diff --git a/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb b/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb
index 9baf1d5ba803799da36b146cd76a52f6694ba906..96efdfd902d2f678053acafdb3424118dc10f002 100644
--- a/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb
+++ b/SAS/TMSS/scripts/notebooks/project_report_poc.ipynb
@@ -7,7 +7,11 @@
"source": [
"# Project Report PoC - TMSS\n",
"\n",
- "This document shows how to generate a report for a project."
+ "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",
+ "---"
]
},
{
@@ -17,13 +21,23 @@
"source": [
"### Prerequirements\n",
"\n",
- "The following modules are needed before proceeding:"
+ "Before proceeding you need to import some modules, as well as specify some configurations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e36852c6",
+ "metadata": {},
+ "source": [
+ "#### Imports\n",
+ "\n",
+ "The Pandas and Requests libraries are required."
]
},
{
"cell_type": "code",
"execution_count": 1,
- "id": "48b766e7",
+ "id": "ae6bff14",
"metadata": {},
"outputs": [],
"source": [
@@ -31,6 +45,35 @@
"import requests"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "6ada730f",
+ "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": "340c050b",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
{
"cell_type": "markdown",
"id": "9812780a",
@@ -40,7 +83,7 @@
"\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. You need to provide your authentication credentials in the `auth` parameter. Since the response will be a JSON object, you can simply store the result of `response.json()` as follows:"
+ "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."
]
},
{
@@ -76,18 +119,17 @@
}
],
"source": [
- "BASE_URL = 'http://localhost:8000/api'\n",
- "project = 'test_for_report' # project id\n",
- "credentials = ('test', 'test') # username, password\n",
+ "project = 'high' # Specify your target project\n",
"\n",
- "response = requests.get(BASE_URL + '/project/%s/report' % project, auth=credentials)\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": 134,
+ "execution_count": 3,
"id": "3276ce6d",
"metadata": {},
"outputs": [],
@@ -162,14 +204,14 @@
"id": "1721b2bc",
"metadata": {},
"source": [
- "## Manage the data\n",
+ "### Manage the data\n",
"\n",
- "Once you have retrieved the data, you need to apply some preprocessing. In the following snippet, we extract the data into variables that will be used afterwards."
+ "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": 135,
+ "execution_count": 4,
"id": "d1b58c3a",
"metadata": {},
"outputs": [],
@@ -188,7 +230,17 @@
"id": "883d53a9",
"metadata": {},
"source": [
- "You can now use Pandas for the data analysis and visualisation parts."
+ "You can now use a library (i.e., Pandas) for the data analysis and visualisation parts.\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2e62c29",
+ "metadata": {},
+ "source": [
+ "## Create tables\n"
]
},
{
@@ -196,14 +248,14 @@
"id": "c9b7d51e",
"metadata": {},
"source": [
- "## Create a report\n",
+ "### 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": 136,
+ "execution_count": 5,
"id": "8a0a7ed9",
"metadata": {
"scrolled": true
@@ -213,22 +265,22 @@
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
- "</style><table id=\"T_8b9a6fac_9d01_11eb_9243_000c299c9be6\" ><caption>Summary Table</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",
+ "</style><table id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6\" ><caption>Summary Table</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_8b9a6fac_9d01_11eb_9243_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >test_for_report</th>\n",
- " <td id=\"T_8b9a6fac_9d01_11eb_9243_000c299c9be6row0_col0\" class=\"data row0 col0\" >2800.000012</td>\n",
- " <td id=\"T_8b9a6fac_9d01_11eb_9243_000c299c9be6row0_col1\" class=\"data row0 col1\" >1400.000006</td>\n",
- " <td id=\"T_8b9a6fac_9d01_11eb_9243_000c299c9be6row0_col2\" class=\"data row0 col2\" >1400.000006</td>\n",
- " <td id=\"T_8b9a6fac_9d01_11eb_9243_000c299c9be6row0_col3\" class=\"data row0 col3\" >800.000006</td>\n",
- " <td id=\"T_8b9a6fac_9d01_11eb_9243_000c299c9be6row0_col4\" class=\"data row0 col4\" >246</td>\n",
+ " <th id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6level0_row0\" class=\"row_heading level0 row0\" >test_for_report</th>\n",
+ " <td id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6row0_col0\" class=\"data row0 col0\" >2800.000012</td>\n",
+ " <td id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6row0_col1\" class=\"data row0 col1\" >1400.000006</td>\n",
