gst-plugins-rs/utils/tracers/scripts/buffer_lateness.py

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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()