def launch(self, view_model):
# type: (NodeCoherenceModel) -> [CoherenceSpectrumIndex]
"""
Launch algorithm and build results.
"""
# --------- Prepare a CoherenceSpectrum object for result ------------##
coherence_spectrum_index = CoherenceSpectrumIndex()
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
dest_path = h5.path_for(self.storage_path, CoherenceSpectrumH5,
coherence_spectrum_index.gid)
coherence_h5 = CoherenceSpectrumH5(dest_path)
coherence_h5.gid.store(uuid.UUID(coherence_spectrum_index.gid))
coherence_h5.source.store(view_model.time_series)
coherence_h5.nfft.store(self.algorithm.nfft)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
input_shape = time_series_h5.data.shape
node_slice = [
slice(input_shape[0]), None,
slice(input_shape[2]),
slice(input_shape[3])
]
# ---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries()
small_ts.sample_period = time_series_h5.sample_period.load()
small_ts.sample_period_unit = time_series_h5.sample_period_unit.load()
partial_coh = None
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_coh = self.algorithm.evaluate()
coherence_h5.write_data_slice(partial_coh)
coherence_h5.frequency.store(partial_coh.frequency)
array_metadata = coherence_h5.array_data.get_cached_metadata()
freq_metadata = coherence_h5.frequency.get_cached_metadata()
coherence_h5.close()
time_series_h5.close()
coherence_spectrum_index.array_data_min = array_metadata.min
coherence_spectrum_index.array_data_max = array_metadata.max
coherence_spectrum_index.array_data_mean = array_metadata.mean
coherence_spectrum_index.array_has_complex = array_metadata.has_complex
coherence_spectrum_index.array_is_finite = array_metadata.is_finite
coherence_spectrum_index.shape = json.dumps(
coherence_h5.array_data.shape)
coherence_spectrum_index.ndim = len(coherence_h5.array_data.shape)
coherence_spectrum_index.fk_source_gid = self.input_time_series_index.gid
coherence_spectrum_index.nfft = partial_coh.nfft
coherence_spectrum_index.frequencies_min = freq_metadata.min
coherence_spectrum_index.frequencies_max = freq_metadata.max
coherence_spectrum_index.subtype = CoherenceSpectrum.__name__
return coherence_spectrum_index
def launch(self, time_series, nfft=None):
"""
Launch algorithm and build results.
"""
# --------- Prepare a CoherenceSpectrum object for result ------------##
coherence_spectrum_index = CoherenceSpectrumIndex()
time_series_h5 = h5.h5_file_for_index(time_series)
dest_path = h5.path_for(self.storage_path, CoherenceSpectrumH5,
coherence_spectrum_index.gid)
coherence_h5 = CoherenceSpectrumH5(dest_path)
coherence_h5.gid.store(uuid.UUID(coherence_spectrum_index.gid))
coherence_h5.source.store(time_series_h5.gid.load())
coherence_h5.nfft.store(self.algorithm.nfft)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
input_shape = time_series_h5.data.shape
node_slice = [
slice(input_shape[0]), None,
slice(input_shape[2]),
slice(input_shape[3])
]
# ---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries()
small_ts.sample_period = time_series_h5.sample_period.load()
partial_coh = None
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_coh = self.algorithm.evaluate()
coherence_h5.write_data_slice(partial_coh)
coherence_h5.frequency.store(partial_coh.frequency)
coherence_h5.close()
coherence_spectrum_index.ndim = len(coherence_h5.array_data.shape)
time_series_h5.close()
coherence_spectrum_index.source_gid = self.input_time_series_index.gid
coherence_spectrum_index.nfft = partial_coh.nfft
coherence_spectrum_index.frequencies = partial_coh.frequency
return coherence_spectrum_index