def launch(self, time_series, nfft=None):
"""
Launch algorithm and build results.
"""
##--------- Prepare a CoherenceSpectrum object for result ------------##
coherence = CoherenceSpectrum(source=time_series,
nfft=self.algorithm.nfft,
storage_path=self.storage_path)
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), slice(self.input_shape[3])]
##---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries(use_storage=False)
small_ts.sample_rate = time_series.sample_rate
partial_coh = None
for var in range(self.input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_coh = self.algorithm.evaluate()
coherence.write_data_slice(partial_coh)
coherence.frequency = partial_coh.frequency
coherence.close_file()
return coherence
def launch(self, time_series, mother=None, sample_period=None, normalisation=None, q_ratio=None,
frequencies='Range', frequencies_parameters=None):
"""
Launch algorithm and build results.
"""
##--------- Prepare a WaveletCoefficients object for result ----------##
frequencies_array = numpy.array([])
if self.algorithm.frequencies is not None:
frequencies_array = numpy.array(list(self.algorithm.frequencies))
wavelet = WaveletCoefficients(source=time_series, mother=self.algorithm.mother, q_ratio=self.algorithm.q_ratio,
sample_period=self.algorithm.sample_period, frequencies=frequencies_array,
normalisation=self.algorithm.normalisation, storage_path=self.storage_path)
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [slice(self.input_shape[0]), slice(self.input_shape[1]), None, slice(self.input_shape[3])]
##---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries(use_storage=False)
small_ts.sample_rate = time_series.sample_rate
small_ts.sample_period = time_series.sample_period
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_wavelet = self.algorithm.evaluate()
wavelet.write_data_slice(partial_wavelet)
wavelet.close_file()
return wavelet
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `ComplexCoherenceSpectrum` built with the given time-series
"""
shape = time_series.read_data_shape()
##------- Prepare a ComplexCoherenceSpectrum object for result -------##
spectra = ComplexCoherenceSpectrum(source=time_series,
storage_path=self.storage_path)
##------------------- NOTE: Assumes 4D TimeSeries. -------------------##
node_slice = [slice(shape[0]), slice(shape[1]), slice(shape[2]), slice(shape[3])]
##---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries(use_storage=False)
small_ts.sample_rate = time_series.sample_rate
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_result = self.algorithm.evaluate()
LOG.debug("got partial_result")
LOG.debug("partial segment_length is %s" % (str(partial_result.segment_length)))
LOG.debug("partial epoch_length is %s" % (str(partial_result.epoch_length)))
LOG.debug("partial windowing_function is %s" % (str(partial_result.windowing_function)))
#LOG.debug("partial frequency vector is %s" % (str(partial_result.frequency)))
spectra.write_data_slice(partial_result)
spectra.segment_length = partial_result.segment_length
spectra.epoch_length = partial_result.epoch_length
spectra.windowing_function = partial_result.windowing_function
#spectra.frequency = partial_result.frequency
spectra.close_file()
return spectra
def launch(self,
time_series,
mother=None,
sample_period=None,
normalisation=None,
q_ratio=None,
frequencies='Range',
frequencies_parameters=None):
"""
Launch algorithm and build results.
"""
##--------- Prepare a WaveletCoefficients object for result ----------##
frequencies_array = numpy.array([])
if self.algorithm.frequencies is not None:
frequencies_array = numpy.array(list(self.algorithm.frequencies))
wavelet = WaveletCoefficients(
source=time_series,
mother=self.algorithm.mother,
q_ratio=self.algorithm.q_ratio,
sample_period=self.algorithm.sample_period,
frequencies=frequencies_array,
normalisation=self.algorithm.normalisation,
storage_path=self.storage_path)
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [
slice(self.input_shape[0]),
slice(self.input_shape[1]), None,
slice(self.input_shape[3])
]
##---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries(use_storage=False)
small_ts.sample_rate = time_series.sample_rate
small_ts.sample_period = time_series.sample_period
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_wavelet = self.algorithm.evaluate()
wavelet.write_data_slice(partial_wavelet)
wavelet.close_file()
return wavelet
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `ComplexCoherenceSpectrum` built with the given time-series
"""
shape = time_series.read_data_shape()
##------- Prepare a ComplexCoherenceSpectrum object for result -------##
spectra = ComplexCoherenceSpectrum(source=time_series,
storage_path=self.storage_path)
##------------------- NOTE: Assumes 4D TimeSeries. -------------------##
node_slice = [
slice(shape[0]),
slice(shape[1]),
slice(shape[2]),
slice(shape[3])
]
##---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries(use_storage=False)
small_ts.sample_rate = time_series.sample_rate
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_result = self.algorithm.evaluate()
LOG.debug("got partial_result")
LOG.debug("partial segment_length is %s" %
(str(partial_result.segment_length)))
LOG.debug("partial epoch_length is %s" %
(str(partial_result.epoch_length)))
LOG.debug("partial windowing_function is %s" %
(str(partial_result.windowing_function)))
#LOG.debug("partial frequency vector is %s" % (str(partial_result.frequency)))
spectra.write_data_slice(partial_result)
spectra.segment_length = partial_result.segment_length
spectra.epoch_length = partial_result.epoch_length
spectra.windowing_function = partial_result.windowing_function
#spectra.frequency = partial_result.frequency
spectra.close_file()
return spectra
