{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TriScale Interactive Plots\n",
"\n",
"This notebook generates and displays all the plots from the _TriScale_ paper.\n",
"By default, large versions of the plots are shown. To exactly reproduce the plots from the paper, set `PaperPlots=True` in the first cell.\n",
"\n",
"- [Figure 1](#Figure-1)\n",
" - [Figure 1.a](#Figure-1a)\n",
" - [Figure 1.b](#Figure-1b)\n",
"- [Figure 3](#Figure-3)\n",
" - [Figure 3.a](#Figure-3a)\n",
" - [Figure 3.b](#Figure-3b)\n",
" - [Figure 3.c](#Figure-3c)\n",
"- [Figure 4](#Figure-4)\n",
"- [Figure 5](#Figure-5)\n",
"- [Figure 7](#Figure-7)\n",
"- [Figure 8](#Figure-8)\n",
"- [Figure 9](#Figure-9)\n",
"- [Figure 10](#Figure-10)\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Set to False to view larger versions of the plots\n",
"# Set to True to reproduce the exact plots from the paper\n",
"PaperPlot = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"import json\n",
"import os\n",
"from pathlib import Path\n",
"import yaml\n",
"import zipfile\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.graph_objects as go\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"notebook\"\n",
"\n",
"import triscale\n",
"import triplots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Download Source Files and Data\n",
"[[Back to top](#TriScale)]\n",
"\n",
"The entire dataset for this case study is available on Zenodo: \n",
"\n",
"[](https://doi.org/10.5281/zenodo.3451417)\n",
"\n",
"The wget commands below download the required files to reproduce this case study.\n",
"> **The .zip file is ~2.7 GB**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nothing to download\n"
]
}
],
"source": [
"# Set `download = True` to download (and extract) the data from this case study\n",
"# Eventually, adjust the record_id for the file version you are interested in.\n",
"\n",
"# For reproducing the original TriScale paper, set `record_id = 3666724`\n",
"\n",
"download = False\n",
"record_id = 3666724 # v3.0.1 (https://doi.org/10.5281/zenodo.3666724)\n",
"\n",
"files= ['UseCase_Pantheon.zip',\n",
" 'UseCase_Glossy.zip'\n",
" 'UseCase_VideoStreaming.zip'\n",
" 'UseCase_FailureDetection.zip']\n",
"if download:\n",
" for file in files:\n",
" print(file)\n",
" url = 'https://zenodo.org/record/'+str(record_id)+'/files/'+file \n",
" os.system('wget %s' %url)\n",
" if file[-4:] == '.zip': \n",
" with zipfile.ZipFile(file,\"r\") as zip_file:\n",
" zip_file.extractall()\n",
" print('Done.')\n",
"else: \n",
" print('Nothing to download')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import UseCase_Pantheon.pantheon as pantheon\n",
"import UseCase_Glossy.flocklab as flocklab\n",
"import UseCase_VideoStreaming.videostreaming as vs\n",
"import UseCase_FailureDetection.failuredetection as fd"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Select the different output format settings\n",
"\n",
"if PaperPlot:\n",
" output_format = 'CCR'\n",
"else:\n",
" output_format = 'online'\n",
"\n",
"if output_format == 'online':\n",
" font_size_px = 14\n",
" linewidth_px = 512\n",
" landscapewidth_px = 654\n",
" plot_path = None\n",
" \n",
" plot_path = Path('plots')\n",
"\n",
"if output_format == 'CCR':\n",
" font_size_pt = 7\n",
" offset = 5 # to compensate for the rounding of unit conversions\n",
" linewidth_pt = 241 - offset \n",
" landscapewidth_pt = 506 - offset\n",
" \n",
" # 1pt = 1.333px\n",
" font_size_px = int(font_size_pt*1.333)+1\n",
" linewidth_px = int(linewidth_pt*1.333)+1\n",
" landscapewidth_px = int(landscapewidth_pt*1.333)+1\n",
"\n",
" plot_path = Path('plots_ccr')\n",
" \n",
"# Create plot directory if don't exist\n",
"if (plot_path is not None) and not (os.path.exists(plot_path)):\n",
" os.mkdir(plot_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 1a"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set to False to see the full range of the axes\n",
"# Set to True to reproduce the figure shown in the paper\n",
"zoom = True\n",
"\n",
"data_path = Path('UseCase_Pantheon/PantheonData/10runs_30s/2019-08-20T15:34:33:+0200')\n",
"perf_file = data_path / 'pantheon_perf.json'\n",
