \n",
+       "3   {'fail-cl': '1', 'fail-count': 4, 'fail-time':...   \n",
+       "4   {'fail-cl': '1', 'fail-count': 10, 'fail-time'...   \n",
+       "5   {'fail-cl': '1', 'fail-count': 4, 'fail-time':...   \n",
+       "6   {'fail-cl': '1', 'fail-count': 5, 'fail-time':...   \n",
+       "7                                                None   \n",
+       "8   {'fail-cl': '1', 'fail-count': 22, 'fail-time'...   \n",
+       "9   {'fail-cl': '1', 'fail-count': 9, 'fail-time':...   \n",
+       "10  {'fail-cl': '1', 'fail-count': 3, 'fail-time':...   \n",
+       "11                                               None   \n",
+       "12                                               None   \n",
+       "13                                               None   \n",
+       "14                                               None   \n",
+       "15                                               None   \n",
+       "16                                               None   \n",
+       "17                                               None   \n",
+       "18                                               None   \n",
+       "19                                               None   \n",
+       "20                                               None   \n",
+       "21                                               None   \n",
+       "22                                               None   \n",
+       "23                                               None   \n",
+       "24                                               None   \n",
+       "25                                               None   \n",
+       "26                                               None   \n",
+       "27                                               None   \n",
+       "28                                               None   \n",
+       "29                                               None   \n",
+       "30                                               None   \n",
+       "31                                               None   \n",
+       "32                                               None   \n",
+       "33                                               None   \n",
+       "34                                               None   \n",
+       "35                                               None   \n",
+       "36                                               None   \n",
+       "37                                               None   \n",
+       "38                                               None   \n",
+       "39                                               None   \n",
+       "40                                               None   \n",
+       "41                                               None   \n",
+       "42                                               None   \n",
+       "43                                               None   \n",
+       "44                                               None   \n",
+       "45                                               None   \n",
+       "46                                               None   \n",
+       "47                                               None   \n",
+       "48                                               None   \n",
+       "49                                               None   \n",
+       "50                                               None   \n",
+       "\n",
+       "                                             statuses  \\\n",
+       "0   [{'count': 12, 'value': 12}, {'count': 11, 'va...   \n",
+       "1   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "2   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "3   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "4   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "5   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "6   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "7   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "8   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "9   [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "10  [{'count': 1, 'value': 12}, {'count': 1, 'valu...   \n",
+       "11  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "12  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "13  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "14  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "15  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "16  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "17  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "18  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "19  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "20  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "21  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "22  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "23  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "24  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "25  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "26  [{'count': 12, 'value': 0}, {'count': 11, 'val...   \n",
+       "27  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "28  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "29  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "30  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "31  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "32  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "33  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "34  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "35  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "36  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "37  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "38  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "39  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "40  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "41  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "42  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "43  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "44  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "45  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "46  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "47  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "48  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "49  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "50  [{'count': 1, 'value': 1}, {'count': 1, 'value...   \n",
+       "\n",
+       "                                               values  \n",
+       "0   [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1...  \n",
+       "1   [12, 0, 12, 12, 0, 12, 12, 1, 12, 12, 12, 12, ...  \n",
+       "2   [12, 0, 12, 12, 0, 12, 12, 1, 12, 12, 12, 12, ...  \n",
+       "3   [12, 0, 12, 12, 0, 12, 1, 1, 12, 12, 12, 12, 0...  \n",
+       "4   [12, 0, 12, 12, 0, 12, 12, 12, 12, 12, 12, 12,...  \n",
+       "5   [12, 0, 12, 12, 0, 12, 1, 1, 12, 12, 12, 12, 0...  \n",
+       "6   [12, 0, 12, 12, 0, 12, 12, 1, 12, 12, 12, 12, ...  \n",
+       "7   [12, 0, 12, 1, 0, 12, 1, 1, 1, 12, 1, 1, 0, 0,...  \n",
+       "8   [12, 0, 12, 12, 0, 12, 12, 0, 12, 12, 12, 12, ...  \n",
+       "9   [12, 0, 12, 12, 0, 12, 12, 0, 12, 12, 12, 12, ...  \n",
+       "10  [12, 0, 12, 12, 1, 12, 12, 1, 12, 12, 12, 12, ...  \n",
+       "11  [1, 0, 1, 1, 12, 1, 1, 12, 1, 1, 1, 1, 1, 1, 1...  \n",
+       "12  [1, 0, 1, 1, 0, 1, 1, 12, 1, 1, 12, 12, 0, 0, ...  \n",
+       "13  [1, 0, 1, 1, 0, 1, 1, 1, 1, 12, 1, 1, 0, 0, 0,...  \n",
+       "14  [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 12, 0, 0, 0,...  \n",
+       "15  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "16  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "17  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "18  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "19  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "20  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "21  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "22  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, ...  \n",
+       "23  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "24  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "25  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "26  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ...  \n",
+       "27  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "28  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "29  [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "30  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "31  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "32  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "33  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "34  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "35  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "36  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "37  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "38  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "39  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "40  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "41  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "42  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "43  [1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, ...  \n",
+       "44  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "45  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "46  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "47  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "48  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "49  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n",
+       "50  [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  "
+      ]
+     },
+     "execution_count": 16,
+     "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": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:55:24.849479Z",
+     "start_time": "2021-01-15T12:55:24.624390Z"
+    },
+    "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": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:58:28.413182Z",
+     "start_time": "2021-01-15T12:58:28.399505Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MTmZHnz4knT2LZ0AATb/5BrfKlUlPSCBsyBBitm2jbOPG3P/DD0T9/jsV27ShlK8vf736KhdWr8a9Zk2a//gjbr6+GBwdOfHttxweNw7IOsnQ4rffqNi6NcnR0Wzt2ZPE06d56MQJVtWrx31ffYWzlxct5s8nZvt2Kj/wAGUDA9k3fDjnFi36/w4qXZpWS5bgUq4c/1q3jk2dOuHbrRsNx43DwcWF6ydOsPuFF0iOisq3PsemTMn1/i0PPUTT776jYsuWGBwdubx1K3sGDiQjKcm6zs3MJOHYMXYPGEC7DRsIGDqU8HffpfnPPxP566+c/e23fMsvXb8+zWbNIi48nFJVqrD/7be5//vvWVWvHgDlQ0JMP5vLU+mAAO4dNYrMtDScy5YlJSaGMvXqcW7xYhqOG8eaRo1M1eyybx/733qLy1u30uTrr6kQEkJmWhpHv/qKE9OnW2yi78MP4+Hvzwp/fzISEwG4vHkz2x57DPfq1XPF5qx33p+tyYelfAa8/jplGzbE6777iFq+nPCRI606rqVx7l6zJiFz5+Lq48PlLVtwdHcneuVKYvfvz5Wfje3bU+PZZ6k/ciQOzs4kRkays18/ks6cwcHNjWazZlGxVSsSz5wh9q+/co1Xa/q7UpcuNP7sMxzd3MhMTWXf8OFEr1gBgHu1anTYtg3PunW5/OefbH/8cYwZGWbndb0RI/CoWZO9gwcD3PSz6ZgPPECTKVMwZmRwes6cm64oyfbo1ascGDWKqj174lmnDmFDh+LXvTtlAwPJTEtjS9eupCckUK5ZM4KnTcOlXDkykpP565VXuLRhA7X/8x/T1QQtFy/m6t69VGjRAvdq1Ug4fpztjz9OwBtv5JrzlzZt4v4ff6RMvXrg4EDE5Mmmq+yy14yqvXpRtlEjHN3c8KxdG4OTE9t69SLx9OncDTAYCJo0Cb8ePTA4OHBx40b2DByIMT3dFOLs5UXLxYtx9vKiy759bOzYke6nT7O8Zk3TFVC9kpNZXrMm1Z56yuxxnb28aPrNN5QPCSE9IYG/X3sNY0ZGvvN0z8CBuJQvT5Np0yh3330YMzOJnDuXgx9+aOr3/SNGULVXLzz8/Tk5axaHx461qj+NGRlU6tKFJlOmkJmezokZM7jnzTdZ36oViadO5eqeMg0a0HTmTNyrVeP6iRPs6t+f6ydPWjW38hu3mcnJudp74O23zc7pVkuXEhceTo3+/dkzcCDRK1fmOwYLYmmemRuXLRctws3HhwfCw9nctSsPHT2ab77d/PxuWgvMtcd/8GCqPf44mzp1wtXHh7qvvcb6Vq24umcPAPGHD7P9qaeo/swzGJydc7Uh+3h5j5965Uq+nyHN587NVX/HUqXy/cz2atIkV/23PfFEgf3Zetky4g4epFxwMK6VKnF17172vPgixrQ0s+PKpXx5q9dM95o1CfnlF9x8fLi6dy8OLi6mY5v728NaBmdnPGrX5vzSpabNzLPz5xP799+kXbtmcTxnz634o0dv+huk/siR1OjXD4xGLvzxB/tef53M1FRCfv2Va+HhWVdjOTgQ+OGHVL3Rx1d372bvf/5DelwcPePjOThmDJUfeIBSfn4cnzGDo5MnA5gdT3Vfe83sWhPwxhvUfuklDA4OJEdHZ12NdOIEkPV50io0FDdfX4589hmHx47FKyiIkF9/ZV3TprnWusTIyFzjyFpl6tfn2NSppr8J4sLD+eP++01fDDCXx7zjcd/w4f9/5ZMVa3VBkqOiiMizOZXfH9+KU5ziFKc4xRVFSWmHtey9Hfaej5LSXnuvX0npF1uXZ+/HLSlx1rrb8lFc7L1f7CUfNt98cq9enabffMO6Zs1IOHYM/0GDuP+HH/ijeXOMGRlUfvBBVt1zDymXLhE0eTINRo9mz4sv0uLXXzk2bRonv/2Wcs2a0So0lBW1amHMyKBsYCAHRowgfNQoqj39NA3ee48Lq1dzzxtvEL1mDYfGjMG5bFmafvcdzmXLAlknQ9a3bk38oUM0nzsX/0GDcm0ebOvZk0djY9nUqRNJZ89Svlkzdg8cyJUdO3K1Jz0+nn2vv06tgQPZ8uCDuFWuTPOff2Z9mzbEhYcTMGwYTb7+mm2PPZZvfQwODrneX/Xxx3GrXJkVN058NJ4wAa8mTYjZurXQfX3mt9+o3rt3rt/5PfJIvuWnx8XhWacOh8eN48y8eZS38M1fS3mK/OUXks6e5fD48QS88QYAF9aupfmcOXjUqsX1kyfxqFWLUlWrcuGPP2g8YQIYDKy85x5cypen0549xGzfzrV9+8we37ttW6JXrjSdZMp2bf9+ru3fb3X/WJMPS/nMTEvDt1s3/ggJMZ1ks4al/ms0fjyXNm3iwNtv49OxI22WL+f8smUY09Jy5celfHmCZ8xgdYMGXD9xguAZM7h35Ej2Dh5MrQEDcK9WjRW1a+Pk4UGH7dtNJ0sDP/zQqv4OmjSJvYMHc/nPPynbuDF1X3nFtPlUqUsXNj/wAMa0NB4ID8enQwfijxwxO6+tYXB0pNmsWex+/nkurF5N7X//G6cyZcz2n5OHB5s6dcJ/0CBCfvmFNQ0bknDsGP9avx7f7t058+uvBE+fztEvvuD0jz9SrXdvgqdNM53gzVlWpU6d2NS5M2Rm0mX/fnw6dODIJ59Qs18/05xv/NlnJEdH82e3brjXrMmDhw5xdsECks+fz1VWlR49TLfIC54+/aZ1BcC3e3cqdenCqnvvBaORTrt2UfXxxznz66+mmLTYWLY//jgtFizIdaWNuf4wd9zAjz4i6fx5llevTvn776ft6tUs9fXNd54CNBw7lrTYWFYGBOBUpgyddu3iyu7dRK9ciTEjA/fq1dncpQulqlThoRMnOJrnVlPm+vPi+vVZ+X3hBS6sXk2D99/Hzcfn5pO4BgMhv/zC/0aP5tzixdzz5ps0mTaNLQ8+aLEPsuU3bvOuS5bmtDEtjYpt2rCyTh3rN/zzYWmemRuXe158Ed+uXQvOd561wFJ7oletMq2JFUJCSDp/3rTxlC09Pr7ADf+czH2G5K1/57/+yv8zO+9aVrFigcc0ZmRQPiSEje3bY3BwoOP27VR78knO/Pqr2XFVmDWz0SefcGnjRg68/TZlGzak0+7dnJ47F8Ds3x7Zt7ktsO5paUQtW0brpUuJ+PxzLq5fT3JUFAlHj1rd53nXI78ePajepw9/NG9OekICLRYswH/wYI59+SVHPv2UtLg4AKo9+SS+Dz3E2iZNyEhMpPnPP3PvyJHsf+stjBkZOHt6sqlDB9x8fel69CjnFi4kMzXV/Pwws9ZETJ5M/XfeYXmNGqTHx1Pzueeo/OCDHJ86FYAy997L6oYNKR0QQJcDB3KtG3nXOpeKFXn47FnTOPJq3NiqPjq7cCFBkybh6u3NhTVriD98mMTISNPrZv+GzDNOyjVtanqPNWt1QRxcXXGvWVNxilOc4hSnuELFWauktMNa9t4Oe89HSWmvtey9n+29X2xdnr0ft6TEWetuy0dxsfd+sZd82HzzqVLnzlzeutV0//9TP/5I8IwZpk2hy1u2kHLpEgDnf/+d+774Avfq1fEMCODkd98BWd/eTT5/ngotWpASE0P69eum26rEHTxIqapVgawdt0qdO3NhzRqu7NnD9scfN9Xj8tatxB86BEDMjh2UDw62Tfu6dCFmxw7iwsMBODFjBo3Gj8fg6GixPtmSo6MpU78+vg89lPVt5hwnhgsr6fx5U78WVH6ZwEAMDg6cmT/fqrLzy5M5mampnF+2DL9HHuHoF19QpWdPzi1ejDEtDb8ePdjRuzcYjaTGxHB2wQKqPPqoxc0nFy8vkov4TJWcrMmHpXxiNHL1r78KtfGUzVz/ef/rXxwaOxaAi3/8wfUbV4UYjcZc+Um9coWlPj6kx8dnxW7cSK0BA0xlnF20CGN6OmnXrnF+yRJcvb0BrO7vpKgoqvfuTVJUFNf27WPPiy+aXjs7fz7pN05uxu7bR6mqVXGvXt3ivC6IZ926OHl4cGH1agBOzp5NkxsnLvMTtXw5AAnHj3P95EnTca8fP46bry8AG1q3Np0gvrRxIx4//JBvWeeWLMGYlgZA/JEjpvUjp/1vvYXDjStDEk+dIjEyEo+aNXNtPkHWupJ9pci1gwfzXVeifv+dSxs2kHnjNpgxO3bg4e9vtq3WMHdc3+7d2fbYYwBc2bWL5f7+puPmx7dbN7b27AlAelwc5xYvpnKXLqYrgM4uWABA0rlzpF+/jlulSjeVkV9/etapg6O7uym/x6dP597Ro296r3v16rhXr865xYsBiJg82XQC2xqWxm02S3PaaDQStXy51RtPTaZMyXXLrYzr11lRu7bFeWbtuDQn71pgqT2Jp06Zrixz9vIq8rOocjL3GZJzE6mgz+zCfNZkOxcaCpmZGDMziV61igotWnB1zx6z46qwa+bh8eMBuHbgAFfDwgpsx6VNm6yu+7YnnqD2Sy/hP2gQzb77jrjDhzk0ZoxpPhVWlR49OPXjj6RduwbAyZkzueeNNzj25Ze5Nhd9u3Xj9Ny5ZFy/DmSty43Gj4e33gLg7I2ruJOjoogNC6P8/ffj6O5u/jOP/NeajOvXMRgMVH/mGc7On3/TrRdPzpoFRiPxR46QnpCQ77pxq45+/jkJx49T6/nnCfzwQ9ITEjg+YwaHx46lVJUqRRqPtlirvYKC6Jbjyk3FKU5xilOc4qyJs1ZJaYe17L0d9p6PktJea9l7P9t7v9i6PHs/bkmJs9bdlo/iYu/9Yi/5sPnmk6uPD6kxMaafM5OTyUhMNJ0cT7161fRaWlwczuXK4erjg6ObG91ynOR39PDA1ds7a/PpxskkAGNmpukkyeFPPsGYkUHTmTNx9fHh8CefmG4rk33yPKsSmYV69kTIvHlU6tgRgM0PPHBT+yq2aUO3HLeSSk9IwNXb22J9sl3+80/+HjqUe954g+Zz5nDmt9/4e+jQm67ysUapKlVu2qQxVz7c6Ps8zzgxJ788WXJ2wQLqDhmStfn06KMc/OgjIKu/Wi5caLoCwsHVlTPz5lksK+XyZdzz2SAoLGvyYSmfQK6xXBjm+s+lQoVczwDJ+c3tXPlxcOCe4cOpfGP8uZQrZ4p1KV+etBzlp165Yqqvtf2985lnuPfdd2m/aRNp167x99ChXFi71lTfbNnzzdXb2+K8LohL+fKkxcbe9H5z0m+cTDVmZOSKM2ZkmOayX48e1Hn5ZRycnTE4OWFwcMi/LDPrR05ejRvT4MMPs9qTmYl7tWr5lpezLHPrimvFijSaOJEy9euD0Yh7zZocmzLFbFutYe64bj4+ufo157jIT971OfXKlVxXHljTvvz6M++4To6OzvfKEVcfH9MJdci6aiT9xkaWNSyN25zHsDSnUwoxpw+MHMm5hQv/v7435qeleWbtuLQk51pgqT3ZmwSQtW7mt7FaWJY+Q7IV9Jmday278d+8Y8ng7Exmjivj0vKsmR7+/gWOq8Ksmal51syC2lEYxrQ0jk2ZwrEpU3AsVYoqjz1Gs++/J/HMmUKVk83Vx4d6jzxCnX//G8jqu/y+kOGWz3zOWff8Poecy5a1OD/yWwMykpLY2KED9d9+m0bjxnFlzx72Dhpkul3mrfy9VRhRy5YRtWwZODhQsVUrms6cSWZqKhf/+MP68ZiDLdbq+IgI9t/Y7MvW6sbmuuIUpzjFKU5x5uKsVVLaYS17b4e956OktNfe61dS+sXW5dn7cUtKnLXutnwUF3vvF3vJh803n1IuXsS1TRvTz47u7ji6u5tOnLhWqGB6zaVcOVJjYkiOjiY9IYHl+Vy6VcbCLYqM6ekcHj+ew+PH41m3Lu02bCjUt5XN2Tt4MI6urlntiYnBo1Yt02vJ0dFcXLeOrY8+mu97ranP2QULOLtgQdYzp+bNo/bgwURMmlToelbv08d0BUFB5UevWZPrGTvGjAwwGEw/O5cunauM/PJkSfSqVTSbNQvPunXxDAjg4oYNQFZ/bX/8cWL//tvqdl3atInmP/+Mc7lyuU5Ilq5fnxp9+hD+7rtWtcOa8VFQPs09l6iolIuccQAAIABJREFU/Zd27RrOZcuaHirvXq1avseq9tRTVOnZkw1t2pAWG0v1vn2pdePh9qlXr+Ls5WWKzfktc2v7O+XiRcJefZWwV1+lcteuhMybxxILt8eyNK/z9oVTnr7Ir85Onp44urtbrKMlpapUodns2axt0oT4Q4coVbVqrpOphdV87lyOTJhg+uZ81xtXWhVF4JgxAKxv2dK0+WmNnP3o4OKS67k05iRfvIirt7fpJHDpe+4xXU2Xn5Q88a4VK9rkKsPscZ3NtVKlfOufcvEiLuXLg4MDZGbi4OKCh78/8YcPm2IszS1rxm1R57S5duXc4Ml5jPzmWWHGpcV856hjge25IWbbNlx9fPBu145LGzeafu/g5sZ9n3/O38OGWX38/D5DTv34Y646WfzMzlH/1NhYMhIT8axTh6t795p+X7ZBg1yb7y75rJkFjisr18y0q1dx8fIiexs7e8201A5rlapWjbKBgaarBzOSkoicM4ca/frhFRTE1b/+svhZkZ/k6GgOjhlz020vb4q7MZ+z5Z3PrhUqmK6My+7TjKQkq8ZTXrFhYWx/8kkMzs40GD2aoC++YOsjjxSqjKJydHenSs+eRM6dm5XzzEwub9nCqdmzKRcUROScOVaPx5yKulbnlHb1KudDQxWnOMUpTnGKK1SctUpKO6xl7+2w93yUlPZay9772d77xdbl2ftxS0qcte62fBQXe+8Xe8lH4b+SXYDo1aup0LIlnnXqAFDrhRe4vGWL6du0FVu3Nm3mVHvySS5t3kzS2bMkHD9OtRvPL3L19qb53Lk4eXpaPFbIL79Q+cazQhLPnCEtLq5I3zIHyExLw+XGyfG0q1dJjo4mOToaY1qa6UH2GAxcWLs2q3116wJZD5e/78svLdYn5/vrDBlCg/ffB4OBtGvXSDx7NutELFmbSa5W3K7Gzc+P4G++wa1SJY7neZaHpfJzSjp3jlJ+fqYNgap5bkmXX54g6xveOTcRTP2XksKFNWtoNGEC55csMV0RcH7pUmoPHgwGAwZnZxpPmkS5Am6BGLVyJbH79tFm+XLKBQfj6O5OxTZtaL1sGckXL1rdDmvyYSmflhS1/67s2kXVXr0AqPzQQ7jXqJFv+dknDdNiY3EuV45azz1nmg8x27dT9bHHMDg741KxIn49epjeZ01/O7q703HHDtNVEvGHDxc4byzN66Rz5yjToEFW2aVK4dut203vTzh2jMzUVFM+/AcPzjr5XUTO5cqRnpDA9RMnMDg6UnfIEAwODjiWKmV1GTnnvGuFCqaNhOp9++Lq7V3g+mOOS4UKXDtwIOt5dY0b49Ohg1Vl5ezHqk8+idGKTZLoFSvwf+klcHDAq0kTOmzfDgaD2Xl6ftkyar3wApDVh1V69TLd4vBWJBw7hsHRkYpt2wJQd8iQm5/3RNaVfomnT1Pz2WcBqP3vfxOU5wS7ublladzmbG9R53RhmJtnlsalMS0Ng5MTjh4epnZak29L7XGvWZMKLVsCWc/WOTx+PCFz5+LbvTtOpUvjGRBAq0WLcPX2Nt2aLZu545v7DMlZ/0J9Zmdmcur772k8aRIe/v44uLnh9+ij1Hr+edNmL2Tl2bFUKRzc3PB9+GEubd5s9biCgtfMak89BUD55s0p27BhVh8U8W+PnJzc3Wkxbx5VevbM+qw1GKjYti3lmzUjZseOAj8rTN2UYz06v3QpNfr2NW3k+w8aRM0bG2nlmjbFMyAgK27ZMqr37p21ke/gQK3nn881n2v06weAh78/Xo0bE7N9e5HmR/mQEFouWoSDmxvGtDTiIyKK/LdW3nlgjcyUFAI/+ogGo0ebvrTgUatW1mfrn38WOY9FXatFRERERERExDo2v/Ip6exZ9gwcSKvQUBxcXEiMjGTXjZMmANFr1nDfV19RpkEDEk+fZsfTTwOwo3dvms6YQeBHH0FmJkcmTSI9IcHisY5MmkTwtGk0mTYNMjM5/dNPXN2zhwohIYWu97nFiwmZN4/wUaM49tVXuV67snMnpfz86H7mDL9XrcruAQNouXAhju7upMfHE/bqqxbrk3T2rOn9a4KCuH/2bLqdOoUxM5Oru3ebHgbfeNIktj/+eL7P7fD096dXcnLWCcG4OKJXrGB969Y3nVCMnDs33/LzPhAs+fx5TsyYQcedO0k8dYrzy5fj06GD6XVzeYpasYKWCxfiXq2a6bkZ2c7Mn0/LhQvZ1KmT6Xf/Gz2aJlOm0PXoURycnDj/++/E7t9vORmZmWzp1o2GY8bQKjQUl4oVSTh6lIMffcTpPM9PsdQOa/JhKZ+WFLX/9r/1FiFz51Kjb18u/PEHF9aty/db2WfmzaNG3750jYgg8exZDrz9Nq1CQwn8+GMOjR2Ld5s2dI+MJDEykrMLFpieg2RNf2ckJnJ8+nTarV+PwcmJjMREdg8YYPGWjJbm9bnQUGoOGEDnsDASIyOJXrkS1zxXoxjT0tjz4os0mTYNg8HAiZkzSb5wAYNT0ZaguPBwolesoOuRI6ReucK+N96gQkgI/1q3jrNWXoKbc86Hv/cerUJDSbt2jRPffcexr76i6cyZrL9xYr8wIiZN4v4ff6TWCy9wZdcu/n79de6fPZvLW7dm3TLKjAOjRhE8fTqJkZFErVhByoULBd6+6sCoUTT99lsePneOtGvX2PH002QmJ5udpwdGjiR4+nS6RkRgzMzk+LRpXFy/vtBtzCszJYW//vMfmv/0E2mxsRybOjXr1ol5x7bRmLXWz5xJw/HjSYiIYFf//rlCzM0tS+M2Z3t3PvNMkeZ0YZibZ8a0NLPjcn2rVlxcv57up0+z5aGHrM53yoULZttT+cEHqfb446Y193/vvUfyhQs0+uQTPPz9Sbl0idM//cTBDz64qVxzxzf3GZKekJCr/oX5zP572DAafvwxHbZtw8XLi7iDB9nxzDNc2bnTFHN582babdyIm68vF1av5lxoKMb0dOvGFZbXzH1vvpm17j77LFd27OD8smU43Fh7ivK3R07xR46wrVcvGrz/ftaVM0YjCSdOsPuFF0zP2rP0WZEt798gZQID6bRnDw7OzsRHRLD7+ecBuOeNN7gWHm56ppRXo0Z0DgvDYDBwafNmDn/yianMpHPn6HLgAM5ly7Jv+HCSo6IACj0/ruzaxfVTp+h65AjGjAySo6Lyfd6aNdKuXcs1jqxhzMhgU+fONJ44kW6nTuHg7EzKpUucnD3b9My4ouTR0lotIiIiIiIiIrfO8BtYfw+iW1R36FDKNmhQ5JMWd5PmP//MhXXrbnqw9+1wu/LU+NNPSY2N5dCNW9+UFAX2341bjgG027iRwxMmEL1ixW2socg/IPvWYkYjBkdHeqWksLhMmSI9z644eNapw7/WrbulW7BJ0bQKDeXM/PlEzplz84vFOK7MfUbVGjiQiq1bszvHF2vszaOxsaxu0ICkc+eKuyoW+XbvTsDQobm+tAL2NR+zx0HgjWdZzs9xG8X8PHFjc1RxilOc4hSnuJxx1iop7bCWvbfD3vNRUtprLXvvZ3vvF1uXZ+/HLSlx1rrb8lFc7L1f7DEfNr/tXoEKqLTYCeXp1pjpv8affkrzn34Cg4HS99yDV1AQsX/9dZsrJ2J7nXbvxv/Ghmv1vn2J+9//7piNJ7EDZtZMjatboM9xERERERERESlGt3/zSawWOGYMTb/9trir8Y9ot3Gj6RkWd5PDEyfi6uND98hIWv/+O3/95z8kR0cXd7VEblnYkCHUeeUVup0+Td1XXsl1u9U7hZufHw+Eh+N1333FXRW5objGVUGfUVV69KDzjefESdE0mjiRJl9/bfZ1e5iPd+vfKiIiIiIiIiK2YPNnPllyNM+D5cW8nX37Ftuxb0eeNrZr948fo7hY6r+UCxfY3LnzbayNyO0Rs20baxo1Ku5qFFnCsWMsdHEp7mrclbY++qjZ14prXFn6jDo5cyYnZ868fZUpglAvr+KuQoH2Dx/O/uHD833NXuZjznGQfds9EREREREREbGOrnwSERERERERERERERERm9Hmk4iIiIiIiIiIiIiIiNiMNp9ERERERERERERERETEZgxGo9FY3JUQERERERERERERERGRkkFXPomIiIiIiIiIiIiIiIjNOJ04caK46yAiIiIiYrf8/f0BMOxtajHOGLxHcYpTnOIUp7ib4uYbDBbjnrhxQxq14/bEWcve21FS8mYte+9n9Uv+cday93aUlP6zdXn23i/Wrn/Wsvd+scd86MonERERERERERERERERsZkStfmUnp7OkiVLbqmMJ598knr16lG/fn3q169P48aN6devH8eOHbNRLW1TT2sNHjyY0NDQ216fKVOm0KJFC4KDg3nttde4fv26TcsvaQ4ePEiHDh1u2/GUn8ILCgoiKiqq2I6/detWq47fsGHDYq1nXtbWW0RERESkpOuZkMATRuNN/+40JaUddxvlTUTuVlr/is8dtfmUkZFh8fUjR44UuNFiTZnjx4/n0KFDHDp0iJ07d9KgQQP++9//FqpcS4pSz3+SreuzatUqli5dSmhoKJs3b+bq1avMmjXLZuXfKTIzM4u7CvlSfrLYa37M+fHHH4mOji4wbuPGjVSqVOmWj2er/rG23iIiIiIid4PEyEj2DR+e69+dqKS0426jvInI3UrrX/Fw+icKXbFiBV9++SXp6el4e3szZswYateuzcyZMwkPDyclJYXo6GhcXV2ZPHkyvr6+nDx5kpEjR3Lp0iXc3d157733CA4OJjw8nBEjRhAQEMCFCxeYM2cOixYtYurUqaSnp+Pn58enn35KmTJleOmll4iLi6Nfv3789NNPZuvx3XffceTIEQ4ePEj79u0ZNmyY2ba4ubnx9NNPM3v2bIxGI3///Tdvvvkma9euBSAsLMz08/fff8/hw4dJSUkhMjKS9PR0pk6dSpUqVUzlJSQk3FTPDRs2MHHiRNLS0qhWrRrjx4/Hx8eH77//noMHD5KamkpERAS+vr4888wz/Pzzz5w8eZIhQ4bQs2dPi/2ak7X9Zq4+eZmLq1KlCuPGjTOdAG/Xrh3/+9//TO1///33CQsLw9nZmWeffZY+ffrk2/f51Tc1NZW+ffvy559/AvDuu+9y5MgRfvvtNwAGDRpEr169cHd3Z+zYsaSkpODi4sKIESNo167dTccYMGAAvXr1onv37rl+rlGjBsOHD6dVq1YcOHCAuLg43n//fUJCQkhJSWHEiBHs3buX6tWr06xZM86ePcvEiRMZNGgQAQEBLFq0iLFjx9K4cWPeeOMNTpw4QWZmJs8//zz9+/cHYNq0acyZMwcvLy+6du2aq15ff/01oaGhGAwGWrZsyTvvvIOLi4vZcZofc3PKVvnZv38/7777LnFxcbi6ujJ69Gh8fX1vS366d+9Oo0aNePnll9m8eTMXL16kd+/ePP/88zbLz+bNm3n//fdxdHSkR48eGHLcJ9aa/BiNRiZPnsySJUvIyMjgueeeY+DAgWRmZvL555+zYsUKABo1asQHH3xA6dKlGTx4MIGBgYSFhREVFUX16tX5+uuvmTlzJps3byYiIoIRI0Zw//33m613u3btWLNmDbGxsQwfPpwOHTqwe/duLl68yOjRo2nbtq3FPDdu3JjBgwfzzTffsHXrVtzd3U1t2rJlS755M1fejBkzctX7119/LXQ+RURERERKkuSoKCI+/TTX7xpPnFhMtSm6ktKOu43yJiJ3K61/xcPmVz6dP3+ekSNHMn36dNatW8cjjzzC8Bs7iY6OjmzevJkxY8awePFiAgMD+eqrrwB47bXXeOSRR1i7di0fffQRr776KqmpqTg7O3P69Gk6dOjAnDlziI2NZdSoUcyaNYuNGzdSq1Ytpk6diqenJyNHjiQ4OJiffvrJYj2cnJzYsGEDU6dOtbjxBJCUlMRPP/1EUFBQrpPP+XFwcGDdunW88847LFy4kIYNG/Lrr7/mislbz0uXLvH666/z+eefs3btWlq2bMno0aNN5W3ZsoUPP/yQZcuWceTIEdauXcusWbP45JNPmDp1aoH9ms3afrNUn5wsxTVs2JDg4GBT7NatW2nWrBkAn3/+OUajkXXr1vHLL78wY8YMDh06dFP55upbs2ZNIGucARw4cIDU1FRSUlIwGo2EhYXRvHlzPv74Yz766CPWr1/P5MmTWbNmjcXc5eXo6MixY8do3749v/32G8OGDWPkyJEALFy4kKioKNavX89nn33GggULcHLK2sd1cnJi9+7drF+/nnbt2jFt2jS8vb3ZsGEDc+bM4ZNPPuHChQscO3aMb775hsWLF7Ns2TKOHz9uOvbatWtZtmwZixYtYuXKlURHR/PLL78Uqv5gfk7ZIj+QtbHUv39/NmzYwMsvv8x777132/IDWfPj+vXrzJkzhzlz5jB58mTOnz9vk/xkZGQwYsQI3n//fdauXUu5cuVISEgArM/P6tWr2bp1K6tXr+b3339n9uzZhIWFsWLFCjZu3MjSpUtZu3YtRqMx11zetm0b06dPZ9myZZw6dYrt27fz0ksvUaNGDSZNmsQDDzxgtt45OTo6EhERQXBwML/88gv//e9/TeuCpTw7OTlx7do1/vrrr1wbT4DZvJkrL2+9i5JPEREREZGSxMHVFfeaNXP9uxOVlHbcbZQ3Eblbaf0rHjbffPrzzz8JDg42nYR+7LHH2LdvH/Hx8QA0a9aMChUqANChQwf++usvzp8/z8mTJ3nyySeBrCsBfHx8CAsLw2AwkJmZyUMPPQSAl5cXu3btonr16gCEhIRw5syZQtXDYDAQGBhoKiOvUaNGERQURFBQEA0bNiQ6OprPP//cqvYHBwfj7e0NQN26dQu83dSWLVu47777CAgIAKB3796sX7/edDvA4OBgypQpg6OjI1WqVKF169YAVK9enYsXL5rKya9fc7K23wqqT2HjvvzyS1JTU025XbduHf369cNgMFCuXDm6du1quorM2vq2aNGCsLAwYmNjcXNzo0GDBoSHh3Ps2DH8/Pzw8vLCx8eHZcuWcfr0aerXr8/YsWMt5iE/np6epv5u3749p0+f5sqVK+zcuZOuXbvi5OREpUqV+Ne//mV6j8FgoH379ri5uQHw5ptv8v777wNQtWpV/Pz8OHv2LLt27eL+++/H29sbg8HAo48+aipj3bp19OzZk9KlS+Po6MhTTz1V6M0ZS3Mqp6LmB2DevHn06NEDgObNm9/2/ACmDQ0fHx/uvfde9u3bZ5P8nDp1isTERNq2bQvA448/broNnbX5Wb9+Pd26dcPNzQ0vLy9WrVpFo0aN2LBhAw8//DDu7u4YDAZ69uxpulIMoFOnTjg7O+Po6EitWrXyXUPM1TuvUqVKmdpfp04dU1mW8mwwGHjggQfy3Ww3l7fCjBtL8suniIiIiEhJ4hUURLeTJ3P9uxOVlHbcbZQ3Eblbaf0rHja/7V5MTAzlypUz/ezq6kqpUqWIiYkBoEyZMqbXPD09iYuLIyYmhpSUlFy33UpKSuLKlSuUK1eOsmXL4uCQtU+WmZnJt99+y5YtWwC4du0afn5+ha6Hl5eX2TaMGTPGtBnQs2dP2rZte9Mt7Mzx8PAw/b/BYCjwOVUxMTHs3r3bdJIbwN3dnStXrgBZJ4+zOTo6mn52dHTM9UyW/Po1p8L0m7n6ZG+qWRNnNBr58MMPOX36NDNmzDDlLyYmhpdffhlHR0cAUlNT6datGz/88IPpqoxXX32Vfv36ma1vSEgIYWFhuLi4EBQURJ06ddi7dy+lS5emZcuWAEyePJkpU6bQp08fSpcuzahRo2jdujWtWrUiJSUFV1dXtm7daik1ufrUyckJNzc34uLiiI2NpWzZsqbX/Pz8cm3k5Rxbhw4d4vPPP+fKlSs4ODgQFRWF0WgkNjY2V/k53xMTE8Mff/zBnDlzgKzcZW8s5jRkyBC2bdsGwOzZs2nYsGGuMszNKaDQ+Tlw4AADBgwAoGXLlnz55ZesW7eOn376ifT0dNLT003j8XblB8iVB09PT65du/aP5Cd7/ShMfmJiYnKVUbp0adPvc65NXl5eprxA7jXEwcEh3zXEXL3z8vT0zFVWdo7M5TlbzvrlZC5vBZVnrfzyKSIiIiJSksRHRLD/rbdy/a7V4sXFVJuiKyntuNsobyJyt9L6VzxsvvlUoUIFdu/ebfo5KSmJpKQkKlasCGTdTi3btWvX8PLyomLFiri7u7N58+abyouIiMj1Dfzly5ezZs0a5s2bR5kyZQgNDWXhwoWFrkdBt9DL9uabb/Lf//6X7t274+HhgaOjY66TvNevX7eqHHO8vb1p1aoV06dPv6Vy8uvXnKztN2vrU1DcuHHjiImJYcaMGTg7O+d635QpU7j33ntzxScmJpqubvPw8LBY35CQEObOnYujoyNNmzbF39+fCRMmULp0adOVOBUqVGD06NGMHj2ajRs3MmTIEPbs2cOSJUswGo2m/Ds4OOTKZ/at1bL7MTs2JSWFpKQkvLy8KF26tOlKPoCoqKhcbck5tv773/8yaNAg05VFHTp0ALJOsucs4/Lly7n66OWXXzZt9pjz0UcfkZqaCty8mWppTkHh85OWlsbKlSsBcHFxITo6mjfffJOlS5dSp04doqKiTFfY3K78AFy9epWqVasCEBcXR7ly5WyWn5wbuNevXycpKcnUR9bkp0KFCrk2laKioihVqtRNv79y5YppXbKWuXpby1yeC2Iub9aWV5R8ioiIiIiUJGlXr3I+NLS4q3HLSko77jbKm4jcrbT+FQ+b33avTZs2/PXXX5w6dQqA3377jWbNmpm+gb9nzx7TVQgrVqzg/vvvx9fXlxo1arBs2TIg61v5Q4cOzXdjJ/vkZJkyZbh27RoLFy4kMTERyLo6JT4+HqPRWGA9clq6dGmuk/85tWjRgnr16jFjxgwAKlWqxMWLF00nprNPyBdGznq2bt2avXv3muq5f/9+Pvjgg0KXmV+/5mRtv1lbH0txO3fu5M8//2TSpEm5NjYAOnbsyNy5czEajaSlpfHxxx9z4MAB3N3d8fb2xtvbG3d3d4v1rVKlCvHx8ezYsYPg4GD8/f05efIk4eHhNG3alKSkJHr16mXadKhdu7bpio+KFSvi7e1tOtlfuXJljh49CsCpU6dyPd8oKSmJVatWAVl5rlu3Ll5eXjRq1Ii1a9eSmZlJdHQ0f/zxh9m8xMbGUr9+fQBCQ0OJiYnh+vXrNGnShF27dnHx4kUyMjJybQR27NiR0NBQ04n5X375Jd+NwrJly5r6LG8/W5pTRcmPs7Oz6VjZGzMeHh5Uq1aNjIwMfvjhBzIzM0lOTr5t+cnuU4DIyEgOHTrEfffdZ5P81KhRA2dnZzZt2gRg2kwrTH7atWvHkiVLiI+P59q1a/Tp04cTJ07QoUMHli1bRlJSEpmZmSxYsCDXFWrmODs7m9Ydc/W2lrk8W2Ipb5bKy1nvouRTRERERERERERE7jw2v/LJ19eXcePGMXjwYNLS0vDz82PChAmm19u0acMHH3zA0aNHqVKlCl988QWQ9cD6UaNGMXnyZAwGAy+88EKu209l69atG0uWLKFjx45UrlyZ4cOHM3jwYD777DOeffZZLly4QOvWrdm6davFeuT08ccf8/XXX5u9+uCNN97g6aef5umnn8bPz4/evXvz2GOPUbVqVdq3b8/27dsL1UdBQUG56jlhwgT+85//kJycjIeHB6NHjy5UeWC+X7MVpt+sqU/FihXNxi1YsIBjx47lug1cQEAAS5Ys4bXXXuODDz6gY8eOpKen06FDB+rVq3dT+ZbqO2zYMJo0acKePXsoX748ANWqVSMpKcl0a7Q+ffrQt29fMjIycHNzY8KECaZby+XUv39/hg4dyvbt26lTpw5t27Y13easWrVqhIWF8dlnn2EwGBg/fjyQ9XyrnTt30rZtWwICAujZsydXr17NNy9Dhw7l3//+N56enjz55JP079+fd955h/nz59OvXz8eeeQRSpcuTZ8+fUwn6zt16kRERASPPvoo6enp1KpVy3TswjA3p2yRn4CAANq1a0fnzp3x8vLi7bff5u+//6Zfv37Mnz//tuQHsjaDu3btSnx8PCNGjMDHx8dm+Rk7dizvvfceRqORJ598kooVK5KRkWF1fh588EEOHjxI165dMRqN9O/fnyZNmpCZmcmRI0d4+OGHMRqN3H///bz00ksF5rNLly4MGTKE119/3WK9rWFtnnMqVaqU2bxZKi9nvYuSTxEREREREREREbnzGI4fP37zg0L+IbNnz+bo0aOmh9SLbahfbe/gwYO88sorrF+/Pt/XMzMzTZslX3zxBUlJSYwYMeJ2VvGuFxQUxKpVq6hcufJNryk/dx5L+RQRKW7+/v4AGPY2tRhnDN6jOMUpTnGKU9xNcfMLuO3/EzduTa123J44a9l7O0pK3qxl7/2sfsk/zlr23o6S0n+2Ls/e+8Xa9c9a9t4v9pgPm992ryDGQiZVrKN+vX3WrVtH586dSUpK4vr166xevZomTZoUd7XuSvmNe+XnzqV1TEREREREREREpGSw+W33REq69u3bs3nzZjp37oyDgwMPPfQQnTt3Lu5qyQ3Kj4iIiIiIiIiIiEjxuq2bTwMGDLidh7trqF9t79577zV7yz1HR0c+/PBDPvzww9tcK8np77//zvf3ys+dyVw+RURERERERERE5M5z22+7JyIiIiIiIiIiIiIiIiWXNp9ERERERERERERERETEZrT5JCIiIiIiIiIiIiIiIjZjMBqNxuKuhIiIiIiIiIiIiIiIiJQMuvJJREREREREREREREREbMbpxIkTxV0HERERERG75e/vD4Bhb1OLccbgPYpTnOIUpzjFKc7O4+YbDBbjnrhxgyB7b0dJibOWvbdD/ZJ/nLXsvR0lZb0qKeuftTROb0+cJbrySURERERERERERERERGymRG0+paens2TJklsbhbdFAAAgAElEQVQq4/XXX2fKlCk2qlHhLFy4MN/f52xXREQErVq1um11Gjx4MKGhobfteP+k2bNn8/bbb//j5T3//POsWbOGMWPGMG3aNA4ePEiHDh0KLO/UqVPs2VO4HXdz789bh7wWLVpE48aNmTBhQpGPZ+/Mzad/QlBQEFFRUbfteCIiIiIiInJreiYk8ITReNM/ERF7Y+v1Suuf3C5OxV2BwsjIyMDR0dHs60eOHCE0NJQePXrYrMzbISMjAwcHByZMmECvXr1uer0o7SrMsYu7/SWNu7s7Hh4euLu74+7ubvX71q9fT2pqKk2bWr6k0Zr3F1SHdevW8frrr9O/f/9cvy8p48FoNJqdT+bcSW2/k+oqIiIiIiJirxIjIzn61Ve5ftd44sRiqo2IiHm2Xq+0/snt8I9sPq1YsYIvv/yS9PR0vL29GTNmDLVr12bmzJmEh4eTkpJCdHQ0rq6uTJ48GV9fX06ePMnIkSO5dOkS7u7uvPfeewQHBxMeHs6IESMICAjgwoULzJkzh0WLFjF16lTS09Px8/Pj008/pUyZMrz00kvExcXRr18/fvrpJ7P1+O677zhy5AgHDx6kffv2DBs2LN92fP/99xw8eJDU1FQiIiLw9fXlmWee4eeff+bkyZMMGTKEnj17WmzXkSNHGDVqFFeuXMHV1ZVhw4bRsWPHm9pVunRpYmJiePDBB5k1axZ+fn4AJCQk5GrXu+++i5OTE1OmTGH+/PlkZGQwduxY2rZtC8DXX39NaGgoBoOBli1b8s477+Di4pKrXfm135r3bdiwgYkTJ5KWlka1atUYP348Pj4+GI1GPv74Y9atW0dmZibNmzdn3LhxODk5sWXLFsaOHUtKSgouLi6MGDGCdu3akZCQwPvvv09YWBjOzs48++yz9OnT56YcvPjii9SpU4fw8HAuX75MYGAgY8eOxfn/2Lv3uJzv//Hjj6urVKhYYkLOLHPILjGEDs6nYRuT07CR+cwhcyxnMtkc5jA+25gZbYjU0LlWYXP8GGMyJnwq2VUqVqGu3x8+Xb8udV1dWehrz/vt5nbrut6v0/P1fr1ff1wvr9fbzKzEcVClShU6derEjz/+SI0aNQDw8/OjoKCAOnXqaMu9c+cOCxYs4Ndff8XExISBAwfy4YcfApRYrr29PXl5ecyePZtTp05Ru3ZtXn311RLHTePGjbG1taVevXrY2NjoXNPXVydOnGDjxo2YmpqSnZ3NzJkz9d6TkvrU3NxcJ7+hNmzatIkjR47wyy+/kJGRgZWVlc54mD59OmvXruXQoUMAtG7dmsWLF2NlZUXbtm3x9vYmPDycpKQkfH19iYmJITExEVNTU7Zu3UqVKlWMitnU1JS2bdsya9YsQkNDuXHjBm+99RYffPBBsT7VNz98+eWXnDhxgi1btgAwZswYevToQUJCgs7zlJeXZ9T84uvry8yZM3F3d+fEiROkpaWxcOFCunbtajAOfb744gsCAgLQaDTUqFGDTz/9FAcHB73zwpkzZ5g1axYREREAOp8ff269vb1Zs2YNBw4cID8/n3fffZf33nsPMG4eEEIIIYQQQoh/utyUFBI/+UTnO/nxVQhREZX3fCXzn3gWyv3YveTkZHx8fNi8eTORkZEMHDiQmTNnAqBUKomLi2PZsmXs37+fli1bsv5/K6xTp05l4MCBREREsHTpUj788EPu37+PmZkZSUlJuLu7s3PnTu7cuYOvry9bt24lNjaWhg0bsmnTJqpWrYqPjw8qlYodO3YYbIepqSkxMTFs2rRJ78ITgImJCfHx8SxZsoSQkBAuXbpEREQEW7duZeXKlWzatMlgXAUFBUyZMoXRo0cTFRXF2rVr8fb2Jj09vVhchQsqoaGh2oUnoFhcAGlpadSpU4cff/yRyZMna/swIiKCkJAQ9u3bx+HDh0lNTSUgIKBYXI/Hb0y+27dv4+3tzdq1a4mIiKBTp04sXLgQeLTjJiEhgbCwMCIiIrhw4QKhoaEALF++nKVLlxIdHc2aNWsIDw8HYO3atWg0GiIjIwkICGDLli1cvHixWFuVSiX/+c9/2L59OyEhIVy+fJlDhw7pHQc2NjZ07tyZgwcPassIDw+nX79+OuV+8sknWFlZERUVxb59+wgODiY2NlZvuQB79+4lJSWFmJgYvvrqK44ePVriuJk+fTqvvPIKb7/9Nj179tS5pq+vOnbsyIABAxg7diwzZ840eE9K6tPH8xtqwwcffEDHjh356KOPmDZtWrHxcOjQIWJjYwkODiYiIgKNRqMz1nNyctixYweTJk1i2rRpTJw4kcDAQG05jzM0PpRKJcnJyWzfvp1du3axfv16cnJyipWhb34YO3YsqampxMfHExERwb179/D09Cz2PBk7vyiVShITE1GpVAQEBDB9+nTt82UojpKkp6fz+eefExwcTExMDO+88w5xcXEG5wVDHr9PYWFhHDlyhLCwMH744Qe2bdvGmTNnjJ4HhBBCCCGEEOKfzsTcnMoNGuj8E0KIiqi85yuZ/8SzUO6LTwkJCahUKhr8b8AOGTKEs2fPkp2dDYCzszO2trYAuLu7c/r0aZKTk/njjz8YOnQo8GinRc2aNTlz5gwKhYKCggL69u0LQLVq1Th+/DgODg4AvP7669y4caNM7VAoFLRs2VJbhiEqlQpra2uUSiV16tTBxcUFAAcHB9LS0rTpSorrxo0bpKSk0L9/fwCaNWtG06ZNOXXqVLG4yqJSpUoMHjwYgLZt25Kamgo8Okpt8ODBWFlZoVQqGTZsmHaxp6jH4zcmX3x8PG3btqVZs2YADB8+nOjoaPLz83F3dycwMBBzc3PMzc1xcnLi+vXrANSsWZOQkBCSkpJwdHTEz89PW+eoUaNQKBRUr16dPn36aHd7PK5Hjx6YmJhgampK165dOXPmjMFxMHDgQEJCQgC4cOEC+fn5ODk56ZQZGxvLO++8A4CVlRU9e/YkISHBYLk///wzvXr1wtTUFCsrK7p3727cDSvCUF8VZeie6OvTJ/X4eIiJiWHAgAFUrlwZhULB4MGDSUhI0KZ3dXUFoH79+tSrV0/7jNWvX1/nmTA25t69ewPw8ssvY2lpyZ9//qmT39D8oFQqWb58OX5+fqxatYrly5djYmJidP6SnkNLS0u6desGQJMmTbTPl7H3rlDlypXRaDQEBweTkZHBm2++yciRIw3OC4Y8fp+io6Pp168fFhYWVKtWjdDQUFq3bm30PCCEEEIIIYQQ/3TVnJzo98cfOv+EEKIiKu/5SuY/8SyU+7F7arWa6tWraz+bm5tjaWmJWq0GwNraWnutatWqZGVloVarycvL0/6oDZCTk0N6ejrVq1fHxsZG+4NyQUEBX3zxBfHx8QBkZmbq7BQyth3VqlUzKh5LS0vt30qlUvtZqVRSUFCgvaYvLhsbGxQKhfaajY0N6enp1K9fXyeusqhatar2bxMTE2071Go1UVFR7Ny5E3jUV4ULYo8rGr8x+dRqNSdOnNAe7wePflxPT0/HxMSEjz/+mCtXrqBQKLh58yajRo0CYM2aNWzYsAFPT0+srKzw9fXFxcUFtVrN5MmTte+tuX//frHdSYWKHhtXtWpVbty4YXAceHh44OPjw/Xr17W7noreg8J4ivaBjY0Nv/32m8FyMzMzddpSeC/LIj09XW9fPd4+ffdEX5/+HY+Ph6LPTrVq1XTiLHyHlImJCRYWFtrvTUxMyM/PL3PMRY/pK6kMQ/MDQMuWLalatSpKpZLmzZsXq78s8wvof76MvXeFLCws+Pbbb9m8eTOrVq2iVatWLF++3OC8UHhUpD6P36ei846VlZX2e2PnASGEEEIIIYT4J8tOTOSX2bN1vuu8f/9zao0QQuhX3vOVzH/iWSj3xSdbW1tOnDih/ZyTk0NOTo72R9U7d+5or2VmZlKtWjVq1KhB5cqViYuLK1ZeYmKizo+0Bw8eJDw8nO+//x5ra2uCgoIIDAwsczseX4z4u0qKy9bWlszMTAoKCrQ/bmdkZDy1NtjZ2TF58mTGjh1batqidRuTz87Ojs6dO7N58+Zi13x8fADYs2cPSqWSuXPnaq/Z2tqycOFCFi5cSGxsLFOmTOHkyZPY2dmxYcMGWrRoUWpbMzIytH9nZWVRrVo1g+PA0tISDw8PDh06RHh4OCtXrixWpq2tLenp6dSrV09bR40aNQyWa2NjQ1ZWlraMwoXMsli9erXevirK0D3R16d/R9HxUNg3hYxZFDHE2Jj1MTQ/wKOdWqampuTl5RETE4Obm5vR+R+fX8o7jldffZX169fz4MED1q9fz9KlS/Hx8dE7LyiVSjQajTb/vXv3dMozdJ9SUlKwtLQs0zwghBBCCCGEEP9kDzIySA4Ket7NEEKIUpX3fCXzn3gWyv3YvS5dunD69GmuXbsGwO7du3F2dtbuJjh58qT2GLNDhw7Rvn17ateuTf369bVHpanVaqZNm1bsh1d49CNt3bp1sba2JjMzk8DAQP766y/g0TtRsrOz0Wg0pbajqODg4GJHfZVVSXE5ODhgb2/PDz/8ADw6Ai4pKYl27doVy29mZkZ+fr42lqKKxmWIh4cHQUFB3L17F4CAgIASF+aeJJ+LiwunTp3S9ucvv/zC4sWLgUcLb82bN0epVHLx4kWOHTvGX3/9RU5ODm+++SYpKSkANG7cWLuLxMPDg127dqHRaHjw4AHLly/n3LlzJbYvNDSU3Nxc8vLyiIqKon379gbHAcCAAQPYuXMnubm5tGrVqliZ7u7u7N69G3i0WBgWFoabm5vBctu2bUtYWBgPHjwgPT1d7zGBhujrK3h0nwsXt/TdE0N9WjT/3+Hu7k5ISAg5OTkUFBSwd+9enV1DZWUoZmMYmh/++usvli5dyqJFi7T//vrrL53nqSzzS3nGcebMGSZNmkReXh5mZmY0aNCAgoICg/NCrVq1SEtL097Hw4cP6y3f1dWVAwcOkJ2dTWZmJp6enly9evWJ5wEhhBBCCCGEEEIIIYQoL+W+86l27dqsWLECLy8vHjx4gL29Pf7+/trrXbp0YfHixVy+fJk6deqwbt06ANauXYuvry9r1qxBoVAwfvx4neO4CvXr148DBw7g4eHByy+/zMyZM/Hy8uLTTz9l9OjR3Lp1CxcXF44cOWKwHUUtX76cjRs3/q3dHSXFpVAoWLduHb6+vnz22WdYWFiwbt06bGxsuHXrlk5+KysrOnbsSNeuXfnqq69o06aN9pqTk5M2rm3btultQ/fu3UlMTGTQoEE8fPiQhg0b8vHHH5fadmPy1ahRA39/fz744ANyc3OpUqUKCxcuBGDcuHHMnDmTPXv20Lp1a+bNm8fs2bNRqVR4enoycuRI8vPzsbCwwN/fHxMTE6ZOncrixYvx8PDg4cOHuLu788orr5TYPmdnZzw9PUlLS6Nr16706NGDzMxMveNgxowZdOnShdzcXAYNGlRimTNmzGD+/Pl4eHhgYmKCp6cnHTt2pFmzZnrLnTRpEidOnKBLly7Y29vTp0+fEt9xZIihvnJ1dWXy5MmkpKSwZs2aEu+JpaWl3j59PP+T6tOnD5cuXWLAgAFoNBrat2/PxIkTn7g8QzEbS9/8sGLFCtzc3LTH7XXq1InVq1fj6+ur8zwZO7+UZxxt2rShbt26dO/eHaVSiZ2dHX5+fgbnBRsbG4YPH86QIUOoW7cubm5uHDt2rMTye/fuzYULF+jTpw8ajYYxY8bw2muvATzRPCCEEEIIIYQQQgghhBDlRXHlyhXD22nK0bZt27h8+TJ+fn7Pqspn4kWNqyLw8vKiT58+vPHGG2XO27NnTzZs2ECzZs2eQsuEEEII8U/RqFEjABSniu9eL0qjOinpJJ2kk3SSTtJJugqebk8pR6+//b9TZyp6HC9KOmNV9DikX0pOZ6yKHseLMl+9KPOfsWScPpt0hpT7sXulKe3ouP+rXtS4KoIn6dvAwEBq1qwpC09CCCGEEEIIIYQQQgghxDNW7sfuCfG8DR06lMzMTLZs2fK8myKEEEIIIYQQQgghhBBC/OM808WnsWPHPsvqnpkXNa6KYPPmzWXOs3v37qfQEiGEEEIIIYQQQgghhBBCGOOZH7snhBBCCCGEEEIIIYQQQgghXlyy+CSEEEIIIYQQQgghhBBCCCHKjSw+CSGEEEIIIYQQQgghhBBCiHKj0Gg0mufdCCGEEEIIIYQQQgghhBBCCPFikJ1PQgghhBBCCCGEEEIIIYQQotyYXr169Xm3QQghhBBCiAqrUaNGAChOtTOYTqM6KekknaSTdJJO0kk6SSfpypDOWBU9DumXktMZq6LH8aKk26NQGEz39v8OSKvocRhLxumzSWeI7HwSQgghhBBCCCGEEEIIIYQQ5eaFWnx6+PAhBw4c+FtleHt7s2HDhnJqUdkEBgaW+H3RuBITE+ncufMza5OXlxdBQUHPrL6nadu2bcydO/eplzdu3DjCw8NZtmwZn3/+OefOncPd3b3U8t577z1tXxcdC0W/N6YtLVq0IDMzkwEDBnD69OliaRcsWECbNm2IiIgotU3/F5XHPGCsCxcuGHVvhRBCCCGEEEIIIYSoCAbfvcvbGk2xf0KUt/9Ti0/5+fkGr1+6dKnMCyWllfks5Ofno9Fo8Pf3L/H6k8RVlrpF+apcuTJVqlShcuXKVK5cGUdHR/bu3VtqvtWrV9OnT59iY6Hwe2NVqVJFp/7HHTx4kN27d9OjRw/tdwUFBUaXX9H9X50HjPEi3SchhBBCCCGEEEII8Xz8df06Z2fO1PknRHkzfRqFHjp0iM8++4yHDx9iZ2fHsmXLaNy4MV9++SXnz58nLy+P1NRUzM3NWbNmDbVr1+aPP/7Ax8eH27dvU7lyZRYsWIBKpeL8+fPMmTOHZs2acevWLXbu3Mm+ffvYtGkTDx8+xN7enk8++QRra2smTpxIVlYWo0aNYseOHXrb8dVXX3Hp0iUuXLiAm5sbM2bMKDGOr7/+mgsXLnD//n0SExOpXbs2I0aM4Ntvv+WPP/5gypQpDB482GBcly5dwtfXl/T0dMzNzZkxYwYeHh7F4rKyskKtVtO7d2+2bt2Kvb09AHfv3tWJa/78+ZiamrJhwwb27NlDfn4+fn5+dO3aFYCNGzcSFBSEQqGgU6dOzJs3j0qVKunEVVL8xuSLiYlh1apVPHjwgHr16vHxxx9Ts2ZNNBoNy5cvJzIykoKCAjp06MCKFSswNTUlPj4ePz8/8vLyqFSpEnPmzMHV1ZW7d++yaNEizpw5g5mZGaNHj8bT07PYPXj//fdp0qQJ58+f588//6Rly5b4+flhZmZW4jioUqUKnTp14scff6RGjRoA+Pn5UVBQQJ06dbTl3rlzhwULFvDrr79iYmLCwIED+fDDDwFKLNfe3p68vDxmz57NqVOnqF27Nq+++mqJ46Zx48bY2tpSr149bGxsuHjxIlOnTiU6Opqvv/6a3377jby8PK5fv87Dhw/ZtGkTderUwdvbm/79+xMaGqozFhYsWED//v0ZNGiQ3rYV9dprr2Fqakrz5s2xtrbWuebl5UVWVhb/+te/mDt3LtOnT8fLy4t///vfHDlyhBs3bpQ4Xi9evMjMmTPp1q0bx48fJysriyVLlrBt2zauXbtGhw4dWLx4cbG++OWXX5g/fz5ZWVmYm5uzcOFCOnbsqC3P3d2dEydOkJaWxsKFC7Xj2Jhx9/7779OxY0fGjRtHdnY2PXv2ZMOGDUydOlVnHtCX//HnwNbWVu+90ReHPvfv32fu3LmcPn2a/Px8VCoVK1aswMLCQu+8tHnzZm7evMmyZcsAdD63adNG5z7dvHmTefPmkZKSQr169Vi1ahX16tXTO48KIYQQQgghhBBCCFEoNyWFxE8+0fmuzapVz6k14kVV7jufkpOT8fHxYfPmzURGRjJw4EBm/m/lVKlUEhcXx7Jly9i/fz8tW7Zk/fr1AEydOpWBAwcSERHB0qVL+fDDD7l//z5mZmYkJSXh7u7Ozp07uXPnDr6+vmzdupXY2FgaNmzIpk2bqFq1Kj4+PqhUKnbs2GGwHaampsTExLBp0ya9C08AJiYmxMfHs2TJEkJCQrh06RIRERFs3bqVlStXsmnTJoNxFRQUMGXKFEaPHk1UVBRr167F29ub9PT0YnEVLqiEhobqLCY8HhdAWloaderU4ccff2Ty5MnaPoyIiCAkJIR9+/Zx+PBhUlNTCQgIKBbX4/Ebk+/27dt4e3uzdu1aIiIi6NSpEwsXLgQgOjqahIQEwsLCiIiI4MKFC4SGhgKwfPlyli5dSnR0NGvWrCE8PByAtWvXotFoiIyMJCAggC1btnDx4sVibVUqlfznP/9h+/bthISEcPnyZQ4dOqR3HNjY2NC5c2cOHjyoLSM8PJx+/frplPvJJ59gZWVFVFQU+/btIzg4mNjYWL3lAuzdu5eUlBRiYmL46quvOHr0aInjZvr06bzyyiu8/fbb9OzZs9iYioyMZN68eQQGBtKqVSu+++47nTT6xoKhthW1ZcsWAJYsWVJsYWrz5s1UqVKFb775Bnd3d0xNTcnMzOT06dNYWFjoHa9KpZLExETc3d3Zs2cPjo6OLFq0iHXr1hEUFMT+/ftJS0sr1pb58+czZswYYmJimDx5MgsWLNDe18TERFQqFQEBAUyfPl07josyNO4WL17M1q1bUavVrFu3jkGDBqFSqXSeF0P5H38ODN0bfXHoExUVxe3bt4mOjiY2NpaaNWvy66+/GpyXDCl6nywtLZk2bRoTJkzgyJEjuLu7a9ujbx4VQgghhBBCCCGEEKKQibk5lRs00PknRHkr98WnhIQEVCoVDf43YIcMGcLZs2fJzs4GwNnZGVtbWwDc3d05ffo0ycnJ/PHHHwwdOhSA1q1bU7NmTc6cOYNCoaCgoIC+ffsCUK1aNY4fP46DgwMAr7/+Ojdu3ChTOxQKBS1bttSWYYhKpcLa2hqlUkmdOnVwcXEBwMHBQefH9pLiunHjBikpKfTv3x+AZs2a0bRpU06dOlUsrrKoVKkSgwcPBqBt27akpqYCEBkZyeDBg7GyskKpVDJs2DDtYk9Rj8dvTL74+Hjatm1Ls2bNABg+fDjR0dHk5+fj7u5OYGAg5ubmmJub4+TkxPXr1wGoWbMmISEhJCUl4ejoiJ+fn7bOUaNGoVAoqF69On369NH7DqIePXpgYmKCqakpXbt25cyZMwbHwcCBAwkJCQEevZMnPz8fJycnnTJjY2N55513ALCysqJnz54kJCQYLPfnn3+mV69emJqaYmVlRffu3Y27YY9RqVTY2dkB0LRpU+39K42xY78sFAoFvXr1QqFQGByvADY2NtpdNA4ODjg7O2Nubo6FhQU1atTg9u3bxcr//vvveeONNwDo0KGDTnstLS3p1q0bAE2aNCmxHwyNO3t7e9577z0++ugjfvzxR6ZOnVqm/CXNA/rujaE4SmJnZ8eVK1eIjY3lwYMHzJ07F5VKVer8qE/R+5ScnExycrJ2YXPs2LFs2LDB4DwqhBBCCCGEEEIIIUShak5O9PvjD51/QpS3cj92T61WU716de1nc3NzLC0tUavVADrHgFWtWpWsrCzUajV5eXm4urpqr+Xk5JCenk716tWxsbHBxOTROllBQQFffPEF8fHxAGRmZhbb3WFMO6pVq2ZUPJaWltq/lUql9rNSqdR5/4q+uGxsbFAoFNprNjY2pKenU79+fZ24yqJq1arav01MTLTtUKvVREVFsXPnTuBRXxUuiD2uaPzG5FOr1Zw4cULnWLTKlSuTnp6OiYkJH3/8MVeuXEGhUHDz5k1GjRoFwJo1a9iwYQOenp5YWVnh6+uLi4sLarWayZMno1QqgUfHlD2+O6mQjY2NTuw3btwwOA48PDzw8fHh+vXr2l1PRe9BYTxF+8DGxobffvvNYLmZmZk6bSm8l2VVpUoV7d8KhcLo9w0ZO/bLqvA5KW286nsWCj+X9D6iyMhIduzYwcOHD3n48KFOGn3juChD487Ozo633noLf39/3n//fSwsLMqUH4rPA/rujaE4StKuXTt8fX358ssvmT59Ov369cPX17fUecmQovfJyspK+72ZmRlmZmZcvXpV7zwqhBBCCCGEEEIIIUSh7MREfpk9W+e7zvv3P6fWiBdVuS8+2dracuLECe3nnJwccnJytO/fuXPnjvZaZmYm1apVo0aNGlSuXJm4uLhi5SUmJur8GH7w4EHCw8P5/vvvsba2JigoiMDAwDK34/HFiL+rpLhsbW3JzMykoKBAu8iUkZHx1NpgZ2fH5MmTGTt2bKlpi9ZtTD47Ozs6d+7M5s2bi13z8fEBYM+ePSiVSubOnau9Zmtry8KFC1m4cCGxsbFMmTKFkydPYmdnx4YNG2jRokWpbc3IyND+nZWVRbVq1QyOA0tLSzw8PDh06BDh4eGsXLmyWJm2trakp6dTr149bR01atQwWK6NjQ1ZWVnaMoxZMChPxo79J1XaeC2r1NRUZs2aRXBwME2aNCElJUW708lYhsYdwPr16xk8eDCBgYF4enpSq1atMuU35hl80jj69OlDnz59yMrKYsqUKezatcvgvGRiYoJGo9Feu3fvXonlPn6f7t+/z40bNwzOo0IIIYQQQgghhBBCFHqQkUFyUNDzboZ4wZX7sXtdunTh9OnTXLt2DYDdu3fj7Oys3eVw8uRJ7ZFVhw4don379tSuXZv69etrj0pTq9VMmzatxB9fMzIyqFu3LtbW1mRmZhIYGMhff/0FPHovSnZ2NhqNptR2FBUcHMyff/75t+IuKS4HBwfs7e354YcfgEdHwCUlJdGuXbti+c3MzMjPz9fGUlTRuAzx8PAgKCiIu3fvAhAQEGDU4oQx+VxcXDh16pS2P3/55RcWL14MPFp4a968OUqlkosXL3Ls2DH++usvcnJyePPNN0lJSQGgcePG2h0jHh4e7Nq1C41GwwdWr4kAACAASURBVIMHD1i+fDnnzp0rsX2hoaHk5uaSl5dHVFQU7du3NzgOAAYMGMDOnTvJzc2lVatWxcp0d3dn9+7dwKPFwrCwMNzc3AyW27ZtW8LCwnjw4AHp6el6jwn8u/SNhdJi/rvKMl6NkZWVRZUqVahXrx75+fls376dgoICcnNzjS7D0Li7ePEiERER2vcxFX5f9HkxlP9pxrF9+3bWrVuHRqPBysqKl19+mYKCAoPz0ssvv8zly5cByM3NJSYmpsSy7e3tsbe3Z////jfKrl27WLZsWZnmUSGEEEIIIYQQQgghhHiayn3xqXbt2qxYsQIvLy88PDyIjIzE399fe71Lly4sXryYbt26kZSUxOTJkwFYu3Yt3333He7u7gwdOpT27dvrHIFVqF+/fqSnp+Ph4cEHH3zAjBkz+O9//8unn36Kk5MTt27dwsXFpdR2FLV8+XLtj8FPqqS4FAoF69atY8eOHXTv3p1Zs2axbt06naPbCllZWdGxY0e6du3K2bNnda4VjcuQ7t2706tXLwYNGoSrqyvh4eGl5jE2X40aNfD39+eDDz7A3d0dHx8f7TF548aNY9euXfTq1YutW7cyb948AgICOHr0KJ6enowcORJXV1fef/99/P39MTExYerUqeTl5eHh4YGHhwcPHjzglVdeKbF9zs7OeHp64uHhQdu2benRo4fBcVB4P3Jzc/Ue5Tdjxgyys7Px8PDgrbfewtPTk44dOxosd9iwYbz00kt06dKF9957jz59+vDw4cNS+7es9I2F0mL+u8oyXo3RrFkzXF1d6dGjB4MHD6Zbt260a9dOeySjMfSNu4KCAnx8fJgzZw4WFha8++67XLlyhYiICJ3nxdC4fZpxDBgwgPPnz9O1a1dcXV25d+8enp6eBuelHj16YGFhwYABA5g6dSrdunUr8Xi/wvu0c+dOOnToQGhoKEuWLAGMn0eFEEIIIYQQQgghhBDiaVJcuXLF8HaacrRt2zYuX76Mn5/fs6rymXhR46oIvLy86NOnD2+88UaZ8/bs2ZMNGzbQrFmzp9AyIYQQQvxTNGrUCADFKcO7gTWqk5JO0kk6SSfpJJ2kk3SSrgzpjFXR45B+KTmdsSp6HC9Kuj2lvH7i7f+dulXR4zCWjNNnk86Qct/5VJrSjo77v+pFjasieJK+DQwMpGbNmrLwJIQQQgghhBBCCCGEEEI8Y6bPuwFClLehQ4eSmZnJli1bnndThBBCCCGEEEIIIYQQQoh/nGe6+DR27NhnWd0z86LGVRFs3ry5zHl27979FFoihBBCCCGEEEIIIYQQQghjPPNj94QQQgghhBBCCCGEEEIIIcSLSxafhBBCCCGEEEIIIYQQQgghRLmRxSchhBBCCCGEEEIIIYQQQghRbhQajUbzvBshhBBCCCGEEEIIIYQQQgghXgyy80kIIYQQQgghhBBCCCGEEEKUG9OrV68+7zYIIYQQQghRYTVq1AgAxal2BtNpVCclnaSTdJJO0km6YumM9aLEYayKHkdFvx8vSrzGquj9XNH7pbzLq+j1vijpjPVPux/PS0Xvl4p4P2TnkxBCCCGEEEIIIYQQQgghhCg3T2XxKTExkXfffZcOHTrg7OzMoEGDSEhIeBpV6Xj48CEHDhx4amU3btwYR0dHHB0dee2113jvvfc4e/asUfmPHDlCSkrKE9dfNP8XX3zB3Llzn7isoorG1aJFC9q2bcuYMWO4du1auZRfSK1WM3bsWHr37q3zfWZmJpMnT8bd3R13d3f+/e9/67Tt448/pkmTJqSnp+vk27BhAx07dkSlUjF16lTu3bsHQHZ2NtOmTUOlUvH666+zYcMGbZ7k5GQ8PT1p1aoV/fr149y5c6W2T59z587h7u5earpr165x8mTpq8BeXl4EBQWVeM3JyelvjZ1C5TlunsS+ffto06YN/v7+T5R/27Zt2vYX7dfnHZcQQgghhBBCCCGEEEIIXU9l8WnSpEn06tWLY8eOcfz4cSZMmMCkSZO4c+fO06hO69KlS3p/wC8v0dHRXLx4kdDQUNq3b8/o0aM5depUqfm++eYbUlNTn7jeovlHjhzJvHnznriskkRHR3PhwgWOHj1K3bp1Wbx4cbmVfffuXUaMGMErr7xS7NqaNWt46aWXiI6O5sCBA+zcuZOffvoJgH/9619YWlpiYqI7TENDQwkODiYoKIi4uDgyMjLYunUrAKtXr6agoIAjR44QGBjIt99+y/HjxwGYMWMGbm5unD59Gk9PT77++utS26ePo6Mje/fuLTVddHS0UYtPz8LTGDdlERkZibe3N7NmzfrbZRXt1+cdlxBCCCGEEEIIIYQQQghdpuVd4IMHD7h+/ToeHh7aRYO+ffvSokULrKyseOONN/Dy8qJPnz4AREVFsXbtWvz9/Zk5cybdunXj+PHjZGVlsWTJErZt28a1a9fo0KEDixcv5ty5c8ycOZPOnTtz7tw5srKyWLRoES1btmTixIlkZWUxatQoduzYwaFDh/jss894+PAhdnZ2LFu2jMaNG/P1119z4cIF7t+/T2JiIrVr12bEiBF8++23/PHHH0yZMoXBgwcbjLNmzZpMmDCB3Nxc/P39+f777wHYuHEjQUFBKBQKOnXqxLx589i2bRtxcXEkJiYyZ84cevXqVWK6SpUqkZiYyLx580hJSaFevXqsWrWKQ4cO6eS/fv06V69eZcWKFVy6dAlfX1/S09MxNzdnxowZeHh4cPHiRWbOnIm7uzsnTpwgLS2NhQsX0rVrV4NxWVpa0rt3b+3i05kzZ5g1axYRERHFPufl5TFnzhxOnTqFg4MDzs7O3Lx5k1WrVumUqVAo2Lx5M3/++ScxMTE61y5fvszIkSMBsLKyomXLlly5coXXX3+dKVOm0KJFCzZu3KiTp06dOqxYsYJatWoB4Orqyq+//gpA586deeWVV7CwsKBOnTq0adOGa9eu8fLLL3P9+nXGjx+PiYkJI0aMYMSIEaW2b9euXRw+fJgdO3bofH/x4kWmTp1KdHQ0X375JefPnycvL4/U1FTMzc1Zs2YN165dY+PGjZiampKdnc1HH33E8uXLiYyMpKCggA4dOrBixQpMTR89homJiQwYMIDbt2/TpUsXli9fTqVKlXTq1Tdu4uPj8fPzIy8vj0qVKjFnzhxcXV118n777bfacWNM+ps3b/LRRx+RlpZGQUEBw4YNY9KkSQbbcfPmTaZPn45araZdu3bk5ubSrVs3bt26xZEjR/jll1/IyMhgzJgxfPTRR1y9epWCggLGjRvHmDFjgEcLe3FxcdjZ2el8LnTs2DGdfq1WrZo2Li8vL1q2bMmZM2dISUnBwcGBjRs3olQqiY+PZ9GiRZiamjJ8+HD+/e9/s3v3burWrYsQQgghhBBCCCGEEEKI8lPuO5/MzMzw8PBgwoQJHDhwgLS0NAAaNGiAUqlk4MCBBAcHa9OHh4fTr18/lEoliYmJuLu7s2fPHhwdHVm0aBHr1q0jKCiI/fv3k5aWhlKp5Pfff8fNzY3du3czY8YMfHx8qFq1Kj4+PqhUKnbs2EFycjI+Pj5s3ryZyMhIBg4cyMyZMx8FbWJCfHw8S5YsISQkhEuXLhEREcHWrVtZuXIlmzZtMjrevn37cubMGe7fv09ERAQhISHs27ePw4cPk5qaSkBAABMnTqR+/fqsXr2aXr166U2n0WiYNm0aEyZM4MiRI7i7u7NgwYJi+QsVFBQwZcoURo8erV3E8/b2Jj09XdufKpWKgIAApk+fzvr160uN5969e+zfv5/u3buXmjYwMJCUlBSio6P59NNP2bt3r3YhpagqVarQoEGDEsvo2LEj4eHhPHz4kD///JPz58/Tvn17AFq0aFFinlatWqFSqbSfjxw5grOzMwDdu3fXLiZkZ2dz7tw5XnvtNX777TcaNGiAr68vXbp0YcSIEfz++++ltq9r165MmzbNYD8olUri4uJYtmwZ+/fvp2XLlqxfv56OHTsyYMAAxo4dy8yZM4mOjiYhIYGwsDAiIiK4cOECoaGh2nISEhLYtWsXMTExnDt3jsOHD+vUo2/cACxfvpylS5cSHR3NmjVrCA8PN9hmY9J/+eWXuLi4EB0dTUhICOfPnyc7O9tgO/z9/Wnfvj3R0dEMGjSIiIgIlEolH3zwAR07duSjjz5i2rRpfP7559jZ2RETE8POnTtZuXIlt27dMtjmQo/36+P34ujRo2zevJmQkBCuXbvGsWPHyM/PZ86cOSxcuJCwsDAyMzNRq9UolUqj6hRCCCGEEEIIIYQQQghhvKdy7N769esZMmQI3333Hd26daN///7aH9L79+9PfHw82dnZ5OfnEx0dTd++fQGwsbHRLioU7qQxNzfHwsKCGjVqcPv2bQCqVq2Ki4sLAG5ubiQlJRV7J1BCQgIqlUq7qDBkyBDOnj1LdnY2ACqVCmtra5RKJXXq1NGW5+DgoF0wM0atWrXIz88nJyeHyMhIBg8ejJWVFUqlkmHDhpX4o76+dMnJySQnJ9OzZ08Axo4dq/POosfduHGDlJQU+vfvD0CzZs1o2rSp9hhAS0tLunXrBkCTJk0MHvvXu3dvnJycaNu2Lb/++ivDhg0rNfaff/6ZPn36YGpqSq1atbR1lcW4ceO4cuUKzs7OuLi40K9fP5o2bWp0/s8++4z79+8zdOhQne9zc3OZOnUq77zzDk2aNCE7O5szZ84wYMAA4uLi6NGjB1OnTi21/Lp16+osdOnj7OyMra0tAO7u7pw+fbpYGnd3dwIDAzE3N8fc3BwnJyeuX7+uvd6vXz+srKywtLSke/funDlzRie/ofFVs2ZNQkJCSEpKwtHRET8/P4PtNSa9nZ0dR48e5ezZs1SpUoWNGzdiZWVlsB3Hjx9nwIABAHTq1EnvrqJZs2axaNEi4FEf29vbc/PmTYNtNlb37t0xMzNDqVTSsGFDUlNTSUpKIicnR7vzz9PTk4cPH5ZLfUIIIYQQQgghhBBCCCF0lfuxe/Bo99Po0aMZPXo0ubm5hIWFMWvWLGrXro2TkxNt2rQhLCyMOnXqULduXRwcHEhMTMTS0lJbhlKpLPa5oKAApVKJtbX1/w/A1BQLCwuysrJ02qBWq6levbr2s7m5OZaWlqjVagC9dRXWY6zU1FQqVaqEtbU1arWaqKgodu7cCTzamVS4IPF420pKp1arsbKy0ulHMzMzvXWr1WpsbGxQKBTa72xsbEhPT6d+/fpUrVpV+72JiYnBuEJDQ6lduzb5+fkcOXKEYcOGERISYjD2O3fuYGNjo/1sb2/PjRs3DOZ53OzZs3F1dWXatGncvXuX8ePHs3///lKPPdRoNCxZsoSkpCS2bNmi816orKws3n//fe3RffDoSL8mTZrQsWNHAEaPHs3KlStJT0/npZdeKlObS1J0TFatWrXYeARIT0/n448/5sqVKygUCm7evMmoUaO014uOFWtra/773//q5Dc0vtasWcOGDRvw9PTEysoKX19f7YJqSYxJP3HiRJRKJXPnzkWtVjNx4kTGjRtnsB0ljYmSXLx4kbVr15Keno6JiQkpKSloNBq97S2LKlWqaP82MTEhPz+fjIwMqlWrpv3ezs6u2JGGQgghhBBCCCGEEEIIIcpHuS8+JScnk5iYqH1/jIWFBW+88QZBQUFcvHgRJycnBg4cyMGDB2nQoAH9+vUrcx2ZmZloNBoUCgV5eXnk5OTo/LAMj37IP3HihPZzTk4OOTk51KhR42/F97jg4GBcXFxQKBTY2dkxefJkxo4dazCPvnT//e9/yczMpKCgABMTE+7fv8+NGzdo3LhxieXY2trqpAfIyMj4WzEqlUq6du1KzZo1OX36NHXq1NFZFLh37572bysrK+1OMoCUlJQy1xcXF4e3tzcmJiZYW1vj5ubGzz//XOri04oVK1Cr1WzZskVngS4vL4/33nuPfv36ad8hBI92tGVkZGg/azQaNBqNwcW9srhz547278zMzGLjEWD16tUA7NmzR7uoU1RmZqb278J3GRVlaHzZ2tqycOFCFi5cSGxsLFOmTOHkyZM6i3JlTW9qaoqXlxdeXl5cu3aNESNG0L59e4PtKBwTtWvXBvSPienTpzNhwgTtjjV3d3ftNaVSqR1z9+/f58GDByWWURaPj9U///yT+/fv/+1yhRBCCCGEEEIIIYQQQhRX7sfuFR53Fh4eTkFBARqNhuPHj/PLL7/g5OQEPDri7fTp04SGhmqP3CuLnJwc7btyDh8+TNOmTalWrRqmpqZkZ2ej0Wjo0qULp0+f5tq1awDs3r0bZ2dnnd1Af8e9e/fYvn0733zzDd7e3gB4eHgQFBTE3bt3AQgICCAwMBB4tIupcDeMvnT29vbY29uzf/9+AHbt2sWyZcuK5S/k4OCAvb09P/zwAwAXLlwgKSmJdu3aGWx7dnY2e/bsKfFaQUEBx44dIykpiebNm1OrVi3S0tK0dRd9D1Hr1q2JiIigoKCA1NRUoqKijOy9/69Zs2ZEREQAjxaOjh49SvPmzQ3m+fnnn0lISGD16tXFFo+2bNlC8+bNdRaeAF555RWsra3Zs2cPGo2Gb775hubNm+vsNCvJzZs3tccYGnLy5Entrq9Dhw5p31tlamqq7bs7d+7QvHlzlEolFy9e5NixY/z111/aMkJDQ8nLyyMvL4/IyEhtGYX0jZucnBzefPNN7UJP48aNDe5yMzb91KlT+fHHHwGoXbs2VatWRaPRGBznrVu3JiwsDIDY2Nhiu7cK3blzB0dHRwCCgoJQq9Xahc1atWpx+fJlbV8W3dlXqGi/GqN+/frk5+dz/PhxALZv317i+8mEEEIIIYQQQgghhBBC/H3l/utro0aN2LhxI+vWrWPu3LkoFArq1avHihUrtD8229jY8Prrr5Oenq73WC5D6tWrx5kzZ/j0009RKBR8/PHHADg5OXHr1i1cXFw4cuQIK1aswMvLiwcPHmBvb4+/v//fjq9wh0alSpVo06YN3377rTau7t27k5iYyKBBg3j48CENGzbUtq1nz55MmTIFb29vxowZU2I6hUKh7Td/f38aNmzIqlWriuUvVJje19eXzz77DAsLC9atW4eNjQ23bt3SG0NaWho+Pj68/fbbxeJSKpXUq1ePlStX0qhRIwCGDx/OkCFDqFu3Lm5ubhw7dkz7/c8//0zXrl1p1qwZgwcP1tldVCg8PFz7fqUHDx7g6OhIo0aNOHjwICtXrmThwoUEBASgUChwcXFh5MiRZGRk0KlTJwDy8/Pp3LkzAPHx8ezdu5fff/+dVq1aaeto1qwZBw4cYM+ePdy+fZu9e/dqr40YMQJfX19Wr17NrFmz8PPzo0mTJnz66aelti8uLo7Dhw+zY8cOQ8OCLl26sHjxYi5fvkydOnVYt24dAK6urkyePJmUlBTGjRvHzJkz2bNnD61bt2bevHnMnj0blUpFfn4+nTp1Yvjw4aSlpdG1a1d69OihU4e+8WVpaYmnpycjR44kPz8fCwsL/P399e56Mjb9+PHjmT9/PgsWLMDExIRBgwbRqlUrWrVqpXecz549m2nTphEUFESnTp3o3LlziYtH06ZNY9KkSVStWpWhQ4cyZswY5s2bx549e/D29mb+/PnUrl0bV1dXatSoQX5+vk7+ov3aokULg/cGHh27uWTJEmbMmIG1tTUjR47E0tKyxLYJIYQQQgghhBBCCCGE+HsUV65cKZ8XrZSRj48Pjo6OjBw5skz5Lly4wL/+9S+io6OfUstEWRQ98m/dunXk5OQwZ86c59yqZ2vbtm1cvnwZPz+/592UCqHomPD09GTChAnaYzifp8Kj/BQKBfn5+Tg6OnL27Fmd978JIYQQJSn8DzmKU4Z3l2tUJyWdpJN0kk7SSbpi6Yz1osRhrIoeR0W/Hy9KvMaq6P1c0fulvMur6PW+KOmM9U+7H89LRe+Xing/yv3YPWMkJiby448/8sYbbzyP6kU5iYyMpEePHuTk5HDv3j3CwsJ47bXXnneznoui78X6J1uxYgUzZsxAo9Fw9epVLly4wKuvvvq8mwXAoEGD+O6774BHR/01bdpUFp6EEEIIIYQQQgghhBDiKXjmLz3x9/cnMDCQZcuWlfq+HVGxubm5ERcXR48ePTAxMaFv377FjooT/yzvv/8+3t7edOnShUqVKrFkyRLs7Oyed7MAWLBgAfPnz2fTpk3Y2tqWyzGcQgghhBBCCCGEEEIIIYp75otPs2bNYtasWU+cv0WLFnLkXgWhVCpZsmQJS5Ysed5Nea7Gjh37vJtQYdSoUYNvvvnmeTejRCqVikOHDj3vZgghhBBCCCGEEEIIIcQL77kcuyeEEEIIIYQQQgghhBBCCCFeTLL4JIQQQgghhBBCCCGEEEIIIcqNLD4JIYQQQgghhBBCCCGEEEKIcqPQaDSa590IIYQQQgghhBBCCCGEEEII8WKQnU9CCCGEEEIIIYQQQgghhBCi3JhevXr1ebdBCCGEEEKICqtRo0YAKE61M5hOozop6SSdpJN0kk7SFUtnrBclDmNV9Dgq+v14UeI1VkXv54reL+VdXkWv90VJZ6x/2v14Xip6v1TE+yE7n4QQQgghhBBCCCGEEEIIIUS5eSqLT4mJibz77rt06NABZ2dnBg0aREJCwtOoSsfDhw85cODAUyu7cePGODo64ujoyGuvvcZ7773H2bNnjcp/5MgRUlJSnrj+ovm/+OIL5s6d+8RlPa5z586cPFn6SuWCBQto06YNERER