def perform(self, simulation_results, result_factory):
value_collection = []
# sample_period = None
sample_times = numpy.arange(self.start_time,
self.stop_time + self.sample_period/2, self.sample_period)
for simulation in simulation_results:
times, values = simulation['filaments'][0]['measurements'
][self.measurement_name]
stimes, sampled_values = interpolation.resample_measurement(
(times, values), sample_times, method='previous_value')
# if not sample_period and len(times) > 1:
# sample_period = float(times[1] - times[0])
value_collection.append(sampled_values)
# calculate velocities collection
velocity_collection = [numpy.diff(v) / self.sample_period
for v in value_collection]
# collection get mean/std -> threshold
mean, std = correlation.collection_stats(velocity_collection)
threshold = mean + self.std_devs * std
# do autocorrelation
if 'above' == self.side.lower():
matched_collection = [numpy.array(velocities > threshold, dtype=int)
for velocities in velocity_collection]
else:
matched_collection = [numpy.array(velocities < threshold, dtype=int)
for velocities in velocity_collection]
measurement = correlation.aggregate_autocorrelation(self.sample_period,
matched_collection)
return result_factory(measurement, label=self.label)
def perform(self, simulation_results, result_factory):
value_collection = []
sample_times = numpy.arange(self.start_time,
self.stop_time + self.sample_period/2, self.sample_period)
for simulation in simulation_results:
times, values = simulation['filaments'][0]['measurements'
][self.measurement_name]
stimes, sampled_values = interpolation.resample_measurement(
(times, values), sample_times, method='previous_value')
values = numpy.array(sampled_values)
matches = numpy.array(values == self.state, dtype=int)
value_collection.append(matches)
measurement = correlation.aggregate_autocorrelation(self.sample_period,
value_collection)
return result_factory(measurement, label=self.label)
def perform(self, simulation_results, result_factory):
value_collection = []
sample_times = numpy.arange(self.start_time,
self.stop_time + self.sample_period / 2,
self.sample_period)
for simulation in simulation_results:
times, values = simulation['filaments'][0]['measurements'][
self.measurement_name]
stimes, sampled_values = interpolation.resample_measurement(
(times, values), sample_times, method='previous_value')
values = numpy.array(sampled_values)
matches = numpy.array(values == self.state, dtype=int)
value_collection.append(matches)
measurement = correlation.aggregate_autocorrelation(
self.sample_period, value_collection)
return result_factory(measurement, label=self.label)