"meta_file = data_path / 'pantheon_metadata.json'\n",
"config_file = Path('UseCase_Pantheon/PantheonData/config.yml')\n",
"\n",
"custom_layout = {\n",
" \"title\":None,\n",
" \"showlegend\":False,\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'range':[100,32],\n",
" 'autorange':False,\n",
" 'title':{'font':{'size':font_size_px}}},\n",
" \"yaxis\":{'range':[-7,127],\n",
" 'title':{'font':{'size':font_size_px}}},\n",
"}\n",
"\n",
"# Adjust the axis ranges and file name\n",
"if zoom:\n",
" custom_layout[\"yaxis\"][\"range\"]=[80,122]\n",
" custom_layout[\"xaxis\"][\"range\"]=[100,38]\n",
" plot_filename = \"plot_zoom_pantheon.pdf\"\n",
"else: \n",
" plot_filename = \"plot_summary_pantheon.pdf\"\n",
"\n",
"if PaperPlot:\n",
" plot_filename = str(plot_path/plot_filename)\n",
"else:\n",
" plot_filename = None\n",
" \n",
" \n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 0.8*landscapewidth_px\n",
" custom_layout['height'] = 500\n",
" custom_layout['margin'] = dict(l=65, r=0, t=70, b=65)\n",
"\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = 0.4*landscapewidth_px\n",
" custom_layout['height'] = 185\n",
" custom_layout['margin'] = dict(l=30, r=0, t=25, b=40)\n",
"\n",
"# Produce the plot\n",
"pantheon.plot_pantheon(perf_file, \n",
" meta_file, \n",
" config_file, \n",
" layout=custom_layout,\n",
" out_name=plot_filename,\n",
" show=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 1b"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set to False to see the full range of the axes\n",
"# Set to True to reproduce the figure shown in the paper\n",
"zoom = True\n",
"\n",
"result_dir_path = Path('UseCase_Pantheon/PantheonData/10runs_30s')\n",
"result_dir_list = [x for x in result_dir_path.iterdir() if x.is_dir()]\n",
"meta_data_file = 'pantheon_metadata.json'\n",
"config_file = Path('UseCase_Pantheon/PantheonData/config.yml')\n",
"out_name = Path('UseCase_Pantheon/PantheonData/metrics_wo_convergence.csv')\n",
"\n",
"# Definition of metrics and KPIs for TriScale analysis\n",
"convergence = {'expected': True,\n",
" 'confidence': 95, # in %\n",
" 'tolerance': 5, # in %\n",
" }\n",
"metric_tput = {'name':'Mean throughput',\n",
" 'unit': 'Mbit/s',\n",
" 'measure':'mean',\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"metric_delay = {'name':'95th perc. of one-way delay',\n",
" 'unit': 'ms',\n",
" 'measure':95,\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"KPI_tput = {'percentile': 25,\n",
" 'confidence': 75,\n",
" 'name': 'KPI of throughput metric',\n",
" 'unit': 'Mbit/s',\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"KPI_delay = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': 'KPI of one-way delay metric',\n",
" 'unit': 'ms',\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"\n",
"metric_list = [metric_tput, metric_delay]\n",
"kpi_list = [ KPI_tput, KPI_delay ]\n",
"metrics, figure = pantheon.compute_metric(result_dir_list, \n",
" meta_data_file, \n",
" convergence,\n",
" metric_list,\n",
" out_name=out_name,\n",
" plot=False,\n",
" verbose=False)\n",
"\n",
"custom_layout = {\n",
" \"title\":None,\n",
" \"showlegend\":False,\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'range':[100,32],\n",
" 'autorange':False,\n",
" 'title':{'font':{'size':font_size_px}}},\n",
" \"yaxis\":{'range':[-7,127]},\n",
"}\n",
"\n",
"# Adjust the Y-axis range and file name\n",
"if zoom:\n",
" custom_layout[\"xaxis\"][\"range\"]=[100,38]\n",
" custom_layout[\"yaxis\"][\"range\"]=[78,122]\n",
" plot_filename = \"plot_zoom_triscale_wo_convergence.pdf\"\n",
"else: \n",
" plot_filename = \"plot_summary_triscale.pdf\"\n",
"\n",
"if PaperPlot:\n",
" plot_filename = str(plot_path/plot_filename)\n",
"else:\n",
" plot_filename = None\n",
" \n",
" \n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 0.8*landscapewidth_px\n",
" custom_layout['height'] = 500\n",
" custom_layout['margin'] = dict(l=65, r=0, t=70, b=65)\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = 0.4*landscapewidth_px\n",
" custom_layout['height'] = 185\n",
" custom_layout['margin'] = dict(l=30, r=0, t=25, b=40)\n",
"\n",
" \n",
"series_label = np.sort(metrics['datetime'].unique())\n",