5VJvYGBguZTztMorqzt37uDs7IyPj4/2u8WLF+Pr66uTbsSIEcW+Gz58ON999x0AGzduxM3NDWdnZzp27Mj8+fPJy8szuh3nzp3D3d39b0Siy8nJqdSx+zSfPX3tKe84jfGkcW7btk37zLZq1apMc0HROJ9lPwshhBBCCCGEEEIIIcSTeiqLT5MmTaJXr14cO3aM48ePM2HCBCZNmsSdO3eeRnValy5dIigo6KnWER0dzcWLFwkNDaV9+/aMHj2aU6dOlZrvm2++ITU19YnrLZp/5MiRzJs374nLelIHDx5k9+7d9OjRQ+f7goKCMpel0Wjw9/cv8Vp5l6dPfn5+mesxJDg4mDFjxnD06FHtYlGXLl04evSoNk1eXh5JSUkcP35c+11ubi7/+c9/cHFxITg4mNDQUL7//ntOnDjBwYMH+f3331m3bp3R7XB0dGTv3r3lF5gRnsWz97inEWdpY6I84oyNjaVWrVpGpy8a5/PoZyGEEEIIIYQQQgghhCircl98evDgAdevX8fDwwMTExMUCgV9+/YlJCQEKysr3njjDQ4fPqxNHxUVxYABA7h48SL9+/dn1apVvP322/Tq1Yuff/4ZLy8vevfuzcKFC4FHuwB69+7N0qVLGTp0KL179+ann37i7t27TJw4kVOnTjFq1CgADh06RO/evenevTvDhw/nypUrAHz99dfMmjWLadOm0bdvX8aPH090dDTjxo3Dzc2N/fv3lxpnzZo1mTBhAuPHj9dZ9Ni4cSM9evSgZ8+eLFq0iPv377Nlyxbi4uLw9vYmLCxMbzp4tGvsrbfeonPnzrzzzjvcuHGjWP5vv/0WPz8/4NGP0W+//TYeHh707duXqKgoAG1/rl69muHDh+Ph4UFcXFypcXl5ebFhwwbGjx9P37598fLyIj8/Hy8vL7KysvjXv/5FdHQ0EyZM4JNPPqFTp07ExcWRkZHB+PHjcXNzo1u3bmzfvh2A+/fvM2PGDNzc3OjatSvTp08nNzeXSZMmoVar6d27N8nJycXKGzt2LD/88IO2XUU/h4aG0qNHDzp37syMGTPIy8srVp6+/OfPn6d///54e3szevRoAGJiYujbty89evRg3LhxpKWlldpPJdm/fz8DBw6kU6dO2t1hHTp0IDk5meTkZABOnz5Ny5YtUSgU3Lp1C4BTp05Ru3Zt6taty++//07r1q2pWbMmAC+99BIbNmzg/fffL1afvr69ePEib731FmD8WDfU30Xt27eP7t274+rqiqenJ8nJySU+e8b0qb5nGR4tQK5evZru3bvTvXt3vL29yc7O1slfNE6NRsPq1avp1q0bLi4ufPnll2RmZvLqq6/y559/avP4+fmxbNkynXJKGhPlGefjXF1duXXrltFzXmGcJdUvhBBCCCGEEEIIIYQQFVG5Lz6ZmZnh4eHBhAkTOHDggPbH2AYNGqBUKhk4cCDBwcHa9OHh4fTr1w+lUkliYiLu7u7s2bMHR0dHFi1axLp16wgKCmL//v2kpaWhVCr5/fffcXNzY/fu3cyYMQMfHx+qVq2Kj48PKpWKHTt2kJycjI+PD5s3byYyMpKBAwcyc+bMR0GbmBAfH8+SJUsICQnh0qVLREREsHXrVlauXMmmTZuMjrdv376cOXOG+/fvExERQUhICPv27ePw4cOkpqYSEBDAxIkTqV+/PqtXr6ZXr15602k0GqZNm8aECRM4cuQI7u7uLFiwoFj+QgUFBUyZMoXRo0cTFRXF2rVr8fb2Jj09XdufKpWKgIAApk+fzvr160uNR6lUcvToUTZv3kxISAjXrl3j2LFjbN68mSpVqvDNN9/g7u6OqakpJ06cIDo6GldXVz7//HPs7OyIiYlh586drFy5klu3bhEVFcXt27eJjo4mNjaWmjVr8uuvv+Ln54eZmRmhoaHY29sXK0+fW7duMX/+fLZt20ZcXBxZWVls27atWHn6mJmZkZSUhLu7Ozt37uT27dt4e3uzdu1aIiIi6NSpk/ZH/7K4fPkyZmZmODg4MGjQIO2iTpUqVXBycuLYsWMAHDt2DJVKhUql0i60/PTTT7i4uADg4eFBcHAwq1at4syZMzx8+BBbW1uqV69erE59fVtUeY71O3fu4Ovry9atW4mNjaVhw4Zs2rSp2LNnbJ/qe5bh0cJxbGwswcHBREREoNFoDLY1LCyMI0eOEBYWxg8//MC2jZltDAAAIABJREFUbdu4evUqnTt35uDBg9p0hfNNUY+PifKOUx9j57xCj9cvhBBCCCGEEEIIIYQQFdVTOXZv/fr1DBkyhO+++45u3brRv39/7W6n/v37Ex8fT3Z2Nvn5+URHR9O3b18AbGxsUKlUADg4OODs7Iy5uTkWFhbUqFGD27dvA49+hC38sd7NzY2kpCTS09N12pCQkIBKpaJBgwYADBkyhLNnz2p3T6hUKqytrVEqldSpU0dbnoODQ5l2vtSqVYv8/HxycnKIjIxk8ODBWFlZoVQqGTZsGOHh4cXy6EtXuEOmZ8+ewKPdJxs2bNBb940bN0hJSaF///4ANGvWjKZNm2qPAbS0tKRbt24ANGnSxOhj/7p3746ZmRlKpZKGDRuWmE+hUODm5oaFhQUAs2bNYtGiRQDUrVsXe3t7bt68iZ2dHVeuXCE2NpYHDx4wd+5c7T02VJ4+CQkJvPbaa9StWxelUslnn33G2LFjjYqrsJ6CggLtmIuPj6dt27Y0a9YMePTupejo6DIfyRcYGMigQYMAaNeuHX/88Yd2x42Li4v26L2ffvqJ119/nfbt25e4+NSmTRv27t1Leno6H374ISqVitmzZ5ORkVGsTmP7trzGerVq1Th+/DgODg4AvP7669y4caNYurL0qb5nOSYmhgEDBlC5cmUUCgWDBw82+N646Oho+vXrh4WFBdWqVSM0NJTWrVszcOBAQkJCALhw4QL5+fk4OTnp5H18TDyNOPUxds4TQgghhBBCCCGEEEKI/0tMn0ahZmZmjB49mtGjR5Obm0tYWBizZs2idu3aODk50aZNG8LCwqhTpw5169bFwcGBxMRELC0ttWUolcpinwsKClAqlVhbW///AExNsbCwICsrS6cNarVaZ7eIubk5lpaWqNVqAL11FdZjrNTUVCpVqoS1tTVqtZqoqCh27twJPNqZZGtrWyyPvnRqtRorKyudfjQzM9Nbt1qtxsbGBoVCof3OxsaG9PT0/8fenYdVVa0PHP8eDggODIaYouKUFt5SFGdJZVBG56HkOqQWouaEV0qhnLXwplJqll2nFEpFEFKZJYdMTLlKyRWHHAqUAgVEEOSc3x887B/TQVRMr/f9/OXe513z2vQ8e7XWpmXLljRo0EC5r6enV+N21a9fv1w6XS/TzczMlH+npKSwdu1asrKy0NPTIz09Ha1WS9euXfH39+err75izpw5uLu74+/v/8D8dMnMzCw3/qXjlpeXV6O2QUkf6enpKfmdPHmSvn37Kr/Xq1ePrKwsLCwslHvbtm1Tdo7NmDGDCRMmKL8VFxcTHh5OXl6ecgTjvXv32LdvH5MnT8bOzo6goCDy8/P59ddf6dChAxYWFnz66afk5eXxyy+/0LNnTyW/l19+mZUrVwJw6dIlAgICmDt3Lps3by7Xjpr2bW3NdY1Gw6ZNmzhy5AgA2dnZVe4yq2mfAjqf5YrPr5mZWaUF5oplls2r9DlydHTEz8+Pa9euKbueyj4vpcrOidpo59KlS5UFxy1btuisd03+5gkhhBBCCCGEEEIIIcR/m1pffEpLSyM1NVU5Os3IyIghQ4YQFhZGSkoKNjY2DB48mP3799OqVatKR2DVRHZ2NlqtFpVKxb1798jPz6+0cGFubs7JkyeV6/z8fPLz82nUqNFjta+i8PBw7OzsUKlUWFhYMH369AfuxNEV9/vvv5OdnY1Go0FPT4/CwkKuX79O27Ztq8zH3Ny8XDzArVu3ar2NupR9iT9nzhy8vLwYPXo0AA4ODspvrq6uuLq6kpOTw8yZMwkKCmLYsGHV5qenp4dWq1Wu79y5A5S0uXTHEJTMhby8vEo7pnSlr1iOhYUFffr0YePGjdW2ddSoUcrOmLKLc1CyA+bll18ut8jwyy+/8N577zF58mRee+01CgoK2L9/P506dUKtVtOkSROg5PtVHTp0UBZO4uLieO2115RvPrVt2xZvb2+mTp1aZb2q6tvu3btX25aqVNdfpfbv3090dDTffvstJiYmhIWFERISUimupn0Kup9lc3PzcotNWVlZ1c7rivHp6enUrVsXMzMzHB0dOXDgANHR0Xz88cdVpi87J2qjnUuXLlW+42ZmZsZPP/30wL4QQgghhBBCCCGEEEKI50WtH7tXUFDArFmziI6ORqPRoNVqSUxM5OzZs8pxVy4uLpw+fZrIyEjlhf7DyM/PJzIyEoCDBw/Srl07zMzM0NfXJzc3F61Wy+uvv87p06e5cuUKALt27aJbt27ldgM9jry8PLZt28b27dvx8fEBSnZZhIWFKS/ug4ODlZfWBgYGyu4sXXGWlpZYWloq3wsKCgpi2bJlldKXsrKywtLSku+++w4oOVbs6tWrdO3atdq65+bmsnv37troBsXt27extrYGICwsjMzMTKWPAgMD0Wq1GBsb06RJEzQaDQYGBhQXF3P37t0q82vSpAkXLlwA4MqVK6SkpADQp08fTp8+zYULFyguLsbPz4/w8PBK+elKX5GdnR2nTp1S5snZs2dZvHhxpbh69ephYWGBhYUF9erVK/fb3r17GTBgQLl7f/vb38jJyeE///kParWaXr16sXXr1nI7nHr06MHWrVvp06ePcu/gwYP4+/srx+Hl5OQQFBRU5Zjq6ttHUZP+unXrFs2bN8fExITs7GxCQkKU/i777NW0T0H3s+zg4EBERAT5+floNBr27NlT7bfA+vfvz759+8jNzSU7OxtPT08uX74MwKBBg9i5cycFBQW89tprD+yL2minqampMl+q2734sMqWL4QQQgghhBBCCCGEEM+qWl98atOmDevXr2fTpk1069aNbt26sXLlSlauXKksTpiamtKzZ0/l20APq0WLFiQlJeHk5MT69etZvnw5ADY2Nty8eRM7OzuaNm3KypUr8fb2xtHRkdjYWOVItMfh4OCAtbU1vXv3Ji4ujh07dijtcnJywtnZmaFDh9K/f3+io6OV79kMHDiQmTNnsm3bNp1xKpWKwMBAdu7cSY8ePYiMjGTJkiWV0pcqjf/6669xcnLC19eXwMBATE1Nq21DRkYGfn5+j90XZc2ePZupU6fi4uJCVlYWEyZMYMGCBXTr1o2ff/6Zvn370r9/f/Ly8vD09MTY2JhevXrRt29fzpw5Uym/CRMmEBsby6hRo/jiiy/o27cvxcXFNGnShOXLl+Pt7U2fPn0wNDRk4sSJlfLTlb6iRo0aERAQwLRp03BwcMDPz++hduPl5OQQFxeHk5NTpd8GDBigLCTa2dmRkpJCjx49lN979OjBuXPnyi0+LV26lGbNmjF8+HA6d+6Mm5sbderUURYhyxo0aFCVffsoatJf7u7uZGVl4ejoyLRp05g7dy6///47n3zySbln72H6VNez7Orqir29PYMGDWLAgAEYGxszZcoUnfV3cXHB3t4eV1dX3NzcGDNmDF26dAHg9ddfp6CgoMbj+iTaWVvKli+EEEIIIYQQQgghhBDPKtWlS5eeyv9C7+fnh7W1NWPHjn2odOfOnePdd98lPj7+CdVMCPFX+Cuf5YEDB7Ju3Trat2//xMsSQgjx/GnTpg0AqlPV7y7X2v4kcRIncRIncRJXKa6mnpd21NSz3o5nfTyel/bW1LPez896v9R2fs96uc9LXE39r43H0/Ks98uzOB61vvOpJlJTU/n+++8ZMmTI0yheCPE/JCQkhMaNG8vCkxBCCCGEEEIIIYQQQvxF9P/qAgMCAggJCWHZsmUYGxv/1cULIf6HjB49muzsbL744ounXRUhhBBCCCGEEEIIIYT4n/GXLz75+vri6+v7yOk7dOggR+4J8Rz4K57lXbt2PdH8hRBCCCGEEEIIIYQQQlT2VI7dE0IIIYQQQgghhBBCCCGEEM8nWXwSQgghhBBCCCGEEEIIIYQQtUYWn4QQQgghhBBCCCGEEEIIIUStUWm1Wu3TroQQQgghhBBCCCGEEEIIIYR4PsjOJyGEEEIIIYQQQgghhBBCCFFr9C9fvvy06yCEEEIIIcQzq02bNgCoTnWtNk5r+5PESZzESZzESVyluJp6XtpRU896O5718Xhe2ltTz3o/P+v9Utv5PevlPi9xNfW/Nh5Py7PeL8/ieMjOJyGEEEIIIYQQQgghhBBCCFFrnsjiU2pqKm+99RY9evSgW7duDB06lKNHjz6Josq5f/8++/bte2J5t23bFmtra6ytrenSpQtvv/02Z86cqVH6Y8eOkZ6e/sjll02/adMm5s+f/8h5VdSnTx9++unBK5UffvghnTp1IiYmplbKDQkJqZV8nlR+D+vy5ctMnTqVLl26YG1tjbOzM1u3bqXsZ9Vyc3NZuHAhvXr1wtramoEDB7Jt2zbl99WrV9O+fXtlnnXv3h0/Pz/u3r37xOt/5cqVGs2DZ9m5c+dwcHB46HQ2NjY6n8+kpCRGjBhBUVHR41YPePR+fpT5vXfvXjp16kRAQMBDp31WRUZG8s477zztagghhBBCCCGEEEIIIarxRBafpk6dirOzM8ePHycxMREvLy+mTp3K7du3n0RxivPnzxMWFvZEy4iPjyclJYXIyEi6d+/O+PHjOXXq1APTbd++nRs3bjxyuWXTjx07lgULFjxyXo9q//797Nq1iwEDBpS7r9FoHjovrVar84V4beenS3Fx8UOXo0taWhqjR4+mdevWhIeH8+9//5vFixezfft2Nm3apJQ3adIkrly5wtatW0lKSmLZsmVs27aN9evXK3mNGDGClJQUUlJS2L9/P7/++iuffvpprdVVl/j4+P/6xafaVlxcjK+vLytXrsTAwEC5/yhztNSj9POjzG+A2NhYfHx88PX1rVH847Trr+Li4kKdOnWe+mKzEEIIIYQQQgghhBBCt1pffCoqKuLatWs4Ojqip6eHSqXCzc2NiIgIjI2NGTJkCAcPHlTi4+LiGDRoECkpKXh4eLBq1SpGjRqFs7MzJ06cwNvbGxcXFxYuXAhAcnIyLi4uLF26lNGjR+Pi4sKPP/7InTt3mDJlCqdOnWLcuHEAHDhwABcXF5ycnBgzZgyXLl0CYOvWrfj6+jJ79mzc3NyYPHky8fHxTJo0CXt7e0JDQx/YzsaNG+Pl5cXkyZPLvRRev349AwYMYODAgSxatIjCwkK++OILDh8+jI+PD1FRUTrjoGTX2MiRI+nTpw9vvvkm169fr5R+x44drFixAihZcBs1ahSOjo64ubkRFxcHoPTn6tWrGTNmDI6Ojhw+fPiB7fL29mbdunVMnjwZNzc3vL29KS4uxtvbm5ycHN59913i4+Px8vLin//8J7179+bw4cPcunWLyZMnY29vT79+/ZTdPIWFhcydOxd7e3v69u3LnDlzKCgoYOrUqWRmZuLi4kJaWlql/CZOnMh3332n1KvsdWRkJAMGDKBPnz7MnTuXe/fuVcpPV/qff/4ZDw8PfHx8GD9+PACHDh3Czc2NAQMGMGnSJDIyMh7YTxV99dVXdO/eHV9fX5o3b46hoSE9e/Zk9+7d/P3vfwdK5vq1a9fYuHEjL7/8MkZGRnTv3p0NGzZgbW1dZb4WFha89dZb/PDDD5V+++233xg5ciT29va8++67TJs2TXkhb21tzR9//KHEll5rNBpWr16Nk5MTTk5O+Pj4kJuby/Hjx1m/fj1btmxh1apVQMmuGScnJ/r374+npydpaWlV1rGq/svNzcXOzo7k5GQATp06Rb9+/bh79y45OTnMmDEDOzs7nJ2dlV2Rd+7c4R//+AeOjo64uLgQFBSklPHrr7/i6enJgAEDGDJkSLkF388//xw7Ozs8PDw4dOiQcr+6/A4fPoyDgwMDBgxg3bp1qFSqKtt24MABWrduTfv27QHo1KkTn3/+Oba2tty9e1fn85eUlFRukbb0uqp+rsn8qzi/dZVb1oYNGzh27BibNm1i7dq13L59m5kzZ+Lo6MiAAQP47LPPlNiK7dI1f2r6d7oqGzduxM7OjiFDhhAcHEzfvn2BkoW11atX069fP+zs7Pjqq6+Akmdq9uzZTJ06lWHDhvHmm28qu9O8vLzYuHGjzrKEEEIIIYQQQgghhBBPV60vPhkYGODo6IiXlxf79u1TXqS2atUKtVrN4MGDCQ8PV+Kjo6Nxd3dHrVaTmpqKg4MDu3fvxtramkWLFhEYGEhYWBihoaFkZGSgVqu5ePEi9vb27Nq1i7lz5+Ln50eDBg3w8/PD1taWr7/+mrS0NPz8/Ni4cSOxsbEMHjyYefPmlTRaT48jR46wZMkSIiIiOH/+PDExMWzevJmPP/6YDRs21Li9bm5uJCUlUVhYSExMDBEREezdu5eDBw9y48YNgoODmTJlCi1btmT16tU4OzvrjNNqtcyePRsvLy+OHTuGg4MDH374YaX0pTQaDTNnzmT8+PHExcWxdu1afHx8yMrKUvrT1taW4OBg5syZU+5lsy5qtZoffviBjRs3EhERwZUrVzh+/DgbN26kfv36bN++HQcHB/T19Tl58iTx8fH079+fzz//HAsLCw4dOsTOnTv5+OOPuXnzJnFxcfzxxx/Ex8eTkJBA48aN+eWXX1ixYgUGBgZERkZiaWlZKT9dbt68yQcffMCWLVs4fPgwOTk5bNmypVJ+uhgYGHD16lUcHBzYuXMnf/zxBz4+Pqxdu5aYmBh69+5d7Qt0XU6ePFlubEqZm5tTv359JaZfv37UrVu3XMwrr7xS7VFxGo0GfX39SvcDAgLo2bMnhw4dYvr06Rw6dAi1Wl1tPQ8cOEBCQgLh4eHExMSg1WrZsGEDvXr1YtCgQUycOJF58+Zx+/Zt/P392bx5MwkJCbRu3brK50JX/xkbG/Phhx+yZMkSiouLWbJkCYsXL6ZevXqsWbOGxo0bc/ToUQICApgxYwb37t1j7dq1aLVaYmNjCQ4O5osvviAlJQWAWbNmMXjwYGJiYli6dCkzZsygsLCQixcv8uWXXxIaGkpERISywAzozK+4uJj333+fRYsWERMTQ8OGDblz506V/RUVFVVuEUlfX5/s7GxOnz6NkZGRzudPl4r9XNP5V3Z+N2nSpEblTps2jV69evGPf/yD2bNn889//hNjY2Pi4uLYu3cv4eHhJCQkVGpXvXr1dNa/pn+nK7p48SKbNm0iNDSU3bt3Exsbq8zpqKgojh07RlRUFN999x1btmwhKSkJtVrN4cOHWbZsGaGhobz66qvK37BOnTqRl5fHhQsXdNZVCCGEEEIIIYQQQgjx9DyRY/c+++wzhg8fzjfffEO/fv3w8PBQdjt5eHhw5MgRcnNzKS4uJj4+Hjc3NwBMTU2xtbUFwMrKim7dumFoaIiRkRGNGjVS/k/8Bg0aYGdnB4C9vT1Xr16t9OL16NGj2Nra0qpVKwCGDx/OmTNnyM3NBcDW1hYTExPUajXNmjVT8rOysnqonS8vvvgixcXF5OfnExsby7BhwzA2NkatVvPGG28QHR1dKY2uuLS0NNLS0hg4cCBQsltn3bp1Osu+fv066enpeHh4ANC+fXvatWun7AqpW7cu/fr1A+Cll16q8bF/Tk5OGBgYoFarad26dZXpVCoV9vb2GBkZAeDr68uiRYsAaN68OZaWlvz2229YWFhw6dIlEhISKCoqYv78+coYV5efLkePHqVLly40b94ctVrNp59+ysSJE2vUrtJyNBqNMueOHDlC586dlZ0tY8aMIT4+/qGP5MvJyaFRo0bK9Zo1a+jRowc9evTAycmpypiauHHjBps3b8be3r7SbydOnMDV1RUo2ZnSoUOHB+Z36NAhBg0aRL169VCpVAwbNqzK77GZmZmRmJiIlZUVAD179uT69euV4qrrv4EDB2Jubo63tzetWrVSFhXj4+MZPnw4ULKIkJCQgKGhIbGxsYwbNw6VSkXDhg1xdXUlJiaGtLQ0fv31V0aPHg1Ax44dady4MUlJSSQmJtK9e3csLCxQqVQMHTpUqZuu/K5cucLdu3eVnTcjR47UedzcmTNneO2115RrlUqFs7MzKpXqgc9fTTzK/HvUchMSEnjzzTcBMDY2ZuDAgcrYl23Xg9T073RZJ0+eVMapTp06DBs2TPktPj4ed3d3jIyMMDMzIzIyko4dOwLQrVs3zM3NAXBwcOD06dNKuo4dO3L27NkH1lcIIYQQQgghhBBCCPHXq7ydohYYGBgwfvx4xo8fT0FBAVFRUfj6+tK0aVNsbGzo1KkTUVFRNGvWjObNm2NlZUVqamq5HSFqtbrStUajQa1WY2Ji8v8N0NfHyMiInJyccnXIzMykYcOGyrWhoSF169YlMzMTQGdZpeXU1I0bN6hTpw4mJiZkZmYSFxfHzp07gZIdK6UvTivWraq4zMxMjI2Ny/Vj2e/MVJWPqalpuRfGpqamZGVl0bJlSxo0aKDc19PTq3G7SnfqlKbT9SLczMxM+XdKSgpr164lKysLPT090tPT0Wq1dO3aFX9/f7766ivmzJmDu7s7/v7+D8xPl8zMzHLjXzpueXl5NWoblPSRnp6ekt/JkyeVhQiAevXqkZWVhYWFhXJv27Ztyq6LGTNmMGHChHJ5vvDCC+UW6d555x3Gjh1LVlYWb7zxBgANGzas0QJgSEiI8u0yU1NTXF1d8fLyqhSXnZ2Nqampcl3T/iv7XJiZmVW5U0ej0bBp0yaOHDmilFXVjrIH9d/YsWOZMGECO3bsKJem7BiWtiEzM5Pp06cru7cKCwtxd3cnMzOTe/fuldsRl5+fT1ZWFrdv3y6XV9k+0JVfxTSlfxuqkpWVxQsvvFDuXmn/Vff81XSRsabzr2IaXeU+qKyy/WNqasp//vOfSu16kJr8na74vOTl5ZUru2nTpuXqVXY8yv4NLHu/QYMG5f7Ol/7NFEIIIYQQQgghhBBCPHtqffEpLS2N1NRU5UWxkZERQ4YMISwsjJSUFGxsbBg8eDD79++nVatWuLu7P3QZ2dnZaLVaVCoV9+7dIz8/v9KLd3Nzc06ePKlc5+fnk5+f/9A7Tx4kPDwcOzs7VCoVFhYWTJ8+/YE7cXTF/f7772RnZ6PRaNDT06OwsJDr16/Ttm3bKvMxNzcvFw9w69atWm+jLmVffs+ZMwcvLy9ld0rZY+RcXV1xdXUlJyeHmTNnEhQUVG7nQ1X56enpodVqlevSY9HMzc358ccflfvZ2dnk5eVV2jGlK33FciwsLOjTp88Dvx8zatQoZbdU2cW5Ur1792bfvn2MGDECKHlR3qBBg3J16NGjBz4+PpUWjS5evEh4eDg+Pj4AjBgxgpUrV1ZbHyh5MZ+dnU3z5s0B+PPPP5Xf1Gq1UnZhYSFFRUVASf+VXaTQtVCyf/9+oqOj+fbbbzExMSEsLEz5nlRZ1fWfRqNhzZo1vPPOO/zzn/9k9+7d6OnpKXVo0aIFAJcvX6ZZs2ZYWFiwbt26Sju40tPTqVevXpXfLNu5c6eym7FiH+jK7+LFi+UWMfLy8sjPz6+UN1Bu/Cqq7vkr2/+lZVSlpvOvpuU+KF3Zfq8uja75U1MVn5eQkJBy41T67aay9Sr7W+mC1u3bt5X72dnZNVpgFUIIIYQQQgghhBBCPH21fuxeQUEBs2bNIjo6Go1Gg1arJTExkbNnz2JjYwOAi4sLp0+fJjIyUnlB+TDy8/OJjIwE4ODBg7Rr1w4zMzP09fXJzc1Fq9Xy+uuvc/r0aa5cuQLArl276NatW7ndQI8jLy+Pbdu2sX37dmXRwNHRkbCwMGWhIzg4WHlhb2BgoLzw1hVnaWmJpaUloaGhAAQFBbFs2bJK6UtZWVlhaWnJd999B8C5c+e4evUqXbt2rbbuubm57N69uza6QXH79m2sra0BCAsLIzMzU+mjwMBAtFotxsbGNGnSBI1Gg4GBAcXFxdy9e7fK/Jo0aaJ8z+XKlSvKt3/69OnD6dOnuXDhAsXFxfj5+REeHl4pP13pK7Kzs+PUqVPKPDl79iyLFy+uFFevXj0sLCywsLCo8ps4kyZNIjU1lQ8++IDffvuN+/fvk5yczD/+8Q+lX/r164e1tTVvv/02ycnJ5Ofnc/LkSby8vKrcIfcgXbp0ISIiAoDTp0+Tmpqq/Pbiiy8q7T9w4ICy4Obg4EBERAT5+floNBr27NmjLBTr6+src+zWrVs0b95cWeAKCQmpcqyq678dO3bQrFkz3n//fRo2bMjXX38NQP/+/QkODkaj0fDzzz8zcuRIoOS5CAoKQqvVUlRUxPLly0lOTqZp06a0bNlSaWtmZiazZ88mLy+PLl26kJiYSEZGBsXFxeUWyHTl17JlSwwMDPj++++BkudM17eyXnjhBZ07iqp7/l588UUyMjKU/iw9drRiP9d0/pWd34/63Ds4OLBr1y6gZCEnKiqqyuMcQff8qamKz0unTp04ceIEt27doqioSKkHlMyHffv2kZubS3Z2Np6enly+fBmAn376STnu8cCBA3Tv3l1Jl5mZWWlXmhBCCCGEEEIIIYQQ4tlQ6zuf2rRpw/r16wkMDGT+/PmoVCpatGjBypUrlZfwpqam9OzZk6ysrCqP8nqQFi1akJSUxCeffIJKpeKjjz4CwMbGhps3b2JnZ8exY8dYuXIl3t7eFBUVYWlpSUBAwGO3r3RHT506dejUqRM7duxQ2uXk5ERqaipDhw7l/v37tG7dWqnbwIEDmTlzJj4+PkyYMKHKOJVKpfRbQEAArVu3ZtWqVZXSlyqN9/f359NPP8XIyIjAwEBMTU25efOmzjZkZGTg5+fHqFGjHrs/Ss2ePZupU6fSoEEDRo8ezYQJE1iwYAGbNm1izZo19O3bFz09PTp27Iinpyf169enV69e9O3bl3/961+V8pswYQKzZ8/m+PHjvPTSS/Tt25fi4mKaNGnC8uXL8fb2Ji8vjz59+jBx4kQMDQ3L5acrfUWNGjUiICCAadOmUVBQQP369Vm4cOFDt9/c3JyQkBBWrVrFsGHDuHPnDlZWVgwePJjJkycDJbuxvvrqK1ac5dJmAAAgAElEQVSvXs3UqVPJysqiVatWTJ8+Xdkx9TDee+89Zs+eTa9evejSpQt9+vRRfvPx8eGDDz6gadOm9O/fn0aNGlFcXIyrqyvnz59n0KBBaLVaunfvzpQpU4CSRYDp06eTnp6Ov78/+/btw9HRkSZNmjBv3jy8vb355JNPmDt37gP77+bNm2zcuFFZDPrggw8YOXIkAwcOxMfHhwULFtC7d2+MjY0JDAzE0NCQWbNmsXjxYhwdHbl//z4ODg688sorAKxduxZ/f3/WrFmDSqVi8uTJ1K9fH2tra8aNG8fgwYMxNjbG09OT5ORkAJ35GRgYsGLFCj788EO0Wi2jR49W+qeiV199leTkZKUeZVX3/JmamjJmzBiGDx9O8+bNsbe35/jx45X6ec2aNTWaf8bGxuXmt65yqzN37lw++OADHB0d0dPTw9PTk169elUZq2v+PKqOHTsyfPhw3N3dsbS0ZOjQoWzevBko+Z8Rzp07h6urK1qtlgkTJtClSxfOnDnD66+/zuLFi7lw4QLNmjUjMDBQyTM5Obnc30MhhBBCCCGEEEIIIcSzQ3Xp0iXd50o9QX5+flhbWzN27NiHSnfu3Dneffdd4uPjn1DNhPjv5O3tjYuLC0OHDn3aVXluhISEEBUVxZdffvm0q/Jfr+wxgT/++CMff/yxssuzKlu2bOHChQusWLGi0m/JycnMmjVL/jsghPjLtGnTBgDVqep3mWptf5I4iZM4iZM4iasUV1PPSztq6llvx7M+Hs9Le2vqWe/nZ71faju/Z73c5yWupv7XxuNpedb75Vkcj1o/dq8mUlNT+f777xkyZMjTKF4IIWpk8ODBXLx4sdyRhuLhZWZm0rlzZ6UfQ0ND6dy58wPT6frm1saNG5k+fXqt1lEIIYQQQgghhBBCCFF7/vLFp4CAAMaNG8fChQsxNjb+q4sXQogaMzAwYNWqVSxYsICioqKnXZ3/Wubm5rz//vu888472NnZkZ2dzYwZMx4pr6ioKO7du/dIR1UKIYQQQgghhBBCCCH+GrX+zacH8fX1xdfX95HTd+jQQY5aEqIKGzdufNpVeC7Z2tqyZ8+ep12N/3pjxoxhzJgxNY6fOHFilfednZ1xdnaurWoJIYQQQgghhBBCCCGegKdy7J4QQgghhBBCCCGEEEIIIYR4PsnikxBCCCGEEEIIIYQQQgghhKg1svgkhBBCCCGEEEIIIYQQQgghao1Kq9Vqn3YlhBBCCCGEEEIIIYQQQgghxPNBdj4JIYQQQgghhBBCCCGEEEKIWqN/+fLlp10HIYQQQgghnllt2rQBQHWqa7VxWtufJE7iJE7iJE7iKsXV1PPSjpp61tvxrI/H89LemnrW+/lZ75fazu9ZL/d5iaup/7XxeFqe9X55FsdDdj4JIYQQQgghhBBCCCGEEEKIWvNEFp9SU1N566236NGjB926dWPo0KEcPXr0SRRVzv3799m3b98Ty7tt27ZYW1tjbW1Nly5dePvttzlz5kyN0h87doz09PRHLr9s+k2bNjF//vxHzqus0nYtWbKk3P3o6Gi8vb0fOj9vb2/CwsJqpW7/K+7du8ewYcPo2rUrf/zxxyPlUbbfQ0JCHjr9uXPncHBweKSyhRBCCCGEEEIIIYQQQoiynsji09SpU3F2dub48eMkJibi5eXF1KlTuX379pMoTnH+/PknvvARHx9PSkoKkZGRdO/enfHjx3Pq1KkHptu+fTs3btx45HLLph87diwLFix45LwqMjIy4uDBg1y4cKHW8qxtxcXFT7sKT8yVK1f4/fffOXHiBBYWFo+Vl1arJSAgoJZqJoQQQgghhBBCCCGEEEI8vFpffCoqKuLatWs4Ojqip6eHSqXCzc2NiIgIjI2NGTJkCAcPHlTi4+LiGDRoECkpKXh4eLBq1SpGjRqFs7MzJ06cwNvbGxcXFxYuXAhAcnIyLi4uLF26lNGjR+Pi4sKPP/7InTt3mDJlCqdOnWLcuHEAHDhwABcXF5ycnBgzZgyXLl0CYOvWrfj6+jJ79mzc3NyYPHky8fHxTJo0CXt7e0JDQx/YzsaNG+Pl5cXkyZPLvexfv349AwYMYODAgSxatIjCwkK++OILDh8+jI+PD1FRUTrjoGTX2MiRI+nTpw9vvvkm169fr5R+x44drFixAihZcBs1ahSOjo64ubkRFxcHoPTn6tWrGTNmDI6Ojhw+fLjKtmi1WmbOnMny5cur/F2j0bB69WqcnJxwcnLCx8eH3NxcAH777TdGjhyJvb09M2bMUNoBcOjQIdzc3BgwYACTJk0iIyOjUt66xhPgX//6F76+vnh4eLB27dpq65GTk8OMGTOws7PD2dlZ2Wl3584d/vGPf+Do6IiLiwtBQUEAFBYWMnfuXOzt7enbty9z5syhoKBA5/2Kjhw5gqurKw4ODri4uJCQkFBteQAbN27Ezs6OIUOGEBwcTN++fcnJyWHq1Knk5ubi7u7On3/+yd69e3FycqJ///54enqSlpampPf39y+XX9lrKFn4zczMxMXFhbS0tGrr8/nnn2NnZ4eHhweHDh2qcuyFEEIIIYQQQgghhBBCiIdV64tPBgYGODo64uXlxb59+5QFh1atWqFWqxk8eDDh4eFKfHR0NO7u7qjValJTU3FwcGD37t1YW1uzaNEiAgMDCQsLIzQ0lIyMDNRqNRcvXsTe3p5du3Yxd+5c/Pz8aNCgAX5+ftja2vL111+TlpaGn58fGzduJDY2lsGDBzNv3rySRuvpceTIEZYsWUJERATnz58nJiaGzZs38/HHH7Nhw4Yat9fNzY2kpCQKCwuJiYkhIiKCvXv3cvDgQW7cuEFwcDBTpkyhZcuWrF69GmdnZ51xWq2W2bNn4+XlxbFjx3BwcODDDz+slL6URqNh5syZjB8/nri4ONauXYuPjw9ZWVlKf9ra2hIcHMycOXP47LPPqmxDcXExb7zxBn/++SexsbGVfj9w4AAJCQmEh4cTExODVqtV+iggIIAePXpw6NAh3n33XY4fPw7AH3/8gY+PD2vXriUmJobevXsrC4hl6RpPAH19fQ4dOsSGDRuYO3dutfVYs2YNjRs35ujRowQEBDBjxgzu3bvH2rVr0Wq1xMbGEhwczBdffEFKSgpxcXH88ccfxMfHk5CQQOPGjfnll1903q9o+fLlLF26lPj4eNasWUN0dDSAzvIuXrzIpk2bCA0NZffu3cTGxqKvr4+JiQnr1q2jadOmREZGoq+vj7+/P5s3byYhIYHWrVs/1HxcsWIFBgYGREZGYmlpWW19vvzyS0JDQ4mIiFAWZoUQQgghhBBCCCGEEEKIx/VEjt377LPPGD58ON988w39+vXDw8ND2e3k4eHBkSNHyM3Npbi4mPj4eNzc3AAwNTXF1tYWACsrK7p164ahoSFGRkY0atRI+R5OgwYNsLOzA8De3p6rV6+SlZVVrg5Hjx7F1taWVq1aATB8+HDOnDmj7JSxtbXFxMQEtVpNs2bNlPysrKyq3KGjy4svvkhxcTH5+fnExsYybNgwjI2NUavVvPHGG8qiRFm64tLS0khLS2PgwIEATJw4kXXr1uks+/r166Snp+Ph4QFA+/btadeunXIMYN26denXrx8AL730ks5j/7RaLXp6evj7+7NixYpyu5egZAfToEGDqFevHiqVimHDhik7i06cOKGM38svv8zf/vY3oGRnUOfOnWnfvj0AY8aMIT4+vsrj83SNp0ql4tVXX8XKyuqB9YiPj2f48OEAdOrUiYSEBAwNDYmNjWXcuHGoVCoaNmyIq6srMTExWFhYcOnSJRISEigqKmL+/PnY2trqvF9R48aNiYiI4OrVq1hbWys70XSVd/LkSbp3746FhQV16tRh2LBhVY6FmZkZiYmJSpt79uzJ9evXq4ytCV31SUxMVOqjUqkYOnToI5chhBBCCCGEEEIIIYQQQpSl/yQyNTAwYPz48YwfP56CggKioqLw9fWladOm2NjY0KlTJ6KiomjWrBnNmzfHysqK1NRU6tatq+ShVqsrXWs0GtRqNSYmJv/fAH19jIyMyMnJKVeHzMxMGjZsqFwbGhpSt25dMjMzAXSWVVpOTd24cYM6depgYmJCZmYmcXFx7Ny5EyjZmWRubl4pja64zMxMjI2Ny/WjgYGBzrIzMzMxNTVFpVIp90xNTcnKyqJly5Y0aNBAua+np/fAdvXs2ZNXXnmFLVu20Lp163LllO1LMzMzZbEvOzsbU1PTcuWXpjl58iR9+/ZVfqtXrx5ZWVmVvmtU3XiamZnVqB6ZmZnl8ilbj+nTp6NWq4GS4/bc3d3p2rUr/v7+fPXVV8yZMwd3d3f8/f113i87X6Bkp9W6devw9PTE2NgYf39/7OzsdJanr69fri1Nmzatcgw0Gg2bNm3iyJEjSv9aWlpWGVsT1dWnbH+VrZsQQgghhBBCCCGEEEII8ThqffEpLS2N1NRU+vfvD4CRkRFDhgwhLCyMlJQUbGxsGDx4MPv376dVq1a4u7s/dBnZ2dlotVpUKhX37t0jPz+/0stzc3NzTp48qVzn5+eTn59Po0aNHqt9FYWHh2NnZ4dKpcLCwoLp06czceLEatPoivv999/Jzs5Go9Ggp6dHYWEh169fp23btlXmY25uXi4e4NatW4/Vxvnz5zN8+HDmzp1brpyyO8uysrKUMkxMTMot/P35559KG/v06cPGjRsfWGZ141l2Ya26epT+1qJFCwAuX75Ms2bNsLCwYN26dXTo0KFSua6urri6upKTk8PMmTMJCgpi8uTJOu+XZW5uzsKFC1m4cCEJCQnMnDmTn376SWd5X3/9tbLrDiA9Pb3Kvti/fz/R0dF8++23mJiYEBYWRkhICFCygKjVapXYvLy8B/atrvrs3LmzXH1Kx00IIYQQQgghhBBCCCGEeFy1fuxeQUEBs2bNIjo6Go1Gg1arJTExkbNnz2JjYwOAi4sLp0+fJjIyUjmy7WHk5+cTGRkJwMGDB2nXrh1mZmbo6+uTm5uLVqvl9ddf5/Tp01y5cgWAXbt20a1bt3K7gR5HXl4e27ZtY/v27fj4+ADg6OhIWFgYd+7cASA4OFhZODAwMFAWaXTFWVpaYmlpSWhoKABBQUEsW7asUvpSVlZWWFpa8t133wFw7tw5rl69SteuXaute25uLrt3767ytxYtWvDGG2/w5ZdfKvccHByIiIggPz8fjUbDnj17lMXFLl26sH//fgD+/e9/k5qaCoCdnR2nTp1S+v/s2bMsXry4yjJ1jWdF1dWjf//+BAcHo9Fo+Pnnnxk5ciRQ0tdBQUFotVqKiopYvnw5ycnJbNu2jcDAQLRaLcbGxjRp0gSNRqPzfsX6jhgxQllAatu2rRKjq7xOnTpx4sQJbt26RVFREbt27aqyL27dukXz5s0xMTEhOzubkJAQ7t69C0CTJk24cOECUPKcHTp0qFJ6AwMDiouLlTS66tOlSxcSExPJyMiguLhYmadCCCGEEEIIIYQQQgghxOOq9Z1Pbdq0Yf369QQGBjJ//nxUKhUtWrRg5cqVWFtbAyVHovXs2ZOsrKxHOlKsRYsWJCUl8cknn6BSqfjoo48AsLGx4ebNm9jZ2XHs2DFWrlyJt7c3RUVFWFpaEhAQ8Njtc3BwAKBOnTp06tSJHTt2KO1ycnIiNTWVoUOHcv/+fVq3bq3UbeDAgcycORMfHx8mTJhQZZxKpVL6LSAggNatW7Nq1apK6UuVxvv7+/Ppp59iZGREYGAgpqam3Lx5U2cbMjIy8PPzY9SoUVX+PnXqVPbu3atcu7q6cv78eQYNGoRWq6V79+5MmTIFgPfee4/Zs2cTGhqKjY0NDg4OFBcX06hRIwICApg2bRoFBQXUr1+fhQsXVlmervGsqLp6+Pj4sGDBAnr37o2xsTGBgYEYGhoya9YsFi9ejKOjI/fv38fBwYFXXnmFZs2a8d5779G3b1/09PTo2LEjnp6e3Lt3r8r7ZdWtWxdPT0/Gjh1LcXExRkZGBAQEoKenp7M8AwMDhg8fjru7O5aWlgwdOpTNmzdXaqO7uzv79u3D0dGRJk2aMG/ePLy9vfnkk0+YNm0ae/bsYdCgQVhaWtKvXz9u3bpVLr2xsTG9evWib9++/Otf/6q2PuPGjWPw4MEYGxvj6elJcnKyjhkjhBBCCCGEEEIIIYQQQtTcE/nmk52dHXZ2dtXGNGrUiNdff125bt++PYcPH1auZ82aVS4+JiYGKNndo9VqWbBgAQsWLCgXY2FhoXwrB/7/WLWKxo8fX+46KCioXB5VvYTX19fn0qVL1bYJYNq0aUybNq3S/RkzZjBjxowHxr388svlFn50pS8bX9Uupvbt23Ps2LEqr9u2bavsUNLX11f+Xap+/fr88MMPyrWenh4+Pj7lFr5KtW7dmn379lW6D2Bvb4+9vX2Vv5WlazxLvxtWk3qYmpqyfv36SvcbNGigLOCV9cILL7Bp06ZK9+vXr1/l/YpGjBjBiBEjalwelCzUzZ8/H4Aff/xR+S5Vhw4diI+PB0qO86u4C+nHH39U/r19+/Yq8y57vOG2bdvK/aarPnPmzGHOnDnK9YOOixRCCCGEEEIIIYQQQgghaqLWj92ridTUVL7//nuGDBnyNIoX4i+XmZlJ586dlYW+0NBQOnfu/JRrJYQQQgghhBBCCCGEEELUviey86k6AQEBhISEsGzZMoyNjf/q4oV4KszNzXn//fd55513KC4u5tVXX+X9999/2tUSQgghhBBCCCGEEEIIIWrdX7745Ovri6+v7yOnL3tEmfjv9780nmPGjGHMmDFPuxpCCCGEEEIIIYQQQgghxBP1VI7dE0IIIYQQQgghhBBCCCGEEM8nWXwSQgghhBBCCCGEEEIIIYQQtUYWn4QQQgghhBBCCCGEEEIIIUStUWm1Wu3TroQQQgghhBBCCCGEEEIIIYR4PsjOJyGEEEIIIYQQQgghhBBCCFFr9C9fvvy06yCEEEIIIcQzq02bNgCoTnWtNk5r+5PESZzESZzESVyluJp6XtpRU896O5718Xhe2ltTz3o/P+v9Utv5PevlPi9xNfW/Nh5Py7PeL8/ieMjOJyGEEEIIIYQQQgghhBBCCFFrnsjiU2pqKm+99RY9evSgW7duDB06lKNHjz6Josq5f/8++/bte2J5t23bFmtra6ytrenSpQtvv/02Z86cqVH6Y8eOkZ6e/sjll02/adMm5s+f/8h5lZWVlUXbtm3Jy8srdz8kJIRx48Y9dH7e3t6EhYWRnJyMg4NDrdTxr3Tu3Lm/pN6P2z81reeVK1f46adH/78CHjV96Tyo6O23367y/pOmqz5CCCGEEEIIIYQQQgghat8TWXyaOnUqzs7OHD9+nMTERLy8vJg6dSq3b99+EsUpzp8//8RfMMfHx5OSkkJkZCTdu3dn/PjxnDp16oHptm/fzo0bNx653LLpx44dy4IFCx45r7+CtbU1e/bsedrVeGb9Vf0THx//WItPj5u+otWrV+Pq6lpr+QkhhBBCCCGEEEIIIYR49ujXdoZFRUVcu3YNR0dH9PRK1rbc3Nzo0KEDxsbGDBkyBG9vb+UFdFxcHGvXriUgIIB58+bRr18/EhMTycnJYcmSJWzZsoUrV67Qo0cPFi9eTHJyMvPmzaNPnz4kJyeTk5PDokWLePXVV5kyZQo5OTmMGzeOr7/+mgMHDvDpp59y//59LCwsWLZsGW3btmXr1q2cO3eOwsJCUlNTadq0KX//+9/ZsWMHv/76KzNnzmTYsGHVtrNx48Z4eXlRUFBAQEAA3377LQDr168nLCwMlUpF7969WbBgAVu2bOHw4cOkpqby/vvv4+zsXGVcnTp1SE1NZcGCBaSnp9OiRQtWrVrFgQMHyqW/du0aly9fZuXKlZw/fx5/f3+ysrIwNDRk7ty5ODo6kpKSwrx583BwcODkyZNkZGSwcOFC+vbt+9BjunXrVv7zn/9w7949rl27xv3799mwYQPNmjXjt99+Y/bs2WRmZvLqq69SWFgIQEpKCrNmzSI+Ph6tVsvy5cuJjY1Fo9HQo0cPVq5cib6+Pr/99htz5swhMzOTrl27UlBQQL9+/ejQoYPO+ms0GtauXcuBAwcA6NixI4sXL8bY2JgjR46wYsUK7t27R506dXj//ffp378/d+7cYdGiRSQlJWFgYMD48ePx9PQE4PPPP2fnzp2YmZnpXBjRNe969uxZbfu8vb159dVXSUpKIj09HSsrK9avX1+pf9asWcO+ffsoLi7mrbfe4u23365Uh+rqWdV8OnXqFOvXr0dfX5/c3FzmzZunc97l5OTg5+dHUlIS9evX54MPPkCtVtc4va55UJGPjw8eHh4MHTqUzp074+vrS2RkJNevX2fkyJFMmzatUpqOHTsyffp0Dh8+TEZGBmPGjGHSpEnVzoOa1kcIIYQQQgghhBBCCCFE7av1nU8GBgY4Ojri5eXFvn37yMjIAKBVq1ao1WoGDx5MeHi4Eh8dHY27uztqtZrU1FQcHBzYvXs31tbWLFq0iMDAQMLCwggNDSUjIwO1Ws3Fixext7dn165dzJ07Fz8/Pxo0aICfnx+2trZ8/fXXpKWl4efnx8aNG4mNjWXw4MHMmzevpNF6ehw5coQlS5YQERHB+fPniYmJYfPmzXz88cds2LChxu11c3MjKSmJwsJCYmJiiIiIYO/evRw8eJAbN24QHBzMlClTaNmyJatXr8bZ2VlnnFarZfbs2Xh5eXHs2DEcHBz48MMPK6UvpdFomDlzJuPHj1cW8Xx8fMjKylL609bWluDgYObMmcNnn332SGOqp6dHbGwsCxYsICQkhNdee41vvvkGgICAAHr06MGhQ4d49913OX78eKX08fHxHD16lKioKGJiYjh37hyRkZFK+u7duxMfH8/QoUOJiYlBrVZXW/8DBw6QkJBAeHg4MTExaLVaZcyWL1/O0qVLiY+PZ82aNURHRwOwdu1atFotsbGxBAcH88UXX5CSksLFixf58ssvCQ0NJSIigkuXLlXZB7rm3YPap1ar+eGHH9i4cSMRERFcuXKlUh9FRUVx7NgxoqKi+O6779iyZQtJSUnlYqqrp6751KtXLwYNGsTEiROZN2+ezjiANWvW0LhxY44ePUpAQAAzZsygS5cuNU5fk3lQVZ+mpaWxbds2goKC+Oyzz8jPz68Up6enR15eHjt37mTnzp2sWbOGtLS0aufBo9RHCCGEEEIIIYQQQgghRO14IsfuffbZZwwfPpxvvvmGfv364eHhwcGDBwHw8PDgyJEj5ObmUlxcTHx8PG5ubgCYmppia2sLgJWVFd26dcPQ0BAjIyMaNWrEH3/8AUCDBg2ws7MDwN7enqtXr5KVlVWuDkePHsXW1pZWrVoBMHz4cM6cOUNubi4Atra2mJiYoFaradasmZKflZWVsmBWEy+++CLFxcXk5+cTGxvLsGHDMDY2Rq1W88YbbyiLH2XpiktLSyMtLY2BAwcCMHHiRNatW6ez7OvXr5Oeno6HhwcA7du3p127dsoxgHXr1qVfv34AvPTSS4917J+trS0WFhYAtGvXTsnrxIkTyvi9/PLL/O1vf6uU1sHBgZCQEAwNDTE0NMTGxoZr164BkJiYyKBBgwDo3bs3zZs3V9Lpqv+hQ4cYNGgQ9erVQ6VSMWzYMOWbYo0bNyYiIoKrV69ibW3NihUrgJI+HzduHCqVioYNG+Lq6kpMTAyJiYl0794dCwsLVCoVQ4cO1dkHuuZdde0DcHJywsDAALVaTevWrSuNQ3x8PO7u7hgZGWFmZkZkZCQdO3YsF1NdPR933pXWYfjw4QB06tSJhIQEDA0Na5y+JvOgKi4uLgA0adKEunXr8ueff1YZV7ro2rhxYzp06MCZM2eqnQePWh8hhBBCCCGEEEIIIYQQj6/Wj90DlGPNxo8fT0FBAVFRUfj6+tK0aVNsbGzo1KkTUVFRNGvWjObNm2NlZUVqaip169ZV8lCr1ZWuNRoNarUaExOT/2+Avj5GRkbk5OSUq0NmZiYNGzZUrg0NDalbty6ZmZkAOssqLaembty4QZ06dTAxMSEzM5O4uDh27twJlOxMMjc3r5RGV1xmZibGxsbl+tHAwEBn2ZmZmZiamqJSqZR7pqamZGVl0bJlSxo0aKDc19PTq7JdpUcjVvytqKgIff3/nx7169dX/q1SqSguLgYgOzsbU1PTcuVXlJWVxUcffcSlS5dQqVT89ttvjBs3DoDbt2+XS2Npaan8W1f9K46tmZmZsvi4Zs0a1q1bh6enJ8bGxvj7+2NnZ0dmZibTp09HrVYDUFhYiLu7O/r6+uXmk5mZWaX6l9I177Rarc72Vew7PT09pe9KZWZmlsu77Bwodfv2bZ31fNx5V1UdqhrH6tLXZB5U5UF9U1V+DRo0IDs7u9p58Kj1EUIIIYQQQgghhBBCCPH4an3xKS0tjdTUVPr37w+AkZERQ4YMISwsjJSUFGxsbBg8eDD79++nVatWuLu7P3QZ2dnZaLVaVCoV9+7dIz8/v9Kigbm5OSdPnlSu8/Pzyc/Pp1GjRo/VvorCw8Oxs7NDpVJhYWHB9OnTmThxYrVpdMX9/vvvZGdno9Fo0NPTo7CwkOvXr9O2bdsq8zE3Ny8XD3Dr1q2HaqOJiQl169blypUrvPbaa8r9CxculFsIqi592YW/qnaurF69GoDdu3ejVquZP3++8puxsTG5ubk0bdoUgPT09AeWaW5uXm6nW1ZWltJmc3NzFi5cyMKFC0lISGDmzJn89NNPWFhYsG7dOjp06FAur507dyq74XTVv5Suebdq1Sqd7auJiu1JT0+nbt265ea0qampzno+7rwrW4cWLVoAcPnyZZo1a1bj9DWZB4/j1q1byq64nJwcGjZsWO08eNL1EUIIIYQQQgghhBBCCKFbrR+7V1BQwKxZs4iOjkaj0aDVaklMTOTs2bPY2NgAJbWq7LYAACAASURBVEdtnT59msjISOVorIeRn5+vfFPn4MGDtGvXDjMzM/T19cnNzUWr1fL6669z+vRprly5AsCuXbvo1q1bud00jyMvL49t27axfft2fHx8AHB0dCQsLIw7d+4AEBwcTEhICFCyi6n0ZbiuOEtLSywtLQkNDQUgKCiIZcuWVUpfysrKCktLS7777jsAzp07x9WrV+natWu1dc/NzWX37t1AyW6TESNGsGLFCq5du8a9e/eIjo5mz549jBo16oH90KVLF/bv3w/Av//9b1JTUyvF3L59m5dffhm1Wk1KSgrHjx/n7t27AHTs2JGoqCgAEhIS+P333x9YpoODAxEREeTn56PRaNizZw/9+/cnPz+fESNGKAtYbdu2VXZLOTo6EhQUhFarpaioiOXLl5OcnEyXLl1ITEwkIyOD4uJiZbyqomveVde+mujfvz/79u0jNzeX7OxsPD09uXz5crmY6upZ3bzT19d/4LwrrUNwcDAajYaff/6ZkSNHPlT6msyDxxEWFgbAtWvXSElJoXPnzjrnwV9RHyGEEEIIIYQQQgghhBC61frOpzZt2rB+/XoCAwOZP38+KpWKFi1asHLlSqytrYGSXRw9e/YkKyurRrtrKmrRogVJSUl88sknqFQqPvroIwBsbGy4efMmdnZ2HDt2jJUrV+Lt7U1RURGWlpYEBAQ8dvscHBwAqFOnDp06dWLHjh1Ku5ycnEhNTWXo0KHcv3+f1q1bK3UbOHAgM2fOxMfHhwkTJlQZp1KplH4LCAigdevWyq6asulLlcb7+/vz6aefYmRkRGBgIKampty8eVNnGzIyMvDz81MWlxYsWMAnn3zCqFGjyMnJoV27dqxevVpZLKzOe++9x+zZswkNDcXGxgYHB4dKR6dNmjSJefPmsXv3bjp27MiCBQt47733sLW1VdKHhf0fe/ceF1W1/3/8NQzkDRS/gve0/KblNRU1L6gwgALeu3iENI96TMzygkWmlbfMk55M856lmUcpzSvmJRAMNe+a2le/miaWgVaAaEZeYH5/8GN/Z5QZBiOxzvv5ePR4ODNr1v6s9dl7TY+9WGuvo23btrRr185uG8GChIWFcfLkSbp164bVaqVVq1YMGTKEMmXKEBkZSd++fcnJyaF06dJMmzYNNzc3RowYwcSJEwkKCuLmzZtYLBYeeeQRPDw86NevH927d8fLy4vIyEiOHTtW4HEdnXfO2ueK0NBQjh8/TlhYGFarlf79+9O8eXO7MvXr13cYp7PzLiAggGHDhpGWlsY777zjsFx0dDRjx46lbdu2eHl5MWvWLEqVKuXy9105D36PKlWqEBYWxpUrVxgzZgyVK1d2eB7cjXhERERERERERERExDHTmTNnrCVx4HHjxlG/fn369u1bpO8dP36c559/nsTExD8oMrnbbLcNjIyM5NlnnzVWsNwrdN6VnKZNm7JlyxaqVq1a0qGIiMh/qDp16gBgOuh8dbnV74DKqZzKqZzKqdxt5Vz1V2mHq+71dtzr+firtNdV93o/3+v9Utz13evH/auUc9V/Wj5Kyr3eL/diPop92z1XnDp1ii+++IIePXqUxOHlHjJ16lRGjx6N1Wrl22+/5fjx4zRs2LCkw5J7jNVaInPkIiIiIiIiIiIiInIHin3bvcJMmzaN1atX88Ybb+Dl5XW3Dy/3mMGDBxMdHU379u257777mDRpEr6+viUdloiIiIiIiIiIiIiI3KG7PvkUExNDTEzMHX+/QYMG2vrsL8THx4ePPvqopMMolM67kvPVV1+VdAgiIiIiIiIiIiIiUgQlsu2eiIiIiIiIiIiIiIiI/DVp8klERERERERERERERESKjSafREREREREREREREREpNiYrFartaSDEBERERERERERERERkb8GrXwSERERERERERERERGRYuP+7bfflnQMIiIiIiL3rDp16gBgOtjCaTmr3wGVUzmVUzmVUzmVUzmVK0I5V93r7VC/FFzOVfd6O1Tu3irnKp2nd6ecM1r5JCIiIiIiIiIiIiIiIsXmLzX5dPPmTdavX18sdU2dOpXmzZuTmppq937Tpk1JS0vj+PHjWCyW27537NixAt//T7BkyRJeeeWVIn3Htr9sv5+SksKBA0WbfS6KqKgo1q1bd9v7f/RxXeUovn/84x8Fvm+rOK8DgF27dpGWllZoufxr41Z3cl6IiIiIiIiIiIiIyJ/Xn2ryKScnx+nnJ0+eLPTGvCt15uTksG3bNp599tki38SvX78+n376aZG+cy8rrM9/L0f9lZiYWCKTQCV1XFfNmDGDsLAwp2Xu5Dpw5qOPPuLChQvFVp+IiIiIiIiIiIiI/LX9IZNPmzZtIjQ0lODgYCIiIjhz5gwA77//PiNHjmTo0KH06tWLPn36GCslzp49S2RkJCEhIfTo0YODBw8C8PXXX9O1a1eio6N55plnAFizZg3BwcEEBAQQGRlJamoqv/zyC0OGDOHgwYP069fPaRwffPABMTExdO3alZkzZ94Wf3JyMs2aNaNnz55Fnnw6ceIETz75JAAffvghMTExjBw5kvDwcAYNGkRiYiIDBw4kMDCQtWvXAmC1WnnjjTcICAigQ4cOvPTSS9y8eROAHTt2EBQUROfOnfnwww9p27Yt58+fByApKYnw8HBCQkIYOHAgP/74IwDnz5+nT58+WCwWAgICmD9/foGxFtSPjvpn7ty5hISE0KlTJyZMmMD169cLrPPq1asMHDiQNm3a0Lt3byO/AwYMYOPGjUa5/Ne2/ZVv9+7dzJ07lyVLljB9+nSX4/7www8ZM2YMo0aN4oknnqBHjx788MMPRp88+eSTBAYG8sILLxQY/63HdZaX8+fP89RTT2GxWIiJiWH48OGsXr0agAULFuDv70+PHj2IjY2lQ4cOxjEc9aMr8QFER0ezefNmAJo1a0ZsbCz9+/fHYrEwb968Aq+DkydP8tRTTxEUFER4eDjbtm0rsO5FixZhsVgIDAzkqaee4rvvvmPhwoUkJycTHR3N1q1byczMZNCgQQQGBtKxY0eWLl1qV8fGjRsJCgqiTZs2fPDBB7cd45dffuHFF18kKCiI0NBQVqxYUWAsIiIiIiIiIiIiIvLnVeyTT6mpqYwbN44FCxaQkJBA9+7deemllwAwm80kJyfzxhtvsHbtWho1asTs2bMBGDFiBN27dyc+Pp7JkycbN+A9PDw4d+4cFouF5cuXc+nSJV599VUWL17M9u3befDBB5k3bx6enp6MGzcOPz8/li1b5jQOd3d3kpKSmDdvHqNHj76tDatXr6Znz55UrVoVHx8fjh49ekd94ebmxo4dO5g0aRJxcXGcPHmS+Ph4Fi9ezFtvvcW8efOAvNU2O3fuZOvWrcTHx3P8+HG2bNlCTk4OY8aMYfz48WzdupWsrCzS09Mxm8389NNPREdHM3PmTOLj42nbti3jx48H8ib5/P39SUxMJC4ujq+//porV67YxeaoHwvqn/j4eOLi4lizZg2bN2/mwoULxMbGFtjm7du3M3bsWHbv3s0DDzxg5Lco2rRpQ7du3RgwYICRM1fidnNzIyEhgbFjx7J69WoaN27Mxx9/DMC0adN47LHHSEpK4vnnn2f37t2FHtdRXvLra9WqFYmJifTs2ZP4+HjMZjOnT59m0aJFrF27llWrVpGQkIC7uzuA0350Jb5bmc1mUlNTWbp0KStWrGD27NmYzWa76yA3N5fhw4fzzDPPsG3bNmbOnEl0dDQZGRl2dWVkZDB//nw2bNhAUlISffr0ITk5mSFDhlC7dm1mzJhB586dmT9/Pr6+viQlJbF8+XLeeustLl68aNRz9uxZEhISiI2N5e23375t28qZM2ditVqNMgsXLuTEiROFtlVERERERERERERE/jyKffJp586d+Pn58cADDwDw+OOPc+TIEWPyo2XLllSqVAkAi8XCoUOHSE1N5ezZs/Tu3RuAJk2aULlyZQ4fPozJZCI3N5fw8HAAvL292bdvH7Vq1QKgdevWfP/990WKw2Qy0ahRI6MOW1lZWXz99de0adMGgJ49exorlO6En58f5cuXx2w2U6NGDfz9/QGoVauWsVLJYrGwevVqSpUqRalSpWjatCnfffcd586dIzs721g5ExkZabciqlmzZtSrVw+AiIgIEhMTycnJwdfXly+//JIjR45Qrlw55s6di5eXl11czvrx1v5JSEigV69eeHl5YTab+dvf/sbnn39eYHtbtGjBQw89BEC3bt04dOjQHfddQQrLv5+fH76+vgDUrVvX2C5u7969xjn08MMP07Bhw0KP5SgvAPv27aNbt24AtG3blpo1awKwf/9+WrVqha+vL/fddx+9evUy6nPWj3cSH0BoaCgAVatWpUyZMvz88892n3///fekpaXRtWtXAOrVq0fdunWNlYX5ypYti9VqZcOGDWRmZvLEE0/Qt2/f244XExPDhAkTAKhZsybVq1c3VuIB9O7dG5PJxAMPPEDDhg05cuSI3fcTEhLo168fJpOJihUrEhYWRnx8vEttFREREREREREREZE/B/firjA9PZ2KFSsar0uVKkWZMmVIT08HoHz58sZnnp6eXL58mfT0dK5du0ZAQIDxWXZ2NhkZGVSsWJEKFSrg5pY3T5abm8uiRYvYsWMHkDdZVL169SLH4e3tXWD8cXFxXLx4kebNmwN5W+Ldd999jB07Fg8PjyL3R5kyZYx/m81m47XZbCY3NxfIW3Xyz3/+kzNnzmAymTh//jz9+vUjMzPTLs78CY389u3fv99uS7eyZcuSkZHBkCFDMJvNvPLKK6SnpzNkyBAGDhxoF1dh/Wh73PT0dLZt28by5cuN7+ZPIN7K9v0KFSpw+fLlIvRW4QqLu1y5csa/TSaT8cyqrKwsKlSoYBdbYRzlBfJWYNnWkR/DrTmrVq2a8W9n/Xgn8d3aXjc3t9ue0ZWenk6FChUwmUx2dd+68ql06dL8+9//ZsGCBUyfPp3GjRszZcoU7r//frtyJ06cYObMmWRkZODm5kZaWhpWq9X43Db/5cuXJysr67Z4hg0bhtlsBuD69et06dLFpbaKiIiIiIiIiIiIyJ9DsU8+VapUif379xuvs7Ozyc7OxsfHB8i7aZ8vKysLb29vfHx8KFu2LMnJybfVd+rUKbsb55999hmff/45n3zyCeXLl2fdunXGs3aKEodtnbbWrFnDihUraNasmfHekCFDSEpKolOnTq52Q5HMmDEDgFWrVhmTRgBeXl522+X9/PPPxrOAfH19adeuHQsWLCiwzqioKKKiokhJSeHpp5+mVatWNGrUyPi8sH607R9fX1+GDRvGgAEDCm2L7WTD5cuXjYkYNzc3u0mKX375pdC6CuJq/m9Vvnx5u4mwW1cIFcRRXuD/cpM/uZT/bKtbc5b/PjjvxzuJzxWVKlUiKyuL3NxcYwI3MzPTuA5sNWzYkNmzZ3Pjxg1mz57N5MmTee+99+zKjBo1imeffdZYpWixWOw+z8rKMiasLl++TMWKFcnOzjY+9/X1Zc6cOTRo0KBY2iciIiIiIiIiIiIi955i33avffv2HDp0iJSUFABWrlxJy5Yt8fT0BODAgQPGNmmbNm2iVatWVKtWjdq1axMXFwfkrY4YOXIkV69eva3+zMxMatasaayqWL16Nb/++iuQ96yiK1euYLVaC43D1oYNG/j55585ffo0aWlpNG3a1O7zkJCQ37X1XmEuXbrEww8/jNls5sSJE+zevZtff/2V2rVrk5OTw759+wBYunSp8fwgf39/Dh48aLTv6NGjTJw4Ech7ftYXX3wB5K288fT0tJv4Aef9eKugoCDWrVtnTBjFxsY6nPDZv38/P/zwA/B/+YW8beG++eYbAFJSUgp9zo+7u3uBq6aKEret5s2b89lnnwHw1VdfcerUqUKP6ygvkLc15NatW4G851zlt/nRRx9l7969ZGZmcuPGDVauXGnU7awfXY3PFbbXQa1atahevTobN24E4Pjx45w7d44WLVrYfefw4cMMHTqUa9eu4eHhwQMPPGCszPPw8LDrk/r16wOwbt060tPT7a7TdevWAXnb/Z04ccJuEje/D1asWIHVauXGjRtMmTKFY8eO3XFbRUREREREREREROTeU+yTT9WqVWPq1KlERUURFBREQkIC06ZNMz5v3749EydOpGPHjpw7d45hw4YBMHPmTD7++GMsFgu9e/emVatWdluK5evSpQsZGRkEBQXx3HPPMXr0aH744QfefvttmjZtysWLF/H39y80DltTpkwhJSWFNWvWEBQUdNuqKIvFQnJyMpmZmcXYU/9n4MCBrFixgs6dO7N48WLGjh1LbGwsO3fuZNKkSYwePZouXbpQvXp1ypQpg8lkwsfHh2nTpvHcc89hsVgYN26csX3ZoEGDmDFjBh07diQ0NJTw8HAaN27scj/eKjg4mM6dO9OzZ08CAgL4/PPPjWdX2crJycFisTB+/Hg6duxISkqKkd/+/fuTkJDAU089xcKFC+nQocNtW8TZCggIYPny5YwaNeqO47b18ssvs3PnTtq2bct7772HxWIp8Pi2x3WUl23btvHyyy+zefNmgoKC2LZtG+3atcNkMtGkSRMef/xxunTpQkREhN1qOWf96Gp8rrC9DkwmE7NmzWLZsmUEBwcTExPDrFmzbtvW79FHH6VmzZoEBwcTEBBAbGwsL7/8MgCdOnVi+PDhLF26lJEjRzJ06FBCQ0PJyMigf//+jB07ltTUVHJzc6lZsybh4eFEREQwZswYKleubHecESNGcO3aNYKCgggKCuLGjRs88sgjd9ROEREREREREREREbk3Ffu2ewBhYWGEhYUV+Jmnpyfvvvvube8/+OCDxrNwbNWrV49du3YZrytVqnTbqps9e/YY/85/FpCzOJ555hmeeeYZ4/XevXsBblsNku+//uu/+J//+R8gb1UK5E2yJSYm3la2cePGxvu2xwBYsWKF8W9fX19jxYefn99tdeVPWlitVrp06WI8v2j8+PHGxEFgYCCBgYG3xdCkSRPWr19fYFvyFdaPt8b+3HPP8dxzzzmt8x//+IfDz+rVq8emTZsK/Cy/7bbb0fn7+3PkyJHfFbdtnh988MFC+6Sg4zrKC8DGjRuNrewiIyONvLz88svGFn179uyxm+hx1I+uxvf+++8b/z5w4IDdZ7avba+Dhx9+mFWrVjmt183NjXHjxjFu3LjbPnvhhRd44YUXjNd9+/a1+zw6OhrIW30H8Pe//93uc9u8enp6Mn36dKexiIiIiIiIiIiIiMifW7GvfCrMrdu/iXM9e/bk448/BvK2NKtbty5lypQp4ahk6tSpjB49GqvVyrfffsvx48dp2LAh6enpNGvWzNg2b+3atbdtPSciIiIiIiIiIiIi8lf2h6x8kuLz+uuv89prrzFv3jwqVarkcOtAubsGDx5MdHQ07du357777mPSpEn4+voCMGbMGAYPHkxOTg6NGjVizJgxJRytiIiIiIiIiIiIiMjdc1cnn2y33xLX+Pn5OdyuTkqOj48PH330UYGfRUREEBERcZcjEhERERERERERERG5N9z1bfdERERERERERERERETkr0uTTyIiIiIiIiIiIiIiIlJsNPkkIiIiIiIiIiIiIiIixcZktVqtJR2EiIiIiIiIiIiIiIiI/DVo5ZOIiIiIiIiIiIiIiIgUG/dvv/22pGMQEREREbln1alTBwDTwRZOy1n9DqicyqmcyqmcyqmcyqlcEcq56l5vh/ql4HKuutfboXL3VjlX6Ty9O+Wc0conERERERERERERERERKTZ/qcmnmzdvsn79+t9VR+/evalbty716tWz++/nn39m0aJFvPLKK0Wq7/jx41gsliJ9Z8mSJcZxoqKiWLduXZG+//rrr/Poo48SHx9v975t/5w6dYp27doVqd5b3Ul/uColJYUDB26fPT127FiR+7OkOMrDn8H169fx9/cnNzfXeC8zM5MWLVqwYsUKh9/bsGEDTZo0YfPmzQ7LfPzxxwQEBFC/fn1at27NxIkTyX/03OrVq2nVqhWLFi0qvsbco2yvx0WLFtGqVSs2btz4hx3P2Vi0evXqYj2WbX0tWrQgJSWlWOsviKtjw65du0hLSytS3bbjUXGMnXfq9+bJ2W9k48aNi9wvJeFO8iciIiIiIiIiInffn2ryKScnx+nnJ0+eLPJETUF1Tp8+nVOnTtn95+PjQ9++fRk7dmyR6i8Jn332GStXriQkJMTu/TvpH2f+yP5ITEwscPKpfv36fPrpp3/IMYubozzYTujcqw4fPkzjxo1xc/u/IeLNN9+kbNmyDr/zwQcfsGnTJh566CGHZdLS0njnnXdYtWoVrVu3ZuvWrezcuZNNmzYB8MknnzBt2jQGDx5cfI25R9lej4MHD+aNN95g5cqVdz0Oq9XKtGnT7tn6XOXq2PDRRx9x4cKFItXtaDy6m4qjX539Bmzfvp0qVar8rvrvBkf5K+z/D0RERERERERE5O76QyafNm3aRGhoKMHBwURERHDmzBkA3n//fUaOHMnQoUPp1asXffr0Mf6C+ezZs0RGRhISEkKPHj04ePAgAF9//TVdu3YlOjqaZ555BoA1a9YQHBxMQEAAkZGRpKam8ssvvzBkyBAOHjxIv379nMbxwQcfEBMTQ9euXZk5c6bL7fr3v//Nm2++CUCzZs2IjY2lf//+WCwW5s2bZ5SbP38+/v7+dO3alaSkJIf1FdQOV126dInhw4cTFBRESEgIs2fPBvJWSl2+fJnnn3+exMREo3xB/ePu7s6cOXPo2LEj/v7+JCcnG+Xnzp1LSEgInTp1YsKECVy/ft1pf0RFRTFnzhwGDRpEeHg4UVFR5OTkcOzYMUJDQ5k8eTK9e/cmNDSUPXv2ALBgwQJeffVVo77817t372bu3LksWbKE6dOn2x3zxIkTPPnkkwB8+OGHxMTEMHLkSMLDwxk0aBCJiYkMHDiQwMBA1q5dC+TdtH3jjTcICAigQ4cOvPTSS9y8eROA8+fP89RTT2GxWIiJiWH48OHG6oKkpCTCw8MJCQlh4MCB/Pjjj8Z3+vTpg8ViISAggPnz59/WN7fm4dlnn+Vf//oXbdu2JTk52WH+Tpw4QdeuXZk+fTpPPfUUnTt3Zu/evURFRREaGsr48eOLdD4AbNmyhZCQENq1a8fo0aO5du2a07xA3uoC29Ude/bsITU11enKkjZt2jB//nzKlStn9/6ZM2eoV68eAD///DPly5fH19cXgHLlyvHhhx/Svn17AK5du0blypWNvM2YMcM4P99//30gb/JuxowZBAcHExwcTHR0NFeuXDH6/d133+Uf//gHHTt2ZPbs2bz33ns8/fTThISEcOLECSBvsuett96iX79+hIWF8dJLL3Hjxg0g7wb9U089RVBQEOHh4Wzbtg3IO9/GjBnDqFGjeOKJJ+jRowc//PADkHd9vfjiiwQFBREaGmq3OqygsaKg67FatWr89ttvBfZtZmYmgwYNIjAwkI4dO7J06VKn9edzZSwaOnQo6enphIaGkpqaytGjR+nRoweBgYGEhoaye/duo+yCBQvw9/enR48exMbG0qFDh0LrA9i5cyehoaH4+fnZxefoGnM1j7ZsxwZH49HChQtJTk4mOjqarVu3Or1u8hU0HjkaO52dB7acHbd+/fr89NNPt72+tV+bNGnCwoULjT5ZvHgxkDdxbDvhnf+6oHPOVkBAABcvXizSGOToN+zatWuMGjWKDh060LdvX2bNmsVLL70EOP6tv9WiRYuwWCwEBgby1FNP8d13392Wv4J+yx39djk6r+90zBUREREREREREeeKffIpNTWVcePGsWDBAhISEujevbtx08lsNpOcnMwbb7zB2rVradSokXHTbcSIEXTv3p34+HgmT57MCy+8wPXr1/Hw8ODcuXNYLBaWL1/OpUuXePXVV1m8eDHbt2/nwQcfZN68eXh6ejJu3Dj8/PxYtmyZ0zjc3d1JSkpi3rx5jB49+o7aaTabSU1NZenSpaxYsYLZs2eTnZ3N6dOnee+991i7di1xcXHGhNetHLXDVf/617/w8vJi27ZtrFmzhg0bNrB9+3YWLFhAuXLl+Oijj+wmCm7tH4Aff/yRGjVq8MUXXzBs2DAjF/Hx8cTFxbFmzRo2b97MhQsXiI2NLbQ/vvzySxYsWEBcXBwpKSns3r0bs9nM6dOnCQwMZOXKlYwePZpx48Y5ratNmzZ069aNAQMGGDkriJubGzt27GDSpEnExcVx8uRJ4uPjWbx4MW+99ZbRn4mJiezcuZOtW7cSHx/P8ePH2bJlCwDTpk2jVatWJCYm0rNnT+Lj4zGbzfz0009ER0czc+ZM4uPjadu2rXET8v3338ff35/ExETi4uL4+uuvjcmPfLfmwd3dnf3795OYmEhAQIDD/JnNZk6dOoXFYmHVqlXUr1+fCRMmMGvWLNatW8fatWuNG/SunA8XL17ktddeY8mSJSQnJ3P58mWWLFlSaF527dpF27Ztgbwt+CZPnsykSZOc5q1BgwaYTKbb3q9ataqRi4cffpicnBxefPFFfvrpJ27cuEGNGjUoX778bd/bunUru3btYuvWrWzcuJElS5Zw+PBhNm3axPbt29mwYQPx8fFYrVajfrPZzJEjR1i0aBEffvghc+bMoVKlSixfvpzu3buzfPlyo9xXX33F0qVLiYuL45tvvmHTpk3k5uYyfPhwnnnmGbZt28bMmTOJjo4mIyMDNzc3EhISGDt2LKtXr6Zx48Z8/PHHAMycOROr1UpCQgKxsbEsXLjQmCApaKwwm823XY/OzJ8/H19fX5KSkli+fDlvvfUWFy9edFh/UcaiN998Ew8PD7Zs2UL16tV57bXX6N+/P0lJSQwbNozXX38dgNOnT7No0SLWrl3LqlWrSEhIwN3dvdD68r+7efNmVq1axbvvtm2VjQAAIABJREFUvkt2drbTa8zVPDriaDwaMmQItWvXZsaMGXTu3NnhdWOroPHI0djp7Dyw5cpxC+tXNzc3rl69yvLly1m+fDnvvPOO0z9gKOg3wFHfuTIGOfsNW716NWlpaSQmJvL222/z6aefGueKo996WxkZGcyfP58NGzaQlJREnz59SE5Ovi1/t/6WO/vtcnRe3+mYKyIiIiIiIiIizhX75NPOnTvx8/PjgQceAODxxx/nyJEjxs35li1bUqlSJQAsFguHDh0iNTWVs2fP0rt3bwCaNGlC5cqVOXz4MCaTidzcXMLDwwHw9vZm37591KpVC4DWrVvz/fffFykOk8lEo0aNjDpuNWHCBB577DHjv8DAwALLhYaGAnk318uUKcPPP//Mvn37aNWqFb6+vphMJnr27Fngd11thyPbt2+nT58+AHh5edGpUyd27tzp8vcB7rvvPnr16gXkrZ7I38ooISGBXr164eXlhdls5m9/+xuff/55ofUFBwfj4eGB2WzmwQcfNOrz9PTE398fgMDAQM6dO0dGRkaRYnXEz8+P8uXLYzabqVGjhnGcWrVqGTcMLRYLq1evplSpUpQqVYqmTZvy3XffAbBv3z66desGQNu2balZsyYAO3bsoFmzZsaKnYiICBITE8nJycHX15cvv/ySI0eOUK5cOebOnYuXl5fTOE0mE4GBgZQuXRpwnr8KFSrg5+dntKNly5aUKlWK0qVL4+PjY7cqIp+j+nbu3Enz5s2pWbMmZrOZd999lwEDBgCO83LlyhV++ukn6tSpA+RNpHXq1In//u//Llpy/r9y5coRHBwM5J1zq1atokaNGly4cIHWrVszadIkfvvtN9LT00lNTTVWPiUmJtKlSxdKly6Nt7c3W7ZsoUmTJiQlJdGtWzfKli2LyWSiV69edud+QEAAJpOJWrVqcfPmTQICAoy+tO27kJAQ3NzccHd3p0OHDhw+fJjvv/+etLQ0unbtCkC9evWoW7eusTrDz8/PWLVVt25du2umX79+mEwmKlasSFhYmN2zvgoaK27l4+PDd999R1ZW1m2fxcTEMGHCBABq1qxJ9erVOX/+vNP6XR2LbvXJJ5/Qo0cPAB577DFjXNq/f79Rn+3Y4Yonn3wSk8lEnTp1KFu2LD///LPTawxcz6MjjsYjW3c6jjobO52dB7/3uLfq3LkzAJUrV6ZBgwYcOXKkyHUUxJUxyNlv2N69ewkLC8Pd3Z0qVarQsWNHAKe/9bbKli2L1Wplw4YNZGZm8sQTT9C3b9/b4rz1t9zZb5ej89rV9oqIiIiIiIiISNHc/mfrv1N6ejoVK1Y0XpcqVYoyZcqQnp4OYLfCwdPTk8uXL5Oens61a9eMm4sA2dnZZGRkULFiRSpUqGA8eyY3N5dFixaxY8cOALKysoy/ri9KHN7e3g7bMHr0aONmLmD33BtbttuLubm5kZOTw6VLl+za6Og4rrbDkfT0dLu6K1SowP/+7/+6/H3I63/b+POfRZSens62bduM1QW5ubnGhKEzBfUH2Ofc3d2d0qVLc/ny5SLF6kiZMmWMf5vNZuO12Ww22pORkcE///lPzpw5g8lk4vz588a2U5cuXaJChQpGHfk5SE9PZ//+/XbbipUtW5aMjAyGDBmC2WzmlVdeIT09nSFDhjBw4MBCY7XNl7P8OWrTre2y5ai+9PR0u/63rctRXk6dOsVjjz0G5G2RFR8fX6zP2apUqRKjRo3i6NGjvPrqq4wdO5Y5c+bwwQcfMGTIEHx8fIw22caYP8F367Xt7e1tN5mZ/1wqs9kMYEz4mc1mu+fC2Obd09OT77//nvT0dCpUqGC3gqtChQpG/bbnuMlkMupLT09n2LBhxjGvX79Oly5djLKOrg1b1apV4+mnn8bf35/9+/cbcUPe1mAzZ840VmClpaVhtVqd1u/qWHSrhIQEli1bxs2bN7l586ZxvmVmZtrVUa1aNZfqg9vHmpycHKfXWP6/ofA8OuJKn9/pOOps7HR2Hvze497q1nM4KyuLqlWrFrmeW7kyBjn7DStoXM2/vhz91tsqXbo0//73v1mwYAHTp0+ncePGTJkyhfvvv/+2WG8dVx39djk6r11tr4iIiIiIiIiIFE2xTz5VqlSJ/fv3G6+zs7PJzs42bihfunTJ+CwrKwtvb298fHwoW7as3TOH8p06dcruRvBnn33G559/zieffEL58uVZt26d8YyeosRR0PZg+by8vIzVDUVVoUIFuy3YClrhUJR2OFKpUiUyMjKMm3GZmZlG234vX19fhg0bZqyQ+b2ysrKwWq2YTCauXbtGdnY23t7euLm52d1Av3r1arEc71YzZswAYNWqVcakUT4vLy+uXLli3EjPfwaZr68v7dq1Y8GCBQXWGRUVRVRUFCkpKTz99NO0atWKRo0aOY3D9pwr7vw5qq9SpUp2z3LKysoy+tlRXmyf95SYmMgPP/xgTBBcvXrV2ObtxRdfLHKcX331FTk5OcYqg9q1a9O/f3/Wr1/Ptm3b6N69OwMHDqR8+fJGm/KlpaVRpkyZ297PyMi4o77LzMw0/n358mW8vb2pVKkSWVlZ5ObmGpPO+X2Z/3yngvj6+jJnzhwaNGhQ5Dhs41m2bBmJiYl2E08Ao0aN4tlnnzVWjDh79lY+V8ciWxcuXCAmJoYNGzbw0EMPkZaWZqxayb9W8uVfK3eqsGvsbiju69DV88DZcc1mszEuXr9+3XgWWUEyMzON1ZqXL1+mYsWKdt+HP25cdfYb5uhccfZbf6uGDRsye/Zsbty4wezZs5k8eTLvvffebeVsx1VHv13OzmsREREREREREfljFPu2e+3bt+fQoUOkpKQAsHLlSlq2bGn8pfiBAweM7W42bdpEq1atqFatGrVr1yYuLg7I++vlkSNHFnjTLP9mW/ny5cnKymL16tX8+uuvQN7qjStXrmC1WguNw9aGDRtcujHriubNm7Nv3z5+/PFHcnJyHE4oOWuHKywWCytXrgTyJhG2bt3qcHvAfLb940xQUBDr1q3jl19+ASA2NrZIE2O3ys7ONp6xtHnzZurWrYu3tzdVq1blm2++AeC3334jKSnJLtbiWh116dIlHn74YcxmMydOnGD37t1GXzdp0oStW7cCeVth5U8w+Pv7c/DgQeP8OXr0KBMnTgTynlnyxRdfAHmrPzw9PQvt01vdSf7upL527dpx6NAhvvnmG3Jychg3bhwbNmwAHOfF9nlPgwYN4tChQ+zdu5e9e/fy5JNP8vLLL/Piiy9y5coVVq1aVWhsV69eJSEhAYAffviB119/3ViBmJOTw/bt23n44YepXr061apVM3IQEBDA+vXruXLlCllZWURGRvLtt99isViIi4sjOzub3NxcPv30U7uVFK7asmULv/32G9euXWPbtm20atWKWrVqUb16dTZu3AjA8ePHOXfuHC1atHBaV1BQECtWrMBqtXLjxg2mTJnCsWPHnH7n1uvx/Pnz1KpVq8CJ70uXLlG/fn0A1q1bR3p6eqGTCq6ORR4eHuTk5PDrr79y+fJlypUrx/33309OTg5Lly4lNzeX3377jUcffZS9e/eSmZnJjRs3jPPNWX3OOLvG/kgeHh7G2OLqdejqeOTqeeDsuFWqVDHGxU2bNhmTKwX167p16wD47rvvOHHiBM2aNaNKlSr8+OOPRrybN2+2a4crvwGucPYb1qRJE+Lj48nNzeXChQts27YNwOXf+sOHDzN06FCuXbuGh4cHDzzwgLH6yDZ/t3L02+XsvBYRERERERERkT9GsU8+VatWjalTpxIVFUVQUBAJCQlMmzbN+Lx9+/ZMnDiRjh07cu7cOYYNGwbkPaj9448/xmKx0Lt3b1q1amW3bVK+Ll26kJGRQVBQEM899xyjR4/mhx9+4O2336Zp06ZcvHgRf3//QuOwNWXKFOMG6O9Vv359+vXrR/fu3QkNDaVFixbcvHmzSO1wxejRo7ly5QpBQUE8+eSTREZG0qZNG6ffse0fZ4KDg+ncuTM9e/YkICCAzz//vNDvOHP//fdz+PBhgoODmTt3LlOmTAHynrlTunRpunXrxogRI+jYsaNxgzEgIIDly5czatSoOz5uvoEDB7JixQo6d+7M4sWLGTt2LLGxsWzbto2XX36ZzZs3ExQUxLZt22jXrh0mkwkfHx+mTZvGc889h8ViYdy4ccb2WYMGDWLGjBl07NiR0NBQwsPDady4cZFiupP83Ul9VatWZcqUKURFRdGuXTtKlSplrAooKC+pqam4u7sbz11y5scff2TcuHHG6x49elC/fn327NnDqFGjqF+/Pps2beLChQs899xzQN55HxYWRo8ePdizZw8BAQHk5OQQFRUFYLcaLjQ0lMDAQMLCwggPDyciIoLmzZsTFhZGYGAg3bp1IyQkBC8vL4YMGVLkPmvZsiWRkZEEBQXRrFkzQkJCMJlMzJo1i2XLlhEcHExMTAyzZs2y20KsICNGjODatWsEBQURFBTEjRs3eOSRR5x+p6Dr0dEWnyNHjmTo0KGEhoaSkZFB//79GTt2LKmpqQ7rd3Us8vLyok2bNnTo0IHs7GwCAgIICQmhV69edOzYkRYtWtCvXz+aNGnC448/TpcuXYiIiKBTp04FHte2PmfPIHJ2jf2ROnXqxPDhw1m6dKnL16Gr45Gr54Gz40ZHR/Paa6/x9NNP89NPP+Hj40NOTk6B/VqlShXCwsKIjIxkzJgxVK5cmSpVqhAREcHjjz/O3//+d+rVq2dsOejqb4ArnP2GRURE4OHhQYcOHRg7diy9evUyJtFc+a1/9NFHqVmzJsHBwQQEBBAbG8vLL78M2OfvVo5+u+rVq+fwvBYRERERERERkT+G6cyZM7//T6BdtGTJEr755hvefPPNu3VIKWHHjx/n+eefJzExsaRDcch2i7XIyEieffbZO1pJ82dyL+RlwIABLFq0CHf3/9v9s0ePHkydOvV3bV/niqioKGMS7F5x9OhRpkyZwieffFLSoThke63s2bOHt956i7Vr15ZwVP+ZmjZtypYtW4rlGU9/BNtzZdasWWRnZzNmzJgSjkrkztWpUwcA00HnK2GtfgdUTuVUTuVUTuVUTuVUrgjlXHWvt0P9UnA5V93r7VC5e6ucq3Se3p1yzhT7yqfCFMd2PyLFZerUqYwePRqr1cq3337L8ePHadiwYUmH9R9hyZIldhNPkPeconPnzt2V499rY9HZs2cLXWFVktLT02nWrBmnTp0CYO3atTRr1qyEo/rPdq+dw/kSEhIICQkhOzubq1evsnXrVpo3b17SYYmIiIiIiIiIyF3kXngRkb+uwYMHEx0dTfv27bnvvvuYNGlSgc/ckbvj73//O2PHjuX8+fMMHjy4pMO5axYtWsQHH3zA9OnTSzoUhypVqsSYMWMYPHgwOTk5NGrUSCtZpECBgYEkJycTEhKCm5sb4eHhhISElHRYIiIiIiIiIiJyF93Vyaf8Z83If44GDRrc01vu+fj48NFHH5V0GHfdvZoXi8XCnj17/vDjLFiw4A8/RlEMHjz4TzHZFhERQUREREmHIcBXX31V0iE4ZDabmTRpEpMmTSrpUEREREREREREpITc9W33RERERERERERERERE5K9Lk08iIiIiIiIiIiIiIiJSbDT5JCIiIiIiIiIiIiIiIsXGZLVarSUdhIiIiIiIiIiIiIiIiPw1aOWTiIiIiIiIiIiIiIiIFBv3b7/9tqRjEBERERG5Z9WpUwcA08EWTstZ/Q6onMqpnMqpnMqp3D1ezlX3ejv+KuVcda+3Q/1ScDlX3evt+Kv0X3HXp34puL7/tHLOaOWTiIiIiIiIiIiIiIiIFJu/1OTTzZs3Wb9+/e+qo3fv3jzyyCPUr1+f+vXr8+ijj9KvXz9Onz5dTFEWT5yuioqKYt26dXc9njlz5tCmTRv8/PwYMWIEV69eLdb6/2qOHz+OxWK5a8dTfoquadOmpKWlldjxd+3a5dLxGzdu/IfFmZKSwoEDRfurkKIq6X4WERERERERERGR3+9PNfmUk5Pj9POTJ08WOtHiSp3//Oc/OXHiBCdOnGDv3r00bNiQUaNGFaleZ+4kzj9SccezZcsWNmzYwLp160hOTiYzM5PFixcXW/1/Frm5uSUdQoGUnzz3an4c+eijj7hw4UKh5bZv306VKlV+9/EK6p/ExMRim3z6s/W/iIiIiIiIiIiIuO4PmXzatGkToaGhBAcHExERwZkzZwB4//33GTlyJEOHDqVXr1706dPH+Av3s2fPEhkZSUhICD169ODgwYMAfP3113Tt2pXo6GieeeYZANasWUNwcDABAQFERkaSmprKL7/8wpAhQzh48CD9+vVzGscHH3xATEwMXbt2ZebMmU7bUrp0afr06cOpU6ewWq0cPnyYkJAQ43Pb1x9++CFjxoxh1KhRPPHEE/To0YMffvjBrr6C4kxKSiI8PJyQkBAGDhzIjz/+aNQXExPDyJEjCQ8PZ9CgQSQmJjJw4EACAwNZu3Ztof1qy9V+cxTPrRyVq1GjBlOnTqVKlSqUK1eOgIAAUlJSjPa/+OKLBAUFERoayooVKxz2fUHxpqSk4O/vb5R57bXX6N27t/H62WefZevWrezYsYOwsDAsFguhoaFs3769wGMMGDCAjRs33vb62LFjhIaGMnnyZHr37k1oaCh79uwB4Nq1a4waNYoOHTrQt29fZs2axUsvvWQc/1//+hdt27Y1JnYGDRpEYGAgHTt2ZOnSpcax5s+fj7+/P127diUpKckurrlz5xISEkKnTp2YMGEC169fd9hPjji6poorP0ePHqVHjx4EBgYSGhrK7t2771p+AJo0acLChQt5+umnCQkJMSbQiis/ycnJWCwWQkJCmDNnDiaTyfjMlfxYrVZmzJhBx44d8ff35/333wfyJl1mzJhBcHAwwcHBREdHc+XKFSBvpeKcOXMYNGgQ4eHhREVFkZOTw8KFC0lOTiY6OpqtW7c6jTsgIICLFy9y4sQJunbtyowZM4iIiCAoKIjk5ORC8/zoo48yf/58/Pz8+PXXX433d+/ezdy5c1myZAnTp08HHI8BzuK7tX5n/VzQGP7JJ58QHR1tlOnUqRNvv/220bfNmzcnMzOTZs2aERsbS//+/bFYLMybN6/Ac0xERERERERERESKX7FPPqWmpjJu3DgWLFhAQkIC3bt3N278ms1mkpOTeeONN1i7di2NGjVi9uzZAIwYMYLu3bsTHx/P5MmTeeGFF7h+/ToeHh6cO3cOi8XC8uXLuXTpEq+++iqLFy9m+/btPPjgg8ybNw9PT0/GjRuHn58fy5YtcxqHu7s7SUlJzJs3j9GjRzttT3Z2NsuWLaNp06Z2N0UL4ubmRkJCAmPHjmX16tU0btyYjz/+2K7MrXH+9NNPREdHM3PmTOLj42nbti3jx4836tuxYweTJk0iLi6OkydPEh8fz+LFi3nrrbeMm6nO+jWfq/3mLB5bzso1btwYPz8/o+yuXbto2bIlADNnzsRqtZKQkEBsbCwLFy7kxIkTt9XvKN4HHngAyDvPAI4dO8b169e5du2aMTn42GOPMWXKFCZPnkxiYiLvvPMOn3/+udPc3cpsNnP69GkCAwNZuXIlo0ePZty4cQCsXr2atLQ0EhMTefvtt/n0009xd3cH8s6t/fv3k5iYSEBAAPPnz8fX15ekpCSWL1/OW2+9xcWLFzl9+jTvvfcea9euJS4uzpgYBYiPjycuLo41a9awefNmLly4QGxsbJHiB8fXVHHkB/Imlvr3709SUhLDhg3j9ddfv2v5gbzr4+rVqyxfvpzly5fzzjvvkJqaWiz5ycnJYcyYMUyYMIH4+HgqVqzIL7/8Arien61bt7Jr1y62bt3Kxo0bWbJkCYcPH2bTpk1s376dDRs2EB8fj9VqtbuWv/zySxYsWEBcXBwpKSns3r2bIUOGULt2bWbMmEHnzp0dxm3LbDZz6tQp/Pz8iI2NZdSoUca44CzP7u7uZGVlcejQIcqWLWvU16ZNG7p168aAAQN46aWXnI4BzuKzrb9UqVIO+9nRGN6mTRsOHz4MQHp6Op6enhw6dAiAU6dOUaNGDSpWrIjZbCY1NZWlS5eyYsUKZs+eTXZ2dpHPMxERERERERERESm6Yp982rlzJ35+fsZN6Mcff5wjR44Yf9nfsmVLKlWqBIDFYuHQoUOkpqZy9uxZY4VEkyZNqFy5MocPH8ZkMpGbm0t4eDgA3t7e7Nu3j1q1agHQunVrvv/++yLFYTKZaNSokVHHrV599VWaNm1K06ZNady4MRcuXCh0hVQ+Pz8/fH19Aahbt26h22Tt2LGDZs2aUa9ePQAiIiJITEw0tgP08/OjfPnymM1matSoYawqqVWrlt2KpIL61Zar/VZYPEUt9+6773L9+nUjtwkJCfTr1w+TyUTFihUJCwsjPj7+tjicxZt/8/nSpUuULl2ahg0b8vXXX3P69GmqV6+Ot7c3lStXJi4ujnPnzlG/fn3efPNNp3koiKenp9HfgYGBnDt3joyMDPbu3UtYWBju7u5UqVKFjh07Gt8xmUwEBgZSunRpAGJiYpgwYQIANWvWpHr16pw/f559+/bRqlUrfH19MZlM9OzZ06gjISGBXr164eXlhdls5m9/+1uRJ2ecXVO27jQ/AJ988gk9evQA4LHHHrvr+QHo3LkzAJUrV6ZBgwYcOXKkWPKTkpLCr7/+SocOHQB48sknjW3iXM1PYmIiXbp0oXTp0nh7e7NlyxaaNGlCUlIS3bp1o2zZsphMJnr16sXOnTuN7wUHB+Ph4YHZbObBBx8scAxxFPetypQpY7T/oYceMupylmeTyUTnzp0LnWx3NgY4i8+2fmf97GgMr1ixIjdv3iQ9PZ2DBw/i7+9PVlYWN27c4MCBA7Rt29aIMTQ0FICqVatSpkwZfv75Z6dtEhERERERERERkeLhXtwVpqenU7FiReN1qVKlKFOmDOnp6QCUL1/e+MzT05PLly+Tnp7OtWvXCAgIMD7Lzs4mIyODihUrUqFCBdzc8ubJcnNzWbRoETt27AAgKyuL6tWrFzkOb29vh2144403jMmAXr160aFDB6pVq+ZS+8uVK2f822QyFfqcqvT0dPbv32/cfAUoW7YsGRkZQN7N43xms9l4bTab7Z6ZUlC/2ipKvzmKJ39SzZVyVquVSZMmce7cORYuXGjkLz09nWHDhmE2mwG4fv06Xbp0YenSpcaqjBdeeIF+/fo5jLd169YcPnyY++67j6ZNm/LQQw9x8OBBvLy8jBvP77zzDnPmzCEyMhIvLy9effVV/P39adeuHdeuXaNUqVLs2rXLWWrs+tTd3Z3SpUtz+fJlLl26RIUKFYzPqlevbjeRZ3tunThxgpkzZ5KRkYGbmxtpaWlYrVYuXbpkV7/td9LT09m2bRvLly8H8nKXP7Foa/jw4Xz55ZcALFmyhMaNG9vV4eiaAoqcn2PHjjFgwAAA2rZty7vvvktCQgLLli3j5s2b3Lx50zgf71Z+ALs8eHp6kpWV9YfkJ3/8KEp+0tPT7erw8vIy3rcdm7y9vY28gP0Y4ubmVuAY4ijuW3l6etrVlZ8jR3nOZxufI87GgPwJe0fx5ddfWD87GsNbt27NoUOH2LdvH/7+/qSlpXH8+HEOHDhgN5HrSl+KiIiIiIiIiIhI8Sv2yadKlSqxf/9+43V2djbZ2dn4+PgAeTcb82VlZeHt7Y2Pjw9ly5Y1nkdi69SpU3Z/gf/ZZ5/x+eef88knn1C+fHnWrVvH6tWrixxHYX/Vny8mJoZRo0bRtWtXypUrh9lstruJevXqVZfqccTX15d27dqxYMGC31VPQf1qy9V+czWewspNnTqV9PR0Fi5ciIeHh9335syZQ4MGDezK//rrr8bqtnLlyjmNt3Xr1qxYsQKz2UyLFi2oU6cO06ZNw8vLy1iJU6lSJcaPH8/48ePZvn07w4cP58CBA6xfvx6r1Wrk383NzS6f+Vt+5fdjftlr166RnZ2Nt7c3Xl5exko+4Lbna9meW6NGjeLZZ581VhZZLBYgb9LEtg7bFRm+vr4MGzbMmOxxZPLkycazhm7Nt7NrCoqenxs3brB582YA7rvvPi5cuEBMTAwbNmzgoYceIi0tzVhhc7fyA3nPFqpZsyYAly9fpmLFisWWH9sJ3KtXrxpbtrman0qVKtlNKqWlpVGmTJnb3s/IyDDGJVc5ittVjvJc1DocjQEREREuxeesn52N4fkTnAcPHuSFF14gNTWVgwcPcvToUaZMmXLHbRIREREREREREZHiUezb7rVv355Dhw6RkpICwMqVK2nZsqXxF/gHDhwwViFs2rSJVq1aUa1aNWrXrk1cXByQ9xfvI0eOLHBiJ/9mc/ny5cnKymL16tX8+uuvQN7qlCtXrmC1WguNw9aGDRscbsfUpk0bHnnkERYuXAhAlSpV+PHHH40bpvk35IvCNk5/f38OHjxoxHn06FEmTpxY5DoL6ldbrvabq/E4K7d371527tzJjBkz7CY2AIKCglixYgVWq5UbN24wZcoUjh07RtmyZfH19cXX15eyZcs6jbdGjRpcuXKFPXv24OfnR506dTh79ixff/01LVq0IDs7myeeeMKYdPjv//5vY8WHj48Pvr6+xs3+qlWr8s033wCQkpJi93yj7OxstmzZAuTluW7dunh7e9OkSRPi4+PJzc3lwoULbNu2zWFeLl26RP369QFYt24d6enpXL16lebNm7Nv3z5+/PFHcnJy7CYCg4KCWLdunTHREhsbW+BEYYUKFYw+u7WfnV1Td5IfDw8P41j5EwblypXj/vvvJycnh6VLl5Kbm8tvv/121/KT36cA3333HSdOnKBZs2bFkp/atWvj4eHBF198AWBMphUlPwEBAaxfv54rV66QlZVFZGQk3377LRaLhbi4OLKzs8nNzeXTTz+1W6HmiIfk391YAAAHLUlEQVSHhzHuOIrbVY7yXBh3d3cjBmdjgKvxOetnZ2N4mzZt2L17Nzdu3KBChQo0b96czZs34+vra7faSUREREREREREREpGsa98qlatGlOnTiUqKoobN25QvXp1pk2bZnzevn17Jk6cyDfffEONGjWYNWsWADNnzuTVV1/lnXfewWQyMWjQoAJvInbp0oX169cTFBRE1apVeemll4iKiuLtt9/mmWee4eLFi/j7+7Nr1y6ncdiaMmUKc+fOdbj64MUXX6RPnz706dOH6tWrExERweOPP07NmjUJDAxk9+7dReqjpk2b2sU5bdo0nnvuOX777TfKlSvH+PHji1QfOO7XfEXpN1fi8fHxcVju008/5fTp03bbwNWrV4/169czYsQIJk6cSFBQEDdv3sRisfDII4/cVr+zeEePHk3z5s05cOAA//Vf/wXA/fffT3Z2trFlV2RkJH379iUnJ4fSpUszbdo0Y2s5W/3792fkyJHs3r2bhx56iA4dOhhbc91///0cPnyYt99+G5PJxD//+U8gb1XH3r176dChA/Xq1aNXr15kZmYWmJeRI0cydOhQPD096d27N/3792fs2LGsWrWKfv360b17d7y8vIiMjDRu/gcHB3Pq1Cl69uzJzZs3efDBB41jF4Wja6o48lOvXj0CAgIICQnB29ubV155ha+++op+/fqxatWqu5IfyJsMDgsL48qVK4wZM4bKlSsXW37efPNNXn/9daxWK71798bHx4ecnByX8xMaGsrx48cJCwvDarXSv39/mjdvTm5uLidPnqRbt25YrVZatWrFkCFDCs1np06dGD58ONHR0U7jdoWreb5VQEAAw4YNIy0tjXfeecfhGOBqfB4eHg77uWbNmg7H8OrVq5OVlUWbNm2AvPPx1KlTha5GExERERERERERkbvDdObMmdsfFPIHWbJkCd988w1vvvnm3TrkfwT1a/E7fvw4zz//PImJiQV+npuba0yWzJo1i+zsbMaMGXM3Q/yP17RpU7Zs2ULVqlVv+0z5ERGR4lSnTh0ATAdbOC1n9Tugciqnciqnciqncvd4OVfd6+34q5Rz1b3eDvVLweVcda+346/Sf8Vdn/ql4Pr+08o5U+zb7hXG9vktUnzUr3dPQkICISEhZGdnc/XqVbZu3Urz5s1LOqz/SAWd98qPiIiIiIiIiIiISMkq9m33RP7qAgMDSU5OJiQkBDc3N8LDwwkJCSnpsOT/U35EREREREREREREStZdnXzS8zj+GOrX4tegQQOHW+6ZzWYmTZrEpEmT7nJUYuurr74q8H3lR0RERERERERERKRk3fVt90REREREREREREREROSvS5NPIiIiIiIiIiIiIiIiUmw0+SQiIiIiIiIiIiIiIiLFxmS1Wq0lHYSIiIiIiIiIiIiIiIj8NWjlk4iIiIiIiIiIiIiIiBQb98uXL5d0DCIiIiIi96zy5cuXdAgiIiIiIiIifypa+SQiIiIiIiIiIiIiIiLFRpNPIv+vvTuqYRAKAiBYDCANsThDATUANE020MKMgvu8y+YBAAAAAACQEZ8AAAAAAADIfIxP8zyfMQcAAJzOrgsAAAC9w/jkGAcA4O7svAAAANDajU+OcAAAnsLuCwAAAJ3N+OT4BgDgaezAAAAA0NiMT9M0nT0HAABcyg4MAAAAjd3P7jm+AQB4CrsvAAAAdHbj0+vlCAcA4P7svAAAANAalmVZrx4CAAB+1TiOV48AAAAAf+Xw5RMAAAAAAAB8Q3wCAAAAAAAgIz4BAAAAAACQGdZ19c8nAAAAAAAAEl4+AQAAAAAAkBGfAAAAAAAAyIhPAAAAAAAAZMQnAAAAAAAAMuITAAAAAAAAGfEJAAAAAACAjPgEAAAAAABARnwCAAAAAAAgIz4BAAAAAACQEZ8AAAAAAADIiE8AAAAAAABkxCcAAAAAAAAy4hMAAAAAAAAZ8QkAAAAAAICM+AQAAAAAAEBGfAIAAAAAACAjPgEAAAAAAJARnwAAAAAAAMiITwAAAAAAAGTEJwAAAAAAADLiEwAAAAAAABnxCQAAAAAAgIz4BAAAAAAAQEZ8AgAAAAAAICM+AQAAAAAAkBGfAAAAAAAAyIhPAAAAAAAAZMQnAAAAAAAAMuITAAAAAAAAGfEJAAAAAACAjPgEAAAAAABARnwCAAAAAAAgIz4BAAAAAACQEZ8AAAAAAADIiE8AAAAAAABkxCcAAAAAAAAy4hMAAAAAAAAZ8QkAAAAAAICM+AQAAAAAAEBGfAIAAAAAACAjPgEAAAAAAJARnwAAAAAAAMiITwAAAAAAAGTEJwAAAAAAADLiEwAAAAAAABnxCQAAAAAAgIz4BAAAAAAAQEZ8AgAAAAAAICM+AQA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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 18,
