diff --git a/MAC/Services/src/PipelineControl.py b/MAC/Services/src/PipelineControl.py
index d9aa782a72d10c19604115fd6e19ca3bf4121c53..abfab5bcc1bcbec4ccc74554293b8ec795f7bb00 100755
--- a/MAC/Services/src/PipelineControl.py
+++ b/MAC/Services/src/PipelineControl.py
@@ -102,15 +102,17 @@ def runCommand(cmdline, input=None):
cmdline,
stdin=subprocess.PIPE if input else None,
stdout=subprocess.PIPE,
- stderr=subprocess.STDOUT,
+ stderr=subprocess.PIPE,
shell=True,
universal_newlines=True
)
# Feed input and wait for termination
logger.debug("runCommand input: %s", input)
- stdout, _ = communicate_returning_strings(proc, input)
+ stdout, stderr = communicate_returning_strings(proc, input)
logger.debug("runCommand output: %s", stdout)
+ if stderr:
+ logger.warn("runCommand stderr output: %s", stderr)
# Check exit status, bail on error
if proc.returncode != 0:
diff --git a/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb b/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..09d0a809ff2ed7e040b84aa7ba78366344b77fe4
--- /dev/null
+++ b/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb
@@ -0,0 +1,1267 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9cdf5a35",
+ "metadata": {},
+ "source": [
+ "# Project Report PoC - TMSS\n",
+ "\n",
+ "This notebook shows how to generate a report for a project.\n",
+ "\n",
+ "The data is retrieved through the *TMSS APIs* and it is analysed and visualised using the *Pandas* library.\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ec22618d",
+ "metadata": {},
+ "source": [
+ "### Prerequirements\n",
+ "\n",
+ "Before proceeding you need to import some modules, as well as specify some configurations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "56ae3f42",
+ "metadata": {},
+ "source": [
+ "#### Imports\n",
+ "\n",
+ "The Pandas and Requests libraries are required."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d169d2ba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import requests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3403df5",
+ "metadata": {},
+ "source": [
+ "#### Configs\n",
+ "\n",
+ "Your authentication credentials are needed to perform HTTP requests to the TMSS APIs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "48b766e7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "BASE_URL = 'http://localhost:8000/api' # TMSS API endpoint\n",
+ "auth = ('test', 'test') # username and password"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "76fe037c",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9812780a",
+ "metadata": {},
+ "source": [
+ "## Retrieve the data\n",
+ "\n",
+ "To retrieve the data, you need to perform a GET request to the following endpoint: `http://127.0.0.1:8000/api/project/<project>/report`\n",
+ "\n",
+ "This can be done by using the `requests` module. To perform the request, you need to provide your target project, by specifying its *id* in the `project` variable, and to pass your authentication credentials in the `auth` parameter. Since the response will be a JSON object, you can simply store the result of `response.json()` in the `result` variable."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "62acf8a9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'project': 'high',\n",
+ " 'quota': [{'id': 1,\n",
+ " 'resource_type_id': 'LTA Storage',\n",
+ " 'value': 1000000000000.0}],\n",
+ " 'SUBs': {'finished': [], 'failed': []},\n",
+ " 'durations': {'total': 12120.0,\n",
+ " 'total_succeeded': 0.0,\n",
+ " 'total_not_cancelled': 12120.0,\n",
+ " 'total_failed': 0.0},\n",
+ " 'LTA dataproducts': {'size__sum': None},\n",
+ " 'SAPs': [{'sap_name': 'placeholder', 'total_exposure': 0}]}"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "project = 'high' # Specify your target project\n",
+ "\n",
+ "# Retrieve the data related to project\n",
+ "response = requests.get(BASE_URL + '/project/%s/report' % project, auth=auth)\n",
+ "result = response.json()\n",
+ "result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "3276ce6d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TODO: Remove, just for testing purposes.\n",
+ "result = {\n",
+ " \"project\": \"high\",\n",
+ " \"quota\": [\n",
+ " {\n",
+ " \"id\": 2,\n",
+ " \"resource_type_id\": \"LTA Storage\",\n",
+ " \"value\": 1300.0\n",
+ " },\n",
+ " {\n",
+ " \"id\": 4,\n",
+ " \"resource_type_id\": \"LTA Storage\",\n",
+ " \"value\": 1000.0\n",
+ " },\n",
+ " {\n",
+ " \"id\": 11,\n",
+ " \"resource_type_id\": \"LTA Storage\",\n",
+ " \"value\": 2400.0\n",
+ " }\n",
+ " ],\n",
+ " \"SUBs\": {\n",
+ " \"finished\": [\n",
+ " {\n",
+ " \"id\": 3,\n",
+ " \"name\": \"amazing_sub\",\n",
+ " \"duration\": 600.000003\n",
+ " },\n",
+ " {\n",
+ " \"id\": 8,\n",
+ " \"name\": \"another_amazing_sub\",\n",
+ " \"duration\": 600.000003\n",
+ " },\n",
+ " {\n",
+ " \"id\": 21,\n",
+ " \"name\": \"another_amazing_sub\",\n",
+ " \"duration\": 800.000003\n",
+ " }\n",
+ " ],\n",
+ " \"failed\": [\n",
+ " {\n",
+ " \"id\": 12,\n",
+ " \"name\": \"horrible_sub\",\n",
+ " \"duration\": 600.000003\n",
+ " },\n",
+ " {\n",
+ " \"id\": 36,\n",
+ " \"name\": \"another_horrible_sub\",\n",
+ " \"duration\": 200.000003\n",
+ " },\n",
+ " {\n",
+ " \"id\": 43,\n",
+ " \"name\": \"yet_another_horrible_sub\",\n",
+ " \"duration\": 350.000003\n",
+ " }\n",
+ " ]\n",
+ " },\n",
+ " \"durations\": {\n",
+ " \"total\": 4000.000018,\n",
+ " \"total_succeeded\": 2000.000009,\n",
+ " \"total_not_cancelled\": 3250.000009,\n",
+ " \"total_failed\": 1150.000009\n",
+ " },\n",
+ " \"LTA dataproducts\": {\n",
+ " \"size__sum\": 246\n",
+ " },\n",
+ " \"SAPs\": [\n",
+ " {\n",
+ " \"sap_name\": \"sap_1\",\n",
+ " \"total_exposure\": 340.0\n",
+ " },\n",
+ " {\n",
+ " \"sap_name\":\"sap_2\",\n",
+ " \"total_exposure\": 195.0\n",
+ " },\n",
+ " {\n",
+ " \"sap_name\":\"sap_3\",\n",
+ " \"total_exposure\": 235.0\n",
+ " },\n",
+ " {\n",
+ " \"sap_name\":\"sap_4\",\n",
+ " \"total_exposure\": 345.0\n",
+ " },\n",
+ " {\n",
+ " \"sap_name\":\"sap_5\",\n",
+ " \"total_exposure\": 137.0\n",
+ " }\n",
+ " ]\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1721b2bc",
+ "metadata": {},
+ "source": [
+ "### Manage the data\n",
+ "\n",
+ "Once you have retrieved the data, you need to extract it in a proper way. In the following snippet, we do such operation by defining some variables that will be used afterwards."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d1b58c3a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "project_id = result['project'] # Project id\n",
+ "quota = result['quota'] # Allocated resources\n",
+ "durations = result['durations'] # Durations\n",
+ "subs_finished = result['SUBs']['finished'] # SUBs succeeded\n",
+ "subs_failed = result['SUBs']['failed'] # SUBs failed\n",
