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Support multiple file sources for osg stats

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pull/3104/head
elsid 3 years ago
parent f8c7664234
commit 36ba56a513
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GPG Key ID: B845CB9FEE18AB40
1 changed files with 91 additions and 76 deletions
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167
scripts/osg_stats.py
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@ -45,30 +45,30 @@ import termtables
help='Start processing from this frame.')
@click.option('--end_frame', type=int, default=sys.maxsize,
help='End processing at this frame.')
@click.argument('path', default='', type=click.Path())
@click.argument('path', type=click.Path(), nargs=-1)
def main(print_keys, timeseries, hist, hist_ratio, stdev_hist, plot, stats,
timeseries_sum, stats_sum, begin_frame, end_frame, path,
commulative_timeseries, commulative_timeseries_sum):
data = list(read_data(path))
keys = collect_unique_keys(data)
frames = collect_per_frame(data=data, keys=keys, begin_frame=begin_frame, end_frame=end_frame)
sources = {v: list(read_data(v)) for v in path} if path else {'stdin': list(read_data(None))}
keys = collect_unique_keys(sources)
frames = collect_per_frame(sources=sources, keys=keys, begin_frame=begin_frame, end_frame=end_frame)
if print_keys:
for v in keys:
print(v)
if timeseries:
draw_timeseries(frames=frames, keys=timeseries, add_sum=timeseries_sum)
draw_timeseries(sources=frames, keys=timeseries, add_sum=timeseries_sum)
if commulative_timeseries:
draw_commulative_timeseries(frames=frames, keys=commulative_timeseries, add_sum=commulative_timeseries_sum)
draw_commulative_timeseries(sources=frames, keys=commulative_timeseries, add_sum=commulative_timeseries_sum)
if hist:
draw_hists(frames=frames, keys=hist)
draw_hists(sources=frames, keys=hist)
if hist_ratio:
draw_hist_ratio(frames=frames, pairs=hist_ratio)
draw_hist_ratio(sources=frames, pairs=hist_ratio)
if stdev_hist:
draw_stdev_hists(frames=frames, stdev_hists=stdev_hist)
draw_stdev_hists(sources=frames, stdev_hists=stdev_hist)
if plot:
draw_plots(frames=frames, plots=plot)
draw_plots(sources=frames, plots=plot)
if stats:
print_stats(frames=frames, keys=stats, stats_sum=stats_sum)
print_stats(sources=frames, keys=stats, stats_sum=stats_sum)
matplotlib.pyplot.show()
@ -92,126 +92,140 @@ def read_data(path):
frame[key] = to_number(value)
def collect_per_frame(data, keys, begin_frame, end_frame):
result = collections.defaultdict(list)
for frame in data:
for key in keys:
if key in frame:
result[key].append(frame[key])
else:
result[key].append(None)
for key, values in result.items():
result[key] = numpy.array(values[begin_frame:end_frame])
def collect_per_frame(sources, keys, begin_frame, end_frame):
result = collections.defaultdict(lambda: collections.defaultdict(list))
for name, frames in sources.items():
for frame in frames:
for key in keys:
if key in frame:
result[name][key].append(frame[key])
else:
result[name][key].append(None)
for name, sources in result.items():
for key, values in sources.items():
result[name][key] = numpy.array(values[begin_frame:end_frame])
return result
def collect_unique_keys(frames):
def collect_unique_keys(sources):
result = set()
for frame in frames:
for key in frame.keys():
result.add(key)
for frames in sources.values():
for frame in frames:
for key in frame.keys():
result.add(key)
return sorted(result)
def draw_timeseries(frames, keys, add_sum):
def draw_timeseries(sources, keys, add_sum):
fig, ax = matplotlib.pyplot.subplots()
x = numpy.array(range(max(len(v) for k, v in frames.items() if k in keys)))
for key in keys:
ax.plot(x, frames[key], label=key)
if add_sum:
ax.plot(x, numpy.sum(list(frames[k] for k in keys), axis=0), label='sum')
for name, frames in sources.items():
x = numpy.array(range(max(len(v) for k, v in frames.items() if k in keys)))
for key in keys:
print(key, name)
ax.plot(x, frames[key], label=f'{key}:{name}')
if add_sum:
ax.plot(x, numpy.sum(list(frames[k] for k in keys), axis=0), label=f'sum:{name}')
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('timeseries')
def draw_commulative_timeseries(frames, keys, add_sum):
def draw_commulative_timeseries(sources, keys, add_sum):
fig, ax = matplotlib.pyplot.subplots()
x = numpy.array(range(max(len(v) for k, v in frames.items() if k in keys)))
for key in keys:
ax.plot(x, numpy.cumsum(frames[key]), label=key)
if add_sum:
ax.plot(x, numpy.cumsum(numpy.sum(list(frames[k] for k in keys), axis=0)), label='sum')
for name, frames in sources.items():
x = numpy.array(range(max(len(v) for k, v in frames.items() if k in keys)))
for key in keys:
ax.plot(x, numpy.cumsum(frames[key]), label=f'{key}:{name}')
if add_sum:
ax.plot(x, numpy.cumsum(numpy.sum(list(frames[k] for k in keys), axis=0)), label=f'sum:{name}')