"for series_ix in [series_label[0]]:\n",
"\n",
" # Get the metrics values for one series\n",
" metric_series = metrics.loc[metrics['datetime'] == series_ix]\n",
"\n",
" # Plot them\n",
" pantheon.plot_triscale_kpi(metric_series, \n",
" result_dir_list[0] / meta_data_file, \n",
" kpi_list,\n",
" config_file,\n",
" layout=custom_layout, \n",
" out_name=plot_filename,\n",
" show=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 3a"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Construct the path to the different test results\n",
"result_dir_path = Path('UseCase_Pantheon/PantheonData/10runs_30s')\n",
"result_dir_list = [x for x in result_dir_path.iterdir() if x.is_dir()]\n",
"# Meta data file name\n",
"meta_data_file = 'pantheon_metadata.json'\n",
"# Config file name and path\n",
"config_file = Path('UseCase_Pantheon/PantheonData/config.yml')\n",
"out_name = Path('UseCase_Pantheon/PantheonData/metrics.csv')\n",
"\n",
"# Inputs\n",
"metric_delay = {'name':'One-way delay',\n",
" 'unit': 'ms',\n",
" 'measure':95,\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"convergence = {'expected': True,\n",
" 'confidence': 95, # in %\n",
" 'tolerance': 5, # in %\n",
" }\n",
"\n",
"# load the metrics\n",
"metrics = pantheon.compute_metric(result_dir_list, \n",
" meta_data_file, \n",
" convergence,\n",
" [metric_delay],\n",
" out_name = out_name)\n",
"\n",
"data_file = str(result_dir_list[0] / 'cubic_datalink_delay_run1_flow1.csv')\n",
"\n",
"# Plot file name\n",
"plot_out_name = \"plot_example_metric.pdf\"\n",
"if PaperPlot:\n",
" plot_out_name = str(plot_path/plot_out_name)\n",
"else:\n",
" plot_out_name = None\n",
"\n",
"# Horizontal Y-axis title\n",
"ytitle = {\n",
" \"x\":0,\n",
" \"y\":1.10,\n",
" \"xref\":\"paper\",\n",
" \"yref\":\"paper\",\n",
" \"text\":metric_delay['name'] +' [ '+ metric_delay['unit'] +' ]',\n",
" \"showarrow\":False,\n",
" \"xanchor\":'left'\n",
"}\n",
"\n",
"custom_layout = {\n",
" \"title\":None,\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px},\n",
" 'text':'Time [ s ]'}\n",
" },\n",
" \"yaxis\":{'title':{'text':''}},\n",
" \"annotations\":[ytitle] \n",
"}\n",
"\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 1.2*landscapewidth_px\n",
" custom_layout['height'] = 400\n",
" custom_layout['margin'] = dict(l=65, r=0, t=70, b=65)\n",
"# custom_layout[\"legend\"] = dict(x=0.1, y=0.1)\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = 0.60*landscapewidth_px\n",
" custom_layout['height'] = 200\n",
" custom_layout['margin'] = dict(l=20, r=0, t=30, b=25)\n",
" custom_layout['showlegend'] = False\n",
" custom_layout[\"legend\"] = dict(xanchor='center',\n",
" x=0.5,\n",
" yanchor='bottom',\n",
" y=0.1, \n",
" font=dict(size=font_size_px-2),\n",
" orientation='h')\n",
" \n",
"# Produce the plot\n",
"triscale.analysis_metric( data_file,\n",
" metric_delay,\n",
" plot=True,\n",
" plot_out_name=plot_out_name,\n",
" custom_layout = custom_layout,\n",
" convergence=convergence);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 3b"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Construct the path to the different test results\n",
"result_dir_path = Path('UseCase_Pantheon/PantheonData/10runs_30s')\n",
"result_dir_list = [x for x in result_dir_path.iterdir() if x.is_dir()]\n",
"# Meta data file name\n",
"meta_data_file = 'pantheon_metadata.json'\n",
"# Config file name and path\n",
"config_file = Path('UseCase_Pantheon/PantheonData/config.yml')\n",
"out_name = Path('UseCase_Pantheon/PantheonData/metrics.csv')\n",
"\n",
"# Inputs\n",
"metric_delay = {'name':'One-way delay',\n",
" 'unit': 'ms',\n",
" 'measure':95,\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"convergence = {'expected': True,\n",
" 'confidence': 95, # in %\n",
" 'tolerance': 5, # in %\n",
" }\n",
"KPI_delay = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': 'One-way delay',\n",
" 'unit': 'ms',\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"\n",
"# Compute/load the metrics\n",
"metrics, figure = pantheon.compute_metric(result_dir_list, \n",
" meta_data_file, \n",
" convergence,\n",
" [metric_delay],\n",
" out_name = out_name\n",
" )\n",
"\n",