+ "lta_dataproducts = result['LTA dataproducts'] # LTA Dataproducts sizes\n",
+ "saps = result['SAPs'] # SAPs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "883d53a9",
+ "metadata": {},
+ "source": [
+ "You can now use a library (i.e., Pandas) for the data analysis and visualisation parts.\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9765847",
+ "metadata": {},
+ "source": [
+ "## Create tables\n",
+ "\n",
+ "Pandas mainly provides two *data structures*:\n",
+ "- **Series**: a one-dimensional data structure that comprises of a key-value pair. It is similar to a python dictionary, except it provides more freedom to manipulate and edit the data.\n",
+ "- **DataFrame**: a two-dimensional data-structure that can be thought of as a spreadsheet. A dataframe can also be thought of as a combination of two or more series."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43dbc054",
+ "metadata": {},
+ "source": [
+ "#### Caveat\n",
+ "\n",
+ "All of the durations retrieved from the APIs are expressed in seconds. In order to better visualise them, you can adopt a custom format to convert *seconds* into *timedeltas*. This will not touch the values contained by the DataFrames, but will only affect their on-the-fly visualisation. In this case, we are specifying the following conversion when displaying any DataFrame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "9647e60b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "to_timedelta = lambda x: '{}'.format(pd.to_timedelta(x, unit='s').round('1s'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af79759e",
+ "metadata": {},
+ "source": [
+ "### Summary Table\n",
+ "\n",
+ "You can create a unique table within all the data related to a project. It might be convenient to create a different DataFrame for each variable of the previous step, as they could be used for subsequent analysis later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8a0a7ed9",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6\" ><caption>Summary Table - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >total</th> <th class=\"col_heading level0 col1\" >total_succeeded</th> <th class=\"col_heading level0 col2\" >total_not_cancelled</th> <th class=\"col_heading level0 col3\" >total_failed</th> <th class=\"col_heading level0 col4\" >size__sum</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >high</th>\n",
+ " <td id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >0 days 01:06:40</td>\n",
+ " <td id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:33:20</td>\n",
+ " <td id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6row0_col2\" class=\"data row0 col2\" >0 days 00:54:10</td>\n",
+ " <td id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6row0_col3\" class=\"data row0 col3\" >0 days 00:19:10</td>\n",
+ " <td id=\"T_d3546088_9dff_11eb_84e4_000c299c9be6row0_col4\" class=\"data row0 col4\" >246</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d6f3f5128>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a DataFrame for each data you want to summarise\n",
+ "df_durations = pd.DataFrame(durations, index=[project_id])\n",
+ "df_lta_dataproducts = pd.DataFrame(lta_dataproducts, index=[project_id])\n",
+ "\n",
+ "# Create a general DataFrame as a summary table\n",
+ "df = pd.concat([df_durations, df_lta_dataproducts], axis=1)\n",
+ "df.style.format({'total': to_timedelta, 'total_succeeded': to_timedelta, 'total_not_cancelled': to_timedelta, 'total_failed': to_timedelta}).set_caption(f'Summary Table - {project_id}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17475585",
+ "metadata": {},
+ "source": [
+ "Note that for the other values, you can follow a similar procedure as illustrated by the following sections."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97374167",
+ "metadata": {},
+ "source": [
+ "### Quota table\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0d86e8a4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6\" ><caption>Quota - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >resource_type_id</th> <th class=\"col_heading level0 col1\" >value</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
+ " <td id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >LTA Storage</td>\n",
+ " <td id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >1300.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
+ " <td id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >LTA Storage</td>\n",
+ " <td id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >1000.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >11</th>\n",
+ " <td id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >LTA Storage</td>\n",
+ " <td id=\"T_d3576666_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >2400.00</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d6f667400>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a DataFrame for quota\n",
+ "df_quota = pd.DataFrame(quota).set_index('id')\n",
+ "df_quota.style.format({'value': '{:.2f}'}).set_caption(f'Quota - {project_id}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1106c43",
+ "metadata": {},
+ "source": [
+ "### SchedulingUnitBlueprints\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a3b5c78",
+ "metadata": {},
+ "source": [
+ "#### Finished SUBs\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "a8588756",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6\" ><caption>Finished SUBs - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >name</th> <th class=\"col_heading level0 col1\" >duration</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
+ " <td id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >amazing_sub</td>\n",
+ " <td id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:10:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
+ " <td id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >0 days 00:10:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >21</th>\n",
+ " <td id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d3593702_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >0 days 00:13:20</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d6f6673c8>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a DataFrame for finished SUBs\n",
+ "df_subs_finished = pd.DataFrame(subs_finished).set_index('id')\n",
+ "df_subs_finished.style.format({'duration': to_timedelta}).set_caption(f'Finished SUBs - {project_id}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a14140a",
+ "metadata": {},
+ "source": [
+ "#### Failed SUBs\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b0e3224a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6\" ><caption>Failed SUBs - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >name</th> <th class=\"col_heading level0 col1\" >duration</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
