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https://github.com/OpenMW/openmw.git
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273 lines
10 KiB
Python
Executable file
273 lines
10 KiB
Python
Executable file
#!/usr/bin/env python3
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"""
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osg_stats.py is a script to analyze OpenSceneGraph log. It parses given file
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and builds timeseries, histograms, plots, calculate statistics for a given
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set of keys over given range of frames.
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"""
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import click
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import collections
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import matplotlib.pyplot
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import numpy
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import statistics
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import sys
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import termtables
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@click.command()
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@click.option('--print_keys', is_flag=True,
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help='Print a list of all present keys in the input file.')
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@click.option('--timeseries', type=str, multiple=True,
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help='Show a graph for given metric over time.')
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@click.option('--commulative_timeseries', type=str, multiple=True,
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help='Show a graph for commulative sum of a given metric over time.')
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@click.option('--hist', type=str, multiple=True,
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help='Show a histogram for all values of given metric.')
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@click.option('--hist_ratio', nargs=2, type=str, multiple=True,
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help='Show a histogram for a ratio of two given metric (first / second). '
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'Format: --hist_ratio <first_metric> <second_metric>.')
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@click.option('--stdev_hist', nargs=2, type=str, multiple=True,
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help='Show a histogram for a standard deviation of a given metric at given scale (number). '
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'Format: --stdev_hist <metric> <scale>.')
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@click.option('--plot', nargs=3, type=str, multiple=True,
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help='Show a 2D plot for relation between two metrix (first is axis x, second is y)'
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'using one of aggregation functions (mean, median). For example show a relation '
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'between Physics Actors and physics_time_taken. Format: --plot <x> <y> <function>.')
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@click.option('--stats', type=str, multiple=True,
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help='Print table with stats for a given metric containing min, max, mean, median etc.')
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@click.option('--timeseries_sum', is_flag=True,
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help='Add a graph to timeseries for a sum per frame of all given timeseries metrics.')
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@click.option('--commulative_timeseries_sum', is_flag=True,
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help='Add a graph to timeseries for a sum per frame of all given commulative timeseries.')
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@click.option('--stats_sum', is_flag=True,
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help='Add a row to stats table for a sum per frame of all given stats metrics.')
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@click.option('--begin_frame', type=int, default=0,
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help='Start processing from this frame.')
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@click.option('--end_frame', type=int, default=sys.maxsize,
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help='End processing at this frame.')
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@click.argument('path', type=click.Path(), nargs=-1)
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def main(print_keys, timeseries, hist, hist_ratio, stdev_hist, plot, stats,
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timeseries_sum, stats_sum, begin_frame, end_frame, path,
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commulative_timeseries, commulative_timeseries_sum):
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sources = {v: list(read_data(v)) for v in path} if path else {'stdin': list(read_data(None))}
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keys = collect_unique_keys(sources)
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frames = collect_per_frame(sources=sources, keys=keys, begin_frame=begin_frame, end_frame=end_frame)
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if print_keys:
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for v in keys:
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print(v)
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if timeseries:
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draw_timeseries(sources=frames, keys=timeseries, add_sum=timeseries_sum)
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if commulative_timeseries:
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draw_commulative_timeseries(sources=frames, keys=commulative_timeseries, add_sum=commulative_timeseries_sum)
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if hist:
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draw_hists(sources=frames, keys=hist)
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if hist_ratio:
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draw_hist_ratio(sources=frames, pairs=hist_ratio)
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if stdev_hist:
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draw_stdev_hists(sources=frames, stdev_hists=stdev_hist)
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if plot:
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draw_plots(sources=frames, plots=plot)
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if stats:
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print_stats(sources=frames, keys=stats, stats_sum=stats_sum)
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matplotlib.pyplot.show()
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def read_data(path):
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with open(path) if path else sys.stdin as stream:
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frame = dict()
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camera = 0
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for line in stream:
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if line.startswith('Stats Viewer'):
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if frame:
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camera = 0
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yield frame
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_, _, key, value = line.split(' ')
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frame = {key: int(value)}
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elif line.startswith('Stats Camera'):
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camera += 1
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elif line.startswith(' '):
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key, value = line.strip().rsplit(maxsplit=1)
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if camera:
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key = f'{key} Camera {camera}'
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frame[key] = to_number(value)
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def collect_per_frame(sources, keys, begin_frame, end_frame):
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result = collections.defaultdict(lambda: collections.defaultdict(list))
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for name, frames in sources.items():
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for frame in frames:
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for key in keys:
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if key in frame:
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result[name][key].append(frame[key])
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else:
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result[name][key].append(None)
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for name, sources in result.items():
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for key, values in sources.items():
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result[name][key] = numpy.array(values[begin_frame:end_frame])
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return result
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def collect_unique_keys(sources):
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result = set()
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for frames in sources.values():
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for frame in frames:
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for key in frame.keys():
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result.add(key)
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return sorted(result)
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def draw_timeseries(sources, keys, add_sum):
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fig, ax = matplotlib.pyplot.subplots()
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for name, frames in sources.items():
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x = numpy.array(range(max(len(v) for k, v in frames.items() if k in keys)))
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for key in keys:
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print(key, name)
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ax.plot(x, frames[key], label=f'{key}:{name}')
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if add_sum:
