#!/usr/bin/env python3
"""
osg_stats.py is a script to analyze OpenSceneGraph log. It parses given file
and builds timeseries, histograms, plots, calculate statistics for a given
set of keys over given range of frames.
"""
import click
import collections
import matplotlib.pyplot
import numpy
import statistics
import sys
import termtables
@click.command()
@click.option('--print_keys', is_flag=True,
help='Print a list of all present keys in the input file.')
@click.option('--timeseries', type=str, multiple=True,
help='Show a graph for given metric over time.')
@click.option('--hist', type=str, multiple=True,
help='Show a histogram for all values of given metric.')
@click.option('--hist_ratio', nargs=2, type=str, multiple=True,
help='Show a histogram for a ratio of two given metric (first / second). '
'Format: --hist_ratio <first_metric> <second_metric>.')
@click.option('--stdev_hist', nargs=2, type=str, multiple=True,
help='Show a histogram for a standard deviation of a given metric at given scale (number). '
'Format: --stdev_hist <metric> <scale>.')
@click.option('--plot', nargs=3, type=str, multiple=True,
help='Show a 2D plot for relation between two metrix (first is axis x, second is y)'
'using one of aggregation functions (mean, median). For example show a relation '
'between Physics Actors and physics_time_taken. Format: --plot <x> <y> <function>.')
@click.option('--stats', type=str, multiple=True,
help='Print table with stats for a given metric containing min, max, mean, median etc.')
@click.option('--timeseries_sum', is_flag=True,
help='Add a graph to timeseries for a sum per frame of all given timeseries metrics.')
@click.option('--stats_sum', is_flag=True,
help='Add a row to stats table for a sum per frame of all given stats metrics.')
@click.option('--begin_frame', type=int, default=0,
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())
def main(print_keys, timeseries, hist, hist_ratio, stdev_hist, plot, stats,
timeseries_sum, stats_sum, begin_frame, end_frame, path):
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)
if print_keys:
for v in keys:
print(v)
if timeseries:
draw_timeseries(frames=frames, keys=timeseries, timeseries_sum=timeseries_sum)
if hist:
draw_hists(frames=frames, keys=hist)
if hist_ratio:
draw_hist_ratio(frames=frames, pairs=hist_ratio)
if stdev_hist:
draw_stdev_hists(frames=frames, stdev_hists=stdev_hist)
if plot:
draw_plots(frames=frames, plots=plot)
if stats:
print_stats(frames=frames, keys=stats, stats_sum=stats_sum)
matplotlib.pyplot.show()
def read_data(path):
with open(path) if path else sys.stdin as stream:
frame = dict()
camera = 0
for line in stream:
if line.startswith('Stats Viewer'):
if frame:
camera = 0
yield frame
_, _, key, value = line.split(' ')
frame = {key: int(value)}
elif line.startswith('Stats Camera'):
camera += 1
elif line.startswith(' '):
key, value = line.strip().rsplit(maxsplit=1)
if camera:
key = f'{key} Camera {camera}'
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])
return result
def collect_unique_keys(frames):
result = set()
for frame in frames:
for key in frame.keys():
result.add(key)
return sorted(result)
def draw_timeseries(frames, keys, timeseries_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 timeseries_sum:
ax.plot(x, numpy.sum(list(frames[k] for k in keys), axis=0), label='sum')
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('timeseries')
def draw_hists(frames, 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),
num=20,
)
for key in keys:
ax.hist(frames[key], bins=bins, label=key, alpha=1 / len(keys))
ax.set_xticks(bins)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('hists')
def draw_hist_ratio(frames, 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),
num=20,
)
for a, b in pairs:
ax.hist(frames[a] / frames[b], bins=bins, label=f'{a} / {b}', alpha=1 / len(pairs))
ax.set_xticks(bins)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('hists')
def draw_stdev_hists(frames, 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])
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))
ax.set_xticks(bins)
ax.grid(True)
ax.legend()
fig.canvas.set_window_title('stdev_hists')
def draw_plots(frames, 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}',
)
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)))
metrics = list(stats[0].keys())
max_key_size = max(len(tuple(v.values())[0]) for v in stats)
termtables.print(
[list(v.values()) for v in stats],
header=metrics,
style=termtables.styles.markdown,
)
def filter_not_none(values):
return [v for v in values if v is not None]
def sum_multiple(frames, keys):
result = collections.Counter()
for key in keys:
values = frames[key]
for i, value in enumerate(values):
if value is not None:
result[i] += float(value)
return numpy.array([result[k] for k in sorted(result.keys())])
def make_stats(key, values):
return collections.OrderedDict(
key=key,
number=len(values),
min=min(values),
max=max(values),
mean=statistics.mean(values),
median=statistics.median(values),
stdev=statistics.stdev(values),
q95=numpy.quantile(values, 0.95),
)
def to_number(value):
try:
return int(value)
except ValueError:
return float(value)
if __name__ == '__main__':
main()