+     "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": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T12:58:59.677201Z",
+     "start_time": "2021-01-15T12:58:59.488965Z"
+    }
+   },
+   "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": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T13:07:31.645328Z",
+     "start_time": "2021-01-15T12:59:49.229777Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"redhat-openshift-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-4.6-blocking\" downloaded \n",
+      "\"redhat-openshift-okd-release-4.6-informing\" downloaded \n",
+      "\"redhat-openshift-okd-release-4.7-blocking\" downloaded \n",
+      "\"redhat-openshift-okd-release-4.7-informing\" downloaded \n",
+      "\"redhat-openshift-presubmit-master-gcp\" downloaded \n",
+      "\"redhat-osd\" downloaded \n"
+     ]
+    }
+   ],
+   "source": [
+    "download = True\n",
+    "if download:\n",
+    "    data_set = {}\n",
+    "\n",
+    "    for dashboard in dashboard_names:\n",
+    "        response_1 = requests.get(f\"https://testgrid.k8s.io/{dashboard}/summary\")\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",
+    "            if \"tests\" in response_2.json().keys():\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": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T13:09:11.747606Z",
+     "start_time": "2021-01-15T13:09:11.744369Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "date = datetime.datetime.today()\n",
+    "file_path = f\"../../../data/raw/testgrid_{date.day}{date.month}.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-15T13:10:01.097520Z",
+     "start_time": "2021-01-15T13:09:12.893886Z"
+    }
+   },
+   "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). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_indepth_EDA.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_indepth_EDA.ipynb
new file mode 100644
index 00000000..1ce43d0d
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_indepth_EDA.ipynb	
@@ -0,0 +1,3459 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TestGrid In-Depth EDA\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",
+    "In our previous notebook, [TestGrid_EDA](testgrid_EDA.ipynb), we did some straightforward data access and preprocessing work in order to take a look at what data TestGrid exposes, how to access it and convert the test grids themselves into 2d arrays. While performing that initial data exploration we came up with a few more questions around how to look at this data in aggregate that we want to address here.     \n",
+    "\n",
+    "In this notebook we will try and answer the following questions about the TestGrid data:\n",
+    "\n",
+    "* How comparable are the TestGrids?\n",
+    "    \n",
+    "* How do we analyze the TestGrids in aggregate to learn from their combined behavior?\n",
+    "\n",
+    "* How many/ which tests are the most common?\n",
+    "    \n",
+    "* Are their time series dates comparable?\n",
+    "\n",
+    "* Are there sub-groups that should only be compared with one another?\n",
+    "    \n",
+    "* Is looking at the grid matrices independent of test names a valid approach for issue identification?\n",
+    "\n",
+    "* What is the expected behavior of a test over time across multiple jobs.\n",
+    "\n",
+    "* How does the entire test platform/specific tests perform on a given day?\n",
+    "\n",
+    "* How does a test/ the entire test platform behavior evolve over time.\n",
+    "\n",
+    "* Is there sufficient data here for useful ML approaches?\n",
+    "\n",
+    "* Can we develop some meaningful alerting/ problem identification based on the results of the above questions?\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T19:15:33.528348Z",
+     "start_time": "2021-01-26T19:15:31.855479Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "import re\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from itertools import groupby\n",
+    "from scipy.stats import kurtosis\n",
+    "from scipy.signal import convolve2d\n",
+    "import datetime\n",
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.cluster import DBSCAN\n",
+    "from sklearn.decomposition import IncrementalPCA\n",
+    "\n",
+    "from IPython.display import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length\n",
+    "\n",
+    "sns.set(rc={\"figure.figsize\": (20, 10)})\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:07.721799Z",
+     "start_time": "2021-01-26T17:35:02.928833Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with gzip.open(\"../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. How comparable are the TestGrids?\n",
+    "\n",
+    "Lets start by answering the first question on our list, \"How comparable are the TestGrids?\"\n",
+    "\n",
+    "TestGrids are made of a set of tests which either pass or fail over time. They are multidimensional time series where the values can take either 12 (fail), 1 (pass) or 0 (not run). There are a few other status codes used, but we will ignore them for now. We want to know if they are always the same shape, and composed of the same tests. And if not, how do they vary?     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:07.736073Z",
+     "start_time": "2021-01-26T17:35:07.724344Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/testgrid_0.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.a) Are there duplicate grids?\n",
+    "\n",
+    "The first thing we will do is look at all the summaries and grid names to see if there is any overlap. Are there duplicate grids for different platforms? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:07.743943Z",
+     "start_time": "2021-01-26T17:35:07.738067Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "403\n",
+      "403\n"
+     ]
+    }
+   ],
+   "source": [
+    "# count all grids and uniquely nameed grids\n",
+    "c = 0\n",
+    "grid_names = set()\n",
+    "for i in data.keys():\n",
+    "    for j in data[i].keys():\n",
+    "        grid_names.add(j)\n",
+    "        c += 1\n",
+    "print(len(grid_names))\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It appears as though the answer to our question, \"are there duplicate grids?\" is no. There a 403 individual grids by name and all grids appear to be unique. However, there seems to be a lot of similarity in naming convention between grids where only the only difference is version number. Let's remove those references and see how many groups there are and how they compare.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.b) Are there  sub-groups?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:07.759749Z",
+     "start_time": "2021-01-26T17:35:07.745899Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "131\n",
+      "403\n"
+     ]
+    }
+   ],
+   "source": [
+    "# count all the unique grids with version numbers removed.\n",
+    "c = 0\n",
+    "grid_names = set()\n",
+    "for i in data.keys():\n",
+    "    for j in data[i].keys():\n",
+    "        remove_version = re.sub(r\"\\d\", \"X\", j)\n",
+    "        grid_names.add(remove_version)\n",
+    "        c += 1\n",
+    "print(len(grid_names))\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, we can now see that there are 131 grid types if we remove the version. This means that on average, a grid type is repeated about 3 times. Or stays useful for 3 versions. \n",
+    "\n",
+    "This might represent a challenge since that is a fair amount of uniqueness when it comes to making statistical claims about the behavior of these grids (since there are only 3 members of each sample). \n",
+    "\n",
+    "Now that we have identified these 131 cross-version grid types, lets look at each type and see what percent of tests they share. Can they be compared directly (do they have the same shape and tests)? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.c) Which tests do the groups all share?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.137981Z",
+     "start_time": "2021-01-26T17:35:07.761695Z"
+    }
+   },
+   "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>group</th>\n",
+       "      <th>num_grids</th>\n",
+       "      <th>num_shared_test</th>\n",
+       "      <th>total_tests</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>release-openshift-ocp-installer-eXe-aws-mirror...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>release-openshift-ocp-installer-eXe-aws-fips-s...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>release-openshift-origin-installer-eXe-aws-upg...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>release-openshift-origin-installer-eXe-aws-sdn...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>39</td>\n",
+       "      <td>209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>periodic-ci-openshift-release-master-ocp-X.X-e...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>54</td>\n",
+       "      <td>2116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>release-openshift-okd-installer-eXe-aws-X.X</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>release-openshift-ocp-installer-eXe-gcp-rt-X.X</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>release-openshift-origin-installer-eXe-gcp-com...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>release-openshift-origin-installer-eXe-azure-u...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>periodic-ci-openshift-release-master-ocp-X.X-e...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>953</td>\n",
+       "      <td>1114</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>77 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                group  num_grids  \\\n",
+       "0   release-openshift-ocp-installer-eXe-aws-mirror...          6   \n",
+       "1   release-openshift-ocp-installer-eXe-aws-fips-s...          6   \n",
+       "2   release-openshift-origin-installer-eXe-aws-upg...          3   \n",
+       "3   release-openshift-origin-installer-eXe-aws-sdn...          2   \n",
+       "4   periodic-ci-openshift-release-master-ocp-X.X-e...          3   \n",
+       "..                                                ...        ...   \n",
+       "72        release-openshift-okd-installer-eXe-aws-X.X          5   \n",
+       "73     release-openshift-ocp-installer-eXe-gcp-rt-X.X          4   \n",
+       "74  release-openshift-origin-installer-eXe-gcp-com...          5   \n",
+       "75  release-openshift-origin-installer-eXe-azure-u...          3   \n",
+       "76  periodic-ci-openshift-release-master-ocp-X.X-e...          2   \n",
+       "\n",
+       "    num_shared_test  total_tests  \n",
+       "0                 3         2796  \n",
+       "1                 0            0  \n",
+       "2                 1         2549  \n",
+       "3                39          209  \n",
+       "4                54         2116  \n",
+       "..              ...          ...  \n",
+       "72                3         2088  \n",
+       "73                3         1212  \n",
+       "74               36         2534  \n",
+       "75               19           80  \n",
+       "76              953         1114  \n",
+       "\n",
+       "[77 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shared_test = {}\n",
+    "results = []\n",
+    "for k in grid_names:\n",
+    "    k_list = []\n",
+    "    for i in data.keys():\n",
+    "        for j in data[i].keys():\n",
+    "            remove_version = re.sub(r\"\\d\", \"X\", j)\n",
+    "            if remove_version == k:\n",
+    "                k_list.append([x[\"name\"] for x in data[i][j][\"grid\"]])\n",
+    "\n",
+    "    total_tests = len(set([item for sublist in k_list for item in sublist]))\n",
+    "\n",
+    "    for g, l in enumerate(k_list):\n",
+    "        if g == 0:\n",
+    "            intersect = set(l)\n",
+    "        else:\n",
+    "            intersect = intersect.intersection(set(l))\n",
+    "\n",
+    "    shared_test[k] = intersect\n",
+    "    if len(k_list) > 1:\n",
+    "        values = (k, len(k_list), len(intersect), total_tests)\n",
+    "        results.append(values)\n",
+    "results = pd.DataFrame(\n",
+    "    results, columns=[\"group\", \"num_grids\", \"num_shared_test\", \"total_tests\"]\n",
+    ")\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.180499Z",
+     "start_time": "2021-01-26T17:35:08.141660Z"
+    }
+   },
+   "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>num_grids</th>\n",
+       "      <th>num_shared_test</th>\n",
+       "      <th>total_tests</th>\n",
+       "      <th>perc_coverage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.532468</td>\n",
+       "      <td>56.610390</td>\n",
+       "      <td>1224.428571</td>\n",
+       "      <td>0.195229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.658982</td>\n",
+       "      <td>173.945481</td>\n",
+       "      <td>1205.698175</td>\n",
+       "      <td>0.309903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>92.000000</td>\n",
+       "      <td>0.002397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>551.000000</td>\n",
+       "      <td>0.014575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>2534.000000</td>\n",
+       "      <td>0.206321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>953.000000</td>\n",
+       "      <td>3462.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       num_grids  num_shared_test  total_tests  perc_coverage\n",
+       "count  77.000000        77.000000    77.000000      72.000000\n",
+       "mean    4.532468        56.610390  1224.428571       0.195229\n",
+       "std     1.658982       173.945481  1205.698175       0.309903\n",
+       "min     2.000000         0.000000     0.000000       0.000000\n",
+       "25%     3.000000         3.000000    92.000000       0.002397\n",
+       "50%     5.000000         9.000000   551.000000       0.014575\n",
+       "75%     6.000000        36.000000  2534.000000       0.206321\n",
+       "max     7.000000       953.000000  3462.000000       1.000000"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[\"perc_coverage\"] = results[\"num_shared_test\"] / results[\"total_tests\"]\n",
+    "results.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each grid type there is between 1 and 7 individual grids, and for the vast majority of these types, there is very little overlap in the tests used across them all. There appear to be some exceptions, here that we can look into more closely later on. But as far as addressing the question \"how comparable are the grids\". I think the answer hear appears to be, not very.   \n",
+    "\n",
+    "Therefore, analyzing the behavior of our jobs based on comparing grids (if we assume tests as the fundamental unit of these grids) does not appear to be a reasonable approach. We may still be able to do this looking purely at the grid patterns independent of tests names, but this will be explored further below. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. How comparable are individual tests?\n",
+    "\n",
+    "Lets ignore the grids as whole units for a minute and examine the occurrence of specific tests. Which are the most common? How many many unique tests are there? How should we expect them to behave?  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.a) What are the  most common tests? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.281952Z",
+     "start_time": "2021-01-26T17:35:08.183603Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Count the number of tests present across the entire platform\n",
+    "all_tests = []\n",
+    "for i in data.keys():\n",
+    "    for j in data[i].keys():\n",
+    "        tests = [x[\"name\"] for x in data[i][j][\"grid\"]]\n",
+    "        all_tests.append(tests)\n",
+    "\n",
+    "all_tests = [item for sublist in all_tests for item in sublist]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.288796Z",
+     "start_time": "2021-01-26T17:35:08.284341Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177291"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(all_tests)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.420874Z",
+     "start_time": "2021-01-26T17:35:08.290622Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Overall                                                                                                                                                                                                                          0.915633\n",
+       "operator.All images are built and tagged into stable                                                                                                                                                                             0.786600\n",
+       "operator.Import the release payload \"latest\" from an external source                                                                                                                                                             0.751861\n",
+       "operator install authentication                                                                                                                                                                                                  0.533499\n",
+       "operator install machine-config                                                                                                                                                                                                  0.533499\n",
+       "                                                                                                                                                                                                                                   ...   \n",
+       "Create a test namespace.creates test namespace test-gpmwe if necessary                                                                                                                                                           0.002481\n",
+       "Create a test namespace.creates test namespace test-btywt if necessary                                                                                                                                                           0.002481\n",
+       "[sig-storage] In-tree Volumes [Driver: cinder] [Testpattern: Dynamic PV (ntfs)][sig-windows] subPath should support readOnly file specified in the volumeMount [LinuxOnly] [Suite:openshift/conformance/parallel] [Suite:k8s]    0.002481\n",
+       "Create a test namespace.creates test namespace test-kzmgo if necessary                                                                                                                                                           0.002481\n",
+       "[sig-storage] Projected optional updates should be reflected in volume [NodeConformance] [Conformance] [Suite:openshift/conformance/parallel/minimal] [Suite:k8s]                                                                0.002481\n",
+       "Length: 7798, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This tells us what percent a specific test is present across all grids.\n",
+    "# Where c is the total number of tests.\n",
+    "pd.Series(all_tests).value_counts() / c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.545217Z",
+     "start_time": "2021-01-26T17:35:08.423031Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Overall                                                                                                                                                            369\n",
+       "operator.All images are built and tagged into stable                                                                                                               317\n",
+       "operator.Import the release payload \"latest\" from an external source                                                                                               303\n",
+       "operator install authentication                                                                                                                                    215\n",
+       "operator install machine-config                                                                                                                                    215\n",
+       "                                                                                                                                                                  ... \n",
+       "operator.Run template e2e-gcp-serial - e2e-gcp-serial container test                                                                                                11\n",
+       "[Disruptive] Cluster upgrade [Top Level] [Disruptive] Cluster upgrade should maintain a functioning cluster [Feature:ClusterUpgrade] [Serial] [Suite:openshift]     11\n",
+       "operator.Find all of the input images from ocp/4.1:${component} and tag them into the output image stream                                                           11\n",
+       "[sig-auth][Feature:OpenShiftAuthorization][Serial] authorization  TestAuthorizationResourceAccessReview should succeed [Suite:openshift/conformance/serial]         11\n",
+       "[sig-network] IngressClass [Feature:Ingress] should not set default value if no default IngressClass [Serial] [Suite:openshift/conformance/serial] [Suite:k8s]      11\n",
+       "Length: 3137, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This tells us the count of a specific test and filters only for tests that occur at least 10 times\n",
+    "test_counts = pd.Series(all_tests).value_counts()\n",
+    "test_counts[test_counts.values > 10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see above that there are **177,291 test instances** across all **403 grids**. Of those tests, there are only **7,798 unique tests** which is over a **~20x reduction**. \n",
+    "\n",
+    "Somewhat contrary to the findings in the earlier section, this tells us that there is a fair amount of overlap of tests among grids. Perhaps our criteria for comparison above was too strict, and we should employ some more fuzzy matching methods. \n",
+    "\n",
+    "We can also see that of these ~7k unique tests, 3,137 (about 1/3) occur across at least 10 grids. So it is probably this subset that we could use for comparisons and determining average behavior of a test overtime. \n",
+    "\n",
+    "Let's look at one of the most common tests and see what we can say about its overall behavior. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.b) Single Test Behavior Analysis \n",
+    "\n",
+    "We will use \"operator.All images are built and tagged into stable\" since it looks like it impacts ~80% of our grids. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.550401Z",
+     "start_time": "2021-01-26T17:35:08.547391Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "target_test = \"operator.All images are built and tagged into stable\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.665403Z",
+     "start_time": "2021-01-26T17:35:08.552372Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "collected_target_tests = []\n",
+    "timestamps = []\n",
+    "for i in data.keys():\n",
+    "    for j in data[i].keys():\n",
+    "        grid = data[i][j][\"grid\"]\n",
+    "\n",
+    "        if len(grid) == 0:\n",
+    "            continue\n",
+    "        if target_test in [x[\"name\"] for x in grid]:\n",
+    "            test_data = [x[\"statuses\"] for x in grid if x[\"name\"] == target_test]\n",
+    "            collected_target_tests.append(test_data)\n",
+    "            timestamps.append(data[i][j][\"timestamps\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.686671Z",
+     "start_time": "2021-01-26T17:35:08.667905Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_timeseries_by_name(data, test_name):\n",
+    "    collected_target_tests = []\n",
+    "    timestamps = []\n",
+    "    for i in data.keys():\n",
+    "        for j in data[i].keys():\n",
+    "            grid = data[i][j][\"grid\"]\n",
+    "\n",
+    "            if len(grid) == 0:\n",
+    "                continue\n",
+    "            if test_name in [x[\"name\"] for x in grid]:\n",
+    "                test_data = [x[\"statuses\"] for x in grid if x[\"name\"] == test_name]\n",
+    "                collected_target_tests.append(test_data)\n",
+    "                timestamps.append(data[i][j][\"timestamps\"])\n",
+    "    ts_data = pd.DataFrame(\n",
+    "        zip(collected_target_tests, timestamps), columns=[\"values\", \"time\"]\n",
+    "    )\n",
+    "    # use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "    ts_data[\"values\"] = ts_data[\"values\"].apply(lambda x: x[0]).apply(decode_run_length)\n",
+    "    ts_data[\"time\"] = ts_data[\"time\"].apply(lambda x: (np.array(x) // 1000))\n",
+    "    ts_data[\"time\"] = ts_data[\"time\"].apply(\n",
+    "        lambda x: [datetime.datetime.fromtimestamp(y) for y in x]\n",
+    "    )\n",
+    "\n",
+    "    return ts_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.700489Z",
+     "start_time": "2021-01-26T17:35:08.689244Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "317\n",
+      "317\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(collected_target_tests))\n",
+    "print(len(timestamps))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, this particular test has 317 examples, which means of the 403 grids its used in 78%. Let see if we have roughly the same number of samples for each instance of this test. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.720214Z",
+     "start_time": "2021-01-26T17:35:08.702483Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      118\n",
+       "1      867\n",
+       "2      181\n",
+       "3       15\n",
+       "4       15\n",
+       "      ... \n",
+       "312     32\n",
+       "313    104\n",
+       "314     30\n",
+       "315     23\n",
+       "316     50\n",
+       "Name: RL, Length: 317, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "pd.DataFrame(collected_target_tests, columns=[\"RL\"])[\"RL\"].apply(\n",
+    "    decode_run_length\n",
+    ").apply(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we can see from the above output that the we have a very different number of data points for a test depending on which grid it is associated with. This again, creates a problem for making direct comparisons between these tests. \n",
+    "\n",
+    "But that's alright, there are a number of ways this issue can be addressed. For now, lets go ahead, and generate some statistics about each time series. We can then define \"normal behavior\" as the average of these statistics as an initial naive approach to comparing tests in aggregate. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.737662Z",
+     "start_time": "2021-01-26T17:35:08.722049Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a dataframe for all of the \"operator.All images are built and tagged into stable\" time series data\n",
+    "ts_data = pd.DataFrame(\n",
+    "    zip(collected_target_tests, timestamps), columns=[\"values\", \"time\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.877222Z",
+     "start_time": "2021-01-26T17:35:08.740062Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "ts_data[\"values\"] = ts_data[\"values\"].apply(lambda x: x[0]).apply(decode_run_length)\n",
+    "ts_data[\"time\"] = ts_data[\"time\"].apply(lambda x: (np.array(x) // 1000))\n",
+    "ts_data[\"time\"] = ts_data[\"time\"].apply(\n",
+    "    lambda x: [datetime.datetime.fromtimestamp(y) for y in x]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:08.893533Z",
+     "start_time": "2021-01-26T17:35:08.879378Z"
+    }
+   },
+   "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>values</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08 20:48:05, 2020-10-08 19:12:01, 202...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              values  \\\n",
+       "0  [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...   \n",
+       "\n",
+       "                                                time  \n",
+       "0  [2020-10-08 20:48:05, 2020-10-08 19:12:01, 202...  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts_data.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:12.982462Z",
+     "start_time": "2021-01-26T17:35:08.895553Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.940677966101695% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "0.9319492502883506% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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FzjAj9HCx7wXK45vUQ5RLChlWAMVb1LfFm9ibkLDSgD4gGvroAnPiAAHL2+J5L6NWt8W7P+veQuWi0OShfbZ4d4UsO/7RSUBPzrY4l+TjSVcMfQv4oG8jh67lZeTfFLIR5ZIAxb8mGTmnc71eHYN3kRW8spFc8t8Dp5EwmBgEIcTu6hAeupDxuVUZMmpTiefJLa+SOM7IOv6x8HrFaHHfQYO0GG6LZ0PYFk/4xnJ7OokVV0OUywpojWndbXHmPTd3W6ypq47QKPR7We8OtTyXZvtxZhCAxbESVcU2+MMSRcc/Zn0dPetgfpkdV5bLHOWS2zccANoNlHU1h5UlflFc4+NIBVEuCfQ7xytl9Oj+VNctTQX00Mopu0vnmamwym8mDA8uupXjxcjL8MtR89XksKSbw9dMou55HjyvJFocbiyXseLL1HKZ1ed07o13+Rw8XvHXybO9uSHKJYFeRLTGPZo3mZ6r6pz6+nMCBgE9ca4g1I2S5dJgfV73yLgA6WTbVrfFuz/XusGl9FSbnHuKrnflc1kWbOTkvSCjf1P8732mxyx6SblkeStElEsKhBeyStHiDFGLFqcWLqNWqA8lxcKgv3Du7TYDeuI5K5VPrHJ5TaOVc6pN2T1leS5dWHi9vGAjZmsMJcAo00+ZQZ/zAMBUrtghR5RLAr3TdjS2V7o/SWkZ9G+pDrHSKJhuWJRoYW1idMyF+Uu6pz+mrfzWIL7cNlWJzMPLOCu8rE28kq10Vz6XrW4oe55snL467YOeEtZiVu5RroLFOX2ZiohySSCZYkH7HuVtcVM+l0aKySi3vOAGjgdBAGDW1zDSLQc/tzdCoone3EtfM0d32ZGJmfd4xcqlq2jxqM7+Z+GiilHWyeS9HA0UodtXN9aioWPAFqJcEuj5kuhrl8oH9OiJlKzCPd08lwYiegShEMoCpLsrboLA1JigEi/0iRN6LATlcYMSBFOWGzPcFq8smjZRDk6OSliI/g5ffGeGnysLZW7I3NvqRJRLCoQOphsEZHuy6q+uerR4X0CPDEaBCUpd0fi+eN11FBMfY6h9Z7aQbF5kNfEztsVV7ilPom6/QXKtsPGz8fiSKFN/Mlc5J0UuClgVyhHlkkAVq6K6tZPHxKBLHBkrA1CokUprueXOmRch7mKEB0EvGqGZM0w1ogMw9M4XL1ZG3Vku+aUiSvkUJ8PFtcthGtCT9LlkIA9nRLmkQPG5jLcIapCnqF6Ds14qzlTF5zLrEh1xZPAKNWA7iXovPVfGf5goX2ks9lkuFevOK7qpQzP7VJviHlEWLd5xZLnMO/4xwrV1Oek+pts+ZemfnMJVLmaIckmgt8Wkk4w3/Knsc+ll/64L+dYKlXYDYwWBJ7Z9Ll2bK5Ez/zRVQ6xAL1pc4x4v/1SfsKyAdCRwVfK2xbnNvSRdjGtAj6P8m00cqqJcVkAvWlxPIeWeRF3B5VLv86rXCoIitn0uPURWGHcBCp7u221pgWaKsU0rjrAe1BbzrGvlAT1uLJdRnZy2xVPkuIOokJVEnUOX84A17VaigyiXBHrzdJD6WwX1ud3UnpmZYlTL9bp5V/pPIzFQtCBYw7Ti1wuxyPBJs0BUla4e4npr1TQUP8VWaUCPmyTqrZzgJC4Wv0quYF5vfWW1Pe6FaxsrmZgiyiWB2JeEkOeSa7T4QP0V/z+8psKrqyDUhMrCkA7Cqdh5FY2GdSooWUVXeS4WaWEI+IRtcb80z6Wb+don5Oysm+x2CLTn/9Dn0oREZpFocXVEuaRAmEl0t8VNUducl9MGoeEyoB0oKwiasDq9IwcOwyDeQtVcGZuqROaRlXi8rElU8lz6DtqpLGenawMF5Zjk+FbPGxjbrh8nxMv4TchClEsCpBN6oHeP645bNjGVyWdiyeevNghNIdmflZKom4wWTwQnmArUo8gAoC/lg8qNmp8zJz5bXCsVkUq0eGXRtMmOfOdDcs3TbR6uFkKuJwdxRJRLAr1Bo69d2na5NDrrJYTPLTaRCKyh648gGGbQChN+ajGgp/tzrfuKFW0l530baif0OLBcEhLCu4B2Qk9W6i73K0q0/yiUI8olgXiLO1aiyju9r2nubPp2VFBlW7zZjy4wR8nn0nil1fJcVl1XB/Jcqt6n+Tl3ynJDZt7jF1sHXftc9ssWvci4XkO8lEFFT5bk8Y9udOe8iNVkrlgH2R4ahCiXFaD4e6nuYJjqS8rFGKyvOyU4n9wEIQu1JOrm6kud6uGY5EK9FkcnKVq8NKAn6BkPLMIxoCcN3emS6/ZzmFaMp2zcEOWSgGbgd3hPPPeoWi55U/YmJYNPEEJ680U6PZfNMU45+AFgsRNpFL+74iUtl2UtUna2uDOfy7I8l0y+O8pS4DsP6OHdpk1AlEsCnqf/RhZZ8Wy/ZMaTnonZL2dhTBWdE7xAmmFEQRUcYXQbKi86weZCleGVo/aIOVkhGrrIlipkGYQ+l/n/HwRBbEW0Sa7PJZN5sxdERsh5nFhHmDwOgGgoUzb61x6iXBIg6JYDideVbyDUYYLSxVX8KQUOEFYetWhx/XJzy0L3tJEg/Cv5uS38xPyzlrOEFW6L5zRKWbQ4N59LLkRNQhEvtrQz2wKLXFx4ScUTUS4rQOr4DUminomCv5YXZbqsMOFyfHRheHCxNex+jUwHISY/K7wr95JmjtLYT1EroEchWtxJnsvwZ97Z4py+IUoqIqD7LIST8KqS/3UzcqBmjiiXBAaskAqdvufzpFhHqj76qHIVZdaE5NZCc6nUry3nuVRyJ0G9ygBlt2UY6SUe17snzzoYW4Jd+FwSThuySoU28XyeHTYZnMfSAMQIUS4JkM5MtbAtbhIFm0b2p6koPxl9Aj9sr1eUAEDjMujOP2XlGSnFPplbySVN0iqwXEafuowW53pCT2+d1M+i3ttS56VdegAkXFwNUS4pUHwuoXePqXmhrvmldOLKm080Gk2sn0ItWM5zmQoCSFoxLS7+XsakpVK/awXFNFnHP5bhFUSLZ32vtsjL2enSTzGrGSjiJHNJs1oFPI+XPIwR5ZJA5lFqpffwNPNTKZpLI4dnss/lsK1oAiuUhqBR7dJ9f45EMGUJYvBIJOLjHzXaoVWoXIY/XZ7QwzbPZWLJ0/VJ1U3dZ4u8xA/CIKJcEkiZ+1Xv0Zzcjc1VdUV1Uz20BcExStHiBjts/oJkb1BkKT8qtQ/bQQhe4fGP2c/qe/kKnFPLZWm0OBfXKn1tzIutsoOfOWW4bES1IsolgZ6i2P2bUoaBK8zcQyNvIg59UqyJIQjK6FgcTK9jWVXbXio9EII/GKznJmkRzuP2fS/XkBAFBjnxufTCWbgRqYg0mydOG8psWzzp4iIUI8plJTQmqL7zyMswdZKH1/ezCikfyIIC47NtK064MoSFMurqI6Z3xbMWJOv6SP+b35ApjipQckMW5bnsBGbmOipFaZI4GPoAEAep/kuASfKqTRpVWFhSGSPKJYHY2VjrJmjd42TdMVC/x+kgZUHokuzPSpYHwwtHL32JuwXJ9zxtf7E8aZu6sEYWsaRyWRY42Co4oSfzNDKLtPxBxZeNYS1x5Khu80THdAbcMpZ7DIOMmCLKJQGKr3Fs5bftc1mTmlpUahV3AUFQoe6+Zfy0KyarERvFwxGtrtaik0S9MFrc0C4NlSLZXJBshyoxOb1y+Dwb0N0Wd1FvAxdTUS4JUCIvGxctXtKbi/87Q7tsynMLQ48LBStWQhKf2VZIPC/tO6gSrNNUC2UecW5uDYWsVZhEPfzpzHKZIRu3qZYiT8pfM/rMYhvnWrMlqEAZUS5J6PlPJu/SUEf1Cs8rpbYBmV9wUHqFILhDZQya7rtZc4V115euAXUtByRQ0vf4vpdr6YwMDC4CeoBi2VzTS9yvvw4lX2o45Tv24PHbqmeKKJcE+o2QygMnx7G/qI6q1DXl5cnnlWnQonEKrlHLRWSM1KlVKdOluTrU5UiY2irU31SDpk84MtH3vVxLp2vLZZFsfL4jSiqi8KergJ7cbCgeYd1fo4hySaDnS6LX8XUc6j2Hi1BelUHJ/0d0XM+4glCAmuXSXN8lhP/VQmx1WcP0zhZPBPSUtElRtHgvz6WbuS4roIfblxyKo9c+WW5kNnOuFlpLeTUvW0S5JECKFtevRf+OrFsc6neiWgpNxaiukHipdPnOmLKgVqy/qWPbJ+a5zD1b3LXl0uN3/nZEFYW7t6XOKzQ7tFwGrLbquSLKJQViIJunMRHYnqy0h0qBgK4nXGENQZjjbS8LuZ4i1gN6ePmvuaBVdEJPztfhR+lnMubugIHPZV5AD5fTlTpBQPa5TD0ag8fx0NsXtykOg0fXRpRLAr1INr2J2sXWSTTBaA/uks+K8t+t7eVLYI9SmkuzYzXIeOOybrk0uS3e0DfHrG3xMopO9YmTqBuQjYLv+3zPFo8giOcnGpTV0/HJKsYeUS4JJKPgup+o3Qd1dxj2U3eRgEUPqTMyZRQLeVQYICrWO6Pjz1VyvD76g+3YzzE1EAeKaEaLh/cM/l801fm+m9YMt8Vz/tPxF5x0myQ4eYX3utryL8hEJNHiaohySYI4ajWixbmHi+daLlH9NJKGGkWEpmB5YfCQbc233c89z0PH0MM3dYh6ntc9cYeiXOZvi7s8oWdALiaKT5VteX/AgMOkz3lAb1+chURsEeWSQK/j62+Lq97hvNuWCFA0cQRZW0XOH0gQQpSixb3k79U6bzKQxvkwSPmwOZfGCV5BUvQs4q30TJ/LXpku8DWfxRnaPpfhzyAIWAW/S8YFdUS5pDCYJUH9NkJAT5U3QOU7FcRSktxT3/oXBBeo9U/DPpcZo6dfIalbP/F1dk7KaLBempm+B/mPVJR4vePYcpkVyZ6VUtUWplLo2cnIkk+upwFx7V+LiHJJIFL2tE/oKfKPyalDq3ztOyqgUllFgWQAC2YJ+n7mY/Rs8USVTo2FEmwHAPB9zVRECpZLV9HieYoyByqlusraHWTyQsNmF4I5olxSoG6LO+iO1DmvTNZCn8t48HEN+ROEYkzrClmKifVo8T7LZaXFv7o4ztDdSm4p+Vy6aREvy+cy+j9Gbg+6a19suQwATouHs3RejL5LVUS5JBB9zb3AFcX7tAJ6dKWqhn6ey6Kyqg2+5g0jwRXKfS35nmN5Wzx3kbcd0ANxWQGireTe36Un9BQql+FPV3NWK0NRZnN2vOFtcavGmdwmDHMRcWlizohySSDu4oQeZjtYnDrCy+rPHehlR1yK5igYoEo30g3oMUHWmLC9k+H1nRRU5Rk5WcV0CROPZ+QVyru+IDdmx7Hl0vc9tJlqOqn+rRvQ0/3JL6AnMX80dwhYQZRLAlRnY71ocTM9V3XO0498N1e3IFjFdioir3p6LnNyMFqpHRFui2tc310li30uDQhGwPc9BEx9Lqvgp7bF+SBrmjqiXBIYTKKucR/TbXFThI9o6NQKbjOLMBRYT6IOHluVkkYlJDcIJkdzUIsWl4Cefjy64TIzroGXYsezzTkhymUF9AN6ANUXZlPjyOh4VHzcygsYr1lEGDaU9sXrqTOVqcW2z6WkCQMQWsV05u5etPjg/2Wc6mmVvOCkps+gScslpy6bDDTicnY7V0S5JOARTfZecn+sDiz29dwYheQjipIoMERNt/QSv1fD87JPhNGMn60oRfVt8WEZzb6mtY9ztHhmnksm2lgVyyXXfJL9wbxCPqJcVoASYa06uRubq2qa9Arf2kxtiwuCI4znucz8wy4e0i+3VRSiJr83+gPpe4rn5KKAHg4+l1y3xauQmeeSA460yyYON1EuCfhEp0u9VCBuu1PpwlOYiqj8GhWYTSsCQyhrj8o9RkdfXmEDY6zeMR8e4iCjys+xJOdeH1kuM+5x7XPpe4NKbwC4Xj4QipA0XVbIc+mgy+YZgZJR7E1+wbKBKJcUiCb7ZCqQ8ms1C88rx0wxWuUWPqOsbYJzVLRLg3kukQxyM7fdri+I2WdqKtTjH4u3xY2Jp0UrY1t8GOgZCJkF9Dg+lrJJiHJJoPf2onmfB6h2S5eO/0rk+lx6laPFOT6uwA16L3GS5zKzErN1lOF76sfPDjPaxz8WRIv3Anoc+lxmKspu5DHVDp6fDOjh02mpa/9aRJRLCkR/EA8aZ4tbNl3WMVi8Kh7dglAXtheGnB0LJ0PCWGqX5g7oPIUs9/p4izbfcunM59LLssLy0HwqBfR0f7pS4vLq7QUa8WhjzohySSB6K+wlRla8r+5o8bK6TZaX3N5LlK3nVyoI9rGdiSi5Le4SX8MtZ5jpT99T1iStojyX3Z8u81wOozXay1DoWaX+GcI2N40olwSoSdTDe+wmUa/N57IwoCcrIZx62TJuhTpRGoMm/RNz3imtKyQG321Zuuoo0iJaLrNP6HHrc5llhQ0Cft+PrjyxtTj+hwfU0/mqV2y7wuqIckmgN1A0t8U1fJ4ofanKm12/WOTJyStJLNzAQSIML7mB3KYrcpxsG0D3hJ6eMrRWh2KYdzTrP7KvLw7o6V7jLFpcT1G2SaWXJ6apiFLR4k4l4c+IykUPPPAA3vnOd+L48ePYsmULbrzxRpx//vmpa2ZmZnDttdfiwIEDWF1dxfOe9zy8+93vxsiIUhWNIlLidMd0ONhU81wacoyuaQgUWi4zTiPRKpt4nyAUkRtUk+UPabATJoPc6qpDTY5ugASv9do6Ld/Doo7lMjpbPON4NQ5J1NtMv9AqLeLHyqURUczBVS6GKFkur7/+elx11VW44447cNVVV+G6664buOYv//IvccEFF+C2227Dpz/9adx333248847jQvMCUpAD1tzuvZoyS44fEYGZhpBSJB8yUp29fyXL7N9N/bPNlqqHqFyGaQ/WINknWpTeL1X4HPpeKrT3eKvG1PNEMc1BLxCZ5JyCcWUKpczMzPYv38/du3aBQDYtWsX9u/fj6NHj6au8zwPc3Nz6HQ6WF5exsrKCs4888x6pHZMPJHEFjrliB5SnksX81ZZnYX/L+NOYEyyexYdY2qKvCC3/irqVlA8eOJziYyAnpJG8f18haIXLc4rFRELkmuYbhL1rmbC9V0ogDtrdVMo3bM+cOAAzjzzTLRaLQBAq9XCGWecgQMHDmDr1q3xdb/zO7+D3/3d38W/+3f/DgsLC3j961+Pn/u5n9MSZmpqo6b4bphdCfdHWiPhCDh9ejKOKCxipOXHWyybNk1genoy99qHZubj3zdsHC+8NiLq61NTG3H6lgkAwPr1YwCA0dFWYRnr1o2m/p6a2ojTNo2nPhsfH43LWL9hLHHthtQ1EZOTPbknNx0P/3/daOmzbJwcH6ivDNXrhHrbytb3MDER9rNkHyuSYXx8FL7vY7pgrCbv837ce3netLl4rJYxPj4aj82NyTExmR5fyTnhxGI79X8TE2pjoeia0bEWRkdbaLV8rFs3itXOoExFTCbka7XCiSyaA7Pu37JlfebnrsfqxMQo/NmlWI7x+WUAwMaN6zJlW+y204aM/598fBYAsHXrBidz1caN69AJglSZExNj8DzPajtv3LgOADC2biSud77d0wzL1p9+Hj+5BADYvGUCIwsrAIDTTlNvYx3mVntyxn1iYnTgMwCYnAyfs+X7GBsbsdbGkyU6gOsxlYUxh8jbb78dT3nKU/C3f/u3mJubwzXXXIPbb78dl112mXIZMzOzfN/CEhw/Fip+K10l88jhU/HbbRGdTif22zl5cgGHD5/KvTZpDZ2bXSy8NiJ6y5uZmUWwsgoAWOhOnCsr7cIylpZWUn/PHJ3Dat9nC4srcRlRuWF9c/Hvi4ur8VbRbELuUycXw/9fWil9ltnZ8NqFhfJrgXBgqVwn1N9Wtr6HxcWwb546NTg2smRYXFxBp9PB4cOnsNrOcJ7LuQ8ATp4oHqtlLC2txPPa7OxSXNbs7FK6nsSccOz4XOr/VMdC0TXt1TaWllfRbnewtLSC5eVwjpjNaMMsTiXk63QVh3ZXQ826//jx+czPXY/VlZU2lhPz4WxXeZlLfDdJThwP5/vjGf3gxInu/x2fx+Gxci8z0+NvcWEFQQAcPHQytp7Ozy8DCKy2c9SXl5dWe334aK8Pl60//USr37Fj81hYand/n1NqY12ScvbWt5WBz4CwjwDAymoby8ur1tr4VIEOUNec7vteJYNf6Te1bds2HDx4EO12+AW3220cOnQI27ZtS123d+9evPKVr4Tv+5icnMQll1yCu+++mywYa8jR4hp55sw5r1gl6dclmwYCSxS22szueHmVg9yMSCF5LgGEwSJZ/pO51ytEi7tMRQRkycZg9q3QKM5S/pTh6Itm8G1qU6pcTk1NYceOHdi3bx8AYN++fdixY0dqSxwAzjnnHHzxi18EACwvL+OrX/0qnvzkJ9cgsnuopwfoJFMmdaYKPVA7nEf8TYSGkg4RsBvQU2MVpXhAfzRTxcKaCTnPZWZAj9to8VaGcsklBKZS90qlIrL/PHltSF371yJKNub3vOc92Lt3L3bu3Im9e/diz549AIBrrrkG99xzDwDgXe96F775zW/i8ssvxxVXXIHzzz8fr33ta+uT3CUFSXXLblM2XBqaq6ipiKgBPcngheoTroxgwTwqQQJmUxElXiodBurl5ndcY2hHi0cn9GQG9HSvcWW5JK5FtWFoPMUBUkweKyYWi5tg/FDyubzgggtwyy23DHx+8803x78/4QlPwN/8zd+Yk4wxA+NEeeCoa5cppZCjlbAoz2XRQ8qYFBiR+5JkOlo8sw4XpsvknxW2LSuK4hLdxONxtDhDy2XmtjiTE3oqydC9t5N6EbT3UHljI2m55NDGnJETeghQj38MT+ixq10pDwAVsZSOzau+ZSBjVlClvuFkQbu0jA/Jzwfob4sXnS3u+vjHItk4ods88fGPQeBkCzp3W1w0SmVEuSRAdTbW6peu+3BJ/SriuX4EQcgitVgpbONVXU/C/JIMgtxMBvQ0eJH1/Gz3gDzFwc+wokWYcwGikSdbc7+dkKxdcU7PxMYNgTGiXBLomcZ1T+ihJVFnSY6A6e18S7IIa45qXas3CMu2v4yQY823PTx8Lz1nsZ9jaqLlecaixSMlw9VCmiUbF7XHq/CGxvUknP4DVIR8RLmkkIpk07tP1RG4rjPB8zBz+GP/NRlXaTwWs3lFGBJS3crSMMu0cA34QFqQA2bWxSbrpf0BPWXzeFHQjHPLZbwtnp271TZezu/a5TBV4lyJ1cTteFEuCfSnI1D92n0df0RCX8q6hdonS6PFi8LFBcEyOt1OKRuPwcmc5kRjHslzGaJ7ZKLP2Oeyp/gmPgzAYx6usInlZT2XzWfK6x4JX1ChGFEuCdDfItTvMzWOVC2gdQwWsmLbwLc0wTGGu4zJ4rzcbXG7/dzT2DlRKaupmIwWd225zMpzOQz0mpNX0p90tHiDB4EFRLkk0G+5VL6PGC1uogsbGQeJQvIGlgw3gSvR0FPxO6zrhJ766lCQos9yuVbHqrbl0iu3XDrLc5lhVQ0QWH9xycLL/aOcXrQ4uYh6YCFEMxDlkkLirUrrNo2UJMbeihSL6a8vs35C0nhBYEH+/nfOpwa3xRMWQ5djwoP4MQNd9yTovei3chKvdxxbLmMljKPlskqbxFHwbp4rd1c8+n8ZSKWIckkgWnh0x3OYkoQn+pHvOZ8bnGO5tpXAh16KH/WOp+TGZVhX4LAWhZZLBoI4hrKV7OVspbv2uWxKnktd/ESDcuyzQSBGzDJEuSSQPvdU50aNs8XtGi5rKZjD1owwpFTpWqlTP3KKN9h18yJo7W+L9ynWFQRosr9Zf/oelRk5z3IZH//oaF/ci54lFf0Od5pP0nUq+TFRoFSbM+hz1BzXaxFRLisQdTDViTY8IUOtbO6KWb58FUIEBYGIzrqjMgS9XJWQgEpOWAuE2+KyLPoZCln5PSXR4o4mO84BPVVaJFbWXT1WSb0yjsoR5ZJArEzWeEQP5SUt8x7HPl6CwA7bR7Cm/nD38uX1my6rlGWmGCe0vGyFrGjO9T0PQUYqyZ7PpSnp9MhLk8Tt+9FtH2rQbN3EciX/EDIR5ZIAWbcE8Y3HQScumwysRNkym1iE4UBpa9hoLqL6q1AhzFZhuVKG9LaS1e/xfQ/trG1xRNHijgN6uGlhQKUO3stzmTxNiwEp7VIoQpRLAtSIsbx8d4WVVEQ9z6WZ+voqFwQraG2LK/T1urpuYblWxosc/0gJgslLXxQ4tlxyDuip4iqQbE9OejPXYyk5IsolhYwcXGq3qUeLc5z3VaJsTThxC4ItbAf0qP1HPfgmT+hp8ND2+7fFFdokL/F6L1rcreUydbZ40PwXh0zLJYNnSu5aMhCHNaJcEqiS60o9WtxM142K0S2t9HoF+So/goxeoYx4ONE6S/5d5jpf3kuWzsuXmUMQ9IMQh5H+aHEVyvNcGhFNG0pwUp3kZUPQXn9sbj8Tvjsmzc0aUS4JUCcS3xseV418a8zaXbSEZhAopDcx2o1zAs/dnNATGFkZmzzK/QyrGFD8TGWWS1c+l1nb4sEQ2NXi7WfwOgCSnIawar1WazODKJcEPOK2+ECiuZJLWWNDPj5zisCMfquf6fFiIZ7HOmFAoWsp3ENJ3+OX5Ll0NV9H9XJMRZRE11LudzUTV76NeQqt5/LtsGGIclkBW9HiVbowvf8X36jkcymDT2CI9fOKK2wPmiQMKEz6sFWaWaoL5Aivu+rp5bn0ivNcOrZcctQtTbRJEJou+RBbLt2K0QREuSRANY3rBfRQBiavCZ8qDa+nEJqATp9JjcHcgJ76e6FthURn/hlmWl2zWFvjhJ68bfFOEDg1YPXyXCaScA7B0YRcUyxVibdYa4hySaDXwTTvc5CKyOQ0k3ZVy1uVjVUnCOpo9TuF3HkGd7+SL4rJcWN7qAxYLiuW1VSiw1/0tsWzrw8Cd/6WQE+5zErw3mSyjlm0+TKW2zMkWlwZUS4J9Dp+oN3BlKPFNcutSr9cTV48BKEIlZckoz6XTMaS+FyGUCKsw2jxwc9dWy6j04bSAT3ufUD7f6eWEwTcdsWZDOYGIMplFTR7vc4brrFUREZKyShXwXBZ9RE4RQkKvDE96bvwoat74fIM5rls8hKbm3i84DsPt8UHzYNhTkn3lksuqYiSVFkLvIxtcQ59zpTyvBYQ5ZIA9fhHgK/lgBKcVBsyaAVFKF4mtoeglaNSVeQAkHr6NTrOYn8+ja1kP8dyGTDxueQeLa5L0nLpxMbArTkbOFZFuSRAPQKq3+ep7FoTxOUYKNDLeRVNJ8tNXVS5TkEwTnmay9oUBq/gr7rxvGwFiVaYoXIcMBAEozAn+15etDgPy2VKNi6KUYV2ybJcckAyoKgjyiUFYjoCarQmxw5tIxBCEHLp71safS1QCOgxuUXNxk/LK/xzzdBLoq5xT+4JPUEcIOSCrOMfuVClWaI2VcnsYBMGIjQGUS4J+EnlUqO36XRMikJZeAfBylqV9FYkYfLjN18Kw4DtFSvHmG/75cv3zPnmsVGYCVC2kn0/74Qet23RyvC5DBDwMEik+jpNnjCgRxaCJiLKJYletDjhNtOXWiO1LikE9FD+XxAo6PSrdHoTAwWWYKEKRbz44T1gzW4t5AXBFLVGK1e5FJ/LOoj9Yh0plnm1rtEhQ0KUSwJxByNsi6tfrFd2WZ2mh6iSeBkXqcjRZKuIwB8V453RHshEu/Q1fL6TZIrZ4CGaGy1eQP7Z4j0FzwWREqbzLLao5H2f4XrGo8slYw14SMQVUS4rwCrC2nCdZQqeShL1VPJoJlODIKiM3HSQmjmcjoOuzzc/NcQuvWhxE2eLu92Czt4W50GVdkme0MMppkf0SXVEuSRA7WAODJcxTgaoDETBEqbHpI2AHtuKZv8JYcq1Z1zY5KHdH2GtdvxjtnWwE7hVOKK62W+La7ZRKhVR/4c2yFkwXX3VTTTOiHJJgPpF67zJGXsbVixGO/I993Nzg4D5dCk0lLSCRXQe1iA38bLl9cIDv9QuLoiPf9Roi6I8ly6Pf/Q8L9yyTz6LY4XXBFxTEaV35oQiRLkkkJ/XseS+GmSpDcPR4lr3NaqhBBcMdhGq6TLnY8tB5NkfGK6v74Qe1WfMmuOaPEZzg2AKnimMFs87ocekdPr4fnYOThckjSLVjn+sJ1ZAQ4Dsj5u1ijtFlEsCZKuizrY4oYqse8wOhfI9NTkeS+COytaw0W1xJuPAA22x5iK/KTITj5fQ8jxk6Jahz6VjhSMvkp0Tui0Ux8y6CujJs5gO2VioE1EuLaJl5bQ8o+umfFB7FhmJgh10epr99CY5VpACoevYDQwtl3JETytOoq7eFl5uEnX3yrfvI6X4aqZfro0qMvR8LpkF9OT+IfQjyiURSr9yMgkRfVfKRHU9oQoCGZUggZr6t+dwdfI88WMGBrfFVabG3DyXcOtzCeSnSXIPfRur53NpUp7qyLqnjiiXVCjb1vUWX41aLCXV7uc2sQjNJnrBUjgLwHD6oZzPbQf0eOYCJJq8yPaSqGvcU3i2uCnJaPi+h3YqoIeBUAbofxni8URupGji1ynKJZHobVUrvRAxF1Elx2j6rcXlqvhc1lS3IPRjPBWRwdk8LwDQ9vjwkF6tVZ+xiQtbEXkBPUWP6fvZLgWu81wC+UdTuqbqWuB33ThcnNKjckLPkA0L44hyaRMt3dJt1y2bL5XkS1wi58MKLhk2BYmC5+lZ6xJ3KnzSHCLDgNbZ4v3pfrp0Om6PfwR4BfSYbgqVI4dtwkCExiDKJRFSNHfN5ReVY2uL2bVSLKwtdLd5+6/PszqZzEfp5Zk77JsukbTJrNWRSjn+sZWT7icIwMPnsu+EHm7fLS37iRcaJXjozSHcGpYxolyS0e9lrrdP9BiUVfctMjM/XgWJBCGCOpRU1ykbL0m2X8T8ZJ5LLXeerA9NSOSG5NGCqnh9EdkRncC95bIJ2+LU+1353ednInL5dtgsRLkkYioPJReMnZMuY0/gjGLebJNjVWmsWCIgHC4+bMOYlOcyL1o8cG806LeqBkxMl1VfnjwPfamVWTyUoIgol0Tq3xY304vrCkygHoPF7/1aGAZU+/mA76+NxUJxrNQtykCey0qBgs1dZf3uqqd1/GN367nf2hlwsFzm+IM2HQ/dNnctSILm9nr7iHJJhDK5ap0trl16MdqWSWJAT57hssmLkTA89K/BNqyKudZRc1Uo0WcI0rtxiMgL6CmanyNrZ3//CUruswHXbfHqvsou09FlV5xa04ZsXJhGlEsqlI6lZbqsVpX2vbpJ1vWCxQWBKeUvSdWryB7M1pXLKLivu3Cu1fHpeV43cl7PcgkM3tMJAviuLZcDwUaBu+82L3aNoInFAT3x3xXkMgULIZqBKJdE6tctDXdie+Hiid+rPgPDt3Gh0agOAyvWqII66jr+kVJ25lzU8DU26aeoEtiTF2HOwedyeLfF+R2k0fBubxVRLolQUyvUWX52QeGPsjGqMoat6acyggVF4uBnYp+xokOmfnfXuQfTkq3dgaZ7ZKKXs5XOweeyP9iIg8IL5Brs9e7nFi3ucOehaYhySYZg5q9BClfk5gd0eAKJIJSjdiqL0WjxnLJc+FyGVF+xmz62Qz9F9etb8ZGRg5ZL93ku9RLC26NqtLiHDgaDqIRmIMolkcjPRmteSb71lAXMGLKMRsqe6eGpIh6Dl2dhaKF1roF1Klfx8zJ/r4qnMQek5TFRd6Qg6d5oUAgm6J5qk3dkpBz/mE/VgwhcWi5tHAs77IhyaRHXb7hFDETRMt5mFIR+VLvdoG5Zv1mRy4LUL0YVsXg8ER1P008xT7nsBHAe0NPyPbT7TugZBkKfy2RAD7Nex0wcbohySaTuju5RTRyWsCHSsEySAiMULZcmoVRRR3a/nuVSMzOEcUnck3ecYx6RAtl/CwfLpae5xV8nuW4mlLI8Dx1Hpz+q+FzahKEKUIoolxaxvQ3mmuSkq7NYSk5MQRuFwRUeTqOWhsdGD+xXSupeQOLig76/y+7LurCJq10C36dZLtt9WlwYPGNUNG1a/cFJDGQCqssQ3s/LxCBrkzqiXBKpO1rcVB+OqzSRfqTvCgoyNAWX9J+tnR/QY3JfvKZydcVInKm91sehbrR4L89l+nMOlsvBPJccIQTAdi2XrEhFi6/1UVSMKJdEehOKRnqhekSxRnKc5zs8l18jCPZJd8ZIWVDpx9Vrzsms4Gh86GciygoUbDa+P+geUPRMrQKfS9fznO97LCOqjZ0t7uDR8qpser+3iSiXREidTMtwaaYb9wyXZkeolUHGb74UuKG5xTuI3eXC5eIU+Q3qJ1EfPnzfNxYt7jpQs99/NHB5Qk+Sqtvi6A/oqVaeETjI0BBEuaRC2RbXsXKa6sTksG/q/9eTwkUQTBCtVdGwsJFyJD/PZUEddbxYJbbFDRXVWHRzQ+adRx4E7vUN3S1+F1DdyFwF9OSRGrOuv3jmiHJJhBb9Zq+uAQyPUCXFMeMSJTFk0AolDIwl5T6Tdro0Gd2qjeV+HrVZJ7b2qgnQdEUyi/Txj+XX+7lJ1Hn4XKbkCgB3k6iX8RuxJJcBPdyixRu4KIpySYQyoWjF8xjqxaql9FszSu/Ltcbk/d68wSEMH6TjIg1s79VQrDa9+jjZgtzge56We4Cfc7Z4h8Hxj1wDepJrGMkYg/R3JCtIsxDlkghtQnGwLd6F39QjCOZQf4nqXl8Wj2dy/DE5/3HghJ41vFpnKmQF7dGLFud4/CP/bXEKntddtxgFK8nZ4uqIckkkXps0epiTkxxqqjN3Tc4zXRLgM6UIw0ZkSc+N5DZaV+J3hysSOS1Z1rGyrs11FQmPf1TPPJ4fLe7ectnq2xYPwNCVgehHltpRY/BQsgOnjiiXZOrtZK67cNni4SmsmNSB6PrZheElWqzKLJd15blMf2w5Ut1gQE/T8Xy9HIrxCT1ZAT2uLZdMzxYHqs3lvufOaFnHCVlrDVEuidSfRN2sz6X5QSoqoMAH3QU+jhavQZaBunL+GhS5Xq/IfsPlWh7BLS8R0KPQ2kWpiFwb1Py+c9I5vjtQX6SCgJeaJ3mc1RHl0iLDdPyjyrPI4BPqRnfhSWTNK7xuGPtu9ExiuaQf/zh4Qo97n0vdc9LrhJ7FYZD+oKshHJJDjSiXREiWS2r5ijdmydSz6NhJop5nQdJ5/+QxTQqcIS80fUnXdfsxhVTUrFOfy2hbXE+W7HnFkFCO0N1K5h4tzndbnL5FEAX0cHoXcuYC0cDxJsolkaiTaX3nrmchy2QuSvbFEISYgVREuf7C9WN7Oki7yGhkrqhDGMdkRVgXbd0WRYu797kE0rFJ7hVeM/A71jLPxUUYRJRLIpRu5SRavEvZGNUewzVmV5EhK9RGHNBDeDkkkpe+xHrkabQtbsDrsuljtKVp7cuLFg8YKHKRzyU3RQyo9gI1ENDDodNxkKEhiHJJpeYZxVgSdZNipvxfyguWtA2CLZT6ecZRcrn31R8sXkwNeoLfty2ujGvtqQY8L2GFVDmhp+D4R9c+l/2nBwXgN/eSpPEc+gfnndBjV4pGI8olEUon01EYTXfisiGqPYQtLMqCYJporSrbRUguzka7dAPHR6bIBXNZEx5RNwgm7/jHDoPjH3tWVadiADD73XueB5uGS6XymfhPNwFRLonQUhHpXKxffo3FkMqtOvg4bvMIPCnraoNRrBajxfP8Oi0vTv0WtrW8OPq6eS5zUxG5dXcC8mXjQJzyi9BGLvNc5rGGh4w2olwSIZ0trnX8Y02WE0OI4VJoMr14nvp7LJsxkSNI6fqtG5jH5oHz8TNO6CnqCnnR4gEDy+VAsFG4L86EKoKEAT2cjAxr+YVMFyXl8oEHHsCVV16JnTt34sorr8SDDz6Yed1nPvMZXH755di1axcuv/xyHDlyxKSsrPAGfqmp/Kr3qI4G3QFMHGVKtcgAFlSJ+61ap4kXqrJt8ZpO6Eltt1u2JPaXv5aHWRgtrnN9+DM7WtygYATyFF8OeBUWSt9Lrxe1K/GJ4lVa0ubX3sSxOqJy0fXXX4+rrroKu3fvxqc+9Slcd911+OhHP5q65p577sGHP/xh/O3f/i2mp6dx6tQpjI2N1SI0C4hmfmfYmne87MWTm4O50HAqLjQ9y2Xx/5uAS8/PHYMlL5bZL63F9fA6V2WQZBJ1FUnzA3rcWy77I9lZGS6r4HE8oWcoWtYKpZbLmZkZ7N+/H7t27QIA7Nq1C/v378fRo0dT133kIx/B1VdfjenpaQDA5OQk1q1bV4PIPKBFv+lsi1MqyCin+7NsiBrKRKR9jSDYpJdAPOyduX3UgrNyURV1LKm6fqe9y4ZvJOsG9LRyTujpcLBc5uTg5EDZS1zhvX0n9HBg+EZCfZRaLg8cOIAzzzwTrVYLANBqtXDGGWfgwIED2Lp1a3zdj370I5xzzjl4/etfj/n5ebz0pS/FW9/6Vq2JaWpqI+ER3DAyEraH53mYnp5UumdyY0/Z3rRpovC+ldXens3k5LhSHb4fvitMTW3E6VsmAAAbJ8djeYvKGB1tpf7Ounbd+Ej8+eMnl+LPp6Y2xL9v3NB7xtO2boivn9x0HAAwvm609Fk2TZ4I61O4tkheIZs628rW9zAxPgog7N/T05NojfTek7NkGB8fhe972Lo17KutVnj92NhI5n2Hjs3Hv2/eXDxWy5icnMgsa341vXIm/+/w7HJa/okxJRmKrtn82MleeeMjGO8++4aNxfNLtO26abIn37ruvVG7J+/3PAABsGXL+sxyOYzV9evDXbXp6Um0u/Nm0Tw7NjEW39d/zYb167SeyfTzb94c9q8tWzZg+rQJrBsbQavlW23njd21bXwiPWd73b4zPq4+l0eMjbYw0vLjsk8/fSMm15vfDY3GYXItX7cue15YSegzE4pj0gSTJfoChzHVj9K2uArtdhv3338//uZv/gbLy8t485vfjO3bt+OKK65QLmNmZpZlxFsWnXao/AVBgMOHTyndMzfXU8hOnlwovO+0rT2F7dSpRaU6Igf1mZlZBCurAIDZ2UUAwOpKu7CM1ZV26u+saxcXV+LPTxzvLb4zM3Px78lnPHZsDhtGwsF46mQox+LSSumznDoVXrukcC0QDizV72CtU3db2foeFhZXAACz3bERjcc8GRYXV9DpBJiZmQUABN2xspzo98n7vJHe1HjiRPFYLSMagwBwMlHWsWNzqeuS9RxPjC8AWFxYVpKh6JpoDALA4uIqgu5cOztbPL9Ec/LJUz35lpfDdmt3X4Kz7j9+fD7zcw5jdXlpFaurHRw+fApHjy8AKJ5n57r97eTJ9DWdToDFRbXvBqhn/M1359zDR04Bq6tYXFpBu6O+LplgdjaUIblGAD0f56VFtbk8Ynp6EqurHbRXO5g9FZZ95MgsFidGDUodcjQah0Gvby4ursb/n5T7WOKlc0HzmapwqkBfqGtO932vksGvdFt827ZtOHjwINrt7mTSbuPQoUPYtm1b6rrt27fjsssuw9jYGDZu3IiXvOQl+M53vkMWjD1RigViBLhi8dbQz6tcnl4leQUvzxlhrdI7/rF4W7ymeB6nSEBPj+hUG53rgYZEizOiiq/9YEBPdXkqwzyLCydKlcupqSns2LED+/btAwDs27cPO3bsSG2JA6Ev5pe+9CUEQYCVlRV87Wtfw1Of+tR6pGZA7QEqOYEx2sV0fzqZdljMBoLQQ3UcpF6SqnbjvLIsj4+8eaRMJ8m6relDW/f4x8g1oD8tDgufy4w8l2y+nipOl7Cb6zgtYna9bNq1ASilInrPe96DvXv3YufOndi7dy/27NkDALjmmmtwzz33AABe8YpXYGpqCi9/+ctxxRVX4Kd+6qfwK7/yK/VJ7hqO0eIVZjlrweQa1zJ8EReYQe0iNo/sy3sRLQzoqaHvD+i1ik2g21KulS0VPB+kaPEsyyWX4x85piKqQnRCD6encvZNN2BM9aPkc3nBBRfglltuGfj85ptvjn/3fR/XXnstrr32WnPSMab2aHFK+ZlVmuuV6Rx9yc/zrhcEOyh38/48l3mpiGpTGLLHkA1cb99ygh4tnnX8o1HRtOlPk8TppbxK03he2nLJovd6Ob8LA8gJPURIqRVqLr9ukn6TudYYA2evcnx2gRfULqK8LU4sP7MsLv2Z7HOpbbvUvN4+fjfNjeq2a/QdZh3/6DqHb5bi66rP9bdFfPwjpSyv63TpQFvOq9H1d90kRLkkQjr+UeMW01aG0vFZR6JLQbCE8qQ/kOcyz3RpQKisYgvKrXtI5bnllClYmT6XBuRxia+pkHmeNxAEFLWb65eHbiYlrROHmoDnDeYVdY3r77pJiHJJxBv4ReEeBz2z/s09O/UJggn0Dos0Q+4pQHpvmyYkMVCGQi0NmAR6W8ka9/hpv8ZIz+Tic5k8oYcPdNOl1z1bfKAsC6jU1IBu7hRRLqlY6FmGbZdGS1MRrqoyzWuSFFij0NXCHTY1a5PJ7a+Ur3Lqc7v0Wy6rPGPT/TdTRyYqbrv6vocgoYx2mFguW3GwET/TZWWfSzg5ubjgmmb3e5uIckmEMjE76ZfKu4WGhrBCoI8g2Ka/L5YtElbGqu0BkrctbqcaVkTfv26uyyzLpWuFw+8/mpJTRE8Xks8l+gJ6LDYztxZsoq+nKJdUiGb+uuvIw/wCQhOO26AV1hY9hcBipUzWhQElSFGuYTTWtAjpe1o+V5/L9LY44F7hjagiRhTQw0lXTmdJ4dHGXBHlkgil4XQHmm7nzbo6LsP0rnhuHISYLgV79GcWUqXserMpvLL/YGK4LJ0bXL4U10V24vEya3Y68Xrsc1l7AuNiWOW5VFkXVIuyHtBTLmMDujYbRLmkUnJ8XMEttV1fO5oDPe1fxu1hhLWIatY8L08jpJAb/WZ3TBhVmBs+nLOixcvot1zGPpeO57b+4x8DDIcSFCZRd6Qw5+YiGoaWtYMol0RoPiQOosVrqlKp3KqVc9oPEXgx6ESpdp9yQI850gE92cE9NhhoMuqNpZfzX4CTAT2qs4zvZ/tcOjZcpoOTmBG/0FDcyDyw86My+c457IhySYSkNxEtl1b6sLZVUk8qZ2+ggpDARSoiCnWcqZw3Z63FkZmXFL0I3/MQJJVLRC8qTCyXkWxD8oV60LMsV65PyWBSuxhDgyiXFnE5B7lI55DtA0osTBBMoBrhW9cWskufy+TpWRoCaMvZgGEbB/ToRIv76eudBIdlkLnF34DvoIz+McohWnwImtUaolwSIZ3QYzzswBx1KJ8yEAVbqPa1uJ9b3BZXrqTmSnOnrDIFK+NG19a6qgxY+4DS9vd9P3V9L8+l27agRL7botrxj2FAD6ddr4EXNGv1WqzMEKJcEiGdLe4goCcqw/Q2W360eN4fBMTnUjBMnD6m5DqTk3negmTbN3Hg3Oea6m/COpgVLV56j4fsaHHHD+xlnNDjSiST70thKiL1aP6qNKHfNglRLolEHZF+Xnj5jSa2o5rgXC8IVdF/cbM3LriMQKrPZWX3Fob0IqyhvG0TRov3/g64WC6zrLBcqBLQg65uyeixmmhBdIUol1RI2+J130BHxUiYOuU1/8Dk3q/EB3A9WQvDT7nLZf1WPevdnFhf41OoZUCJsPZz8ly6fl6/z3+0jmAwF4Tb4hafJfE9KrVhA/q5S0S5JGKjXxmxOtYkqFJgnQw+oSaoXUs16fowdl2/b0BWGp8NbyCPkOfSzz2hx3G0ePdZgpSizOMLinf4SEnU++5h8EgMRGgMolwSIQX0UCchjj1axedSELiioV1W1R3S3jAJyz6Tty/zxiEez1VEVhBMmdT9eS47ijlT64ZzQE+VrhBui3MK5wGa0Le5IMolERsBPdwMl7ll1TTeeE0qwjAQ5ya0G+tpsa58+i2XVeDxRHRIAT2+1xct3v3cteWyW336bHE3sih4S2mU5VldA1TmBDlbXB1RLiuj3sEc6JYxpq0TKoom1TIjQ1bQRamvBYGyn5wcldhDty2a8LjJVETKJ/QM+FzysFz28ly6lSOLSk2TDhZvRL8SeohySYTS0bUnaROjqVtG+byje0QPTTiG85+wBrHpc5mXI8K2UjLgwqZYf+ZlrjWqilCSqPefLd57UXEcLR5vi3cA8MrgVqVt/G5AD6cApYZ3e6uIckmENGhqDhevu9+ruIs7jYYV1iylXa3bGfsVgjy7lY2+219FcputjvU0b84y7dXWhHEfWS6DSnkueVguvYQVNv7MlTAGCfNcJv+uu0KVS5I+0zXKMgSIcmkRF30xHgyWkqgLQhMo778GO7jB4KBKYlDrzriv6cPfJwTB+L6fslyy8blkvC1ehSighxVN7/gWEeWSCNVBWev6+Gf9PdrUGC47HkvrSZjNKwI/dBefOKCnZCwaPaEnN7WC3ZXK6/tjLQck+N2VT+c8bt9LK6NsUhF5Hrw+2bh8tXEO9QYE9KjApFkbgSiXRGipiOq9vqgM04NU6fnFvCnUxIBipNrVFAeC0Z6bp1uarEOB3DFbdrR4ZlmVxXFK5tniZff4HrpujQDUc6bawPc8JlY+sxb/9CPV29LJ0vOasun93iaiXBLx+n7q3MMRDtOSINSNcj+3kbbHcUCPev26gYicZ7qQ5FayqlLGNYk6EAb1RJZLHkpmSJXdNx+hwszoceBqFefQx3QR5ZKKjW1xxh1KRTK+0gvDhnJfi/3kaihbpSzFMV3Hepof0LP26I+wVr0n6/hHn8Eq2p+Dk4/LQwU5+iyXHJZDDjI0BQbDopmQ+piDjlmbgqriRlax6rW46An1EkdGl/lcGqwzN7G0wTpUoBouh3FB7UWL693TyTyhx30D9cvGDpIxxnwmg7L66rx+rSHKJRGSz2UNcqTKL5KpbIwqjGHdhLYy9gRuqCdRN1lrdmG2lZJcl8uSfcdhHMdZ0eJl1r7BbfHu5wwayPe9OGdnALD50qp08dCP1I2RIa9OUSjVEeWSiJVo8QqRdnEZ9FtLCs5ZMOsrWhAK0M3EUBYtbtDnkkt/NvpM+WWxed4C4oAeDYc+3+Prc9m/Lc4Nagvx8rfk5G7AH1EuK6Izr+h2S5Pd2HiiZJVrGEy6wnBD7dU2u2bKU8TL/t0GA5O98r64YUEYQD1bvJ3hc8lhmuv3B3WFybbwPS9ct6xpmCoZUJK/MvjiGSPKJREbqYhYzFq6NFFmofGodrvY2qRSJl0cWkG1+83YKaoJM0CLlETdS53ow9nn0r1EIXHTUAQaSEVkkZyKubRrExDlkghprBCTqJvA9CBVs1xWq4NTSg2BGRX7llqe1mp1VCq+hr7ff5KMquVlGC00vVREGmeLe/2Wy/B3Douo7+s9iy2q9B2PZbS4o1RETmqtBodx0Uis9DEDdShbdHQ3GGuNgG3iUBJcotpjdNZfU0pV6jzi9Ca5kfKrUtomWWIWmi55PFcRWUnUS4O8/PQRi/3n1LvE9/1EnstGfAWlRInhGW2KV7xhbSHKJRkL2+Ls0MujlrwimojVJgp+b+BC8wnQ61lWx2Lei5jl+YCDEsSFzOMfSxjMcxltixsVjQQXn8sBoqBUoiaWzlBSb0MHOb/3w+DrbgSiXBKxebZ4NeoZCrmP4uX+IQju0VAmTCkNXIZE//PUlYWiCaO+lQjoUe0S/dHiUYpMDkq77/WsqjZzQ5ZRpWW8KKCHG+6/7kYgyiURon+y3vU2Jy2lMayXfigrMlbniThM2kJDKNvS7P6Munm//2GVsmu+3VwZiWdOWoFUl+9h8r30MrbFy+hP9xP7XDKYpwZTETHxDey2DckYg+67YM36ZVbMUfHLk1d6jSDKJR0b2qUBokFty9fbbBASw7dWodHEPUolnqeG8ZpawArKr+X4R6P3FZTWgFU3K4l66T1e9vGPDHTL1Nniw0K8dsUfuJIkDYfvuwmIckmE8havew87w2WCeq2KMnoFRaIFXnts9a9cmVeRRMqoLKd0u/08d8iWndAzhMPR98LW19HHWr6HAD0/zUjR5LDDktqyZxTQU3VbHHBjZBguNd0NolwSoflcal6vX0VBGXaGS2rrjckEJwgxsTJazvD5XHp9f1cpq+D/6MVaRfdUG68v8TonyyXXE3pM9DFbj6Uqa6XcnWsIUS6J2FAujVBTnWppAmX0CfVA7VlRgIDNNJdqwW/1k3u2ePmdhiXhQXRWuKru0hpQLhn5XCZycPJTMasGwKoffGAHPpJwRpRLMpGjsnpH01e2zHViMxMOvxMgBAGAeofUGQi1+Fwmg2pqr85I+VlTXGHAAwNlSwVda1//eeQdbpbLYLjm51i1dKEtF9TJ4ftuAqJcErGyLe6lf6piJTcYgxNOBEGXXp5Le50zry7rAT0DFa7tAdofoFN6fY7lkoMyzSbP5YDrBX0PubctzqedgWR0OQ95uCLKJRGO3apwsSqZd3TfDlV2+kxaSgShCGXDpYa1qY7Fg2XfLhn7mSJzfA5NWr6HtsbE5/f5ADpJyJ9DygobBEPx/fQCehwL0s8QtK0NRLkkQnmL0k6ibjaiRxAEHf+t2seO3cE5eLb42qZ/W7xsfm71pS/iZLnUtcLaolq0ePjTVrR48nssPqHH/ffdBES5pGIlWpxxJ1YwXTKYc4U1gmpf0zkP2tT2V15V1scHNaAn0+cyX/imjHvf00ui3h8t3okDeszLpkvSCstKxfRSPzRv5W25tNnPmzKmkohySYQ2WOyjXqfeCFYrt9oTs5tUBHZQu4hKzzSXiojHytCvBCmnXmEiv2liP0XFiabVd6pPdBuHaHGv3wo7BN9Zv88lF5rfsnYQ5ZIIafC6SHRZE7lBCslo2MQlOvMD48cWuEDsJHqnfRjTLpmQLQhp27Hgmdg8bgle31nhZcQBPUH/trh52XThEtCTFzJWJRURM92SxffdBES5pELoYLrbJya25Uz6rTAb44KQQHVfPLpafVvcJMmFqXCRqmGwperTebghXUx1j0wcjBYPP+fic9lOyuVepC4V1q7uzyAIrD9O8XrJpnFZI8olESvzCbES9bTAyXv0YONHJgiKBEFibNj0l8r93O5gMTk2i4tqxiQQ5obs/V0m9WCeSz6WS993c0xinfQMI27l6IfB190IRLkkQjpbXDdaXLf8gk9djE9lK40gVES5f8V+cgbLLC0o+auX+XnGn8ahWtiGdejqJlEfjBbvlsNgctO1wtqi2vGP0ba4ndRKusc/MvjaWSPKJRGbSdSroLvoKperdI2MPoEJkRWk/wMrVfMYBwP+cIqTQ9ZVhWeL83jcUqL0PapTX9ReHJOo96cici9RSM/nkmKMCX8y1JkFBUS5rIjOmHE64I0P0BqfhsvMKAwdWknUDSkNuS4kRkpXJ+95uG072qL/yMQyWgMBPeHnDHTLgS3+YSBluWSEm5cJBp1ME1EuiZD6l4NOqVyjIasqOWhAECqg3tX4WJuKZKD4TSvVmfN78U2ZtsvqwjhG//jH8GenE/5kZbn0vVRyd2cS5YSLk5bL7s9wV7z+JyLYVmuQYngQ5ZIIpbPrNrbVSavCWpaflkgQaoZo1VBy66ihA7vWQ7LGalkLDus4rhot3uFkudRMq2QLLplOTMLh+24ColwSoVkuiZdX6cw1DYT8rT4vcU21yrlNKgIfBhYtxa4Wd6kBH8ysOvTKzi2H0WJkLDH8UPhc6s0x/dHi0b1cAno45LnMhRSjELW3YVkq4v7bbgaiXFKhDBaydqlJxmAsG5/aqYhkiAkNRqn3mvK5ZDRWKG4rDHSnWoi3khUnv0iJ7I8W59A+A5HvHIQCKmlivVsDXqn/utcxaWG2iHJJhJaKSLeO6lhf2ExEuMuwFTRR7TOx4ZLL4mudrOfWNw0VtV5Txq+uta//hJ44zyWD59X1H7VFpZZxGC1eZNB2/203A1EuicS5rrTuSW4Zm5WnW2r+f5neYi6RXwagwJE4CEPhWlN9OJ3v1e3ISOb3XOtj1NOMFs8/oce4aNq0fA8BQoXXUlpIJSjrZERkKWbnHsWlcZkjyiVrjJgBAShsi2sO4DzJ8vzUdIqvK1JWEGJsapcU6hoCGc9UNjYzFeJCp0s9kVzR8tIBPWVKYmtAueTjc+n1yeZMDpNlxQE9Bgstqk/zOptfO4Mupo0ol0QoFgjts8UZdyi6bIwfSmgumgE9KgqB9Z5qYcB74i8WE/opalzfl0Sd0/GPScWX16t5ZLokuJFFp8sxs1y63n1oCqJcEiF1L+LxjyaCxY2Mz1QZueHi3R8VUlDI0icoUueyYy6JesIdxkiJdFL117RIun5GVaIk6qp9qN/nsrct7v6J+4ONGIgEoJocScsll+cR1BHlkgjp+Mfab7BH2fPLZCDYRLW7RaqEza1MTkPBmMJspBS3aCdR7z50e+D4R+OiaZNSfJlZ+gCqMSb8YS1/5xrPnmAaUS6rotHT9KPFq/di2+OgXGZ+E5/QPMgTfF+ey1rqGCgo53cHUETRboeGrL5Vo8V1XCzqpt8flAtVWqYX0GNGFlO4/7abgSiXRCgWAFvbJ+nda7tDQelYSEFwhM46ZcIthUpt8TyUgB7lD5uFp3mqzeAJPQwtl8yUywoul31ni9tt5GI/TwZfeAMQ5ZKInaSumpczil4zIQuzaVJgjHJ3c5A+hpHh0kI6NPfPqMrg8Y/FkrfigJ7w7yjlDw+fy/CnRk74WjDpLx/HCxgrUa2+0utihdn9984ZUS6JULqVdrQ4oY46ysgut6zkKp7c9FsFIYveQqWT+NpQR2S0CGVaLsuW74yb+DwRHd+P8kKqqS8D2+II2CgYkWztrubLRS4TZ4t3Aksn9DCmiY8vyiUVC4eLmxxQZROoyvyqEiwuCE5QHCw6ia+NncNdR6FE6p+1nD+iMr7v03wuE0nUuTwr21REldrHnc9l4aY4k++cO6JcEiFN0tRwcQPpHEyTV2ycR08GoMAYTi4kNjEXLc7ooYi0tKPFI+tgz+eSi4UwzsHJSrOsRrTTZyvPpep3GfV9Ht88X0S5JEI7/pFWhw1MD18ZeIINYkuk9p3ld9Qx/oqKLKyujsj1iLLBP6SD2fP10txkRYvrujrVRW9bnJd2GQfFVcjd5+I4SytjccgR5ZIIKVp8iHpl3vN7A78IAh966WPKr3U5Xusy1iTT5lSyug3B+O4P6Clrjv4TegKOlkvXET19VGmddLQ4H3h84/xRUi4feOABXHnlldi5cyeuvPJKPPjgg7nX/vjHP8aznvUs3HjjjaZkZAmpg1kK6OE1FEN05gcZvEJdaHmkGYvnqT9CuwrlhktdX3GGD5lBmERd/fpWhuWSy6P257l0JldOvVUCYDv2MxEVjwnKtuUaREm5vP7663HVVVfhjjvuwFVXXYXrrrsu87p2u43rr78el156qVEhWULoWNpbKAY6b+/tr+RCBe1PywVARp5gEeXFtP/IvoJ+X+dOtCuSc5CqXFlty+mZqPjaSdTDnx2OPpcJxZdVSE+l9hHLpdtKq1GqXM7MzGD//v3YtWsXAGDXrl3Yv38/jh49OnDt//gf/wMvfvGLcf755xsXlBs05UnTAtDEHhWRK3qDn0loPBoH9MRU7rGsunxGwJ3htZvV4xbgd5Ooq+ouWdHi3HwuY8ulS2ESVHGT8svf/4yiqgczeZ9gz0jZBQcOHMCZZ56JVqsFAGi1WjjjjDNw4MABbN26Nb7ue9/7Hr70pS/hox/9KP78z/+cJMzU1EbSfS7YuHEdAKDV8jA9Pal0z8pqbw9m06aJ0vtGR8M236xwbShL+K4wtXUDpqc2AAAOnVoGEA6IojKieyOyrh1bN5L5+emn9763zZsnAIQTd/LaTZuOAQDGx7PLSLL58ByA8PlV21b1OqHetrL1PaxfPwYgHIfT05MYG+tNZVkyTEyMwfM8bNw4Ht8HAKMF942MhGNi85b1lZ5r1euNra1bN+SWtSVRz+Yj82n5x0eVZCi7Jnqm8fExbNgQtsHE+rHC+6K23bRpPL4uav9Wt7zk/dFnW3LajctYnZwM+8KWLesBlM/JkQVtfCJsr/HxUfi+r/08dTz/1mMLAIDJTRMYHWlhdKx8njVJNJ4mJtL9dHQsXMM2blinLc/m7vfSGvEH1hOTzLfD7zX5XRbNJ9EYojwTlTIdgMuYSlKqXKqwsrKC//Sf/hP+9E//NFZCKczMzPI7viqHubklAGF03uHDp5TuaSccfE6eXCi8b3p6Equr7e61i0p1tNth+TNH59Dq1nX8eLhIdQIUlhHdG5F17dLSaubnR47Mxr+fPBlOckGQbpeTJxcBAIuL2WUkicpYXm4rPff09KTyd7DWqbutbH0P8/PhS9Ps7BIOHz6F5eXVQhkWFpYRBAFOnQr71lz3/pWc+6anJ9HuLjonjs9Xeq6jx3qK4tGjc1iXY/k4nqjnxIm0crmwuKIkQ9k1ne68sLi4jLn5cPpfmF8uvC9q2+Q8NL8Qtl+7+8KcvD+aS47ntBuXsbrQfYaZo+HL7KmSORkIX9JPdftc2AfV53+gvvF3qju/Hj06h5WVNlqe3XaenQ3Xw4WFdD9dXQnXsLm54j7Wz/T0ZGodCErWryoc637/nU4nrqNoPonmhbm5JWttfKKgb9bVp3zfq2TwK1Uut23bhoMHD6LdbqPVaqHdbuPQoUPYtm1bfM3hw4fx0EMP4S1veQsA4OTJkwiCALOzs3jf+95HFo4zTYkW74lp9xAt2ToQOKKzLW6sD5uK0DZAVv1lPnpZEjfaZadLfxCMCn4iNyaraPG+YCNXE7DJWiN7v7U8l4avW+uUKpdTU1PYsWMH9u3bh927d2Pfvn3YsWNHakt8+/btuPvuu+O/P/ShD2F+fh5/9Ed/VI/UDOAcLZ4uQ60U88M3r95mWKaFZqG7yLs6W9w1Xup3Ncm021branf0kqKrh4y3fC9W4DgdS+hzPaGnCslgVNvR4oVH9EQ/mXz5TFGKFn/Pe96DvXv3YufOndi7dy/27NkDALjmmmtwzz331CogW+g5YTVuIHbejJFh2ym6X3IZh4JJyP2pP1q8sA4zndbL+d0FmZZLwtwwDOO5PwhGBS8RYd4J0nlDXdLqS6LOQ6pqY8j2CT2qcGlb7ij5XF5wwQW45ZZbBj6/+eabM6//3d/93WpSNQBSrLgLC0CDR8IQvYMLTNDaFjdVKaMoVDu6UDMmnVQeRUVaA9viNQhGoHf84/DNmR1rJ/Tw3RhvohuKnNBDJD5DW/c+rTrSP1WvJ6EwJ6m8QZrZMWjeQBL4EwSB3nmRdXRDxTLr0hFIvuJDOhz7rX0q343ve2gnk6gzmatS2+KM9EvdNSx9b3ST/QcqMmxIDnU1RLkkQp1wbTuAywAQ1gJlwypSAoK+v23ARQEB7MwHTVFGB4JgFO8JWFouw586irJJ6lD/orbllkCGyVfOHlEuiVA7mKvJqPSAHlPTg4w8gTE6wbQ1BIs7Hx4pWSoIw0WpqkIc0NPWiRbvKXABI5/LtKLsUBvzsv+s4kZmS4lXroPHV84eUS6pWOjtRqycjgaCEdmZvbEKw4NinHT3xzCtJmYCevRq4AnFcjkQLc7kiJ6Bs8W5fAsVxg5fy2XXJY5JE3NFlEsidMslU9OlIfImNU5+QMLwoTqqIr9htWjxCgKlyjFkLjQA7WzxwSvZKC8VIEWLpwJ6GB3/GAX0qGdVskuFvNDcosWHoOtbQZRLKrFRQzMCXONyM4bLyNfM7gDNl12nAUxIIgwz5H5tcVucFd7AL6Qk6mZvcAMliXpouQx/55pEnZMuVqV10vE8PNoZ4CQJb0S5JEL1tdGKFifV4Hg3WUaeYIEBy5nieNRJRVRHX1a2sJqvOqzfmDnWTDEu6U/fo2KN9X0v5XPJRLcczHPJRK7YCEO5Nd4WZ6Qtw1Hbcvk+NRDl0jJak7t2XsyM7SvFbA5r1e9KWGPoJFE31IvZLPToG5fKOc5062D0wAX4/amIFO8JOgmfSybP6rk+oacGBbC3Le5gPSl8HB7fOXdEuSRC7l5D3i97u24S0CPww0WX4jTkM5Vqygk91UVxTs9PUe9s8TZDn0vKFn8d9CvbXgXTZdRVbanLVvJJryFEuaRC7GA2tsU5YMTXRhAUUe4yXQtLfLSckUJLSHRo112bkhZpWH0uqZbLTpxEnZHPpaf/LDaolu6qZ7nkRPRIXL57rohySYS6HaK3LU6qoq++6mXo1CHjTeBMvE4pbYubgdOQIBkus27i9FBE/O7qp5VEPRktDj7zXaQoh6dQORbGEEmfS9vtLLvi1RHlkgj5hB6ta831YuvzTa7oQzLzCbxQHCpaSdTrWEQcR/Sk5pSaFsmmrL2t/m1xBcEH8lwy0S4HAnpcCpMBKaCn+9OW5VJ1veXiZ8sdUS6J0I9/rL+OVBmKA8HcAPYS/yY+JTyLqKFCKfVlIkJeX66pMitkjsOSNmQkvlFIxz96YJ7nMrCedg7I70LV3KMS1lhOVIiAr1hloxDlkoiVbXHqPRlj0fb45PJGL6wNdMej1STqqd/djosqKWHS5eSX1JShT0mi7vvJJOp8LJf9aXu4yBVBW/fCn0x1S6EEUS6pcOxhDGRq8ImVQpMgdhIdq465eB4+PTopi65UjpLc1AY1oKcd9CyXXL5az/PQSuTgdCdH/9/0BrJuuUyKWlAnk6+cPaJcEqF2MMvxPAnKBuhwLRyCkIm6e10tq4hrZSQ7oEd/7Bc/RzOWX1IqIt+Lj1jkZLkEQmWsM5QBPbxe0GLBGInEEVEuqUT9S7OD6QwSEwPK9pjspWlIf85ta0MYLkr7ebTFFl+/NleGlOXSgLtNk6EEwSSjxTudgI3PJdANNmKWiqgKvYAeXs/E6CtnjSiXRGwc/2gS6z6XhP8ZgNmkIjSfIPZJK7/W5Qk9dW9BJ2Uqq0lXCW2K3t5//KMKqWhx8HpJiY6m5DRrekQjTHhPtC1uUKCi+hK/F1XJ6CtnjSiXtrEcLW4dEzI38bmF4aOGgJ7C6ywMeNIi3/3JSWkxASmgx+sP6KlFNBK+BwTdLXvreSFr6BxcLZcRroPzuCPKJRHqQkCxeDazCzdTamG4idYplXForgdX2Io2TKXjHxPXFT1GU0Z+9RN63H+fSVqJYCNu0LIURJZls7JUxcV3zqibKSPKJRHO33VyLKoOBJXxqzJvRW9zTRwMQvMgrztK2+Jm4DQWGIniHDOpiGoRjYTne+h0Oqy8iapFi4c/7UWLM/oyhwBRLi2jl0Rd09dJUxZBaCr9fV217/cCelQqGb4RlQ7oqVQS6b84EQXj6FjGfK+X7qcT0H3v66CViGTnQpXWSfpcMmpmQRFRLolEHV+3z3t9P1WurULPb8VAYToVGoDRC7gwLEQBPQodNS/zgRX6On+dVtTys8Wj64ZrRLa6h4t3uhqZyvfs+z1LWhAErJTLnuLL6HuqMIhsWy7VfaNrFWNoEOWSCP34Rx3TJa0OShmmx2+VASiO0oIuuu4fKqd/GDuhh1F39nJ+V71H5d6mjN/Icqnjc5lMVM7Nopb0B+XkC0olGUjG6WmcvnQ2CFEuidjoVybrsGV16FlmZeQJDHFg1EmOBdcLUnZAj1qjcPLlMwE5Wrx7Obck6hzzXOrs1A3c67BtC/s6o++cM6JcUiFbLs2KUVqf3eoU4DX5CWuL/iTqVsYjo0GYel7lhx+8rvBWRs9bRJREXcvnMhXQw0vPiGRzOcMOtEe1LSyrqIrK6CtnjSiXRKiWOb1t8eZ1Y2/gl+6fhEcZNkuJ4J44iXr8d/61piwnlK1oTjRwGlIi+n61UhElT+hhZrn0vcS2uGNZBiAIlPJnZdTO8el8LiptEKJcUsnTolRvM3xtfiH1d0pdnyxBcA2ntSqLut6rsgJQlNNcKl7IvGljWgPb4uWS+4lckkEAVsc/Rif0cNocqhYtbkwMo4jLlxqiXBIhTypaZ4vTqsiMrrMWLe6lf0bVM5rwhOFB5zhHXWoJ6HG9YnqZv6reMkCThzU1z2WQynPJR9FI+VwyESteDij3GpXEHIy+ctaIckmG1sNqfdPN6PUyDoShhPziFd2qnoqoOnxGYZYypPril/TmK1Kq+DxtMZQTelhHiye2xYeBVE5Wh3LkwunLZ4gol0Rs9CuTb8W201yaWHpk7AomCaIdQ2WTnSGfS4K1sC5S49OE6bLBRC4COgqZl1DgOkHAaovUZUBPWa2cLLwqFOXWbNaTuEOUSyL0XXHLbsCK1ZlOVGviMYfoJVxgg4YiUaMUrsgelyWKQS+LeuKz7kdZg7RBioRu+p6W74UvKUHAz+fS61lhXYnVr2xXWe98y29lqrI2TVF2hSiXVLJdC8tv0wkWJ9zjGiOyNuh5hWaQjA734LkbUwX12jiJpMrCqCtdE14OvUT0t9IJPd1rOkGAAAx9LoPA3lncdcOnaTOxKR6jbqaMKJdEqJOK1l0WO5TKdDQkU5YwhOiMxwrpHUlwSsNFMgZppoNo0jrYSkR/q5AMAmLnc+n7iYAeHoJVkcKWVVh3qDFpWvaIckmE3r80FkFyHYkyHI0ETm/0ghAR6REqvdOUP13qhB4jJdLJqr9Mt6LK3ISXUd/XjxYHgE6HY57LUC5WVGge2wE9fL7J4UCUSyLUjqj3Ntbc7t5cyYUmotrfwq1MxTJNdWJGgyG1YGs+YHK7NelmMFgHRTI3+J6nFy2eSLwedAJePpe+3rPYhFEzVSYeN8P0UDUgyiUV6gyq43Op689ZpTpDc5IMPIE/loPqrNZWTKblsuwesumSp6KTJJm3UgUvPjIyQCfgtUMT+VwCDvpcThNW2xbnuS/O5xvnzYhrAZoKOVpcumYp0kJCGeRxFPnJKdxuzHBZwVpoGmMBPd1y+KuPxSRP3FFpmVbK51LdCm4DXzPyvRYG2qOCsSHpH2wl95/h69Y4YrkkQjZcEqLFqzmuqF1mekqS8SdYRaOfK68hTjWHuiJ6KFXR2qEJiqduKqJkbsyAmeUyqVwyEosM10fw+n4K2YhySYQcLa5zW6N7b57wTVhyhKElDujhN7hs7CJTfATjOSsjz2WW0JwUrjJ0fS5T0eIa/rs2aGk+iw16XlL6DeV5dkapdos5+NIZdTNlRLm0jk60ePUuZdTSr7D65RwtTkzHwmuiFPiiE9CjO6xMKEt8Fgf6foh2nkvN612gu5UcWy47oc+lNb9ABbwoz6VrQRJUbh2LzZusqmjp4fON80aUSyLUOUXLcmCxF9c9IYmeKHAgSqKu5HNpcvzlvHTZJmv+KT26L+szL7q32eiex+13V8x2N1k5J+VSd4vfJvT1kk/7RvQMKPxk44Qol0TIVkWSz6UeqbnS9nGTXanzay2XR4asoA3NmdkaKvOFFbUgubWgbbrUlJCnnpOipZm+h3US9Whb3MGbfG6NhtqnznaWHbJ6EOWSSN72b+l9Na5snCY60RAFjgQau+J1WCZcD4vMR1LMRZR5WcPzXHpeIhWRgtzJbfGAWxJ134sVJWeHZwz8XU0Ou8/Rq6toSHD6zjkjyiUVG9HiBvqwchGm3t5k3AkWoXRbVaOd0V1xzcJqO/4x9buaUOQdlAaYLnUtl3Eqoq6BkJOeET0Lq1av2D4c0yZzkoUzolwSob6R6d3V3WLm0ptzBRn8nGM0rjA8DASMKdwTIFJ4FPtmLV24oFAL23OV8lxmiJelyjRp5Ps+9HwuUwE9zCyXmv6jTYBR8/bgKBNDRLkkQu70GjfatFyampK8gV8ohcjoFWqiuy2u0sVcWi7rgrIrnnWTklLVAD1H98hEP3FCTxDQUjvVRSrPpWNZIiI56Hmh7T2J8rGw9YrBqNJqiHJpGZ3JyEh/MtkpNd6KGzgWhAZTujA4j27msQuRPi1I8Z6C/8ucElw/pAa+56HT0bi+O4G32259G7OIFWUHnTwvKKZq83gZv5kmU/TCtY7HWOaOKJdErHSsJvbeBoosrCF0/OSaOP5KyHok1XfGpAKh6obAnVbK2lf+VLFy2dVIOXUR3wu/S0ou17ohu5FxauAuDEViiSiXRHopdzS9KB2lBirDtKuODECBIz0fwfIOOpTb4gQ5tOc4/Sqc4enmuew24CpDy2Xy3HNnDDRHxWjxSnfXUxefb5w3olwSsXu2uB4uLQa9haj6EBwy33SBAQHUFQKTegMXS1+2oqhWc+oqFZfLBozflqbTZKvPcsnN5xIAqyMgvYrLATXlX1UKW5BhBDtHRLm0jFaHNNCJXb1YV6lXBq2givYyGqU0tN3JqIurWSlSz63uHpD/X9k+lzoSucXX1A45Wy79RJokPlJVw2r7KvsgD0vr1osol0TInV4nWpxWg1MYzbXCGkJnPCoHshjszLEbjW6RxrXLCrcEyc+42GKroXu8YL91kNN81/KiYCONCKWaqbqPxal9YzjKxBBRLonYGCzc3pB0looqkvNfkoSmopHl0uzoU9lGzrzN7ByQpUypjrfs67LyXPKat4pIbYsriB2dLb7aVeA4PauXSJPERgOqqB1ysgxHxBLZTJPE5fvUQJRLIlRfEq1O0rz+1KPJsguNQ7m7dY9VUbq+hj6sHwBIu69cDo0yiVU3wefSI26LR6mIOPlcthj4XOb2KSZuISZgqO+yRJRLKsQeZrtjujtjVkagwI/+ZTcvP59plEZDMPir6eGbOR8omy4HL2x4mkvtgJ5oW3w1TkXE52F9DtHifVRtHVcBPcWwEoYtolwSIW+LW6iDgqlF1up56IKgSRAFOyh0MpP9kB41a95iqVt09KKYnCGG5IAebctjayCJummJ6PSOpmQkV0U5bCjvWUeYFqZQl2hxJUS5JGLlOCtq7+WwHyUjT7CJ+r64xrVG1UvSJeYtl4Oflc0WRTJwmGqqQI8W52e5bKV8LnlBT6JuWJCiuhRl5PON80aUS8bEnd1BdCuVXnJ5QeBHZLlUWUjqyHNZWGSGTmA8WLzCQyV1Ft1tfq4kA5xUnqk/WpyTz2VSUeYiVuVt8Xg94fJEYGQW5o0ol0R0U1hEaBkuTWwxOxoHJqptwNokuCaIju5TJ32+dv6dsULoci2p0XJZ12M1aenV9rlkbLmkrkkmyDOWklNwRfdbeCRdQ6838IsF+HQzZUS5pEL0u7B//KNBLGl7jOZrgSm0PjLoXVXoa2zScqkQmJCULpLL9HyRVVqZv3WmCApyZfmycUM7WrzP51J3W71OdBXlOqjNjcPGoyXrKHS6rFuQ4UCUSyI2AnoajWiIAkeCbtdUcoF034dr3RbXLDwz8CFDMeVkzSujpSnrQLS4cYnoVPlua6NqQA+bB+nBTyKeiHJJpSHb4raVPImkE9xQ3ON6Ec8a1jSjlkuaCcZGQE/pPRkyD43PJfVscYbHP3KwXPZT2eeS3yNJXIEiolwSIXcsreMfmXXfHHE4TgCCkEkc0MOQTGXM8LZ4KoBF23Sp8lGj0I8WD3+yTEXkV/hua6bqccn2d8XtuMsMM6JcErGyLR5bAfVqS+WjU73H8CphZNJlmFJD4Ilqf9MJ/+GwhhjfFs/4rHSYZfpcltfVhNHb0oyw7kWLh9viLoNo+vE5ruYVm4ehMZbFvNAEOHbHRuCpeOhn3hf9onCtnkjS6QWhhCBQH7JGUxHRpgs7J/SU3dP9qaosMtK3StFtj160OD/Lpa7/qEny+kbVLWQrSdR134Lisczoy2eIKJdEWCdRN11GhMIgjJ5PBp5gE/Xe5sZ2yXFx1S06GbwT31owJzRh44F6/GOb8fGPANhYGqo2T3y7hXZOVVG4K26/cZl8nVqIcmkZrp2kAeuAIAxA6bcuLJdcTFyx/pEQp7QNeYheC7rbrnG0OEPLJae0SKbg1L4RHGXiiCiXRKhvrHqGy+q92PY4sGwoFdYstJ4WQH1c9fQwO+Mwq78bX8gMFRjNf8VjlP8Iphz/6CF5Qg8fTYPjCT3VqT+gpwn5WJuIKJfW0YgWtzpDmB1gVWTntNUkNATlyDWNa2s4/1Hb59KcBPn1lyVRj9I4KU4RTRq/6a1kNbl930uc0FOHVDRYpiJqVECP4ksnv2ZmyYjKRQ888ADe+c534vjx49iyZQtuvPFGnH/++alrbrrpJnzmM5+B7/sYHR3F29/+drzwhS+sQ2YWUDs9qWM2SVHzUj8EgRU6r1As+nCNJ/Sozg2FlxVonI3wuSS0r+97seWSkyLNyYraj5UYBUtwS/PEFSXl8vrrr8dVV12F3bt341Of+hSuu+46fPSjH01d88xnPhNXX301JiYm8L3vfQ9veMMb8KUvfQnj4+O1CO4cYv4tK0nUGzCpC4JJVCf8IAicLA0qW+xJZSz6nUO0eIRytDi5BvvoHv8IhEpcm6HlMrUtbluunDeJyooY0eKvBTVa3Lggw0XptvjMzAz279+PXbt2AQB27dqF/fv34+jRo6nrXvjCF2JiYgIA8JSnPAVBEOD48ePmJWYCOfpT60573deUlcFmdJ8gkFCO6DFZJdFH25wIXTkGP6sSFNX091jKVrLve1gVy2UmAyJ4A79oYXNb3MG0MNSUWi4PHDiAM888E61WCwDQarVwxhln4MCBA9i6dWvmPf/wD/+AJzzhCTjrrLO0hJma2qh1vUvGJpYAAK2Wj+npSeX7JiZGAQCbN02U3rdh/Vh47ebyawFgZCR8Vzht64b4+on55fj/i8ronySzrh1bN5L5+emn976307ZuAACsG0tfu2nTMQDA+Hh2GUkOnVruPk9LuW11voO1Tp1tZet7iMbGhg3rMD09ifHx0UIZJtaPwvM8jI2NYKTlY/OW9QCA0dGR3PvWT4R1bNmyvvJzRUrM9PRkbhBJcpxv2nQivK8VjumJiVElGcqu2TQZ7iRNjI9i86bQGLAuZ1xHbNiwLvy5fiy+buPGsBw/8VwR68ZHus+QPW9xGqubum0AAFu2KM6zLS+eL08j9I26nn8lofZMTIxZbeeoj6xfvy5VbzSGNm8eJ7XT6Gikd+itszrMzK8AAEYT683ISCslR5L13blnclL/maiUzUGcxlSE0ra4Dl//+tfx3//7f8df//Vfa987MzOLTqcZ78Knukpbu93B4cOnlO9bWgo78omTC4X3TU9PYr5bx4kTxddGRE7mx47OYcNIONHMLa7E/19cRrrds65dWlrN/PzIkdn492PH5gEAyyvpa0+eXAQALC5ml5Hk+PGwjJXVttJzT09Pan0Ha5m628rW9zDXHRtzc0s4fPgUFpeK+/nC/AqCIMDi0io6nQ5ORH1sZTXzvunpSSwuhGUePz6Pw4dHUYVOd2vg8JFTuRamEyfmYxmi8RLNhwsLK0ptW3bN7Gz4UrywuIKTJxcAAEslY3J+PrxnttvWYTmhfNExiMn7l5bCNs2b4ziN1fm5pfj3E8fV5lkPwGL3GU+WzOP91Dn+jh9fiH9X7S+mmOu24/z8UqrehYVwnJ48uUhqp/ZquKbprrM6RGtWcr1ZWW3H/99f70J3Xjg1q/dMVThxfD63rrr6lO97lQx+pdvi27Ztw8GDB9Fuh43dbrdx6NAhbNu2beDab3/72/gP/+E/4KabbsKTnvQkslBNgL4dYjdaXDmIthk6vSCk0ei31JM4TFKnj3Z95Q3eVFRMk7YNKdviHtNocQ7b4gNUFcnmtrjqdQybmSOlyuXU1BR27NiBffv2AQD27duHHTt2DGyJf+c738Hb3/52/Nmf/RkuuuiieqRlBJfj3LhhMjegIOSR69pVRhCoR0nrCGS4rCA+SchwtHjGsyvr3JrKedCAN1aKQtbiGi3uMKCHcjy9ClZOfMsQXqXryhpXjFKey/e85z3Yu3cvdu7cib1792LPnj0AgGuuuQb33HMPAGDPnj1YXFzEddddh927d2P37t24//7765PcMVUHi8611bqwYhRtpToyapVxJzAiGYCi3DWN9mFiQI9pyyWh7Oiy1BxRdHODBj/lVJtktDinRNEs81xWHESc2jeiQd3bKUo+lxdccAFuueWWgc9vvvnm+Pdbb73VnFSNoP7ozyb2YRl4gguU+53GW1S8MJpwT4l3OhwPEEL1RSI3wTpZBEm59D0srfA+W9ydVOmaKzePzfZ189Y5tHB8MWgEFlwuySSne0ZzHwB+8ghriwBguzbY0NP8jN0QVQUxed2w+Fwmt8VV56bQctk9/pGRtZClz2UEUTTf3PtdLrrHP8auXzabm/N3m4Mol5bxdYaJrj+n3uVpDC9szRsKwlogTqKuMFlzmM/rlaHeB2yCUdMnrIChzyW/gB6O2+JNIr2Fn995OX3nnBHlkgj5LbE+3ZIVedtFTVhwhCai6XBiuSOqTBdZEpkOGjBm3RoOl0tatLjnYbXNMaAn8QcTuWJ3ELLPMY/nEPQR5ZJK/bql1QlCd2sgD6OTgSiiQgm6XSQINAJZDHZlT3d5ren4x6QQ6u0QXpiljxfp6KbmlDohR4vHyqVpieg43aKv6auOH6nOYHFN2UXhVUOUSyLk7kWwXFbpy00cB02UWbBLfx/R6TPK2+JGz3+k3md2MGQe/1iyuGZJMCxDlBbQ00uKz8nPMeU/6kiGwXorOk06CugpGhN8vnHeiHJJhNrndSYjM0nU7Q4FJ87OgqCDat80rVuWlZexoJk3XNJLzFpvC/VS/oZLknLoMp9kEZ7nsZIHqN5/vYq6aS2wEoYvolySkR4mCE0jDOhRG7tGRzhx1a/zhJ4qmVeKtgabtG1IzXMZwS2RNtegHnpyFX7PIwYUNUS5JEKO57HQIVOpRUy6QKodWyAI1lHtdi6NaaULpYWxY17xy2/RBhguScpli6nlEkgovkzkqto+PNNc2m9cJl+nFqJcEqEmRaac0KNRuub1CUynIjIwAJsQECA0EM1tcRMTu1IZweCvppUFL/cPFdTGY5MWQmq0eAQnn0uAV97NJNWTq/B5Lj6S8EaUSyLk1Ao1XVtnGZT6qtTLcStEYI7i6hUEOrqluX7oecQFtsZt8YiyHYkiEQpvbcC7ITVaPIKZbhnLZlusvK+6qpHBhouF7ilTVdMrrRVEuaRCdyIhXNvATtxAkYXhJ0CQUggKo0It9+EsS73xgJ4K/oLK3jYNGvvU4x8juPmXupYnv/pqPsc2HitZRQPei9gjyiURal/XihY3MEurVmdsMDGbbIXhJrI6KPc6R+c/0hd9s7JmlVY69gtkLzZc8l+iKYoi5chIW3AL6KkeLc7reQCeMnFElEsi0sGKyWsdaTbBFZGq4yKJOhXz0eLRtqnOS67qh6X/xQ6KLpZUSLn6XLLZsm1QQI8qTd5QtIkolxUguVBZ75B2K+wNPAP18jd8CA0jgM6IMOhzqXJRMPh7nSf06D5epgtB0Qk9DRi/FEsf1zyXAD9lN6JqQI+Vp1J+66xXjGFBlMsqePr9TMtiYLETm14IKokug1fQRbXP9EX0FI0xo92QGNBj/mxxggwZ9xTL1ZwBTEqinuo/vJ41VpaZiNXYbfHCE3pcuNVYr7IyolxWgNLJtI+pq4jtTtnAMSCsIfrXDFvWNfK4MD6gvIzf1MjyoWyCX2URw5bn0nPkc5kbcR27YdCwkxeadh+zr54dolxWoPaOr51Ds0plZhcJbpOuMNzoGS7Vrjbbhz1QliPTwyhL91BeXJPR4kUW3waN/aon9HDbhuYW0FOV2HJpOVq88LrhauLaEOXSMnrR4t2fTerMJbLqvCU22yYisMXBeGJzopeiS0BahoILG+5zOWypiPyKlkLTVA1+cbcrnt95ubQtd0S5rABpYrG9Ta1cn1nBqky6MniFugjPFlfrY6atJmVDIns54zMuVXXFJo1fks8l421xn9mKXvn4x/hnfQ2t/Q7E7IhNrjDris3CI+x0aV1utfOaMTOwSYEhrCl0U+uo9HaTPZnq32h6lzOruDK/ySIRmp7nsmq0ONttcV5ikdcFm5ZhCRY3iyiXFaB0Mq2zxQnlD5ZR/1BIPpK81Ak2qLTouFhFPGJxppXL1LNrtoOiz2WTBj8tWpyz5ZKZQBVx9jRF70XD1cS1IcplFSi74jrR4hZnLuP+UTIABZso9rcoE5HStngVeQbKKi8tawyafjnMLE1x7GtbIvkbLklb3K0G+FxyoapriZWAHt2zxeOfvNqaG6JcVoBr50qNFZ4iKtGEgAChWQTQeMEzuVDTgsVrsFxm/14swuCF0Wc2FOI6IW2Lp6LFTUpTnZbjE3pMK9vMdGUA/F4ouCLKZQUofYzyZqm8FmqXXB+VZOH0IMJwwfqNZVA248HiGfOPciai4TNc0hLbJ1ZNbooGO3mq3s/rcQQNRLmsAKnja22LE8qnV2eE3uRWoeYmrEoCK1R7m07XMh3Qo2NNihS5Oi1BZl4AM02XjcHzPO0Xfs5J1GPLJRe5Ku5qR/2/1l1xzevjnXoubcwUUS4rob/5oBvV2lTyBp6ez6kZWQQhItwWV+tYRnfFqT5n5kTolqe/2g/7ONRN35MO6OHVOBLQU6Eu1XmhZjmGBVEuK0CLFq/r4rwiZCgIa4A6TJdGoY1D08M3szzF/e6sy7J9LpuFrkKWTkVkWppqsAvoQTXLY2y55PRYrIThiyiXFag7h7rNLqyyvuj4XJmQnbV7nMACrT4SbzUPfJRJb0vOxEseeGhdXs7virfo3Jp73jQzfM3vmbPl0tXxj7V91RYeJ0t0bpmIuPUzFUS5rADthB6d/Sj94l1jwuWyic8t2KW/i6gqBgGCRgXImV5Usixb6keLq13ZtHVQVyHj7HPpels8t3ZiQ/ksRqFAQZRLy9QdBMSFJqUjEYafeNzp5CIy6XOpUJxtQ5/yGI3aSzPFWTPslvoKvMfYcukzC+ipfPxjfH+ND6Qd0VOLFEOHKJcVCDu+5sQU/yy/T7sPM+j0DEQQhFwC6KT2MqxdUm4zPKAyLZcli2uRCNn3NmsWqGS5NC1MRfj5XKZ/kguwTNGYiJ+JWVtzQ5TLCtACejSixZvceXNE17PONMX2IbhGdaj09z9bI8yDV7uPdt2kDZfDY7qkBvR44DdHu/K5zKVi+9hUlpU3NJh951wR5bIKlIToTPul9tFueRjISyZb6kJ9BKnOaU3/oYfLmhWDENBTdF3WvMF1jssjVmAU5e5tPfN70Citku05tGwcmdseN4+xtU9IIcplBYYpWtw0DOddQeieLa7WOU2f/kgZ0aaHEUUhiu9IrMEqxTRlyda19kWXc5zjfN2knabxCv/UxqrlMvVXfu81EbO6FhDlsgKkzsU1WtyU4dJMMYKghG63dRTPo4QN98UqJ/RkWniGIM+lR90WZ6hd+tw0H0MWSy6PA4CZMHwR5bICJCvAmumYa+ZBBRfY2GYzOFhVosWTRL6hpi03WaWV5aMkK1ENyXOpb7n0uj/rkKYarlMRGcfG42h2U3HbUkOUy4rozrt62+Je6qcqTn1IDG4ZNWRtEhig3N+6LpcqfasXFUoUKlUYjwUplUZH817lZNNMnlUVXX2sxdhyyS2gp0nb4qr9luHXzhJRLitAy6GuEy2uWXaFoWxaj8uTROWZZPAKdREgUB+Dhn0uOUSLV9pmTPlclpfQlHfDZPS31vUM56neaUM86B3fWHWrwYAwGjSl73JGlMsKUPo7l0FvkqRSO4zPJ/BH+cUq4wjI/DLNQV1bTVvHbLjyNG0O0LWO9bbF+T2pu23xetQxG9Zh7Rzq/L52lohyWQHa8Y865esXz4ZGCy8MK5wtEjbO4k6NSs0xqnlAT2PcWrR9LhlbLuNncSSbcUu74fJM1BW7qzH8/jkhyqVldLaum+g47BnclmnI2iRwQNVwGWhEizNYPUyLkFVelRN6zNzgluGKFuclE+f8lmQ4ysQQUS4r4Hn6/YzlYKmDtfKcQsNw88qiq4hEQXkmX9a6BfZ+1b23KaZITVrkbfE6pKkG12hx6roXK8s1dj3dbu2ihZuoN4hyWQGKZVGrk3h9PwVB6EN9ZQi6KpuqomfeakiYL8yKkDnhl7ZgV27Vlm7adOVrbiU3IVqcy65XZSnq1y0H6lK/jkcbc0WUyyqQXC51tsWbS5NlF/jT378o72ymrlMqi1pYjU5sVYLmlU7oaYilU/ts8UgXZTjJsVN4vWrKbvQ81rtSQX1cFHfuiHJZgdojL4l92OWc3kt1IgNQ4IOXNIEku2bRYGGwUBu3XFZ4JtV5pWljXztavAmWS8ui1bXm9B6jzkVNr2yGXztLRLmsAKmP6USL69bQgE6vNQk1w/AhcEA1oEf9UrOWS/KNFga1ckSPXrx4U4YvNVqco3ujc5/Lvv5aVZrY5dJCZ9Jdb0XJLEaUywrQMhFpbIs3sPOacEdp4nMLzcDZVq3nZfbr3K4eH/9oWgwLfp8NG7/a2+KMLZfOlct+KsYN+K62xQvg+L1zRJTLqugaFxvcL3UW5gY/pjDsODqhpwwr1pkMQVSrTcqn1ISMFIIidPWx+BQchpMct+MfKxN7s9TXmbLGXVFtTqLFG7iiinJZAZrPZfM6iR56kZdZcHpLFZqB6uQbBH1ds2A8Gh2pXk55JZXUeUKPetS85nah1tXu8TUjrDlHi3MTqaLhMu0rXTPDfhKVbUS5rEDdZwUbz3HHABsBTYKQR5iKKPlB/qplUnnIU1ySn2dZZ2oLFk8UrGy5zCpH8XrO6EeL81UuXQX05FG1jSykudSGS9tyR5TLClD6mE7HbGIfNhotzmlGEVgS6YbK44pDKgWte9yLEDOkiS61k6hLQI8+RLFspCLKKrrYBaz67txaQJTLSpDUS+NScKTKIiZvhkIpFTqJ8nYwuYasOvWuj5Y2075WxgJ6uDiRGkCOfzRHXZb2On0udWH4tbNElMsKhMc/1pe+gLsPiIwxoWnopCIybjXM+iy1RV/y/yZkyCpPcd1WXeCbFnxATUXEUcnoPQsP4XrS0OTxetolG3i0LH9EuaxA3dvi3BgY3w1+FmGNoqFdmlSS6AENZkmWpzwXZVyn0jaM9IFCdKO/e6dF8psAXVsuB6goTrwtbkCUXHQLZ/z9c0KUyyqQXKiGJ6Il61lin0sjLpdNWZ6EpuCqT5G3UI1bLgcLVG4T1RN6eE9bA+j6Kbb8cNnk6N7Yi3znBbn7RwE9Flws1N+1HLQuty9UAVEuK0BaMAjb4lz7VdHjVxmA8kYo6KI6FAdSESmUaUxZqv1ltJ6yo+uC9IelNMTlUv/4x7hf8JunuOW5rNp/ObaxLE9qiHJZAUof0xn73Ppwrjxe8lfmGrEgMOqcpWunYVEzFamy0x/5NFctDJPPpW5wkinyXiSqtlF0e6fWaHHNs8UHfhGyEOWyArSB02DtUhDYomJK61ouVX0uDY6/MPgv83/i36wY+ryc3xVoiiVSF09zFYxTETGzEgK9tEquFN/aAtBsbIsrHypQsyBDgiiXlchdMfLv0NItab3Y6SIghkuBOSUB2onr6u/FZfOBaQmq6UO91ioqpmmLr7blkvHxjxwV3iqwDOiR1U0JUS4rQHK5rLFf2p7sCuvL+T8txXdILSVCdbS7enyDjtOlbiUFRXke2X/M7LDOCugpuYMoQFMC8rR9LjnnuWSmXFb1W+4ZLvn0Je6xEFwQ5bICtHie5m6LqxxO58U/mQkvDDWqvS3MRNS7mvB+VB9566fh7XlqsanjH2sK5nOBfrR4d1u8DmEqwi2gpyo2TujJoqi+4Wrh+uA4PhoDaRKl6JYM35ABtmIJQi5BX57LwjXLpFJXoQqj+Taz4nnKAnqo9fMxNhVC3xbnNwG6ynOZZ6WO+k7lJOo1Irvi9SDKZRUInUynwRnOXSmSO43xZyXbINyfSWgm7PuVpnaZ3AY0G1iUWOyrmC6Lbub+XfShq5AZT1FlEHZb9lWjxbv327BcKgf6RQozkybmiiiXFfBA8f3SuYN572UuniD0EwSBk2TJHnj4aJOKixZ4zdsaYrjU3hb3PA9+BR/aOuHmc1mV3hi025ts7WgMM6JcVoDSx7RUy4Z04vSWiDmhm7I4Cc0hgJtURHmFqSiwdVgu0xSPtKw7in0umwUl+tv3PZ4n9DALNqkqh0d8sdFB1yrKpW25I8plBShvrk1RGFWIT+7IGJxVnnOY2kioF+3tsgDQXR5MWDBVdjnyo6vriRfXfS7diF1OEb5FUKx9vs9o6zkBu4CeqtHijgJ6ilALBxREuawAbZtL/SZq17WWAiRDwN7jycAT6sPGy4sV5UGhCvOJqdMFli7cWeNc83rOUJTLli/b4jawebZ4isJw8eFq47oQ5ZIz+g6ddUhRWlum5dKqJMJaR3Wh7wsWLy6TLE1GWXkBbpblAHgHpLiAYu0LfS5rEKYicXCSbdnyjn+sKIiVJOrU4x+FQkS5rEDd2+K9NA486T1LPUOf01aIMCTonP9onOJ6k/1dNTCbJEVfeeWGy4ytyUKfS64zVjaU7hD6XPJ7Ttfb4v1roqmzxW1sxuke/8jw62eFKJcV0e1gWgop+86b/1YpA0+wiWp3iyyXKuOw/kAapTtrUNZ0o6NptTTl5TBSyHTama3lMn6W4cBGQI9QD0rK5QMPPIArr7wSO3fuxJVXXokHH3xw4Jp2u409e/bg0ksvxUtf+lLccsstpmVlB+2EnnqudYFuFKkguCZSeGz7cIUKbcbnDvbFqcattOEyv5CmzQEUC6QvPpdaVHm5Auodr1lFF9XG8XvniJJyef311+Oqq67CHXfcgauuugrXXXfdwDW33XYbHnroIdx55534u7/7O3zoQx/CI488YlxgTpA62RD2y+xo8SF8UGE4UO6aNvqwggW17ipV123daHGtq91BihbnarlkJlTVdSD6aqwkUa/5+rVGqXI5MzOD/fv3Y9euXQCAXbt2Yf/+/Th69Gjqus985jN4zWteA9/3sXXrVlx66aW4/fbb65GaCZTOpTP4uXfeTEsMe6mFoUSh23WCAO1Ox8m2eJVhUVe0eKWo+SEa5hQ/Ra7R4vGzMJONbLdk9hyAm6Z99PAsfvToCSyttO1XTqRUuTxw4ADOPPNMtFotAECr1cIZZ5yBAwcODFy3ffv2+O9t27bh8ccfNywuL9aPj2D9uhG9e7rXrx8vv2/9+KjytQDwlCdsAQBsWj828H9nbJkovPdp558W/z6xrpV5zQVnb079/fQnTQEARls+zjtrMvzQA8bHWgMyn3naegDAE7dtKpQDACa78j+1+zyC0M/Zp28EAJx7RvjznOmNhdePj7aw2g6wsNTGurEWNm8M+9iF527B2advyLxnQ7cP540HHTasG80cx8940tb49zNO643Rs7rj5fxtk1ifc28/WzetU5NlfATrx0cwORG2wVNKxtnpm8cBAE/a3hv/46Nhm0xl1Bl/J33tesH28rHvgg3deXZc43veMDEa9w9OtFoe1o21tNelqmzeGPaDjROjqc+jfqvTtknGx8L7zppaX0G6Yqa7a+OTzu71z6edd1re5fG6PGHh+1/Xff6/vf1+vP9/fROf+H8/qL1OU3hBiTPDvffeiz/6oz/CP/7jP8afvfzlL8cHP/hBXHTRRfFnl19+Od7//vfjmc98JgDg5ptvxsGDB/Hud7+7JtHdc3JuGZ1OgC2TapN6xMMHT+GcMzYqvZU9fPAUzj1zUqncdruDg0fnsb1voT1yfCFUhMdHc+4EFpdXcWJ2GaMjPsZGW6lJYm5hBT969Die/qTTU1tIK6ttzJxYxFlTG7CwtIpT88s447T1eHxmDls3jWNsND2h6Dz3Y0dmccZp6zHSkpizJkAdC1XoHxv/3w8O44JztgwscACwuLSK/Q8eBQLgqeefhvXjo3j08CzOmtqApeVVzC2sYvq09AtYEAR45NCs8vgrYm5hBYvLq5janK4jGkMrq52BeqLxcuT4IjauH8VEgcIwc2IBE+uKx3jE4WMLmFw/ivF1I3js8CzO3LoerZJx1j92gyDA/geOYvq0CZxx2uDCnzVvLS6t4mR3juAE5Xs+enJxYJ7kQt78Wycrqx18/6FjeMp5p6Xm7KpjqNMJ8N0Hj+Ls6Y21zi0PHzyFs6c3xuvbaruDBx47gdM3T+C0TeOZ16uuZVVYXFrFV+89gE0bui+C521l2eeyKFUuZ2ZmsHPnTtx9991otVpot9t43vOehzvvvBNbt/beut/ylrfg1a9+NS677DIAwHvf+15s374db37zm5WFmZmZRafTFE+depmensThw6dci9EIpK3UkbZSQ9pJHWkrdaSt1JB2UqeutvJ9D1NTxTtChfeXXTA1NYUdO3Zg3759AIB9+/Zhx44dKcUSAC677DLccsst6HQ6OHr0KO666y7s3LmTLJggCIIgCILQPJT2HN/znvdg79692LlzJ/bu3Ys9e/YAAK655hrcc889AIDdu3fjnHPOwcte9jK89rWvxdve9jace+659UkuCIIgCIIgsKN0W9wmsi3eQ7YF1JG2UkfaSg1pJ3WkrdSRtlJD2kmdxm6LC4IgCIIgCIIqolwKgiAIgiAIxhDlUhAEQRAEQTCGKJeCIAiCIAiCMUS5FARBEARBEIwhyqUgCIIgCIJgDFEuBUEQBEEQBGOIcikIgiAIgiAYQ5RLQRAEQRAEwRiiXAqCIAiCIAjGEOVSEARBEARBMIYol4IgCIIgCIIxRLkUBEEQBEEQjDHiWoAkvu+5FoEV0h7qSFupI22lhrSTOtJW6khbqSHtpE4dbVW1TC8IgsCQLIIgCIIgCMIaR7bFBUEQBEEQBGOIcikIgiAIgiAYQ5RLQRAEQRAEwRiiXAqCIAiCIAjGEOVSEARBEARBMIYol4IgCIIgCIIxRLkUBEEQBEEQjCHKpSAIgiAIgmAMUS4FQRAEQRAEY4hyKQiCIAiCIBiD1dniHDl27Bje8Y534KGHHsLY2BjOO+88vPe978XWrVvxb//2b7juuuuwtLSEs88+Gx/84AcxNTWFBx54ANdddx0OHz6MkZERPOMZz8D111+P8fFxAMDnP/95fOADH0C73cZFF12EP/3TP8XExERm/UXX/tVf/RVuu+02rK6u4lnPehbe9773YWxsbKCMb33rW/jABz6AkydPAgB+4Rd+Ae94xzvgeR7uvvtuvOUtb8H5558PABgbG8Mtt9wi7dTXTh/96Edx6623xtc+/PDDeM1rXoNrr71W2qqvrdrtNm644QZ89atfxcrKCi655JL4/5rSTsvLy3jrW9+Ke++9FwBw9913K7ehanuX1SFt1bv20KFD+O3f/m202210Oh088YlPxPve9z5s3rxZ2qrv2kceeQQve9nL8OQnPzm+9iMf+QhOO+00aafEtbfffjv+4i/+Ir7u8ccfx3Oe8xx8+MMf1mqntdBWgPraEBMIhRw7diz42te+Fv99ww03BNdee23QbreDSy+9NPjXf/3XIAiC4Kabbgre+c53BkEQBA8//HBw3333BUEQBO12O/i93/u94MMf/nAQBEEwOzsb/PzP/3zwwAMPBEEQBO9617uCD33oQ5l1F137L//yL8GuXbuCubm5oNPpBP/xP/7H4K/+6q8yy7n//vvjMpaWloLXve51wSc/+ckgCILga1/7WvCqV72K1jgJhr2dkiwvLwfPf/7zg+985zvqDZRg2NvqE5/4RHD11VcHy8vLwcrKSvCmN70p2LdvX6PaaWVlJfjyl78c7N+/P3juc5+b+j9T7V1Uhy7D3lbLy8vB/Px8fO373//+4E/+5E9UmyfFsLfVww8/XLk/BcHwt1M/u3fvDj772c+WtEo2w95WOmtDhGyLl7BlyxY873nPi//+6Z/+aTz22GO49957sW7dOjz72c8GALzuda/D7bffDgA455xz8LSnPQ0A4Ps+nvnMZ+Kxxx4DAHzxi1/E05/+9NhS+LrXvQ6f/exnM+suuvZ73/senv3sZ2P9+vXwPA8vetGLcNttt2WWc+GFF6Ysk0972tNieUyxltrpn/7pnzA9PY1nPOMZGi3UY9jb6nvf+x4uvvhijI6OYmRkBC94wQtyy+HaTiMjI/j5n/95TE5ODvyfqfYuqkOXYW+r0dHR2ILSbrcxPz8P36ctX8PeVqZYS+1033334fHHH8cll1xS0irZDHtb6awNEaJcatDpdPDxj38cl1xyCQ4cOIDt27fH/7d161Z0Oh0cP348dc/i4iJuvfXWuNP237d9+3YcOHAgs76iay+66CJ85StfwdGjR7G6uorPfvazePTRR0ufYWZmBnfccQde/OIXx589+OCDeNWrXoXXvOY1+OQnP1laRhnD2k4Rt956K1796leXlqHCMLbVRRddhM9//vOYn5/H/Pw87rrrLqVyirDdTkWYau+6GOa22r17Ny6++GL85Cc/wdve9jZtefoZ1raam5vDq1/9arz61a/G//yf/xNBEGjLk2RY2yni1ltvxeWXX168zavIMLYVZW0Q5VKD973vfVi/fj3e8IY3KF2/urqKt7/97Xj+85+Pl7zkJUZlufjii3HVVVfhTW96E97whjfgvPPOw8hIsQvt7Ows3vrWt+Lqq6+O35guuugi/PM//zM++clP4r/+1/+Km266CV/5ylcqyTaM7RRx6NAhfO1rX8MrX/lKI/INY1u9+tWvxnOe8xz86q/+Kq655ho885nPLC2nDE7txJ1hbqtPfepT+PKXv4wnPelJ+PjHP165vGFsqzPOOAP//M//jL//+7/HzTffjDvvvBP/9//+30plDmM7RSwvL2Pfvn345V/+ZSPlDWNbUdYGUS4VufHGG/GTn/wE/+2//Tf4vo9t27altkyPHj0K3/exZcsWAOHWzR/+4R9i8+bNePe73x1f13/fY489hm3btgEA9uzZg927d2P37t348Y9/XHgtAPzGb/wGPvnJT+ITn/gELrzwQlxwwQWZ5QDAwsICfvu3fxsveMELcPXVV8dlbNy4MTann3vuubj00kvxrW99S9qpr50i/uEf/gG/8Au/gK1bt5LbaNjbyvd9vP3tb8enPvUpfOxjH8PWrVvjcprSTkWYbG/TrIW2Gh0dxate9Sp8+tOf1miZQYa1rcbGxjA1NQUAmJqawuWXX964Ob0I033qc5/7HM455xw89alP1WyZQYa5rfLWhlwKPTKFIAiC4L/8l/8SvOENb0g5lLfb7eAlL3lJpqNuu90O/vAP/zD4/d///WB1dTVV1qlTp4KLL75YycG27NpDhw4FQRAEx48fD6644orgc5/7XGY5i4uLwa//+q8HH/jABwb+7+DBg0Gn0wmCIHRK3rVrV245ZQxzO0W87GUvC77whS8UtIIaw9xWi4uLwcmTJ4MgCIJHH300+MVf/MXg3nvvLWuSTFy1U0RWcITJ9s6rg8Iwt9Vjjz0WzM7OxnLfcMMNwe///u+XtEg+w9xWR44cCZaXl4MgCIL5+fngN3/zN4OPfOQjJS2SzTC3U8Qb3/jGYO/evYVyqDDsbaW6NkR4QVDRGWPI+cEPfoBdu3bh/PPPj1MEnHPOObjpppvwrW99C9dff30qxcDpp5+OL3zhC/it3/otXHjhhbHT+c/+7M/i+uuvBwDcdddd+OAHP4hOp4MdO3bghhtuwPr16zPrL7r28ssvR6fTwerqKl7/+tfj13/91zPL+NjHPoY//uM/xoUXXhh/dtlll+Gtb30r9u7di49//OMYGRlBu93GFVdcgTe/+c3STn3tBADf/OY38e///b/HF77wBbRaLe02WittdeTIEfzar/1aLOfb3vY2vPzlL29cO/3yL/8yDh48iJmZGUxPT+OFL3wh3v/+92uXU3RtUR3SVr1rv/KVr+CGG25AEAQIggBPfepT8a53vYu0gzDsbXXnnXfiz/7sz+D7PlZXV/HiF78Yf/AHf6A9Zw17OwGhn+Ev/dIv4Ytf/CI2bdqk1T5rra1U14YIUS4FQRAEQRAEY4jPpSAIgiAIgmAMUS4FQRAEQRAEY4hyKQiCIAiCIBhDlEtBEARBEATBGKJcCoIgCIIgCMYQ5VIQBEEQBEEwhiiXgiAIgiAIgjH+f9cXy7nhtDsvAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "0.9668508287292817% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "0.7333333333333333% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "1.0% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "1.0% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "0.9333333333333333% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "1.0% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "0.8333333333333334% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "0.8333333333333334% pass \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Plot the first ten time series to see what we are working with\n",
+    "\n",
+    "sns.set(rc={\"figure.figsize\": (11, 8)})\n",
+    "for i in range(len(ts_data))[0:10]:\n",
+    "    print(f\"{sum(ts_data.loc[i,'values'])/len(ts_data.loc[i,'values'])}% pass \\n\")\n",
+    "    plt.plot(ts_data.loc[i, \"time\"], ts_data.loc[i, \"values\"])\n",
+    "    plt.show()\n",
+    "    print(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since these time series are of unequal length, and we want to encode their general behavior to make comparisons, lets convert each of them into a vector of features based on some statistical properties of the time series. \n",
+    "\n",
+    "Selected Features\n",
+    "* pass rate\n",
+    "* regression slope\n",
+    "* regression intercept\n",
+    "* longest failure streak ratio\n",
+    "* kurtosis ratio\n",
+    "* largest frequency ratio\n",
+    "\n",
+    "There are obviously many ways to convert time series into a fixed length feature vector. But we'll use these as I think they are fairly interesting and will allow us to move forward. **But I encourage future contributors to question/update/change these selected features. :)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:12.994741Z",
+     "start_time": "2021-01-26T17:35:12.984509Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Functions for calculating each feature from the time seires.\n",
+    "\n",
+    "\n",
+    "def get_regression_coefs(data):\n",
+    "    lr = Ridge()\n",
+    "    x = np.array(range(len(data))).reshape(-1, 1)\n",
+    "    y = np.array(data).reshape(-1, 1)\n",
+    "    lr.fit(x, y)\n",
+    "    line = lr.predict(x)\n",
+    "    return lr.coef_, line[0]\n",
+    "\n",
+    "\n",
+    "def longest_failure_streak(x):\n",
+    "    prev_streak = 0\n",
+    "    for n, c in groupby(x):\n",
+    "        if n == 12:\n",
+    "            count = sum(1 for i in c)\n",
+    "            if count > prev_streak:\n",
+    "                prev_streak = count\n",
+    "\n",
+    "    return prev_streak\n",
+    "\n",
+    "\n",
+    "def largest_frequncy(x):\n",
+    "    f = np.fft.fft(x).real / len(x)\n",
+    "    f = f[range(int(len(x) / 2))]\n",
+    "    if len(f[1:]) > 0:\n",
+    "        return f[1:].argmax() / len(x)\n",
+    "    else:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:13.009794Z",
+     "start_time": "2021-01-26T17:35:13.001210Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_test_individual_behavior(test):\n",
+    "    description = []\n",
+    "    for i, ts in enumerate(test):\n",
+    "        pass_rate = sum([x for x in ts if x == 1]) / len(ts)\n",
+    "        coef, intercept = get_regression_coefs(ts)\n",
+    "        failure_streak = longest_failure_streak(ts) / len(ts)\n",
+    "        kurtosiss = kurtosis(ts) / len(ts)\n",
+    "        frequency = largest_frequncy(ts)\n",
+    "        description.append(\n",
+    "            (\n",
+    "                pass_rate,\n",
+    "                float(coef),\n",
+    "                float(intercept),\n",
+    "                failure_streak,\n",
+    "                kurtosiss,\n",
+    "                frequency,\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "    return description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:13.457096Z",
+     "start_time": "2021-01-26T17:35:13.013725Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "indivudial_metric = pd.DataFrame(\n",
+    "    get_test_individual_behavior(ts_data[\"values\"]),\n",
+    "    columns=[\n",
+    "        \"pass_rate\",\n",
+    "        \"slope\",\n",
+    "        \"intercept\",\n",
+    "        \"failures_streak\",\n",
+    "        \"kurtosis\",\n",
+    "        \"frequency\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:13.476940Z",
+     "start_time": "2021-01-26T17:35:13.459200Z"
+    }
+   },
+   "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>pass_rate</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>intercept</th>\n",
+       "      <th>failures_streak</th>\n",
+       "      <th>kurtosis</th>\n",
+       "      <th>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.940678</td>\n",
+       "      <td>0.001494</td>\n",
+       "      <td>0.853298</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.101019</td>\n",
+       "      <td>0.161017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.931949</td>\n",
+       "      <td>0.000109</td>\n",
+       "      <td>0.884678</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.011266</td>\n",
+       "      <td>0.085352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.966851</td>\n",
+       "      <td>0.000391</td>\n",
+       "      <td>0.931698</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.139232</td>\n",
+       "      <td>0.016575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.046263</td>\n",
+       "      <td>0.409490</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.059091</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.200000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>0.968750</td>\n",
+       "      <td>0.002382</td>\n",
+       "      <td>0.931832</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.844758</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>313</th>\n",
+       "      <td>0.990385</td>\n",
+       "      <td>0.000549</td>\n",
+       "      <td>0.962088</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.952016</td>\n",
+       "      <td>0.298077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>-0.004225</td>\n",
+       "      <td>1.027930</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.834483</td>\n",
+       "      <td>0.066667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315</th>\n",
+       "      <td>0.782609</td>\n",
+       "      <td>0.002962</td>\n",
+       "      <td>0.750032</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.005314</td>\n",
+       "      <td>0.304348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>316</th>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>0.006434</td>\n",
+       "      <td>0.722368</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.069394</td>\n",
+       "      <td>0.360000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>317 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     pass_rate     slope  intercept  failures_streak  kurtosis  frequency\n",
+       "0     0.940678  0.001494   0.853298              0.0  0.101019   0.161017\n",
+       "1     0.931949  0.000109   0.884678              0.0  0.011266   0.085352\n",
+       "2     0.966851  0.000391   0.931698              0.0  0.139232   0.016575\n",
+       "3     0.733333  0.046263   0.409490              0.0 -0.059091   0.200000\n",
+       "4     1.000000  0.000000   1.000000              0.0 -0.200000   0.000000\n",
+       "..         ...       ...        ...              ...       ...        ...\n",
+       "312   0.968750  0.002382   0.931832              0.0  0.844758   0.250000\n",
+       "313   0.990385  0.000549   0.962088              0.0  0.952016   0.298077\n",
+       "314   0.966667 -0.004225   1.027930              0.0  0.834483   0.066667\n",
+       "315   0.782609  0.002962   0.750032              0.0 -0.005314   0.304348\n",
+       "316   0.880000  0.006434   0.722368              0.0  0.069394   0.360000\n",
+       "\n",
+       "[317 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indivudial_metric"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've now converted our 317 variable length binary time series data into a tabular data set with 6 features that represent the behavior of our tests over time. The benefit of doing this is that we can now apply some classic machine learning approaches, like  dimensionality reduction and clustering to our data.  \n",
+    "\n",
+    "Lets visualize the instances of this test and see if any obvious structure jumps out.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualizing our tests\n",
+    "\n",
+    "First we will apply PCA to reduce our 6 dimensional data down to 2 for visualization purposes. Then we will apply DBSCAN to identify any clusters or outliers that might be present in our data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:13.517831Z",
+     "start_time": "2021-01-26T17:35:13.478950Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = indivudial_metric\n",
+    "ipca = IncrementalPCA(n_components=2, batch_size=10)\n",
+    "ipca.fit(X)\n",
+    "two_d = ipca.transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:13.890427Z",
+     "start_time": "2021-01-26T17:35:13.519875Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(two_d[:, 0], two_d[:, 1])\n",
+    "plt.title(\"PCA - operator.All images are built and tagged into stable\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:13.904025Z",
+     "start_time": "2021-01-26T17:35:13.892603Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = indivudial_metric\n",
+    "cluster = DBSCAN().fit(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:14.632763Z",
+     "start_time": "2021-01-26T17:35:13.905908Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "scatter = ax.scatter(two_d[:, 0], two_d[:, 1], c=cluster.labels_)\n",
+    "legend1 = ax.legend(*scatter.legend_elements(), loc=\"lower left\", title=\"Classes\")\n",
+    "plt.title(\"DBSCAN - operator.All images are built and tagged into stable\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like this method works to cluster individual test behavior across multiple instances of the same test. In this case we have divide the space into 4 clusters with a couple of outliers that we could investigate further.\n",
+    "\n",
+    "For the purposes of this notebook,we have established at least one method for encoding and categorizing the behavior of tests that are all of the same type. We will now move on to another topic. \n",
+    "\n",
+    "In the future this could be used to classify different types of observed errors for tests found in TestGrids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.c) Multiple Test Behavior \n",
+    "\n",
+    "Lets just quickly take a look at a larger portion of th entire data set that we have and see if we notice anything from clustering across multiple tests, i.e. are there types of behavior clusters we can identify.\n",
+    "\n",
+    "_note: Using all 117k tests is rather slow, so I'll limit it only to ~12k from the first 200 grids for the purposes of this notebook_ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:34.180437Z",
+     "start_time": "2021-01-26T17:35:14.634854Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first_pass = True\n",
+    "\n",
+    "for i in all_tests[1:200]:\n",
+    "    if first_pass:\n",
+    "        tsx = get_timeseries_by_name(data, i)\n",
+    "        tsx[\"name\"] = i\n",
+    "        first_pass = False\n",
+    "    else:\n",
+    "        next_ts = get_timeseries_by_name(data, i)\n",
+    "        next_ts[\"name\"] = i\n",
+    "        tsx = tsx.append(next_ts, ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:34.187481Z",
+     "start_time": "2021-01-26T17:35:34.182808Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11927, 3)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tsx.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:49.299773Z",
+     "start_time": "2021-01-26T17:35:34.189667Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "indivudial_metric = pd.DataFrame(\n",
+    "    get_test_individual_behavior(tsx[\"values\"]),\n",
+    "    columns=[\n",
+    "        \"pass_rate\",\n",
+    "        \"slope\",\n",
+    "        \"intercept\",\n",
+    "        \"failures_streak\",\n",
+    "        \"kurtosis\",\n",
+    "        \"frequency\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:49.315793Z",
+     "start_time": "2021-01-26T17:35:49.302041Z"
+    }
+   },
+   "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>pass_rate</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>intercept</th>\n",
+       "      <th>failures_streak</th>\n",
+       "      <th>kurtosis</th>\n",
+       "      <th>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.016949</td>\n",
+       "      <td>-0.036272</td>\n",
+       "      <td>3.765972</td>\n",
+       "      <td>0.016949</td>\n",
+       "      <td>0.021369</td>\n",
+       "      <td>0.084746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.237601</td>\n",
+       "      <td>0.001266</td>\n",
+       "      <td>1.793203</td>\n",
+       "      <td>0.008074</td>\n",
+       "      <td>0.000992</td>\n",
+       "      <td>0.003460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.076442</td>\n",
+       "      <td>10.962582</td>\n",
+       "      <td>0.372881</td>\n",
+       "      <td>-0.015939</td>\n",
+       "      <td>0.237288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.024003</td>\n",
+       "      <td>10.001448</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>-0.008263</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.056302</td>\n",
+       "      <td>12.105453</td>\n",
+       "      <td>0.388889</td>\n",
+       "      <td>0.002778</td>\n",
+       "      <td>0.022222</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pass_rate     slope  intercept  failures_streak  kurtosis  frequency\n",
+       "0   0.016949 -0.036272   3.765972         0.016949  0.021369   0.084746\n",
+       "1   0.237601  0.001266   1.793203         0.008074  0.000992   0.003460\n",
+       "2   0.000000 -0.076442  10.962582         0.372881 -0.015939   0.237288\n",
+       "3   0.000000 -0.024003  10.001448         0.166667 -0.008263   0.000000\n",
+       "4   0.000000 -0.056302  12.105453         0.388889  0.002778   0.022222"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indivudial_metric.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:50.437532Z",
+     "start_time": "2021-01-26T17:35:49.317748Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = indivudial_metric\n",
+    "ipca = IncrementalPCA(n_components=2, batch_size=10)\n",
+    "ipca.fit(X)\n",
+    "two_d = ipca.transform(X)\n",
+    "plt.scatter(two_d[:, 0], two_d[:, 1])\n",
+    "plt.title(\"PCA - All Tests\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:51.537492Z",
+     "start_time": "2021-01-26T17:35:50.439754Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = indivudial_metric\n",
+    "cluster = DBSCAN().fit(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:53.067084Z",
+     "start_time": "2021-01-26T17:35:51.539944Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "scatter = ax.scatter(two_d[:, 0], two_d[:, 1], c=cluster.labels_)\n",
+    "legend1 = ax.legend(*scatter.legend_elements(), loc=\"lower left\", title=\"Classes\")\n",
+    "plt.title(\"DBSCAN - All Tests\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the image above, its looks like we get something close to a 2d uniform distribution of our dataset with a few outliers and smaller clusters when we take a larger portion of our tests at once with this feature set. \n",
+    "\n",