"# File name for saving plot\n",
"plot_out_name = \"plot_example_KPI.pdf\"\n",
"if PaperPlot:\n",
" plot_out_name = str(plot_path/plot_out_name)\n",
"else:\n",
" plot_out_name = None\n",
" \n",
"metric_data = np.array(metrics.loc[(metrics['cc'] == 'cubic') &\n",
" (metrics['datetime'] == result_dir_list[1].stem)].delay_value)\n",
"\n",
"# Compute the KPI\n",
"out = triscale.analysis_kpi(metric_data,\n",
" KPI_delay);\n",
"# KPI annotation\n",
"note = go.layout.Annotation(\n",
" x=0.5,\n",
" y=1,\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" yanchor=\"bottom\",\n",
" text=\"KPI: %2.2f ms\" % out[1],\n",
" showarrow=False,\n",
" font={'size':font_size_px+2 }\n",
" )\n",
"\n",
"custom_layout = {\n",
" \"title\":None,\n",
" \"width\":0.5*linewidth_px,\n",
" \"height\":100,\n",
" \"margin\":dict(l=0, r=0, t=5, b=40),\n",
" \"font\":{\"size\":font_size_px},\n",
" \"showlegend\": False,\n",
" \"yaxis\":{'title':{'font':{'size':font_size_px}}},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px},\n",
" 'text':'One-way delay metric [ms]'}},\n",
" \"annotations\":[note]\n",
"}\n",
"\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 0.5*linewidth_px\n",
" custom_layout['height'] = 100\n",
" custom_layout['margin'] = dict(l=0, r=0, t=5, b=40)\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = 0.22*landscapewidth_px\n",
" custom_layout['height'] = 60\n",
" custom_layout['margin'] = dict(l=0, r=10, t=15, b=25)\n",
" \n",
"# Produce the plot\n",
"out = triscale.analysis_kpi(metric_data,\n",
" KPI_delay,\n",
" to_plot=['horizontal'],\n",
" plot_out_name=plot_out_name,\n",
" custom_layout=custom_layout);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 3c"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Invalid metric data (only one data point)\n",
"Invalid metric data (only one data point)\n",
"Invalid metric data (only one data point)\n",
"Invalid metric data (only one data point)\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"KPI_tput = {'percentile': 25,\n",
" 'confidence': 75,\n",
" 'name': 'Average Throughput',\n",
" 'unit': 'Mbit/s',\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"KPI_delay = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': 'One-way delay',\n",
" 'unit': 'ms',\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"score_delay = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': '95th perc. of One-way delay',\n",
" 'unit': 'ms',\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"# Compute the KPIs\n",
"kpis = pantheon.compute_kpi(metrics,\n",
" [KPI_tput, KPI_delay],\n",
" 'datetime',\n",
" plot=False,\n",
" verbose=False)\n",
"\n",
"plot_out_name = \"plot_example_var_score.pdf\"\n",
"if PaperPlot:\n",
" plot_out_name = str(plot_path/plot_out_name)\n",
"else:\n",
" plot_out_name = None\n",
"\n",
"kpi_data = np.array(kpis.loc[(kpis['cc'] == 'cubic')].delay_value)\n",
"\n",
"# Compute the variability score\n",
"out = triscale.analysis_variability(kpi_data,\n",
" score_delay);\n",
"\n",
"note = go.layout.Annotation(\n",
" x=0.5,\n",
" y=1,\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" yanchor=\"bottom\",\n",
" text=\"Var. Score: %2.2f ms\" % out[3],\n",
" showarrow=False,\n",
" font={'size':font_size_px+2 }\n",
" )\n",
"custom_layout = {\n",
" \"title\":None,\n",
" \"showlegend\": False,\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px},\n",
" 'text':'One-way delay KPI [ms]'}\n",
" },\n",
" \"annotations\":[note]\n",
"\n",
"}\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 0.5*linewidth_px\n",
" custom_layout['height'] = 100\n",
" custom_layout['margin'] = dict(l=0, r=0, t=5, b=40)\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = 0.22*landscapewidth_px\n",
" custom_layout['height'] = 60\n",
" custom_layout['margin'] = dict(l=0, r=10, t=15, b=25)\n",
" \n",
"# Produce the plot\n",
"triscale.analysis_variability(kpi_data,\n",
" score_delay,\n",
" to_plot=['horizontal'],\n",
" plot_out_name=plot_out_name,\n",
" custom_layout=custom_layout);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 4"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TriScale metric definition\n",
