forked from mirrors/gecko-dev
8cbb53fb3d
because the correction is proportional to differences in input buffering frame counts. This intends to make behavior independent of the nominal resampling ratio. TEST(TestDriftController, BasicResampler) confirms that because the results now match those of TEST(TestDriftController, Basic) without resampling. The step size in BigRangesInRates* is increased to 2 because the range was increased and these tests were taking a couple of seconds each to run in an opt build. Differential Revision: https://phabricator.services.mozilla.com/D207667
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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