def deserialize_ExperimentPoint(experiment, id_mappings, ioHelper):
# coordinate_id = ioHelper.readId()
# sampleCount = ioHelper.readInt()
# mean = ioHelper.readValue()
# meanCI_start = ioHelper.readValue()
# meanCI_end = ioHelper.readValue()
# standardDeviation = ioHelper.readValue()
# median = ioHelper.readValue()
# medianCI_start = ioHelper.readValue()
# medianCI_end = ioHelper.readValue()
# minimum = ioHelper.readValue()
# maximum = ioHelper.readValue()
# metricId = ioHelper.readId()
# callpathId = ioHelper.readId()
coordinate_id, sampleCount, \
mean, meanCI_start, meanCI_end, \
standardDeviation, \
median, medianCI_start, medianCI_end, \
minimum, maximum, metricId, callpathId = ioHelper.read_pattern('qqdddddddddqq')
coordinate = id_mappings.coordinate_mapping[coordinate_id]
metric = experiment.metrics[metricId]
callpath = id_mappings.callpath_mapping[callpathId]
point = Measurement(coordinate, callpath, metric, None)
point.minimum = minimum
point.maximum = maximum
point.mean = mean
point.median = median
point.std = standardDeviation
return point
def make_measurement(c, ms: Sequence[Measurement]):
if len(ms) == 1:
measurement = copy.copy(ms[0])
measurement.coordinate = Coordinate(c)
return measurement
measurement = Measurement(Coordinate(c), ms[0].callpath, ms[0].metric, None)
if self.use_median:
value = np.mean([m.median for m in ms])
else:
value = np.mean([m.mean for m in ms])
measurement.mean = value
measurement.median = value
if measurement.mean == 0:
measurement.maximum = np.mean([m.maximum for m in ms])
measurement.minimum = np.mean([m.minimum for m in ms])
measurement.std = np.mean([m.std for m in ms])
else:
measurement.maximum = np.nanmean([m.maximum / m.mean for m in ms]) * measurement.mean
measurement.minimum = np.nanmean([m.minimum / m.mean for m in ms]) * measurement.mean
measurement.std = np.nanmean([m.std / m.mean for m in ms]) * measurement.mean
return measurement