+ " <td id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >horrible_sub</td>\n",
+ " <td id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:10:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >36</th>\n",
+ " <td id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >another_horrible_sub</td>\n",
+ " <td id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >0 days 00:03:20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >43</th>\n",
+ " <td id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >yet_another_horrible_sub</td>\n",
+ " <td id=\"T_d35ac8e2_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >0 days 00:05:50</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d6f667a20>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a DataFrame for failed SUBs\n",
+ "df_subs_failed = pd.DataFrame(subs_failed).set_index('id')\n",
+ "df_subs_failed.style.format({'duration': to_timedelta}).set_caption(f'Failed SUBs - {project_id}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "901b2937",
+ "metadata": {},
+ "source": [
+ "### SAPs\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "e8907f52",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6\" ><caption>SAPs - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >total_exposure</th> </tr> <tr> <th class=\"index_name level0\" >sap_name</th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >sap_1</th>\n",
+ " <td id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >0 days 00:05:40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >sap_2</th>\n",
+ " <td id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >0 days 00:03:15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >sap_3</th>\n",
+ " <td id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >0 days 00:03:55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6level0_row3\" class=\"row_heading level0 row3\" >sap_4</th>\n",
+ " <td id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6row3_col0\" class=\"data row3 col0\" >0 days 00:05:45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6level0_row4\" class=\"row_heading level0 row4\" >sap_5</th>\n",
+ " <td id=\"T_d35c8b46_9dff_11eb_84e4_000c299c9be6row4_col0\" class=\"data row4 col0\" >0 days 00:02:17</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d6f6677b8>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a DataFrame for SAPs\n",
+ "df_saps = pd.DataFrame(saps).set_index('sap_name')\n",
+ "df_saps.style.format({'total_exposure': to_timedelta}).set_caption(f'SAPs - {project_id}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6261c701",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b7a4b0b9",
+ "metadata": {},
+ "source": [
+ "## Create a plot\n",
+ "\n",
+ "To better visualise the data, you could plot it in several ways. The following sections show some examples."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc9a3b19",
+ "metadata": {},
+ "source": [
+ "### Quota\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "18b5ce1d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot a pie graph\n",
+ "ax_quota = df_quota.plot.pie(title=f'Quota - {project_id}', y='value', autopct='%.2f%%')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3458db6",
+ "metadata": {},
+ "source": [
+ "### Durations\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "da3340db",
+ "metadata": {
+ "scrolled": false
+ },
+ "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": [
+ "# You can associate a color for each duration\n",
+ "colors = {'total': '#58a5f0', 'total_not_cancelled': '#ffd95a', 'total_succeeded': '#60ad5e', 'total_failed': '#ff5f52'}\n",
+ "# Plot a horizontal bar graph\n",
+ "ax_durations = df_durations.plot.barh(title=f'Durations - {project_id}', color=colors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf9b7e2c",
+ "metadata": {},
+ "source": [
+ "### Scheduling Unit Blueprints\n",
+ "\n",
+ "You can plot either the finished or the failed SUBs. In addiction, you can also plot a unified bar graph. Here all of the three options are shown."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1816784",
+ "metadata": {},
+ "source": [
+ "#### Finished SUBs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "0869ef70",
+ "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": [
+ "# Plot a bar graph\n",
+ "ax_subs_finished = df_subs_finished.plot.bar(title='Finished SUBs', color='#60ad5e')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfa3909f",
+ "metadata": {},
+ "source": [
+ "#### Failed SUBs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "ac375a19",
+ "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": [
+ "# Plot a bar graph\n",
+ "ax_subs_failed = df_subs_failed.plot.bar(title='Failed SUBs', color='#ff5f52')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "77084d49",
+ "metadata": {},
+ "source": [
+ "#### SUBs Summary\n",
+ "\n",
+ "To summarise both finished and failed SchedulingUnitBlueprints, you can concatenate the prior DataFrames as well as adding a new column to distinguish them in the new DataFrame:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "9755ccd4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6\" ><caption>SUBs Summary - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >name</th> <th class=\"col_heading level0 col1\" >duration</th> <th class=\"col_heading level0 col2\" >status</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >amazing_sub</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row0_col2\" class=\"data row0 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row1_col2\" class=\"data row1 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >12</th>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >horrible_sub</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row2_col2\" class=\"data row2 col2\" >failed</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6level0_row3\" class=\"row_heading level0 row3\" >21</th>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row3_col0\" class=\"data row3 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row3_col1\" class=\"data row3 col1\" >0 days 00:13:20</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row3_col2\" class=\"data row3 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6level0_row4\" class=\"row_heading level0 row4\" >36</th>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row4_col0\" class=\"data row4 col0\" >another_horrible_sub</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row4_col1\" class=\"data row4 col1\" >0 days 00:03:20</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row4_col2\" class=\"data row4 col2\" >failed</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6level0_row5\" class=\"row_heading level0 row5\" >43</th>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row5_col0\" class=\"data row5 col0\" >yet_another_horrible_sub</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row5_col1\" class=\"data row5 col1\" >0 days 00:05:50</td>\n",