+ " <td id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6row0_col2\" class=\"data row0 col2\" >1400.000006</td>\n",
+ " <td id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6row0_col3\" class=\"data row0 col3\" >800.000006</td>\n",
+ " <td id=\"T_b5d46e6c_9d24_11eb_bb28_000c299c9be6row0_col4\" class=\"data row0 col4\" >246</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
- "<pandas.io.formats.style.Styler at 0x7f4c158faa90>"
+ "<pandas.io.formats.style.Styler at 0x7f6bb40e6f28>"
]
},
- "execution_count": 136,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -245,16 +297,24 @@
},
{
"cell_type": "markdown",
- "id": "8f9b1da2",
+ "id": "ddfa7824",
"metadata": {},
"source": [
- "For the other values, you can follow a similar procedure:"
+ "Note that for the other values, you can follow a similar procedure as illustrated by the following sections."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2f231ba",
+ "metadata": {},
+ "source": [
+ "### Quota table\n"
]
},
{
"cell_type": "code",
- "execution_count": 257,
- "id": "aec5ebb1",
+ "execution_count": 6,
+ "id": "1b94ce76",
"metadata": {},
"outputs": [
{
@@ -309,20 +369,37 @@
"11 LTA Storage 2400.0"
]
},
- "execution_count": 257,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
+ "# Create a DataFrame for quota\n",
"df_quota = pd.DataFrame(quota)\n",
"df_quota.set_index('id')"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "47f4503c",
+ "metadata": {},
+ "source": [
+ "### SchedulingUnitBlueprints\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93b336e7",
+ "metadata": {},
+ "source": [
+ "#### Finished SUBs\n"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 138,
- "id": "a2621ab9",
+ "execution_count": 7,
+ "id": "10537659",
"metadata": {},
"outputs": [
{
@@ -346,52 +423,60 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>total_exposure</th>\n",
+ " <th>name</th>\n",
+ " <th>duration</th>\n",
" </tr>\n",
" <tr>\n",
- " <th>sap_name</th>\n",
+ " <th>id</th>\n",
+ " <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>sap_1</th>\n",
- " <td>340.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>sap_2</th>\n",
- " <td>195.0</td>\n",
+ " <th>8</th>\n",
+ " <td>amazing_sub</td>\n",
+ " <td>600.000003</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>sap_3</th>\n",
- " <td>235.0</td>\n",
+ " <th>21</th>\n",
+ " <td>another_amazing_sub</td>\n",
+ " <td>800.000003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " total_exposure\n",
- "sap_name \n",
- "sap_1 340.0\n",
- "sap_2 195.0\n",
- "sap_3 235.0"
+ " name duration\n",
+ "id \n",
+ "8 amazing_sub 600.000003\n",
+ "21 another_amazing_sub 800.000003"
]
},
- "execution_count": 138,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df_saps = pd.DataFrame(saps)\n",
- "df_saps.set_index('sap_name')"
+ "# Create a DataFrame for finished SUBs\n",
+ "df_subs_finished = pd.DataFrame(subs_finished)\n",
+ "df_subs_finished.set_index('id')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c67ef360",
+ "metadata": {},
+ "source": [
+ "#### Failed SUBs\n"
]
},
{
"cell_type": "code",
- "execution_count": 139,
- "id": "192cea33",
+ "execution_count": 8,
+ "id": "6487f39e",
"metadata": {},
"outputs": [
{
@@ -426,40 +511,49 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>8</th>\n",
- " <td>amazing_sub</td>\n",
+ " <th>12</th>\n",
+ " <td>horrible_sub</td>\n",
" <td>600.000003</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>21</th>\n",
- " <td>another_amazing_sub</td>\n",
- " <td>800.000003</td>\n",
+ " <th>36</th>\n",
+ " <td>another_horrible_sub</td>\n",
+ " <td>200.000003</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " name duration\n",
- "id \n",
- "8 amazing_sub 600.000003\n",
- "21 another_amazing_sub 800.000003"
+ " name duration\n",
+ "id \n",
+ "12 horrible_sub 600.000003\n",
+ "36 another_horrible_sub 200.000003"
]
},
- "execution_count": 139,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df_subs_finished = pd.DataFrame(subs_finished)\n",
- "df_subs_finished.set_index('id')"
+ "# Create a DataFrame for failed SUBs\n",
+ "df_subs_failed = pd.DataFrame(subs_failed)\n",
+ "df_subs_failed.set_index('id')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "265697ce",
+ "metadata": {},
+ "source": [
+ "### SAPs\n"
]
},
{
"cell_type": "code",
- "execution_count": 140,
- "id": "02d2a788",
+ "execution_count": 9,
+ "id": "669389c5",
"metadata": {},
"outputs": [
{
@@ -483,45 +577,55 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>name</th>\n",
- " <th>duration</th>\n",
+ " <th>total_exposure</th>\n",
" </tr>\n",
" <tr>\n",
- " <th>id</th>\n",
- " <th></th>\n",
+ " <th>sap_name</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>12</th>\n",
