{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9979f0ae",
   "metadata": {},
   "source": [
    "# **Amazon Lookout for Equipment** - SDK Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f9246b",
   "metadata": {},
   "source": [
    "#### Temporary cell to be executed until module is published on PyPI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bbc8dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 21.2.4 is available.\n",
      "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/python3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install --quiet --use-feature=in-tree-build .."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d534c994",
   "metadata": {},
   "source": [
    "## Initialization\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cb7bb1",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94c9c9ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import sagemaker\n",
    "import sys\n",
    "\n",
    "from lookoutequipment import plot, dataset, model, evaluation, scheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4fe2864",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c71972d1",
   "metadata": {},
   "source": [
    "<span style=\"color: white; background-color: OrangeRed; padding: 0px 15px 0px 15px; border-radius: 20px;\">**Note:** Update the value of the **bucket** and **prefix** variables below **before** running the following cell</span>\n",
    "\n",
    "Make sure the IAM role used to run your notebook has access to the chosen bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7a81916",
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket   = '<<YOUR-BUCKET>>'\n",
    "prefix   = '<<YOUR_PREFIX>>/' # Keep the trailing slash at the end\n",
    "\n",
    "plt.style.use('Solarize_Light2')\n",
    "plt.rcParams['lines.linewidth'] = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58d300e",
   "metadata": {},
   "source": [
    "### Dataset preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "205d730b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Component subsystem-17: 100%|██████████| 24/24 [02:23<00:00,  5.97s/it]\n"
     ]
    }
   ],
   "source": [
    "data = dataset.load_dataset(dataset_name='expander', target_dir='expander-data')\n",
    "dataset.upload_dataset('expander-data', bucket, prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6d7b4d",
   "metadata": {},
   "source": [
    "## Role definition\n",
    "--- \n",
    "Before you can run this notebook (for instance, from a SageMaker environment), you will need:\n",
    "* To allow SageMaker to run Lookout for Equipment API calls\n",
    "* To allow Amazon Lookout for Equipment to access your training data (located in the bucket and prefix defined in the previous cell)\n",
    "\n",
    "### Authorizing SageMaker to make Lookout for Equipment calls\n",
    "You need to ensure that this notebook instance has an IAM role which allows it to call the Amazon Lookout for Equipment APIs:\n",
    "\n",
    "1. In your IAM console, look for the SageMaker execution role endorsed by your notebook instance (a role with a name like `AmazonSageMaker-ExecutionRole-yyyymmddTHHMMSS`)\n",
    "2. On the `Permissions` tab, click on `Attach policies`\n",
    "3. In the Filter policies search field, look for `AmazonLookoutEquipmentFullAccess`, tick the checkbox next to it and click on `Attach policy`\n",
    "4. Browse to the `Trust relationship` tab for this role, click on the `Edit trust relationship` button and fill in the following policy. You may already have a trust relationship in place for this role, in this case, just add the **\"lookoutequipment.amazonaws.com\"** in the service list:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"Version\": \"2012-10-17\",\n",
    "  \"Statement\": [\n",
    "    {\n",
    "      \"Effect\": \"Allow\",\n",
    "      \"Principal\": {\n",
    "        \"Service\": [\n",
    "          \"sagemaker.amazonaws.com\",\n",
    "            \n",
    "          // ... Other services\n",
    "            \n",
    "          \"lookoutequipment.amazonaws.com\"\n",
    "        ]\n",
    "      },\n",
    "      \"Action\": \"sts:AssumeRole\"\n",
    "    }\n",
    "  ]\n",
    "}\n",
    "```\n",
    "\n",
    "5. Click on `Update the Trust Policy`: your SageMaker notebook instance can now call the Lookout for Equipment APIs\n",
    "\n",
    "### Give access to your S3 data to Lookout for Equipment\n",
    "When Lookout for Equipment will run, it will try to access your S3 data at several occasions:\n",
    "\n",
    "* When ingesting the training data\n",
    "* At training time when accessing the label data\n",
    "* At inference time to run the input data and output the results\n",
    "\n",
    "To enable these access, you need to create a role that Lookout for Equipment can endorse by following these steps:\n",
    "\n",
    "1. Log in again to your [**IAM console**](https://console.aws.amazon.com/iamv2/home)\n",