+ " <td id=\"T_d3bf2de6_9dff_11eb_84e4_000c299c9be6row5_col2\" class=\"data row5 col2\" >failed</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d69ab3978>"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Add a status column to differentiate colors later\n",
+ "df_subs_finished['status'] = 'finished'\n",
+ "df_subs_failed['status'] = 'failed'\n",
+ "# Create a new DataFrame, within index sorting, as a concatenation of finished and failed SUBs.\n",
+ "df_subs = pd.concat([df_subs_finished, df_subs_failed]).sort_index()\n",
+ "df_subs.style.format({'duration': to_timedelta}).set_caption(f'SUBs Summary - {project_id}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee01dc60",
+ "metadata": {},
+ "source": [
+ "Then, you can plot a bar graph discriminting colors by status:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "9cbe3a9f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEZCAYAAABsPmXUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYEklEQVR4nO3dfZRddX3v8fcHApQHJTyMKebBsCTKxaqII6KybEuwEnSZVJSClQRuumIt+HB1eaV679Xa9i7turcU9JbeXBAGK0/yIFmICg2g9bZQgmAQgpcRhSQCiUBAnkTgc//YvymHYSZzZubMOZlfPq+1Zp29f7/f3vu7k7U+s+d39jlbtomIiLrs0OsCIiKi8xLuEREVSrhHRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R5dI+lwSf8i6RFJD0n6v5LeVPo+L+kfR9jGkg4oy9dLekrSY2Uf35f02gnW8hlJPyv72iDposmdXcS2JeEeXSHppcCVwJeBvYHZwF8Avx7nrk6xvUfZx/XA1yZQyzLgBODIsq9+YPV499Nrkmb0uobYdiXco1teBWD7AtvP2n7S9tW2105kZ7afBS4EDhpqk3SopDWSHpX0gKS/HWXzNwHftf3Tsq/7ba9s2c/PJR3Zsv7vf1VIml/+mjhJ0npJD0v6U0lvkrRW0hZJX2nZ9sTyF8pppe9uSW8t7eslbSq/bIbGv0vSLeUc1kv6fEvf0LGXS7oXuFbStyR9pPXkSh1/OJF/16hHwj265f8Bz0oakLRI0l6T2ZmknYE/Bm5oaT4dON32S4FXAhePsvkNwFJJn5LUL2nHCZTwZmAB8EfA3wGfBY4EXgMcK+l3h41dC+wDnE/zS+lNwAHAB4GvSNqjjH0cWArMBN4FfFjSkmHH/l3gPwDvBAbKPgCQ9Hqav4q+NYFziook3KMrbD8KHA4Y+D/AZkmrJM0a567OkLQF+BVwCs3UzpDfAAdI2tf2Y7ZvGGkHtv8R+AhNOH4P2CTp0+Os4y9tP2X7appAvsD2JtsbgX8G3tAy9me2zyl/bVwEzAW+YPvXZfunaYIe29fbvs32c+WvmgtowrzV520/bvtJYBXwKkkLSt8JwEW2nx7n+URlEu7RNbbX2T7R9hzgd4CX01z1AjwD7NQ6XtLQ+m9amj9qeyawK/Bu4BJJryt9y2mmf+6UdJOkd2+llq/bPpLmCvlPgb+U9M5xnM4DLctPjrC+x1bGYnvE8ZLeLOk6SZslPVJq23fYsde3nMdTNL8wPihpB+B4JvA+RNQn4R49YftO4FyakAe4F5g/bNj+NKG/cYTtn7P9z8Ag8Ael7S7bxwMvA75EE/y7j1HHb2x/g2baZKiWx4HdWob9dtsnNnnn01yNz7W9J/APgIaNGf5VrgM0U1QLgSds/+uUVxnbvIR7dIWkAyV9UtKcsj6X5ipzaOrkO8CBkk6QtJOkvYH/Dlxq+5lR9vkWmjdUby/rH5TUZ/s5YEsZ9twI251Y3rh8iaQdJC2imSu/sQy5FTiu1NEPvG/S/wDtewnwkO2nJB0KfGCsDUqYPwf8T3LVHkXCPbrlVzRvLN4o6XGaUP8x8EkA25uARcCHgE2lbwvw4WH7+Uq5N/0xmiD7L7a/XfqOAm4vfacDx5V56eEeBT5D89fCFuBvgA/b/kHp/680b8g+TDOnf/6kznx8/gz4gqRfAf+N0d8UHu484LXAiz4rENsn5WEdEdOfpKXACtuH97qW2Dbkyj1impO0G80V/8qxxsb2I+EeMY2VO3w209yR083po9jGZVomIqJCuXKPiKhQwj0iokLbxLfK7bvvvp4/f36vy4iImFZuvvnmX9ruG6lvmwj3+fPns2bNml6XERExrUi6Z7S+TMtERFQo4R4RUaGEe0REhRLuEREVSrhHRFSorXCX9J8k3S7px5IukPRbkvaXdKOkQUkXlceeIWmXsj5Y+udP6RlERMSLjBnukmYDHwX6bf8OsCNwHM3DEE6zfQDNV6MuL5ssBx4u7aeVcRER0UXtTsvMAHaVNIPmCTX3AUcAl5T+AWBJWV5c1in9CyUNf5JMRERMoTE/xGR7o6T/QfNggyeBq4GbgS0tT8jZQPPEdcrr+rLtM+U5kPsAv2zdr6QVwAqAefPmTf5MIrYhJ122rKvHO+e9A2MPiu1KO9Mye9Fcje9P80Dj3WmeeDMptlfa7rfd39c34qdnIyJigtqZljkS+JntzbZ/A1wGvA2YWaZpAObw/EOMNwJzAUr/nsCDHa06IiK2qp1wvxc4TNJuZe58IXAHcB3PPzh4GXBFWV5V1in91zpfGh8R0VVjhrvtG2neGP0hcFvZZiXwaeATkgZp5tTPLpucDexT2j8BnDoFdUdExFa09a2Qtj8HfG5Y893AoSOMfQp4/+RLi4iIiconVCMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAq184DsV0u6teXnUUkfl7S3pGsk3VVe9yrjJekMSYOS1ko6ZOpPIyIiWrXzmL2f2D7Y9sHAG4EngMtpHp+32vYCYDXPP05vEbCg/KwAzpyCuiMiYivGOy2zEPip7XuAxcBAaR8AlpTlxcB5btwAzJS0XyeKjYiI9ow33I8DLijLs2zfV5bvB2aV5dnA+pZtNpS2iIjokrbDXdLOwHuAbwzvs23A4zmwpBWS1khas3nz5vFsGhERYxjPlfsi4Ie2HyjrDwxNt5TXTaV9IzC3Zbs5pe0FbK+03W+7v6+vb/yVR0TEqMYT7sfz/JQMwCpgWVleBlzR0r603DVzGPBIy/RNRER0wYx2BknaHXgH8KGW5i8CF0taDtwDHFvarwKOBgZp7qw5qWPVRkREW9oKd9uPA/sMa3uQ5u6Z4WMNnNyR6iIiYkLyCdWIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAol3CMiKpRwj4ioUMI9IqJCCfeIiAq1Fe6SZkq6RNKdktZJeoukvSVdI+mu8rpXGStJZ0galLRW0iFTewoRETFcu1fupwPfsX0g8HpgHXAqsNr2AmB1WQdYBCwoPyuAMztacUREjGnMcJe0J/B24GwA20/b3gIsBgbKsAFgSVleDJznxg3ATEn7dbjuiIjYinau3PcHNgPnSLpF0lmSdgdm2b6vjLkfmFWWZwPrW7bfUNoiIqJLZrQ55hDgI7ZvlHQ6z0/BAGDbkjyeA0taQTNtw7x588az6b876bJlE9puos5578DYgzqo9vPjxGO6e7xzL+3u8SJ6qJ0r9w3ABts3lvVLaML+gaHplvK6qfRvBOa2bD+ntL2A7ZW2+2339/X1TbT+iIgYwZjhbvt+YL2kV5emhcAdwCpg6NJyGXBFWV4FLC13zRwGPNIyfRMREV3QzrQMwEeAr0vaGbgbOInmF8PFkpYD9wDHlrFXAUcDg8ATZWxERHRRW+Fu+1agf4SuhSOMNXDy5MqKiIjJyCdUIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIqlHCPiKhQwj0iokIJ94iICiXcIyIq1Fa4S/q5pNsk3SppTWnbW9I1ku4qr3uVdkk6Q9KgpLWSDpnKE4iIiBcbz5X779s+2PbQ4/ZOBVbbXgCsLusAi4AF5WcFcGanio2IiPZMZlpmMTBQlgeAJS3t57lxAzBT0n6TOE5ERIxTu+Fu4GpJN0taUdpm2b6vLN8PzCrLs4H1LdtuKG0REdElM9ocd7jtjZJeBlwj6c7WTtuW5PEcuPySWAEwb9688WwaERFjaOvK3fbG8roJuBw4FHhgaLqlvG4qwzcCc1s2n1Pahu9zpe1+2/19fX0TP4OIiHiRMcNd0u6SXjK0DPwB8GNgFbCsDFsGXFGWVwFLy10zhwGPtEzfREREF7QzLTMLuFzS0PjzbX9H0k3AxZKWA/cAx5bxVwFHA4PAE8BJHa86IiK2asxwt3038PoR2h8EFo7QbuDkjlQXERETkk+oRkRUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUKOEeEVGhhHtERIUS7hERFUq4R0RUqO1wl7SjpFskXVnW95d0o6RBSRdJ2rm071LWB0v//CmqPSIiRjGeK/ePAeta1r8EnGb7AOBhYHlpXw48XNpPK+MiIqKL2gp3SXOAdwFnlXUBRwCXlCEDwJKyvLisU/oXlvEREdEl7V65/x3wn4Hnyvo+wBbbz5T1DcDssjwbWA9Q+h8p4yMiokvGDHdJ7wY22b65kweWtELSGklrNm/e3MldR0Rs99q5cn8b8B5JPwcupJmOOR2YKWlGGTMH2FiWNwJzAUr/nsCDw3dqe6Xtftv9fX19kzqJiIh4oTHD3faf255jez5wHHCt7T8GrgPeV4YtA64oy6vKOqX/WtvuaNUREbFVk7nP/dPAJyQN0sypn13azwb2Ke2fAE6dXIkRETFeM8Ye8jzb1wPXl+W7gUNHGPMU8P4O1BYREROUT6hGRFQo4R4RUaGEe0REhRLuEREVSrhHRFQo4R4RUaGEe0REhcZ1n3tExHbhxGO6e7xzL+34LnPlHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoTHDXdJvSfo3ST+SdLukvyjt+0u6UdKgpIsk7Vzadynrg6V//hSfQ0REDNPOlfuvgSNsvx44GDhK0mHAl4DTbB8APAwsL+OXAw+X9tPKuIiI6KIxw92Nx8rqTuXHwBHAJaV9AFhSlheXdUr/QknqVMERETG2tubcJe0o6VZgE3AN8FNgi+1nypANwOyyPBtYD1D6HwH2GWGfKyStkbRm8+bNkzqJiIh4obbC3faztg8G5gCHAgdO9sC2V9rut93f19c32d1FRESLcd0tY3sLcB3wFmCmpKGvDJ4DbCzLG4G5AKV/T+DBThQbERHtaedumT5JM8vyrsA7gHU0If++MmwZcEVZXlXWKf3X2nYHa46IiDG087CO/YABSTvS/DK42PaVku4ALpT0V8AtwNll/NnA1yQNAg8Bx01B3RERsRVjhrvttcAbRmi/m2b+fXj7U8D7O1JdRERMSD6hGhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoXaeoTpX0nWS7pB0u6SPlfa9JV0j6a7yuldpl6QzJA1KWivpkKk+iYiIeKF2rtyfAT5p+yDgMOBkSQcBpwKrbS8AVpd1gEXAgvKzAjiz41VHRMRWjRnutu+z/cOy/CtgHTAbWAwMlGEDwJKyvBg4z40bgJmS9ut04RERMboxH5DdStJ8modl3wjMsn1f6bofmFWWZwPrWzbbUNrua2lD0gqaK3vmzZs33rojopdOPKa7xzv30u4erwJtv6EqaQ/gUuDjth9t7bNtwOM5sO2Vtvtt9/f19Y1n04iIGENb4S5pJ5pg/7rty0rzA0PTLeV1U2nfCMxt2XxOaYuIiC5p524ZAWcD62z/bUvXKmBZWV4GXNHSvrTcNXMY8EjL9E1ERHRBO3PubwNOAG6TdGtp+wzwReBiScuBe4BjS99VwNHAIPAEcFInC46IiLGNGe62fwBolO6FI4w3cPIk64qIiEnIJ1QjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIirUzjNUvyppk6Qft7TtLekaSXeV171