135 lines
4.7 KiB
Python
Executable file
135 lines
4.7 KiB
Python
Executable file
#! /usr/bin/env python3
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#
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# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#
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# This scripts plots graphs produced by our drift correction code.
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#
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# Install dependencies with:
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# > pip install bokeh pandas
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#
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# Generate the csv data file with the DriftControllerGraphs log module:
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# > MOZ_LOG=raw,sync,DriftControllerGraphs:5 \
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# > MOZ_LOG_FILE=/tmp/driftcontrol.csv \
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# > ./mach gtest '*AudioDrift*StepResponse'
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#
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# Generate the graphs with this script:
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# > ./dom/media/driftcontrol/plot.py /tmp/driftcontrol.csv.moz_log
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#
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# The script should produce a file plot.html in the working directory and
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# open it in the default browser.
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import argparse
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from collections import OrderedDict
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import pandas
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from bokeh.io import output_file, show
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from bokeh.layouts import gridplot
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from bokeh.models import TabPanel, Tabs
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from bokeh.plotting import figure
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def main():
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parser = argparse.ArgumentParser(
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prog="plot.py for DriftControllerGraphs",
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description="""Takes a csv file of DriftControllerGraphs data
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(from a single DriftController instance) and plots
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them into plot.html in the current working directory.
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The easiest way to produce the data is with MOZ_LOG:
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MOZ_LOG=raw,sync,DriftControllerGraphs:5 \
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MOZ_LOG_FILE=/tmp/driftcontrol.csv \
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./mach gtest '*AudioDrift*StepResponse'""",
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)
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parser.add_argument("csv_file", type=str)
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args = parser.parse_args()
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all_df = pandas.read_csv(args.csv_file)
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# Filter on distinct ids to support multiple plotting sources
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tabs = []
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for id in list(OrderedDict.fromkeys(all_df["id"])):
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df = all_df[all_df["id"] == id]
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t = df["t"]
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buffering = df["buffering"]
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desired = df["desired"]
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buffersize = df["buffersize"]
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inlatency = df["inlatency"]
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outlatency = df["outlatency"]
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inrate = df["inrate"]
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outrate = df["outrate"]
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hysteresisthreshold = df["hysteresisthreshold"]
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corrected = df["corrected"]
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hysteresiscorrected = df["hysteresiscorrected"]
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configured = df["configured"]
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p = df["p"]
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i = df["i"]
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d = df["d"]
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kpp = df["kpp"]
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kii = df["kii"]
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kdd = df["kdd"]
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control = df["control"]
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output_file("plot.html")
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fig1 = figure()
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fig1.line(t, inlatency, color="hotpink", legend_label="In latency")
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fig1.line(t, outlatency, color="firebrick", legend_label="Out latency")
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fig1.line(t, buffering, color="dodgerblue", legend_label="Actual buffering")
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fig1.line(t, desired, color="goldenrod", legend_label="Desired buffering")
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fig1.line(t, buffersize, color="seagreen", legend_label="Buffer size")
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fig1.varea(
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t,
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[d - h for (d, h) in zip(desired, hysteresisthreshold)],
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[d + h for (d, h) in zip(desired, hysteresisthreshold)],
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alpha=0.2,
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color="goldenrod",
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legend_label="Hysteresis Threshold (won't correct in rate within area)",
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)
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fig2 = figure(x_range=fig1.x_range)
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fig2.line(t, inrate, color="hotpink", legend_label="Nominal in sample rate")
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fig2.line(t, outrate, color="firebrick", legend_label="Nominal out sample rate")
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fig2.line(
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t, corrected, color="dodgerblue", legend_label="Corrected in sample rate"
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)
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fig2.line(
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t,
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hysteresiscorrected,
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color="seagreen",
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legend_label="Hysteresis-corrected in sample rate",
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)
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fig2.line(
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t, configured, color="goldenrod", legend_label="Configured in sample rate"
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)
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fig3 = figure(x_range=fig1.x_range)
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fig3.line(t, p, color="goldenrod", legend_label="P")
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fig3.line(t, i, color="dodgerblue", legend_label="I")
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fig3.line(t, d, color="seagreen", legend_label="D")
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fig4 = figure(x_range=fig1.x_range)
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fig4.line(t, kpp, color="goldenrod", legend_label="KpP")
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fig4.line(t, kii, color="dodgerblue", legend_label="KiI")
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fig4.line(t, kdd, color="seagreen", legend_label="KdD")
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fig4.line(t, control, color="hotpink", legend_label="Control Signal")
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fig1.legend.location = "top_left"
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fig2.legend.location = "top_right"
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fig3.legend.location = "bottom_left"
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fig4.legend.location = "bottom_right"
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for fig in (fig1, fig2, fig3, fig4):
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fig.legend.background_fill_alpha = 0.6
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fig.legend.click_policy = "hide"
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tabs.append(
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TabPanel(child=gridplot([[fig1, fig2], [fig3, fig4]]), title=str(id))
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)
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show(Tabs(tabs=tabs))
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if __name__ == "__main__":
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main()
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