+    "This might be noise or it might show that most time series behave in a similar way, or cluster into one of the other 10 failure types.  \n",
+    "\n",
+    "In either case, we can use these methods outlined above to investigate this approach further in a later notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.d)  Comparing tests in aggregate \n",
+    "\n",
+    "Now that we've looked at individual tests, lets derive a new object that describes a single test as the aggregation of the features we've defined above for every instance of the same test. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:53.079619Z",
+     "start_time": "2021-01-26T17:35:53.069240Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_test_behavior(test):\n",
+    "    description = {}\n",
+    "    pass_rate = 0\n",
+    "    avg_coef = 0\n",
+    "    avg_intercept = 0\n",
+    "    avg_failure_streak = 0\n",
+    "    avg_kurtosis = 0\n",
+    "    avg_frequency = 0\n",
+    "    for i, ts in enumerate(test):\n",
+    "        pass_rate += sum([x for x in ts if x == 1]) / len(ts)\n",
+    "        coef, intercept = get_regression_coefs(ts)\n",
+    "        avg_coef += coef\n",
+    "        avg_intercept += intercept\n",
+    "        avg_failure_streak += longest_failure_streak(ts) / len(ts)\n",
+    "        avg_kurtosis += kurtosis(ts) / len(ts)\n",
+    "        avg_frequency += largest_frequncy(ts)\n",
+    "\n",
+    "    description[\"pass_rate\"] = pass_rate / (i + 1)\n",
+    "    description[\"avg_coef\"] = float(avg_coef / (i + 1))\n",
+    "    description[\"avg_intercept\"] = float(avg_intercept / (i + 1))\n",
+    "    description[\"avg_failure_streak\"] = avg_failure_streak / (i + 1)\n",
+    "    description[\"avg_kurtosis\"] = avg_kurtosis / (i + 1)\n",
+    "    description[\"avg_frequency\"] = avg_frequency / (i + 1)\n",
+    "\n",
+    "    return description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:53.643450Z",
+     "start_time": "2021-01-26T17:35:53.081553Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'pass_rate': 0.911815196098699,\n",
+       " 'avg_coef': -0.00031722304811730484,\n",
+       " 'avg_intercept': 0.9199496749854382,\n",
+       " 'avg_failure_streak': 0.0,\n",
+       " 'avg_kurtosis': 0.25686846515261513,\n",
+       " 'avg_frequency': 0.1669826612570952}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "single_test = get_timeseries_by_name(\n",
+    "    data, \"operator.All images are built and tagged into stable\"\n",
+    ")\n",
+    "get_test_behavior(single_test[\"values\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alright, this looks like it might be a reasonable representation for a summary of expected behavior for a test. What can this tell us? \n",
+    "\n",
+    "For the test \"operator.All images are built and tagged into stable\" we should expect this test to pass about 91% percent of the time regardless of the platform its run on. We have a small negative slope (avg_coef), which means on average we have more \"not run\" instances as we move forward in time, but this is such a small slope its practically flat, i.e., this is a reliable test. If it does fail, there is 0% average failure streak, meaning if it does fail, it could fail for up to 0% of the total number of tests runs to date in a row (i.e, this test never fails, seems to only pass or not run). This value basically tells us how often we see small flips vs longer outages in our data. Finally, kurtosis and frequency are two additional measures of \"steadiness\" of our data, but since they are normalized here their interpretation is less direct, but can be understood in relation to other test vectors.\n",
+    "\n",
+    "Now, is it useful to represent tests this way? Maybe. Its not fully clear at this point. My concern here would be that we have over-summarized the different dynamics occurring in these individual test runs. Especially, as we saw about 4 clear clusters for this test above. However, we can validate the usefulness of these baselines by repeating the above for all the tests and seeing if there is stronger inter-test relationship than between different tests. (Basically, will the individual tests land closer to the baseline for their test on average or not if we measure their vector distances?) \n",
+    "\n",
+    "Lets find out and produce these vectors for each test, and compare them similarly to how we did above. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:35:53.656417Z",
+     "start_time": "2021-01-26T17:35:53.645614Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_timeseries_for_test(x, target_test):\n",
+    "    collected_target_tests = []\n",
+    "    timestamps = []\n",
+    "    for i in x.keys():\n",
+    "        for j in x[i].keys():\n",
+    "            grid = x[i][j][\"grid\"]\n",
+    "\n",
+    "            if len(grid) == 0:\n",
+    "                continue\n",
+    "            if target_test in [x[\"name\"] for x in grid]:\n",
+    "                test_data = [x[\"statuses\"] for x in grid if x[\"name\"] == target_test]\n",
+    "                collected_target_tests.append(test_data)\n",
+    "                timestamps.append(x[i][j][\"timestamps\"])\n",
+    "\n",
+    "    ts_data = pd.DataFrame(\n",
+    "        zip(collected_target_tests, timestamps), columns=[\"values\", \"time\"]\n",
+    "    )\n",
+    "    # use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "    ts_data[\"values\"] = ts_data[\"values\"].apply(lambda x: x[0]).apply(decode_run_length)\n",
+    "    ts_data[\"time\"] = ts_data[\"time\"].apply(lambda x: (np.array(x) // 1000))\n",
+    "    ts_data[\"time\"] = ts_data[\"time\"].apply(\n",
+    "        lambda x: [datetime.datetime.fromtimestamp(y) for y in x]\n",
+    "    )\n",
+    "\n",
+    "    return ts_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:50:58.110301Z",
+     "start_time": "2021-01-26T17:35:53.658466Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vector_set = {}\n",
+    "test_names = list(set(all_tests))\n",
+    "for test in test_names:\n",
+    "    test_set = get_timeseries_for_test(data, test)\n",
+    "    vector = get_test_behavior(test_set[\"values\"])\n",
+    "    vector_set[test] = vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:50:58.118198Z",
+     "start_time": "2021-01-26T17:50:58.112775Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'pass_rate': 0.4001886802824165,\n",
+       " 'avg_coef': 0.005342678117091565,\n",
+       " 'avg_intercept': 7.116541139292677,\n",
+       " 'avg_failure_streak': 0.33080526429387325,\n",
+       " 'avg_kurtosis': 0.05301396234931552,\n",
+       " 'avg_frequency': 0.13468357069313408}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector_set[test_names[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:50:59.087516Z",
+     "start_time": "2021-01-26T17:50:58.120187Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transpo = pd.DataFrame(vector_set).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our data converted from individual time series into one data point for each test with 6 features representing the average behavior of a test across all of its instances. Lets visualize it and get a sense for the distribution. To plot this 6d data in 2d we will again use PCA to reduce the dimensions.     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:50:59.343718Z",
+     "start_time": "2021-01-26T17:50:59.089890Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = transpo\n",
+    "ipca = IncrementalPCA(n_components=2, batch_size=20)\n",
+    "ipca.fit(X)\n",
+    "two_d = ipca.transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:50:59.661706Z",
+     "start_time": "2021-01-26T17:50:59.346120Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(two_d[:, 0], two_d[:, 1])\n",
+    "plt.title(\"PCA - Tests\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clearly we can see some differentiation in this representation. Let's use DBSCAN on the 6d data and see if there is still some meaningful grouping. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:51:00.454348Z",
+     "start_time": "2021-01-26T17:50:59.664220Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = transpo\n",
+    "cluster = DBSCAN().fit(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:51:01.157339Z",
+     "start_time": "2021-01-26T17:51:00.456842Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Kbm5azJw+iTOoiYiIiDIgX8yeVigUWLhgNo4ePY4dO/fB29sLr73aBSVLFnd0aERERER5Qr5IGgFAEAQ0atQAjRo1cHQoRERERHlOvuieJiIiIqLsYdJIRERERHYxaSQiIiIiu5g0EhEREZFdTBqJiIiIyC4mjURERERkF5NGIiIiIrJLtqRx2rRpaNWqFSpUqIDr16/LVSwREREROQHZksbWrVtj5cqVKFKkiFxFEhEREZGTkG1HmLp168pVFBERERE5GY5pJCIiIiK7nGbv6cBAL0eH4HBBQd6ODsGlsD7lxfqUD+tSXqxPebE+5eNqdek0SWNUVAJEUXJ0GA4TFOSNyMh4R4fhMlif8mJ9yod1KS/Wp7xYn/LJq3WpUAhpNuSxe5qIiIiI7JItaZwyZQqaNWuG8PBw9OvXDx07dpSraCIiIiJyMNm6pz/77DN89tlnchVHRERERE6E3dNEREREZBeTRiIiIiKyi0kjEREREdnFpJGIiIiI7GLSSERERER2MWkkIiIiIruYNBIRERGRXUwaiYiIiMguJo1EREREZBeTxnzCaDTiwsXLuHPnnqNDISIiojxItm0EyXn9tX03Rn70KcxmM8wmM0qVLoGlv/yIYkWLODo0IiIiyiPY0ujirl2/iSFDR+Hp0zgkJCQiWafD1as30LPnO5AkydHhERERUR7BpNHF/frrKhgMxlTHRFHEk6goHD9x2kFRERERUV7D7mkXFxoaDrPZbHVcEARERj5xQETO6UloJGYP/wZhd0IBAMHFQzDi+zEILhbs4MiIiIicA1saXVzLlk3g7u5uddxgMKJO7Zq5H5ATMhlM+LTHR5aEEQAi7ofhs9c+hi5Z58DIiIiInAeTRhfXs2c3FA4JhlartRzz8HDHO2/3RkgIW9EAYPPi9TAZTVbHzSYzNi9a54CIiIiInA+7p12ch7s7/tr2J35evAybN2+Ht483+r/bB506tXN0aE7j1oUbaZ+7eDMXIyEiInJeTBrzAW9vL4wcMRgjRwx2yPMlScKxHUewbekmxEU9RfnaFfHqkNcRXDzEIfG8qGiZYrh87ILNc0VKc1kiIiIigN3TlAu2LtmIpZN/wsMb9xEX/RSn9hzHxLfG4/HDCEeHBgDoNqgnBIVgdVwQBLw65HUHREREROR8mDRSKlFhT7Bu/h9Y9Nk8HFy/F/pkfbbK0yfrsGXxehh0/5UjSRIMOj22LN6A62eu4uLR89A7cMKJm4cbxi+dBE9fL8sxDx9PjF38JTx8vNK5k4iIKP9g9zRZXD9zFbOHfQOzyQyT0YTT+05i29JN+Hz5FHhmMXmKuB8OhUoJvJB7imYRR7YcxMk9xyAAMJtFvD3+Pbz0StPsv5EsKF2lLObu/Qm6JB1EUYSHl4dD4iAiInJWbGkkACmtfz999gP0yXrLTGKDTo/oiChsXbIxy+X6BfnbnJkMpCwyrktMRnJiMgw6PZZO+Rlhdx5l+VlycPNwY8JIRERkA5NGApCyuHV8TJzVcZPRhJN7jmW5XJ8AX9RoWgtqjdrutWaTCYc27rd5LjkxGUe3Hca+P3c5zVhIIiKi/ITd0wQA0Gg1ENPYi1rjprV5PKP6TxyMX7/6OaUrWhCgVClhMhitWiBFs4j42Hir+6+evIzvRkyHyWiCJAEKpQJte3fAa8PeyFZcRERElHFMGgkA4FvADyUqlsSdS7cgmkXLcY2bBi1fa5Pmfcd3HcXvs1YgLuYpfAP98cbHfVGnVb1U12jdtRgwZQj6fvoekuITIZpFfPrqx1Zlad21qNmsjuX1o9sPcWj9XuxavR2S+F9CK5rN2LZ0E7Yt3WQ5tuTUqiy9byIiIsoYdk+TxaCvP0RgoQJw83CD1l0LtVaDWs3rouVrL9u8fs/vO7Bg7PeIeRwNs9GM6PAn+GH0LBxcv9fm9W4ebggIDkSBwkFo99YrqVowte5alKxcBjWb1YZoFvHtkKn4vOdo7Pztr1QJY1rercNWRyIiopzElkayCCgUiK83zMa1k5cRExmDUlXKIKRk4TSv/2POSpvHV327DM26t0r3WbVb1IMuUYf71+5C46ZFww6N0aBdIyhVSmxfvgWX/rG92HZ63q3zBgqVDMHkP2Zk+l4iIiJKH5NGSkWhUKBS/ap2rxNFEUaD0ea59NZ2NBqM+G7EDNw8dx2SJEGpVMDT1wuV6lWBSp3ycdy5cmvWggcQfjcM79d/CwDg7umO5MTklD97eWDsT1+gWPkSWS6biIgoP2PSSFmiUKQzssF6cxWLv37djBtnr8GoNwAAjAAMegN++uwHjPnpCwCAQWc7Gc2sZwkjACQnJOHLN8Zah6oQULBoMJp1a4WXe3ewJK5ERESUGsc0UpaVrVnB5vHK9aulec+hjfssCeMzolnEzfPXkRSfCACo3bKerVtzhCRKiLgfjjXf/4ZpAyfn2nOJiIjyGiaNlGWjfvjUasxjkbLFMGLOJ2neYzLYXujbbDJjeOuBWPvD73htWC+4e+f+Atu3zl3H5p/X5fpziYiI8gL2xVGWadw0+Grtt4h4EIE7F26gTI1yCCoSnO49dds0wP51e2C2sUuMaDZj65INiI+Jw8wtc7FlyQac3HMcSqUSkY8iYDaZc+qtWGxcuBYd+3WDQsnfp4iIiJ7Hfxkp24KLBaPhK03sJowA0HXgawgMDoTWPe0Fww+u3wt3Lw/0HN4b0zbOwdR132LAlKFQazXQerhBk8692SWKIu5cvpVj5RMREeVVTBopV3n5emHyH9PRZ9x76V43puuH2PTTOstM7HovN8Ss7T+g77j30NfOvdllyoUWTSIioryGSSPlOrVWg0Ydm6Z7TeTDx9j6ywZMGzDJskONp48XXnqlCRp1bIpO/bvnWHx+gX45VjYREVFexaSRHMKgM0BQpLM2DwCj3oh7V+/gi16f4J/tf6ca01iqUqkci+362as5VjYREVFexYkwlC1Po2KxY/lWXD5xEQHBgWjfpxPK16po975jO49kaHtASZQQevsRFo2fh92rt2PczxNw89w1zP14lhzhp/FQ+3ERERHlN0waKctiI2Pw5RtjkZyQBJPRhPtX7+Li0XN4ufcrKFmxFM4dPo3zh89C665Fi1fboH2fTlCqlACAa6cuZ/p5dy7ewqm9x7F61nK530oqZlGEKIo4tv0IDm3cB7PZjMYdm6FRp2Zc/JuIiPIt/gtIWbZlyQYkxSem6jY2GUz4a+mmVNclxMZj80/rcPfKbQyZPhJAyg4tmSVJEg5v2o/YyJjsBW7Hsik/49CG/Qi99cAyEefelbs4sfsYPpo3FoKQfrc6ERGRK+KYRsqyi0fPZ3jtRIPegPOHzyD8XhgAwGTM2gxlpUqV7jaFcrlz8WaqPbQNOj1unruGy8cv5vzDiYiInBCTRsoyn0DfTF2vVCpx/9pdAIC7p3uWnhkf8xRw0JBDfbIeV5g0EhFRPsWkkbKsQ59O0LhlfKFtSZIQGBIEs8mMGs1qZ+peAKjf9iU8uHE/s2HKRq1Rwycgc4kyERGRq2DSSFlWq0VddOnfHWqtBiqN/eGx7l4euHn+Goa3HoBfJi2EyWiEQqmAUqW0TJBJT4lKpSDkRt90GgSFAg3aN3LY84mIiBxJkCTnWF8kKioBYgaWYHFVQUHeiIyMd3QYWZKcmIwHN+7hu+HTkZyYnOZ1SpUSoiimWmpHpVGhXM0KqNemIUpWKo2v3v0C5jTGOwoKIUPL9MhNEARo3bWo93JDVG9SCzWa1s53s6jz8ufT2bAu5cX6lBfrUz55tS4VCgGBgV62z+VyLOSC3D3dUb5mRXy7/UfUaJaSUCmU1i2HZpPZKukzGUy4c/EWmnVrhZKVS6Ng0UJpPscRCSOQ0q0uShIObz6In7+cj7HdRuD4ziOIfBThkHiIiIgcIX81l1COcvNww4ezRwMABjftB12S/RnSyWYD7kZFYsbMuejwShuUqlIGYXce5XSomWb4dya1PkkHfZIOC8bNBQD4Bwdgwsqv4e3vY7n22ZJAfkH+uR8oERFRDmHSSLI7sfsf6JJ0dq8LTY7Ghaf3AUHA9R8WYuFPv6B7uw5QqpQZXsrH0WIiovFhm4HwLeCHqi/VwJ1Lt/D4YTgAIKREYQz8ejgKlyri4CiJiIiyj93TJLsz+0/ZvcYomnHh6X2IkCBKIsxmEcnJOmzYuR3VXq6TC1HK6+mTWPy9+QBCbz+EyWCCyWDCg5v38fV7E6BPtp9AExEROTsmjSS7oCIF0zxXuHRRVKpXBQkqI5Q2xj1W0xbFmW3HcjK83COljNk8uee4oyMhIiLKNiaNJLuO/bpCUNheGmfA5CEYveAzvPflQGjdU6/TGKDygr/G06W26TPo9IgOf+LoMIiIiLKNSSPJTuOmwagfxkOt1ViOCQoF+ox9F8UrlgQAtGjRBGZz6nGLVX2L5WaYuULjrkWpKmUBADfPX8fkPuPRv96bGN56ALYs2QBRFB0cIRERUcZwIgzliEr1q2DhkV9x+8JNJCcno1LdKlAo/vsdxcvLC/PmzsCQoaMgQIDJbE513hWotWoUKV0UlRtUxcMb9zFz0Fcw6AwAgITYeGxZvB5Pn8TizU/ecWygREREGcCkkXJU6Wpl0zz3SoeXceLYXmzZsgNJyclQR5lwdP3+3AsuJwlA9cY10X/SECgUCmxevN6SMD5j0BlwcP1edB/UEx7eng4KlIiIKGOYNFKGbf1lI9b/+IelS9W/UCAmrf4Gnt62V47PiAIFAvHOO71hMppwYvc/rpM0SsDVk5ctO8dcPXnJ5mVKtQpPQiNRvMJ/SaMkSbh1/gbC74WhSJmiKFm5tEuN8yQioryJSSNlyNG/DmPtvNWpjsWER+Hj9kMwZtEX8C8YkOXFrJ9GxWLim59aFsV2FSajCY8fhCP0TigSnyameU1gSAHL66T4RMwY9BXC74ZCAgApZc/tj+aOgdbdLXcCJyIissG1BpFRjlk5banN4wadAdMGTMYnXT7EvI9nQf/vzikZZTabMW3AZJdLGAFAn6zHmQOnsOmnP9Oc8NKwfWN4+vzXUrti2i94dPMB9Ml6GJL1MOj0uHPpFv6cuyq3wiYiIrKJSSOl687lWzi4fi+S4m23lAEpy8qYDEZcOHIWy6b+nOGyDckGDGrSD+F3Q+UI1Smt+/F3PLh+3+Y5pUqJjv26Wl6LoogTu/6ByWhKdZ3JYMSRrYdzNE4iIiJ72D1NNhl0Bsz5cBpuX7qFlH5S+4wGI07sPoa+n76Xoa7UWcOmwmQwZjNS5yaa015SRxIlREdEIbh4oX8PIM0WSVevJyIicn5saSSbNv20Frcu3LB0kWaUACA5ITlD1944dyOL0bkGURTx3YfTcePsNQCAQqlA+ZoVrSa9CAoBVRvVcESIREREFkwayabDm/bDqM9865aHjyd8An0zeHUGmzBdmEFvSDVese/4/nD38rAsjK5x08DL1xtvjOoLIGVmdUxkNBKeJjgkXiIiyr/YPU02vTiu7nmCQoAkvpDwCYBGq8FbY/tleJHukpVK486lW9kJ0yU8vPHfmMeQkoXxzYbZOLzpAB7cuIeSlUujcafm8PD2wM1z1/HzFz8i+nE0IEkoW6MCBkwZkuVZ60RERJnBlkayqUazOlAolVbHBcFGwgggqEgwRi/4HHVa1s/wM4Z++xHXHwSQnJSMb4d8DV2SDgDg5eeN9n074f3JQ/DyGx3g4e2B6PAofDtkKh4/jIDJYITJaML1M1cwfeBkSBJbbImIKOcxaSSb/je8N3wDfaB11wJI2RJP46aB2k1j8/rCpYugTDq7v7xox4qtGP3KMCY8ACAB109fwfJvlqR5yf71e2A2pd6rWzSLiImMwfUzV1MXJ0m4d+UOLhw5i4TY+BwJmYiI8h92T5NNvgX8MHXdLBzddhi3L95E4VJFUal+VXzV7wurazVuGlSsUznd8gw6AzYu/BPXz1yFWqtJc4eU/MpoMOL4jiN457P3odaorc4/vh+e5pCB6PCoVH/+dujXiA5/AoVCAZPRhI79uqLLgFdzLHYiIsofmDRSmrTubmjxahu0eLWN5ViDdo1wYtc/lhnVSpUSHt6eaNq1ZZrlREdEYWy3kS63bIzGPWWSSnxMPIx6A5QqJSQppQtfrVVDEsVMLXZuNpth1BttJo3la1fE2YOnrWayi2YRJSuVsrz+fuQMRNwPS7XUz7ZfN6N4xVKo2ax2Ft4lERFRCiaNlCn9vhiAkpVLY+/vO6BL0qFW8zroMuBVeHh7pHnP3I+/dbmEEQAkERgyfSSeRsXi/OEziHgQjtsXb8FkMEISRdRoWgcn9x6D+EK3ctoFAka9AbBRl8UrlIJKrbJKGgMKBaBQycIAgIj7YQh/IWEEUhZf373qLyaNRESULbIljXfu3MHYsWMRGxsLPz8/TJs2DSVLlpSreMoFp/aewLZfNiD2SSwq1K6Erh+8huBihVJdo1Ao0Pp/bdH6f20zXO79a3dljtQ5uHu5o1SVMgCAqLAnOLL1sCWpM5vMOHvwFBSCAiIymDQC+KjDYJSuUhate7WDt78PipQphl+nLMKV45dgMll3T8dERGPfn7vQqmdbJMYl2py8BIBL9BARUbbJljR++eWX6N27N7p27YqNGzfiiy++wLJly+QqnnLYjpVbsf7HNZak5/jOozh76DQm/PY1ChYNzmbpAlxxTUZd4n+LmG9evN6qFdCg02d6drgkSrh14QZuXbgBrYcWRp0REASIZtuJp0FnwI7lW9GqZ1sUK1/C5jVqjRq1W9bLVBxEREQvkmX2dFRUFC5fvoxOnToBADp16oTLly8jOjpajuIphxl0BmyYvyZV0iOKIgw6PbYsXp/t8stUzfis6rzEoDNgUp/xOLbjCOKintq8Jjuzw/VJeoiimGbC+MyzGdJqjRpvjekHjZvGkqyqtRr4BfmjTa/2iIt+iq2/bMSiz37A7tXbkZyQlOXYiIgo/5GlpTEsLAzBwcFQ/ts1plQqUbBgQYSFhSEgIECOR1AOinwUYbNFTDSLuH76qo07UouLfoodK7biwt/n4FfQH+3e6ogqDapZzg+b9TE+6fwh9Mk6WeN2Bncv38aSiQuh9XCDPsn6/Xn7eUOv08OgM1iOKVVKFCtfAtERUUiOT4Ixm+M9y9WqaPlzo45NEVKqMPas3oGYx9Go3qQWmnVvhajQSHz93gSYTCYY9Uac3ncCW5dsxBcrvoJ/Qf6MEhGRfU4zESYw0MvRIThcUJC3Q56rURaBOY3WrELFC6UbV+yTWEzoPRYJTxNgMpjw8OZ93DhzFe982g+d3k5peQ4K8sYfV/7Anz+uwfLpy3PkPTiSUW+wfUIAhk0fjr3r9uLUvpMQBAFmkxlaNy1MegOMegMKFC6AiIcREE2i7TLsUCgVGDjhfShFI07sOQ6FUoH6LzdA/eZjUl03Z+jXSH6uO92g08NkNGLTgj8wau7oDD3LUZ9PV8S6lBfrU16sT/m4Wl3KkjSGhIQgIiICZrMZSqUSZrMZjx8/RkhISIbLiIpKgGhjp5H8IijIG5GRjlqIWYEaTWrj3KHTqVq9NG4atO3TKd24/pizGgmxCanWENQn67F06i+o2bKhZXFwAChUugQUSoXV7F5XpVarofbwxPtThsH4yRycO3QaZpMZSQlJSPq3azg5IRkKpQJKldJq8W57FEoFXhv2Bo7sOoE/Zq+AoFBAEID5n83H/0a8iaJliqFg8ULw8PLELRvbNYpmEcd2H8/Q586xn0/XwrqUF+tTXqxP+eTVulQohDQb8mRJGgMDA1GpUiVs2bIFXbt2xZYtW1CpUiV2Tech700chKWTF+HUvhNQKhVQqVXo9VEfVK5fNd37Lh49b3PRaUGhQOjth5bZxQDg6esFlVoNgznjaxc6SvnaFXHz3A274wnTo1Sr8CQsEnqdHhePnktzcW7RLELrroVao07ZSvC5eUNqrRqePl6o0bQWDm3YDwj/XV+xbhXUaFIbE94ca9XFvXLaUrh5uMFkMqN2y7ppxmhrTUgiIiJbZOuenjBhAsaOHYsff/wRPj4+mDZtmlxFUy7QumsxcOowJMUnIeFpPAILFYBSZXv5luf5Bfnh4c37VsfNJhO8A3xSHStWrjgCCgUi4n6Yzf2rn+cfHICYiNydSCUoBFRvXAud3uuG+1fv4u7l2zBkI2k0G00oUbEUdq36C3pd+omyUW/EDweXQOOmwd0rt7Hrt78QHR6Fqo1qoOVrbeDp44VuH/TE8R1HER8bj8r1q6JCnUrYvHh9ml3bz/ayPrv/FIKKFMST0MhUrZlqrTrdRdmJiIieJ1vSWKZMGaxZs0au4shBPLw90l2o+0Xt+nTC9TNXrSZ6lKpcBgVCgizH9Mk6/D5nJaJCI+0mjEDK+oMNOzTGP3/9bfdapVoFcxqteJmh1qrx4ZyU8X0FCgdh9ewV1hcJAhQKwW4Xu0KhQL22L6FA4SCoNWooFOl3y7t5uEGtVUMQBJSqXAYDpgy1usY30A8v9+6Q6pjZaIIopR+LQW/A06inCClVBJEPHwOQIEkSylYvj64DeqR7LxER0TNOMxGG8qYqDaqh5/De+HPuKggKBcwmE0pVLoMPvh6OI1sP4fjOI1BrNQi/F4aI++EZ3xlGAE7vPZGhSyVRnjGSzyezvoF+eH/yYPz8+Y8QJREmw79JqSSlbBWoUKT7XO8AH/QZ0w///PU3Ep8mQBAUAGxfr3HTosM7naFQZH4FrNot6mHHiq2pknZbdInJeH/yYOgSdXj8IBzFypdA8QolM/08IiLKvwQpOwvJyYgTYfLmgNln9Ml6hN5+CO8AHwQUDMTMwVNx++JNqwWvM8oRE2a8A3xQrVENPLz5AFFhTxAQHIiG7Rth7bzfIb6QIKo1apSsXAo3zl63WVb1JrXw8MZ9JMYlQp+ss0x0EQTBau3GwMIF8NWf30Kj1aQbnz5Zh6jwKPgV8Le0BkuShG/6T0gzjhff33sTBqFaoxqZXnQ8r38+nQnrUl6sT3mxPuWTV+syxyfCEGndtZZJL6f2nsCdS1lPGAFYJWm5ISE2AUe2HLK8TnyagAfX79m81mgwwq9gAMpUL4/bF25YJYIPrt9D7JMYS+vls7GEtn5Hi4+Kw97fd6J93042nyVJEjYu/BPbl22BQqmA2WRG487N8OYn/bDn9x24d9V2jFbPiY7DnOHToNKo8d6EgYh5HIOnUbGoVK8qqr5UPUstnURElH8waSTZXDt1Gau+XY771+9me9dAtUadMiM4FxufM9PNLQgC3D3d8dG8sVgyYQFO7zuRKiGMeZzxSTwGvQGHNu1PM2nc9usmbFmyIVXL65Gth+Dm6Y6/Nx/MdHJuMhix8NN5UGnUMBmM2P/nHpSsXAof//ApVGp+JRARkW38F4IyJTEuAUe3HUbE/XCUrloWdds0gFqjxq0LNzB7+DS7Y+syymQyp0xwMWR/gktOUKlVaNatFdw93dFn3Ls4d/j0f+MesyKNUSKPH0Rg3bzfrVooDToD9vy+M+2FxTPg2fhSfbIOdy7dwm8zliI5IRlqrRqNOzdHhdqVslw2ERG5HiaNlGEPbz7AN/0nwGQ0waAz4PDmA9i48E98tmwK1s9fI1vCCACSWYQ5G2MabY0dlJPZLOLKiYsoXbUs7l65A7VGk+WkUa3VoHHn5jbPrZn7W5rvw2w0oUCRgnjy6LHVOY1WA0EhQJ+csVZIg86AA+v2pjxLAI7v/Adt3+yAtm92xP61u3H34g0EFC6INq+3h0+gL+5fuwtvP2+ElCqS8TdKRER5GpNGyrDFX85HUnyS5bU+SYcogwkb5v+BRzbWanQUQSFAUAiQTDmXNIpmMzb9tA61W9aHX5B/mtswKpQKSKKUdgIrCChRsSRefqO9zdOXj11MMwa/gv5446M+WPjpXBiea3FUazUYMn0kFCoFfvr8R8RFP83Qe7LEKKVsM/jXss3Y/+du6HV6GPVGKFVK7F2zCwqFALVaDbPZjEIlCuPD70bDP4gL+RMRuTqOfKcMSYpPTHMR7xO7j6FQycIOiMo2SZSyvJdzZogmEaf3HkexcsURXKwQFMrUi6Fr3DR4feRb6PB2l5RdXmwIKVkYY3/+Emo7M6dteWNUX9RqURdDv/0YpaqUgYe3J0pXK4sR332Cak1qokrD6vj4x3FQZGCRdlvMRjMSnibAqE/pxjabzBBNZpgMJiQnJsOgM+Dhzfv4fsTMLJVPRER5C5NGyhAhnZm1SpUS3T7oCY1b5hOfPE0AIAgQBAEfzRuLstXLQaVRQ+uuhZefNwZMGYqWr72Mtm+9gvK1Klklbxo3LboP6pnmrOXo8Cjok3U2z5WuVg51WtaHJEnw8PZA2zdfwfhfJmL0/M9w98ptfPnGWEx5+zPcvXIH0zbMQfXGNeHm6S77RBfRLCLs7iOE3Q2VtVwiInI+7J6mDHH3dEe5mhVx7fSVVLOM1Ro1mnRJmTQxeNoIrJy+FJE2xti5IkEQULd1fQApi4GP/flLxERGIzkhGQWLBmPzz+vw0+c/wGwWodGq4R8UgPiYp1CqVDAZjWjftxPqtm6QZvlnD56CSqWCwWw9VrRczQpIiI3HzMFTEXE/DIIgwGQyQ61Rw2Q0WSbIPLz5ELVb1sWI78cAAP6cuwrblm6StR6USiUSYvPeWmRERJQ5TBopw/pPGoSv35uAxKcJKbOblQqUqFQKnd7tDiBlQevqTWoh8WkCDm06gCNbD+LRrQcZ2jbQWQkKIc34JUmCT6BfqmP+QQHwDwI2LFiDHSu2WZbDMRmMMBlN6D7ofyheoSSKVygBTx/bi6c+o1AqbHZrK5QKaN00+GXyIjy69SDVftIv7rhj0Olxau8JPLr1AEXKFEPNZrVlTxrNZhHFK5SQtUwiInI+TBopwwKCA/HNhjm4ePQcnoQ9QYkKJVGmejmr3UUEhQCtmwbVG9WEJIp4dOuhgyKWg4C0FotUa9Q4suUgoiOicOvCTRQtWwxte7+CAoWDsHPlNqv1E5/NUJ667tsMPblW87pYNXOZ1XGlSoVaLeph6y8bUyWMab4DAbhx9hoKly6KhePnZejZ6Xl+tx6NmwY9h78BrbtbtsslIiLnxqSRMkWpUqJG09ppnr975Ta+6T8RRoMRkihBUGRuuzpnk96C32aTGWu++w2iKMJkNOHmuev4e/MBDP32o5SFyW2Iicz4ot++BfzQd3x/LJv6MwDBspZjtw9eQ1CRghle+FyhUMA30A93Lt1CwtMEm9cULVvc5kSntISULIzgEiFo2/sVVKxbOcP3ERFR3sWkkWQjSRJmDp6aar3GvNw1bY/JmLIu47OlakSzGfpkM1bPWgFPXy/ERVkvdVO0bLFMPaNxp2ao0qAaTu87AbNZRK3mdVCgcBAAoFDJwnh060H6BQgpS/BUa1wT189chSKNJN7Lzwv1272E4zuO2o1JNIvwDvDF8FmjMvVeiIgob+PsaZLN3at3kBSX6Ogwcofw73hHG+svht1+hFeHvG41mzylK7d3ph/lF+SPVv9ri5ffaG9JGAHg3QkDofVws8yI1mg18PDxhJefN7QebtC4aRBcLARjfvoCKrUKpauWtdmdrXHToF6bhvhg6nDM3fczilcqaTcmWwuKExGRa2NLI8km9KadVi8XolKp0l7QW6VEo47N4O3vgw0L/sSTsEgULVsMrw19A2VrlJcthlKVy2Dq2m+xb+1uhN15hLLVy6Np1xZw83THo1sPoFKrUahEiGXMqZuHG3qPfge/zVhqGT6gcdeiUPEQy440nj6eaNGjDX6bsTTNHW4EQUCpqmVsnjPoDLhw5BwMOh0q168G3wJ+sr1fIiJyLCaNJJsimex6zctMRlNKa+ML2xWqNGo0bN8ISpUSNZvVQc1mdXI0Dv+CAegx6H9Wx4uVsz2buVm3lihRoST2/bkLcdFxqNWyLhq2bwy1Rm25pky1smmuHQmktEx2G/ia1fHrZ65izofTAaR02YsmM7p90BMd3u6c2bdFREROiEkjyaZExVLwDw5ETESUo0PJHRIAIWU2saBQQBJFlKlWDm9+8o6jI0tXiUql8M7nA9I8X6xcCVRrVOPfFsN/x6f+myBXrl8VPT98E0XKpP4FQZeYjG+HTLXsHvPMxkV/okKdSihdtazs74OIiHIXk0aSjSAIGD7rY0x881NHh5JrJEmCJAIwm6BUK3H38i3cOHsNfgX8EVg4CO6e7o4OMcOeRsVi48I/cebAKWjdtaj6Ug2E3wuFyWhGq+4t0PS1tjbfjyiK+Ob9iVYJIwAY9UYc3rSfSSMRkQtg0kiyKlGxFLQebtAn2d7+ziX92z1tNpphNpoxa+g30Hq4QTSb0fr1dug5vLfVWpaZIYoiwu6GQqFQpBqjKKek+CRMfPNTxEfHWcZqxkbGoE6r+nh/8hAEBXkjMtL2ri8X/j6L0Du2txGUJAlx0fFY8OlcXDl+Ee5e7uj0Xnc0+XcMJRER5R1MGsmuU3uPY9H4eZa1B70DfPDlyq8RUDDA6lqT0ZS/EsY0PKuDvX/sgl9QANr27pClcm6cvYYF475DUnwSJEmCX5A/hs74CEXLFZczXBzauA9JcYmpJvcYdAac3HMMXQe+hqAg7zTvPX/4jNVONM87vf+4ZU3J+Jg4LJmwABePnsMHU4fLFj9RRhgMBly/fgu+fj4oVrSIo8MhynO45A6l68H1e/hh9OxUi1XHR8dh1CtDbF4vCILNre/yK4NOj/Xz/8BnPUdhTNcPsXjCfEQ+zNhyNXExcZg19BvEPI6BPlkPg86Axw8iMG3A5FRrYcrh2qkrMOity1SqVLh35U6693r6ekGpUqZ9gY2lOo/vOIp367yBQU37Yd+fuzMbLlGmrVu3GdWqN0b3V99Cs2avoFOXXnj8ONLRYRHlKUwaKV0LP0tj2zkJGNdjJGIjY1IdVqqUKBASZPuefEqfpEPo7UeIfPgYf28+iDHdPsSMQV/h0rEL6SZ///x1GKJovayPyWTCmQMnZY0xuEQhKNXWHQ+SKMIn0Be7/9iFn7+cj00/rbPa1aZx5+ZQKtNJGtOhT9Jh+deLcWDdnizdT5QRZ89ewKhPPkdcfDwSEhKh0+tx9uwFvPlW2hPCiMgak0ZKV+SDiDTPRdwLx6iOQ7FjxVbLsQ0L1iAq7EluhJZ3ScCV4xfx7eCpGNLiPexevd3mZbGRMTYnl5iNJjyNipU1pFY921olfkqVEgUKB2HJhAVY+MVCHNlyEFuWrMenPT7GzfPXLdcFFyuEdyd8AI2bFm6e7tC4azP9/N9m/prt90CUlkU/LYXuhb3gzWYzbt2+i6tXr6dxFxG9iEkjpUupSX/Yq2gW8fvsFbh49Bwe3riP7cu22NwlhWwzG034ffYKLJ4wH2f2n0y1Y0uF2pWgdXezukehVKBczQqyxhFUpCAGTBkCvyB/qNQqqNQqVKxbBeVrVUDM42jo/h2jaTKkjFn96fMfUv0912/7Er7bvQCDv/kQb3/6ntVuOPYY9UbMHPwVrp68DLPJjMS4BIjp7PtNlBmhYRE2v5dUKiUeP+YvuUQZxYkwlK66bRvi8Lp9dq+bNfQb+AX52xwXR+kzm8z4e/NBnNxzHH5B/hi/ZCK8/LxRrVFNFC1XDA+u3bPUq8ZNiyoNq6FUZds7smSFyWjC0smLcHzXP1CplZCAlFnTkwZjTNcPLXtsPy82MgYxj6MREBxoeQ/nDp3B2YOn4enrBW9/X0SFR9ocz5iWy8cu4tqpKykLpgPw8PLAa8PeQNOuLWR5n5R/tWzRBOfOXbBqbTQYjKhWrbKDoiLKe5g0UrpeG/R6hpJGAFbjGylz9Ek6RNwPw/xx32P0/PFQKBX4ZMFn2LtmF45sPQSlUoFm3VuhadeWsj5348I/cXLPMZgMRsss6LMHTmLjorWpdop5niRKUP17zmQ0Yeagr3Dv6h3ok/VQKBVQKpVwc3cDhJTWaFEUEVwsBKF3HkIS084kn29pjY+Jw8rpv8DD2wN1WtWX8R1TfvPO272xbPlqPHkSBcO/n3EPD3cMHvQe/P39HBscUR7CpJHS5RPgi+DiwYi4n/bYRpLRv+MdV327HB7eHkiIjUe1RjXw5cqp6W7tlx171+yympBj0Bmw94+d6PJ+D6z74fdULcgKpQIlK5eGj78PAODY9r9x9+odGJJTWnFEswjRLEIQBPSbMBDJCckoV7MCipQuir+3HMTiCfMz3AJp0BmwYeGfqNOqPgw6A07tPYawu2EoWrYYaresB5WNyTtEL/L19cGuHeuxYOEv2LlzL/wD/DFwwDto3661o0MjylP4jUt2fb1+DpZ9vRj7uTRKlr24R7U9u37bBoVSAdEsYt+fu+AfFID3Jg5ChTqVZF3cW5Ik6BKTbZ5LTkhCy54v4/qZq7h49DyEf7cS9PLzTrXG4rEdRy0J4/OUKiU8vDzQoG0jy7HGnZrBzcMNa77/DY/TmWT1vOjwKESFPcGUdz6HLjEZ+mQ9tB5uWDtvNcb/OtmSvBKlJyDAH5+O+wifjvvI0aEQ5VlMGilD+o57D0GFC2LN9785OpQ8xSfQFwVCgtC8R2ssnbIo3a7ZF4lm0fL/qPAn+HbIVNRv1wj9Jw6SLXEUBAElK5fCnUu3bZ5f9e0yDJ35EZKio3Dq8AUEBAeiUr0qqVo93b1sb5UoQbI5kadOq/qo06o+Tu4+hp+/nA+DzjrhfF6RssWwdMpPePok1nJMn6RDlM6A32evwPuTBmfgnRIRUXZx9jRlWIe3O+PLlVMdHUaeEhf1FEVKF0X52hUhZHPVc7PJjBO7juLSPxdkii7Fm5/0s7k4tyRJ+HvTAcRERqNEhZJo2qUFqjSoZtVN3qJHa2jcrJfZ0bppUbZ6+TSfW7dNA3Qd+Cq07ilL9SiVSgiK1HWkcdPg1SGv49I/563uF0URx3ceyejbJCKibGJLI2VKiYql8MWKrzDprfGODiXPOLRpP0RRlGUJGZPBhO8/moHg4iFo3Kk5Wv/vZai1mVve5kWlq5ZFiYqlcPviTatzKrUKD67dQ/nKJdK8v1L9qmjftxO2Ld0EpUoJAQJUGhVGzh0LhTL930s79O2MVj3bIuJBOHwDfXH+8Bn8OXc1EuMS4BPohzdHv40yVculeb/ZaIYkSTmyHzcREaXGlkbKtJKVSuOHg0scHUae8veWg7KVZTKY8OjmA/wxZwXGdf8IJpP1kjiZVax8CZsJntlsRkChAnbv7zbwNUzbNAdvj++PD74ehlnbf0TxCiUz9GytuxbFy5eAPkmPP+ashC4xGaJZREJMHBZ/OR83zl1N9/7wu6EZeg4REWUPk0bKEndPd3y3e6HNreco90RHRGHhuLnpXmPUG3DlxCXcPHfdMk7yRS/37mA1E1mpUqJo2eIoWrZYhmLxDwpAw/aNUb1JrSzNal49axmS4pMs+5ybjCboknRY8/2qtFsSBUChUuJp9FMsn/YLvn5vAlbPWo4nodxTmIhIboLkJNt3REUlQMzEJAFXExTkjcjIeEeHkWk3z9/E1H6fOzqMfM/L3xuV6lZBlQbVEPM4GkaDEbVa1EXskxgs/mI+IAiAJEHjpsWHc0ajVBXrxcEv/XMev0xahPjYOEiihMoNqqH/pMHw8vXKlc/nB43etrk4vKAQUKVhdVw8cs7qnF+QP94Y1RcLxn6fana6WqvBJws/Q5lqaXdtO0pe/Vl3VqxPebE+5ZNX61KhEBAY6GXzHJNGJ5FXP1wAsHrWcuxcuc3RYVAGuXt5YPaO+Ta3+pMkCTGPo+Hm4QYPb0/L8dz4fA5q2g/6f7crfJ5KrcKMLXPx5RtjERf91HJc467FR3PHYvqAyTbHixYuXQRT1szM0ZizIi//rDsj1qe8WJ/yyat1mV7SyO5pyrZeH/VBUJGCjg7DKQSG2B//52iSKOLcodM2zwmCgIDgwFQJY24wPrcbzYsKFgvGl2+MhS4pZT1JQaGAWqvBpz9PwL2rd9OcYBR2NxTJaaxBSUREmcekkWQx/tfJjg7B4QSlAjO2zMX0Ld879U4loigiMS7B0WGk8vDGfcu2hC+KjohGfGy8ZdcaSRRhMhjx28xf0926UoDg1H8PRER5DZNGkoWPvw++3f6Do8NwKO2/S98UCAnCJ4s+R4HCBVPWHRQEePrabup3BJPRhEr1qjg6jFQ8vD0gpdFiqE/SWZ2TJAk3zl1D2Zrlba4xCQAV61VJc+9sIiLKPCaNJBv/oAAsOPqro8NwGDcvD8ufy1Yvj+mbv8PsnQvww4HF+GbDbKfpwhfNIr7sPQ7TBkzCrfM3HB0OACC4eAiCi4dYLRyudddC7WY78VMoFKj2Ug2UqlLGalFwTx9PDJ42Is3nJcUnIjkhKdtxExHlJ5wI4yTy6oDZtJiMJqyYsRQH1+5xdCi5rk2v9nh95FtWLWCiKGLb0k3YsOBPiGazg6KzVqNZbbw/aXC64xhz4/MZHR6FmYO/QszjaCgUChiNJnTo2xnJiUnYv3ZPqjGPSpUKdVvXx8Cpw2A0GLFvzS4cXL8HJpMZDdo1Qpf3X7XZAhl2NxQ/ff4DHly/ByBlYfP3Jw9BgcJBOfrenudqP+uOxvqUF+tTPnm1Ljl7Og/Iqx8uexJi47F6zgqcPXASyfHJcJKPW67o9kFPNO3SAveu3cHjBxEoWq44KtatDEmUcOvCDfz61c8Iu/PI0WFaePp64fNlU1CwaLDVudz6fEqShLuXbyMu+ilKVS0LH38f6JN1mDX0G9y7dheCBEAQULBoQXyy6HN4+mS821+XpMPoTsOQFJdo+RwKCgE+Ab6YsWVuro1/dNWfdUdhfcqL9SmfvFqXTBrzgLz64cqqb4d+jUtHrfcTdkXPFkBXqVUILlYIYxZ9Dvd/u7LD7oRix4otuHryEqLCo2A2Zb4FUqVWQa3VpHS3CgCy8WOkUCnww/4l0Lqn3kva0Z9PSZJw59ItPLr1AMHFQ1CuZoVMbx14cMM+rJr5K/TJ+lTH3Tzc0O+Lgaj3ckM5Q06To+vS1bA+5cX6lE9ercv0kkZOLSSH+HjeOOiSdLh66jKWTl6EuKin9m/Ko8xGk+X/obcfYu281Xhr7LsAgJBShfHO5wMApCRGX/Qag0c3H2S4bHcvD7Tv0xEd3u4ChVIBQRBSJVO7Vv2F1bOWQ8rgL2SiScShTftRvHwJ+BcMyJFxmLcv3sTvs1fg3tW78Pb3Rsd+XdG8R+t0k0BBEFC6almUrlo2y8+NfBhhlTACKcv9PAlL2UHGqDfgxO5juHv5FgqVLIyG7ZvAw9vD6h4iovyIE2HIYdw83FCzaW18ungiCpcuanPvY1djMppwdNthm+cEQUDz7q1sLrqdlprN66Bz/x5QqVVQKBRWidfLb3SAT4BvpmJcNeNXzPlwOj7rOQozB30l64SR+9fuYvrAybhx9hoMOj2iwp5g9awV2PzTOtmekZaSlcvAzcPN6rhKo0aJiqUQFxOH8a+NwvKvl2D36h34Y85vGNNluFMNISAiciTX/1eanF7BYsGYsmYGJv8xA58vn4JF/yx3dEg5KjkxOc3FtVu+9jIq1asKjZv23+V60i5HpUnp7ranTNXMbaUnSRJ0ickw6o24evIyfv5yfqbuT8/GhWth1KdexNug02Pbss2WdRgzSxRF3LpwE1dOXEq3jJrNaiOgUKDV2EWj3oDjO49g1cxfEfM4GvpknSWupPgkLJm0MEtxERG5Go5pdBJ5dexDTlv42Vwc++uIo8PIEcUrlkTz7q3RvHsrq1bWO5du4ca5a/Ar4I8aTWvhi15jERUWCdH833qFWg83TF33LfyDAtJ9zv1rd/FVvy+skrWMEgQBrXq2xq2Lt+FbwA9te7+S5XUeR3UciujwKKvjWg83fLliKgqVCMlUefev38Oc4dOgS0yGIAgQRQn9vhiA+m1fsnl9UnwSNixcg31rdqUaP6pUKSGaRZsTtRRKBX44YD3OM6v4sy4v1qe8WJ/yyat1yW0EKc8aOGUYRs0fn6l75PrHPafdv3oXy79ejP4N3sTZQ6dw4+w1xDyOBgCUqlIGbXu/gvptX4LW3Q1jf/oC5WpWSJn0olEjuFghjPrxU7sJIwAUr1ASoxd8hpKVS2cpTkmSsOeP3bh7+TbOHTyN70bMwJ7fd2SprELFbSeFotkM3wJ+mSrLZDRh5qCvEBsZA12SDsmJydAn67Bk4gKE3Q21eY+Htwfqtm5g1dpoNpnTnNkvCAIUisxNuiEickWcCENOr3L9qlhyahUA4MyJ0zi69hBO7vrH+kIB+GLZVzh75BQ2zc/4GDmFUglBSNnTWDSbU/Yyzs1Gbwn4fsTMf2NRoGazOhj41VCotf+NbfQvGIAxi75AwtMEmAxG+Bbwy9Ts4bLVy+OL5V9hxbRfsPePndkK16DT4/fZKxAX/RSCQkDtFvVQvELJDN3bZcCruHn+eqpuZI2bBk26tIC7p3um4rj0z3mY/p1k9DyT0YxDG/bhfyPetHnfg+v3YDbb3n1GoVCk2staoVSgcv1qqf4uiIjyKyaNlKfUqlcbterVBr75EAAw75NZSIiJw3sTByOocMpMX1EjZihpDCpSEBXrVkGjTk1Rplo5xMfGwdvPBwmx8fio/eAcfR9pEc0iTu8/gVXfLkPfT/tbnffK5naELXu+jH1/7srwbOq0mIwmbP55PQBg00/rULFOFYz68VO7k5nK16qID77+EL/NWIro8CiotRq0+l9b9Bj8v0zHkPjceovPE81mxEWnPRs/qEhBqFTKVIuFA4Baq4ZvoB/iY+IgmkUoVUp4+/ug3xcDMh0bEZErYtJIedrQ6R9ZHStdNmPLsvxvxFuo06qe5fWzrt771+/B3csdyQnJWYpJoVDAJ8AHsU9is3Q/JGD/uj1Qu2lx4e+z8PLzRrs3X0HtlvUyvTbhi4qULopi5Yrj/rV72SonFQm4evIShrbqjw59OqH16+3TXaamZrPaqNG0Fgw6A9RatdXWgRlVoXYliDbWtdS6a1Gjae0076v6Ug14+XnDoDf8N0ZUANQaDb5cORWPbj3Eg+v3EFQ0GFUbVs8Xs/qJiDKC34bkkj75cUy65/0LBqRKGJ8XUDAAZpPt7ssMEQTM2jEfC48uQ88Rb1rti5whErB71V8IvxuKm2ev4efPf8SGBX9mPabnlK9VUZZyXqRLSMb6BWswrvsIJMYlpnutIAjQumuznDACQGBIAbTu1Q4at//GsGrctChWvgRqt7T9dwukdDmPWzIBlepVgVKlhFKlRKnKZTBuyQR4+nihfK2KaP16O1RvXJMJIxHRczh72knk1VlWzupZfU7p9wVuX7wBNw93GHUGKBQK1GpRF/0nDoZKk3ZD+6Q+43H/2t1Us5UBoHDZYogOjYQuSZfmvaWrlsVnv062vBZFEdMHTMH1M1ey9Z5UahVm7Zif7S7q+9fvYUrfz6zHAwqAb6Af9Em6dN9fRmjdtOgy4FW83LtDjm7PJ0kSLvx9FvvX7oYuSY+G7RuhUadmGX6mPlkPURTTHE9p1Btwat8JRIdHoWTl0qhUr0q2W3v5sy4v1qe8WJ/yyat1yW0E84C8+uFyVtmtz6iwSIzpOhKiOXX3p4e3B2ZsnYcFY7/HhSNnre4TFAp8s2G2zZ1U/vj+N2z/dXOWYwKACrUr48PvRttcpDozdq36C2u+X4Vn+Y9CocCwWaNQuX5VzBkxHecPnclW+c94+Xvjm/Vz8uSuKhEPwjG135cw6g0w6I1Qa9QoUrYYPlnwWaYWYH8Rf9blxfqUF+tTPnm1Lpk05gF59cPlrLJbn0e2HsLyrxdbbTunddfijVFvo1m3ljh36DRWzvgVUeGRUAgKlK1RHu9NGoQChYLSLPfhrQeYNfRrxD6OyXJsAFCgSEG8PuJN1GlVP8tlPI2KxeVjF6DWaFCtcQ1o3VMS0VsXbmDGB1NsL5QtAIVLFUHo7YzvklKoRAi+XDnVUn5eMbnvZ7h7+XaqyTYpC6qHQJeYDP/gALzyTlfUbJb2+Elb+LMuL9anvFif8smrdcmkMQ/Iqx8uZ5Xd+tz6y0as+/EPSKL12MauA19F1wGvZSc8PAmNxMH1+7Br9Tbok6z3Q84IjZsGb4/vj5deaZqtWGw5d+g0Vkz7BbGRMVAoFajbsi7a9+uGkBKFoVKrcHr/SSydvAgJsRmr42LliiMwJAhhd0NRsFgw2vbugCoNq8sed1Y8fhCBJ2GRKFq2mGXLxfiYOHzcYYjNJX2ep3HT4n8jeqNVz7YZfh5/1uXF+pQX61M+ebUumTTmAXn1w+WsslufV45fxPcfzbRuafRww5BpI1C1UY3shmhx7tBprJy+FE9CI1MOCMjwOpF+Qf749q8fsj3OzhZJkpCckAyNmwYhhf2t6lOSJJzaexzzx3yX5sLYaVFrNej2wWvo0LeznCFnSnJCEuaNmoWb529ApVbCaDChRY9WeGPU24iPjceoDCSNAODm6Ybv9/yU4XGU/FmXF+tTXqxP+eTVukwvaeSSO0Q2VKxXBSUqlsKdy7dh1Kd006q1ahQpXRSVG1aT9Vk1mtZGjaa1kRiXgLMHTyM6/Ak2LPgzQ4nY06hYmAzGHFl8WhCEdMciCoKAuq0bYOjMj7Dos3lWCXZ6jHoDNixYg+bdW8HD21OOcDNEFEUc3XoI+9buRtidUOiSkiGJEoz/hn5ww36ElCqClq+9jJBSRfDgxj27CbwkAZEPIxBSqkjOvwEiIgfiehJENgiCgI9/GIeu7/dAcPFCCC5WCJ3e645PFn6erWVi0uPp44XGnZqhc/8eaNC+UYbu8fDygEqjzpF4MqpWi7qYt38xmvdonan7FEolrpy8hLiop7h57jpunrueaj/onLBkwgIsn/YLbl+4ieSEJKtFzg06PXau3AYAGPDVUHh6e1q2pUxr6SSzyQTvf7u1iYhcGVsaidKg1mrwSr+ueKVf11x/9oApQxEQHIjty7ak2tbueRo3LTq+2y1HuqYzS6lS4u3x/dG0a0tMeefzlOY3O/RJOvz4yRxL4iYIAlQaNfpPGox6bRrIHmPonUc4sfsfGPXGdK9Lik8CkLIQ+vQtc3Fi1z94EhoJhUKBv5ZtSnW/WqNGzeZ1sr0MEhFRXsCkkchJvTbsDXQd8Coe3X6IR7ce4p/th3Hz3A0Y9Qa4ebij47td0e6tjo4O00IURWxfvhkKpcLmTi22PN/SJ0kSjHoD5o+Zg+3VymLkd2NkTcZunLlqN8EWFAIqN6hqee3u6Y5m3VpaXvsV9Mea71ZCNIswm0XUbFEH737xgWwxEhE5MyaNRE5MrdWgZKXSKFmpNBp3agZJkqBL0mV4N5VbF27g780HYDSYUL9tQ1R9qUaOtUwe33kUF/4+m+GEMT13LtzEyHaD8M362QgMKSBDdIBvAb90d3hRqVXQumvx6pBeaV7TokdrNOncHE9CI+Ht7w1PH7YwElH+waSRKA8RBCHN3UtetHHRWvz1a0p3qiRJOLn7H9RsXgcDpgzNkcTx8KYDmZoMY4/ZaMLMoVPxzvj3UaZauWzvLFP1pRrQaLUpu90813uuUCpQrFwJVGlQDW3eaA+/IP90y1GpVShUIiRbsRAR5UVMGolc0JOwSGz7ZSOMhv/G3+mT9Th74BSunbqCinUry//QNMYxqtQqBAQHIOZxDBQqJQDAqDOkOVbzeRF3wzBtwGS4uWsx9NuPUbl+Vbv3pEWlVmHMos8x9+NvER0RBUFQQOOmwcCpw7JVLhFRfsGkkcgFXTx63uZsX71Oj9P7T+RI0tikSwvcvHADBhtrW361dhZiI2OQnJCE4BIhGNT4nYwX/G+X/HcjZmDG1rnw8ffJcowhpYrgq7XfIuJ+OIwGA4qULpZul7WzkiIfQ9qxDQh9BFSqDKFVWwieubd0ERHlT3nv25KI7NK6aSDYGPOoUCjh5pGx7u3Mqt/2JVR7qTo0bloICgEaNw207loMmT4SSpUSgSEFULRccajUqgy1Mr5INJtxYufRbMcpCAIKlQhBsXIl8mbCePUypJFDgC0bgRP/AKtXQBo5GFJM9ramJCKyJ9vfmBs3bkTnzp1RuXJlrFixQo6YiCibajarY7O7WKlS4KVXmuTIMxVKBQZPH4lR8z9Ft4Gv4fURb2HGlrlWrZpPn8RmaXyi2WRG4tMEucLNkyRJgvTDHECvA8z/7lZjMABxTyH9sdKhsRGR68t20lipUiXMnj0bnTp1kiMeIpKBu5cHhs78CFoPN7h5usPNww1qrRpvftIPISUL59hzBUFA2erl0bl/D7Ts+TK8/Lytrpk3ejbM5szPsFZr1KhYr4ocYeZdT2OBqCfWx81m4MSxXA+HiPKXbI9pLF++PADk2C4ZRJQ1VRpWx5ydC3Dx6HmYTSZUblDN4YtQPwmLxIPrd612YrFHEARUeak6ytWskEOR5REabdoLp7u55W4sRJTvOM1EmLQ2x85PgoKsW2Uo61ifAOCNosVbyVKSHPWZEBkJlUpld1eWZxRKBYKLBuN/w19Hyx4toVQqU503m8w4ufckHt15hBIVSqBWs1p54hfYrNelN2Jq14bh9GnAZPrvsFYLr1d7wDOffub5sy4v1qd8XK0u7SaN3bt3R2hoqM1zR44csfoSz6qoqASImWx9cCVBQd6IjIx3dBgug/UpL7nq083PH7CxRqQgCBAUAkSzCLVWDUFQYNislCV2Tu87iQObDuDg5sNo3KkZarWoC0EQ8PRJLL5690skxMTBaDBCpVHD3dMd3v4+kEQRTbq1RPNurSx7RzuL7NalNGA4MPkzIDwcUAiAyQzUbYDEZm2RlA8/8/xZlxfrUz55tS4VCiHNhjy7SeP69etlD4iI8ieVWoW+n76HXyYttCw6rtaq4e3ng7fGvIM7l2/D298H9ds1go+/DxZ9Ng9n9p+0LBp++dgF1G5ZD+9PHoJfp/6M6PAoiP+OjzSbzNAn6RAbmTKLePXMZdj7+05MXPWN0yWO2SH4+gIzvgduXgciHwOlykAIyblxqkREzzhN9zQR5Q8N2jVCcPFC2LVqO6LCnqB6oxpo8VobeHh7ombzupbrbl+8idP7TsCgM1iO6ZP1OLX3OFr/rx3OHz4D0Zz+0j2PH4bj8OYDaP2/tjn2fhxBEASgXIWU/4iIckm2k8YtW7Zg+vTpiIuLw549e7Bo0SIsWbIEZcuWlSM+InJBJSuVxvuTBqd7zaVjF2A0mKyOm4wmXD5+IdVWgGmSgKPbDrlc0khE5AjZTho7derE5XaISHYe3p5Qa1SpWhqBlC5uD29PVKpfBVeOX7K7ULiHN3dKISKSg/NPMySifKneyw3TOCOg3ssN8fZn78PL39vueMVX3ukif3BERPkQk0Yicko+/j4YOvNjuHu6w83THe6e7nD3csfQmR/B298HBUKCMG3jd3hrTD+0e+sV+BcMAF6YmN26V3tUrCP/PttERPmRIElprRSbu7jkTt6cmu+sWJ/ycmR9Gg1GXD9zFYIgoFzNClBr1DavM5vMOHPgJI7v+gfuHm7o8HYXFCoRkm7ZT8IicWLXPzDqjajZvA6Kly+RE28hFX425cX6lBfrUz55tS6zteQOEZEjqTVqVGlQze51SpUSdVs3QN3WDTJU7uFNB7D8m8WQRAlms4htv2xEi9faoNdHfbIbMhGRS2L3NBHlO/ExcVj+zWIY9UaYjCZIogiD3oD9a/fg5vnrjg5PFmazGU7SkURELoJJIxHlO+f/Pmtzu0Gj3oDjO486ICL5XLlyDZ279EKxElVRqkwNjPzoUyQmJjo6LCJyAeyeJqJ8RxAEm9sZWs7lUeHhEeja7U3EJyQAAPR6A9at34K79x5g/drlDo6OiPI6tjQSUb5TvUkty/aDz1Nr1WjYvrEDIpLHsuWrYTCkXtfSYDDg3LkLuHz5moOiIiJXwaSRiPIdL18v9PtiIFRqFVRqFZRqFdRaNdq91RGlqpRxdHipSNeuQpz8OcQP3oH41ZeQbqQ95vLSpavQv5A0AoBSqcTt23dyMkwiygeYNBJRvhMdHoXNP6+DQqmAIAiQRBHNurVC90H/c3RoqUjnzkCaNB44fxaIigLOnoY04VNIly7YvL5mzWrQaq0XOzeZzChfoVwOR0tEro5JIxHlO9+NnIGIe+Ew6AwwGowQzSIObdyPswdPOzq0VKRfFgEvthwa9JCW/mzz+j5vvQ43N22qcZluWi0aNaqP8uWcqwWViPIeJo1ElK+E3wtDxP0wqz2rDTo9dq/6y0FRWZMkCXj00PbJe3cgPbgP6XFEqsMFCgTir61r0LpVM2i1Gvj6+qDfu29i8U9zcyFiInJ1nD1NRPlKUnwiFEqlzXMJT1NmHV85fhHrF6xBxP1wFC5dBN0H/Q/la1XMzTBTus09vYDEBJvnpU8/BkQRUuEiEEZ9CiG4EACgVKkSWL5sYW6GSkT5BFsaiShfKVa+BADrRa/VGjXqtKqHswdP47uRM3Dz3HXEx8Th2qkrmDX0a1w5fjH3g+3cHbAxRhGSBOh0KV3X9+5C+nIcJBuzwYmI5MSkkYjyFbVGjT5j34XGTWMZ+6fRauBX0B+tX2+PVd8ug0H3wrI1OgNWz16R67EK3V8D2nUENBrAzQ1QKADhha9tSQKSEoGL53M9PiLKX9g9TUT5zkuvNEVIqSLYvXoHYh5Ho0aTWmjarSW0blpEPoywec+jW2mML8xBgkIBoU8/SP97A4iOhvT7SuDvg9YXShIQG5Pr8RFR/sKkkYjypZKVSqP/xEGpjkmSBE8fTyTGWW+75xvom1uhWRG0bkBIYaBmbUgnjwN6XeoLzCJQPnfHXBJR/sPuaSKifwmCgA7vdIHGLfU4Qo2bFp3e6+6gqJ7TqClQIAhQq/87ptUCjZpCCCnsuLiIKF9gSyMR0XM69O0MfZIOO1dugyRKUCgV6PRuN7R4tbWjQ4Og0QBTZ0LaujGlm1rrBqF9R6B5K0eHRkT5gCBJkvU0QgeIikqAKDpFKA4RFOSNyMh4R4fhMlif8sqP9Wk0GJEQGw9vfx+o1PL9fu3sdXnlyjWcP38JRYsWxksv1YdC4dwdUs5en3kN61M+ebUuFQoBgYFeNs+xpZGIyAa1Rg3/ggGODiPXGI1GvPf+cBw+fBSCIEAQBAQHF8T6tctRsGCQo8MjIifg3L9CEhFRrvhx/mIcOnQUyck6JCUlIzExCffuPcCw4WMcHRoROQkmjUREhBUr/oBOl3pWttlsxtF/TiA+3vauNESUvzBpJCIi6A16m8cFAdAbDDbPEVH+wqSRiMgFSCYTpPAwSInWa0xmRIf2L0NtY8JPiRLFUSAw/4ztJKK0cSIMEVEeJ+7aDqz4JWWRb9EMqUEjCB8Mg2Br3+o0jB49HHv3HkR0TAySkpKh1WqhVqvw/Xff5GDkRJSXMGkkIsrDpNMngV9/BvTPdS8fOwpJECAM/zjD5Xh6eODjj4di9+79SEhIRP36dfDWmz05c5qILJg0EhHlYdK6P1InjABgNAD//A3pvYEQPG2vt/a8Gzdvo1v3N6HX66HXG6DRqGE0GTFkcP8cipqI8iKOaSQiysuintg+rlACcXEZKmLgByMRExOLxMQkmEwmJCUl4/Tp8/jp519lDJSI8jomjUREeVnFyoBg46tcqUjZp9qOsLAI3L59Fy9uDqbT6bBq9Vq5oiQiF8DuaSIiB5MiH0P6fSVw7gzg6QV07gqhVVsIgmD3XqHnG5BOHQd0ekASUw5qtUDvtyGo1XbvN4tmpPUYs9mcmbdBRC6OSSMRkQNJsTGQPhkBJCUCogjExgC//ATpwQMI79gfUygULgJ8MxvSH78BVy8DgQUg9OgJoU79DD2/SOEQFClSGLdu3Ul13E2rRc/XumblLRGRi2LSSETkQNLWTYAuOSVhfEavB3Zug9S9JwRfX7tlCIWLQBgxOkvPFwQBC36chVd79oXRaEJycjI8PT1QtmxpDBr0XpbKJCLXxKSRiMiRLl8ETCbr42o1cP8uUK1GjodQtWolnDi2Fxs2bkVoaDjq1K6BVq2aQalU5viziSjvYNJIRORIIYWBG9f/G4/4jMmUoYksGSGKIv5Ysx7Llv8Oo9GIV3t0xtt9e8Pd3c1yjY+PN/r26SXL84jINTFpJCJyIKFzN0hH/wae3/tZpQLKVYAQUliWZwweOgo7d+5DcnIyAODmzTvYtHk7Nm34DSoV/xkgoozhkjtERA4klCgFYdRYICAQUGtSEsbadSGMHi9L+VeuXMOOHXstCSOQspzO9es3sWv3flmeQUT5A3/FJCJyMKFWXWDBL0B0NODuDsHDQ7ayjx07BUCyOp6YmITDh4+iQ/s2sj2LiFwbk0YiIicgCAIQGCh7uQWCAv/tgk691aBWq0GhQsGyP4+IXBe7p4mIXNjLbVpCo9FYLRSuVCrxv57dHBMUEeVJTBqJiFyYVqvBuj+XoUSJYnB3d4enpweCggpg2dL5CA4u6OjwiCgPYfc0EZGLq1ChHI4c3oGbt+7AaDCiYsVyUCjYZkBEmcOkkYgoHxAEAeXKlnZ0GESUh/FXTSIiIiKyi0kjEREREdnF7mkiIspFEtTQQwERJqhhhtrRARFRBjFpJCKiXKGEEZ54CuG5xcaN0CAJPgCEtG8kIqfA7mkiIsoFEjwRB4UgQRBg+U8NAzQvLDxORM6JSSMREeU4BcwQIFodFwRAi0QHREREmcWkkYiIcpxgY//rZxQQoYApF6Mhoqxw2jGNkiQhIeEpkpMTIIpmR4cjO5VKA3//ICiVTvtXQEQkGzNUkCCkmTxqoIMOXrkcFRFlhtNmLDExkRAEAQEBwVAqVVb7puZlkiQhMTEOMTGRKFAgxNHhEBHlAgEGuEErJcOFvs6J8hWn7Z42GHTw8wuESqV2qYQRSNmZwdPTByaTwdGhEBHlGj3c0zxnhCYXIyGirHDalkZAgiA4bU6bba6WCBNR/iNAhAbJUMIEM1QwwB1SOm0REpRIhifcpdQTXwxw43qNRHmAEyeNRETkrBQwwRuxSPkFH5AkA7RIRgL8IUKZ5n0GeMAEDTTQAUhpYUxJGPmLNJGzc8mmvMWLF2LSpM8dHQYRkcvyQDyeJYzAv+suQoIbEmxeL0CEO+LhgyfwQiwAQAdPmKFBdhJGvV6Plb+tweu93kX/AR/i0KGjWS6LnJskSVi8ZAVq1m6KYiWqok3bbjhy5Jijw8pX8nRL486d2/H77ytx//5deHh4oGzZCujb911Hh0VE5OIkKGGymtAiCIBaenGstgQlDPBEPITnkkytlAwVjEiAH7KaNBoMBrRq3RsXLl5BUlIyAGDv3oMYMvg9fPzR0CyVSc5r1uwf8MOPi5GcnPJ3fenSVbzZZyD+/GMp6tSp6djg8ok829K4evUKfP/9t+jbtx82bdqJtWu3onv313D48AFHh0ZElG9JqRJACZ54Ci/EpUoYgZQEUwkTVDBm+Vmbt+zAxUtXLQkjACQnJ2PuvEV4/Dgyy+WS89Hp9Phx/n8J43/HdZg+83sHRZX/5MmkMSEhAYsXL8RHH41B8+at4O7uDpVKhSZNmmHIkA+trv/sszHo0qUd2rVrjiFD3sft27cs544ePYy33uqJl19uhm7dOuC335YDAGJjY/HJJyPQvn0LdOjQCoMH94copuxm8ORJJMaPH41OndqgZ88uWLNmtaW8y5cv4r33+qBt2+bo3Lkt5s6dlcO1QUSU2wQYoIX0wpKLkpQyqeUZDVJaE59tGWiLMhuLeu/YsQeJiUlWx9VqNf7552SWyyXnExmZ9i8BV6/eyMVI8rc82T198eJ5GAwGNGvWIkPXN2zYCJ9++gVUKjXmz5+LSZM+x9KlvwEAvvlmMiZN+gY1atRCXFwcwsJCAaS0ZAYFFcSWLbsBAJcuXYAgCBBFEZ98MhJNmzbHhAlT8fhxBEaMGILixUugQYOX8N1336Jnz15o374jkpKSUiWoRESuIhleUMIMpWSyLNptgho6eFqu0UBvd03G9CbN2BMY6A+lUgmz2XoDCF8/nyyXS86nQIECkF78LeVfZcuWyuVo8q882dIYF/cUvr5+UKkylvN26tQVHh6e0Gg0ePfdAbh58zoSElIGayuVKty5cxuJiQnw8fFBhQoVAQAqlQpRUU8QHh4GlUqFGjVqQRAEXLlyGbGxMejX732o1WoUKVIUXbp0w549Oy33PXr0ELGxsfDw8EDVqtVyphKIiBxKgQT4IwF+SIYX4uGPxEyMT5QkQIIiW+szvvXm69BorJfqMZtFjB79BWrXbY5Jk6cjLi4+y88g5+Du7ob+7/WBu3vqtT7d3dwwetRwB0WV/+TJpNHHxxdPn8bCZLLfrWE2mzF//lz8739d0bZtc/Ts2RkA8PRpLADgq6+m459//sarr3bG0KEDcPHieQBA7959UKRIMYwcORQ9e3bF8uVLAQDh4WGIinqC9u1bWP5btuwXREdHAQDGjv0c9+/fw5tvvor+/fvi778PyV8BREROwgw1jHCDaKPjylYXNpCSMJqgRnw2JsEAQJUqFTFv7lS4u7vD29sLnh4eUKvVMJvNePDwEcLCIrB4yQp06tILBgM3U8jrxo4ZiQ+HfwA/P18AQOnSJfHTT9+jYYO6Do4s/8iT3dNVq1aHWq3GoUP70bJlm3Sv3bVrOw4fPoA5c35ESEhhJCQkoEOHlpZm7kqVquCbb2bBZDJh7drf8cUX47Bu3VZ4eHhi2LCRGDZsJG7fvonhwwehUqXKCA4ORkhIYaxevd7m84oVK46JE6dCFEUcOLAXn38+Blu37rH67YiIyNUZ4A41DFBJqSe7JMD336V2su/tvq+jebPmOHnqLO7fe4gJk75JNTHGYDAgNDQM27fvQZcuHWR5JjmGQqHAh8MH4sPhAyGKIhSKPNnulaflyRr38vLCe+99gFmzpuPgwf3Q6XQwmUw4evRv/Pjjd6muTUpKglqtga+vL3Q6HRYu/MFyzmg0YufOv5CQkACVSgVPT0/LTi1//30IDx8+gCRJ8PT0glKpgEKhQKVKVeDh4YEVK5ZCr9fBbDbj9u2buHLlEgBgx45tiImJgUKhgJeXNwBAoeCitUSUHwlIhC8S4QsdPJAMLzxFoGwJ4zOenp5o3qwx9AY9zCbr8Y2JiUk4eeqMrM8kx2LC6Bh5sqURAN544y0EBgbi118XY9Kkz+Dh4YkKFSqib993cfz4P5br2rfviOPHj6Jbt1fg4+OD/v0/wIYNf1rOb9++DbNmTYcoiihevAS++GIKAODhw/uYPXs6YmNj4O3tg+7de6J27ZQm8OnT52Du3Nno2bMrDAYDihcvgfffHwQAOHbsKObOnQ29Xofg4BBMmDAVWq0biIjyJwEmaGDKhb2lixQpDLVGDf0LXdHu7m4oUaJ4jj+fyNUJUlrTkXJZVFQCRPG/UMLD76FQoRIOjCjnPf8eg4K8ERnJwdpyYX3Ki/UpH9alvJ6vT6PRiPoN2+Dx40jLEmkA4OPjjWNHd1vGwlHa+PmUT16tS4VCQGCgl+1z2S184sSJaN++Pbp06YJevXrhwoUL2S2SiIgo09RqNTZtWIlatapDo1ZDo9GgQoVyWPfnciaMRDLIdvd0s2bN8Omnn0KtVmPfvn0YOXIkdu/eLUdsREREmVKsWFFs2bQa0dExMJvNCAoq4OiQiFxGtpPGli1bWv5cs2ZNhIeHc1YTERE5VECAv6NDIHI5sk6EWblyJVq0aJGlhPHF/vPHjxVQqVw78VQoFAgK8ra8fv7PlH2sT3mxPuXDupQX61NerE/5uFpd2k0au3fvjtDQUJvnjhw5AqUyZQuorVu3YvPmzVi5cmWWAnlxIowoijCZxHTuyPtEUbQMks2rA2adFetTXqxP+bAu5cX6lBfrUz55tS7TmwhjN2lcv972ItbP27VrF2bPno2lS5eiQAGOHyEiIiJyNdnunt63bx++/vpr/PLLLyhatKgcMRERERGRk8l20jhu3Dio1WoMH/7fhuFLly6Fvz8HIRMRERG5imwnjf/884/9i4iIiIgoT3Pt6clEREREJAuXSxrXrduMevVboXDRSqhXvxXWrduc6zHMmzcHPXt2QZMmdXH79s1cfz4RERGR3FwqaVy3bjNGffI5Hj4KhSRJePgoFKM++TzXE8emTVtg3rxFKFQoJFefS0RERJRTXCpp/Pqb2UhO1qU6lpysw9ffzM7VOGrUqIng4EK5+kwiIiKinORSSeOj0LBMHSciIiKijJF1G0FHK1I4BA8fWe9eU6SwfN3E7777JiIiwm2e27Rpp2WHHCIiIiJX4lJJ47ixIzHqk89TdVG7u7th3NiRsj1jyZKsbZNIRERElJe5VNLYo0dnACljGx+FhqFI4RCMGzvScpyIiIiIssalkkYgJXF0dJI4Z84MHDiwD9HRURgxYgh8fHyxYsUfDo2JiIiIKDtcLml0BiNGjMaIEaMdHQYRERGRbFxq9jQRERER5QwmjURERERkF5NGIiIiIrKLSSMRERER2cWkkYiIiIjsYtJIRERERHZxyR0iIiInIEkSDh06is1btkOjUaNnz26oWaOao8MismDSSERE5GCSJGH4iLHYtm0nkpKSoVAosGrVWnz44SB8OHygo8MjAsDu6Uy7f/8eBg7sh169emDgwH548OC+o0MiIqI87ug/JywJIwCIoohknQ6z5/yAR4/CHBwdUQqXSxqP/nUYozsOw7t138DojsNw9K/DspY/c+bX6NGjJ1avXocePXpixoypspZPRET5z1/bdyM5WWd1XKFQYt/+Qw6IiMiaSyWNR/86jF+n/ISo8CeABESFP8GvU36SLXGMiYnG9etX0aZNOwBAmzbtcP36VcTExMhSPhER5U8e7u5QKKz/SVYoFHBzc3NARETWXCppXDfvdxh0hlTHDDoD1s37XZbyIyIiUKBAQSiVSgCAUqlEgQJBePw4QpbyiYgof3r11S5Qq62nGYiiiHZtWzkgIiJrLpU0RkU8SeN4VC5HQkRElHHly5XBhC/HQqvVwtPTA15envDwcMfPP30Pb28vR4dHBMDFZk8HBhdI6Zq2Oh4oS/nBwcF48uQxzGYzlEolzGYznjyJRMGCwbKUT0RE+dfbfd9Ax47tcODA39Co1WjVqik8PT0dHRaRhUu1NPYY+jo0bppUxzRuGvQY+ros5fv7B6Bs2fLYvXsHAGD37h0oV64C/P39ZSmfiIjytwKBAXi1R2d07tyeCSM5HZdqaXypQxMAKWMboyKiEBgciB5DX7ccl8Po0Z9iypQv8csvP8Pb2xuffz5RtrKJiIiInJVLJY1ASuIoZ5L4ohIlSuKnn37NsfKJiIiInJFLdU8TERERUc5g0khEREREdjFpJCIiIiK7mDQSERERkV1MGomIiIjILpebPU1ERJRfaI7/DnX4VctrSVAiqfVHgIeHA6MiV8WWRiIiojxIfXEH1OFXIQD//SeZ4bl7hoMjI1fFpJGIiCgP0tz+x+qY8Oz/987kbjCULzBpJCIiyqOENI5rHl7I1Tgof3C5MY2Pth3H9bkboQuPhluhAJQf1hVFXqkvW/nz5s3BgQN7ERYWimXLVqN06bKylU1ERJQZEmwnjsYiVXM7FMoHXKql8dG247g4aSV0YdGABOjConFx0ko82nZctmc0bdoC8+YtQqFCIbKVSURElFmGEnWsjkn//l8sWTt3g6F8waWSxutzN0LUGVIdE3UGXJ+7UbZn1KhRE8HBhWQrj4iIKCuMNTrBVKA0JMDyHyAgsc0IR4ZFLsyluqd14dGZOk5ERJSX6Rv1gR4AjEZArXZ0OOTiXKql0a1QQKaOExERuQQmjJQLXCppLD+sKxRumlTHFG4alB/W1UEREREREbkGl+qefjZLOidnTxMRERHlRy6VNAIpiWNOJolz5szAgQP7EB0dhREjhsDHxxcrVvyRY88jIiIicgYulzTmtBEjRmPEiNGODoOIiIgoV7nUmEYiIiIiyhlMGomIiIjILiaNRERERGQXk0YiIiIisotJIxERERHZxaSRiIiIiOxi0khEREREdjFpJCIiIiK7uLh3Jjx9GovJk7/Ao0cPoVarUbRocYwe/Sn8/f0dHRoRERFRjnK5lkbx0H6Ig96F+L8uKf8/tF+2sgVBQO/efbFq1TosW/Y7ihQpigUL5spWPhEREZGzcqmkUTy0H1gwD3gSCUhSyv8XzJMtcfTx8UXt2nUtr6tUqYrw8HBZyiYiIiJyZi6VNOK3ZYBBn/qYQZ9yXGaiKGL9+rVo0qSZ7GUTERERORvXGtMY9SRzx7Nh9uwZ8PBwx6uv/k/2somIiBwtKSkJR4+egEqtwksN60Gj0Tg6JHIw10oaAwukdEnbOi6jefPm4OHD+5g2bTYUCtdqrCUiItqyZQeGjxgLlUoJABAEBX5ZMg+NXqrv4MjIkVwr4+ndF9BoUx/TaFOOy2Thwh9w7doVfP31t/yti4iIXM6DBw8x7MMxSE5ORnx8AuLjExAXF4e+b3+AhIQER4dHDuRSSaOiaQvgg6FAgSBAEFL+/8HQlOMyuH37FpYv/wVPnkTigw/exTvv9Ma4caNkKZuIiMgZrF23GWaz2ea5v7bvyeVoyJm4Vvc0/k0cZUoSX1S6dBkcPnwyR8omIiJyBk+fxsFoNFodN5vNbGnM51yqpZGIiIiyp3Wr5vDw8LA6LklAs6aNHBAROQsmjURERGTRuHEDNG/eGB4e7pZjHh7u6NvndZQpU8qBkZGjuVz3NBEREWWdIAj4edF32PbXLqxbtxkajRq9er2K5s0aOzo0cjAmjURERJSKQqFAp47t0KljO0eHQk4k20nj/PnzsW3bNiiVSkiShIEDB+KVV16RIzYiIiIichLZThrfeustDBo0CAAQERGBDh06oHHjxvD19c12cERERETkHLI9Ecbb29vy56SkJAiCAFEUs1ssERERETkRWcY0rlq1Cr/++ivCw8MxdepU+Pv7Z7qMwECvVK8fP1ZApXLtyd0KhQJBQf8l3c//mbKP9Skv1qd8WJfyYn3Ki/UpH1erS0GSJCm9C7p3747Q0FCb544cOQKlUml5fe3aNYwaNQrLli3LdOIYFZUAUfwvlPDweyhUqESmyshrnn+PQUHeiIyMd3BEroP1KS/Wp3xYl/JifcqL9SmfvFqXCoVg1ZD3jN2WxvXr12f4QRUqVEDBggVx/PhxtGvnmBlXqocXoLmyB0LyU0juvjBUag1T0WqylT9u3McIDQ2FQiHA3d0DI0eORrlyFWQrn4iIiMgZZbt7+ubNmyhbtiwA4MGDB7hy5YrldW5TPbwA7bnNEMwp2x8JyU+hPbcZAGRLHMePnwgvr5QM/NCh/fj660lYsmSlLGUTEREROatsJ41z587FzZs3oVKpoFQq8dlnn6FMmTJyxJZpmit7LAnjM4LZCM2VPbIljc8SRgBISEiAILj2uEsiIiIiQIak8bvvvpMjDlkIyU8zdTyrvvlmMo4f/wcAMHPm97KWTUREROSMXKqZTHK3vTZkWsezauzYz7Fu3VYMGDAYP/7oPEkzERERUU5xqaTRUKk1JKU61TFJqYahUusceV779h1x+vQpPH0amyPlExERETkLl0oaTUWrQV+jM0R3X0gARHdf6Gt0lm08Y1JSEiIiwi2vDx8+CB8fH/j4cPcbIiIicm2yLO7tTExFq8m6xM7zdLpkfP75WOh0yVAolPDx8cG0abMhCEKOPI+IiIhcS1JSEpb+ugqbNv0FLy9PvPNOb3R8pW2eyCVcLmnMSQEBgVi0aKmjwyAiIqI8SK83oFOXXrhz5z50Oh0A4MzZ8zh+4jQmTRjn4Ojsc6nuaSIiIiJntXHjVty798CSMAJAUlIyli1bjUePwhwYWcYwaSQiIiLKBXv2HkRSUrLVcbVaheMnTjkgosxh0khERESUCwoVKgilUmnzXIHAwFyOJvOYNBIRERHlgj5vvQ6NJvXSgIIgwNvbC40a1XdQVBnHpJGIiIgoF5QtWxrz5s6Aj483vLw84e7ujjKlS+LPP361aoG8e/c++r79AUqWroGKletj4qRp0On0Doo8BWdPExEREeWSVzq8jJfbtMDFi1fg4emB8uXKWC23ExUdgw4deyIuLh6iKEKv1+OXpb/h6rUbWLXyZwdFzqSRiIiIKFep1WrUqlU9zfPLl6+GTqeDKIqWY3q9HseOncS1azdQoUK53AjTCruniYiIiJzIufOXbHZFK5VKXLt20wERpWDSmEVLlixCkyZ1cfu24/7yiIiIyPVUrlQBGo3G6rhoFlG6dMncD+hfLpc0qqGDN6Lgi0h4Iwpq6OzflEnXrl3FpUsXUahQiOxlExERUf7Wt8/r0GpTJ40ajQZVq1VG1aqVHBSViyWNaujggXgoBRGCACgFER6IlzVxNBgMmDVrGkaNGitbmURERETPBAcXxMb1K1Gvbi0oFApoNBp0794JK5cvdGhcLjURxg2JeHG/b0EA3KREGOEmyzN+/nkB2rbtgJCQwrKUR0RERPSiSpUqYNPGVTCZTFAoFFAoHN/O5/gIZKSAmKnjmXXx4nlcu3YFPXr0lKU8IiIiovSoVCqnSBgBF0saxTTeTlrHM+vMmdO4e/cOevbsgtde64zIyMf46KNhOH78H1nKJyIiInJWLtU9rYMnPKT4VF3UkpRyXA59+ryDPn3esbx+7bXOmD59NkqXLitL+URERETOyqWSRiPckISUMYwKiBChgA6eso1nJCIiIsqvXCppBFISx9xKEv/8c3OuPIeIiIjI0VxqTCMRERER5QwmjURERERkF5NGIiIiIrLLiZNGAZIkz/qKzkiSJEeHQERERJRhTps0ajRuiI19ApPJ6HIJliRJSEyMg0plvRk5ERERkTNy2tnT/v5BSEh4iujoCIii2dHhyE6l0sDfP8jRYRARERFliNMmjYIgwNvbD97efo4OhYiIiCjfc9ruaSIiIiJyHkwaiYiIiMgup+meVigE+xe5ONaBvFif8mJ9yod1KS/Wp7xYn/LJi3WZXsyC5GpTk4mIiIhIduyeJiIiIiK7mDQSERERkV1MGomIiIjILiaNRERERGQXk0YiIiIisotJIxERERHZxaSRiIiIiOxi0khEREREdjFpJCIiIiK7mDQSERERkV1Os/d0fjNx4kQcPXoUGo0GHh4eGD9+PKpVq2Z13bp16zB16lQUKVIEAFC0aFH88MMPuR2uU7pz5w7Gjh2L2NhY+Pn5Ydq0aShZsmSqa8xmM6ZMmYJDhw5BEAQMGDAAPXv2dEzATiwmJgaffPIJ7t+/D41GgxIlSmDSpEkICAhIdd3YsWNx5MgR+Pv7AwDat2+PQYMGOSJkp9aqVStoNBpotVoAwKhRo9C0adNU1yQnJ2PcuHG4dOkSlEolxowZg5YtWzoiXKf28OFDDBkyxPI6Pj4eCQkJOH78eKrr5s6di99++w0FCxYEANSuXRtffvllrsbqrKZNm4YdO3bg0aNH2Lx5M8qXLw8gY9+hAL9Hn2erLjP6/Qm4wHeoRA6xd+9eyWAwWP7cunVrm9etXbtWGjZsWG6Glmf06dNH2rBhgyRJkrRhwwapT58+VtesX79eevfddyWz2SxFRUVJTZs2lR48eJDboTq9mJgY6Z9//rG8/uabb6Rx48ZZXTdmzBhp+fLluRlantSyZUvp2rVr6V4zd+5cafz48ZIkSdKdO3ekRo0aSQkJCbkRXp42ZcoUaeLEiVbHv//+e+mbb75xQETO78SJE1JoaKjV5zIj36GSxO/R59mqy4x+f0pS3v8OZfe0g7Rs2RJqtRoAULNmTYSHh0MURQdHlXdERUXh8uXL6NSpEwCgU6dOuHz5MqKjo1Ndt23bNvTs2RMKhQIBAQFo06YNtm/f7oiQnZqfnx8aNGhgeV2zZk2EhoY6MCLX99dff+H1118HAJQsWRJVq1bFwYMHHRyVczMYDNi8eTNeffVVR4eSp9StWxchISGpjmX0OxTg9+jzbNVlfvr+ZNLoBFauXIkWLVpAobD913H8+HF07doVb775Jvbv35+7wTmpsLAwBAcHQ6lUAgCUSiUKFiyIsLAwq+sKFy5seR0SEoLw8PBcjTWvEUURq1atQqtWrWye/+WXX9C5c2cMHjwYt27dyuXo8o5Ro0ahc+fOmDBhAuLi4qzOh4aGWoadAPxsZsTevXsRHByMKlWq2Dy/detWdO7cGe+++y7OnDmTy9HlLRn9Dn12Lb9HM8be9yeQt79DOaYxh3Tv3j3N3zSOHDli+UHdunUrNm/ejJUrV9q8tkWLFnjllVfg5uaGy5cv4/3338eyZctQpkyZHIud8rfJkyfDw8MDb731ltW5kSNHIigoCAqFAhs2bED//v2xe/duy+eZUqxcuRIhISEwGAz46quvMGnSJMycOdPRYeV5a9euTbOVsVevXvjggw+gVqvx999/Y/Dgwdi2bZtl7BhRbkjv+xPI+9+hbGnMIevXr8exY8ds/vfsw7Fr1y7Mnj0bixcvRoECBWyWExAQADc3NwBA5cqVUbt2bZw/fz7X3oezCgkJQUREBMxmM4CUgdqPHz+26jYICQlJlbyHhYWhUKFCuRprXjJt2jTcu3cPc+bMsdnyHRwcbDnerVs3JCUlscXBhmefQ41Gg969e+P06dNW1xQuXBiPHj2yvOZnM30RERE4ceIEOnfubPN8UFCQZchP48aNERISghs3buRmiHlKRr9Dn13L71H77H1/Ann/O5RJo4Ps27cPX3/9NRYvXoyiRYumeV1ERITlz48ePcLZs2dRoUKF3AjRqQUGBqJSpUrYsmULAGDLli2oVKmS1Wy19u3bY82aNRBFEdHR0di9ezfatWvniJCd3qxZs3Dx4kX88MMP0Gg0Nq95/vN46NAhKBQKBAcH51aIeUJSUhLi4+MBAJIkYdu2bahUqZLVde3bt8fvv/8OALh79y4uXLhgNcOa/rN+/Xo0b948zZbD5z+bV65cwaNHj1CqVKncCi/Pyeh3KMDv0YzIyPcnkPe/QwVJkiRHB5EfNWzYEGq1OtUP6NKlS+Hv74/x48ejVatWaN26NWbNmoU9e/ZYWif79euH7t27Oypsp3Lr1i2MHTsWcXFx8PHxwbRp01C6dGm8//77GD58OKpVqwaz2YxJkybh77//BgC8//77lskH9J8bN26gU6dOKFmypKVl+9nyTl27dsWiRYsQHByMd955B1FRURAEAV5eXvjkk09Qs2ZNxwbvZB48eIBhw4bBbDZDFEWUKVMGn332GQoWLJiqLpOSkjB27FhcuXIFCoUCo0ePRps2bRwdvtNq164dxo8fj2bNmlmOPf+zPmbMGFy6dAkKhQJqtRrDhw9H8+bNHRix85gyZQp27tyJJ0+ewN/fH35+fti6dWua36EA+D2aBlt1OWfOnDS/PwG41Hcok0YiIiIisovd00RERERkF5NGIiIiIrKLSSMRERER2cWkkYiIiIjsYtJIRERERHYxaSQiIiIiu5g0EhEREZFd/wdgLPifJwfEbgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "scatter = ax.scatter(two_d[:, 0], two_d[:, 1], c=cluster.labels_)\n",
+    "legend1 = ax.legend(*scatter.legend_elements(), loc=\"lower left\", title=\"Classes\")\n",
+    "plt.title(\"DBSCAN - Tests\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see some clustering going on in the plot above, but also a fairly even distribution between tests.\n",
+    "\n",
+    "However, what we want to know is if these \"baseline\" vectors for each test are actually meaningful. We will make that assessment by determining if individual tests are closer to their \"baselines\" on average than the datasets overall average."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:51:01.558853Z",
+     "start_time": "2021-01-26T17:51:01.159648Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "indivudial_metric = pd.DataFrame(\n",
+    "    get_test_individual_behavior(ts_data[\"values\"]),\n",
+    "    columns=[\n",
+    "        \"pass_rate\",\n",
+    "        \"slope\",\n",
+    "        \"intercept\",\n",