KuySdIWlQ0lpJh0xl8RERMbJ2rtzPBY4a1nYqsNr2AmB1WQdYBCwoPyuAMztTZkREjMeY4W77+8BDw5oXAwNleQBY0tJ+nhs3ADMl7dehWiMiok0TnXOfZfu+snw/MKsszwbWt4zbUNpeRNIKSWskrdm8efMEy4iIiJFM+g1V2wY8ge1W2u633d/X1zfZMiIiosVEw/2BoemW8rqptG8E5raMm1PaIiKiiyYa7quAZWV5GXBFS/vSctfMYcAjLdM3ERHRJTPGGiDpAuD3gH0lbQA+B3wRuFjScuAe4Ngy/CrgaGAQeAI4aQpqjoiIMYwZ7raPH6Vr4QhjDZw82aIiImJy8gnViIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKJdwjIiqUcI+IqFDCPSKiQgn3iIgKTUm4SzpK0k8kDUo6dSqOERERo+t4uEvaEfhfwCLgIOB4SQd1+jgRETG6qbhyPxQYtH237aeBC4HFU3CciIgYhZpnWndwh9L7gKNs/0lZPwF4s+1Tho1bAawoq68GftLRQrZuX+CXXTxet+X8pq+azw1yfp32Ctt9I3XM6GIRL2B7JbCyF8eWtMZ2fy+O3Q05v+mr5nODnF83TcW0zEZgbsv6nNIWERFdMhXhfhOwQNL+knYGjgNWTcFxIiJiFB2flrH9jKRTgO8COwJftX17p48zST2ZDuqinN/0VfO5Qc6vazr+hmpERPRePqEaEVGhhHtERIUS7hERFerZfe7dJOlQwLZvKl+FcBRwp+2relzapLTcjfQL2/8k6QPAW4F1wErbv+lpgRHRM9W/oSrpczTfczMDuAZ4M3Ad8A7gu7b/uoflTYqkr9Oc127AFmAP4DJgIc3/7bLeVReTIenbthf1uo7JKHfNXWj7l5IOAL4KvI7m0+h/Yvu2nhY4CZJ2A04BDHyZ5iLrvcCdwBdsP9bD8oDtI9xvAw4GdgHuB+bYflTSrsCNtl/Xy/omQ9Ja26+TNIPmg2Ivt/2sJAE/ms7nNkTSS4E/p/kw3Ldtn9/S9/e2/6xnxU2SpENG6wKutL1fN+vpNEm3235NWf4WcJbtyyX9HvDXtt/Wy/omQ9LFwHpgV5qvT1kHXAS8B/ht2yf0sDxg+5iWecb2s8ATkn5q+1EA209Keq7HtU3WDmVqZneaq/c9gYdofpHt1MvCOugc4C7gUuA/SjoG+IDtXwOH9bSyybsJ+B5NmA83s7ulTInWfHmZ7csBbF8v6SU9qqlTXmX72HIhdR9wpG1L+gHwox7XBmwf4f60pN1sPwG8cahR0p7AdA/3s2n+DNwR+CzwDUl304Tehb0srINeafuYsvxNSZ8FrpX0nl4W1SHrgA/Zvmt4h6T1Pain0y6RdC7wBeBySR8HLgeOAO7tYV0dUwL9KpcpkLK+TUyHbA/TMruUq7zh7fsC+03neT8ASS8HsP0LSTOBI4F7bf9bTwvrEEnrgNfYfq6l7UTgU8Aetl/Rq9omq3yD6m22X/SNqJKW2P5m96vqrPJ/9WHglTR/Ua4Hvgl8yfYjvatsciSdBXx8+Ny6pFcCA7YP701lLbXUHu4xvUn6G+Bq2/80rP0o4Mu2F/Smss6QdCAwm+b9n8da2o+y/Z3eVdYZw+5Uew3NnWrrpvudaiORdJ7tpZLkbSBYE+4xbUk6yfY5va5joiR9FDiZZnrmYOBjtq8ofT+0PdobrtPCCHeqHQpcTx13qg3/MkQBvw9cC2C759OGCfeYtiTda3ter+uYqHIn11tsPyZpPnAJ8DXbp0u6xfYbelvh5FR+p9otwO3AWTS3Qwq4gOaWSGx/r3fVNbaHN1RjGpO0drQuYFY3a5kCOwxNxdj+eblF8BJJr2DkO2imm5rvVHsj8DGaGxk+ZftWSU9uC6E+JOEe27pZwDuBh4e1C/iX7pfTUQ9IOtj2rQDlCv7dNB/2eW1PK+uMau9UK2/wnybpG+X1AbaxPN2miokYwZU0d8XcOrxD0vVdr6azlgLPtDbYfgZYKul/96akjnr70J1qrXc70XwGo4pPT9veALxf0ruAR3tdT6vMuUdEVCjfChkRUaGEe0REhRLuEcNIGvGNWknnlk+VRmzzEu4Rw9h+a69riJis3C0TMYykx2zvUb7x78s0n6hcDzzd28oi2pcr94jR/SHNd3UfRHPbYq7oY9pIuEeM7u3ABbaftf0LyveGREwHCfeIiAol3CNG933gjyTtKGk/mm/9i5gW8oZqxOiGnhp0B82Tg/61t+VEtC9fPxARUaFMy0REVCjhHhFRoYR7RESFEu4RERVKuEdEVCjhHhFRoYR7RESFEu4RERX6/8IZPYDyP7PiAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Associate colors\n",
+ "colors = {'finished': '#60ad5e', 'failed': '#ff5f52'}\n",
+ "# Plot the concatenated DataFrame\n",
+ "ax_subs = df_subs.plot.bar(title='SUBs Summary', y='duration', legend=False, color=list(df_subs['status'].map(colors)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "892416f7",
+ "metadata": {},
+ "source": [
+ "#### SAPs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "b323083e",
+ "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": [
+ "# Plot a bar graph\n",
+ "ax_saps = df_saps.plot.bar(title='SAPs', color=['#ffd95a'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6825282e",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4bbe1fb",
+ "metadata": {},
+ "source": [
+ "## Tables and Plots all in one\n",
+ "\n",
+ "In this section you can see a complete overview of the project report, generated by following the above documentation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "336df2b9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6\" ><caption>Summary Table - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >total</th> <th class=\"col_heading level0 col1\" >total_succeeded</th> <th class=\"col_heading level0 col2\" >total_not_cancelled</th> <th class=\"col_heading level0 col3\" >total_failed</th> <th class=\"col_heading level0 col4\" >size__sum</th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >high</th>\n",
+ " <td id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >0 days 01:06:40</td>\n",
+ " <td id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:33:20</td>\n",
+ " <td id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6row0_col2\" class=\"data row0 col2\" >0 days 00:54:10</td>\n",
+ " <td id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6row0_col3\" class=\"data row0 col3\" >0 days 00:19:10</td>\n",
+ " <td id=\"T_d3f6afaa_9dff_11eb_84e4_000c299c9be6row0_col4\" class=\"data row0 col4\" >246</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d699a9eb8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 504x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_table = df.style.format({'total': to_timedelta, 'total_succeeded': to_timedelta, 'total_not_cancelled': to_timedelta, 'total_failed': to_timedelta}).set_caption(f'Summary Table - {project_id}')\n",
+ "colors = {'total': '#58a5f0', 'total_not_cancelled': '#ffd95a', 'total_succeeded': '#60ad5e', 'total_failed': '#ff5f52'}\n",
+ "ax_durations = df_durations.plot.barh(title=f'Durations - {project_id}', color=colors, figsize=(7,5))\n",
+ "display(df_table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "42ec4db1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6\" ><caption>Quota - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >resource_type_id</th> <th class=\"col_heading level0 col1\" >value</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
+ " <td id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >LTA Storage</td>\n",
+ " <td id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >1300.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >4</th>\n",
+ " <td id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >LTA Storage</td>\n",
+ " <td id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >1000.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >11</th>\n",
+ " <td id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >LTA Storage</td>\n",
