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Support moving average for timeseries in osg_stats.py

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elsid 2026-01-10 14:40:03 +01:00
parent acb36cb7c6
commit 0fb97efa90
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1 changed files with 26 additions and 14 deletions
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scripts/osg_stats.py
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@ -72,12 +72,14 @@ import termtables
help='Threshold for hist_over.')
@click.option('--show_common_path_prefix', is_flag=True,
help='Show common path prefix when applied to multiple files.')
@click.option('--moving_average_window', type=click.IntRange(min=1),
help='Transform timeseries values to moving average with window of given number of points.')
@click.argument('path', type=click.Path(), nargs=-1)
def main(print_keys, regexp_match, timeseries, hist, hist_ratio, stdev_hist, plot, stats, precision,
timeseries_sum, stats_sum, begin_frame, end_frame, path,
cumulative_timeseries, cumulative_timeseries_sum, frame_number_name,
hist_threshold, threshold_name, threshold_value, show_common_path_prefix, stats_sort_by,
timeseries_delta, timeseries_delta_sum, stats_table_format):
timeseries_delta, timeseries_delta_sum, stats_table_format, moving_average_window):
sources = {v: list(read_data(v)) for v in path} if path else {'stdin': list(read_data(None))}
if not show_common_path_prefix and len(sources) > 1:
longest_common_prefix = os.path.commonprefix(list(sources.keys()))
@ -94,15 +96,19 @@ def main(print_keys, regexp_match, timeseries, hist, hist_ratio, stdev_hist, plo
if regexp_match:
return [key for pattern in patterns for key in keys if re.search(pattern, key)]
return patterns
convolve = None
if moving_average_window is not None:
convolve = numpy.ones(moving_average_window) / float(moving_average_window)
if timeseries:
draw_timeseries(sources=frames, keys=matching_keys(timeseries), add_sum=timeseries_sum,
draw_timeseries(sources=frames, keys=matching_keys(timeseries), add_sum=timeseries_sum, convolve=convolve,
begin_frame=begin_frame, end_frame=end_frame)
if cumulative_timeseries:
draw_cumulative_timeseries(sources=frames, keys=matching_keys(cumulative_timeseries), add_sum=cumulative_timeseries_sum,
begin_frame=begin_frame, end_frame=end_frame)
draw_cumulative_timeseries(sources=frames, keys=matching_keys(cumulative_timeseries),
add_sum=cumulative_timeseries_sum, convolve=convolve, begin_frame=begin_frame,
end_frame=end_frame)
if timeseries_delta:
draw_timeseries_delta(sources=frames, keys=matching_keys(timeseries_delta), add_sum=timeseries_delta_sum,
begin_frame=begin_frame, end_frame=end_frame)
convolve=convolve, begin_frame=begin_frame, end_frame=end_frame)
if hist:
draw_hists(sources=frames, keys=matching_keys(hist))
if hist_ratio:
@ -173,45 +179,45 @@ def collect_unique_keys(sources):
return sorted(result)
def draw_timeseries(sources, keys, add_sum, begin_frame, end_frame):
def draw_timeseries(sources, keys, add_sum, convolve, begin_frame, end_frame):
fig, ax = matplotlib.pyplot.subplots()
x = numpy.array(range(begin_frame, end_frame))
for name, frames in sources.items():
for key in keys:
y = frames[key]
y = maybe_convolve(frames[key], convolve)
ax.plot(x[:len(y)], y, label=f'{key}:{name}')
if add_sum:
y = sum_arrays_with_none([frames[k] for k in keys])
y = sum_arrays_with_none([maybe_convolve(frames[k], convolve) for k in keys])
ax.plot(x[:len(y)], y, label=f'sum:{name}', linestyle='--')
ax.grid(True)
ax.legend()
fig.canvas.manager.set_window_title('timeseries')
def draw_cumulative_timeseries(sources, keys, add_sum, begin_frame, end_frame):
def draw_cumulative_timeseries(sources, keys, add_sum, convolve, begin_frame, end_frame):
fig, ax = matplotlib.pyplot.subplots()
x = numpy.array(range(begin_frame, end_frame))
for name, frames in sources.items():
for key in keys:
y = cumsum_with_none(frames[key])
y = cumsum_with_none(maybe_convolve(frames[key], convolve))
ax.plot(x[:len(y)], y, label=f'{key}:{name}')
if add_sum:
y = sum_arrays_with_none([cumsum_with_none(frames[k]) for k in keys])
y = sum_arrays_with_none([cumsum_with_none(maybe_convolve(frames[k], convolve)) for k in keys])
ax.plot(x[:len(y)], y, label=f'sum:{name}', linestyle='--')
ax.grid(True)
ax.legend()
fig.canvas.manager.set_window_title('cumulative_timeseries')
def draw_timeseries_delta(sources, keys, add_sum, begin_frame, end_frame):
def draw_timeseries_delta(sources, keys, add_sum, convolve, begin_frame, end_frame):
fig, ax = matplotlib.pyplot.subplots()
x = numpy.array(range(begin_frame + 1, end_frame))
for name, frames in sources.items():
for key in keys:
y = diff_with_none(frames[key])
y = diff_with_none(maybe_convolve(frames[key], convolve))
ax.plot(x[:len(y)], y, label=f'{key}:{name}')
if add_sum:
y = sum_arrays_with_none([diff_with_none(frames[k]) for k in keys])
y = sum_arrays_with_none([diff_with_none(maybe_convolve(frames[k], convolve)) for k in keys])
ax.plot(x[:len(y)], y, label=f'sum:{name}', linestyle='--')
ax.grid(True)
ax.legend()
@ -422,5 +428,11 @@ def sum_arrays_with_none(arrays):
return numpy.array(result)
def maybe_convolve(values, convolve):
if convolve is None:
return values
return numpy.convolve([v or 0 for v in values], convolve, 'valid')
if __name__ == '__main__':
main()