mirror of
https://gitlab.freedesktop.org/gstreamer/gst-plugins-rs.git
synced 2024-11-29 06:50:59 +00:00
98 lines
3.2 KiB
Python
98 lines
3.2 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 queue levels")
|
|
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")
|
|
parser.add_argument("--no-latency", help="do not include latency (enabled by default)", action="store_true")
|
|
parser.add_argument("--late-only", help="display only late buffers (disabled by default)", action="store_true")
|
|
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) != 7:
|
|
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]] = {
|
|
'buffer-clock-time': [],
|
|
'pipeline-clock-time': [],
|
|
'lateness': [],
|
|
'latency': [],
|
|
}
|
|
|
|
lateness = float(row[5])
|
|
latency = float(row[6])
|
|
is_late = lateness > latency
|
|
|
|
wallclock = float(row[0]) / 1000000000.0
|
|
pads[row[1]]['buffer-clock-time'].append((wallclock, float(row[3]) / 1000000000.0))
|
|
pads[row[1]]['pipeline-clock-time'].append((wallclock, float(row[4]) / 1000000000.0))
|
|
pads[row[1]]['lateness'].append((wallclock, lateness / 1000000000.0, is_late))
|
|
pads[row[1]]['latency'].append((wallclock, latency / 1000000000.0))
|
|
|
|
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("time (s)")
|
|
ax1.tick_params(axis='y')
|
|
|
|
for (i, (pad, values)) in enumerate(pads.items()):
|
|
# cycle colors
|
|
i = i % len(colors)
|
|
|
|
ax1.plot(
|
|
[x[0] for x in values['lateness'] if not args.late_only or x[2]],
|
|
[x[1] for x in values['lateness'] if not args.late_only or x[2]],
|
|
'.', label = '{}: lateness'.format(pad),
|
|
color = colors[i],
|
|
)
|
|
|
|
late = [x[1] for x in values['lateness'] if x[2]]
|
|
n_late = len(late)
|
|
n_buffers = len(values['lateness'])
|
|
|
|
print("{} late buffers: {}/{} ratio: {:.2f}% min: {} max: {} mean: {}".format(
|
|
pad, n_late, n_buffers, (n_late / n_buffers) * 100,
|
|
min(late) if n_late > 0 else "",
|
|
max(late) if n_late > 0 else "",
|
|
statistics.mean(late) if n_late > 0 else ""))
|
|
|
|
if not args.no_latency:
|
|
ax1.plot(
|
|
[x[0] for x in values['latency']],
|
|
[x[1] for x in values['latency']],
|
|
'-', label = '{}: latency'.format(pad),
|
|
color = colors[i],
|
|
)
|
|
|
|
fig.tight_layout()
|
|
plt.legend(loc='best')
|
|
|
|
plt.show()
|