    "2. On the left menu bar click on `Roles` and then on the `Create role` button located at the top right\n",
    "3. On the create role screen, selected `AWS Service` as the type of trusted entity\n",
    "4. In the following section (`Choose a use case`), locate `SageMaker` and click on the service name. Not all AWS services appear in these ready to configure use cases and this is why we are using SageMaker as the baseline for our new role. In the next steps, we will adjust the role created to configure it specifically for Amazon Lookout for Equipment.\n",
    "5. Click on the `Next` button until you reach the last step (`Review`): give a name and a description to your role (for instance `LookoutEquipmentS3AccessRole`)\n",
    "6. Click on `Create role`: your role is created and you are brought back to the list of existing role\n",
    "7. In the search bar, search for the role you just created and choose it from the returned result to see a summary of your role\n",
    "8. At the top of your screen, you will see a role ARN field: **copy this ARN and paste it in the following cell, replacing the `<<YOUR_ROLE_ARN>>` string below**\n",
    "9. Click on the cross at the far right of the `AmazonSageMakerFullAccess` managed policy to remove this permission for this role as we don't need it.\n",
    "10. Click on `Add inline policy` and then on the `JSON` tab. Then fill in the policy with the following document (update the name of the bucket with the one you created earlier):\n",
    "\n",
    "```json\n",
    "{\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Statement\": [\n",
    "        {\n",
    "            \"Effect\": \"Allow\",\n",
    "            \"Action\": [\n",
    "                \"s3:ListBucket\",\n",
    "                \"s3:GetObject\"\n",
    "            ],\n",
    "            \"Resource\": [\n",
    "                \"arn:aws:s3:::<<YOUR-BUCKET>>/*\",\n",
    "                \"arn:aws:s3:::<<YOUR-BUCKET>>\"\n",
    "            ]\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "```\n",
    "10. Give a name to your policy (for instance: `LookoutEquipmentS3AccessPolicy`) and click on `Create policy`.\n",
    "11. On the `Trust relationships` tab, choose `Edit trust relationship`.\n",
    "12. Under policy document, replace the whole policy by the following document and click on the `Update Trust Policy` button on the bottom right:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"Version\": \"2012-10-17\",\n",
    "  \"Statement\": [\n",
    "    {\n",
    "      \"Sid\": \"\",\n",
    "      \"Effect\": \"Allow\",\n",
    "      \"Principal\": {\n",
    "        \"Service\": \"lookoutequipment.amazonaws.com\"\n",
    "      },\n",
    "      \"Action\": \"sts:AssumeRole\"\n",
    "    }\n",
    "  ]\n",
    "}\n",
    "```\n",
    "\n",
    "And you're done! When Amazon Lookout for Equipment will try to read the datasets you just uploaded in S3, it will request permissions from IAM by using the role we just created:\n",
    "1. The **trust policy** allows Lookout for Equipment to assume this role.\n",
    "2. The **inline policy** specifies that Lookout for Equipment is authorized to list and access the objects in the S3 bucket you created earlier.\n",
    "\n",
    "<span style=\"color: white; background-color: OrangeRed; padding: 0px 15px 0px 15px; border-radius: 20px;\">Don't forget to update the **role_arn** variable below with the ARN of the role you just create **before** running the following cell</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14ceef15",
   "metadata": {},
   "outputs": [],
   "source": [
    "role_arn = '<<YOUR_ROLE_ARN>>'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e64c65e",
   "metadata": {},
   "source": [
    "## Lookout for Equipment end-to-end walkthrough\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f1a854",
   "metadata": {},
   "source": [
    "### Dataset creation and data ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a88889f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"my_dataset\" does not exist, creating it...\n",
      "\n",
      "2021-09-03 06:59:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:00:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:01:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:02:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:03:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:04:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:05:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:06:53 | Data ingestion: IN_PROGRESS\n",
      "2021-09-03 07:07:53 | Data ingestion: SUCCESS\n"
     ]
    }
   ],
   "source": [
    "lookout_dataset = dataset.LookoutEquipmentDataset(\n",
    "    dataset_name='my_dataset',\n",
    "    access_role_arn=role_arn,\n",
    "    component_root_dir=f's3://{bucket}/{prefix}training-data'\n",
    ")\n",