"metric_tput = {'name':'Median throughput',\n",
" 'unit': 'Mbit/s',\n",
" 'measure':50,\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"convergence = {'expected': True,\n",
" 'confidence': 95, # in %\n",
" 'tolerance': 5, # in %\n",
" }\n",
"\n",
"# Selection of the data to plot\n",
"result_dir_path = Path('UseCase_Pantheon/PantheonData/10_20_30_40_50_60s')\n",
"result_dir_list = [x for x in result_dir_path.iterdir() if x.is_dir()]\n",
"result_dir_list = sorted(result_dir_list)\n",
"meta_data_file = 'pantheon_metadata.json'\n",
"sample = {'cc':'ledbat',\n",
" 'run':1}\n",
"\n",
"# Horizontal Y axis label\n",
"ytitle = {\n",
" \"x\":0,\n",
" \"y\":1.15,\n",
" \"xref\":\"paper\",\n",
" \"yref\":\"paper\",\n",
" \"text\":'Throughput [ '+ metric_tput['unit'] +' ]',\n",
" \"showarrow\":False,\n",
" \"xanchor\":'left'\n",
"}\n",
"\n",
"# General layout\n",
"custom_layout = {\n",
" 'title':None,\n",
" \"font\":{\"size\":font_size_px},\n",
" \"legend\":{'x':.1, 'y':.9,'font':{'size':font_size_px-2}},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px},\n",
" 'text':'Time [ s ]',},\n",
" 'zeroline':False\n",
" },\n",
" \"yaxis\":{'title':{'text':''}}, \n",
" \"annotations\":[ytitle]\n",
"}\n",
"\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 0.6*linewidth_px\n",
" custom_layout['height'] = 220\n",
" custom_layout['margin'] = dict(l=40, r=0, t=35, b=40)\n",
" custom_layout[\"legend\"] = dict()\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = 0.27*landscapewidth_px\n",
" custom_layout['height'] = 125\n",
" custom_layout['margin'] = dict(l=25, r=0, t=25, b=30)\n",
" custom_layout[\"yaxis\"][\"range\"]=[0,110]\n",
" custom_layout[\"legend\"] = dict(xanchor='right',\n",
" x=1, \n",
" y=1, \n",
" font=dict(size=font_size_px-2))\n",
" custom_layout['showlegend'] = False\n",
"\n",
"# ordered of tests is all messed-up...\n",
"runtimes = [30,10,20,60,40,50]\n",
"\n",
"# Do only the 30s and 60s plots\n",
"## 30s\n",
"i = 0\n",
"plot_out_name = \"plot_ledbat_%s_runtime.pdf\" % runtimes[i]\n",
"metrics_design, figure = pantheon.compute_metric( [result_dir_list[i]], \n",
" meta_data_file, \n",
" convergence,\n",
" [metric_tput],\n",
" plot=True,\n",
" showplot=False,\n",
" layout=custom_layout,\n",
" verbose=False,\n",
" sample=sample)\n",
"\n",
"# Adjust the figure and save\n",
"figure.update_traces(marker=dict(size=font_size_px/4))\n",
"figure.write_image(str(plot_path/plot_out_name))\n",
"figure.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" figure.write_image(str(plot_path/plot_out_name))\n",
"\n",
"## 60s\n",
"i = 3\n",
"custom_layout['width'] = custom_layout['width']*1.68 # make the x axis (roughly) the same scale\n",
"plot_out_name = \"plot_ledbat_%s_runtime.pdf\" % runtimes[i]\n",
"metrics_design, figure = pantheon.compute_metric( [result_dir_list[i]], \n",
" meta_data_file, \n",
" convergence,\n",
" [metric_tput],\n",
" plot=True,\n",
" showplot=False,\n",
" layout=custom_layout,\n",
" verbose=False,\n",
" sample=sample)\n",
"\n",
"# Adjust the figure and save\n",
"figure.update_traces(marker=dict(size=font_size_px/4))\n",
"figure.write_image(str(plot_path/plot_out_name))\n",
"figure.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" figure.write_image(str(plot_path/plot_out_name))\n",
" \n",
"if PaperPlot:\n",
" # Create a figure with only the legend visible\n",
" custom_layout['showlegend'] = True\n",
" custom_layout['width'] = 0.133*landscapewidth_px\n",
" custom_layout['height'] = 100\n",
" custom_layout['margin'] = dict(l=0, r=0, t=0, b=0)\n",
" custom_layout['xaxis'] = dict(visible=False, showline=False)\n",
" custom_layout['yaxis'] = dict(visible=False, showline=False)\n",
" custom_layout['annotations'][0]['visible']=False\n",
"\n",
" figure.update_layout(custom_layout)\n",
" figure.show()\n",
" figure.write_image(str(plot_path/'plot_ledbat_legend.pdf'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 5"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_file = Path('UseCase_Glossy/Data_FlockLab/2019-08_FlockLab_sky.csv')\n",
"df = flocklab.parse_data_file(str(data_file), active_link_threshold=50)\n",
"plot_name = 'plot_flocklab_autocorr.pdf'\n",
