mirror of
https://gitlab.freedesktop.org/gstreamer/gst-plugins-rs.git
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66030f36ad
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>
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
import argparse
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import csv
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import re
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import statistics
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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 pad push timings")
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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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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) != 4:
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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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'push-duration': [],
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}
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push_duration = float(row[3]) / 1000000.0
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wallclock = float(row[0]) / 1000000000.0
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pads[row[1]]['push-duration'].append((wallclock, push_duration))
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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("push duration (ms)")
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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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push_durations = [x[1] for x in values['push-duration']]
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ax1.plot(
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[x[0] for x in values['push-duration']],
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push_durations,
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'.', label = pad,
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color = colors[i],
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)
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print("{} push durations: min: {}ms max: {}ms mean: {}ms".format(
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pad,
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min(push_durations),
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max(push_durations),
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statistics.mean(push_durations)))
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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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