mirror of
https://gitlab.freedesktop.org/gstreamer/gst-plugins-rs.git
synced 2024-05-20 17:28:49 +00:00
d5740ea844
Help to easily spot places where buffers are late when plotting big pipelines. Part-of: <https://gitlab.freedesktop.org/gstreamer/gst-plugins-rs/-/merge_requests/1437>
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
import argparse
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import csv
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import re
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import matplotlib
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import matplotlib.pyplot as plt
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parser = argparse.ArgumentParser()
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parser.add_argument("file", help="Input file with queue levels")
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parser.add_argument("--include-filter", help="Regular expression for element:pad names that should be included")
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parser.add_argument("--exclude-filter", help="Regular expression for element:pad names that should be excluded")
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parser.add_argument("--no-latency", help="do not include latency (enabled by default)", action="store_true")
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parser.add_argument("--late-only", help="display only late buffers (disabled by default)", action="store_true")
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args = parser.parse_args()
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include_filter = None
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if args.include_filter is not None:
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include_filter = re.compile(args.include_filter)
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exclude_filter = None
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if args.exclude_filter is not None:
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exclude_filter = re.compile(args.exclude_filter)
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pads = {}
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with open(args.file, mode='r', encoding='utf_8', newline='') as csvfile:
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reader = csv.reader(csvfile, delimiter=',', quotechar='|')
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for row in reader:
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if len(row) != 7:
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continue
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if include_filter is not None and not include_filter.match(row[1]):
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continue
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if exclude_filter is not None and exclude_filter.match(row[1]):
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continue
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if not row[1] in pads:
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pads[row[1]] = {
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'buffer-clock-time': [],
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'pipeline-clock-time': [],
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'lateness': [],
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'latency': [],
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}
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lateness = float(row[5])
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latency = float(row[6])
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is_late = lateness > latency
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wallclock = float(row[0]) / 1000000000.0
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pads[row[1]]['buffer-clock-time'].append((wallclock, float(row[3]) / 1000000000.0))
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pads[row[1]]['pipeline-clock-time'].append((wallclock, float(row[4]) / 1000000000.0))
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pads[row[1]]['lateness'].append((wallclock, lateness / 1000000000.0, is_late))
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pads[row[1]]['latency'].append((wallclock, latency / 1000000000.0))
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matplotlib.rcParams['figure.dpi'] = 200
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prop_cycle = plt.rcParams['axes.prop_cycle']
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colors = prop_cycle.by_key()['color']
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fig, ax1 = plt.subplots()
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ax1.set_xlabel("wallclock (s)")
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ax1.set_ylabel("time (s)")
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ax1.tick_params(axis='y')
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for (i, (pad, values)) in enumerate(pads.items()):
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# cycle colors
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i = i % len(colors)
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ax1.plot(
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[x[0] for x in values['lateness'] if not args.late_only or x[2]],
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[x[1] for x in values['lateness'] if not args.late_only or x[2]],
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'.', label = '{}: lateness'.format(pad),
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color = colors[i],
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)
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if not args.no_latency:
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ax1.plot(
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[x[0] for x in values['latency']],
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[x[1] for x in values['latency']],
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'-', label = '{}: latency'.format(pad),
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color = colors[i],
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)
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fig.tight_layout()
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plt.legend(loc='best')
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plt.show()
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