"\n",
"link_quality_bounds = [0,100]\n",
"link_quality_name = 'PRR [%]'\n",
"\n",
"# Produce the plot\n",
"fig_theil, fig_autocorr = triscale.network_profiling(df, link_quality_bounds, link_quality_name)\n",
"\n",
"# Adjust layout\n",
"custom_layout = {\n",
" 'title':None,\n",
" \"font\":{\"size\":font_size_px},\n",
" \"legend\":{'x':.4, 'y':0.9,'font':{'size':font_size_px}},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px},\n",
" 'text':'Lag'}\n",
" },\n",
"}\n",
"\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 1.5*linewidth_px\n",
" custom_layout['height'] = 400\n",
" custom_layout['margin'] = dict(l=40, r=0, t=0, b=35)\n",
" custom_layout[\"legend\"] = dict(xanchor='right',\n",
" x=1, \n",
" y=1, \n",
" font=dict(size=font_size_px))\n",
" # convert the displayed values from lag to days\n",
" fig_autocorr.update_xaxes(\n",
" tickvals=[12,84,168,252],\n",
" ticktext=[1,7,14,21])\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = linewidth_px\n",
" custom_layout['height'] = 180\n",
" custom_layout['margin'] = dict(l=20, r=0, t=0, b=35)\n",
" custom_layout[\"legend\"] = dict(xanchor='right',\n",
" x=1, \n",
" y=1, \n",
" font=dict(size=font_size_px))\n",
" # convert the displayed values from lag to days\n",
" fig_autocorr.update_xaxes(\n",
" tickvals=[12,84,168,252],\n",
" ticktext=[1,7,14,21])\n",
"\n",
"# Show plot\n",
"fig_autocorr.update_layout(custom_layout)\n",
"fig_autocorr.update_xaxes(title_text='Lag (days)')\n",
"fig_autocorr.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" fig_autocorr.write_image(str(plot_path/plot_name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 7"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Path to test results\n",
"result_dir_path = Path('UseCase_VideoStreaming/FCC/linear')\n",
"\n",
"# Metadata\n",
"protocol_list = [x.stem for x in result_dir_path.iterdir()]\n",
"protocol_list = list(set(protocol_list)) # make list elements unique\n",
"config_file = Path('UseCase_VideoStreaming/config.yml')\n",
"\n",
"# Define the KPIs\n",
"KPI_percentiles = np.arange(2,100,2) # percentiles\n",
"KPI_confidence = 95 # confidence level\n",
"KPI_base = {'confidence': KPI_confidence,\n",
" 'bound': 'lower',\n",
" 'bounds': [-3,3],\n",
" 'unit': '',\n",
" }\n",
"KPI_list = []\n",
"for p in KPI_percentiles:\n",
" kpi = copy.deepcopy(KPI_base)\n",
" kpi['percentile'] = p\n",
" kpi['name'] = 'P%d'%p\n",
" KPI_list.append(kpi)\n",
" \n",
"# Compute/load KPIs values\n",
"out_name = Path('UseCase_VideoStreaming') / 'kpis.csv'\n",
"QoE = vs.compute_kpi(\n",
" protocol_list,\n",
" KPI_list,\n",
" result_dir_path,\n",
" out_name=out_name\n",
")\n",
"\n",
"plot_out_name = \"plot_vs_pensieve.pdf\"\n",
"sample = dict(\n",
" sample_cdf=True,\n",
" protocol=['pensieve']\n",
")\n",
"figure = vs.plot_cdf(\n",
" QoE,\n",
" config_file,\n",
" result_dir_path,\n",
" sample=sample\n",
")\n",
"\n",
"# Set layout\n",
"custom_layout = {\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px}},\n",
" 'rangemode':'nonnegative'},\n",
" \"yaxis\":{'title':{'font':{'size':font_size_px}}},\n",
" \"legend\": dict(x=0.97, \n",
" y=0.1, \n",
" orientation='v',\n",
" xanchor='right',\n",
" font=dict(size=font_size_px))\n",
"}\n",
"\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = 1.5*linewidth_px\n",
" custom_layout['height'] = 300\n",
" custom_layout['margin'] = dict(l=40, r=0, t=35, b=40)\n",
" custom_layout[\"legend\"] = dict()\n",
"if output_format == 'CCR':\n",
" custom_layout[\"width\"] = 0.8*linewidth_px\n",
" custom_layout[\"height\"] = 120\n",
" custom_layout[\"margin\"] = dict(l=35, r=0, t=0, b=25)\n",
" \n",
"figure.update_layout(custom_layout)\n",
"figure.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" figure.write_image(str(plot_path/plot_out_name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 8"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result_dir_path = Path('UseCase_Pantheon/PantheonData/10runs_30s')\n",
"result_dir_list = [x for x in result_dir_path.iterdir() if x.is_dir()]\n",
"meta_data_file = 'pantheon_metadata.json'\n",
"config_file = Path('UseCase_Pantheon/PantheonData/config.yml')\n",
