def get_sampled_measurements(self, simulation_results):
raw_measurements = utils.get_measurement(simulation_results,
self.measurement_name,
self.measurement_type)
sample_times = workalike.arange(self.start_time, self.stop_time,
self.sample_period)
if not sample_times:
_log.error('Sample time length is 0. ' +
'Measurement name: %s, stop_time: %s, period %s.',
self.measurement_name, self.stop_time,
self.sample_period)
return [interpolation.resample_measurement(
rm, sample_times, method=self.interpolation_method)
for rm in raw_measurements]
def perform(self, simulation_results, result_factory):
fluctuations = self.get_fluctuations(simulation_results)
taus = workalike.arange(self.tau_min,
self.tau_max + self.sample_period/2,
self.sample_period)
variances = []
for delta, tau in enumerate(taus):
local_fluctuations = []
for measurement in fluctuations:
local_fluctuations.extend(_calculate_local_fluctuations(
measurement, delta, self.sample_period))
variances.append(numpy.var(local_fluctuations))
errors = [0 for tau in taus]
return result_factory((taus, variances, errors), label=self.label)
def get_sampled_measurements(self, simulation_results):
raw_measurements = utils.get_measurement(simulation_results,
self.measurement_name,
self.measurement_type)
sample_times = workalike.arange(self.start_time, self.stop_time,
self.sample_period)
if not sample_times:
_log.error(
'Sample time length is 0. ' +
'Measurement name: %s, stop_time: %s, period %s.',
self.measurement_name, self.stop_time, self.sample_period)
return [
interpolation.resample_measurement(
rm, sample_times, method=self.interpolation_method)
for rm in raw_measurements
]
def perform(self, simulation_results, result_factory):
fluctuations = self.get_fluctuations(simulation_results)
taus = workalike.arange(self.tau_min,
self.tau_max + self.sample_period / 2,
self.sample_period)
variances = []
for delta, tau in enumerate(taus):
local_fluctuations = []
for measurement in fluctuations:
local_fluctuations.extend(
_calculate_local_fluctuations(measurement, delta,
self.sample_period))
variances.append(numpy.var(local_fluctuations))
errors = [0 for tau in taus]
return result_factory((taus, variances, errors), label=self.label)
def run(self):
full_filename = _ospath.join(self.base_directory, self.filename)
f = _comments.CommentFilter.from_filename(full_filename)
reader = _csv.reader(f, dialect=_DatDialect)
results = []
for row in reader:
new_row = map(float, row)
results.append(new_row)
raw_results = zip(*results)
if not self.interpolate_data:
return raw_results
sample_times = _workalike.arange(self.xmin, self.xmax,
self.sample_period)
return _interpolation.resample_measurement(raw_results, sample_times)
def test_interpolation_times(self):
results = self.dr.run()
expected_times = workalike.arange(self.xmin, self.xmax, self.sample_period)
self.assertEqual(expected_times, results[0])
def test_interpolation_times(self):
results = self.dr.run()
expected_times = workalike.arange(self.xmin, self.xmax,
self.sample_period)
self.assertEqual(expected_times, results[0])