    "lookout_dataset.create()\n",
    "response = lookout_dataset.ingest_data(bucket, prefix + 'training-data/', wait=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d638e1b",
   "metadata": {},
   "source": [
    "### Building an anomaly detection model\n",
    "#### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b14f44f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-09-03 07:32:19 | Model training: IN_PROGRESS\n",
      "2021-09-03 07:37:19 | Model training: IN_PROGRESS\n",
      "2021-09-03 07:42:20 | Model training: IN_PROGRESS\n",
      "2021-09-03 07:47:20 | Model training: IN_PROGRESS\n",
      "2021-09-03 07:52:20 | Model training: IN_PROGRESS\n",
      "2021-09-03 07:57:20 | Model training: IN_PROGRESS\n",
      "2021-09-03 08:02:21 | Model training: IN_PROGRESS\n",
      "2021-09-03 08:07:21 | Model training: IN_PROGRESS\n",
      "2021-09-03 08:12:21 | Model training: IN_PROGRESS\n",
      "2021-09-03 08:17:21 | Model training: IN_PROGRESS\n",
      "2021-09-03 08:22:22 | Model training: SUCCESS\n"
     ]
    }
   ],
   "source": [
    "lookout_model = model.LookoutEquipmentModel(model_name='my_model', \n",
    "                                            dataset_name='my_dataset')\n",
    "lookout_model.set_time_periods(data['evaluation_start'],\n",
    "                               data['evaluation_end'],\n",
    "                               data['training_start'],\n",
    "                               data['training_end'])\n",
    "lookout_model.set_label_data(bucket=bucket, \n",
    "                             prefix=prefix + 'label-data/',\n",
    "                             access_role_arn=role_arn)\n",
    "lookout_model.set_target_sampling_rate(sampling_rate='PT5M')\n",
    "response = lookout_model.train()\n",
    "lookout_model.poll_model_training(sleep_time=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c22e5d",
   "metadata": {},
   "source": [
    "#### Trained model evaluation overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1a0189f",
   "metadata": {},
   "outputs": [],
   "source": [
    "LookoutDiagnostics = evaluation.LookoutEquipmentAnalysis(model_name='my_model', tags_df=data['data'])\n",
    "predicted_ranges = LookoutDiagnostics.get_predictions()\n",
    "labels_fname = os.path.join('expander-data', 'labels.csv')\n",
    "labeled_range = LookoutDiagnostics.get_labels(labels_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8a355a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TSViz = plot.TimeSeriesVisualization(timeseries_df=data['data'], data_format='tabular')\n",
    "TSViz.add_signal(['signal-028'])\n",
    "TSViz.add_labels(labeled_range)\n",
    "TSViz.add_predictions([predicted_ranges])\n",
    "TSViz.add_train_test_split(data['evaluation_start'])\n",
    "TSViz.add_rolling_average(60*24)\n",
    "TSViz.legend_format = {'loc': 'upper left', 'framealpha': 0.4, 'ncol': 3}\n",
    "fig, axis = TSViz.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a30e4101",
   "metadata": {},
   "source": [
    "### Scheduling inferences\n",
    "#### Preparing inferencing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d98e613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data from 2015-11-21 04:00:00 to 2015-11-21 04:04:00\n",
      "Extracting data from 2015-11-21 04:05:00 to 2015-11-21 04:09:00\n",
      "Extracting data from 2015-11-21 04:10:00 to 2015-11-21 04:14:00\n",
      "Extracting data from 2015-11-21 04:15:00 to 2015-11-21 04:19:00\n",
      "Extracting data from 2015-11-21 04:20:00 to 2015-11-21 04:24:00\n",
      "Extracting data from 2015-11-21 04:25:00 to 2015-11-21 04:29:00\n",
      "Extracting data from 2015-11-21 04:30:00 to 2015-11-21 04:34:00\n",
      "Extracting data from 2015-11-21 04:35:00 to 2015-11-21 04:39:00\n",
      "Extracting data from 2015-11-21 04:40:00 to 2015-11-21 04:44:00\n",
      "Extracting data from 2015-11-21 04:45:00 to 2015-11-21 04:49:00\n",
      "Extracting data from 2015-11-21 04:50:00 to 2015-11-21 04:54:00\n",
      "Extracting data from 2015-11-21 04:55:00 to 2015-11-21 04:59:00\n"
     ]
    }
   ],
   "source": [
    "dataset.prepare_inference_data(\n",
    "    root_dir='expander-data',\n",
    "    sample_data_dict=data,\n",
    "    bucket=bucket,\n",
    "    prefix=prefix,\n",
    "    start_date='2015-11-21 04:00:00',\n",
    "    num_sequences=12\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b06138",
   "metadata": {},
   "source": [
    "#### Configuring and starting a scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40afd58d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Polling Inference Scheduler Status =====\n",
      "\n",
      "Scheduler Status: PENDING\n",
      "Scheduler Status: RUNNING\n",
      "\n",
      "===== End of Polling Inference Scheduler Status =====\n"
     ]
    }
   ],
   "source": [
    "lookout_scheduler = scheduler.LookoutEquipmentScheduler(\n",
    "    scheduler_name='my_scheduler',\n",