+ " <td id=\"T_d417480a_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >2400.00</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d69929630>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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TwspGPLR3He2I6ShudFRg9VrTGQpQPc4F00NadtlrpLCycxPOfedEHxYGVuwynaFAjQPuCscSRaaDDBcprIOJhz4HfNZ0DDc7Wr1RULeacpljgatNhxguUljvJx6aDfzcdAy3mx7YIGdMzbo8HEs0mg4xHKSw+uMcZL8XHy6+l2u1tHrqNmU+pIDbw7HEZNNB8k0Kq3+3AtNMh/CCIqXHhGjfbTpHgavFmVhaYjpIPklh9SUeugznziYiS3MCb8mZQvPmA664wWq+SGEdKB5aBPzIdAyvWRhYsdt0BgHAJX6+UFoKq7d4qAbngubB3MGkoM0NvCkLq7nHLeFYwpd35ZbC2t91wETTIbxomtpQbTqDeFclcG84lvDdCSMprL3ioRMAWShtkGpol6J3l1nAf5oOkWtSWADxUBnwS9MxvCygdH0tLTtM5xD7+VY4lphqOkQuSWE5rkCmMAzZnMBb60xnEPspA35mOkQuSWE5s9m/YTqGHywMrJBVG9zntHAscbbpELlS2IUVDxUBtyB3wM6JuYG3TEcQfftJOJaoMB0iFwq7sODLwDzTIfxiqtooZwrdaRLwX6ZD5ELh3pcwHhoLvImzgqPIAVurHVO676wznSPVuo3tiR9jd+wGFJVHnUL1vLPY9uA1JHeuB8Du6iBQNoJxF773EI/d1c6OR2+gZ7szeb/+9MsoHT8DgNbnH6bthQRKBSifOo+a4z9L1/rX2fn4L1BFQerP+CbFteOxu9rZ9uA1jPrElSjlinFBD3Bk85LGVaaDDEUh7wp9FymrnAooXTeKXdu2UtNgNkgRNcd/jtIx07C7O9nU9BXKwnNoOOvydx+y84lbCJT2/de/8083UzblaBrO/jY6nUQnuwHoWvMKe958hnEX/gwVLCbdsRuA1n/ez6iPx0m1bqHtpUepPeEiWv52N6EPnOuWsgIowVl55GTTQYbCNX+awyoemgJcZDqGH80JvLnedIZgZS2lY5yTvoHSCorrJpJu2zfjQmtN58rljJjx4fc81+7uoGvda1Qe+REAVFExgTJn9Zy2Fx+hetG5qKBzIUTRiJHOYwJBdKobnexGBYIkd20i1badsklH5vNtDsZJ4VjiE6ZDDEVhFhZciVx+kxcLAytddaYw1bKFni1vUzpu35Uq3etfo2jESIpr37sqTmr3FooqqtnxyE/Y+Jsvs+PRG7B7ugBI7tpA97rX2HT719j82xjdm94AILToXLYv/TEtz/yeqrkfZfdTtzPymH8dnjc4cD8OxxKeXb+s8AorHpoFfMp0DL+aE3hTmc6wl92zh233/ze1J15MoHTfSbKO1//S5+gKQNtpejavpmrO6Yy78AZUcSmtz/w+84Jp7K42xnz6OmqOu5BtD16D1pqS0VMY+5nrGHP+D0i1bKao0rmp0rYHr2H7w9eS7nDVCtLjgS+aDjFYhVdYznKyhfi+h8UUtbnGdAYAnU6x7f7/ZsTM46g4bN9t/LSdpvONv1NxeN+FFayqp6iq/t0RWcVhH6Jny2oAiqrqqTj0gyilKB13GEop7D37BpRaa+fY1QfPY/fTv6XmuAupnH0Krc8/nMd3OihfD8cS5aZDDEZh/eDGQwuBs0xsel2LzfFNHcy8sZ0jftHOT59xDuT+/rUkR/yincCVrTy3se+bKPf3XICXNqdZdEsHR93Uzryb23l2g/Ma973uvO4xv+lgR6ez5PrqnTafvLczr++zmo5Jed1AFrTW7Hj0pxTXTaR6wf5zJruaX6K4bgLB6vo+n1tUWUOwup7kDudQXNealymud95SxfRFdK19BYDkzg3odIpA+b6ZHB2vPkH5lHkUlVc5B+qVAqXePWjvIqOAz5sOMRiFNa0hHvoTcIKJTW9qs9nUrpk7toi2bs3RN3fwwHnlKCCg4N+XdnHtR8qYN+69N0Dp77kzG4r4yB0dfHVRCadNL+aRN5P88Okenvy3ERx3WwePXFDB/61IsmsPXLqwhPPv6+R7x5UyvS6/N1n5QNfPNm+iztjtp7rWv8aWOy+nuCHslAZQ8+HPUD51PtsT11M67jCq5pz+7uNTbTvY8dgNjD73SgB6trzNjsduQKdTBEeOoe70r1BUVolOJ9nxyE/p2fo2qqiYkcd/lvLJswGwk11svfdKRn/iKlRRkK51r7Lz8f/ZN9WhznW3s9wATG1e0ui6Nn0/hTOtIR46BkNlBTC2KsDYKufzqlLFjIYAG1o1J089+F9Bf8+d2eD8PLZm/sm1dMG4KucHNKCgOwWdSSgugr+uSTFmRCDvZQUwN/DmhoRtrrDKJhzB5MuX9vm9+savvudrwaq6d8sKcI5JRX/ynsepomLqz+j7Kq5AcRljzv/BvgwTZzHuczcOMPmwGo+zOslNpoMMRCHtEn7ZdIC9mnfbvLgpzcIJAy+PA5/7k1PK+OayLiZe38Y3lnXxgxOdJZD+c3EpJ93RwcNvpDh/VjFXPdXNd48tzen76M/CwIr2YdmQGKpvhmMJT3WAp8IOWjw0AfiY6RgA7T2af7mnk5+cWkZ16cBOqPX13P95Lsn1p5Sx7qtVXH9KGZ97aA8AJ08N8vznK3n4/AoeXJXk9OlB3tiR5uP3dHLxQ3voTObvUMCcwFsFc2NPj5uCoWO6g1UYhQVfwAW7v8m0UzgXRIo5Z8bApoH199yml3s4Z4bz1s6dGXz3oPtenUnNbS8luWR+CVc82U3Tx8pZPKmIO19JDv0N9WOy2jwyby8ucu29+8gu5v/CiodKgYtNx9Ba87mHuphRX8TXPjCwXbP3e+64qgB/WeOU1BPvpJlet/9f6Y+e7uHLC0soLlLsSTrHvAKKvI6wqthj/EyhyNox4VjCMwsAGB91DINPAmavbQOeXpfmjleSREYFOOom5xDPf59YSncKLn20i22dmsbfdnLUmAB/+NcRbGyzueihLh65oKLf554+vZhfnVHGZY91kbKhLAg3f3Tf9JqNbTbPbkxzxXFOyV26oIT5v+pgZJnigU/mbxqOUlRPUNs2rtcN4/K2EZFLXwUuMB0iG/6f1hAPPYtzvzYxjC7rueS5B+0PeeZ/7gKXBCY3L2ncZDrIwfh7l9CZKCplZcDCwIoO0xlE1oqBc02HyIa/CwsuMR2gUB0lZwq9xhM3X/VvYTl3wvHNWtZeM0ltrTWdQQzIB8KxhOum4x/Iv4UFp+LcUFIYMIKuyeD3A6S+ogDXr5Xl58L6uOkAhUwpRkxWWzaYziEGRArLiHioBDjDdIxCN0+94fqzTmI/C8OxxGTTId6PPwsLPgLIHVwMWxhYkd+1bEQ+uHqU5dfCkt1BF5gdWF0IE5P9RgprWMVDxXjsgk6/mqC2yZlC75kXjiWmmA7RH/8VFpwEjDQdQkAF3WGFbZvOIQbMtaMsPxaWHGx3CaUoP0RtXmc6hxgwKaxh1PfdBYQR8wMrt5jOIAbsqHAsMdJ0iL74q7DioVpgpukYYp+FgZVyptB7FLDQdIi++KuwYDHOH7ZwiYh6W25Y602LTAfoi98KS3YHXWa82t73/bSE233AdIC++K2wjjEdQOyvnJ5wALvvGy4KN1sYjiVct7fin8KKh0YAc03HEPtTitLpav1a0znEgI0EDjcd4kD+KSxnn1tmVrvQ/MAqOVPoTa47juWnwpLdQZdaEFjZZTqDGBTXHcfyU2HJ+uEuNUs1l5jOIAZFRlh5dJjpAKJv49R243ctEoNyRDiWqDIdojd/FJaz/tUhpmOIvpWSnFxEOmU6hxiwALDAdIje/FFYMBWQmx64lFKUHKbWrTGdQwzKHNMBevNLYcnuoMvND6zcajqDGJSJpgP0JoUlhsWCwKpu0xnEoLjqTjpSWGJYHKGaS01nEIMy3nSA3qSwxLAYq3aOMp1BDIqMsPJACsvlSkhOLibVYzqHGLAx4VjCNSe0vF9YzjWEdaZjiPenFMEZao2cKfSeImCs6RB7HbSwlFKjlVK/Vko9mvn9TKXU5/IfLWsyKdEj5gdWbTOdQQyKa3YLsxlh3Qb8ARiX+f0bwFfylGcwpLA8YkFgpewSepOnCqtea30PYANorVOAm9Y3kgXiPGKGWlNmOoMYFNcUVjbLsXQopeoADaCUWgS05DXVANxdVVlZrPWz9el0aV3arqhNp6tqbDtUpnW56Wxif2PUrjGmM4hB8VRhfQ14CJiqlHoaZxfMNXdWvrq+djR9Xe+kdXcAWoKatlKtOyu03VVl2z0j03aqxrZ1fTqt6lPpooZ0uqQ+nS6rS6cratN2VY2dDpVqZCSQB8WkJpXS09VNifz5eot3Cktr/YJS6licqQMKWKW1TuY9Wfb6vppcqVIbRvUoRvWgaCNA1qvIad2VKbv2Uq07KrTdVW3byZFpO1WbTuu6tK3q0+lgQzpdnCm7EXVpu2pkOh0qAZkg2Q+lCMxUa9a8qKfLNBRvGWk6wF4HLSyl1GcO+NJcpRRa69vzlGmgcr/8hVJlNpT1KEYPouz2BKClWOt2Z2Snu6rTds9I2047ZZdWDWk7WL9vZDeiLp2uCqXtUAn4ft2oBYGV219MS2F5jGtW8s0myPxen5cBJwIvAP4trKFQqtyG8m6l6AZagc3Z/nVr3dm77EZo3f3esksH69PpkoaUXV6XTo+otdOVobQ9shg8cTut+YGVyV+m5ebcHuOaf1vZ7BJe2vv3SqmRwF35CjQIlaYD5IxSFTZU9C67TdmXXUcAWou1bi/TunOErbur7X1lV59OB+rT6aKGVLq0Pp0ur0/bFTXpdFXItkcGh/F/0BmBtRXDtS2RM94prD504K7F8lwzXDVKqRE2jNhbdi1FsDHb52rdXrSv7PaMsPWeattOjrTTdm3adsoulQ5mTlCU16fTI2rSdlW1bYcGWnaj2C1nCr3HO4WllHqYzJQGnHlbM4F78hlqgOQGB0OlVGUaKtNK0QXsLoIN2T5X67beZVdp664q207WpNN2rW3r+lQ6sHc3tj6drqhLpctL09vW9lAsJyc8Q7WbTrBXNv87Xtvr8xSwRmu9Pk95BmOP6QAFTamqNFT1LruDKeE6/59d8JdN8CnTGYDsjmH9ZTiCDIGMsITIL9dMY+q3sJRSbezbFdzvW4DWWlfnLdXAyAhLiPxyzQ1E+i0srbW7pgv0T0ZYQuSX+0dYB1JKjYJ9l6xordfmJdHAyQhLiPxyzQgrm/WwzlRKvQm8A/wFaAYezXOugZDCEiK/XPMzls3yMlfh3LL6Da31ITgz3Z/Ja6qBkV1CIfJrs+kAe2VTWEmt9Q4goJQKaK3/DMzLc66BcE37C+FTWc9BzrdsjmHtVkpVAn8F7lRKbcWZ7e4WUlhC5Ncm0wH2ymaE9WcgBFwGPAasBtx09aqsEy5EfrlmhJVNYQWBx4EncVZGuDuzi+gWq00HEMLnvFNYWusrtdZHAJfg3O7nL0qpP+Y9WZasqLUNaDOdQwgf805h9bIV52zBDsBtd/F923QAIXzKhuzXr8y3bOZhfVEp9STwJ5wbll6stT4y38EGSApLiPzYZkUt10wczeYs4UTgK1rrl/KcZSiksITID9fsDkJ2qzX853AEGSI58C5EfrhmSgMM7BiWm8kIS4j8cNVgwC+F5ao/VCF85HnTAXrzS2GtAdKmQwjhQ8+ZDtCbLwrLilpJwC3L3QjhFx3ACtMhevNFYWW46n8CIXzgRStq2aZD9Oanwvqr6QBC+IzrBgFSWEKI/khh5dErQIvpEEL4iBRWvmT2tZ82nUMIn2gF3jAd4kC+KawM2S0UIjdesKJWX7f5M0oKSwjRl3+aDtAXvxXWP5GbUgiRC8tMB+iLrwrLilo9wLOmcwjhcS04Kwy7jq8KK0N2C4UYmkczV4+4jh8L6wnTAYTwuAdMB+iPHwvrKZxlnIUQA9eDu+7svh/fFVZmOdcHTecQwqP+bEWtVtMh+uO7wsq413QAITzK1f/Z+7Ww/gjsNh1CCI/RwEOmQ7wfXxZW5gzHA6ZzCOExz1lRa4PpEO/Hl4WVcafpAEJ4jKt3B8HfhfUEsN50CCE85PemAxyMbwsrs3qDjLKEyM6TVtRy3eoMB/JtYWXcbjqAEB5xk+kA2fB1YVlR63VcuAiZEC6zFfg/0yGy4evCyrjedAAhXO43br128ECFUFh3A82mQwjhUjbwS9MhsuX7wrKiVhr4sekcQrjUA1bUesd0iGz5vrAyfg1sNx1CCBfy1H/mBVFYVtTqBH5uOocQLvOsFbU8deOWgiisjJ8DnaZDCOEinjshVTCFZUWtHcAtpnMI4RKr8eCqJgVTWBk/BlKmQwjhAt/OrB3nKQVVWFbUWgPcZTqHEIY9a0Wte0yHGIyCKqyMa3DmnghRqL5hOsBgFVxhWVHrVeBXpnMIYchDVtTy7J2lCq6wMr4D7DQdQohhlgYuNx1iKAqysDJnDL9jOocQw+wWK2qtNB1iKAqysDJuBl40HUKIYdIOXGE6xFAVbGFlFvj7Es7C+0L43bVW1NpiOsRQFWxhAVhR62/AHaZzCJFnm4FrTYfIhYIurIzLAdfeOFKIHPiiFbU6TIfIhYIvLCtqbQauNJ1DiDy5w4pa95sOkSsFX1gZNwCvmw4hRI6tAy41HSKXlNZyzBkg0hQ5GvgbUGI6ixut+voqAuUBlFJQBNPi02h5toWtD2yle1M3U//fVMoPKe/zuW2vtLHpt5vAhpoP19Dw0QYAtNZsvW8rLf9sQQUUtSfUUndyHS3/bGHr/Vspqixi0pcnEawM0r21my33bmHSFycN59v2Mg2cbEWtP5kOkktB0wHcwopaz0eaIjE8tqDZcDrk8kMIVu37J1M6oZRJl05iw2393yxY25qNd2zkkG8eQrA2yNtXvk3VnCrKxpexe/lukjuTTP/BdFRAkWp1rsXd8ccdTL1iKq3Pt9Ly9xbqTq5j631bGX3O6Ly/Rx/5ud/KCmSXcD9W1LoeWGo6h1eUjSujdGzp+z5mz9t7KB1dSsmoEgLBAKGFIdpebANg5xM7aTirARVQAASrnTJUAYVOaeweG1Wk6FjVQTAUpHTM+29LvGsVHp/R3h8ZYb3XvwEvA+MN53AXBc3XNgNQe3wttcfVZvW05K4kxbXF7/4+WBNkz9t7AOjZ2kPLP1pofaGVYFWQsReMpXRMKQ2NDbzzw3coHlnMhH+fwNob1zLxCxNz/pZ8Kg18xopae0wHyQcprANYUWtHpCnyKZxb3ReZzuMWU74zheKaYlKtKZp/1Ezp2FJGHDZiSK+pU5pAccA5HvZcCxtu3cCUb0+hclYl02ZNA2DX07uoOrKKns09bHxsI0UVRYy9YCyBUtk56McPrKj1rOkQ+SJ/632wotZTwFWmc7hJcY0zSgpWB6maW/XuKCmb5yV37rvlXWpXat9r1QSpnlcNQPXR1XSt69rvuXa3ze7lu6k7sY6tD2xlwsUTqDi0gt1/352Dd+RLLwDfMx0in6Sw+ncV8KTpEG5gd9uk96Tf/bz9tXZKx2d3PKn8kHK6t3TTs60HO2XT8o8WquZUAVA9t5qOFc58xo6VHe85RrX90e3UnVSHCirsnswSZop9n4vetgL/4pUbog6WTGt4H5GmyDic41n1prOY1LO1h7U/WwuATmtCi0KMOnMUrc+3svF/N5JuSxOoCFA+qZzwN8IkdyXZ8JsNhL8WBqDtZWdag7Y1NcfUMOrMUQCkO9Ks++U6kjuTBEoDjIuOo3ySMzXiwNfYO4WiqCIz1aFajmb00gWcYEWtv5sOkm9SWAcRaYqcDjyMjEaFe33Kilq/Mx1iOMgP4UFYUesR4JumcwjRj+8VSlmBjLCyFmmK/BT4sukcQvRylxW1zjcdYjjJCCt7XwUeMB1CiIx/ABeaDjHcZIQ1AJGmSDnO/KxFprOIgrYGWOiHBfkGSkZYA5CZPdwIvGY6iyhYbcAZhVhWIIU1YFbU2gl8BHjHdBZRcPYA51hRyzIdxBQprEGwotZG4CRgk+ksomDsAc60otYfTQcxSQprkKyo9TbOSGuH6SzC96SsMqSwhiBzF+kP46zsKEQ+SFn1IoU1RFbUeh34IHIgXuReG3C6lNU+Ulg5YEWt9cAxwNOmswjf2IFzfeCThnO4ihRWjlhRaxdwMs51h0IMxQbgw1bUes50ELeRwsqhzDyts4Ffm84iPOstYHHmUIM4gMx0z5NIU+Qq4L9M5xCe8ihwQWa0LvogI6w8saLWd4EvAbLanDgYjbNg5EelrN6fjLDyLLOeVhMFvgig6FcL8GkrasmxzyxIYQ2DzMqldwLHGY4i3OU1nEtt3jAdxCtkl3AYZC7lORGI49yGSYjfA4ukrAZGRljDLNIUORZntCX3PSxMaSBmRa1rTQfxIiksAyJNkXrgNpylakThWA9Eraj1hOkgXiW7hAZYUWs7cAbwNcDXt2USgHOm+OfATCmroZERlmGRpsg8nLOIM01nEXnxGnBxIdyCazjICMuwzOUXs4GvA62G44jc6QauAOZKWeWOjLBcJNIUGQNcA3waUIbjiMFbjjOqWmk6iN9IYblQpCnyQZxjHnNMZxED0gpcDvzSilryg5UHUlguFWmKBIDPA98Hag3HEe8vDdwBfCcz507kiRSWy0WaInU4pXUxcszRbdLAb3HuvvyW6TCFQArLIyJNkQjO6g8fR4rLNBu4C7hSZqoPLyksj4k0RQ4Dvg18CggajlNobOAenBHVCtNhCpEUlkdFmiKHAN8EokCF4Th+p4F7cUZUsna/QVJYHpc5xvXvOGtvjTUcx286cI5R/ayQb17qJlJYPhFpipQAn8QprgWG43jdK8BNwJ1W1JLJvC4iheVDkabINOA84Hzkkp9s7cZZ8uVWK2o9YziL6IcUls9FmiJH4hTXeUDYbBrXSQGPAbcDD1lRq9twHnEQUlgFJNIUWYRTXp8AxhiOY8p24I/A48BSK2ptM5xHDIAUVgGKNEWKgA8DJ2R+XQCUGQ2VPz3A33AK6nHgBblsxruksMTeA/bzce5efQzwISBkNNTQrGJfQT1pRa12w3lEjkhhiffIXMcYYV+BzQcmAUUmc/WhHXgdZ82pvR+vyPV8/iWFJbISaYoEgcnAVGBKr1/3fl6Vp01rnFUQVuMU0qvsK6c1sntXWKSwRE5k1qmfCozGKa/eHyOAEpxLifZ+FAN7gF39fOzM/NpiRS2505AApLCEEB4iV/0LITxDCksI4RlSWEIIz5DCEkJ4hhSWEMIzpLDEgCmlblVKbVVKvdrra+cqpV5TStlKqXkm8wn/ksISg3EbcOoBX3sVOAd4atjTiIIha4KLAdNaP6WUCh/wtRUASsn9X0X+yAhLCOEZUlhCCM+QwhJCeIYUlhDCM+TiZzFgSqnfAccB9cAW4Aqc1RV+BjTg3NDhJa31KYYiCp+SwhJCeIbsEgohPEMKSwjhGVJYQgjPkMISQniGFJYQwjOksIQQniGFJYTwDCksIYRnSGEJITxDCksI4RlSWEIIz5DCEkJ4hhSWEMIzpLCEEJ4hhSWE8AwpLCGEZ0hhCSE8QwpLCOEZUlhCCM/4/3jxvHSxTpOWAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_quota_table = df_quota.style.format({'value': '{:.2f}'}).set_caption(f'Quota - {project_id}')\n",
+ "ax_quota = df_quota.plot.pie(title=f'Quota - {project_id}', y='value', autopct='%.2f%%', figsize=(5,5))\n",
+ "display(df_quota_table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "0ff27ce1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6\" ><caption>Finished SUBs - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >name</th> <th class=\"col_heading level0 col1\" >duration</th> <th class=\"col_heading level0 col2\" >status</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >amazing_sub</td>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row0_col2\" class=\"data row0 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row1_col2\" class=\"data row1 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >21</th>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >0 days 00:13:20</td>\n",
+ " <td id=\"T_d439f346_9dff_11eb_84e4_000c299c9be6row2_col2\" class=\"data row2 col2\" >finished</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d69888278>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_subs_finished_table = df_subs_finished.style.format({'duration': to_timedelta}).set_caption(f'Finished SUBs - {project_id}')\n",
+ "df_subs_finished.plot.bar(title=f'Finished SUBs - {project_id}', color='#60ad5e', figsize=(5,5))\n",
+ "display(df_subs_finished_table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "f2256a8e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6\" ><caption>Failed SUBs - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >name</th> <th class=\"col_heading level0 col1\" >duration</th> <th class=\"col_heading level0 col2\" >status</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >horrible_sub</td>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row0_col2\" class=\"data row0 col2\" >failed</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >36</th>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >another_horrible_sub</td>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >0 days 00:03:20</td>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row1_col2\" class=\"data row1 col2\" >failed</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >43</th>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >yet_another_horrible_sub</td>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >0 days 00:05:50</td>\n",
+ " <td id=\"T_d458f7be_9dff_11eb_84e4_000c299c9be6row2_col2\" class=\"data row2 col2\" >failed</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d697fc128>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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ZZcnhZ2ZZcviZWZYcfmaWJYefmWXJ4WdmWXL4mVmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpYlh5+ZZcnhZ2ZZcviZWZY6DT9J9ZJ+JukXkp6X9N3UPk7Sk5LWSfqhpA+n9kFpfl1a3lTl92Bm1mXljPx+DZwVEScDk4FzJU0FbgBujIjjgLeAean/POCt1H5j6mdm1qt0Gn5R2J1mB6ZXAGcB96T2xcCFaXpmmictnyZJlSrYzKwSyjrnJ2mApDXAVuBh4BVgR0TsTV1agDFpegywASAt3wmMaGeb8yWtkrSqtbW1R2/CzKyrygq/iHg/IiYDjcBpwISe7jgiFkZEc0Q0NzQ09HRzZmZd0qWrvRGxA1gJ/HtgmKS25/42AhvT9EZgLEBafgSwvRLFmplVSjlXexskDUvTHwHOBtZShOAXU7fZwLI0vTzNk5Y/EhFRwZrNzHqsrvMujAYWSxpAEZZ3R8QDkl4A7pL0V8DTwG2p/23A/5K0DngTuKQKdZuZ9Uin4RcRzwCntNP+KsX5vwPb9wBfqkh1ZmZV4l94mFmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpYlh5+ZZcnhZ2ZZcviZWZYcfmaWJYefmWXJ4WdmWXL4mVmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpYlh5+ZZcnhZ2ZZcviZWZYcfmaWJYefmWXJ4WdmWXL4mVmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpYlh5+ZZcnhZ2ZZcviZWZYcfmaWpU7DT9JYSSslvSDpeUnfSO3DJT0s6Zfp75GpXZK+L2mdpGckTan2mzAz66pyRn57gW9FxERgKnClpInAAmBFRIwHVqR5gOnA+PSaD9xS8arNzHqo0/CLiE0R8VSa3gWsBcYAM4HFqdti4MI0PRO4IwpPAMMkja504WZmPVHXlc6SmoBTgCeBURGxKS3aDIxK02OADSWrtaS2TSVtSJpPMTLkmGOO6Wrd1pvN+UKtK+i+RffWugI7RMq+4CFpMHAv8GcR8XbpsogIILqy44hYGBHNEdHc0NDQlVXNzHqsrPCTNJAi+JZExD+l5i1th7Pp79bUvhEYW7J6Y2ozM+s1yrnaK+A2YG1E/G3JouXA7DQ9G1hW0n5Zuuo7FdhZcnhsZtYrlHPO73Tgj4BnJa1Jbd8GrgfuljQPeA2YlZY9BJwHrAPeAeZWsmAzs0roNPwi4nFAHSye1k7/AK7sYV1mZlXlX3iYWZYcfmaWJYefmWXJ4WdmWXL4mVmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpYlh5+ZZcnhZ2ZZcviZWZYcfmaWJYefmWXJ4WdmWXL4mVmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpYlh5+ZZcnhZ2ZZcviZWZYcfmaWJYefmWXJ4WdmWXL4mVmWHH5mliWHn5llyeFnZlly+JlZlhx+ZpalTsNP0j9K2irpuZK24ZIelvTL9PfI1C5J35e0TtIzkqZUs3gzs+4qZ+S3CDj3gLYFwIqIGA+sSPMA04Hx6TUfuKUyZZqZVVan4RcRPwXePKB5JrA4TS8GLixpvyMKTwDDJI2uUK1mZhXT3XN+oyJiU5reDIxK02OADSX9WlLb75A0X9IqSataW1u7WYaZWff0+IJHRAQQ3VhvYUQ0R0RzQ0NDT8swM+uS7obflrbD2fR3a2rfCIwt6deY2szMepXuht9yYHaang0sK2m/LF31nQrsLDk8NjPrNeo66yDpB8CZwFGSWoDvANcDd0uaB7wGzErdHwLOA9YB7wBzq1CzmVmPdRp+EfHlDhZNa6dvAFf2tCgzs2rzLzzMLEsOPzPLksPPzLLk8DOzLDn8zCxLDj8zy5LDz8yy5PAzsyw5/MwsSw4/M8uSw8/MsuTwM7MsOfzMLEsOPzPLksPPzLLk8