def _process_without_pandas(self, measurements_csv):
per_frame_fps = []
start_vsync, end_vsync = None, None
frame_count = 0
for frame_data in measurements_csv.iter_values():
if frame_data.Flags_flags != 0:
continue
frame_count += 1
if start_vsync is None:
start_vsync = frame_data.Vsync_time_us
end_vsync = frame_data.Vsync_time_us
frame_time = frame_data.FrameCompleted_time_us - frame_data.IntendedVsync_time_us
pff = 1e9 / frame_time
if pff > self.drop_threshold:
per_frame_fps.append([pff])
if frame_count:
duration = end_vsync - start_vsync
fps = (1e6 * frame_count) / float(duration)
else:
duration = 0
fps = 0
csv_file = self._get_csv_file_name(measurements_csv.path)
with csvwriter(csv_file) as writer:
writer.writerow(['fps'])
writer.writerows(per_frame_fps)
return [DerivedMetric('fps', fps, 'fps'),
DerivedMetric('total_frames', frame_count, 'frames'),
MeasurementsCsv(csv_file)]
def get_data(self, outfile):
if os.stat(self.raw_data_file).st_size == 0:
self.logger.warning('"{}" appears to be empty'.format(
self.raw_data_file))
return
all_channels = [c.label for c in self.list_channels()]
active_channels = [c.label for c in self.active_channels]
active_indexes = [all_channels.index(ac) for ac in active_channels]
with csvreader(self.raw_data_file, skipinitialspace=True) as reader:
with csvwriter(outfile) as writer:
writer.writerow(active_channels)
header = next(reader)
ts_index = header.index('timestamp ms')
for row in reader:
output_row = []
for i in active_indexes:
if i == ts_index:
# Leave time in ms
output_row.append(float(row[i]))
else:
# Convert rest into standard units.
output_row.append(float(row[i]) / 1000)
writer.writerow(output_row)
return MeasurementsCsv(outfile, self.active_channels,
self.sample_rate_hz)
def _process_with_pandas(self, measurements_csv):
data = pd.read_csv(measurements_csv.path)
data = data[data.Flags_flags == 0]
frame_time = data.FrameCompleted_time_us - data.IntendedVsync_time_us
per_frame_fps = (1e6 / frame_time)
keep_filter = per_frame_fps > self.drop_threshold
per_frame_fps = per_frame_fps[keep_filter]
per_frame_fps.name = 'fps'
frame_count = data.index.size
if frame_count > 1:
duration = data.Vsync_time_us.iloc[-1] - data.Vsync_time_us.iloc[0]
fps = (1e9 * frame_count) / float(duration)
else:
duration = 0
fps = 0
csv_file = self._get_csv_file_name(measurements_csv.path)
per_frame_fps.to_csv(csv_file, index=False, header=True)
return [
DerivedMetric('fps', fps, 'fps'),
DerivedMetric('total_frames', frame_count, 'frames'),
MeasurementsCsv(csv_file)
]
def _process_with_pandas(self, measurements_csv):
data = pd.read_csv(measurements_csv.path)
# fiter out bogus frames.
bogus_frames_filter = data.actual_present_time_us != 0x7fffffffffffffff
actual_present_times = data.actual_present_time_us[bogus_frames_filter]
actual_present_time_deltas = actual_present_times.diff().dropna()
vsyncs_to_compose = actual_present_time_deltas.div(VSYNC_INTERVAL)
vsyncs_to_compose.apply(lambda x: int(round(x, 0)))
# drop values lower than drop_threshold FPS as real in-game frame
# rate is unlikely to drop below that (except on loading screens
# etc, which should not be factored in frame rate calculation).
per_frame_fps = (1.0 /
(vsyncs_to_compose.multiply(VSYNC_INTERVAL / 1e9)))
keep_filter = per_frame_fps > self.drop_threshold
filtered_vsyncs_to_compose = vsyncs_to_compose[keep_filter]
per_frame_fps.name = 'fps'
csv_file = self._get_csv_file_name(measurements_csv.path)
per_frame_fps.to_csv(csv_file, index=False, header=True)
if not filtered_vsyncs_to_compose.empty:
fps = 0
total_vsyncs = filtered_vsyncs_to_compose.sum()
frame_count = filtered_vsyncs_to_compose.size
if total_vsyncs:
fps = 1e9 * frame_count / (VSYNC_INTERVAL * total_vsyncs)
janks = self._calc_janks(filtered_vsyncs_to_compose)
not_at_vsync = self._calc_not_at_vsync(vsyncs_to_compose)
else:
fps = 0
frame_count = 0
janks = 0
not_at_vsync = 0
janks_pc = 0 if frame_count == 0 else janks * 100 / frame_count
return [
DerivedMetric('fps', fps, 'fps'),
DerivedMetric('total_frames', frame_count, 'frames'),
MeasurementsCsv(csv_file),
DerivedMetric('janks', janks, 'count'),
DerivedMetric('janks_pc', janks_pc, 'percent'),
DerivedMetric('missed_vsync', not_at_vsync, 'count')
]
def process(self, measurements_csv):
if isinstance(measurements_csv, basestring):
measurements_csv = MeasurementsCsv(measurements_csv)
if pd is not None:
return self._process_with_pandas(measurements_csv)
return self._process_without_pandas(measurements_csv)
def get_data(self, outfile=None, **to_csv_kwargs):
if outfile is None:
return self.data
self.data.to_csv(outfile, **to_csv_kwargs)
return MeasurementsCsv(outfile, self.active_channels)