def load_kinsim(filename, sample_period, duration):
time, factin_d, pi_d, atp_d = io.data.load_data(filename)
factin = time, factin_d
pi = time, pi_d
atp = time, atp_d
sample_times = numpy.arange(0, duration + float(sample_period)/2,
sample_period)
sampled_factin = interpolation.resample_measurement(factin, sample_times)
sampled_pi = interpolation.resample_measurement(pi, sample_times)
sampled_atp = interpolation.resample_measurement(atp, sample_times)
return sampled_factin, sampled_pi, sampled_atp
def load_kinsim(filename, sample_period, duration):
time, factin_d, pi_d, atp_d = io.data.load_data(filename)
factin = time, factin_d
pi = time, pi_d
atp = time, atp_d
sample_times = numpy.arange(0, duration + float(sample_period) / 2,
sample_period)
sampled_factin = interpolation.resample_measurement(factin, sample_times)
sampled_pi = interpolation.resample_measurement(pi, sample_times)
sampled_atp = interpolation.resample_measurement(atp, sample_times)
return sampled_factin, sampled_pi, sampled_atp
def perform(self, run, target):
log.debug('Performing SimpleDataFit of %s.', self.measurement_name)
sim_result = run.analyses[self.measurement_name]
data = run.experiment.objectives[self.label].measurement
sim_result = _measurements.skip_beginning(sim_result,
self.skip_beginning)
sim_result = _measurements.scale(sim_result, self.scale_simulation_by)
if self.interpolate_simulation:
interp = _interpolation.resample_measurement(sim_result, data[0])
# log.debug('interp times: %s', interp[0])
log.debug('Sim values: %s', interp[1])
log.debug('Data values: %s', data[1])
target.value = self.residual_function(interp, data)
else:
target.value = self.residual_function(sim_result, data)
log.debug('Sim values: %s', sim_result[1])
log.debug('Data values: %s', data[1])
log.debug('Objective value: %s.', target.value)
if target.value <= 0:
log.warn('Negative or zero residual found %s.', target.value)
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, run, target):
log.debug('Performing SimpleDataFit of %s.', self.measurement_name)
sim_result = run.analyses[self.measurement_name]
data = run.experiment.objectives[self.label].measurement
sim_result = _measurements.skip_beginning(sim_result,
self.skip_beginning)
sim_result = _measurements.scale(sim_result, self.scale_simulation_by)
if self.interpolate_simulation:
interp = _interpolation.resample_measurement(sim_result, data[0])
# log.debug('interp times: %s', interp[0])
log.debug('Sim values: %s', interp[1])
log.debug('Data values: %s', data[1])
target.value = self.residual_function(interp, data)
else:
target.value = self.residual_function(sim_result, data)
log.debug('Sim values: %s', sim_result[1])
log.debug('Data values: %s', data[1])
log.debug('Objective value: %s.', target.value)
if target.value <= 0:
log.warn('Negative or zero residual found %s.', target.value)
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 _get_timecourse(run, resample_times, poly_duration):
# Grab the analysis
times, values, errors = run.analyses['length']
times = numpy.array(times)
values = numpy.array(values)
times -= poly_duration
# resample
sampled_values = numpy.array(_interpolation.resample_measurement(
(times, values), resample_times)[1])
numpy.putmask(sampled_values, sampled_values < 50, float('nan'))
return sampled_values
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 _get_timecourse(run, resample_times, poly_duration):
# Grab the analysis
times, values, errors = run.analyses['length']
times = numpy.array(times)
values = numpy.array(values)
times -= poly_duration
# resample
sampled_values = numpy.array(
_interpolation.resample_measurement((times, values),
resample_times)[1])
numpy.putmask(sampled_values, sampled_values < 50, float('nan'))
return sampled_values
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 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)
def _get_pyrene_measurements(self, simulation):
# Loop over each weight,
times = numpy.arange(0, self.stop_time, self.sample_period)
total = []
for name, weight in self.weights.iteritems():
raw = [f['measurements'][name] for f in simulation['filaments']]
interpolated = []
for rm in raw:
junk_times, values = interpolation.resample_measurement(rm, times,
method='previous_value')
values = numpy.array(values)
if 0 == len(interpolated):
interpolated = values
else:
interpolated += values
interpolated *= weight
if 0 == len(total):
total = interpolated
else:
total += interpolated
return times, total
def _get_pyrene_measurements(self, simulation):
# Loop over each weight,
times = numpy.arange(0, self.stop_time, self.sample_period)
total = []
for name, weight in self.weights.iteritems():
raw = [f['measurements'][name] for f in simulation['filaments']]
interpolated = []
for rm in raw:
junk_times, values = interpolation.resample_measurement(
rm, times, method='previous_value')
values = numpy.array(values)
if 0 == len(interpolated):
interpolated = values
else:
interpolated += values
interpolated *= weight
if 0 == len(total):
total = interpolated
else:
total += interpolated
return times, total