"convergence = {'expected': False,\n",
" 'confidence': 95, # in %\n",
" 'tolerance': 5, # in %\n",
" }\n",
"metric_tput = {'name':'Median throughput',\n",
" 'unit': 'MBit/s',\n",
" 'measure':'mean',\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"metric_delay = {'name':'95th perc. of one-way delay',\n",
" 'unit': 'ms',\n",
" 'measure':95,\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"KPI_tput = {'percentile': 25,\n",
" 'confidence': 75,\n",
" 'name': 'KPI throughput',\n",
" 'unit': 'MBit/s',\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"KPI_delay = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': 'KPI One-way delay',\n",
" 'unit': 'ms',\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"score_tput = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': 'Throughput',\n",
" 'unit': 'MBit/s',\n",
" 'bounds':[0,120], # expected value range\n",
" 'tag':'throughput' # do not change the tag\n",
" }\n",
"score_delay = {'percentile': 75,\n",
" 'confidence': 75,\n",
" 'name': 'One-way delay',\n",
" 'unit': 'ms',\n",
" 'bounds':[0,100], # expected value range\n",
" 'tag':'delay' # do not change the tag\n",
" }\n",
"\n",
"out_name = Path('UseCase_Pantheon/PantheonData/metrics_wo_convergence.csv')\n",
"\n",
"metric_list = [metric_tput, metric_delay]\n",
"kpi_list = [ KPI_tput, KPI_delay ]\n",
"score_list = [ score_tput, score_delay ]\n",
"\n",
"metrics, figure = pantheon.compute_metric(result_dir_list, \n",
" meta_data_file, \n",
" convergence,\n",
" metric_list,\n",
" out_name=out_name,\n",
" force_computation=False,\n",
" plot=False,\n",
" verbose=False)\n",
"KPIs = pantheon.compute_kpi(metrics,\n",
" kpi_list,\n",
" series='datetime',\n",
" plot=False,\n",
" verbose=False)\n",
"scores = pantheon.compute_score(KPIs,\n",
" score_list,\n",
" plot=False,\n",
" verbose=False)\n",
"custom_layout = {\n",
" 'title':None,\n",
" 'font':{\"size\":font_size_px},\n",
" 'xaxis':{'tickangle':-45,\n",
" 'title':{'font':{'size':font_size_px-2}}},\n",
" 'xaxis2':{'tickangle':-45,\n",
" 'title':{'font':{'size':font_size_px-2}}}\n",
"}\n",
"\n",
"# Adjust the plot size \n",
"if output_format == 'online':\n",
" custom_layout['width'] = linewidth_px\n",
" custom_layout['height'] = 650\n",
" custom_layout['margin'] = dict()\n",
" \n",
" # Plot two graphs in column\n",
" in_column = True\n",
"\n",
"if output_format == 'CCR':\n",
" custom_layout['width'] = linewidth_px\n",
" custom_layout['height'] = 350\n",
" custom_layout['margin'] = dict(l=40, r=10, t=0, b=55)\n",
" \n",
" # Plot two graphs in column\n",
" in_column = True\n",
" \n",
"\n",
"# Produce the figure\n",
"plot_out_name = \"plot_score_matrix.pdf\"\n",
"figure = pantheon.plot_triscale_scores_matrix( scores,\n",
" score_list,\n",
" config_file,\n",
" layout = custom_layout,\n",
" in_column=in_column,\n",
" show=False\n",
" )\n",
"# Adjust the column title font size\n",
"for i in figure['layout']['annotations']:\n",
" i['font'] = dict(size=font_size_px)\n",
"\n",
"# Save and show\n",
"figure.write_image(str(plot_path/plot_out_name))\n",
"figure.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 9"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# === TPR ===\n",
"\n",
"plot_out_name = \"plot_fd_tpr.pdf\"\n",
"\n",
"# Construct the path to the different test results\n",
"result_dir = Path('UseCase_FailureDetection')\n",
"config_file = Path('UseCase_FailureDetection/config.yml')\n",
"out_file = result_dir / 'kpis.csv'\n",
"\n",
"# KPI definition\n",
"KPI = { 'percentile' : 50,\n",
" 'confidence' : 95,\n",
" 'bounds': [0,1],\n",
" 'bound': 'lower'\n",
" }\n",
"\n",
"# Retrieve KPIs\n",
"metric_df = None\n",
"kpis = fd.compute_kpis(metric_df,KPI,config_file,out_name=out_file)\n",
"\n",
"# Generate the figure\n",
"figure = fd.plot_TPR(kpis,config_file)\n",
"\n",
"# Set layout\n",
"custom_layout = {\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px}}},\n",
" \"yaxis\":{'title':{'font':{'size':font_size_px}}},\n",
" \"legend\": dict(x=0.5, \n",
" y=0.1, \n",
" orientation='h',\n",
" xanchor='center',\n",
" font=dict(size=font_size_px-2))\n",
"}\n",
"if output_format == 'online':\n",
" custom_layout['width'] = 1.5*linewidth_px\n",
