gst-plugins-rs/utils/tracers/scripts/pad_push_timings.py
Sebastian Dröge 66030f36ad tracers: Add a pad push durations tracer
This tracer measures the time it takes for a buffer/buffer list push to return.

Part-of: <https://gitlab.freedesktop.org/gstreamer/gst-plugins-rs/-/merge_requests/1506>
2024-04-17 16:20:43 +03:00

77 lines
2.1 KiB
Python

import argparse
import csv
import re
import statistics
import matplotlib
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument("file", help="Input file with pad push timings")
parser.add_argument("--include-filter", help="Regular expression for element:pad names that should be included")
parser.add_argument("--exclude-filter", help="Regular expression for element:pad names that should be excluded")
args = parser.parse_args()
include_filter = None
if args.include_filter is not None:
include_filter = re.compile(args.include_filter)
exclude_filter = None
if args.exclude_filter is not None:
exclude_filter = re.compile(args.exclude_filter)
pads = {}
with open(args.file, mode='r', encoding='utf_8', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in reader:
if len(row) != 4:
continue
if include_filter is not None and not include_filter.match(row[1]):
continue
if exclude_filter is not None and exclude_filter.match(row[1]):
continue
if not row[1] in pads:
pads[row[1]] = {
'push-duration': [],
}
push_duration = float(row[3]) / 1000000.0
wallclock = float(row[0]) / 1000000000.0
pads[row[1]]['push-duration'].append((wallclock, push_duration))
matplotlib.rcParams['figure.dpi'] = 200
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']
fig, ax1 = plt.subplots()
ax1.set_xlabel("wallclock (s)")
ax1.set_ylabel("push duration (ms)")
ax1.tick_params(axis='y')
for (i, (pad, values)) in enumerate(pads.items()):
# cycle colors
i = i % len(colors)
push_durations = [x[1] for x in values['push-duration']]
ax1.plot(
[x[0] for x in values['push-duration']],
push_durations,
'.', label = pad,
color = colors[i],
)
print("{} push durations: min: {}ms max: {}ms mean: {}ms".format(
pad,
min(push_durations),
max(push_durations),
statistics.mean(push_durations)))
fig.tight_layout()
plt.legend(loc='best')
plt.show()