DOzLDn8zCxLDj8zy5LDz8yy5PAzsyw5/MwsSw4/M8uSw8/MsuTwM7MsOfzMLEsOPzPLksPPzLLk8DOzLDn8zCxLDj8zy5LDz8yy5PAzsyw5/MwsSw4/M8tSVcJP0rmSXpK0TtKCauzDzKwnKh5+kgYANwHTgYnAlyVNrPR+zMx6ohojv9OAdRHxakT8BrgLmFmF/ZiZdVtdFbY5BthQMt8CfOrATpLmA/PT7G5JL1WhlkPhKGBbVba8WFXZbD/gz/zQ66uf+bEdLahG+JUlIhYCC2u1/0qRtCoimmtdR078mR96/fEzr8Zh70ZgbMl8Y2ozM+s1qhF+PwfGSxon6cPAJcDyKuzHzKzbKn7YGxF7JX0V+AkwAPjHiHi+0vvpRfr8oXsf5M/80Ot3n7kiotY1mJkdcv6Fh5llyeFnZlly+JlZlhx+ZpYlh5+Z7SPpMEnXSLpaUr2kOZKWS/pvkgbXur5KcvhZryXpq5KOStPHSfqppB2SnpQ0qdb19VOLgFHAOOBBoBn4G0DALbUrq/L8VZcukDQU+HOKX638KCLuLFl2c0RcUbPi+iFJz0fEx9P0g8CtEbFU0pnAdRFxei3r648krYmIyZIEbAJGR0Sk+V9ExCdqXGLFeOTXNbdT/A94L3CJpHslDUrLptaurH6r9Ev4IyNiKUBEPAoMqUlFmYhiVPRQ+ts2369GSg6/rvlYRCyIiPsiYgbwFPCIpBG1LqyfukfSIkkfBZZK+jNJx0qaC7xe6+L6qVVt5/Yi4o/bGiV9DNhVs6qqwIe9XSBpLfDxiPigpG0OcDUwOCI6vH2OdU/6fC8HPgYMorhd2n3ADRGxs3aV5UPSHRFxmSRFPwqMmt3Sqo+6HzgL+Oe2hohYJGkz8Pc1q6p/ewH4akT8XNLHgXOBtQ6+6pB04E1IBHxW0rA0P+PQVlQ9HvlViKS5EXF7revoTyR9h+JxCHXAwxR3CX8UOBv4SURcV7vq+idJTwPPA7dSnOMT8AOKuzMREf9Su+oqy+FXIZJej4hjal1HfyLpWWAyxeHuZqAxIt6W9BHgyf505bG3kPQh4BvAecDVEbFG0qsR8dEal1ZxPuztAknPdLSI4rtRVll7I+J94B1Jr0TE2wAR8a6kDzpZ17ohnc++UdL/SX+30E9zol++qSoaBfwB8NYB7QL+9dCX0+/9RtJhEfEO8Mm2RklHAA6/KoqIFuBLks4H3q51PdXg8OuaByiu6q45cIGkRw95Nf3fGRHxa9g3ImkzEJhdm5LyEhEPUvzSo9/xOT8zy5K/5GxmWXL4mVmWHH7W60lq92JS+unbFw91PdY/OPys14uI36t1Ddb/+Gqv9XqSdkfE4HRbpb+n+IXHBuA3ta3M+jKP/KwvuQg4AZgIXAZ4RGjd5vCzvuQM4AcR8X5EvAE8UuuCrO9y+JlZlhx+1pf8FLhY0gBJo4HP1rog67t8wcP6kqUU91N8geJOzv9W23KsL/PP28wsSz7sNbMsOfzMLEsOPzPLksPPzLLk8DOzLDn8zCxLDj8zy9L/B8JGuiPbXRGpAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_subs_failed_table = df_subs_failed.style.format({'duration': to_timedelta}).set_caption(f'Failed SUBs - {project_id}')\n",
+ "ax_subs_failed = df_subs_failed.plot.bar(title=f'Failed SUBs - {project_id}', color='#ff5f52', figsize=(5,5))\n",
+ "display(df_subs_failed_table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "9cc39543",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6\" ><caption>SUBs Summary - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >name</th> <th class=\"col_heading level0 col1\" >duration</th> <th class=\"col_heading level0 col2\" >status</th> </tr> <tr> <th class=\"index_name level0\" >id</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >amazing_sub</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row0_col1\" class=\"data row0 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row0_col2\" class=\"data row0 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row1_col1\" class=\"data row1 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row1_col2\" class=\"data row1 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >12</th>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >horrible_sub</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row2_col1\" class=\"data row2 col1\" >0 days 00:10:00</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row2_col2\" class=\"data row2 col2\" >failed</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6level0_row3\" class=\"row_heading level0 row3\" >21</th>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row3_col0\" class=\"data row3 col0\" >another_amazing_sub</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row3_col1\" class=\"data row3 col1\" >0 days 00:13:20</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row3_col2\" class=\"data row3 col2\" >finished</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6level0_row4\" class=\"row_heading level0 row4\" >36</th>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row4_col0\" class=\"data row4 col0\" >another_horrible_sub</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row4_col1\" class=\"data row4 col1\" >0 days 00:03:20</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row4_col2\" class=\"data row4 col2\" >failed</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6level0_row5\" class=\"row_heading level0 row5\" >43</th>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row5_col0\" class=\"data row5 col0\" >yet_another_horrible_sub</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row5_col1\" class=\"data row5 col1\" >0 days 00:05:50</td>\n",
+ " <td id=\"T_d46ef3ca_9dff_11eb_84e4_000c299c9be6row5_col2\" class=\"data row5 col2\" >failed</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d69808438>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_subs_table = df_subs.style.format({'duration': to_timedelta}).set_caption(f'SUBs Summary - {project_id}')\n",
+ "colors = {'finished': '#60ad5e', 'failed': '#ff5f52'}\n",
+ "ax_subs = df_subs.plot.bar(title=f'SUBs Summary - {project_id}', y='duration', legend=False, figsize=(7,5), color=list(df_subs['status'].map(colors)))\n",
+ "display(df_subs_table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "ebb46f8e",
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\" >\n",
+ "</style><table id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6\" ><caption>SAPs - high</caption><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >total_exposure</th> </tr> <tr> <th class=\"index_name level0\" >sap_name</th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >sap_1</th>\n",
+ " <td id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6row0_col0\" class=\"data row0 col0\" >0 days 00:05:40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6level0_row1\" class=\"row_heading level0 row1\" >sap_2</th>\n",
+ " <td id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6row1_col0\" class=\"data row1 col0\" >0 days 00:03:15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6level0_row2\" class=\"row_heading level0 row2\" >sap_3</th>\n",
+ " <td id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6row2_col0\" class=\"data row2 col0\" >0 days 00:03:55</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6level0_row3\" class=\"row_heading level0 row3\" >sap_4</th>\n",
+ " <td id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6row3_col0\" class=\"data row3 col0\" >0 days 00:05:45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6level0_row4\" class=\"row_heading level0 row4\" >sap_5</th>\n",
+ " <td id=\"T_d48650ba_9dff_11eb_84e4_000c299c9be6row4_col0\" class=\"data row4 col0\" >0 days 00:02:17</td>\n",
+ " </tr>\n",
+ " </tbody></table>"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7f1d697fc208>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_saps_table = df_saps.style.format({'total_exposure': to_timedelta}).set_caption(f'SAPs - {project_id}')\n",
+ "ax_saps = df_saps.plot.bar(title=f'SAPs - {project_id}', color=['#ffd95a'], figsize=(7,5))\n",
+ "display(df_saps_table)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d40699f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
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