" custom_layout['height'] = 300\n",
" custom_layout['margin'] = dict(l=40, r=0, t=35, b=40)\n",
" custom_layout[\"legend\"] = dict()\n",
" \n",
"if output_format == 'CCR':\n",
" custom_layout[\"width\"] = linewidth_px\n",
" custom_layout[\"height\"] = 125\n",
" custom_layout[\"margin\"] = dict(l=35, r=0, t=0, b=30)\n",
" custom_layout[\"legend\"]['yanchor'] = 'bottom'\n",
" custom_layout[\"legend\"]['y'] = 0.97\n",
"\n",
"figure.update_layout(custom_layout)\n",
"figure.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" figure.write_image(str(plot_path/plot_out_name))\n",
"\n",
" \n",
"# === Speed ===\n",
" \n",
"plot_out_name = \"plot_fd_speed.pdf\"\n",
"\n",
"# Generate the figure\n",
"figure = fd.plot_speed(kpis,config_file)\n",
"\n",
"# Ajust layout\n",
"if output_format == 'online':\n",
" custom_layout['width'] = 1.5*linewidth_px\n",
" custom_layout['height'] = 300\n",
" custom_layout['margin'] = dict(l=40, r=0, t=35, b=40)\n",
" custom_layout[\"legend\"] = dict()\n",
"if output_format == 'CCR':\n",
" custom_layout['showlegend'] = False\n",
" custom_layout[\"height\"]-= 20\n",
" \n",
"figure.update_layout(custom_layout)\n",
"figure.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" figure.write_image(str(plot_path/plot_out_name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 10"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output retrieved from file. Skipping computation.\n"
]
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
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"source": [
"# Path to test results\n",
"result_dir_path = Path('UseCase_VideoStreaming/FCC/linear')\n",
"\n",
"# Metadata\n",
"protocol_list = [x.stem for x in result_dir_path.iterdir()]\n",
"protocol_list = list(set(protocol_list)) # make list elements unique\n",
"config_file = Path('UseCase_VideoStreaming/config.yml')\n",
"\n",
"# Define the KPIs\n",
"KPI_percentiles = np.arange(2,100,2) # percentiles\n",
"KPI_confidence = 95 # confidence level\n",
"KPI_base = {'confidence': KPI_confidence,\n",
" 'bound': 'lower',\n",
" 'bounds': [-3,3],\n",
" 'unit': '',\n",
" }\n",
"KPI_list = []\n",
"for p in KPI_percentiles:\n",
" kpi = copy.deepcopy(KPI_base)\n",
" kpi['percentile'] = p\n",
" kpi['name'] = 'P%d'%p\n",
" KPI_list.append(kpi)\n",
" \n",
"# Compute/load KPIs values\n",
"out_name = Path('UseCase_VideoStreaming') / 'kpis.csv'\n",
"QoE = vs.compute_kpi(\n",
" protocol_list,\n",
" KPI_list,\n",
" result_dir_path,\n",
" out_name=out_name\n",
")\n",
"\n",
"plot_out_name = \"plot_vs_all.pdf\"\n",
"sample = dict(\n",
" sample_cdf=False,\n",
")\n",
"figure = vs.plot_cdf(\n",
" QoE,\n",
" config_file,\n",
" result_dir_path,\n",
" sample=sample\n",
")\n",
"\n",
"# Set layout\n",
"custom_layout = {\n",
" \"font\":{\"size\":font_size_px},\n",
" \"xaxis\":{'title':{'font':{'size':font_size_px}},\n",
" 'rangemode':'nonnegative'},\n",
" \"yaxis\":{'title':{'font':{'size':font_size_px}}},\n",
" \"legend\": dict(x=0.97, \n",
" y=0.1, \n",
" orientation='v',\n",
" xanchor='right',\n",
" font=dict(size=font_size_px))\n",
"}\n",
"\n",
"\n",
"# Ajust layout\n",
"if output_format == 'online':\n",
" custom_layout['width'] = 1.5*linewidth_px\n",
" custom_layout['height'] = 300\n",
" custom_layout['margin'] = dict(l=40, r=0, t=35, b=40)\n",
" custom_layout[\"legend\"] = dict()\n",
" custom_layout[\"xaxis\"][\"rangemode\"] = 'normal'\n",
" \n",
"if output_format == 'CCR':\n",
" custom_layout[\"width\"] = linewidth_px\n",
" custom_layout[\"height\"] = 225\n",
" custom_layout[\"legend\"]['x'] = 1\n",
" custom_layout[\"legend\"]['y'] = 0.1\n",
" custom_layout[\"legend\"]['yanchor'] = 'bottom'\n",
" custom_layout[\"legend\"]['orientation'] = 'v'\n",
" custom_layout['margin'] = dict(l=40, r=0, t=0, b=30)\n",
"\n",
"figure.update_layout(custom_layout)\n",
"figure.show()\n",
"\n",
"# Save plot\n",
"if output_format == 'CCR':\n",
" figure.write_image(str(plot_path/plot_out_name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Back to top](#TriScale-Interactive-Plots)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
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