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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"]
avgbuffered = df["avgbuffered"]
desired = df["desired"]
buffersize = df["buffersize"]
inlatency = df["inlatency"]
outlatency = df["outlatency"]
inframesavg = df["inframesavg"]
outframesavg = df["outframesavg"]
inrate = df["inrate"]
outrate = df["outrate"]
steadystaterate = df["steadystaterate"]
nearthreshold = df["nearthreshold"]
corrected = df["corrected"]
hysteresiscorrected = df["hysteresiscorrected"]
configured = df["configured"]
output_file("plot.html")
fig1 = figure()
# Variables with more variation are plotted after smoother variables
# because latter variables are drawn on top and so visibility of
# individual values in the variables with more variation is improved
# (when both variables are shown).
fig1.line(
t, inframesavg, color="violet", legend_label="Average input packet size"
)
fig1.line(
t, outframesavg, color="purple", legend_label="Average output packet size"
)
fig1.line(t, inlatency, color="hotpink", legend_label="In latency")
fig1.line(t, outlatency, color="firebrick", legend_label="Out latency")
fig1.line(t, desired, color="goldenrod", legend_label="Desired buffering")
fig1.line(
t, avgbuffered, color="orangered", legend_label="Average buffered estimate"
)
fig1.line(t, buffering, color="dodgerblue", legend_label="Actual buffering")
fig1.line(t, buffersize, color="seagreen", legend_label="Buffer size")
fig1.varea(
t,
[d - h for (d, h) in zip(desired, nearthreshold)],
[d + h for (d, h) in zip(desired, nearthreshold)],
alpha=0.2,
color="goldenrod",
legend_label='"Near" band (won\'t reduce desired buffering outside)',
)
slowConvergenceSecs = 30
adjustmentInterval = 1
slowHysteresis = 1
avgError = avgbuffered - desired
absAvgError = [abs(e) for e in avgError]
slow_offset = [e / slowConvergenceSecs - slowHysteresis for e in absAvgError]
fast_offset = [e / adjustmentInterval for e in absAvgError]
low_offset, high_offset = zip(
*[
(s, f) if e >= 0 else (-f, -s)
for (e, s, f) in zip(avgError, slow_offset, fast_offset)
]
)
fig2 = figure(x_range=fig1.x_range)
fig2.varea(
t,
steadystaterate + low_offset,
steadystaterate + high_offset,
alpha=0.2,
color="goldenrod",
legend_label="Deadband (won't change in rate within)",
)
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,
steadystaterate,
color="orangered",
legend_label="Estimated in rate with drift",
)
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"
)
fig1.legend.location = "top_left"
fig2.legend.location = "top_right"
for fig in (fig1, fig2):
fig.legend.background_fill_alpha = 0.6
fig.legend.click_policy = "hide"
tabs.append(TabPanel(child=gridplot([[fig1, fig2]]), title=str(id)))
show(Tabs(tabs=tabs))
if __name__ == "__main__":
main()