+    "        \"failures_streak\",\n",
+    "        \"kurtosis\",\n",
+    "        \"frequency\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:51:02.542572Z",
+     "start_time": "2021-01-26T17:51:01.561251Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pass_rate             0.436850\n",
+       "avg_coef             -0.001499\n",
+       "avg_intercept         0.730979\n",
+       "avg_failure_streak    0.006771\n",
+       "avg_kurtosis          0.351694\n",
+       "avg_frequency         0.184582\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This is the center of our baselines.\n",
+    "\n",
+    "tests_baseline = pd.DataFrame(vector_set).mean(axis=1)\n",
+    "tests_baseline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:06:49.429539Z",
+     "start_time": "2021-01-26T17:51:02.544891Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# for each test, get the averge distance of their test instances from their baseline and the global baseline\n",
+    "results = []\n",
+    "for i in test_names:\n",
+    "    indivudial_metric = get_timeseries_by_name(data, i)\n",
+    "    indivudial_metric = pd.DataFrame(\n",
+    "        get_test_individual_behavior(indivudial_metric[\"values\"]),\n",
+    "        columns=[\n",
+    "            \"pass_rate\",\n",
+    "            \"slope\",\n",
+    "            \"intercept\",\n",
+    "            \"failures_streak\",\n",
+    "            \"kurtosis\",\n",
+    "            \"frequency\",\n",
+    "        ],\n",
+    "    )\n",
+    "    baseline = pd.Series(vector_set[i])\n",
+    "\n",
+    "    group_baseline_distance = indivudial_metric.apply(\n",
+    "        lambda x: np.linalg.norm(x.values - baseline.values), axis=1\n",
+    "    ).mean()\n",
+    "    absolute_basline_distance = indivudial_metric.apply(\n",
+    "        lambda x: np.linalg.norm(x.values - tests_baseline.values), axis=1\n",
+    "    ).mean()\n",
+    "\n",
+    "    results.append((i, group_baseline_distance, absolute_basline_distance))\n",
+    "\n",
+    "local_vs_global = pd.DataFrame(\n",
+    "    results,\n",
+    "    columns=[\"test\", \"local average distance\", \"global average distance\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:06:49.442639Z",
+     "start_time": "2021-01-26T18:06:49.432053Z"
+    }
+   },
+   "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>test</th>\n",
+       "      <th>local average distance</th>\n",
+       "      <th>global average distance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Overall</td>\n",
+       "      <td>4.136537</td>\n",
+       "      <td>6.522205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[sig-api-machinery] ResourceQuota should creat...</td>\n",
+       "      <td>0.487039</td>\n",
+       "      <td>0.671237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[sig-network] NetworkPolicy NetworkPolicy betw...</td>\n",
+       "      <td>0.457299</td>\n",
+       "      <td>0.569890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[sig-storage] In-tree Volumes [Driver: gcepd] ...</td>\n",
+       "      <td>0.283472</td>\n",
+       "      <td>0.587822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[Suite: service-definition] [OSD] NodeLabels M...</td>\n",
+       "      <td>0.341153</td>\n",
+       "      <td>0.856538</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                test  local average distance  \\\n",
+       "0                                            Overall                4.136537   \n",
+       "1  [sig-api-machinery] ResourceQuota should creat...                0.487039   \n",
+       "2  [sig-network] NetworkPolicy NetworkPolicy betw...                0.457299   \n",
+       "3  [sig-storage] In-tree Volumes [Driver: gcepd] ...                0.283472   \n",
+       "4  [Suite: service-definition] [OSD] NodeLabels M...                0.341153   \n",
+       "\n",
+       "   global average distance  \n",
+       "0                 6.522205  \n",
+       "1                 0.671237  \n",
+       "2                 0.569890  \n",
+       "3                 0.587822  \n",
+       "4                 0.856538  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "local_vs_global.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:06:49.463748Z",
+     "start_time": "2021-01-26T18:06:49.444824Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9842267248012311"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(\n",
+    "    local_vs_global[\"local average distance\"]\n",
+    "    <= local_vs_global[\"global average distance\"]\n",
+    ") / len(local_vs_global)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, so in 98% of cases the local average distance of individual tests to their baseline is closer than an overall baseline. This tells us that there is some usefulness in describing the expected behavior of a test based on the mean of its individual features.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What have we accomplished up to now?**\n",
+    "\n",
+    "So far we have looked at the girds as a whole based on their shared collection of tests, determined that that the grids themselves are all unique and not directly comparable with regard to their set of tests. \n",
+    "\n",
+    "However, some tests are repeated across many grids and we showed a couple methods to visualize, encode and analyze them both individually and in aggregate.    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. How does the entire test platform (or a specific test) perform on a given day?\n",
+    "\n",
+    "Now that we've looked at encoding and comparing tests from a \"horizontal perspective\" with regard to the grids and examined the tests independent of time. Lets switch to a \"vertical perspective\" and determine how the entire platform, specific grids, or particular tests behave at specific days and across time. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.a) Platform performance for a given day"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to iterate over and search by the epoch time in our dataset, lets quickly collect all the unique times where a test value was logged. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:06:49.524213Z",
+     "start_time": "2021-01-26T18:06:49.466009Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "times = []\n",
+    "for i in data.keys():\n",
+    "    for j in data[i].keys():\n",
+    "        times.extend(data[i][j][\"timestamps\"])\n",
+    "times = set(times)\n",
+    "times = sorted(list(times))\n",
+    "date = set([datetime.datetime.fromtimestamp(x // 1000) for x in times])\n",
+    "\n",
+    "date = list(date)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`times` should contain every possible time stamp in our dataset.  \n",
+    "\n",
+    "Now let's iterate through our dataset and collect **all** the data on our platform that occurred on our query date. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:07:01.107329Z",
+     "start_time": "2021-01-26T18:06:49.526274Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tests_on_day = []\n",
+    "query_date = datetime.date.fromtimestamp(1601671083)\n",
+    "for i in data.keys():\n",
+    "    for j in data[i].keys():\n",
+    "        for t in data[i][j][\"timestamps\"]:\n",
+    "            if datetime.date.fromtimestamp(t // 1000) == query_date:\n",
+    "                index = data[i][j][\"timestamps\"].index(t)\n",
+    "                v = {}\n",
+    "                for k in data[i][j][\"grid\"]:\n",
+    "                    # use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "                    value = decode_run_length(k[\"statuses\"])[index]\n",
+    "                    name = k[\"name\"]\n",
+    "                    v[name] = value\n",
+    "\n",
+    "                tests_on_day.append((i, j, t, v))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:07:01.130028Z",
+     "start_time": "2021-01-26T18:07:01.109820Z"
+    }
+   },
+   "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>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>tests_values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>1601677764000</td>\n",
+       "      <td>{'Application behind service load balancer wit...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>1601676485000</td>\n",
+       "      <td>{'Application behind service load balancer wit...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            tab  \\\n",
+       "0  \"redhat-openshift-informing\"   \n",
+       "1  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                              grid      timestamp  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade  1601677764000   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade  1601676485000   \n",
+       "\n",
+       "                                        tests_values  \n",
+       "0  {'Application behind service load balancer wit...  \n",
+       "1  {'Application behind service load balancer wit...  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_day_behaviour = pd.DataFrame(\n",
+    "    tests_on_day, columns=[\"tab\", \"grid\", \"timestamp\", \"tests_values\"]\n",
+    ")\n",
+    "one_day_behaviour.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:07:01.192077Z",
+     "start_time": "2021-01-26T18:07:01.132102Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_day_behaviour[\"passes\"] = one_day_behaviour.tests_values.apply(\n",
+    "    lambda x: sum([y for y in x.values() if y == 1])\n",
+    ")\n",
+    "one_day_behaviour[\"length\"] = one_day_behaviour.tests_values.apply(\n",
+    "    lambda x: len(x.values())\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:07:01.214387Z",
+     "start_time": "2021-01-26T18:07:01.195031Z"
+    }
+   },
+   "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>tab</th>\n",
+       "      <th>grid</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>tests_values</th>\n",
+       "      <th>passes</th>\n",
+       "      <th>length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>1601677764000</td>\n",
+       "      <td>{'Application behind service load balancer wit...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>\"redhat-openshift-informing\"</td>\n",
+       "      <td>release-openshift-okd-installer-e2e-aws-upgrade</td>\n",
+       "      <td>1601676485000</td>\n",
+       "      <td>{'Application behind service load balancer wit...</td>\n",
+       "      <td>23</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            tab  \\\n",
+       "0  \"redhat-openshift-informing\"   \n",
+       "1  \"redhat-openshift-informing\"   \n",
+       "\n",
+       "                                              grid      timestamp  \\\n",
+       "0  release-openshift-okd-installer-e2e-aws-upgrade  1601677764000   \n",
+       "1  release-openshift-okd-installer-e2e-aws-upgrade  1601676485000   \n",
+       "\n",
+       "                                        tests_values  passes  length  \n",
+       "0  {'Application behind service load balancer wit...       8      77  \n",
+       "1  {'Application behind service load balancer wit...      23      77  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_day_behaviour.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:07:01.227190Z",
+     "start_time": "2021-01-26T18:07:01.217229Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6142468337152454"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "platfrom_pass_rate = one_day_behaviour.passes.sum() / one_day_behaviour.length.sum()\n",
+    "platfrom_pass_rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, so we can use this method to calculate the pass rate, or any other derived value, for the entire platform for a specific day. As we can see above, there was a 60% pass rate across the entire platform on our query day. \n",
+    "\n",
+    "We could also use this approach on a subset of the data to get behavior metrics for specific tests, grids, or custom subsets.   \n",
+    "\n",
+    "Now that we know how to look at behavior for a single day across the entire platform, we could roll this out across each day to see how the platform evolves over time. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.b) Platform performance over time\n",
+    "\n",
+    "Lets create a list of dates that we are interested in reviewing and plot the platform pass rate.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:07:01.236310Z",
+     "start_time": "2021-01-26T18:07:01.229821Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dates = [datetime.date(2020, 9, i) for i in range(1, 25)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:40.993590Z",
+     "start_time": "2021-01-26T18:07:01.238913Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "platform_pass_rate = []\n",
+    "for d in dates:\n",
+    "    tests_on_day = []\n",
+    "    query_date = d\n",
+    "    for i in data.keys():\n",
+    "        for j in data[i].keys():\n",
+    "            for t in data[i][j][\"timestamps\"]:\n",
+    "                if datetime.date.fromtimestamp(t // 1000) == query_date:\n",
+    "                    index = data[i][j][\"timestamps\"].index(t)\n",
+    "                    v = {}\n",
+    "                    for k in data[i][j][\"grid\"]:\n",
+    "                        # use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "                        value = decode_run_length(k[\"statuses\"])[index]\n",
+    "                        name = k[\"name\"]\n",
+    "                        v[name] = value\n",
+    "\n",
+    "                    tests_on_day.append((i, j, t, v))\n",
+    "\n",
+    "    one_day_behaviour = pd.DataFrame(\n",
+    "        tests_on_day, columns=[\"tab\", \"grid\", \"timestamp\", \"tests_values\"]\n",
+    "    )\n",
+    "    one_day_behaviour[\"passes\"] = one_day_behaviour.tests_values.apply(\n",
+    "        lambda x: sum([y for y in x.values() if y == 1])\n",
+    "    )\n",
+    "    one_day_behaviour[\"length\"] = one_day_behaviour.tests_values.apply(\n",
+    "        lambda x: len(x.values())\n",
+    "    )\n",
+    "    platfrom_pass_rate_one_day = (\n",
+    "        one_day_behaviour.passes.sum() / one_day_behaviour.length.sum()\n",
+    "    )\n",
+    "    platform_pass_rate.append(platfrom_pass_rate_one_day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:41.414897Z",
+     "start_time": "2021-01-26T18:08:40.996093Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(dates, platform_pass_rate)\n",
+    "plt.title(\"platfrom pass rate\")\n",
+    "plt.ylabel(\"pass rate (%)\")\n",
+    "plt.xlabel(\"date\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great, from the above, we can now select any time period of interest (that we have data for) and examine the behavior of the entire platform over time.\n",
+    "\n",
+    "And just to do our due diligence, below we will show that this can be done for individual test as well. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.c) Aggregate single test performance over time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:41.594815Z",
+     "start_time": "2021-01-26T18:08:41.417285Z"
+    }
+   },
+   "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>values</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08 20:48:05, 2020-10-08 19:12:01, 202...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08 20:52:49, 2020-10-08 20:52:45, 202...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08 20:15:59, 2020-10-08 18:38:28, 202...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[2020-10-08 04:21:00, 2020-10-07 04:20:17, 202...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[2020-10-07 22:04:59, 2020-10-06 22:04:21, 202...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              values  \\\n",
+       "0  [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...   \n",
+       "1  [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, ...   \n",
+       "2  [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+       "3      [1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]   \n",
+       "4      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
+       "\n",
+       "                                                time  \n",
+       "0  [2020-10-08 20:48:05, 2020-10-08 19:12:01, 202...  \n",
+       "1  [2020-10-08 20:52:49, 2020-10-08 20:52:45, 202...  \n",
+       "2  [2020-10-08 20:15:59, 2020-10-08 18:38:28, 202...  \n",
+       "3  [2020-10-08 04:21:00, 2020-10-07 04:20:17, 202...  \n",
+       "4  [2020-10-07 22:04:59, 2020-10-06 22:04:21, 202...  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_name = \"operator.All images are built and tagged into stable\"\n",
+    "ts = get_timeseries_by_name(data, test_name)\n",
+    "ts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:41.638536Z",
+     "start_time": "2021-01-26T18:08:41.596868Z"
+    }
+   },
+   "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>values</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08, 2020-10-08, 2020-10-08, 2020-10-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08, 2020-10-08, 2020-10-08, 2020-10-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[2020-10-08, 2020-10-08, 2020-10-08, 2020-10-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[2020-10-08, 2020-10-07, 2020-10-06, 2020-10-0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
+       "      <td>[2020-10-07, 2020-10-06, 2020-10-05, 2020-10-0...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              values  \\\n",
+       "0  [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...   \n",
+       "1  [0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, ...   \n",
+       "2  [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+       "3      [1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]   \n",
+       "4      [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]   \n",
+       "\n",
+       "                                                time  \n",
+       "0  [2020-10-08, 2020-10-08, 2020-10-08, 2020-10-0...  \n",
+       "1  [2020-10-08, 2020-10-08, 2020-10-08, 2020-10-0...  \n",
+       "2  [2020-10-08, 2020-10-08, 2020-10-08, 2020-10-0...  \n",
+       "3  [2020-10-08, 2020-10-07, 2020-10-06, 2020-10-0...  \n",
+       "4  [2020-10-07, 2020-10-06, 2020-10-05, 2020-10-0...  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# epoch time is too specific, we only care about the day a test was run.\n",
+    "simple_dates = ts.time.apply(\n",
+    "    lambda x: [datetime.date(y.year, y.month, y.day) for y in x]\n",
+    ")\n",
+    "ts[\"time\"] = simple_dates\n",
+    "ts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:41.955243Z",
+     "start_time": "2021-01-26T18:08:41.640664Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results = {}\n",
+    "for t in dates:\n",
+    "    result = []\n",
+    "    for i in range(len(ts)):\n",
+    "        if t in ts.loc[i, \"time\"]:\n",
+    "            RL_spot = ts.loc[i, \"time\"].index(t)\n",
+    "            result.append(ts.loc[i, \"values\"][RL_spot])\n",
+    "    results[t] = result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:41.963924Z",
+     "start_time": "2021-01-26T18:08:41.957944Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pass_rate = []\n",
+    "date = []\n",
+    "count = []\n",
+    "for k in results.keys():\n",
+    "    date.append(k)\n",
+    "    pass_rate.append(sum(results[k]) / len(results[k]))\n",
+    "    count.append(len(results[k]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:42.743067Z",
+     "start_time": "2021-01-26T18:08:41.966177Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "color = \"tab:red\"\n",
+    "ax1.set_xlabel(\"time (d)\", fontdict={\"fontsize\": 10})\n",
+    "ax1.set_ylabel(\"pass rate\", color=color)\n",
+    "ax1.plot(date, pass_rate, color=color)\n",
+    "ax1.tick_params(axis=\"y\", labelcolor=color, labelsize=10)\n",
+    "ax1.tick_params(axis=\"x\", labelsize=10)\n",
+    "\n",
+    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
+    "\n",
+    "color = \"tab:blue\"\n",
+    "ax2.set_ylabel(\"count\", color=color)  # we already handled the x-label with ax1\n",
+    "ax2.plot(date, count, color=color)\n",
+    "ax2.tick_params(axis=\"y\", labelcolor=color, labelsize=10)\n",
+    "\n",
+    "plt.title(\n",
+    "    'Pass Rate and Count: \"operator.All images are built and tagged into stable\"',\n",
+    "    fontdict={\"fontsize\": 10},\n",
+    ")\n",
+    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What have we accomplished?** \n",
+    "\n",
+    "Above we have explored a couple of methods for visualizing and calculating metrics about the TestGrid environment given a specific time or time range. Moving forward this could be used for overall trend analysis or prediction or root cause analysis for a time period with a known issue. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Grid matrices as 2d signals\n",
+    "\n",
+    "Is looking at the grid matrices independent of test names a valid approach for issue identification?\n",
+    "\n",
+    "I don't see why not. Let's take an example: Looking at the image below we can see that it was annotated manually by a human subject matter expert. Most of these errors seem to lie either purely on the vertical or horizontal axes and can be identified by analyzing either a single test or a single day, which we've already explored above. Let's look for an issue that is signaled by behavior across both days and tests.  \n",
+    "\n",
+    "### 4.a) Identify Infrastructure Flakes\n",
+    "\n",
+    "What about the infrastructure flake case? That seems to be characterized by diagonal test failures over multiple days. We currently do not have any way of addressing that type of data. Lets see if we can use a simple hand crafted 2d filter to start identifying these types of errors.    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:42.943596Z",
+     "start_time": "2021-01-26T18:08:42.745543Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(\n",
+    "    \"https://user-images.githubusercontent.com/161888/89903201-a62b3800-dbe7-11ea-9b1c-2ec62fe8952f.png\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:42.953358Z",
+     "start_time": "2021-01-26T18:08:42.946145Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1,  1],\n",
+       "       [ 1, -1]])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 2d filter that will have its highest value when convolved with a diagonal pattern.\n",
+    "infra_flake_filter = np.array([[-1, 1], [1, -1]])\n",
+    "infra_flake_filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:42.969634Z",
+     "start_time": "2021-01-26T18:08:42.965765Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# get data for example grid\n",
+    "grid = data['\"redhat-openshift-ocp-release-4.3-informing\"'][\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\"\n",
+    "][\"grid\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:42.999170Z",
+     "start_time": "2021-01-26T18:08:42.973304Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset of the data where we see this infra flake pattern\n",
+    "# use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "grid = np.array(list(pd.DataFrame(grid).statuses.apply(decode_run_length)))\n",
+    "grid = grid[0:21, 0:21]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:43.012522Z",
+     "start_time": "2021-01-26T18:08:43.001536Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# we want to re-map the values so that the output of the convolution will be more interpretable.\n",
+    "def normalize(x):\n",
+    "    if x == 1:\n",
+    "        return 1\n",
+    "    if x == 12:\n",
+    "        return -1\n",
+    "    if x == 0:\n",
+    "        return 0\n",
+    "\n",
+    "\n",
+    "x = pd.DataFrame(grid).apply(lambda x: [normalize(y) for y in x])\n",
+    "x = np.array(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:43.377077Z",
+     "start_time": "2021-01-26T18:08:43.014551Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(x, cmap=\"bone\")\n",
+    "plt.title(\"Test Grid: release-openshift-origin-installer-e2e-gcp-compact-4.3\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:43.712983Z",
+     "start_time": "2021-01-26T18:08:43.379267Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8p6WlFTi/mjVrKicnR5mZmUpNTdWWLVuc3myuXLmi1q1bO+5f/33T9ftaZGSkTp06pQkTJujcuXN67LHH9Pzzz8vHx+eG9Ypr8eLFevPNNyVd3b/j4+N1+vRpp9e5Vq1aunLlijIyMhyP1ahRo8D2Tp8+rTvvvFO33Xab47Hg4GDHacqCFDaXadOmOT7oPfPMMxo1alS+utWqVXOqK13dDwIDAzVv3jy99dZbmjJlilq0aKGYmBg1aNCgwDF8//33mjNnjg4fPqycnBxlZ2crPDy80DFfk5qaqpdeekkJCQmOx4wxSktLczyH1/aPr7/+WtHR0ZKu7iebNm1yqf7ly5c1atQoPfDAA04fwG9UNzEx0el1HTt2rK5cueK0r15/++TJk7rrrrsKnef1r7enp6eCgoJ0+vRpbdiwQdOnT5cktWzZUsuWLdOpU6cKbMuVtXT961mhQgWn+xUrVsy3n18/h1q1ajneG9LT0zVr1ix9/fXXunjxoowxuuOOOxxzrVmzpkvf3xf0nvDaa6/pscceU+3atYusf71rB3h/+9vfdNddd6lz584u13W7MA4JCZGvr6927NhR6EKqXr26UlNTHZ+ST5486fIX6ffcc49q1qypTz/91ClEi9tujx49lJCQoFOnTmn79u1atWqVpKs7vK+vr3bv3l3gjhIYGOj0pnajN7hrz8VHH32k7t27uzS/glSvXt2pn9TU1GK3cf2Ph4KDg/Xggw/q7bffLrJecHCwRo0apWeffTbftpSUFE2dOlUrVqxQSEiIvLy8FBkZ6dgeGBjo+B7+66+/1rBhw/Tggw8W2f+3337rdP/a63q9kydPql27dk73r7/t4+OjypUrKzg4WJGRkY5xFIePj4/GjBmjMWPG6MSJExo5cqTq16+v/v37F7utGxk1alS+kKtevbrT0VVqaqq8vb1VtWpVx3ddhf0gLDAwUP/5z3906dIlRzjeaD+9kfj4+EK/13ZFu3bt1K5dO12+fFnz58/Xn/70J7377rsFjn3ixIkaNGiQli1bpgoVKmjWrFmOD3Y3cm0fLejD5DXX+gsNDc23fxVVPzs7W6NHj1ZQUFC+5+JGdVu0aOH0uubm5srb21unTp1S/fr1JTm/LsHBwTf87cT133Hm5eUpLS1N1atXd/wW4no1atTQsWPH1LBhQ6fHXVlLxXXy5EnHe25qaqrjPXfu3Lny8PBQYmKiAgICtGPHDsfzd+3DYUEHbb/eN679+v96//u//6tTp07pvffekySdOXNG48eP14gRIzRy5Mgix5ybm6vjx48Xa55ud5r69ttv17hx4xQfH68dO3bo0qVLysnJ0SeffKKXX35Z0tVTzW+88YbOnDmjM2fOaNGiRY4fYbkiIiJCf/3rX7Vnzx6nwC9Ou1WqVFGrVq304osvqnbt2o5P69WrV1ebNm00e/ZsXbhwQXl5eTp27JjjdFmPHj3097//XadOndJ//vMfLVmypNBx3nHHHRo9erRmzJihLVu2ONo7dOiQLl265PJ8w8PDtXbtWv3zn//UpUuX9Nprr7lctyAdO3ZUcnKy1q1bp5ycHOXk5Gjv3r06cuRIvrL9+/fX+++/r++//17GGGVlZWnnzp26cOGCLl26JA8PD1WpUkWS9MEHH+jw4cOOups3b3a8gdx5553y8PCQp6dnsfqXpA4dOig5OVmJiYm6cuWKkpKS9M9//lMdO3Z0lNmwYYPj+Xn11VfVvXt3eXl56bHHHtPHH3+sXbt2KTc3V7/88ou+/PJLl368sXv3bv3444/Kzc2Vv7+/vL29S+VX6NWqVSvyjeDaPn78+HFdvHhR8+bNU48ePVw6kqhVq5aaNm2qhQsXKjs7W99++63TmYFbJT09XTt27FBWVpZ8fX3l5+fneP6qVq2qtLQ0px9YXrx4UXfeeacqVKigvXv3Ok53FiUqKkpLlixx7Hvnz5/X5s2bXR7njern5ORo3LhxqlChghISEvK9/sXp28vLS127dtVrr72mS5cu6ciRI1q/fr1je8eOHfXvf/9bK1asUHZ2ti5cuOB0GvrAgQPatm2brly5or/+9a/y9fV1Oo19vf79++vVV19VcnKyjDH64YcflJmZ6dJaKq7ly5frP//5j06ePKm//e1v6tmzp6Srr6efn59uv/12paWladmyZY46zZo1U2BgoP7yl78oKytLv/zyi/7v//5PUsH7xq+tWLFCGzdu1Lp167Ru3TpVr15dM2bM0JNPPpmv7JEjR/TJJ5/o8uXLysnJ0fr16/X111/rwQcfLNY83e7IWJKGDx+uatWq6fXXX9ekSZNUqVIl3XfffY5Pic8995wuXrzo+DQXHh6u5557zuX2IyIiNHfuXLVv394RBDfTbkREhGJiYvTCCy84Pf7yyy9rzpw56tmzpy5evKg6deo4Tm098cQTSk5OVmRkpCpVqqSnn35au3fvLrSP6OhoBQUFadmyZYqJidFtt92mOnXqaNKkSQoJCXFpvh06dNBTTz2lp556Sh4eHho/frzTd8QbNmzQm2++6fheuyj+/v5avny5Zs+erdmzZ8sYo0aNGunFF1/MV/b+++/Xn//8Z8XHx+vo0aOqWLGiWrRoodDQUN1zzz0aPny4oqKi5OHh4fgl7DX79u3TSy+9pAsXLqhq1aqaMmWK6tSpI0ku9y9JlStX1uLFi/XSSy8pLi5OdevW1eLFi51e+8jISMXGxupf//qXWrVqpbi4OElXP4G//vrreuWVVzRx4kR5enqqWbNmju03kp6erunTpystLU1+fn7q2bOn05H/zRoyZIhiY2P13nvvKTIyUlOnTs1Xpm/fvkpLS9OgQYP0yy+/qG3btvrTn/7kch9z5sxRbGysWrdurWbNmqlnz543fZGPm5WXl6cVK1YoJiZGHh4eaty4seN5f+ihh3TPPfeobdu28vDw0Jdffqnp06crISFB8fHxatWqlXr06OH069vCdO3aVRcvXtSECROUkpKi22+/XY888oh69Ojh0jhvVP/aB5mKFSs6vXkvXbpUoaGhxe572rRpio2NVZs2bVS/fn09+uij2r9/v6Sr6/Ktt97SrFmztGjRIvn6+uqpp55yBG7nzp2VlJSkmJgY1a1bVwsXLnR8ZfJr175THj58uDIzM3X33Xdr0aJFqlGjRpFrqbg6d+6sPn366MKFC3r88cfVr18/SdKYMWMUExOj0NBQ3XXXXYqMjNSKFSskXf1gsnjxYs2cOVOdOnWSdPVUfsuWLQvcN36tcuXKTve9vLx05513Ov7cbNq0aZLkOBJ/7bXXNH78eHl5ealu3bqaN2+e7rvvvmLN08OYG1wZAoAGDx6sxx57rNRPH5cn48eP1913353vT/pg1yuvvKL09HSn75wLsnDhQh09elRz5sy5RSNzTaNGjbRt2zbVrVvX9lDKnNudpgZg3969e3Xs2DHl5eXp008/1UcffaQuXbrYHtbv3pEjR/TDDz/IGKO9e/dqzZo16tq1q+1hwQVueZoagF3p6ekaO3aszp49qxo1aiguLk5NmjSxPazfvYsXL2rixIk6ffq0qlatquHDhxfrF72wh9PUAABYxmlqAAAsI4wBALDMbb4zfmt1ks5dKPoKROOH9dP8t137Rwe7txb+TxhuVu16BV/9p6TmzB6vSbHzS73doWP6lXqbkhRx3RXGipKcnKx69eoVWa5f1PibH5AFc2aP15rPC/+ztJs1P2ZaqbfZ7+miL2Rwje01JrnfOrOtOPOq3ah4V51yVVnsC++/v9Bt1liNGkFas+bvhW53mzA+dyFLZ89fcKmsq+VOnUovulAxVby9aqm3eU16xtlSbzP7umsRl6ajR4+WevmymH9Zu3D5cqm3eeJE8a+QVhRX10xxy5fFGpPcb539Frg6L//zAWXSf1ntC+6yxorCaWoAACwjjAEAsIwwBgDAMsIYAADLCGMAACwrcRj//PPPGjBggLp3764BAwYoOTk5X5nc3FzNmDFDXbp0UdeuXbV69eqSdgsAQLlR4jCePn26Bg4cqK1bt2rgwIGOfy11vcTERB07dkzbtm3TqlWrtHDhQp04caKkXQMAUC6UKIwzMjJ08OBBRURESLr6/3sPHjyoM2fOOJVLSkpS//795enpqSpVqqhLly7asmVLSboGAKDcKNE/iti/f79iYmKc/ul8z5499corrzj9Y+VevXpp1qxZatasmaSr/zg7LS2twH96DgDA743bXIFr/ttrXLrqT9y4oYpbsMKlNnd+uLWEo8qv3r2NSr1NSVqxNE5Do+NKvd1xU4aWepuS1LJ+fZfLGmPk4eFRZLmnRkwvyZBuuRVL47Tio52l3m7c02NKvc2hEya53r/lNSa53zqzrTjzqnd/vTIZQ1nsCzt3vuc2a6x27Zr67LNthW4v0Wnq4OBgpaWlKTc3V9LVH2qdPn1awcHB+cqlpv6/y4udPHlSNWrUKEnXAACUGyUK46pVq6px48bauHGjJGnjxo1q3LixqlSp4lQuPDxcq1evVl5ens6cOaMdO3aoe/fuJekaAIByo8S/po6Li9M777yj7t2765133tGMGTMkSdHR0dq3b58kKTIyUrVr11a3bt30xBNPaPTo0apTp05JuwYAoFwo8XfGDRo0KPDvhpcuXeq47eXl5QhpAADgjCtwAQBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgmdtcgevEjyeUnnHWpbLJ+5JdKldWV/FxJ306RpRJu8W9yqor5evVa3qzw7mhjl37lUm7krTz/Z2l3mZZjNfVNVPc8qwx91PcfcFVZbUvuMsaq1Y14IbbOTIGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwzNv2AGBXx679yqTdodFxLpddsTTOpfJlNVYAsI0jYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwzLsklTMzMzV58mQdO3ZMvr6+qlu3ruLj41WlShWncrGxsfriiy9UuXJlSVJ4eLieffbZknQNAEC5UaIw9vDw0IgRI9S6dWtJUkJCgubMmaOXXnopX9mRI0dq0KBBJekOAIByqUSnqQMCAhxBLEnNmzdXampqiQcFAMDvSYmOjK+Xl5en9957T2FhYQVuf/vtt7Vq1SrVqVNHEydOVIMGDUqra/wG7dy+phil41wqn5y8/+YHdAP16jUtk3aluDJqF0B542GMMaXR0IwZM5SWlqbXXntNnp7OB9xpaWkKDAyUp6en1q1bp1dffVU7duyQl5dXaXQNAIBbK5UwTkhI0I8//qjFixfL19e3yPKtW7fWhx9+qFq1arncx6TY+UrPOFtkuRVL4zQ0Os7ldt2Fu82rOEfGycn7XTo6dbcj4+Tk/W71mrnK3fbF4iivc2Ne9lWrGqA5s8cXur3Ef9o0d+5c7d+/X4sWLSo0iNPS0hy3d+3aJU9PTwUFBZW0awAAyoUSfWd8+PBhvfnmm6pXr56ioqIkSbVr19aiRYsUGRmpJUuWKCgoSDExMcrIyJCHh4f8/f31xhtvyNu71L6uBgDArZUoEe+99179+OOPBW5bv3694/aKFStK0g0AAOUaV+ACAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwzNv2AFA+dezar9TLD42Ou8nRlLxvAChLHBkDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGCZt+0BAK7auX1NmbQbt/y1MmlXkurdX6/U29z54dZSb7PevY1KvU2UrY5RHcukfNzTY4o/GFf679qvTNp1lzV2+Xy1G27nyBgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwrMR/ZxwWFiZfX19VqFBBkjRp0iS1a9fOqcylS5f04osv6sCBA/Ly8lJMTIw6depU0q4BACgXSuWiHwsWLFDDhg0L3b58+XL5+/tr+/btSk5O1pNPPqlt27apUqVKpdE9AABu7Zacpt68ebMGDBggSapXr56aNm2qTz/99FZ0DQDAb56HMcaUpIGwsDD5+/vLGKOWLVtqwoQJuuOOO5zKhISE6KOPPlKVKlUkSXFxcapbt66GDRtWkq4BACgXSnyaeuXKlQoODlZ2drZmzZql+Ph4zZkzpzTG5mRS7HylZ5wtstyKpXEaGh1X6v3bVl7nJbk+N3e7NvXQzh0Vt2BFqbdr+9rU7Iu/DcW5NvXQzh214qOdLpV1p2tTr1ga5zZrrEaNanr//YWFbi/xaerg4GBJkq+vrwYOHKhvvvkmX5maNWsqJSXFcf/kyZOqUaNGSbsGAKBcKFEYZ2Vl6fz585IkY4ySkpLUuHHjfOXCw8O1atUqSVJycrL27duX7xfXAAD8XpXoNHVGRobGjh2r3Nxc5eXlqUGDBpo+fbokKTIyUkuWLFFQUJCefvppxcbGqmvXrvL09FR8fLz8/f1LZQIAALi7EoVxnTp1tG7dugK3rV+/3nHbz89PCxYsKElXAACUW1yBCwAAywhjAAAsI4wBALCMMAYAwLJSuTY1cCuUxUUDJGnn+zvLpN2hnTuWycUD5q74/0q9zT4dI4pROs7lC7CU1WuG4l2cY2jyfpfLl9VFcMpqnZXFGuvYp3uptxlw+43/gogjYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALPO2PQCgPKt3b6NSb3PBrBWl3mbHrv3KtDxKX1m9Zjvf33kTo7GnLNZY8r7kUm+zWtWAG27nyBgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALPMuSeUTJ05o9OjRjvvnz5/XhQsX9NVXXzmVW7hwod59911Vr15dktSiRQtNnz69JF0DAFBulCiMa9eurfXr1zvuz5o1S7m5uQWW7d27t2JiYkrSHQAA5VKpnabOzs5WYmKi+vbtW1pNAgDwu+BhjDGl0dCWLVv0xhtvOB0pX7Nw4UKtXr1ad955pwIDAzV27FiFhISURrcAALi9Ugvj6OhotWvXTkOGDMm37d///rcCAgLk4+Ojzz//XJMmTVJSUpIqV67scvuTYucrPeNskeVWLI3T0Oi4YozcPZTXeUnld27My/2U17kxL/uqVQ3QnNnjC91eKqep09LStGfPHvXq1avA7YGBgfLx8ZEktWnTRsHBwTp8+HBpdA0AgNsrlTBeu3atOnToUOiRblpamuP2oUOHlJKSovr165dG1wAAuL0S/Zr6mrVr12rKlClOj0VHR2vcuHG6//77NXfuXB04cECenp7y8fHRyy+/rMDAwNLoGgAAt1cqYbx169Z8jy1dutRxOyEhoTS6AQCgXOIKXAAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgWZFhnJCQoLCwMDVq1Eg//fST4/Gff/5ZAwYMUPfu3TVgwAAlJycXWD83N1czZsxQly5d1LVrV61evbrUBg8AQHlQZBh37txZK1euVK1atZwenz59ugYOHKitW7dq4MCBmjZtWoH1ExMTdezYMW3btk2rVq3SwoULdeLEidIZPQAA5UCRYRwaGqrg4GCnxzIyMnTw4EFFRERIkiIiInTw4EGdOXMmX/2kpCT1799fnp6eqlKlirp06aItW7aU0vABAHB/HsYY40rBsLAwLV68WA0bNtT+/fsVExOjTZs2Obb37NlTr7zyiu677z6ner169dKsWbPUrFkzSdLSpUuVlpamqVOnluI0AABwX962B+CqSbHzlZ5xtshyK5bGaWh0XJmP51Yrr/OSyu/cmJf7Ka9zY172VasaoDmzxxe6/aZ+TR0cHKy0tDTl5uZKuvojrdOnT+c7nX2tbGpqquP+yZMnVaNGjZvpFgCAcummwrhq1apq3LixNm7cKEnauHGjGjdurCpVquQrGx4ertWrVysvL09nzpzRjh071L1795KNGgCAcqTIMJ45c6bat2+vU6dOadiwYXr00UclSXFxcXrnnXfUvXt3vfPOO5oxY4ajTnR0tPbt2ydJioyMVO3atdWtWzc98cQTGj16tOrUqVNG0wEAwP0U+Z3x1KlTC/yxVYMGDQr9m+GlS5c6bnt5eTkFNQAAcMYVuAAAsIwwBgDAMsIYAADLCGMAACwjjAEAsIwwBgDAMsIYAADLCGMAACwjjAEAsIwwBgDAMsIYAADLCGMAACwjjAEAsIwwBgDAMsIYAADLCGMAACwjjAEAsIwwBgDAMsIYAADLCGMAACwjjAEAsIwwBgDAMsIYAADLCGMAACwjjAEAsIwwBgDAMm/bAwBQPDu3ryn1NuOWv1as8h2jOrpUbuf7O4s/GLjE1deguOXjnh5T/MG40n/XfmXSbnnBkTEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWObtSqGEhARt3bpVKSkpSkxMVMOGDZWZmanJkyfr2LFj8vX1Vd26dRUfH68qVarkqx8bG6svvvhClStXliSFh4fr2WefLd2ZAADgplw6Mu7cubNWrlypWrVqOR7z8PDQiBEjtHXrViUmJqpOnTqaM2dOoW2MHDlS69ev1/r16wliAACu41IYh4aGKjg42OmxgIAAtW7d2nG/efPmSk1NLd3RAQDwO+BhjDGuFg4LC9PixYvVsGFDp8fz8vI0fPhwhYWFaciQIfnqxcbGas+ePfLz81OdOnU0ceJENWjQoOSjBwCgHHDpO+Oi/PnPf5afn58GDRpU4Pbnn39egYGB8vT01Lp16zRixAjt2LFDXl5eLvcxKXa+0jPOFlluxdI4DY2Oc7ldd1Fe5yWV37kxL/dTXufGvOyrVjVAc2aPL3R7iX9NnZCQoKNHj2r+/Pny9Cy4uaCgIMe23r17KysrS6dOnSpp1wAAlAslCuO5c+dq//79WrRokXx9fQstl5aW5ri9a9cueXp6KigoqCRdAwBQbrh0mnrmzJnatm2b0tPTNWzYMAUEBGj+/Pl68803Va9ePUVFRUmSateurUWLFkmSIiMjtWTJEgUFBSkmJkYZGRny8PCQv7+/3njjDXl7l8oZcgAA3J5LiTh16lRNnTo13+M//vhjoXXWr1/vuL1ixYrijwwAgN8JrsAFAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlLoVxQkKCwsLC1KhRI/3000+Ox8PCwhQeHq7IyEhFRkZq165dBda/dOmSxo8fr65duyo8PFwff/xx6YweAIBywNuVQp07d9aQIUP05JNP5tu2YMECNWzY8Ib1ly9fLn9/f23fvl3Jycl68skntW3bNlWqVOnmRg0AQDni0pFxaGiogoODb7qTzZs3a8CAAZKkevXqqWnTpvr0009vuj0AAMoTD2OMcbVwWFiYFi9e7DgSDgsLk7+/v4wxatmypSZMmKA77rgjX72QkBB99NFHqlKliiQpLi5OdevW1bBhw0ppGgAAuC+XTlMXZuXKlQoODlZ2drZmzZql+Ph4zZkzp7TG5mRS7HylZ5wtstyKpXEaGh1XJmOwqbzOSyq/c2Ne7qe8zo152VetaoDmzB5f6PYS/Zr62qlrX19fDRw4UN98802B5WrWrKmUlBTH/ZMnT6pGjRol6RoAgHLjpsM4KytL58+flyQZY5SUlKTGjRsXWDY8PFyrVq2SJCUnJ2vfvn1q167dzXYNAEC54lIYz5w5U+3bt9epU6c0bNgwPfroo8rIyNDgwYPVq1cvRURE6Oeff9b06dMddSIjI5WWliZJevrpp3Xu3Dl17dpVzzzzjOLj4+Xv7182MwIAwM249J3x1KlTNXXq1HyPr1u3rtA669evd9z28/PTggULij86AAB+B7gCFwAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABYRhgDAGAZYQwAgGWEMQAAlhHGAABY5u1KoYSEBG3dulUpKSlKTExUw4YNdeLECY0ePdpR5vz587pw4YK++uqrfPUXLlyod999V9WrV5cktWjRQtOnTy+lKQAA4N5cCuPOnTtryJAhevLJJx2P1a5dW+vXr3fcnzVrlnJzcwtto3fv3oqJiSnBUAEAKJ9cCuPQ0NAbbs/OzlZiYqKWL19eKoMCAOD3xMMYY1wtHBYWpsWLF6thw4ZOj2/ZskVvvPGG05Hy9RYuXKjVq1frzjvvVGBgoMaOHauQkJCSjRwAgHLCpSPjonzwwQfq27dvodujoqI0atQo+fj46PPPP9dzzz2npKQkVa5c2eU+JsXOV3rG2SLLrVgap6HRcS636y7K67yk8js35uV+yuvcmJd91aoGaM7s8YVuL/GvqdPS0rRnzx716tWr0DKBgYHy8fGRJLVp00bBwcE6fPhwSbsGAKBcKHEYr127Vh06dLjhUW5aWprj9qFDh5SSkqL69euXtGsAAMoFl05Tz5w5U9u2bVN6erqGDRumgIAAbdq0SdLVMJ4yZUq+OtHR0Ro3bpzuv/9+zZ07VwcOHJCnp6d8fHz08ssvKzAwsHRnAgCAm3IpjKdOnaqpU6cWuG3r1q0FPr506VLH7YSEhJsYGgAAvw9cgQsAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwjDAGAMAywhgAAMsIYwAALCOMAQCwzKX/Zwzg5iQf/rHU2+zYp3upt5m8L7nU20TZKu6+5Wr5sti/pLLbx9xljQXc7n/D7RwZAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWOZtewCuqlz5DpfLVqsaUHYDsai8zksqv3OrUaNaqbcZcLt/qbdZ3Oe/vL5ekvvM7fL54u1bru6LZbF/SWX3vLrLGrvD3++G2z2MMabUewUAAC7jNDUAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWEYYAwBgGWEMAIBlhDEAAJYRxgAAWOY216a+3s8//6zY2FidPXtWAQEBSkhIUL169ZzK5ObmaubMmdq1a5c8PDw0cuRI9e/f386AXZSZmanJkyfr2LFj8vX1Vd26dRUfH68qVao4lYuNjdUXX3yhypUrS5LCw8P17LPP2hiyy8LCwuTr66sKFSpIkiZNmqR27do5lbl06ZJefPFFHThwQF5eXoqJiVGnTp1sDNclJ06c0OjRox33z58/rwsXLuirr75yKrdw4UK9++67ql69uiSpRYsWmj59+i0da1ESEhK0detWpaSkKDExUQ0bNpTk2lqTftvrraC5ubrWpN/ueivsNXNlrUm/7fVW0NxcXW+Se6y5fIwbGjx4sFm3bp0xxph169aZwYMH5yuzdu1aM3z4cJObm2syMjJMu3btzPHjx2/1UIslMzPT7N6923F/9uzZ5sUXX8xXLiYmxvz973+/lUMrsU6dOpkff/zxhmUWLlxopkyZYowx5ueffzaPPPKIuXDhwq0YXqmYOXOmmTFjRr7HFyxYYGbPnm1hRK7bs2ePSU1Nzfc6ubLWjPltr7eC5ubqWjPmt7veCnvNXFlrxvy211thc7teYevNGPdYc7/mdqepMzIydPDgQUVEREiSIiIidPDgQZ05c8apXFJSkvr37y9PT09VqVJFXbp00ZYtW2wM2WUBAQFq3bq1437z5s2VmppqcUS31ubNmzVgwABJUr169dS0aVN9+umnlkflmuzsbCUmJqpv3762h3JTQkNDFRwc7PSYq2tN+m2vt4LmVh7WWkHzKo7f8noram7uvt4K4nZhfPLkSQUFBcnLy0uS5OXlperVq+vkyZP5ytWsWdNxPzg4WKdOnbqlYy2JvLw8vffeewoLCytw+9tvv61evXrpueee05EjR27x6G7OpEmT1KtXL8XFxencuXP5tqempqpWrVqO++70mv3jH/9QUFCQ7rvvvgK3b9q0Sb169dLw4cP17bff3uLR3RxX19q1su663opaa5L7rbei1ppUvteb5H5rzu3C+Pfiz3/+s/z8/DRo0KB8255//nlt375diYmJ6tatm0aMGKHc3FwLo3TdypUrtWHDBn3wwQcyxig+Pt72kErVBx98UOin9KioKH300UdKTEzU008/reeee06ZmZm3eIQozI3WmuR+6628rzXpxutNcs8153ZhHBwcrLS0NMdiyM3N1enTp/Od0ggODnY67XTy5EnVqFHjlo71ZiUkJOjo0aOaP3++PD3zv0RBQUGOx3v37q2srKzf/Cfaa6+Pr6+vBg4cqG+++SZfmZo1ayolJcVx311es7S0NO3Zs0e9evUqcHtgYKB8fHwkSW3atFFwcLAOHz58K4d4U1xda9fKuuN6K2qtSe633lxZa1L5XW+Se645twvjqlWrqnHjxtq4caMkaePGjWrcuHG+X0GGh4dr9erVysvL05kzZ7Rjxw51797dxpCLZe7cudq/f78WLVokX1/fAsukpaU5bu/atUuenp4KCgq6VUMstqysLJ0/f16SZIxRUlKSGjdunK9ceHi4Vq1aJUlKTk7Wvn37CvwV6G/N2rVr1aFDB8evbX/t+tfr0KFDSklJUf369W/V8G6aq2tNcs/15spak9xrvbm61qTyu94k91xzHsYYY3sQxXXkyBHFxsbq3LlzuuOOO5SQkKC7775b0dHRGjdunO6//37l5uYqPj5en3/+uSQpOjra8WOF36rDhw8rIiJC9erVU8WKFSVJtWvX1qJFixQZGaklS5YoKChIQ4cOVUZGhjw8POTv76/JkyerefPmdgd/A8ePH9fYsWOVm5urvLw8NWjQQFOnTlX16tWd5pWVlaXY2FgdOnRInp6eeuGFF9SlSxfbwy9S9+7dNWXKFLVv397x2PX7YkxMjA4cOCBPT0/5+Pho3Lhx6tChg8UR5zdz5kxt27ZN6enpqly5sgICArRp06ZC15okt1lvBc1t/vz5ha41SW6x3gqa1+LFiwtda5LcZr0Vtj9KBa83yf3W3K+5ZRgDAFCeuN1pagAAyhvCGAAAywhjAAAsI4wBALCMMAYAwDLCGAAAywhjAAAs+/8B8pZICtf57x8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 792x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(convolve2d(infra_flake_filter, x, mode=\"valid\"), cmap=\"bone\")\n",
+    "plt.title(\"Convolved Grid: release-openshift-origin-installer-e2e-gcp-compact-4.3\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:43.720695Z",
+     "start_time": "2021-01-26T18:08:43.714961Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1, 3]), array([5, 6]))"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the spots on the map where the convolution had its maximum value.\n",
+    "spots = convolve2d(infra_flake_filter, x, mode=\"valid\")\n",
+    "infra_falkes = np.where(spots == 4)\n",
+    "infra_falkes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T18:08:43.738265Z",
+     "start_time": "2021-01-26T18:08:43.722663Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "possible infra flake occuring on 2020-10-02\n",
+      "possible infra flake occuring on 2020-10-01\n"
+     ]
+    }
+   ],
+   "source": [
+    "dates = data['\"redhat-openshift-ocp-release-4.3-informing\"'][\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\"\n",
+    "][\"timestamps\"]\n",
+    "infra_flake_dates = np.array(dates)[list([infra_falkes][0][1])]\n",
+    "for i in infra_flake_dates:\n",
+    "    print(f\"possible infra flake occuring on {datetime.date.fromtimestamp(i//1000)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What have we accomplished?**\n",
+    "\n",
+    "The above example has shown one way to automatically determined if there was an infrastructure flake in our TestGrid (assuming they can be identified following this diagonal failure pattern rule). This example identified 2 such flakes between October 1st and 2nd for the \"release-openshift-origin-installer-e2e-gcp-compact-4.3'\" grid. Moving forward we should follow up with an SME to validate these results and refine this approach. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "In this notebook, we have done some additional exploration of the TestGrid data; familiarized ourselves with it further and determined a number of facts about the nature of the grids that will help us move forward developing additional analysis and ML tools for CI. We have looked at the entire TestGrid platform, individual grids and individual tests both from a stationary and time-series perspective. We have also done a couple of small experiments with encoding and clustering to explore the dataset's viability for applying ML approaches.  \n",
+    "\n",
+    "\n",
+    "In this notebook we have tried to address the following questions and come up with the following answers:\n",
+    "\n",
+    "* **How comparable are the TestGrids?**\n",
+    "    * The test grids contain nearly unique sets of tests and therefore are not directly comparable. We will either have to consider the aggregation of shared test or define grids purely by their patterns (independent of specific test)\n",
+    "    \n",
+    "    \n",
+    "* **How do we analyze the TestGrids in aggregate to learn from their combined behavior?**\n",
+    "    * I don't think we can analyze the entire grids directly due to the above stated issue of uniqueness, other than addressing purely the patterns independent of the specific test names. It will likely be better to analyze the data one step lower and learn from the tests, then use the tests the grids are composed of to understand the grids better. \n",
+    "\n",
+    "\n",
+    "* **How many/ which tests do they all have in common?**\n",
+    "    * The set of tests in each grid appears to be unique and there is not one test that is included in all grids.\n",
+    "    * However, there are about 3k tests identified above that that occur in 10 grids or more. \n",
+    "    \n",
+    "\n",
+    "* **Are their time series dates comparable?**\n",
+    "    * We have included the timestamps for the time series, so we know when a test passed/failed and we can compare tests on specific days or time ranges. \n",
+    "    * Each grid contains different dates and lengths of runs, however, so they aren't all equally sampled points. \n",
+    "\n",
+    "\n",
+    "* **Are there sub-groups that should only be compared with one another?**\n",
+    "    * We removed version identifiers to compare subgroups, but still saw little overall overlap between sets of tests in grids appeared. But further analysis could still be done in this regard. \n",
+    "    \n",
+    "    \n",
+    "* **Is looking at the grid matrices independent of test names a valid approach for issue identification?**\n",
+    "    \n",
+    "    * We were able to create a toy example using a 2d convolution and an handcrafted filter to identify diagonal failures in the TestGrids. If the annotated image we have is accurate, then we have shown at least one method to achieve this goal. So I would consider this to indeed be a useful approach.   \n",
+    "\n",
+    "\n",
+    "* **What is the expected behavior of a test over time across multiple jobs.**\n",
+    "    * We developed some basic encoding and clustering examples to compare individual tests, aggregated tests and tests of the same type. We should build this analysis out further in a later notebook.  \n",
+    "\n",
+    "\n",
+    "* **How does the entire test platform/specific tests perform on a given day?**\n",
+    "    * We were able to develop a number of methods to perform and display this type of analysis. We should build this analysis out further in a later notebook.   \n",
+    "\n",
+    "* **How does a test or the entire test platform behavior evolve over time.**\n",
+    "    * Given the above step, we were able to roll that analysis out across time and plot trends in platform pass rate over September. We should build this analysis out further in a later notebook.   \n",
+    "\n",
+    "\n",
+    "* **Is there sufficient data here for useful ML approaches?**\n",
+    "    * Yes, especially if we focus on the level of individual time-series for tests. However, there are currently not clear labels, so much of the initial work would either be unsupervised learning or pure statistical analysis. \n",
+    "    * If we look only at the Red Hat grids, which are themselves a subset of the entire ecosystem.\n",
+    "        * There are ~400 grids\n",
+    "        * 61 days of data for each grid\n",
+    "        * 117,000 time series\n",
+    "    * Other platforms have also opened their data on [Testgrid.io](https://testgrid.k8s.io/) that we could leverage as well. \n",
+    "        \n",
+    "\n",
+    "* **Can we develop some meaningful alerting/ problem identification with the results of the above questions?**\n",
+    "\n",
+    "    * Yes. Given all of the above, I think there are a number of avenues available for developing altering and classification tools for CI based on this data. \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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_metadata_EDA.ipynb b/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_metadata_EDA.ipynb
new file mode 100644
index 00000000..0fb9be65
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/TestGrid/testgrid_metadata_EDA.ipynb	
@@ -0,0 +1,2950 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TestGrid: exploring in-depth metadata\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 metadata present at a Test level within the existing TestGrid data at [testgrid.k8s.io](testgrid.k8s.io).\n",
+    "\n",
+    "For initial exploration of the Testgrid data visit [testgrid_EDA.ipynb](testgrid_EDA.ipyb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T15:38:57.701898Z",
+     "start_time": "2021-03-10T15:38:57.115609Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import datetime as dt\n",
+    "\n",
+    "from IPython.display import Image"
+   ]
+  },
+  {
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets look into the various metadata fields present on a test level within each job for the testgrid dataset.\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",
+    "Here we will get the data for 2 dashboard and jobs for further exploration as examples.\n",
+    "\n",
+    "1. **dashboard** : \"redhat-openshift-ocp-release-4.2-informing\" ,\n",
+    "    **job** :  \"release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\" \n",
+    "\n",
+    "2. **dashboard** : \"jetstack-cert-manager-master\" ,\n",
+    "    **job** : \"ci-cert-manager-bazel\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:04.241939Z",
+     "start_time": "2021-03-10T17:41:03.922366Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "openshift_testgrid = 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": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:05.576282Z",
+     "start_time": "2021-03-10T17:41:05.235739Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "jetstack_testgrid = requests.get(\n",
+    "    \"https://testgrid.k8s.io/jetstack-cert-manager-master/table? \\\n",
+    "    &show-stale-tests=&tab=ci-cert-manager-bazel\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`openshift_testgrid` has all the fields expected by TestGrid's javascript client. It represents a grid of test results, with additional annotations for metadata."