    "    model_name='my_model'\n",
    ")\n",
    "\n",
    "scheduler_params = {\n",
    "    'input_bucket': bucket,\n",
    "    'input_prefix': prefix + 'inference-data/input/',\n",
    "    'output_bucket': bucket,\n",
    "    'output_prefix': prefix + 'inference-data/output/',\n",
    "    'role_arn': role_arn,\n",
    "    'upload_frequency': 'PT5M',\n",
    "    'delay_offset': None,\n",
    "    'timezone_offset': '+00:00',\n",
    "    'component_delimiter': '_',\n",
    "    'timestamp_format': 'yyyyMMddHHmmss'\n",
    "}\n",
    "\n",
    "lookout_scheduler.set_parameters(**scheduler_params)\n",
    "response = lookout_scheduler.create()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21854297",
   "metadata": {},
   "source": [
    "#### Post-processing the inference results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6995eede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>subsystem-01\\signal-026</th>\n",
       "      <th>subsystem-01\\signal-027</th>\n",
       "      <th>subsystem-01\\signal-028</th>\n",
       "      <th>subsystem-01\\signal-029</th>\n",
       "      <th>subsystem-01\\signal-040</th>\n",
       "      <th>subsystem-01\\signal-041</th>\n",
       "      <th>subsystem-01\\signal-055</th>\n",
       "      <th>subsystem-01\\signal-056</th>\n",
       "      <th>subsystem-01\\signal-068</th>\n",
       "      <th>...</th>\n",
       "      <th>subsystem-15\\signal-089</th>\n",
       "      <th>subsystem-16\\signal-090</th>\n",
       "      <th>subsystem-17\\signal-108</th>\n",
       "      <th>subsystem-18\\signal-099</th>\n",
       "      <th>subsystem-19\\signal-067</th>\n",
       "      <th>subsystem-20\\signal-076</th>\n",
       "      <th>subsystem-21\\signal-084</th>\n",
       "      <th>subsystem-22\\signal-097</th>\n",
       "      <th>subsystem-23\\signal-098</th>\n",
       "      <th>subsystem-24\\signal-083</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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>2021-09-03 09:35:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     prediction  subsystem-01\\signal-026  \\\n",
       "timestamp                                                  \n",
       "2021-09-03 09:35:00           1                      0.0   \n",
       "\n",
       "                     subsystem-01\\signal-027  subsystem-01\\signal-028  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                      0.0                     0.02   \n",
       "\n",
       "                     subsystem-01\\signal-029  subsystem-01\\signal-040  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                     0.04                      0.0   \n",
       "\n",
       "                     subsystem-01\\signal-041  subsystem-01\\signal-055  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                      0.0                      0.0   \n",
       "\n",
       "                     subsystem-01\\signal-056  subsystem-01\\signal-068  ...  \\\n",
       "timestamp                                                              ...   \n",
       "2021-09-03 09:35:00                      0.0                      0.0  ...   \n",
       "\n",
       "                     subsystem-15\\signal-089  subsystem-16\\signal-090  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                      0.0                      0.0   \n",
       "\n",
       "                     subsystem-17\\signal-108  subsystem-18\\signal-099  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                      0.0                      0.0   \n",
       "\n",
       "                     subsystem-19\\signal-067  subsystem-20\\signal-076  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                      0.0                      0.0   \n",
       "\n",
       "                     subsystem-21\\signal-084  subsystem-22\\signal-097  \\\n",
       "timestamp                                                               \n",
       "2021-09-03 09:35:00                      0.0                      0.0   \n",
       "\n",
       "                     subsystem-23\\signal-098  subsystem-24\\signal-083  \n",
       "timestamp                                                              \n",
       "2021-09-03 09:35:00                      0.0                      0.0  \n",
       "\n",
       "[1 rows x 123 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = lookout_scheduler.get_predictions()\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38173db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_details = pd.DataFrame(results_df.iloc[0, 1:]).reset_index()\n",
    "fig, ax = plot.plot_event_barh(event_details, fig_width=12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_python3",
   "language": "python",
   "name": "conda_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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}