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