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ax.plot(x, numpy.sum(list(frames[k] for k in keys), axis=0), label=f'sum:{name}')
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ax.grid(True)
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ax.legend()
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fig.canvas.set_window_title('timeseries')
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def draw_commulative_timeseries(sources, keys, add_sum):
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fig, ax = matplotlib.pyplot.subplots()
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for name, frames in sources.items():
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x = numpy.array(range(max(len(v) for k, v in frames.items() if k in keys)))
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for key in keys:
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ax.plot(x, numpy.cumsum(frames[key]), label=f'{key}:{name}')
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if add_sum:
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ax.plot(x, numpy.cumsum(numpy.sum(list(frames[k] for k in keys), axis=0)), label=f'sum:{name}')
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ax.grid(True)
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ax.legend()
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fig.canvas.set_window_title('commulative_timeseries')
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def draw_hists(sources, keys):
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fig, ax = matplotlib.pyplot.subplots()
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bins = numpy.linspace(
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start=min(min(min(v) for k, v in f.items() if k in keys) for f in sources.values()),
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stop=max(max(max(v) for k, v in f.items() if k in keys) for f in sources.values()),
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num=20,
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)
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for name, frames in sources.items():
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for key in keys:
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ax.hist(frames[key], bins=bins, label=f'{key}:{name}', alpha=1 / (len(keys) * len(sources)))
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ax.set_xticks(bins)
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ax.grid(True)
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ax.legend()
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fig.canvas.set_window_title('hists')
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def draw_hist_ratio(sources, pairs):
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fig, ax = matplotlib.pyplot.subplots()
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bins = numpy.linspace(
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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()),
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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()),
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num=20,
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)
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for name, frames in sources.items():
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for a, b in pairs:
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ax.hist(frames[a] / frames[b], bins=bins, label=f'{a} / {b}:{name}', alpha=1 / (len(pairs) * len(sources)))
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ax.set_xticks(bins)
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ax.grid(True)
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ax.legend()
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fig.canvas.set_window_title('hists_ratio')
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def draw_stdev_hists(sources, stdev_hists):
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for key, scale in stdev_hists:
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scale = float(scale)
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fig, ax = matplotlib.pyplot.subplots()
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first_frames = next(v for v in sources.values())
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median = statistics.median(first_frames[key])
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stdev = statistics.stdev(first_frames[key])
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start = median - stdev / 2 * scale
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stop = median + stdev / 2 * scale
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bins = numpy.linspace(start=start, stop=stop, num=9)
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for name, frames in sources.items():
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values = [v for v in frames[key] if start <= v <= stop]
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ax.hist(values, bins=bins, label=f'{key}:{name}', alpha=1 / (len(stdev_hists) * len(sources)))
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ax.set_xticks(bins)
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ax.grid(True)
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ax.legend()
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fig.canvas.set_window_title('stdev_hists')
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def draw_plots(sources, plots):
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fig, ax = matplotlib.pyplot.subplots()
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for name, frames in sources.items():
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for x_key, y_key, agg in plots:
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if agg is None:
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ax.plot(frames[x_key], frames[y_key], label=f'x={x_key}, y={y_key}:{name}')
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elif agg:
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agg_f = dict(
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mean=statistics.mean,
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median=statistics.median,
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)[agg]
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grouped = collections.defaultdict(list)
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for x, y in zip(frames[x_key], frames[y_key]):
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grouped[x].append(y)
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aggregated = sorted((k, agg_f(v)) for k, v in grouped.items())
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ax.plot(
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numpy.array([v[0] for v in aggregated]),
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numpy.array([v[1] for v in aggregated]),
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label=f'x={x_key}, y={y_key}, agg={agg}:{name}',
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)
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ax.grid(True)
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ax.legend()
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fig.canvas.set_window_title('plots')
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def print_stats(sources, keys, stats_sum):
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stats = list()
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for name, frames in sources.items():
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for key in keys:
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stats.append(make_stats(source=name, key=key, values=filter_not_none(frames[key])))
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if stats_sum:
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stats.append(make_stats(source=name, key='sum', values=sum_multiple(frames, keys)))
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metrics = list(stats[0].keys())
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max_key_size = max(len(tuple(v.values())[0]) for v in stats)
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termtables.print(
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[list(v.values()) for v in stats],
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header=metrics,
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style=termtables.styles.markdown,
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)
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def filter_not_none(values):
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return [v for v in values if v is not None]
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def sum_multiple(frames, keys):
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result = collections.Counter()
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for key in keys:
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values = frames[key]
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for i, value in enumerate(values):
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if value is not None:
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result[i] += float(value)
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return numpy.array([result[k] for k in sorted(result.keys())])
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def make_stats(source, key, values):
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return collections.OrderedDict(
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source=source,
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key=key,
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number=len(values),
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min=min(values),
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max=max(values),
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mean=statistics.mean(values),
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median=statistics.median(values),
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stdev=statistics.stdev(values),
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q95=numpy.quantile(values, 0.95),
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)
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def to_number(value):
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try:
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return int(value)
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except ValueError:
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return float(value)
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if __name__ == '__main__':
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main()
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