+   ]
+  },
+  {
+   "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:06.876862Z",
+     "start_time": "2021-03-10T17:41:06.865096Z"
+    }
+   },
+   "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 openshift_testgrid.json().keys():\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now explore each metadata within the json and try to understand the significance of the values it contains"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T15:15:33.131160Z",
+     "start_time": "2021-03-10T15:15:33.126466Z"
+    }
+   },
+   "source": [
+    "The summary field is always empty, hence we will ignore that here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## test-group-name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:08.146983Z",
+     "start_time": "2021-03-10T17:41:08.141394Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "releaseopenshiftorigininstallere2eawsupgraderollback41to42\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"test-group-name\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see above, the`test-group-name` is a string value which contains the particular job name for which all the tests are executed. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:09.032665Z",
+     "start_time": "2021-03-10T17:41:09.027136Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "origin-ci-test/logs/release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"query\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above `query` is a string value which contains the `gcs_prefix` pertaining to the job. The `gcs_prefix` is the path to the test data stored in gcs (some-bucket/some/optional/path). The metadata can also be seen [here](https://testgrid.k8s.io/q/config/redhat-openshift-ocp-release-4.2-informing/release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2) and is documented [here](https://github.com/GoogleCloudPlatform/testgrid/blob/master/pb/config/config.proto)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T13:21:12.880962Z",
+     "start_time": "2021-03-10T13:21:12.877814Z"
+    }
+   },
+   "source": [
+    "## status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:09.794349Z",
+     "start_time": "2021-03-10T17:41:09.788891Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Served 1174 results in 0.27 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"status\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `status` aggregates the tests for which a result - Pass, Fail, Flaky, Running was displayed and the time taken to serve the status. The time taken is calculated by aggregating times for different phases, which are further broken down in `phase-timer`. The status line is displayed on the Status bar for the job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## phase-timer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:10.470808Z",
+     "start_time": "2021-03-10T17:41:10.465236Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'phases': ['config load', 'cache peek (stale)', 'state fetch', 'state decompress', 'state unmarshal', 'table creation', 'table serialize (gob+snappy: 13KiB)', 'table querying'], 'delta': [2.5623e-05, 0.06963295, 0.17168032, 0.000316809, 0.000535445, 0.000140227, 0.000774511, 0.025254081], 'total': 0.268359966}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"phase-timer\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T13:55:37.254179Z",
+     "start_time": "2021-03-10T13:55:37.249095Z"
+    }
+   },
+   "source": [
+    "The `phase-timer` is a dictionary that contains the duration of each update cycle and stores the time taken for different phases. The aggregate time is used to populate the `status` displayed on the status bar of the job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## cached"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:11.369470Z",
+     "start_time": "2021-03-10T17:41:11.364255Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"cached\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T14:59:20.034576Z",
+     "start_time": "2021-03-10T14:59:20.028642Z"
+    }
+   },
+   "source": [
+    "`cached` holds a True or False value to determine if the response is loaded from a cache."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## bugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:18.657261Z",
+     "start_time": "2021-03-10T17:41:18.651605Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"bugs\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`bugs` is an empty dictionary here, but it should contain any linked bugs created for the job"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## changelists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:20.749288Z",
+     "start_time": "2021-03-10T17:41:20.744144Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['1372535122330390528', '1372353507234942976', '1372171845851156480', '1371990292286148608', '1371808757339656192', '1371627211211149312', '1371445677782994944', '1371264283366658048', '1371082838371209216', '1370901281094242304', '1370719715751628800', '1370538213357129728', '1370358921604108288', '1370302741443776512', '1369994168893444096', '1369811862551531520', '1369630643398381568', '1369449096883998720', '1369267681374507008', '1369086139733905408', '1368904775671746560', '1368723410221273088', '1368542014391455744', '1368360542443409408', '1368179004518961152', '1367997486991413248', '1367816194010124288', '1367634626750713856', '1367453420490854400', '1367271859506122752']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"changelists\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`changelists` list consisting of IDs for each test within a job. The `changelists` can be used in a URL template to visit the Prow Dashboard after clicking on a cell such as `https://prow.k8s.io/view/gcs/<gcs_prefix>/<changelists>` for eg. https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2/1365457253460611072"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## column_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:22.777503Z",
+     "start_time": "2021-03-10T17:41:22.771663Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['\\ue0001372535122330390528', '\\ue0001372353507234942976', '\\ue0001372171845851156480', '\\ue0001371990292286148608', '\\ue0001371808757339656192', '\\ue0001371627211211149312', '\\ue0001371445677782994944', '\\ue0001371264283366658048', '\\ue0001371082838371209216', '\\ue0001370901281094242304', '\\ue0001370719715751628800', '\\ue0001370538213357129728', '\\ue0001370358921604108288', '\\ue0001370302741443776512', '\\ue0001369994168893444096', '\\ue0001369811862551531520', '\\ue0001369630643398381568', '\\ue0001369449096883998720', '\\ue0001369267681374507008', '\\ue0001369086139733905408', '\\ue0001368904775671746560', '\\ue0001368723410221273088', '\\ue0001368542014391455744', '\\ue0001368360542443409408', '\\ue0001368179004518961152', '\\ue0001367997486991413248', '\\ue0001367816194010124288', '\\ue0001367634626750713856', '\\ue0001367453420490854400', '\\ue0001367271859506122752']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"column_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:25.235829Z",
+     "start_time": "2021-03-10T17:41:25.229569Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(openshift_testgrid.json()[\"column_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:26.014565Z",
+     "start_time": "2021-03-10T17:41:26.010506Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1369630643398381568\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\ue0001369630643398381568\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:27.022889Z",
+     "start_time": "2021-03-10T17:41:27.017572Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "57344"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord(\"\\ue000\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:00:35.427591Z",
+     "start_time": "2021-03-10T17:00:35.422613Z"
+    }
+   },
+   "source": [
+    "`column_ids` are timestamps from `changelists` preceded by a unicode character `\\ue000`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## custom-columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:32.205335Z",
+     "start_time": "2021-03-10T17:41:32.200164Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', 'missing', '1', 'missing', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1'], ['', '', '', '', '', '', '', '', '', '', '', '', 'missing', '', 'missing', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"custom-columns\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:43.375615Z",
+     "start_time": "2021-03-10T17:41:43.368068Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '442d7dbc0', '433cc2fa0', '433cc2fa0', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', 'fedea03a1', '03b3e2f20', '38ebe359a', '65a7a880f', '65a7a880f', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', '51340d0c8', 'f40ffc87c', 'f40ffc87c', 'f40ffc87c', 'f40ffc87c', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', '9f343ec58', 'c01603dd3', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', 'c2634d353', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '07964edea', '75a46ff90', 'deb55110a', 'deb55110a', 'deb55110a', 'deb55110a', 'deb55110a', 'deb55110a', 'deb55110a', 'deb55110a', 'deb55110a', '28fc97699', '28fc97699', '28fc97699', '28fc97699', 'eced645c8', 'eced645c8', 'eced645c8', 'eced645c8', 'eced645c8', 'eced645c8', 'eced645c8'], ['missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing', 'missing']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"custom-columns\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the first dashboard, `custom-columns` did not contain any values, but for the second dashboard, we see that `custom-columns` is nothing but a list of column headers with commit-ids for each test.\n",
+    "\n",
+    "For example, as we see below '9f343ec58' is common header and commit-id for the first tests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T19:02:48.522590Z",
+     "start_time": "2021-03-10T19:02:48.513483Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/metadata-3.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:46.981579Z",
+     "start_time": "2021-03-10T17:41:46.975064Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(openshift_testgrid.json()[\"custom-columns\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## column-header-names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:48.063572Z",
+     "start_time": "2021-03-10T17:41:48.057562Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Date', 'Time', 'Build Number', 'Commit', 'Infra-Commit']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"column-header-names\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is not relevant to our current dashboards which we are looking at, since the values for these column headers are vacant as seen below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:48.826451Z",
+     "start_time": "2021-03-10T17:41:48.820585Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/metadata-1.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, in other dashboards like https://testgrid.k8s.io/jetstack-cert-manager-master#ci-cert-manager-bazel, these consist of 'Date', 'Time', 'Build Number', 'Commit', 'Infra-Commit' for each Test. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T17:41:49.449779Z",
+     "start_time": "2021-03-10T17:41:49.443249Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/metadata-2.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T19:01:22.426818Z",
+     "start_time": "2021-03-10T19:01:22.419969Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"groups\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not sure what this means"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T19:01:38.896535Z",
+     "start_time": "2021-03-10T19:01:38.893217Z"
+    }
+   },
+   "source": [
+    "## metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T19:14:40.575452Z",
+     "start_time": "2021-03-10T19:14:40.570466Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['test-duration-minutes']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"metrics\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`metrics` is a list consisting of string labels for viewing additional metrics such as `test-duration-minutes`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-03-10T19:06:21.588950Z",
+     "start_time": "2021-03-10T19:06:21.582204Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PT5fD4VBeXp7T9c3Psdvt+vzzz/Xmm28afXch3Db7xS9+oS+//FKS9Oqrr2rUqFFO12jsOyWp1f2zf/9+/fCHP5QkTZo0SX/+85+1ceNGvfHGG6qqqlJVVZXxeeys/pHk1A9+fn7Kz8/vkP658POjNf1jt9udruHv79/gz6agoCCjXy7+GWK1Whv8GdLYfV/Mz8/P6VoX+qixfr7AfH9mjfVbc9drqYb6EwCAnqDi4BGdXPIrp31Dvn2fhgEAoJO5LAANCQnRrl27nEKX0tJShYaGSqqd2nxBfn6+goKCFBoaKh8fH2N9OrMjR444jUT6+OOP9dlnn+mdd95RQECAPvjgA7333nutvo/mprNfcN9992nlypVasGCBfH19ZbPZnIKG9lbPDgsL0+TJk/XSSy+16zoNtatZS9utpffT3HlPPPGE7Ha7Xn75Zbm7uzs97/nnn9fw4cOdzi8pKTFG+fr6+jZ5vxMnTtRbb70lm82mK664QgMHDtSTTz4pf39/Y0RiSEiIfve73+l3v/udNm/erF/84hfavXu31q5dK4fDYfS/1Wp16s8L05wvtOOFc8vLy1VaWqqgoCD5+/sbI5ol1Vtv1fzZWrlypW6//XZjhOWFSu+BgYFO18jJyXFqo5/+9KdG4NiYRx991Fh78uL+buo71Zb+qays1H/+8x9JkoeHhzIyMnTffffpww8/1ODBg5Wenm6MNOys/pGkc+fOKSYmRpJUUFCg4OBgl/WP+T8RiouLVVpa2qr+CQkJcQo209PT5e3tXW9/bm6u8XOppRq779b83Gmon1vy872hfmvp9drSnwAA9AQ19nNKT/7Qad8QmgUAgE7nsinwU6dOVWJiolFUZc2aNRo/frwxEmn37t3GaKxPPvlEEyZMUFRUlPr166d169ZJqh2ddPfddzcYLl74B3JAQIDy8/P13nvvGYU63NzcVFhYKIfD0ex9mH344YdOAZTZVVddpdjYWL388suSpIiICGVlZRnhyIVQqDXM9zllyhTt2bPHuM9vvvlGDz/8cKuv2VC7tqXdWno/TZ23c+dObdu2Tc8884xTuCZJ8fHxeuutt+RwOFRZWanHHntM+/fvl4+Pj8LCwhQWFiYfH58m77d3794qLCzUjh07NG7cOA0cOFDHjx/XgQMHdMUVV6i0tFSLFy82gq9BgwYZI99CQ0MVFhZmBE6RkZE6evSoJOnEiRNO612Wlpbq008/Nfp5yJAhCgoK0ujRo7VhwwbV1NQoIyNDn3/+eaP9kpeXp2HDhkmSPvjgA9ntdhUXF2vs2LH6+uuvlZWVperqaqcwOj4+Xh988IERDr399tsNhtWBgYFGm13czk19p9rSP+7u7sZrXQgHfX191adPH1VXV+u1115TTU2NysrKOq1/LrSpJJ06dUoHDx7UmDFjXNI//fr1k7u7u7744gtJMgLd1vTPjBkztHbtWhUWFio/P1/Lli3TsWPHNGvWLK1bt06lpaWqqanRv/71L6eRuo1xd3c3fu40dt8t1Vg/N6Wpfmvqeub7bkt/AgAAAADgKi4bARoVFaUnnnhCd9xxhyorKxUdHa0nn3zSOD516lQ9/PDDOnr0qHr37q0//elPkmqLaDz44IN69tlnZbFYdNtttzlNBb1g/vz5Wrt2reLj4xUZGalf/vKXuuOOO/T000/r+9//vjIzMzVlyhRt3769yfswe+yxx/SXv/yl0VFY9957r2666SbddNNNio6O1s0336zvfOc7iomJ0cyZM/XVV1+1qo3i4uKc7vPJJ5/UT37yE5WVlcnX17fBokPNaaxd29JuLbmf0NDQRs/717/+pZSUFKcp2UOHDtXatWt111136eGHH1Z8fLyqqqo0a9YsxcbGtqqfV61apbFjx2r37t3q1auXJKlPnz4qLS01pikvW7ZMt9xyi6qrq+Xl5aUnn3zSmOZtduutt+ruu+/WV199pcGDB2vatGnGlOM+ffooKSlJTz/9tCwWi/7whz9Iql3vdOfOnZo2bZqGDh2qG264QefOnWuwX+6++27deeed8vPz09KlS3XrrbfqgQce0Lvvvqvly5fruuuuk7+/v5YtW2YERrNnz9aRI0d0/fXXq6qqSgMGDDBeuzUa+065on+GDh2qGTNmaM6cOQoKCtL/+3//T3v37tXy5cv17rvvdkr/SLX/ITFv3jwVFhbq/vvvV3h4uMv65/HHH9dvf/tbORwOLV26VKGhoaqurm5x/8ydO1fJycmaN2+eHA6Hbr31Vo0dO1Y1NTU6fPiwFi5cKIfDoQkTJmjFihXN9ufVV1+tX/ziF7rnnnuavO+WaGk/m3l7ezfab01dz3zfbelPAAAAAABcxZKamuro6Bd59dVXdfTo0TYXPAHt2lmSk5P1s5/9TJs2bWrweE1NjRHY/elPf1Jpaanuv/9+Gq4TxcXF6dNPP1VkZCT908P7EwAutYEDB9b+wpw2usnzHH2+4TzOM87bYrE1ed40RzXtx3kddh4AoGHWznqhhgp1gHbtTjZu3Kg5c+aotLRUxcXFWr9+vcaOHUvDdJHPPf3DzzEAAAAAABriRhMALTNz5kxt2bJFc+bMkdVq1bXXXqs5c+bQMPQPAAAAAADowjolAG2uajJo165i+PDhjU5/t9lseuSRR/TII4/QUJfQ3r176Z//gv4EAAAAAMBVrDQBAAAAAAAAgJ6KABQAAAAAAABAj0UACgAAAAAAAKDHsjgovwsAAAAAAACgh2IEKAAAAAAAAIAeiwAUAAAAAAAAQI/lRhMAAAAAgGvtPZWuTQeO6ZQ9X6UVlbosKlQLxlymoVGhNA4AAJ2MNUABAAAAwIX2nkrXixu+bvDYqmsnE4ICANDJmAIPAAAAAC70+f7URo+tSzxEAwEA0MkIQAEAAADAhY5k2Bs9lpZbQAMBANDJWhSAfnXkVLtepDXPv/v1j5VXXOqyN3jwTLZxvQ3fpOiNbUmd3sjtfd0ah0Nfp5zm0woAAHAJzJw5UxaLpd4foE1YgQwAgE7XbADqkPTe7uS2//3ezuc3p6aZXyASko/pXHGZJGn68AFacuWoTm/k9r7umdwC7UhN49MKAABwCWzevFkOh6PeH6AxQyNDGj3WJySQBgIAoJM1WwX+5Y1fq7C0XA+/t0m/mHuVKqqq9X/b9qqgtFye7jbdNHG0Bkb0UnVNjV7bmqTUzFw5HA4NigjR8qlxWp2wx+n5wb7eTtdPPp2lt776RjaLRRMGx0jn/zf9eNY5/WNLoh5eEl/v8cb9qTpzrkCn7Hka3TdS140bpnd3HtC+k+lyOBy6LCpUy6eO0Yb9KTpwOktnzhVoyYSRyikoVkZBoZZPGaMzuQV6c/s+FZWVy81m0w1XDNOovpE6nVugV7/Yo9F9I5WSYVd+SZluumq0hseE12ubTQeOKS03X1U11TqTW6hgP2/NiO2vhIPHlZlfpOvGxOrKIX30RfJx43XvfuMTLb5iuBJPnlVOQbEmX9ZPcy8f2uj7fWDRdP1lw06VlFfo2f9s18p5k3UgLVP/3pWs6poahfn7aPnUMQr08ZK9sESvbklUXkmZHA6Hpp6/NgAAAIDOEz9qUKPT4BeOjaWBAADoZM2OAL1lSpxsVqt+t3iWgn299deE3bpyUB89vCRe35sUp5c37VJ1TY32ncxQQUm5fr90jh5berWCfDx1Kie/3vPNahwOvbY1ScuuGq2Hl8TLz8tDZRWVzd60zWbRN2kZumP2lVo0bpgOnMrQwdNZenhxvB5ZMlun7Pnac+ysrhk9RBEBvvrxjCs0pn+U8XyHQ3olYZdmjhigR747Wz+eeYX+tnmPisoqZLVIZ84VaHBEiFbNn6KFY4fpo6TDDd6HxSp9ezpTN0+6XA/eMENncvO191S6fnHNVfrBtLH6ZN+R+g1ukXJLSnXX3ElaNX+qPkw8pIqq6kbfq6e7m5ZeOVKDI0O0ct5kFZSW62+b9+jHM8fp4SXxiu0dpre//EaS9NmBFI3oHabff3e2fnP9TJ3MyVNZRRWfcgAAAKATxfWNUmSgnxw1kqXm/L9BaqTbpo+jAjwAAJdAq4og5RaVKiu/SJMv6ydJ6hcWpEAfL6VmnlOgt6fS8wr1bVqmqmtqtHjCSA2O6NXk9bLzi1VeWWWMrpw8pJ9aOpmoX2iQwvx9JEkj+0bq/uumyc1mlZvNqoHhwbIXFTf6XHthsc4VleqKgTGSpOhgf0UF+yslM1eS5OnmphHn7yk62F/nmliTdHBkiHw83GW1WBTq56Ph0bXPC/P3VV5JWYPPGds/WpIU5OslTzc3FZaWt7gPkk9naWB4sKKDAyRJUy/rr32nMlTjcCjQx0vJZ7J1IvucPN3dtCJ+grw83PiUAwAAAJ2ooLRMGflFslglx/l/cVmskpuNGrQAAFwKrUrHCsvKVVFdrV+v+czYV1FRraKyco0dEK0bJ47SZ/tT9LeE3bpiYG8tnei87uWpnDw99+mXkqRh0WGKHzFIPp7udTdjs8rDamvRvfh6ehjbRWUV+vfXB3Q2r1BWWZRdVKL44QOafB++nh6yXHS94vJyST7ycq9rFoulbp3Rv27apYNnsyVJd8+dJEnydLOZzrXI4/xjq9XS6NpQnhddv7oVa0gVlpYrJcOuB975zOl6RWUVmjt6iGwWi17fulcFZeWad/kQxY8YxKccAAAA6ERfp5xpcP+ZcwUaOyCaBgIAoJO1KgAN9PaUl5ubHr/x6gaPjx0QrbEDolVSUam/btqlLQdPaOKQPsbx3r0C9dDi2jUu3a1W5ZWWqaS8bsp7eWWVKmpqp4NbLc4BYlml81Ru8/+dfrintsjSrxZOk9Viabbiur+Xp4rLK+RwGEuOqqisXAFenk0+75bJcaqsqZ3D4uvpodSsXJd1RHPv94IAX08N6x2mO2df2eDxa0YP0TWjhyiroFhPf7xNQyJC1TeUhdYBAACAzpJ4oi4AHRzRy5hpdia3gMYBAOASaHYOhpvVqhpHjSqqqhXk662wAF/tOnZaklRYVqG/b96t8soqbUo+pnVJh+SQ5O3hrmBfbzkcDqfn26wWBXh7KsDbU96e7goP8JPNZtW3p7MkSVsOnjACySBfL+WVlKnk/Jqg5l8iLlZUVqHevQJktVh0OrdAh87mqKyiNki1Wa3GNS4ICfBViJ+Pdh2vfR+n7fnKKijW4IiQJtvC29PduH+b1eLSjmjq/VrPvweHpGHR4UrJzFVWQe0U/5PZeXpnx35J0t8Sdhtt2cvXW14ebnKICqUAAABAZ8ktKlVq1jnj8fVXDDe2z+Tm00AAAFwCzY4A9fJw07DoMN3/z/X6xTVX6cczr9Cb2/fpw90HZbFYNGfkYHm6u2nCwBi9tiVRD/zzM1ksFvUPC9K0Yf3l6e78/P5hwca1bVaLlk8Zoze375NUW7U80NtL1TUOhfh7a/qw/vrD2i8U4u+jUX0jdfBsToP3OGfkYL36xR5tPXxSA8OC9d2JI/WPL5I0OLKXxvaL0iubdun6K4YZ51sk4318tOeQ3N1sun3meHl7uutcSekl6YhAH69G3+/A8GDlF5fp/rfX6483X6MfThurlzZ+rcqqKnm6u+vmq0YZ7fDml3v15vZ9slikiUP6qF9oEJ9yAAAAoJPsOVY3kGFYdJgGRfSSRZJDUlZhicorq5yWxAIAAB3P4nA4GCIIAAAANPYLs6Xxtd0v2LNnj+Lj4xUZGSlJmjRpklavXq2UlBT94Ac/UG5urqZPn64XXnhBFouFRu3B/rD2Cx3PyZMkLZ8cpymx/fTo+wk6fX76+/0Lp2pAeC8aCgCATkQZQgAAAKCdCgoKdNNNN+nQoUM6dOiQVq9eLUlasWKFnnrqKSUnJ8tutyshIYHG6sFyi0qM8NNikcb2j5Ik9e4VYJxz2s46oAAAdDYCUAAAAKCdCgsL5efn57SvqqpKqampmjhxoiRpyZIlWr9+PY3Vg+26aPq7j5eHJKlPcF1R0jPnCEABAOhsBKAAAABAOxUUFGjbtm2aMGGCrrzySn3xxRey2+0KDq5b/z48PFwZGRk0Vg+22xSAXjGwt7EdbR4BSiEkAAA6HatvAwAAAM1Yvnx5vX1vvPGGsR0XF6eVK1dq6dKlSk5O1vz587V7926n8x0OB+t/9mC5RSU6Za8NNy0WaUzfKONYjCkAvXAOAADoPASgAAAAQDOuueaaJo+PHDlSI0eOlCSNGDFCvXr1UmVlpfLy8oxzMjIyFB0dTWP2UF+nnja2h/cON6a/S1Kgj5d8PNxVUlGp8qpq5RaVqpefN40GAEAnIQAFAAAAmnHLLbc0eXz16tXKzs7Wr371K6WlpSkvL08RERGKi4vT1q1bNXXqVK1Zs0b33nsvjdlD7T521tgeP6B3veN9QgJ1OD1HknQmN58AFACATsQaoAAAAEA7LV68WFu2bNHYsWN1/fXX65VXXpHVatXjjz+uu+66S7GxsRo4cKAmT55MY/VAmflFSss1TX/vH1XvnD6mafAUQgIAoHMxAhQAAABop8DAQH388cf19g8bNkyJiYk0UA+XeLyu+NGImAh5ebjXO6e3qRJ8Wi4BKAAAnYkRoAAAAADQDruamf4uSb1DqAQPAMClQgAKAAAAAG2UmV9kTGm3WiyK6xfZ4Hm9g+sC0My8IlVV19B4AAB0EgJQAAAAAGijPal1099HxoQ3OP1dktxsVkUE+EqSHJLOniuk8QAA6CQEoAAAAADQRjtS04ztKxqZ/n5BTEjdOqBnmAYPAECnIQAFAAAAgDY4e65QmQXFtf+wslh0eSPT3y/oTSV4AAAuCQJQAAAAAGiDPabq76P7RjQ6/f0C50rwjAAFAKCzEIACAAAAQBvsSj1tbI8b2LvZ8/uYRoCeyiEABQCgsxCAAgAAAEAr7Tl2Rll5ddPfx/SNavY5If4+8nSzSZJKKipVUFpOQwIA0AncaAIAAAAAaJl1SYf0UeLh2gfnh5NE+PvJ/Xyw2ZyYXoFKzcqVJJ2252t4TDiNCgBAB2MEKAAAAAC0wOffptaFnybpBYX6/NvUFl3DqRBSLoWQAADoDIwABQAAALqwkopK7TiSpnMlpfJ0d5OPu7smDu0jn2YK7sD19p5Ib/JY/IhBzV4jJoRK8AAAdDYCUAAAAKCLsheW6OlPtsteVOK0f+O3qVp17WSF+PvQSJ3oSIa9TcfMzJXgTzMCFACATsEUeAAAAKCL+ijpUL3wU5LsRSX6KOkQDdTJvN3d2nTMzFwJPi2XSvAAAHQGAlAAAACgizqcntPEMTsNpNolAkoqKjvltS6LDm3TMTNPdzeF+Hkbj88yDR4AgA7HFHgAAADARYqLizVs2DC98MILWrBggVJSUvSDH/xAubm5mj59ul544QVZLJYWX89eVNrEsZL/6rZel3RInx84ptLz4ae3h7tmjxykBWMu67DXXDgmVgfPZKu8qtppv7e7mxaOiW3xdWJ6BRp9e+ZcgaKDA/jyAADQgRgBCgAAALjIQw89pIiICOPxihUr9NRTTyk5OVl2u10JCQmtul5Mr4A2HevpvjpySh8lHjbCT0kqrajUusRD+urIqQ573ZiQQI3tH6UaSY4aKcjHSwvGXqbf3DBTMSGBLb6OuRL86RxGgAIA0NEIQAEAAAAXOHDggE6fPq34+HhJUlVVlVJTUzVx4kRJ0pIlS7R+/fpWXTOuf1SbjvV0Xx491aZjrnA8O09WSRardMvky7VwTGyri1HFBJsrwbMOKAAAHY0AFAAAAGgnh8Oh++67T0899ZSxz263Kzg42HgcHh6ujIyMVl03fsQgubvZ6u2vkRTXJ/K/tr1dUY29LfKKy5SRX2Q8HhoV2qbrmEeLnj5XyBcIAIAOxhqgAAAAQDPWrl1bb9+iRYuM7VdffVVz585V7969jX0eHh5O5zscjlat/ylJJ7LOqbKqWpYayeZuVWSAr87YC2W1SlsOn9T3QoP+K/vD291NpZVVjR7rKIfOZhvbQyJD5NnG14oI9DO2zxWXqrS8Ut6e7nzRAADoIASgAAAAQDNeffXVevvMAejHH3+stLQ0vfXWW0pLS9MHH3ygN954Q3l5ecY5GRkZio6ObtXrbj18QpLksEqThvTV2P7Reu7TLyVJ24+c1HVjY+Xv7flf1x+XRYdq78mGR9MG+np12Ot+eybL2B7WO6xd1+obEqhT9trp72m5+W0eTQoAAJpHAAoAAAA044MPPmjy+HvvvWds33///RowfLQy3QIU2X+w3vrgIy27foHWrFmje++9t8WvWVharsQT6cbjabH91Sck0AjOqmsc2pR8TIvGDfuv64/4EYMaDUAz8oq0/chJTR7az+Wve9A0AnREdHi7rtXHFICeOVdAAAoAQAdiDVAAAADARUoqKvXVkVN6f1ey1iUeUp/4RbrrrrsUFtNXffv11+TJk1t8LXMxn74hgepzft3IuaOHGPs3fXtMZaZK6P8thkaFamBY7fqqjhopMtBPwb7ectTUHn996159czLDpa95OrdAhaXlkiRPN5v6hbVv+QGnSvC5FEICAKAjMQIUAAAAcJF3d+xX7PwbjcfBUX30nd8+I0kaO6RPq661+eBxY3vaZf2N7TEDohWyy1v2olKVVVZp66GTmjN6cJdvm5KKSm369phOnx/1GBMSqFkjBsrHo/VrXxaXV+pY9jlJtdXYH1g0XTarVc/+Z7tSMnMlSS9t+lqr5k3WoMgQl9z/obN109+H9w5v9XquFzMHoGfsBXx5AADoQIwABQAAAFzkcHpOo8fSWhFyHT6bo9yiUkm1ow0nDI6p+wXeYtG80UONxxu/TVV1TU2Xbhd7YYkeeGeD1iUeUtLJdCWdTNe6xEN64J0NsheWtPp6SSfOGNvDe4fJ091Nbjarfn71REWeLzBUXePQnz/bobScPJe8h+QzddPfh8WEtft6fXrVVYI/xQhQAAA6FAEoAAAA4CL286FlQ9JaEXJdKH4kSRMGxdSrNj51WH8FeNVWmc8rKdPXKae7dLus2blfpQ1M1S+tqNSanftbfb09x88a22P71RWW8vJw193zJinwfGGossoqPfPpl8opKG73ezh61m5st3f9T0ny9fQw7rO6xqEsF9wjAABoGAEoAAAA4CIhft6NHosxTXluSlF5hXYdqxvhOC22f4PnxY8cZGx/+s3RLt0uafb8Jo61bvp3aXml02jMuAHRTseDfb1197zJ8nSzSZJKyiv12/c2acXf12rF39fq9+9v1t5T6a16zSPpOaqoqZYk9fL1VmiAr0vaJSakbhTomVymwQMA0FEIQAEAAAAXGdMvqtFjMcEtC0B3pqQZ2316BapvaMPFdmYMG2iEfBn5RS4v+uNKTY2MtRe1bgr8vlN173NoZIj8z4+ENYsO9tfPr7nKeGxeIiAtN18vbvi6VSGoufr7yD7hLmuX3sEUQgIAoDMQgAIAAAAuMn9srDGt2cxRI53Ja9kIv60HTxjb0xsZ/SlJXh5umjF8gPH402+OdNl2aWr0a0tHxl6QeKJu+vuYi0Z/mg2JDFF0kH+jxz/fn9ri10w+U1cAaVi06wJQcyjOCFAAADoOASgAAADgIj4e7qo8P9qwRrXrd0q1lcrT7AVa38xU9aMZdqXnF0mqX/yoIbOGD5T1fDXy1KxzSs2wd8l2Gd7EqMm4/lEtvk5ZRaUOnM40Ho/rH93k+WfzChs9dqSFbVVaXqkT2XWFlIZFh7msXcyV4NMYAQoAQIchAAUAAABc5OCZbJWU1xb7CfH10m0zxmnJ+BHG8X/vSlZmXlGjz9925KSx3VDxo4sF+Xpr0tA+xuP1+1O6ZLuUV1TJ0kChekuNNCN2QIuv883pTFXXOCRJ/cOCFOjj1eT53k20n3czbXvBofQcY3tAaJC8Pd1d1i7mNUBzCktUXlnFlwgAgA5AAAoAAAC4yO7jddXYxw7oLam2WFEf00i/1Vv2yOFw1HtuSUWldqXWFT+aMrRfi17z6lFDjO19pzKUmV/UpdokPa9QXxw8Icf5f3ksuXKEogL9JEkOq7T54PEWXyvxmKn6ezOjPyWpjylgbM0xM/P099jeYS5vH/M0/bPnmAYPAEBHIAAFAAAAXKCmxqHEE3WFdcafD0CtVot+POMKY6r6iew8bUo+Vu/5O1PSjGI9vYP91T88uEWvGxHo51R8aV3SIZVUVHaZdnlz+z5je2SfCM0ZOVgLrxhm7Nv07TFVVdc0e53Kqmp9k5ZRr32bsnBsbKPH5l0+tEX331Hrf14QE2IqhGQnAAUAoCMQgAIAAAAucOhs3fT3Xr7eGmAKMCOD/bVg7GXG43/vSlZuoXP1862HThjbrZkWLknzLh+iGtWuO7or9YxWvvGJ7n7jE32UdPiStsmeY2d09Pxam1aLRTddOVKSNKZflEL9fSTVjnz98vDJZq+1P61u+nt0kL96nX9+U4ZGherOORM0aUgfhfh5y2qpO5aeX9js8wtKy5Vzvp9sVqsGR/ZyeRuZR6KeYQQoAAAdggAUAAAAcIHdx+umr18xsP707LmjhxhT4auqa/T3L+qmwp/MztOZc7WBnM1q1ZVD+rTqtc+eK5T1ol/uSysqtS7xkL46cuqStEdVdbXe2bHfeDx75ECFnZ/6brVYFD9ykHHss29SGlwWwCzppGn6+4DoFt9HXN8o3TptrB6/8WrdPHG0sf/jpMOqrKpu8rnfptUVXIqNDpXN6vp/PvUODlSNJEeNdCQ9t0uN3gUAoKcgAAUAAABcwDz9/YoB9au326xW/WjGOONxSmausf7l1sMnjP0TBzdf/OhiXx491aZjHek/3xxVfmm5JMnfw0MLxjhPR58ytJ+sWm2DAAAgAElEQVS8zr/P7OIS7TuV2eT19p2sm/7ekvU/GzJt+AAFnS+cVFxeqS2mdm/IobPZxvbwDpj+LklJJ87KKslilc7k5WvlG59ozc4DfKEAAHAhAlAAAACgnb49naXS8yP3Qvx81C8sqMHzooMDdN24uiDwvR3fatO3x/Xl0TSjSvq02P6tfv0j56eZt/ZYRzlXXKpP9x01Hn9nwoh6oa6Hm03Th9W9188PNF7B/kBapsrPj9YM9/dRb1NRqdZaEFe3FMF/9h1tcv3Rb00B6LAOKIC0LumQtjYw/f/zA6lal3SILxYAAC5CAAoAAAC00+5jTU9/N5sfd5migmsrf1c6avTOjm9UXVMjh1XydLMp/Pw08dbwbmLEqHcrR5O6wr92HjCCxb4hgZp0Wd8Gz5s5fKCxfSTDrjR7foPnJZ4wV3/v3a57mzqsvzEKtLC0XF8cargK/ZncAhWaRrC2J3RtzF7TqOGL7TuRwRcLAAAXIQAFAAAA2inJFNCNa0F18n69AhvcX15VrY8TWz/y77Lo0EaPubvb2vSeSioqtenAMb22JUmvbUnSpgPHWrQ+5ZF0u3Yfr2uPZZNGN3pusK+3xg+sa6/P9h9tpH3rgsIx/aPa3V9zLx9ibH+690iDo0APpdeN/hzRp2Omv5/ObbzoUVpuPl8sAABchAAUAAAAaIcDaZkqLCnR5y/9f/rgkXu0YOZUvf/++5KklJQUTZkyRcOHD9edd95pFPo53US178PprZ+yHj9iUKPHCkrK9ermPa26nr2wRA+8s0Hv7NyvL4+e0pdHT+mdnfv1wDsbZL+oev3F3tnxjbE9cVCMBoQ3XTl9zsjBxvbXqWd0rrjU6fjBM9lG8Brk46X+4cHt7rOZwwcao0ALyioaHAV68HSWsR0bHdYhn50QP+9GjwV6e/LlAgDARQhAAQAAgHbYdfyMTu3braDIGL383jp98sknuvfeeyVJK1as0FNPPaXk5GTZ7XYlJCRIcv3Iv6FRoXp86RxNGtJHQyNDNDQyRJGBfnKcH9i4I/W03ty+t8XXW7Nzv7GmqVlpRaXW7Nxfb/9pe772nsz4/9m77/goy3z//+8p6Z0UkhB6L8EkCAQINURpiq4uKyp6XP0telxdUdf163p2LSu767GvdVdl1YOsHcRGDVKkE2qQkFBCSSgT0nsyvz8CkxmSmRRCG17Px4PHIzP3Pfdcc90zkztvPtd1ae6abTpysu61mU1G3Ti4f5PP1Tk8WD3a14eky3ftc9je2tXfm9JUFeieo/VBdL+Y81MB2jE0yOm2U2UV2mw3tQIAAGg9M10AAAAAtE5Nba22HshRt6uHS1cPV0KXDjq8N10xMTGqrq5WVlaWEhMTJUk333yzFi1apHHjxinU30eW4rJGj+mqKtCV0ABf3TkqwXbbarXq/R+3aEPWYUnSyp8PymQ06pZhA5s8lrO5OCVp79E8ZeRY1CUiWNU1tXp76QbtyTlZv8PpEouU2O4K9vNuVtuT+3dX5rG80+08oMlxveTt6SGr1aqNdiFgfOe2C0BH9u6i77dmqKCsQoXllVqxe5/Gn65GzcixqLK2btGlqCB/Bfl6n5f3z7Shsdpz9KTKqqod7jfUSgZJ/0zdpNvKKzWqX1c+bAAAnAMCUAAAAKCV0g8fV/np8CoiwFfXJA1VYWGhFi1aJIvFopCQ+uHaERERys2tW9imd1SYftp7qNFj9o4Ka5O2GQwG3T1mkEwGg9Zm1j1Xavp++Xl76rr4Pk4fdySv0Gk4K0klVZV68bvVkuoWByqqrGx0v/LK6ma3NaFrtEL9fWUpLlV5VbXW7M22haKlFVW25+oVFdp2fwiZjJoY10v/WVtX0frD9kyN6dtNZpPRYf7Pvuep+lOqC61n33KNlu3KUt7pqQW8PM1K25+j/NJySdLctdt1orhENw0ZwAcOAIDW/t6nCwAAAADXPDw8GtxXVVXluPp79xg9+/PPWrdunaZPn64ff/zRYX+r1SqDwSBJ+mVirPbknGwQNIb6++iXibFt2vb/Gp2gqpoa28JECzft0Z6jJ2U83ZaY0CAl9+um/JIyfbstQ7sOH1etnM+VZb+toLLS6X6ZuXktauf4/t31yenh9Yu3Zyq5f3dttVtcKr5bVJuf17H9utmqQM+sCJ/cv7vSj9TP/9k3Kvy8vrd8PT0aBNKTr+qtV39Ya5sOYfGOLBWUVWhqQl/beybU30ehAb58OAEAaAYCUAAAAKAJZWWNV0Ruza6r6Dx5IEth8V0kSYmJibJaraqpqVF+fr5t39zcXEVH1w3h9vX00OxfXaO1Gdk6WVJX+Rfm56thvTqdl/b/f+MGq3rpem3bnysZpb259fNb7sk5qWU7s2SolazNWCGgc2iQqqprlFtQ7HJBgZbOZTqidyct2LJb5VXVyi8t1+Z9RxxWk2/L4e/2JsT10idnqkC3ZmhItxjtP37Ktr13dNgFf78F+HjpsetG6q2l65V+5IRqJa3PPKz1mYcd9ovvHKU7RsXL19ODDykAAC6wCBIAAADQBLPZ3ODf1uwc2/D38txszXnzH5KkI0eOqLCwUGFhYYqLi9OqVaskSZ9++qkmT57scNxhvTrpuvg+ui6+z3kLP8+4b/xQ+fs5X1m8xu4vg/7R4RoQ3XDo9/CeHfXkDWP09M3JeuX2SQpwsVJ5S+cy9fIwa2TvzpLqqkw/XL1Vp4rLVSvJ28N83hYiSurdWf7enpLqVoR/Z/lG27ae7dvJy+Pi1Ix4mk363YThGtojxuk+aQdztGxXFh9QAACaupajCwAAAICWs1+h++57fq35b7yg2NhYGQwGvfXWWzIajZo9e7Zuu+02lZaWavLkyRoxYsRFbXN1dY3L7QldojThql7qHBYsSSqtrNLh0wsixYQGOVQa+nh5KDYmok3nMk0e0F1Lt2fJaJTKq6plMNYtBhTi53Pe+sTTZNKkuF76dN1OSdKe09WxRknhgf4X/X3269GDtOvwcRWXNz7X6rYDuS7ndAUAAASgAAAAQItV1tRo28Fc2+2hPbvoFx9+2GC/vn37asuWLZdMu89ebdyeUdLM5CEO9/l6eqiXiyCzrecyXZ1xsNFh+Dn5RVqY9vN5C/qiQgIc+uGMn/Zma1jPji774EJwFn5KLZ9qAACAKxEBKAAAANBCuw4dV8XpasqoIH91aBd4WbS7V2SoMuzm/zx7W0u19VymWw/kON12Pisdv0/LcLpt4Zaf9cjkpIt63kL9fRqEzPbbAACAawSgAAAAQAtt2l8//H1Q9w6XTbvjukQ5DUDjurR+lfW2mr/0cF6h023ns9LxkKXAxfMWXvTz1jsqrE2nGgAA4ErDIkgAAABAC1RV12jrwfpKxUFdLp8ANLl/d01J6C0fu4V9fDzMmpLQW8n9u1/09rmqZjyflY6upgYoq6y66P0yJb5Po68/1N9HU5j/EwCAJlEBCgAAADSDpahUazMPadP+I6quqVWtpAhfH0XbzR95OTiz6rylqG7IemiA7yXTto6hQU6HencMDTpvz9vWUwO0tdAAXz1541it3ZutvNPnrV2Ar4b17OSwMBUAAGgcASgAAADQhMOWAr3w3RqHakCjpPyKSh22FCjmPIZz58ulFHyeMW1orPYcPdmgItPHw6xpQ2PP2/Mmx3Z3GoAmx3a/JPrG19PjkqjSBQDgcmSwWq1WugEAAABwcsFsMOjNJeu01W7Vd3txnSN13/ihdFQbKa2s0rJdWTpZUKK8kjL1ig5Tcv/u573ScWt2jpbtyLIFob0iQ5Uc211xnaI4KQAAXOaoAAUAAACasOfoSefbcix0UBvy9fQ4b6u9uxLXKYqwEwAAN8UiSAAAAEATLvVFcgAAAOAcASgAAADQBFcL4VwKi+QAAADAOQJQAAAAoAlxXaJatQ0AAAAXHwEoAAAA0ITk/t01JaF3g/unJPRmZW4AAIBLHKvAAwAAAK4umA0G2V8yH7YUSJJiQoPoHAAAgMvheo4AFAAAAHBxwXxWAAoAAIDLC0PgAQAAAAAAALgtAlAAAADgHFmtVj3wwAOKjY3VwIEDtXjxYklSZmamkpKS1K9fP913331UkgIAAFwEBKAAAADAOfr222915MgRbd++XV9++aVmzZolSZo5c6ZeeOEFpaeny2KxKDU1lc4CAAC4wAhAAQAAgHM0btw4zZkzRwaDQVFRUSorK1N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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/metadata-4.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## row_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Overall', 'Operator results.operator conditions authentication', 'Operator results.operator conditions cloud-credential', 'Operator results.operator conditions monitoring', 'Operator results.operator conditions network', 'Operator results.operator conditions openshift-samples', 'openshift-tests.Monitor cluster while tests execute', 'openshift-tests.[Disruptive] Cluster upgrade should maintain a functioning cluster [Feature:ClusterUpgrade] [Suite:openshift] [Serial]', 'operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container test', 'Operator results.operator conditions dns', 'Operator results.operator conditions operator-lifecycle-manager-packageserver', 'Operator results.operator conditions console', 'Operator results.operator conditions service-ca', 'Operator results.operator conditions image-registry', 'Pod', 'Operator results.operator conditions machine-config', 'Operator results.operator conditions cluster-autoscaler', 'Operator results.operator conditions ingress', 'Operator results.operator conditions insights', 'Operator results.operator conditions kube-apiserver', 'Operator results.operator conditions kube-controller-manager', 'Operator results.operator conditions kube-scheduler', 'Operator results.operator conditions machine-api', 'Operator results.operator conditions marketplace', 'Operator results.operator conditions node-tuning', 'Operator results.operator conditions openshift-apiserver', 'Operator results.operator conditions openshift-controller-manager', 'Operator results.operator conditions operator-lifecycle-manager', 'Operator results.operator conditions operator-lifecycle-manager-catalog', 'Operator results.operator conditions service-catalog-apiserver', 'Operator results.operator conditions service-catalog-controller-manager', 'Operator results.operator conditions storage', 'Symptom Detection.Bug 1812261: iptables is segfaulting', 'Symptom Detection.Infrastructure - AWS simulate policy rate-limit', 'Symptom Detection.Infrastructure - GCP quota exceeded (route to forum-gcp)', 'Symptom Detection.Node process segfaulted', 'Symptom Detection.Undiagnosed panic detected in pod', 'operator.All images are built and tagged into stable', 'operator.Find all of the input images from ocp/4.1:${component} and tag them into the output image stream', 'operator.Import the release payload \"initial\" from an external source', 'operator.Import the release payload \"latest\" from an external source', 'operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container setup', 'operator.Run template e2e-aws-upgrade - e2e-aws-upgrade container teardown']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"row_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`row_ids` is a list consisting of all the various tests executed for the job `release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2`. Each job also has an `Overall` row which summarises all the test runs for the job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "43"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(openshift_testgrid.json()[\"row_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that as of right now, `43` tests were run for the job `release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2` in the dashboard `redhat-openshift-ocp-release-4.2-informing`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Pod', 'Overall', '//cmd/ctl/pkg/create/certificaterequest:go_default_test', '//cmd/ctl/pkg/inspect/secret:go_default_test', '//cmd/ctl/pkg/renew:go_default_test', '//cmd/ctl/pkg/status/certificate:go_default_test', '//hack:verify-bazel', '//hack:verify-boilerplate', '//hack:verify-codegen', '//hack:verify-crds', '//hack:verify-errexit', '//hack:verify-gofmt', '//pkg/acme/accounts:go_default_test', '//pkg/acme/util:go_default_test', '//pkg/api/util:go_default_test', '//pkg/controller/acmechallenges/scheduler:go_default_test', '//pkg/controller/acmechallenges:go_default_test', '//pkg/controller/acmeorders/selectors:go_default_test', '//pkg/controller/acmeorders:go_default_test', '//pkg/controller/certificaterequests/acme:go_default_test', '//pkg/controller/certificaterequests/approver:go_default_test', '//pkg/controller/certificaterequests/ca:go_default_test', '//pkg/controller/certificaterequests/selfsigned:go_default_test', '//pkg/controller/certificaterequests/util:go_default_test', '//pkg/controller/certificaterequests/vault:go_default_test', '//pkg/controller/certificaterequests/venafi:go_default_test', '//pkg/controller/certificaterequests:go_default_test', '//pkg/controller/certificates/internal/secretsmanager:go_default_test', '//pkg/controller/certificates/issuing:go_default_test', '//pkg/controller/certificates/keymanager:go_default_test', '//pkg/controller/certificates/readiness:go_default_test', '//pkg/controller/certificates/requestmanager:go_default_test', '//pkg/controller/certificates/trigger/policies:go_default_test', '//pkg/controller/certificates/trigger:go_default_test', '//pkg/controller/certificates:go_default_test', '//pkg/controller/clusterissuers:go_default_test', '//pkg/controller/ingress-shim:go_default_test', '//pkg/controller/issuers:go_default_test', '//pkg/internal/api/mutation:go_default_test', '//pkg/internal/api/validation:go_default_test', '//pkg/internal/apis/acme/install:go_default_test', '//pkg/internal/apis/acme/validation:go_default_test', '//pkg/internal/apis/certmanager/identity/certificaterequests:go_default_test', '//pkg/internal/apis/certmanager/install:go_default_test', '//pkg/internal/apis/certmanager/validation/util:go_default_test', '//pkg/internal/apis/certmanager/validation:go_default_test', '//pkg/internal/apis/meta/install:go_default_test', '//pkg/internal/vault:go_default_test', '//pkg/issuer/acme/dns/acmedns:go_default_test', '//pkg/issuer/acme/dns/akamai:go_default_test', '//pkg/issuer/acme/dns/azuredns:go_default_test', '//pkg/issuer/acme/dns/clouddns:go_default_test', '//pkg/issuer/acme/dns/cloudflare:go_default_test', '//pkg/issuer/acme/dns/digitalocean:go_default_test', '//pkg/issuer/acme/dns/rfc2136:go_default_test', '//pkg/issuer/acme/dns/route53:go_default_test', '//pkg/issuer/acme/dns/util:go_default_test', '//pkg/issuer/acme/dns:go_default_test', '//pkg/issuer/acme/http:go_default_test', '//pkg/issuer/venafi/client:go_default_test', '//pkg/issuer/venafi:go_default_test', '//pkg/issuer:go_default_test', '//pkg/metrics:go_default_test', '//pkg/scheduler:go_default_test', '//pkg/util/pki:go_default_test', '//pkg/util/predicate:go_default_test', '//pkg/util:go_default_test', '//pkg/webhook/authority:go_default_test', '//pkg/webhook/handlers/testdata/apis/testgroup/install:go_default_test', '//pkg/webhook/handlers/testdata/apis/testgroup/validation:go_default_test', '//pkg/webhook/handlers:go_default_test', '//pkg/webhook/server/tls:go_default_test', '//pkg/webhook/server:go_default_test', '//test/integration/certificates:go_default_test', '//test/integration/conversion:go_default_test', '//test/integration/ctl:go_default_test', '//test/integration/validation:go_default_test', '//test/integration/webhook:go_default_test', '//tools/cobra:go_default_test']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"row_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "79"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(jetstack_testgrid.json()[\"row_ids\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similary, we see the various tests executed for the job `ci-cert-manager-bazel` in dashboard `jetstack-cert-manager-master`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## timestamps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1616072850000, 1616029551000, 1615986240000, 1615942953000, 1615899672000, 1615856389000, 1615813107000, 1615769859000, 1615726599000, 1615683312000, 1615640024000, 1615596750000, 1615554026000, 1615540610000, 1615467683000, 1615423576000, 1615380370000, 1615337085000, 1615293832000, 1615250550000, 1615207308000, 1615164068000, 1615120820000, 1615077555000, 1615034271000, 1614990999000, 1614947770000, 1614904482000, 1614861279000, 1614817991000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"timestamps\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`timestamps` returns the timestamp of when the test runs were executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(openshift_testgrid.json()[\"timestamps\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['18-03-21',\n",
+       " '18-03-21',\n",
+       " '17-03-21',\n",
+       " '17-03-21',\n",
+       " '16-03-21',\n",
+       " '16-03-21',\n",
+       " '15-03-21',\n",
+       " '15-03-21',\n",
+       " '14-03-21',\n",
+       " '14-03-21',\n",
+       " '13-03-21',\n",
+       " '13-03-21',\n",
+       " '12-03-21',\n",
+       " '12-03-21',\n",
+       " '11-03-21',\n",
+       " '11-03-21',\n",
+       " '10-03-21',\n",
+       " '10-03-21',\n",
+       " '09-03-21',\n",
+       " '09-03-21',\n",
+       " '08-03-21',\n",
+       " '08-03-21',\n",
+       " '07-03-21',\n",
+       " '07-03-21',\n",
+       " '06-03-21',\n",
+       " '06-03-21',\n",
+       " '05-03-21',\n",
+       " '05-03-21',\n",
+       " '04-03-21',\n",
+       " '04-03-21']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t1 = openshift_testgrid.json()[\"timestamps\"]\n",
+    "dates = [dt.datetime.fromtimestamp(item / 1000).strftime(\"%d-%m-%y\") for item in t1]\n",
+    "dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can convert them to datetime format for further analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1616097568000, 1616090308000, 1616083050000, 1616075827000, 1616068570000, 1616061307000, 1616054049000, 1616046787000, 1616039527000, 1616032268000, 1616025068000, 1616017807000, 1616010576000, 1616003321000, 1615996053000, 1615988868000, 1615981605000, 1615974348000, 1615967085000, 1615959825000, 1615952564000, 1615945305000, 1615938046000, 1615930784000, 1615923526000, 1615916264000, 1615909010000, 1615901797000, 1615894537000, 1615887337000, 1615880137000, 1615872938000, 1615865738000, 1615858540000, 1615851338000, 1615844077000, 1615836878000, 1615829617000, 1615822359000, 1615815127000, 1615807930000, 1615800668000, 1615793866000, 1615786147000, 1615778888000, 1615771629000, 1615764369000, 1615757109000, 1615749850000, 1615742665000, 1615735466000, 1615728265000, 1615721004000, 1615713745000, 1615706485000, 1615699225000, 1615691964000, 1615684704000, 1615677444000, 1615670184000, 1615662924000, 1615655660000, 1615648448000, 1615641189000, 1615633928000, 1615626668000, 1615619409000, 1615612149000, 1615604888000, 1615597628000, 1615590369000, 1615583108000, 1615575848000, 1615568632000, 1615561428000, 1615554229000, 1615547027000, 1615539827000, 1615532568000, 1615525368000, 1615518107000, 1615510847000, 1615503587000, 1615496327000, 1615489068000, 1615481837000, 1615474638000, 1615467437000, 1615460238000, 1615453038000, 1615438637000, 1615431437000, 1615424177000, 1615359061000, 1615351801000, 1615344541000, 1615337283000, 1615330024000, 1615322760000, 1615315502000, 1615308246000, 1615300982000, 1615293730000, 1615286578000, 1615279310000, 1615272050000, 1615264852000, 1615257591000, 1615250331000, 1615243131000, 1615235932000, 1615228732000, 1615221531000, 1615214272000, 1615207072000, 1615199810000, 1615192620000, 1615185361000, 1615178103000, 1615170840000, 1615163580000, 1615156321000, 1615149060000, 1615141800000, 1615134572000, 1615127312000, 1615120051000, 1615112789000, 1615105569000, 1615098308000, 1615091050000, 1615083792000, 1615076528000, 1615069269000, 1615062029000, 1615054771000, 1615047514000, 1615040251000, 1615032990000, 1615025731000, 1615018470000, 1615011211000, 1615003951000, 1614996691000, 1614989431000, 1614982172000, 1614974924000, 1614967665000, 1614960398000, 1614953138000, 1614945878000, 1614938621000, 1614931358000, 1614924097000, 1614916838000, 1614909578000, 1614902318000, 1614895059000, 1614887798000, 1614880537000, 1614873315000, 1614866057000, 1614858866000, 1614851658000, 1614844455000, 1614837262000, 1614830002000, 1614822741000, 1614815482000, 1614808225000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"timestamps\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "170"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(jetstack_testgrid.json()[\"timestamps\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"clusters\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"clusters\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not very sure what `clusters` should return. According to: https://github.com/GoogleCloudPlatform/testgrid/blob/ce1b1405fc889f371fa047ac03adfab23447c804/README.md#clustered-failures, `clusters` should probably return the clustered failure tests. However, this does not seem to be the case since for `release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2` in the dashboard `redhat-openshift-ocp-release-4.2-informing`, we see that when we filter the dashboard tab to \"Display Clustered Failure Tests\" it returns the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/testgrid_02.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## test_id_map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"test_id_map\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"test_id_map\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This seems to be a lookup map for shortening long test IDs in each row: https://github.com/GoogleCloudPlatform/testgrid/blob/master/pb/response/types.proto#L133"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## test-metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"test-metadata\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"test-metadata\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## stale-test-threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"stale-test-threshold\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"stale-test-threshold\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`stale-test-threshold` probably means the number of times a test is _not_ run for a particular job, before it gets labeled as \"stale\" and hidden in the UI. For both the jobs in this example, this threshold is 10."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## num-stale-tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"num-stale-tests\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that for job`release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2` in the dashboard `redhat-openshift-ocp-release-4.2-informing`, the number of stale tests is `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"num-stale-tests\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similary, for the job `ci-cert-manager-bazel` in dashboard `jetstack-cert-manager-master`, the number of stale tests were `0`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## add-tabular-name-option"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"add-tabular-names-option\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"add-tabular-names-option\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`add-tabular-names-option` seems to return a True/False value, indicating whether additional tabular names can be added."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## show-tabular-names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"show-tabular-names\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"show-tabular-names\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`show-tabular-names` also returns a True/False value, but doesn't seem too relevant for our analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"description\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Runs 'bazel test //...'\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"description\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `description` field seems to be a general description of the job. However, most of them seem to return empty values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## bug-component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"bug-component\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"bug-component\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not too sure what this should return."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## code-search-path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://github.com/openshift/origin/search\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"code-search-path\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "github.com/kubernetes/kubernetes/search\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"code-search-path\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `code-search-path` seems to return the relevant GitHub repository path related to the job and the test runs executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## overall-status"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `overall-status` field returns a status code describing the entire \"job\" (the individual tabs within a dashboard in TestGrid are jobs). The mapping and definition of the `overall-status` codes are defined here: https://github.com/GoogleCloudPlatform/testgrid/blob/1dbc0e8b6ef997b9c90b3fb79518c35ce4d27ea2/pb/summary/summary.proto#L163\n",
+    "\n",
+    "* 0 = NOT_SET\n",
+    "* 1 = UNKNOWN\n",
+    "* 2 = PASS\n",
+    "* 3 = FAIL\n",
+    "* 4 = FLAKY\n",
+    "* 5 = STALE\n",
+    "* 6 = BROKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"overall-status\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hence, for the job `release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2` in the dashboard `redhat-openshift-ocp-release-4.2-informing`, the overall status is `3` which is `Fail`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"overall-status\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Similarly, the overall status for the job `ci-cert-manager-bazel` in dashboard `jetstack-cert-manager-master` is `3`, which attributes it as `Fail`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is interesting to note is that the `overall-status` field is different from the `Overall` row we see in the TestGrid dashboard. The `overall-status` defines the status on a \"job\" level, whereas the `Overall` defines the status on a \"build\" level. \n",
+    "\n",
+    "For eg: in the dashboard `redhat-openshift-ocp-release-4.2-informing`, under the tab aka job `release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2` there is an \"Overall\" row as highlighted below, which defines the overall status for each \"build\" executed in the job. Each build comprises of multiple tests and the overall status of the build is an aggregate of all its test statuses. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"../../../docs/assets/images/testgrid_03.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## about-dashboard-url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "''"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"about-dashboard-url\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "''"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"about-dashboard-url\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not entirely sure, but maybe this field shows the URL which describes this particular dashboard."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## results-text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "See these results on Prow\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"results-text\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field usually just says \"see results on prow\". If we manually go the testgrid website : https://testgrid.k8s.io/redhat-openshift-ocp-release-4.2-informing#release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2&show-stale-tests= and click on any test, we will be re-directed to the prow dashboard (https://prow.ci.openshift.org/view/gcs/origin-ci-test/logs/release-openshift-origin-installer-e2e-aws-upgrade-rollback-4.1-to-4.2/1371445677782994944) where we can find these results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## latest-green"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"latest-green\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"latest-green\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not too sure what this should be returning. Manually checked this for a few other dashboards/tabs, but all were empty."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## triage-enabled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"triage-enabled\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"triage-enabled\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to the [testgrid](https://testgrid.k8s.io/) dashboard, Google has another dashboard called [triage](https://go.k8s.io/triage). This dashboard \"shows clusters of similar test failures across all jobs\", and suggests what general subject these failures related to e.g. storage. So this the value, **True** or **False**, returned in this field probably tells whether triage is enabled or not for this particular dashboard/tab."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## notifications"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"notifications\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jetstack_testgrid.json()[\"notifications\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This value looks like should be returning the notifications if any. For various dashboards and jobs we looked at it however returned **None**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `xyz-template`-type fields\n",
+    "\n",
+    "\n",
+    "These fields are [instances](https://github.com/GoogleCloudPlatform/testgrid/blob/master/pb/response/types.proto#L147) of the [LinkTemplate](https://github.com/GoogleCloudPlatform/testgrid/blob/master/pb/config/config.pb.go#L1775) struct on the testgrid repo. These structs/jsons have three keys:\n",
+    "\n",
+    "1. url\n",
+    "2. name\n",
+    "3. options\n",
+    "\n",
+    "Together, these keys describe the format of various URLs where additional information regarding the tests can be found, where bugs can be filed, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### open-test-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://prow.ci.openshift.org/view/gcs/<gcs_prefix>/<changelist>',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"open-test-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://prow.k8s.io/view/gs/<gcs_prefix>/<changelist>',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"open-test-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field seems to be the template for the URL where more details for a particular test can be found. That is, it's the \"general format\" for the Prow page for each test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### file-bug-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://bugzilla.redhat.com/enter_bug.cgi',\n",
+       " 'name': '',\n",
+       " 'options': {'cf_environment': 'test: <test-name>',\n",
+       "  'cf_internal_whiteboard': 'buildcop',\n",
+       "  'classification': 'Red Hat',\n",
+       "  'comment': 'test: <test-name> failed, see job: <link>',\n",
+       "  'product': 'OpenShift Container Platform',\n",
+       "  'short_desc': 'test: <test-name>'}}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"file-bug-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://github.com/kubernetes/kubernetes/issues/new',\n",
+       " 'name': '',\n",
+       " 'options': {'body': '<test-url>', 'title': 'E2E: <test-name>'}}"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"file-bug-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field seems to be the template for the URL where bugs can be filed for the respective organization/dashboard/job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### attach-bug-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': '', 'name': '', 'options': {}}"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"attach-bug-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': '', 'name': '', 'options': {}}"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"attach-bug-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is probably the template for URL where bugs can be attached. However, it's empty for all of the instances we looked into."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### results-url-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://prow.ci.openshift.org/job-history/<gcs_prefix>',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"results-url-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://prow.k8s.io/job-history/<gcs_prefix>',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"results-url-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field seems to be the template for the URL where historical results can be viewed. That is, the \"Job History\" tab on Prow for each test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### code-search-url-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://github.com/openshift/origin/compare/<start-custom-0>...<end-custom-0>',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"code-search-url-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://github.com/jetstack/cert-manager/compare/<start-custom-0>...<end-custom-0>',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"code-search-url-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field is the template for the URL where you can view diffs / changes across branches (basically to the git \"Compare changes\" page)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### open-bug-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://github.com/openshift/origin/issues/',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"open-bug-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': 'https://github.com/jetstack/cert-manager/issues/',\n",
+       " 'name': '',\n",
+       " 'options': {}}"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"open-bug-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field seems to be the template for the URL that contains the link to issues page for the respective repository, however the name and options look empty."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### context-menu-template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': '', 'name': '', 'options': {}}"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "openshift_testgrid.json()[\"context-menu-template\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'url': '', 'name': '', 'options': {}}"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jetstack_testgrid.json()[\"context-menu-template\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field is mostly empty for all intances we looked into. So not sure what this URL is supposed to be."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metadata within the `statuses` field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['name', 'original-name', 'alert', 'linked_bugs', 'messages', 'short_texts', 'statuses', 'target', 'user_property'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(openshift_testgrid.json()[\"tests\"][0].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['name', 'original-name', 'alert', 'linked_bugs', 'messages', 'short_texts', 'statuses', 'target', 'user_property'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0].keys()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### linked_bugs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((openshift_testgrid.json()[\"tests\"][0])[\"linked_bugs\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0])[\"linked_bugs\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking OpenShift dashboard\n",
+      "Checking non-OpenShift dashboard\n"
+     ]
+    }
+   ],
+   "source": [
+    "# is this non-empty for any other test in any other tabs?\n",
+    "print(\"Checking OpenShift dashboard\")\n",
+    "for t in openshift_testgrid.json()[\"tests\"]:\n",
+    "    if len(t[\"linked_bugs\"]):\n",
+    "        print(t[\"linked_bugs\"])\n",
+    "\n",
+    "print(\"Checking non-OpenShift dashboard\")\n",
+    "for t in jetstack_testgrid.json()[\"tests\"]:\n",
+    "    if len(t[\"linked_bugs\"]):\n",
+    "        print(t[\"linked_bugs\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linked bugs is empty."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['', '', '', '', '', '', '', '', '', '', '', '', 'Build failed outside of test results', '', 'Build failed outside of test results', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((openshift_testgrid.json()[\"tests\"][0])[\"messages\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[\"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\", \"podinfo.json not found, please install prow's GCS reporter\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0])[\"messages\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "170"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len((jetstack_testgrid.json()[\"tests\"][0])[\"messages\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Messages contains messages for first few tests of openshift testgrid but good information for jetstack testgrid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### short_texts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['', '', '', '', '', '', '', '', '', '', '', '', 'F', '', 'F', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((openshift_testgrid.json()[\"tests\"][0])[\"short_texts\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!', '!']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0])[\"short_texts\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 'F', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 'F', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][1])[\"short_texts\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not sure what `F` or `!` signify here. No documentation found about what this could signify."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### user_property"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((openshift_testgrid.json()[\"tests\"][0])[\"user_property\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0])[\"user_property\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking OpenShift dashboard\n",
+      "Checking non-OpenShift dashboard\n"
+     ]
+    }
+   ],
+   "source": [
+    "# is this non-None for any other test in any other tabs?\n",
+    "print(\"Checking OpenShift dashboard\")\n",
+    "for t in openshift_testgrid.json()[\"tests\"]:\n",
+    "    if t[\"user_property\"] is not None:\n",
+    "        print(t[\"user_property\"])\n",
+    "\n",
+    "print(\"Checking non-OpenShift dashboard\")\n",
+    "for t in jetstack_testgrid.json()[\"tests\"]:\n",
+    "    if t[\"user_property\"] is not None:\n",
+    "        print(t[\"user_property\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This field is empty for all tests in all the dashboards we looked at. Therefore we can't determine exactly what this field means. Nonetheless, based off of the comment [here](https://github.com/GoogleCloudPlatform/testgrid/blob/1dbc0e8b6ef997b9c90b3fb79518c35ce4d27ea2/pb/state/state.pb.go#L537), it seems that `user_property` is any custom, user-defined property that can be associated with a test result."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overall\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((openshift_testgrid.json()[\"tests\"][0])[\"target\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pod\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0])[\"target\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking OpenShift dashboard\n",
+      "Checking non-OpenShift dashboard\n"
+     ]
+    }
+   ],
+   "source": [
+    "# is \"target\" same as \"name\"?\n",
+    "print(\"Checking OpenShift dashboard\")\n",
+    "for t in openshift_testgrid.json()[\"tests\"]:\n",
+    "    if t[\"target\"] != t[\"name\"]:\n",
+    "        print(t[\"target\"])\n",
+    "        print(t[\"name\"])\n",
+    "\n",
+    "print(\"Checking non-OpenShift dashboard\")\n",
+    "for t in jetstack_testgrid.json()[\"tests\"]:\n",
+    "    if t[\"target\"] != t[\"name\"]:\n",
+    "        print(t[\"target\"])\n",
+    "        print(t[\"name\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This value in this field is the same as the value in `name` for all tests in all the dashboards we looked at. We couldn't find any strong evidence in the testgrid repo that could explain the difference. Therefore we can't determine exactly how this field is different than test `name`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### original-name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overall\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((openshift_testgrid.json()[\"tests\"][0])[\"original-name\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pod\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((jetstack_testgrid.json()[\"tests\"][0])[\"original-name\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking OpenShift dashboard\n",
+      "Checking non-OpenShift dashboard\n"
+     ]
+    }
+   ],
+   "source": [
+    "# is \"original-name\" same as \"name\"?\n",
+    "print(\"Checking OpenShift dashboard\")\n",
+    "for t in openshift_testgrid.json()[\"tests\"]:\n",
+    "    if t[\"original-name\"] != t[\"name\"]:\n",
+    "        print(t[\"original-name\"])\n",
+    "        print(t[\"name\"])\n",
+    "\n",
+    "print(\"Checking non-OpenShift dashboard\")\n",
+    "for t in jetstack_testgrid.json()[\"tests\"]:\n",
+    "    if t[\"original-name\"] != t[\"name\"]:\n",
+    "        print(t[\"original-name\"])\n",
+    "        print(t[\"name\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This value in this field is the same as the value in `name` for all tests in all the dashboards we looked at. We couldn't find any strong evidence in the testgrid repo that could explain the difference. Therefore we can't determine exactly how this field is different than test `name`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "In this notebook, we explored all the metadata fields in the testgrid json data. From the results above, it seems that not all metadata is created equal and that some fields are more useful for data analysis than the others. Specifically, the following fields seem to have more valueable information:\n",
+    "\n",
+    "- `query`: The \"gcs_prefix\" value here can be used for contstructing prow URL for each test\n",
+    "- `changelists`: The \"ids\" value here can be used for contstructing prow URL for each test\n",
+    "- `custom-column`: Although not properly populated for OpenShift dashboards, in general the values here show the commit ids corresponding to each test run. These can later be used for other types of analysis such as determining what code resulted in passing tests vs failing test, etc.\n",
+    "- `metrics`: The \"test-duration-minutes\" value here is useful\n",
+    "- `row_ids`: This provides the list of test names. Maybe this can be used in the labelwise_encode function (instead of getting test names in a for loop, we can directly set test names to be the value in this field)\n",
+    "- `timestamps`: Run timestamps\n",
+    "- `clusters`: Seems useful because it provides similar tests, but it's not heavily populated for OpenShift dashboards\n",
+    "- `num-stale-tests`: Gives an idea of \"health\" of test runs (are all tests getting run? how many have not been run in a long time?)\n",
+    "- `stale-test-threshold`: Might be useful, as it gives context for how severe of a job health indicator the number above is\n",
+    "- `code-search-path`: Provides the GitHub link for diff'ing across PRs/commits\n",
+    "- `overall-status`: Gives job summary\n",
+    "- `open-test-template`: The template here can be used with query and changelists values to get relevant URLs\n",
+    "- `file-bug-template`: The template here can be used with query and changelists values to get relevant URLs\n",
+    "- `open-bug-template`: The template here can be used with query and changelists values to get relevant URLs\n",
+    "- `results-url-template`: The template here can be used with query and changelists values to get relevant URLs\n",
+    "- `code-search-url-template`: The template here can be used with query and changelists values to get relevant URLs\n",
+    "- `messages`: Contains some error messages from running the test, but not very densely populated.\n",
+    "\n",
+    "\n",
+    "Lastly, the following fields seem to be either noise or empty or repetiions of data already included in other fields.\n",
+    "\n",
+    "- linked-bugs\n",
+    "    - NOTE: Maybe this could have been a very useful field, but it was found to be always empty. \n",
+    "- bugs\n",
+    "    - NOTE: Maybe this could have been a very useful field, but it was found to be always empty. \n",
+    "- cached\n",
+    "- phase-timer\n",
+    "- test-group-name\n",
+    "- status\n",
+    "- column_ids\n",
+    "- column-header-names\n",
+    "- groups\n",
+    "- test-id-map\n",
+    "- test-metadata\n",
+    "- add-tabular-name-option\n",
+    "- show-tabular-names\n",
+    "- description\n",
+    "- bug-component\n",
+    "- about-dashboard-url\n",
+    "- results-text\n",
+    "- latest-green\n",
+    "- triage-enabled\n",
+    "- notifications\n",
+    "- short_texts\n",
+    "- attach-bug-template\n",
+    "- context-menu-template\n",
+    "- target\n",
+    "- original-name\n",
+    "- user_property"
+   ]
+  }
+ ],
+ "metadata": {
+  "finalized": {
+   "timestamp": 1615408695249,
+   "trusted": true
+  },
+  "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/data-sources/gcsweb-ci/ec506/ec506.ipynb b/Red Hat NLP/notebooks/data-sources/gcsweb-ci/ec506/ec506.ipynb
new file mode 100644
index 00000000..affe8039
--- /dev/null
+++ b/Red Hat NLP/notebooks/data-sources/gcsweb-ci/ec506/ec506.ipynb	
@@ -0,0 +1,34 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USE THIS DIRECTORY To Write your notbeooks"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "devenv",
+   "language": "python",
+   "name": "devenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/failure-type-classification/README.md b/Red Hat NLP/notebooks/failure-type-classification/README.md
new file mode 100644
index 00000000..5275e396
--- /dev/null
+++ b/Red Hat NLP/notebooks/failure-type-classification/README.md	
@@ -0,0 +1,49 @@
+# Failure type classification with the TestGrid data
+
+*Sanket Badhe, Michael Clifford and Marcel Hild*,  *2020-10-27 v0.1.0-dev*
+
+## Overview
+
+In continuous integration (CI) project workflow, developers frequently integrate code into a shared repository. Each integration can then be verified by an automated build and numerous different automated tests. Whenever a failure occurs in a test, developers manually need to analyze failures. Failures in the build can be a legitimate failure or due to some other issues like infrastructure flake, install flake, flaky test, etc. SME can analyze the TestGrid data and determine if failures are legitimate or not. However, it takes a lot of manual effort and reduces the productivity of a team.