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('commulative_timeseries')
def draw_hists(frames, keys):
def draw_hists(sources, keys):
fig, ax = matplotlib.pyplot.subplots()
bins = numpy.linspace(
start=min(min(v) for k, v in frames.items() if k in keys),
stop=max(max(v) for k, v in frames.items() if k in keys),
start=min(min(min(v) for k, v in f.items() if k in keys) for f in sources.values()),
stop=max(max(max(v) for k, v in f.items() if k in keys) for f in sources.values()),
num=20,
)
for key in keys:
ax.hist(frames[key], bins=bins, label=key, alpha=1 / len(keys))
for name, frames in sources.items():
for key in keys:
ax.hist(frames[key], bins=bins, label=f'{key}:{name}', alpha=1 / (len(keys) * len(sources)))
ax.set_xticks(bins)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('hists')
def draw_hist_ratio(frames, pairs):
def draw_hist_ratio(sources, pairs):
fig, ax = matplotlib.pyplot.subplots()
bins = numpy.linspace(
start=min(min(a / b for a, b in zip(frames[a], frames[b])) for a, b in pairs),
stop=max(max(a / b for a, b in zip(frames[a], frames[b])) for a, b in pairs),
start=min(min(min(a / b for a, b in zip(f[a], f[b])) for a, b in pairs) for f in sources.values()),
stop=max(max(max(a / b for a, b in zip(f[a], f[b])) for a, b in pairs) for f in sources.values()),
num=20,
)
for a, b in pairs:
ax.hist(frames[a] / frames[b], bins=bins, label=f'{a} / {b}', alpha=1 / len(pairs))
for name, frames in sources.items():
for a, b in pairs:
ax.hist(frames[a] / frames[b], bins=bins, label=f'{a} / {b}:{name}', alpha=1 / (len(pairs) * len(sources)))
ax.set_xticks(bins)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('hists')
fig.canvas.set_window_title('hists_ratio')
def draw_stdev_hists(frames, stdev_hists):
def draw_stdev_hists(sources, stdev_hists):
for key, scale in stdev_hists:
scale = float(scale)
fig, ax = matplotlib.pyplot.subplots()
median = statistics.median(frames[key])
stdev = statistics.stdev(frames[key])
first_frames = next(v for v in sources.values())
median = statistics.median(first_frames[key])
stdev = statistics.stdev(first_frames[key])
start = median - stdev / 2 * scale
stop = median + stdev / 2 * scale
bins = numpy.linspace(start=start, stop=stop, num=9)
values = [v for v in frames[key] if start <= v <= stop]
ax.hist(values, bins=bins, label=key, alpha=1 / len(stdev_hists))
for name, frames in sources.items():
values = [v for v in frames[key] if start <= v <= stop]
ax.hist(values, bins=bins, label=f'{key}:{name}', alpha=1 / (len(stdev_hists) * len(sources)))
ax.set_xticks(bins)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('stdev_hists')
def draw_plots(frames, plots):
def draw_plots(sources, plots):
fig, ax = matplotlib.pyplot.subplots()
for x_key, y_key, agg in plots:
if agg is None:
ax.plot(frames[x_key], frames[y_key], label=f'x={x_key}, y={y_key}')
elif agg:
agg_f = dict(
mean=statistics.mean,
median=statistics.median,
)[agg]
grouped = collections.defaultdict(list)
for x, y in zip(frames[x_key], frames[y_key]):
grouped[x].append(y)
aggregated = sorted((k, agg_f(v)) for k, v in grouped.items())
ax.plot(
numpy.array([v[0] for v in aggregated]),
numpy.array([v[1] for v in aggregated]),
label=f'x={x_key}, y={y_key}, agg={agg}',
)
for name, frames in sources.items():
for x_key, y_key, agg in plots:
if agg is None:
ax.plot(frames[x_key], frames[y_key], label=f'x={x_key}, y={y_key}:{name}')
elif agg:
agg_f = dict(
mean=statistics.mean,
median=statistics.median,
)[agg]
grouped = collections.defaultdict(list)
for x, y in zip(frames[x_key], frames[y_key]):
grouped[x].append(y)
aggregated = sorted((k, agg_f(v)) for k, v in grouped.items())
ax.plot(
numpy.array([v[0] for v in aggregated]),
numpy.array([v[1] for v in aggregated]),
label=f'x={x_key}, y={y_key}, agg={agg}:{name}',
)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('plots')
def print_stats(frames, keys, stats_sum):
stats = [make_stats(key=key, values=filter_not_none(frames[key])) for key in keys]
if stats_sum:
stats.append(make_stats(key='sum', values=sum_multiple(frames, keys)))
def print_stats(sources, keys, stats_sum):
stats = list()
for name, frames in sources.items():
for key in keys:
stats.append(make_stats(source=name, key=key, values=filter_not_none(frames[key])))
if stats_sum:
stats.append(make_stats(source=name, key='sum', values=sum_multiple(frames, keys)))
metrics = list(stats[0].keys())
max_key_size = max(len(tuple(v.values())[0]) for v in stats)
termtables.print(
@ -235,8 +249,9 @@ def sum_multiple(frames, keys):
return numpy.array([result[k] for k in sorted(result.keys())])
def make_stats(key, values):
def make_stats(source, key, values):
return collections.OrderedDict(
source=source,
key=key,
number=len(values),
min=min(values),

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