- " <td>horrible_sub</td>\n",
- " <td>600.000003</td>\n",
+ " <th>sap_1</th>\n",
+ " <td>340.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>36</th>\n",
- " <td>another_horrible_sub</td>\n",
- " <td>200.000003</td>\n",
+ " <th>sap_2</th>\n",
+ " <td>195.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>sap_3</th>\n",
+ " <td>235.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " name duration\n",
- "id \n",
- "12 horrible_sub 600.000003\n",
- "36 another_horrible_sub 200.000003"
+ " total_exposure\n",
+ "sap_name \n",
+ "sap_1 340.0\n",
+ "sap_2 195.0\n",
+ "sap_3 235.0"
]
},
- "execution_count": 140,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df_subs_failed = pd.DataFrame(subs_failed)\n",
- "df_subs_failed.set_index('id')"
+ "# Create a DataFrame for SAPs\n",
+ "df_saps = pd.DataFrame(saps)\n",
+ "df_saps.set_index('sap_name')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "193c3998",
+ "metadata": {},
+ "source": [
+ "---"
]
},
{
@@ -529,23 +633,23 @@
"id": "b7a4b0b9",
"metadata": {},
"source": [
- "### Create a plot\n",
+ "## Create a plot\n",
"\n",
"To better visualise the data, you could plot it in several ways. For example, you might want to observe the differences between all of the four durations retrieved, as follows:"
]
},
{
"cell_type": "markdown",
- "id": "3425a1bd",
+ "id": "b5a0d68a",
"metadata": {},
"source": [
- "#### Quota\n"
+ "### Quota\n"
]
},
{
"cell_type": "code",
- "execution_count": 141,
- "id": "c14dacaa",
+ "execution_count": 10,
+ "id": "8b6ad3ba",
"metadata": {},
"outputs": [
{
@@ -562,21 +666,21 @@
}
],
"source": [
- "# Plot a horizontal bar graph\n",
+ "# Plot a bar graph\n",
"ax_quota = df_quota.plot.bar(title='Quota', x='id', color=['#58a5f0'])"
]
},
{
"cell_type": "markdown",
- "id": "cffa43f9",
+ "id": "619ec2aa",
"metadata": {},
"source": [
- "#### Durations\n"
+ "### Durations\n"
]
},
{
"cell_type": "code",
- "execution_count": 142,
+ "execution_count": 11,
"id": "da3340db",
"metadata": {},
"outputs": [
@@ -602,18 +706,26 @@
},
{
"cell_type": "markdown",
- "id": "f7eb2288",
+ "id": "982e28e7",
"metadata": {},
"source": [
- "#### Scheduling Unit Blueprints\n",
+ "### 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": "07b8933f",
+ "metadata": {},
+ "source": [
+ "#### Finished SUBs"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 250,
- "id": "c84e2c5e",
+ "execution_count": 12,
+ "id": "4835b891",
"metadata": {},
"outputs": [
{
@@ -630,17 +742,23 @@
}
],
"source": [
- "# Plot a horizontal bar graph\n",
+ "# Plot a bar graph\n",
"ax_subs_finished = df_subs_finished.plot.bar(title='Finished SUBs', x='id', color='#60ad5e')"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "e385353c",
+ "metadata": {},
+ "source": [
+ "#### Failed SUBs"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 249,
- "id": "2ae02964",
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 13,
+ "id": "18074170",
+ "metadata": {},
"outputs": [
{
"data": {
@@ -656,24 +774,24 @@
}
],
"source": [
- "# Plot a horizontal bar graph\n",
+ "# Plot a bar graph\n",
"ax_subs_failed = df_subs_failed.plot.bar(title='Failed SUBs', x='id', color='#ff5f52')"
]
},
{
"cell_type": "markdown",
- "id": "340f067d",
+ "id": "51e4faea",
"metadata": {},
"source": [
- "##### SUBs Summary\n",
+ "#### 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": 254,
- "id": "a2e8f9cb",
+ "execution_count": 14,
+ "id": "d709f04e",
"metadata": {},
"outputs": [
{
@@ -746,7 +864,7 @@
"36 another_horrible_sub 200.000003 failed"
]
},
- "execution_count": 254,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -762,7 +880,7 @@
},
{
"cell_type": "markdown",
- "id": "3be9b79e",
+ "id": "b7e56fc8",
"metadata": {},
"source": [
"Then, you can plot a bar graph discriminting colors by status:"
@@ -770,8 +888,8 @@
},
{
"cell_type": "code",
- "execution_count": 253,
- "id": "117d845e",
+ "execution_count": 15,
+ "id": "0ed4233a",
"metadata": {},
"outputs": [
{
@@ -796,7 +914,7 @@
},
{
"cell_type": "markdown",
- "id": "f5be0a1d",
+ "id": "a2682239",
"metadata": {},
"source": [
"#### SAPs"
@@ -804,7 +922,7 @@
},
{
"cell_type": "code",
- "execution_count": 256,
+ "execution_count": 16,
"id": "b323083e",
"metadata": {},
"outputs": [
@@ -822,14 +940,22 @@
}
],
"source": [
- "# Plot a horizontal bar graph\n",
+ "# Plot a bar graph\n",
"ax_saps = df_saps.plot.bar(title='SAPs', x='sap_name', color=['#ffd95a'])"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "9cc1a590",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "91042123",
+ "id": "a5d9b0db",
"metadata": {},
"outputs": [],
"source": []