+
+In this project, our objective is to automate the failure type classification task with the Testgrid data. As we don’t have labeled data to address this problem, we will focus on unsupervised learning methods and heuristics. Figure 1 shows the TestGrid data with different patterns to analyze the type of failure.
+
+![image alt text](https://user-images.githubusercontent.com/4494906/99848891-95745180-2b48-11eb-9a4e-f9cf5da59ab0.png)
+
+ Figure 1: Different type of failures in TestGrid
+
+ In the following section, we discuss the different patterns in more detail.
+
+* Rows with red interspersed with green: This usually means a flakey test.  Flakey tests pass and fail across multiple runs over a certain period of time. We can trigger this test behavior by using the concept of edge. Edge is the transition of a particular test case from pass to fail on the successive run. We can model edges using a different technique to detect a flakey test.
+
+* Rows with solid red chunks: This behavior is almost always a regression either in the test or the product.  We can analyze each row to check continuous red cells for detecting install flakes.
+
+* Rows with solid red chunks and white to the right: This behavior usually means a new test was added that is failing when running in the release job. For each cell, we will check if there exist all failed test cases to the left and all passing test cases to the right. If there exists this pattern, we will trigger this error.
+
+* Repeating vertical red bars: This behavior usually means the subsystem has a bug, and we will find a set of rows that all fail together on the same runs. For this failure type we can also analyze each column to check for continuous red cells to detect subsystem bugs.
+
+* Failure waterfall:  If there are meandering failures moving from bottom to top, right to the left, this almost always means Infrak flake. We can generate convolutional filters manually to detect Failure waterfall patterns. If it’s hectic to encode all the patterns manually, we can also develop a method to create convolution filters to detect ‘Failure waterfall’ patterns automatically.
+
+If this project is successful, we will develop a tool to automatically analyze the TestGrid data. This tool will perform failure type classification with the Testgrid data to address an existing manual process executed by subject matter experts. Using the tool, the developers can focus on real issues and become more productive. Furthermore, we will provide insights about overall statistics about failures so that test developers can improve on existing test suites.
+
+### A. Problem Statement
+
+Given a TestGrid, we want to classify/detect different failure patterns occurring over a certain period of time. In the later part, we will aggregate the results to conclude about the primary reasons behind failures for each release.
+
+### B. Checklist for project completion
+
+1. A notebooks that shows classification and analysis of different types of test failures on TestGrid data.
+
+2. Jupyterhub image to reproduce the results.
+
+3. Public blog explaining analysis and results.
+
+4. Results hosted for SME to review
+
+### C. Provide a solution in terms of human actions to confirm if the task is within the scope of automation through AI.
+
+Without AI and automation tooling, SME will need to go to TestGrid data of a particular release and look at the failures. An SME will determine if that failure is following any of the patterns that we have discussed in earlier sections. Based on detected patterns, an SME tries to determine the reason behind failure.
+
+### D. Outline a path to operationalization.
+
+Once we have notebooks ready, we will build a Notebook-based Pipeline using Elyra. The results will be stored in S3. We can then use Superset as our dashboard and visualization tool, which SME/developers can access and give feedback. If the tool is deemed useful, we could also look into integrating it with the existing TestGrid project.
diff --git a/Red Hat NLP/notebooks/failure-type-classification/background/testgrid_flakiness_detection.ipynb b/Red Hat NLP/notebooks/failure-type-classification/background/testgrid_flakiness_detection.ipynb
new file mode 100644
index 00000000..f47833e0
--- /dev/null
+++ b/Red Hat NLP/notebooks/failure-type-classification/background/testgrid_flakiness_detection.ipynb	
@@ -0,0 +1,2159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Flake Detection for TestGrid \n",
+    "\n",
+    "\n",
+    "In the continuous integration (CI) project workflow, developers frequently integrate code into a shared repository. Each integration can then be verified by an automated build and tests. Whenever a failure occurs in a test, developers need to manually analyze failures. Failures in the build can be legitimate or due to some other issues like an infrastructure flake, install flake, flaky test, or some other type of failure. SME's can analyze the TestGrid data manually and determine if failures appear to be legitimate or not. However, it takes a lot of human effort and reduces the productivity of a team. In this notebook we will try to reliably detect one of these failure types: Flaky test. \n",
+    "\n",
+    "## What is a flaky test?\n",
+    "\n",
+    "A flaky test is one that passes or fails in a nondeterministic way. [See this paper for more details](http://mir.cs.illinois.edu/~eloussi2/publications/fse14.pdf). \n",
+    "\n",
+    "In data science terms, if a test passes and fails in an apparently random sequence then it is likely to be a flake. Tests can also be considered flaky if they pass and fail in a non-random but inconsistent pattern. Therefore, our goal here is to develop a technique for finding apparently random or inconsistent pass/fail sequences, which we can then apply to the TestGrid dataset.       \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:54:49.160755Z",
+     "start_time": "2021-01-26T17:54:49.154099Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import colors\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "\n",
+    "import gzip\n",
+    "import json\n",
+    "\n",
+    "sns.set()\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:54:49.581262Z",
+     "start_time": "2021-01-26T17:54:49.576764Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# adding path to notebook consisting of modules to be imported\n",
+    "module_path = os.path.abspath(os.path.join(\"../../data-sources/TestGrid\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:54:50.000173Z",
+     "start_time": "2021-01-26T17:54:49.996611Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Access\n",
+    "\n",
+    "_In depth details for data access and preprocessing can be found in the [testgrid_EDA notebook](../../data-sources/TestGrid/testgrid_EDA.ipynb)._ \n",
+    "\n",
+    "Here we provide two data access methods: download the latest or use example data. If you would like to download the most recent data for a specific grid, then please set `download_data` to `True` on line #2 below and update `dashboard_name` and `job_name` appropriately. Be warned, the output and results will likely change if the most recent data is used instead of the fixed example data.   \n",
+    "\n",
+    "Otherwise, we will use the repo's example dataset `data/raw/testgrid_810.json.gz`\n",
+    "\n",
+    "Regardless of the method used, we will be looking at \"redhat-openshift-ocp-release-4.6-informing/periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-proxy\" as our example throughout. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:54:56.049375Z",
+     "start_time": "2021-01-26T17:54:50.766865Z"
+    }
+   },
+   "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>statuses</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Overall</td>\n",
+       "      <td>[{'count': 21, 'value': 12}, {'count': 1, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[sig-api-machinery][Feature:APIServer][Late] k...</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[sig-auth][Feature:SCC][Early] should not have...</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 1, 'valu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 20, 'val...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[sig-node] pods should never transition back t...</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 18, 'val...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  \\\n",
+       "0                                            Overall   \n",
+       "1  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "2  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "3  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "4  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "5  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "6  [sig-api-machinery][Feature:APIServer][Late] k...   \n",
+       "7  [sig-auth][Feature:SCC][Early] should not have...   \n",
+       "8     [sig-arch] Monitor cluster while tests execute   \n",
+       "9  [sig-node] pods should never transition back t...   \n",
+       "\n",
+       "                                            statuses  \n",
+       "0  [{'count': 21, 'value': 12}, {'count': 1, 'val...  \n",
+       "1  [{'count': 9, 'value': 12}, {'count': 1, 'valu...  \n",
+       "2  [{'count': 9, 'value': 12}, {'count': 1, 'valu...  \n",
+       "3  [{'count': 9, 'value': 12}, {'count': 1, 'valu...  \n",
+       "4  [{'count': 9, 'value': 12}, {'count': 1, 'valu...  \n",
+       "5  [{'count': 9, 'value': 1}, {'count': 1, 'value...  \n",
+       "6  [{'count': 20, 'value': 0}, {'count': 1, 'valu...  \n",
+       "7  [{'count': 20, 'value': 0}, {'count': 1, 'valu...  \n",
+       "8  [{'count': 20, 'value': 0}, {'count': 20, 'val...  \n",
+       "9  [{'count': 20, 'value': 0}, {'count': 18, 'val...  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Do you want to use download data or stored data?\n",
+    "download_data = False\n",
+    "\n",
+    "dashboard_name = \"redhat-openshift-ocp-release-4.6-informing\"\n",
+    "job_name = \"periodic-ci-openshift-release-master-ocp-4.6-e2e-aws-proxy\"\n",
+    "\n",
+    "if download_data:\n",
+    "    payload = {\"show-stale-tests\": job_name, \"tab\": job_name}\n",
+    "    response = requests.get(\n",
+    "        \"https://testgrid.k8s.io/\" + dashboard_name + \"/table\", params=payload\n",
+    "    )\n",
+    "    details = pd.DataFrame(response.json()[\"tests\"]).drop(\n",
+    "        [\n",
+    "            \"linked_bugs\",\n",
+    "            \"messages\",\n",
+    "            \"user_property\",\n",
+    "            \"target\",\n",
+    "            \"original-name\",\n",
+    "        ],\n",
+    "        axis=1,\n",
+    "    )\n",
+    "else:\n",
+    "    with gzip.open(\"../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "        data = json.load(read_file)\n",
+    "    details = data['\"' + dashboard_name + '\"'][job_name][\"grid\"]\n",
+    "    details = pd.DataFrame(details)\n",
+    "\n",
+    "details.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the column \"statuses\" above we can see that the time series data is run length encoded. Let's add a decoded column so we can get the data in an easier to use array format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:54:56.065938Z",
+     "start_time": "2021-01-26T17:54:56.051971Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "details[\"values\"] = details[\"statuses\"].apply(decode_run_length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:54:56.168492Z",
+     "start_time": "2021-01-26T17:54:56.068203Z"
+    }
+   },
+   "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>statuses</th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Overall</td>\n",
+       "      <td>[{'count': 21, '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>1</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 12, 12...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 12, 12...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 12, 12...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 12}, {'count': 1, 'valu...</td>\n",
+       "      <td>[12, 12, 12, 12, 12, 12, 12, 12, 12, 0, 12, 12...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>operator.Run multi-stage test e2e-aws-proxy - ...</td>\n",
+       "      <td>[{'count': 9, 'value': 1}, {'count': 1, 'value...</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[sig-api-machinery][Feature:APIServer][Late] k...</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 1, 'valu...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[sig-auth][Feature:SCC][Early] should not have...</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 1, 'valu...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[sig-arch] Monitor cluster while tests execute</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 20, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[sig-node] pods should never transition back t...</td>\n",
+       "      <td>[{'count': 20, 'value': 0}, {'count': 18, 'val...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                name  \\\n",
+       "0                                            Overall   \n",
+       "1  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "2  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "3  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "4  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "5  operator.Run multi-stage test e2e-aws-proxy - ...   \n",
+       "6  [sig-api-machinery][Feature:APIServer][Late] k...   \n",
+       "7  [sig-auth][Feature:SCC][Early] should not have...   \n",
+       "8     [sig-arch] Monitor cluster while tests execute   \n",
+       "9  [sig-node] pods should never transition back t...   \n",
+       "\n",
+       "                                            statuses  \\\n",
+       "0  [{'count': 21, 'value': 12}, {'count': 1, 'val...   \n",
+       "1  [{'count': 9, 'value': 12}, {'count': 1, 'valu...   \n",
+       "2  [{'count': 9, 'value': 12}, {'count': 1, 'valu...   \n",
+       "3  [{'count': 9, 'value': 12}, {'count': 1, 'valu...   \n",
+       "4  [{'count': 9, 'value': 12}, {'count': 1, 'valu...   \n",
+       "5  [{'count': 9, 'value': 1}, {'count': 1, 'value...   \n",
+       "6  [{'count': 20, 'value': 0}, {'count': 1, 'valu...   \n",
+       "7  [{'count': 20, 'value': 0}, {'count': 1, 'valu...   \n",
+       "8  [{'count': 20, 'value': 0}, {'count': 20, 'val...   \n",
+       "9  [{'count': 20, 'value': 0}, {'count': 18, 'val...   \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, 1, 12, 12...  \n",
+       "2  [12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 12, 12...  \n",
+       "3  [12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 12, 12...  \n",
+       "4  [12, 12, 12, 12, 12, 12, 12, 12, 12, 0, 12, 12...  \n",
+       "5  [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, ...  \n",
+       "6  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "7  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "8  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "9  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "details.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TestGrids are made of a set of tests that either pass or fail over time. They are multidimensional time series where the values can take either 0 (not run), 1 (pass), 12 (fail), or 13(flaky). \n",
+    "\n",
+    "Now that we have all our data unrolled, lets plot it. We will use green for pass (1), red for fail (12), white for not run (0) and purple for flaky (13). We will also just plot the first 40 rows to save some space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:03.659709Z",
+     "start_time": "2021-01-26T17:54:56.170886Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = colors.ListedColormap([\"white\", \"green\", \"red\", \"purple\"])\n",
+    "norm = colors.BoundaryNorm(boundaries=[0, 1, 12, 13], ncolors=3)\n",
+    "sns.heatmap(\n",
+    "    np.array(list(details[\"values\"].values))[:40],\n",
+    "    fmt=\"\",\n",
+    "    cmap=cmap,\n",
+    "    norm=norm,\n",
+    "    cbar=False,\n",
+    "    linewidths=0.1,\n",
+    "    linecolor=\"Black\",\n",
+    ")\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.title(\n",
+    "    \"Redhat-openshift-ocp-release-4.6-informing/periodic-ci-\\\n",
+    "openshift-release-master-ocp-4.6-e2e-aws-proxy\\n\",\n",
+    "    fontsize=20,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-11-16T16:42:31.790008Z",
+     "start_time": "2020-11-16T16:42:31.786912Z"
+    }
+   },
+   "source": [
+    "Cells with Purple color in the above graph are the existing flake labels defined by the build process. Currently, each failed test is retried, and if it passes on a subsequent run it is considered to be flaky.\n",
+    "\n",
+    "We can see from the grid above that there is a large chunk of flakes between test 7 and 15, but there is also a fair amount of irregular pass/failure patterns on the grid that are not being flagged. Let's see if we can create a method to better capture these flakes. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-11-06T14:48:18.171340Z",
+     "start_time": "2020-11-06T14:48:18.169258Z"
+    }
+   },
+   "source": [
+    "## Flaky test detection\n",
+    "\n",
+    "In the following section, we will explore different methods to detect flaky tests. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-11-06T14:49:12.344626Z",
+     "start_time": "2020-11-06T14:49:12.342633Z"
+    }
+   },
+   "source": [
+    "## Naive flakiness method\n",
+    "\n",
+    "This method calculates flakiness as a ratio of failed or flaky tests to total tests. The idea being that a score of 50 would mean a 50/50 (random) chance of a pass or fail occurring, indicating a flaky test. The major drawback of this method is that it evaluates the time series as a whole and can't account for specific pass/fail patterns or sub-sequences. If the first half of a series are all pass and the second half are all fail, we would not consider that to be a flake, despite this method flagging it as such.\n",
+    "\n",
+    "This is all to say, this is our naive baseline we can use to ensure our later methods are performing well.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:10.582668Z",
+     "start_time": "2021-01-26T17:55:10.577749Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def naive_flake_calc(test_row):\n",
+    "    return (\n",
+    "        100\n",
+    "        * np.logical_or(np.array(test_row) == 12, np.array(test_row) == 13).sum()\n",
+    "        / len(test_row)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:11.192861Z",
+     "start_time": "2021-01-26T17:55:10.893599Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Flake Score 45.833333333333336\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_array = [1] * 6 + [12] * 11 + [1] * 7\n",
+    "plt.figure(figsize=(20, 5))\n",
+    "plt.plot(test_array)\n",
+    "plt.title(\"Consecutive Failures\", fontsize=20)\n",
+    "plt.xlabel(\"time\")\n",
+    "plt.show()\n",
+    "print(f\"Naive Flake Score {naive_flake_calc(test_array)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above cell, we have shown a drawback of the naive flakiness method. As we can see, we have a test array that consist of consecutive test failures, which is not an attribute of a flaky test. However, we got a very high flakiness score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Flip flakiness method\n",
+    "\n",
+    "As we discussed earlier, flaky tests pass and fail across multiple runs over a certain period of time. We can characterize this behavior by using the concept of an _edge_. Here we will define an edge as the transition of a particular test case from pass to fail (or fail to pass). Let's look at a couple examples below to illustrate the idea further. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:12.866741Z",
+     "start_time": "2021-01-26T17:55:12.351561Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 10))  # instantiating figure to be 5\"x5\", 100 dpi resolution\n",
+    "\n",
+    "\n",
+    "test_array = [1] * 6 + [12] * 11 + [1] * 7\n",
+    "x = np.arange(len(test_array))\n",
+    "plt.subplot(2, 1, 1, aspect=0.3)\n",
+    "plt.step(x, test_array)\n",
+    "plt.title(\"Single edge and multiple fails\", fontsize=20)\n",
+    "\n",
+    "plt.subplot(2, 1, 2, aspect=0.3)\n",
+    "test_array_1 = [1] * 5 + [1, 12] * 8 + [1] * 3\n",
+    "plt.step(x, test_array_1)\n",
+    "plt.title(\"multiple edges and multiple fails\", fontsize=20)\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the images above, we have shown example results for 25  test runs. In the first image we can see there are multiple fails but only a single edge. In the second image, we have fewer fails but many more edges. Therefore, the second test exhibits a more erratic behavior pattern, and one we would associate more with a flaky test. \n",
+    "\n",
+    "We calculated the flakiness score as the number of edges divided by total runs. The most common approach to detect flaky test is to run flaky test multiple times and if it passes in any run then it is not considered to be a flaky test ([please see paper for details](http://mir.cs.illinois.edu/~eloussi2/publications/fse14.pdf)). At Google, for example, if a test fails three times in a row, only then is it reported as a real failure; otherwise, it's considered a flake [please see blog for details](https://testing.googleblog.com/2016/05/flaky-tests-at-google-and-how-we.html). We will follow suite and also ignore three or more consecutive failures when calculating a flakiness score.\n",
+    "\n",
+    "For the flip flakiness method below the possible scores will lie between 0 and 50; where 0 is no flakiness and 50 is maximum flakiness. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:12.884926Z",
+     "start_time": "2021-01-26T17:55:12.869218Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def calc_consecutive_failures(test_row, i):\n",
+    "    \"\"\"This function calculates number of consecutive failures\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_row : array\n",
+    "            array of test runs with 0, 1, 12 values as not run, pass, fail respectively\n",
+    "    i : float, int\n",
+    "            index in array i\n",
+    "\n",
+    "    Returns\n",
+    "    ----------\n",
+    "    result: int\n",
+    "            number of consecutive failures from index i\n",
+    "    \"\"\"\n",
+    "\n",
+    "    result = 0\n",
+    "    while i < len(test_row) and (test_row[i] == 12 or test_row[i] == 0):\n",
+    "        if test_row[i] == 12:\n",
+    "            result += 1\n",
+    "        i += 1\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "def calc_flakiness_score(test_row, ignore_failures_in_a_row=3):\n",
+    "    \"\"\"This function calculates flakiness score as the number of edges divided by total runs.\n",
+    "    At google, If the test is failing three times in a row, then only it reported as real failures;\n",
+    "    otherwise, it's considered a flaky test.\n",
+    "    (https://testing.googleblog.com/2016/05/flaky-tests-at-google-and-how-we.html)\n",
+    "    Hence, we ignored three or more than three consecutive failures\n",
+    "    and test cases that are not run while calculating the flakiness score.\n",
+    "\n",
+    "    We always consider label 13 as an edge.\n",
+    "    since currently, each failed test is retry, and if it's passed on a subsequent run it is considered as flaky.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_row : array\n",
+    "            array of test runs with 0, 1, 12, 13 values as not run, pass, fail, flaky respectively\n",
+    "\n",
+    "    Returns\n",
+    "    ----------\n",
+    "    flakiness: int\n",
+    "            Flakiness score lies between 0 and 100; 0 is no flakiness, and 100 is maximum flakiness.\n",
+    "    \"\"\"\n",
+    "    flips = 0\n",
+    "    i = 0\n",
+    "    ## ignore not run instances in the starting\n",
+    "    while i < len(test_row) and test_row[i] == 0:\n",
+    "        i += 1\n",
+    "\n",
+    "    ## intializing last_passing variable value\n",
+    "    if i >= len(test_row):\n",
+    "        return 0\n",
+    "    elif test_row[i] == 1:\n",
+    "        last_passing = True\n",
+    "    elif test_row[i] == 13:\n",
+    "        last_passing = True\n",
+    "        flips += 1\n",
+    "    elif test_row[i] == 12:\n",
+    "        last_passing = False\n",
+    "    else:\n",
+    "        last_passing = True\n",
+    "    considerd = 1\n",
+    "    i += 1\n",
+    "\n",
+    "    while i < len(test_row):\n",
+    "        ## ignoring more than three consecutive failures\n",
+    "        ## If the test is consecutively failing for three or more than three runs,\n",
+    "        ## we considered did not consider it an edge.\n",
+    "        cf = calc_consecutive_failures(test_row, i)\n",
+    "        if cf >= ignore_failures_in_a_row:\n",
+    "            i = i + cf\n",
+    "            if i >= len(test_row):\n",
+    "                break\n",
+    "\n",
+    "        s = test_row[i]\n",
+    "        if s == 1:\n",
+    "            ## run is pass\n",
+    "            considerd += 1\n",
+    "            last_passing = True\n",
+    "        elif s == 0:\n",
+    "            ## not run\n",
+    "            pass\n",
+    "        elif s == 13:\n",
+    "            ## flaky\n",
+    "            flips += 1\n",
+    "            considerd += 1\n",
+    "            last_passing = True\n",
+    "        elif s == 12:\n",
+    "            ## run is fail\n",
+    "            considerd += 1\n",
+    "            if last_passing:\n",
+    "                flips += 1\n",
+    "            last_passing = False\n",
+    "        i += 1\n",
+    "    if considerd == 0:\n",
+    "        return 0\n",
+    "    ## multiplying by 2 since flakiness score lies between 0 and 100\n",
+    "    flakiness = (flips / (considerd)) * 100\n",
+    "    return flakiness"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, we have tested our function using some basic test cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:13.301406Z",
+     "start_time": "2021-01-26T17:55:13.001889Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "x = np.arange(len(test_array))\n",
+    "plt.step(x, test_array)\n",
+    "plt.title(\"Single edge and multiple fails\", fontsize=20)\n",
+    "plt.ylim(1, 13)\n",
+    "plt.show()\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above figure, we can see there is only one edge with more than three consecutive failures. Hence we did not consider the above test as a flaky test. Hence, the total flakiness score using the `flip flakiness` method is 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:13.642467Z",
+     "start_time": "2021-01-26T17:55:13.358647Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NNtggnTt3zq233upcB2XQ0HybMGGCcx2UyXPPPZfRo0enT58++dSnPpUk2XTTTXPJJZdk+vTpOf3001f5ivhu3bq1cGIoDyUQAAAAQAG4HQwAAACgAJRAAAAAAAWgBAIAAAAoACUQAAAAQAEogQAAAAAKQAkEAAAAUABKIAAAAIAC+P8A3rCqYcHdmG8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  33.33333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "x = np.arange(len(test_array_1))\n",
+    "plt.step(x, test_array_1)\n",
+    "plt.ylim(1, 13)\n",
+    "plt.title(\"multiple edges and multiple fails\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array_1),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above figure, we can see there are multiples edges. Hence, this test exhibits a more flake-like behavior pattern. Therefore, our flakiness score is greater. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:14.029300Z",
+     "start_time": "2021-01-26T17:55:13.749090Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  50.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "test_array_7 = [1, 12] * 11\n",
+    "x = np.arange(len(test_array_7))\n",
+    "plt.step(x, test_array_7)\n",
+    "plt.title(\"more multiple edges and multiple fails\", fontsize=20)\n",
+    "plt.show()\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array_7),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above figure, we have simulated an extremely flaky test with an inconsistent pass behavior pattern. And as you can see we get a maximum flakiness score of 50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:14.413435Z",
+     "start_time": "2021-01-26T17:55:14.131154Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "test_array_2 = [1] * 24\n",
+    "x = np.arange(len(test_array_2))\n",
+    "plt.step(x, test_array_2)\n",
+    "plt.ylim(0, 13)\n",
+    "plt.title(\"all tests pass\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array_2),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above figure, we can see all the test runs passed. And as we would expect, our flakiness score is 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:14.780046Z",
+     "start_time": "2021-01-26T17:55:14.502899Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "test_array_3 = [12] * 24\n",
+    "x = np.arange(len(test_array_3))\n",
+    "plt.step(x, test_array_3)\n",
+    "plt.ylim(1, 13)\n",
+    "plt.title(\"all tests fail\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array_3),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above example, we can see that there are no edges and consistent failures occurring for each run. We do not consider this pattern to represent a flake, so the flakiness score using the `flip flakiness` method is 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:15.217383Z",
+     "start_time": "2021-01-26T17:55:14.887397Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "test_array_4 = [12, 0] * 2 + [12] * 5 + [0] * 2 + [12] * 7\n",
+    "x = np.arange(len(test_array_4))\n",
+    "plt.step(x, test_array_4)\n",
+    "plt.ylim(0, 13)\n",
+    "plt.title(\"fail or not run tests\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array_4),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the above example we have simulated consistent failures with irregular non-run instances. As we can see, this method has ignored the not run instances in our calculation of flakiness score.\n",
+    "\n",
+    "In addition to the examples above, we have also included the [test cases](https://github.com/GoogleCloudPlatform/testgrid/blob/2a016ff75a27fff1550f5e9f5319cf0114182547/pkg/summarizer/analyzers/flipanalyzer_test.go) used by the TestGrid developers below. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:15.242148Z",
+     "start_time": "2021-01-26T17:55:15.228957Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty list: Passed\n",
+      "All passing: Passed\n",
+      "Multiple flakes: Passed\n",
+      "Short run: Passed\n",
+      "Long run: Passed\n",
+      "Long run interupted by flakes: Passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_array_google_1 = []\n",
+    "print(\n",
+    "    \"Empty list:\",\n",
+    "    \"Passed\" if calc_flakiness_score(test_array_google_1) == 0 else \"Failed\",\n",
+    ")\n",
+    "test_array_google_2 = [1, 1, 1]\n",
+    "print(\n",
+    "    \"All passing:\",\n",
+    "    \"Passed\" if calc_flakiness_score(test_array_google_2) == 0 else \"Failed\",\n",
+    ")\n",
+    "test_array_google_3 = [1, 12, 1, 1, 13, 1, 1, 1, 1, 12]\n",
+    "print(\n",
+    "    \"Multiple flakes:\",\n",
+    "    \"Passed\" if calc_flakiness_score(test_array_google_3) == 30 else \"Failed\",\n",
+    ")\n",
+    "test_array_google_4 = [1, 12, 12, 1, 1, 1, 1, 12, 1, 1]\n",
+    "print(\n",
+    "    \"Short run:\",\n",
+    "    \"Passed\" if calc_flakiness_score(test_array_google_4) == 20 else \"Failed\",\n",
+    ")\n",
+    "test_array_google_5 = [1, 12, 12, 12, 12, 12, 1, 12, 1, 12]\n",
+    "print(\n",
+    "    \"Long run:\",\n",
+    "    \"Passed\" if calc_flakiness_score(test_array_google_5) == 40 else \"Failed\",\n",
+    ")\n",
+    "test_array_google_6 = [1, 12, 12, 13, 12, 12, 1, 12, 1, 12]\n",
+    "print(\n",
+    "    \"Long run interupted by flakes:\",\n",
+    "    \"Passed\" if calc_flakiness_score(test_array_google_6) == 50 else \"Failed\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Excellent! All tests passed. From our hand-crafted examples and the tests cases above we can conclude that the flip flakiness method we have used here performs at least on par with the methods implemented by TestGrid. \n",
+    "\n",
+    "One drawback of this method you may have noticed is that it evaluates the entire pass/fail time series for each test, and does not account for possible flaky subset within an otherwise stable test. Let's extend this method to find flaky regions (subsets) in our test data.   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flip Flakiness with Optimal Distance\n",
+    "\n",
+    "One of the downsides of finding a single value for each test case is that there might be two different consecutive time periods for which the test case is behaving flaky, or the number of edges is relatively low compared to total runs. This could lead to a low flakiness score, despite the test is still being flaky. Let's illustrate this statement with an example. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:16.035489Z",
+     "start_time": "2021-01-26T17:55:15.753590Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "flip-flakiness score:  8.620689655172415\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "test_array_5 = [1] * 8 + [1, 12] * 2 + [1] * 23 + [1, 12, 1, 1, 12, 1, 12] + [1] * 16\n",
+    "x = np.arange(len(test_array_5))\n",
+    "plt.step(x, test_array_5)\n",
+    "plt.ylim(1, 14)\n",
+    "plt.title(\"two flaky regions\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\n",
+    "    \"flip-flakiness score: \",\n",
+    "    calc_flakiness_score(test_array_5),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see in the above graph, we have two separate time periods for which behavior is flaky, between runs [8,11] and [35,41]. However, our flakiness score is rather low since the total number of runs is high in comparison. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To overcome this limitation, instead of calculating the flakiness score on the entire run we will calculate the flakiness score between edges to maximize our flakiness score for flaky subsets of the test. \n",
+    "\n",
+    "Specifically, we calculate the flakiness score between the two farthest edges, which have a flakiness score greater than a user defined threshold. Currently, we use a threshold of flakiness score of 30, but this can tuned according to needs of an application. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:16.419063Z",
+     "start_time": "2021-01-26T17:55:16.394587Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def calc_flake_edges(test_array):\n",
+    "    \"\"\"This function calculates the number of edges, the transition of a\n",
+    "    particular test case from pass to fail.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_array : numpy array\n",
+    "            array of test runs with 0, 1, 12, 13 values as not run, pass, fail, flaky respectively\n",
+    "\n",
+    "    Returns\n",
+    "    ----------\n",
+    "    flake_edges: numpy array\n",
+    "                        array where the value are the starting of the edge\n",
+    "\n",
+    "    \"\"\"\n",
+    "    flake_edges = []  ## array to store edges\n",
+    "    ignore_failures_in_a_row = 3\n",
+    "    i = 0\n",
+    "    valid = 0\n",
+    "    while i < len(test_array) and test_array[i] == 0:\n",
+    "        i += 1\n",
+    "    ## intializing last_passing variable value\n",
+    "    if i >= len(test_array):\n",
+    "        return 0\n",
+    "    elif test_array[i] == 1:\n",
+    "        last_passing = True\n",
+    "        valid = i\n",
+    "    elif test_array[i] == 13:\n",
+    "        last_passing = True\n",
+    "    elif test_array[i] == 12:\n",
+    "        last_passing = False\n",
+    "    else:\n",
+    "        last_passing = True\n",
+    "    i += 1\n",
+    "\n",
+    "    ## Finding all the edges in our test runs\n",
+    "    while i < len(test_array):\n",
+    "        ## ignoring more than three consecutive failures\n",
+    "        ## If the test is consecutively failing for three or more than three runs,\n",
+    "        ## we do not consider it an edge.\n",
+    "        cf = calc_consecutive_failures(test_array, i)\n",
+    "        if cf >= ignore_failures_in_a_row:\n",
+    "            i = i + cf\n",
+    "            if i >= len(test_array):\n",
+    "                break\n",
+    "\n",
+    "        s = test_array[i]\n",
+    "        if s == 1:\n",
+    "            ## run is pass\n",
+    "            last_passing = True\n",
+    "            valid = i\n",
+    "        elif s == 0:\n",
+    "            ## not run\n",
+    "            pass\n",
+    "        elif s == 13:\n",
+    "            last_passing = True\n",
+    "            flake_edges.append(i)\n",
+    "        elif s == 12:\n",
+    "            ## run is fail\n",
+    "            if last_passing:\n",
+    "                flake_edges.append(valid)\n",
+    "            last_passing = False\n",
+    "        i = i + 1\n",
+    "    return flake_edges\n",
+    "\n",
+    "\n",
+    "def flake_edge_end(test_array, flake_edges, index):\n",
+    "    \"\"\"This function calculates the end of the edges. Starting of the edge will always be 1 or 13\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_array : numpy array\n",
+    "            array of test runs with 0, 1, 12, 13 values as not run, pass, fail, flaky respectively\n",
+    "\n",
+    "    Returns\n",
+    "    ----------\n",
+    "    flake_edges: numpy array\n",
+    "                        array where the value are the starting of the edge\n",
+    "\n",
+    "    \"\"\"\n",
+    "    flake_end = flake_edges[index]\n",
+    "    while test_array[flake_end] != 12 and test_array[flake_end] != 13:\n",
+    "        flake_end = flake_end + 1\n",
+    "        if flake_end > len(test_array):\n",
+    "            break\n",
+    "    return flake_end\n",
+    "\n",
+    "\n",
+    "def calc_optimal_flakiness_score(test_array, threshold=30):\n",
+    "    \"\"\"Calculate the flakiness score between edges since it will maximize the flakiness score.\n",
+    "    Specifically, we calculate the flakiness score between the two farthest edges\n",
+    "    which have a flakiness score greater than a threshold.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    test_array : array\n",
+    "            array of test runs with 0, 1, 12, 13 values as not run, pass, fail, flaky respectively\n",
+    "    threshold: int default 30\n",
+    "\n",
+    "    Returns\n",
+    "    ----------\n",
+    "    modified_test_array: numpy array\n",
+    "                        modified test grid where the value of failure due to flake is 13\n",
+    "    flake_edges_dict: dictionary\n",
+    "            key of the dictionary is a tuple of time period, and value is the flakiness score between the time period\n",
+    "    \"\"\"\n",
+    "    modified_test_array = test_array.copy()\n",
+    "    flake_edges_dict = {}\n",
+    "    flake_edges = calc_flake_edges(test_array)\n",
+    "    ## flakiness score between the two farthest edges\n",
+    "    p = 0\n",
+    "    q = 0\n",
+    "    while p < len(flake_edges):\n",
+    "        possible_flake = False\n",
+    "        for q in range(p + 1, len(flake_edges)):\n",
+    "            flake_end = flake_edge_end(test_array, flake_edges, q)\n",
+    "            curr_flake = calc_flakiness_score(\n",
+    "                test_array[flake_edges[p] : flake_end + 1]\n",
+    "            )\n",
+    "            if curr_flake > threshold:\n",
+    "                possible_flake = True\n",
+    "                max_flake = curr_flake\n",
+    "                max_p = flake_edges[p]\n",
+    "                max_q = flake_end\n",
+    "            else:\n",
+    "                break\n",
+    "        p = q\n",
+    "        if possible_flake:\n",
+    "            for k in range(max_p, flake_end + 1):\n",
+    "                if modified_test_array[k] == 12:\n",
+    "                    modified_test_array[k] = 13\n",
+    "                    curr = k + 1\n",
+    "                    while curr < len(modified_test_array):\n",
+    "                        if modified_test_array[curr] == 12:\n",
+    "                            modified_test_array[curr] = 13\n",
+    "                        else:\n",
+    "                            break\n",
+    "            arr = []\n",
+    "            arr.append(max_p)\n",
+    "            arr.append(max_q)\n",
+    "            flake_edges_dict[tuple(arr)] = max_flake\n",
+    "        if p == len(flake_edges) - 1:\n",
+    "            break\n",
+    "    return modified_test_array, flake_edges_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:16.822473Z",
+     "start_time": "2021-01-26T17:55:16.529934Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flip Flakiness with Optimal Distance results: \n",
+      " flake between (8, 11) with score of 50.0\n",
+      " flake between (35, 41) with score of 42.857142857142854\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "x = np.arange(len(test_array_5))\n",
+    "plt.step(x, test_array_5)\n",
+    "plt.ylim(1, 14)\n",
+    "plt.title(\"two flaky regions\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "modified_test_array, flake_dict = calc_optimal_flakiness_score(test_array_5)\n",
+    "print(\n",
+    "    \"Flip Flakiness with Optimal Distance results: \",\n",
+    ")\n",
+    "for i in flake_dict.keys():\n",
+    "    print(f\" flake between {i} with score of {flake_dict[i]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see in the graph above, we have two separate time periods for which behavior is flaky, between runs [8,11] and [35,41]. Using `flip flakiness with optimal distance` we have a successfully identified these two flaky regions. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison of different methods\n",
+    "\n",
+    "Now that we have explored 3 different methods for identifying flakes in TestGrid data, lets compare them directly and make sure we move forward with the best method. \n",
+    "\n",
+    "### Comparison of custom test cases\n",
+    "Below we use our custom test cases to compare performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:17.420477Z",
+     "start_time": "2021-01-26T17:55:17.149899Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Score:  45.833333333333336  Number of Flaky Tests Detected:  11\n",
+      "Flip-Flakiness Score:  0.0  Number of Flaky Tests Detected:  0\n",
+      "Flip Flakiness with Optimal Distance score:  {}  Number of Flaky Tests Detected:  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def flake_annotation(val_array, flake_score, flake_score_threshold):\n",
+    "    # for illustration purposes, we are removing the flaky labels\n",
+    "    val_array = [12 if (x == 13) else x for x in val_array]\n",
+    "    if flake_score > flake_score_threshold:\n",
+    "        return [13 if (x == 12) else x for x in val_array]\n",
+    "    else:\n",
+    "        return val_array\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(20, 5))\n",
+    "x = np.arange(len(test_array))\n",
+    "plt.step(x, test_array)\n",
+    "plt.title(\"Single edge and multiple fails\", fontsize=20)\n",
+    "plt.ylim(1, 13)\n",
+    "plt.show()\n",
+    "\n",
+    "naive_score = naive_flake_calc(test_array)\n",
+    "total_error_naive = flake_annotation(test_array, naive_score, 10).count(13)\n",
+    "flip_score = calc_flakiness_score(test_array)\n",
+    "total_error_flip = flake_annotation(test_array, flip_score, 10).count(13)\n",
+    "modified_test_array, flake_dict = calc_optimal_flakiness_score(test_array, 30)\n",
+    "total_error_optimal_flip = modified_test_array.count(13)\n",
+    "\n",
+    "print(\n",
+    "    \"Naive Score: \",\n",
+    "    naive_score,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_naive,\n",
+    ")\n",
+    "print(\n",
+    "    \"Flip-Flakiness Score: \",\n",
+    "    flip_score,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_flip,\n",
+    ")\n",
+    "print(\n",
+    "    \"Flip Flakiness with Optimal Distance score: \",\n",
+    "    flake_dict,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_optimal_flip,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the above example, which we do not consider to be a flake, we can see that both `Flip-Flakiness with optimal distance` and `Flip-Flakiness` performed equally well, whereas the `Naive` method incorrectly flagged this example as flaky. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:17.812472Z",
+     "start_time": "2021-01-26T17:55:17.541626Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Score:  100.0  Number of Flaky Tests Detected:  24\n",
+      "Flip-Flakiness Score:  0.0  Number of Flaky Tests Detected:  0\n",
+      "Flip Flakiness with Optimal Distance score:  {}  Number of Flaky Tests Detected:  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "x = np.arange(len(test_array_3))\n",
+    "plt.step(x, test_array_3)\n",
+    "plt.ylim(1, 13)\n",
+    "plt.title(\"all tests fail\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "naive_score = naive_flake_calc(test_array_3)\n",
+    "total_error_naive = flake_annotation(test_array_3, naive_score, 10).count(13)\n",
+    "flip_score = calc_flakiness_score(test_array_3)\n",
+    "total_error_flip = flake_annotation(test_array_3, flip_score, 10).count(13)\n",
+    "modified_test_array, flake_dict = calc_optimal_flakiness_score(test_array_3, 30)\n",
+    "total_error_optimal_flip = modified_test_array.count(13)\n",
+    "\n",
+    "print(\n",
+    "    \"Naive Score: \",\n",
+    "    naive_score,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_naive,\n",
+    ")\n",
+    "print(\n",
+    "    \"Flip-Flakiness Score: \",\n",
+    "    flip_score,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_flip,\n",
+    ")\n",
+    "print(\n",
+    "    \"Flip Flakiness with Optimal Distance score: \",\n",
+    "    flake_dict,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_optimal_flip,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we do not consider the above  example to be a flake as it shows consistent failing behavior. And as expected, both `Flip-Flakiness with optimal distance` and `Flip-Flakiness` performed equally well again, whereas the `Naive` method incorrectly flagged this example as flaky. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:18.116446Z",
+     "start_time": "2021-01-26T17:55:17.836007Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Score:  8.620689655172415  Number of Flaky Tests Detected:  0\n",
+      "Flip-Flakiness Score:  8.620689655172415  Number of Flaky Tests Detected:  0\n",
+      "Flip Flakiness with Optimal Distance score:  {(8, 11): 50.0, (35, 41): 42.857142857142854} \n",
+      "\tNumber of Flaky Tests Detected:  5\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 5))\n",
+    "x = np.arange(len(test_array_5))\n",
+    "plt.step(x, test_array_5)\n",
+    "plt.ylim(1, 13)\n",
+    "plt.title(\"two flaky regions\", fontsize=20)\n",
+    "plt.show()\n",
+    "\n",
+    "naive_score = naive_flake_calc(test_array_5)\n",
+    "total_error_naive = flake_annotation(test_array_5, naive_score, 10).count(13)\n",
+    "flip_score = calc_flakiness_score(test_array_5)\n",
+    "total_error_flip = flake_annotation(test_array_5, flip_score, 10).count(13)\n",
+    "modified_test_array, flake_dict = calc_optimal_flakiness_score(test_array_5, 30)\n",
+    "total_error_optimal_flip = modified_test_array.count(13)\n",
+    "\n",
+    "print(\n",
+    "    \"Naive Score: \",\n",
+    "    naive_score,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_naive,\n",
+    ")\n",
+    "print(\n",
+    "    \"Flip-Flakiness Score: \",\n",
+    "    flip_score,\n",
+    "    \" Number of Flaky Tests Detected: \",\n",
+    "    total_error_flip,\n",
+    ")\n",
+    "print(\n",
+    "    \"Flip Flakiness with Optimal Distance score: \",\n",
+    "    flake_dict,\n",
+    "    \"\\n\\tNumber of Flaky Tests Detected: \",\n",
+    "    total_error_optimal_flip,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see in the above example, we have two separate time periods for which behavior is flaky, between runs [8,7] and [31,37]. However, our flakiness score using `flip-flakiness` and `naive flakiness` method is rather low since the total number of runs is high compared to the number of edges and both methods performed poorly at detecting flaky tests. \n",
+    "\n",
+    "However, `Flip Flakiness with Optimal Distance` method is able to  correctly detect the flaky test regions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the results of the above tests, it is safe to conclude that `Flip Flakiness with Optimal Distance` is the best method to identify flakes moving forward. However, that is only based on these handcrafted test examples. Now lets look at some read testgrid data and see how each method performs. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison of TestGrids\n",
+    "\n",
+    "We will now apply each method to real testgrid data and see how well each performs.\n",
+    "\n",
+    "\n",
+    "### Original TestGrid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:25.755091Z",
+     "start_time": "2021-01-26T17:55:18.549802Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = colors.ListedColormap([\"white\", \"green\", \"red\", \"purple\"])\n",
+    "norm = colors.BoundaryNorm(boundaries=[0, 1, 12, 13], ncolors=3)\n",
+    "sns.heatmap(\n",
+    "    np.array(list(details[\"values\"][:40].values)),\n",
+    "    fmt=\"\",\n",
+    "    cmap=cmap,\n",
+    "    norm=norm,\n",
+    "    cbar=False,\n",
+    "    linewidths=0.1,\n",
+    "    linecolor=\"Black\",\n",
+    ")\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.title(\"raw data\\n\", fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cells with Purple color in the above graph are the existing flake labels for individual test runs provided by the underlying CI system. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:25.762612Z",
+     "start_time": "2021-01-26T17:55:25.757458Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total number of flakes labeled by the ci system's algorithm: 207\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_existing_flake_label = np.count_nonzero(\n",
+    "    np.array(list(details[\"values\"][:40].values)) == 13\n",
+    ")\n",
+    "print(\n",
+    "    \"total number of flakes labeled by the ci system's algorithm:\",\n",
+    "    total_existing_flake_label,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-11-17T12:44:43.917643Z",
+     "start_time": "2020-11-17T12:44:43.913369Z"
+    }
+   },
+   "source": [
+    "### Naive flake method\n",
+    "\n",
+    "Below we are annotating a testgrid using the naive flake detection method. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:25.865533Z",
+     "start_time": "2021-01-26T17:55:25.765105Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flake_score_threshold = 30\n",
+    "# calculate the flakiness score by naive method for each test in our dataset.\n",
+    "details[\"naive_flakiness_score\"] = details[\"values\"].apply(\n",
+    "    lambda x: naive_flake_calc(x)\n",
+    ")\n",
+    "details[\"naive_flakiness\"] = details.apply(\n",
+    "    lambda x: flake_annotation(\n",
+    "        x[\"values\"], x[\"naive_flakiness_score\"], flake_score_threshold\n",
+    "    ),\n",
+    "    axis=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:33.063067Z",
+     "start_time": "2021-01-26T17:55:25.867970Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = colors.ListedColormap([\"white\", \"green\", \"red\", \"purple\"])\n",
+    "norm = colors.BoundaryNorm(boundaries=[0, 1, 12, 13], ncolors=3)\n",
+    "sns.heatmap(\n",
+    "    np.array(list(details[\"naive_flakiness\"][:40].values)),\n",
+    "    fmt=\"\",\n",
+    "    cmap=cmap,\n",
+    "    norm=norm,\n",
+    "    cbar=False,\n",
+    "    linewidths=0.1,\n",
+    "    linecolor=\"Black\",\n",
+    ")\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.title(\"Naive Method\\n\", fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-11-17T12:45:37.629922Z",
+     "start_time": "2020-11-17T12:45:37.624408Z"
+    }
+   },
+   "source": [
+    "Cells with Purple color in the above graph are the labels given by the naive flake algorithm. We can see the `naive flake algorithm` is unable to detect many flaky tests such as test number 19, 22, 23, 24, 25, 34, 35, 37. Also, for tests 1, 3 from the time-stamp 0 to 18 consistent failures are occurring, which is not a flake behavior but this method still incorrectly identifies them as flaky. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:33.072153Z",
+     "start_time": "2021-01-26T17:55:33.065071Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of flake labels given by the naive flakiness method:  332\n",
+      "Total flaky tests detected by the `naive flakiness method` is 1.6 times \n",
+      "the total flaky test detected by the existing testgrid method \n"
+     ]
+    }
+   ],
+   "source": [
+    "total_naive_flake_label = np.count_nonzero(\n",
+    "    np.array(list(details[\"naive_flakiness\"][:40].values)) == 13\n",
+    ")\n",
+    "print(\n",
+    "    \"Number of flake labels given by the naive flakiness method: \",\n",
+    "    total_naive_flake_label,\n",
+    ")\n",
+    "print(\n",
+    "    \"Total flaky tests detected by the `naive flakiness method` is\",\n",
+    "    round(total_naive_flake_label / total_existing_flake_label, 2),\n",
+    "    \"times \\nthe total flaky test detected by the existing testgrid method \",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-11-17T12:46:24.574634Z",
+     "start_time": "2020-11-17T12:46:24.569437Z"
+    }
+   },
+   "source": [
+    "### Flip flake method \n",
+    "Below we are annotating testgrid using the flip flake detection method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:33.212620Z",
+     "start_time": "2021-01-26T17:55:33.074416Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flake_score_threshold = 30\n",
+    "## calculate the flakiness score by flip method for each test in our dataset.\n",
+    "details[\"flip_flakiness_score\"] = details[\"values\"].apply(\n",
+    "    lambda x: calc_flakiness_score(x)\n",
+    ")\n",
+    "details[\"flip_flakiness\"] = details.apply(\n",
+    "    lambda x: flake_annotation(\n",
+    "        x[\"values\"], x[\"flip_flakiness_score\"], flake_score_threshold\n",
+    "    ),\n",
+    "    axis=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:40.395724Z",
+     "start_time": "2021-01-26T17:55:33.214820Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = colors.ListedColormap([\"white\", \"green\", \"red\", \"purple\"])\n",
+    "norm = colors.BoundaryNorm(boundaries=[0, 1, 12, 13], ncolors=3)\n",
+    "sns.heatmap(\n",
+    "    np.array(list(details[\"flip_flakiness\"][:40].values)),\n",
+    "    fmt=\"\",\n",
+    "    cmap=cmap,\n",
+    "    norm=norm,\n",
+    "    cbar=False,\n",
+    "    linewidths=0.1,\n",
+    "    linecolor=\"Black\",\n",
+    ")\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.title(\"Flip Flakiness\\n\", fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cells with Purple color in the above graph are the labels given by the `flip flake algorithm`. Though it performed better than `naive flake algorithm`, but it was still unable to detect flaky behaviors for test number 23 and 24 due to a low occurrence of failures in those tests. Also, for tests 1,2,3,4 from the time-stamp 0 to 18 and for test 17 for time-stamp 74 to 96 consistent failures are occurring, which not a behavior of flaky test but still incorrectly detected as flaky."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:40.405422Z",
+     "start_time": "2021-01-26T17:55:40.398925Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of flake labels given by the `flip flakiness` method: 333\n",
+      "Total flaky test detected by the `flip flake` method is 1.61 times \n",
+      "the total flaky tests detected by the existing testgrid method \n"
+     ]
+    }
+   ],
+   "source": [
+    "total_flip_flake_label = np.count_nonzero(\n",
+    "    np.array(list(details[\"flip_flakiness\"][:40].values)) == 13\n",
+    ")\n",
+    "print(\n",
+    "    \"Number of flake labels given by the `flip flakiness` method:\",\n",
+    "    total_flip_flake_label,\n",
+    ")\n",
+    "print(\n",
+    "    \"Total flaky test detected by the `flip flake` method is\",\n",
+    "    round(total_flip_flake_label / total_existing_flake_label, 2),\n",
+    "    \"times \\nthe total flaky tests detected by the existing testgrid method \",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Flip flakiness with optimal distance method\n",
+    "Below we are annotating testgrid using the `Flip flakiness with optimal distance` method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:40.432826Z",
+     "start_time": "2021-01-26T17:55:40.407580Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "details[\"flip_flakiness_optimal\"] = details[\"values\"][:40].apply(\n",
+    "    lambda x: calc_optimal_flakiness_score(x, 10)[0]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:47.986291Z",
+     "start_time": "2021-01-26T17:55:40.434967Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20, 10))\n",
+    "cmap = colors.ListedColormap([\"white\", \"green\", \"red\", \"purple\"])\n",
+    "norm = colors.BoundaryNorm(boundaries=[0, 1, 12, 13], ncolors=3)\n",
+    "sns.heatmap(\n",
+    "    np.array(list(details[\"flip_flakiness_optimal\"][:40].values)),\n",
+    "    fmt=\"\",\n",
+    "    cmap=cmap,\n",
+    "    norm=norm,\n",
+    "    cbar=False,\n",
+    "    linewidths=0.1,\n",
+    "    linecolor=\"Black\",\n",
+    ")\n",
+    "plt.ylabel(\"Tests\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.title(\"Flip Flakiness with Optimal Distance\\n\", fontsize=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cells with purple color in the above graph are the labels given by the `flip flake with optimal distance algorithm`.\n",
+    "\n",
+    "In the above figure, we can see that `flip flakiness with optimal distance` method is able to detect the flaky behavior in test number 4, 19, 22, 23, 24, 25, 34, 35, 37, which `naive flake algorithm` were failed to detect and test 4 and 23  which `flip flake algorithm` was failed to detect. Also, it correctly classified flaky tests for test cases 1, 3, 17.  \n",
+    "\n",
+    "Given the above, its safe to conclude that `Flip flakiness with optimal distance` works better than `naive flakiness` and `flip flakiness` methods on both are example and real world data sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T17:55:47.994893Z",
+     "start_time": "2021-01-26T17:55:47.988349Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of flake labels given by the `flip flake optimal distance` method: 424\n",
+      "Total flaky test detected by the `flip flake optimal distance method` is 2.05 times \n",
+      "the total flaky tests detected by the existing testgrid method \n"
+     ]
+    }
+   ],
+   "source": [
+    "total_flip_flake__optimal_label = np.count_nonzero(\n",
+    "    np.array(list(details[\"flip_flakiness_optimal\"][:40].values)) == 13\n",
+    ")\n",
+    "print(\n",
+    "    \"Number of flake labels given by the `flip flake optimal distance` method:\",\n",
+    "    total_flip_flake__optimal_label,\n",
+    ")\n",
+    "print(\n",
+    "    \"Total flaky test detected by the `flip flake optimal distance method` is\",\n",
+    "    round(total_flip_flake__optimal_label / total_existing_flake_label, 2),\n",
+    "    \"times \\nthe total flaky tests detected by the existing testgrid method \",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "\n",
+    "**TLDR: Flip Flakiness with Optimal Distance is best!**\n",
+    "\n",
+    "In this notebook, we explored 3 different methods for automatically identifying flaky tests.\n",
+    "\n",
+    "1) **Naive Flakiness**: This was our baseline method to compare the performance of the other two methods. This method simply calculates flakiness as a ratio of failed tests over total tests. As we expected, this method performed the worst and should not be used in the future for flake identification. \n",
+    "\n",
+    "\n",
+    "2) **Flip Flakiness**: This method relied on counting the number of edges (flips from pass to fail) for a test. Although it performed much better than the naive method, it fell short for longer test examples with flaky regions in an otherwise stable time-series. Moving forward, this method should not be used either. \n",
+    "\n",
+    "\n",
+    "3) **Flip Flakiness with Optimal Distance**: This method extended the flip flakiness method by identifying irregular subsets within a test. This was the best performing method explored in this notebook and should be carried forward into the [failure type classification](https://github.com/aicoe-aiops/ocp-ci-analysis/blob/master/notebooks/failure-type-classification/README.md) tool and serve as the new baseline.   \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The current upstream [testgrid repo](https://github.com/GoogleCloudPlatform/testgrid) uses both `Naive flakiness` and `Flip flakiness`  methods for detecting flakes. We should open an issue and suggest including `Flip flakiness with optimal distance` into their workflow to further improve flake identification. \n",
+    "\n",
+    "\n",
+    "* One limitation of the work in this notebook is that we only used the results of previous runs as our data to detect flaky tests. This is not the only data available to us. Next, we can try incorporating additional metadata (like git revisions or linked bugzillas) to improve upon our existing flake identification methods. See [this article](https://medium.com/fitbit-tech-blog/a-machine-learning-solution-for-detecting-and-mitigating-flaky-tests-c5626ca7e853) for possible avenues of future development.  "
+   ]
+  }
+ ],
+ "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Red Hat NLP/notebooks/failure-type-classification/stage/failure_type_classifier.ipynb b/Red Hat NLP/notebooks/failure-type-classification/stage/failure_type_classifier.ipynb
new file mode 100644
index 00000000..4829f460
--- /dev/null
+++ b/Red Hat NLP/notebooks/failure-type-classification/stage/failure_type_classifier.ipynb	
@@ -0,0 +1,805 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Failure type classification \n",
+    "\n",
+    "This is a POC notebook for analyzing testgrids and generating a report that will identify the tests and dates where 4 different types of failures may have occurred.   \n",
+    "\n",
+    "Goal: Auto label test grids with the following types of failures:\n",
+    "\n",
+    "* Infra Flake\n",
+    "* Flaky Tests\n",
+    "* Install Flake \n",
+    "* New Test Failure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next Steps/To Do:  \n",
+    "* Create visualized results on one grid\n",
+    "* Aggregate results for the whole platform. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:10:30.227730Z",
+     "start_time": "2021-01-26T20:10:29.130177Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "import sys\n",
+    "import gzip\n",
+    "import datetime\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy.signal import convolve2d\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:10:30.243422Z",
+     "start_time": "2021-01-26T20:10:30.230062Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# adding path to notebooks consisting of modules to be imported\n",
+    "module_path_1 = os.path.abspath(os.path.join(\"../../data-sources/TestGrid\"))\n",
+    "module_path_2 = os.path.abspath(os.path.join(\"../background\"))\n",
+    "if module_path_1 not in sys.path:\n",
+    "    sys.path.append(module_path_1)\n",
+    "if module_path_2 not in sys.path:\n",
+    "    sys.path.append(module_path_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:11:52.632399Z",
+     "start_time": "2021-01-26T20:11:52.627983Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipynb.fs.defs.testgrid_EDA import decode_run_length  # noqa: E402\n",
+    "from ipynb.fs.defs.testgrid_flakiness_detection import (  # noqa: E402\n",
+    "    calc_optimal_flakiness_score,  # noqa: E402\n",
+    ")  # noqa: E402"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:19.434489Z",
+     "start_time": "2021-01-26T20:08:19.237007Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(\n",
+    "    \"https://user-images.githubusercontent.com/161888/89903201-a62b3800-dbe7-11ea-9b1c-2ec62fe8952f.png\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hand annotated example\n",
+    "\n",
+    "The image above is a hand annotated testgrid. The aim of this notebook is to automate this process. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:19.443709Z",
+     "start_time": "2021-01-26T20:08:19.436923Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def run_length_encode(x):\n",
+    "    \"\"\"run length encoding \"\"\"\n",
+    "\n",
+    "    rle = []\n",
+    "    count = 1\n",
+    "    for i, j in enumerate(x):\n",
+    "        key = j\n",
+    "        if i == len(x) - 1:\n",
+    "            rle.append((key, count, i))\n",
+    "            break\n",
+    "        if key == x[i + 1]:\n",
+    "            count += 1\n",
+    "        else:\n",
+    "            rle.append((key, count, i))\n",
+    "            count = 1\n",
+    "    return rle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.171456Z",
+     "start_time": "2021-01-26T20:08:19.445628Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# load sample data\n",
+    "with gzip.open(\"../../../data/raw/testgrid_810.json.gz\", \"rb\") as read_file:\n",
+    "    data = json.load(read_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.176784Z",
+     "start_time": "2021-01-26T20:08:24.173591Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "grid = data['\"redhat-openshift-ocp-release-4.3-informing\"'][\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\"\n",
+    "][\"grid\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.209554Z",
+     "start_time": "2021-01-26T20:08:24.178578Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "x = np.array(list(pd.DataFrame(grid).statuses.apply(decode_run_length)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.492928Z",
+     "start_time": "2021-01-26T20:08:24.213561Z"
+    }
+   },
+   "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": [
+    "# Display example testgrid\n",
+    "plt.imshow(x, aspect=\"auto\")\n",
+    "plt.title(\"Test Grid: release-openshift-origin-installer-e2e-gcp-compact-4.3\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.499723Z",
+     "start_time": "2021-01-26T20:08:24.495606Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# We want to re-map the values so that the output of the convolution will be more interpretable.\n",
+    "def normalize(x):\n",
+    "    if x == 1:\n",
+    "        return 1\n",
+    "    if x == 12:\n",
+    "        return -1\n",
+    "    if x == 0:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Detect Infrastructure Flakes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.524299Z",
+     "start_time": "2021-01-26T20:08:24.501706Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def detect_infra_flake(grid, tab_name, grid_name):\n",
+    "    \"\"\"\n",
+    "    This function takes a 2d numpy array \"grid\" and uses a diagonal edge detecting\n",
+    "    filter to identify time windows in which 'infrastructure flakes' occured.\n",
+    "\n",
+    "    Returns a list of dates and test indexes\n",
+    "    \"\"\"\n",
+    "    infra_flakes_found = []\n",
+    "\n",
+    "    # 2d filter that will have its highest value when convolved with a diagonal pattern.\n",
+    "    infra_flake_filter = np.array([[-1, 1], [1, -1]])\n",
+    "\n",
+    "    # Find the spots on the map where the convolution had its maximum value.\n",
+    "    spots = convolve2d(infra_flake_filter, x, mode=\"valid\")\n",
+    "    infra_flakes = np.where(spots == 4)\n",
+    "\n",
+    "    dates = data[tab_name][grid_name][\"timestamps\"]\n",
+    "    infra_flake_dates = np.array(dates)[list([infra_flakes][0][1])]\n",
+    "    infra_flake_dates = [\n",
+    "        datetime.date.fromtimestamp(x // 1000) for x in infra_flake_dates\n",
+    "    ]\n",
+    "\n",
+    "    infra_flake_tests = list([infra_flakes][0][0])\n",
+    "\n",
+    "    infra_flakes_found = list(zip(infra_flake_dates, infra_flake_tests))\n",
+    "\n",
+    "    return infra_flakes_found"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.589769Z",
+     "start_time": "2021-01-26T20:08:24.526421Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = pd.DataFrame(x).apply(lambda x: [normalize(y) for y in x])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.599674Z",
+     "start_time": "2021-01-26T20:08:24.592056Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(datetime.date(2020, 10, 2), 1), (datetime.date(2020, 10, 1), 3)]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array(x)\n",
+    "detect_infra_flake(\n",
+    "    x,\n",
+    "    '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Detect Install Flakes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.611294Z",
+     "start_time": "2021-01-26T20:08:24.601708Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def detect_install_flake(grid, tab_name, grid_name):\n",
+    "    \"\"\"\n",
+    "    If greater than 90% of tests are not run for 2 or more consecutive days,\n",
+    "    then we will record this period as as an install flake.\n",
+    "    \"\"\"\n",
+    "    install_flakes = []\n",
+    "\n",
+    "    n_rows, n_cols = grid.shape\n",
+    "    grid = pd.DataFrame(grid)\n",
+    "    not_run_percent = grid.apply(lambda x: sum(x == 0) / n_rows, axis=0)\n",
+    "    install_errors = not_run_percent > 0.90\n",
+    "    install_error_streaks = run_length_encode(install_errors)\n",
+    "\n",
+    "    for i in install_error_streaks:\n",
+    "        if i[0] is True and i[1] >= 2:\n",
+    "            install_flakes.append((i[2] - i[1], i[2]))\n",
+    "\n",
+    "    dates = data[tab_name][grid_name][\"timestamps\"]\n",
+    "    install_flake_dates = np.array(dates)[list([install_flakes][0][0])]\n",
+    "    install_flake_dates = [\n",
+    "        datetime.date.fromtimestamp(x // 1000) for x in install_flake_dates\n",
+    "    ]\n",
+    "\n",
+    "    return install_flake_dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.656692Z",
+     "start_time": "2021-01-26T20:08:24.613392Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[datetime.date(2020, 9, 2), datetime.date(2020, 8, 31)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detect_install_flake(\n",
+    "    x,\n",
+    "    '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detect New Test Failures "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:24.673654Z",
+     "start_time": "2021-01-26T20:08:24.658829Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def detect_new_test_failures(grid, tab_name, grid_name):\n",
+    "    \"\"\"\n",
+    "    If 6 or more consecutive failures occur, then we will record this period\n",
+    "    as a new test failure\n",
+    "    \"\"\"\n",
+    "    grid = pd.DataFrame(grid)\n",
+    "    new_test_failures = grid.apply(single_new_test_failure, axis=1)\n",
+    "    none_empties = new_test_failures[new_test_failures.apply(lambda x: len(x)) > 0]\n",
+    "\n",
+    "    dates = data[tab_name][grid_name][\"timestamps\"]\n",
+    "\n",
+    "    for i, j in enumerate(none_empties):\n",
+    "        none_empties[i] = [np.array(dates)[[x[0], x[1]]] for x in j]\n",
+    "\n",
+    "        none_empties[i] = [\n",
+    "            (\n",
+    "                datetime.date.fromtimestamp(x[0] // 1000),\n",
+    "                datetime.date.fromtimestamp(x[1] // 1000),\n",
+    "            )\n",
+    "            for x in none_empties[i]\n",
+    "        ]\n",
+    "\n",
+    "    idx = list(none_empties.index)\n",
+    "    new_test_failures = [(idx[i], none_empties[i]) for i in range(len(none_empties))]\n",
+    "\n",
+    "    return new_test_failures\n",
+    "\n",
+    "\n",
+    "def single_new_test_failure(test):\n",
+    "\n",
+    "    \"\"\"given a test as an array of values, uses run length encoding to\n",
+    "    find occurences of 6 or moe consecutive failures for a test.\"\"\"\n",
+    "\n",
+    "    new_test_failure = []\n",
+    "    rle = run_length_encode(test)\n",
+    "\n",
+    "    #     if rle[-1][0] == 0 and rle[-2][0] == -1 and rle[-2][1] >= 6:\n",
+    "    #         new_test_failure.append((rle[-2][2]-rle[-2][1],rle[-2][2]))\n",
+    "\n",
+    "    if rle[-1][0] == 0 and rle[-2][0] == -1:\n",
+    "\n",
+    "        for i, j in reversed(list(enumerate(rle[:-2]))):\n",
+    "            if j[0] == 1:\n",
+    "                break\n",
+    "\n",
+    "        end_of_grid = rle[i:]\n",
+    "\n",
+    "        count = 0\n",
+    "        for streak in end_of_grid:\n",
+    "            if streak[0] == -1:\n",
+    "                count += streak[1]\n",
+    "\n",
+    "        if count >= 6:\n",
+    "            new_test_failure.append((end_of_grid[0][2], end_of_grid[-1][2]))\n",
+    "\n",
+    "    return new_test_failure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:25.267178Z",
+     "start_time": "2021-01-26T20:08:24.675781Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(0, [(datetime.date(2020, 10, 7), datetime.date(2020, 8, 9))])]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detect_new_test_failures(\n",
+    "    x,\n",
+    "    '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detect Flaky Tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:25.278530Z",
+     "start_time": "2021-01-26T20:08:25.269495Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def detect_flaky_test(grid, tab_name, grid_name):\n",
+    "    \"\"\"\n",
+    "    Apply run calc_optimal_flakiness_score to out grid\n",
+    "    \"\"\"\n",
+    "\n",
+    "    flaky_tests = []\n",
+    "    dates = data[tab_name][grid_name][\"timestamps\"]\n",
+    "    for i, j in enumerate(grid):\n",
+    "\n",
+    "        # use the calc_optimal_flakiness_score function imported from testgrid_flakiness_detection notebook\n",
+    "        found_flakes = calc_optimal_flakiness_score(grid[i])\n",
+    "        if len(found_flakes[1].keys()) > 0:\n",
+    "            times = [np.array(dates)[[x[0], x[1]]] for x in found_flakes[1].keys()]\n",
+    "            times = [\n",
+    "                (\n",
+    "                    datetime.date.fromtimestamp(x[0] // 1000),\n",
+    "                    datetime.date.fromtimestamp(x[1] // 1000),\n",
+    "                )\n",
+    "                for x in times\n",
+    "            ]\n",
+    "\n",
+    "            flaky_tests.append((i, found_flakes[1], times))\n",
+    "\n",
+    "    return flaky_tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:25.430563Z",
+     "start_time": "2021-01-26T20:08:25.280450Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "detect_flaky_test(\n",
+    "    x,\n",
+    "    '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Failure Type Identification \n",
+    "\n",
+    "Bring all 4 methods together to identify the different failure types for a single testgrid and generate a report."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:25.439187Z",
+     "start_time": "2021-01-26T20:08:25.432666Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "grid = data['\"redhat-openshift-ocp-release-4.3-informing\"'][\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\"\n",
+    "][\"grid\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:25.449702Z",
+     "start_time": "2021-01-26T20:08:25.441065Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def detect_failures(grid, tab_name, grid_name):\n",
+    "    \"\"\"\n",
+    "    This takens in a grid and runs all of our detectors and outputs a report\n",
+    "    \"\"\"\n",
+    "\n",
+    "    failure_report = {}\n",
+    "\n",
+    "    # use the decode_run_length function imported from TestGrid_EDA notebook\n",
+    "    x = np.array(list(pd.DataFrame(grid).statuses.apply(decode_run_length)))\n",
+    "\n",
+    "    failure_report[\"flaky_tests\"] = detect_flaky_test(x, tab_name, grid_name)\n",
+    "\n",
+    "    x = pd.DataFrame(x).apply(lambda x: [normalize(y) for y in x])\n",
+    "    x = np.array(x)\n",
+    "\n",
+    "    failure_report[\"infra_flake\"] = detect_infra_flake(x, tab_name, grid_name)\n",
+    "    failure_report[\"install_flake\"] = detect_install_flake(x, tab_name, grid_name)\n",
+    "    failure_report[\"new_test_failure\"] = detect_new_test_failures(\n",
+    "        x, tab_name, grid_name\n",
+    "    )\n",
+    "\n",
+    "    return failure_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:26.249588Z",
+     "start_time": "2021-01-26T20:08:25.451777Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "results = detect_failures(\n",
+    "    grid,\n",
+    "    '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:26.260839Z",
+     "start_time": "2021-01-26T20:08:26.251867Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_report(results, tab_name, grid_name):\n",
+    "    print(\n",
+    "        f\"Failure Report for: \\n\\\n",
+    "    {tab_name}/{grid_name}\",\n",
+    "        end=\"\\n\\n\",\n",
+    "    )\n",
+    "\n",
+    "    print(\"Flaky Tests:\")\n",
+    "    for ft in results[\"flaky_tests\"]:\n",
+    "        print(f\"Test number {ft[0]} had flakes at:\")\n",
+    "        for i in ft[2]:\n",
+    "            print(f\"{i[1]} to {i[0]}\")\n",
+    "\n",
+    "    print(\"\\b\")\n",
+    "    print(\n",
+    "        \"Infra Flake:\",\n",
+    "    )\n",
+    "    for infr in results[\"infra_flake\"]:\n",
+    "        print(f\"Test number {infr[1]} had an infra flake at {infr[0]}\")\n",
+    "\n",
+    "    print(\"\\b\")\n",
+    "    print(\"Install Flake:\")\n",
+    "    for inst in results[\"install_flake\"]:\n",
+    "        print(f\"An install flake started on {inst}\")\n",
+    "\n",
+    "    print(\"\\b\")\n",
+    "    print(\"New Test Failures:\")\n",
+    "    for ntf in results[\"new_test_failure\"]:\n",
+    "        print(f\"Test number {ntf[0]} had new test failures at:\")\n",
+    "        for i in ntf[1]:\n",
+    "            print(f\"{i[1]} to {i[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-01-26T20:08:26.281232Z",
+     "start_time": "2021-01-26T20:08:26.262772Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failure Report for: \n",
+      "    \"redhat-openshift-ocp-release-4.3-informing\"/release-openshift-origin-installer-e2e-gcp-compact-4.3\n",
+      "\n",
+      "Flaky Tests:\n",
+      "Test number 6 had flakes at:\n",
+      "2020-09-18 to 2020-09-22\n",
+      "2020-09-10 to 2020-09-14\n",
+      "\b\n",
+      "Infra Flake:\n",
+      "Test number 1 had an infra flake at 2020-10-02\n",
+      "Test number 3 had an infra flake at 2020-10-01\n",
+      "\b\n",
+      "Install Flake:\n",
+      "An install flake started on 2020-09-02\n",
+      "An install flake started on 2020-08-31\n",
+      "\b\n",
+      "New Test Failures:\n",
+      "Test number 0 had new test failures at:\n",
+      "2020-08-09 to 2020-10-07\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_report(\n",
+    "    results,\n",
+    "    '\"redhat-openshift-ocp-release-4.3-informing\"',\n",
+    "    \"release-openshift-origin-installer-e2e-gcp-compact-4.3\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now take a single test grid and identify the tests and dates where different error types occurred using the 4 heuristic based methods above.\n",
+    "\n",
+    "Next, we will use this report to annotate the grids themselves. And finally we will apply this to the entire testgrid dataset to generate some aggregated metrics about this failure types."
+   ]
+  }
+ ],
+ "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": "3.6.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}