def launch(self, view_model):
# type: (NodeCoherenceModel) -> [CoherenceSpectrumIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the node coherence for the specified time series
"""
# -------------------- Prepare result entities -----------------------##
coherence_spectrum_index = CoherenceSpectrumIndex()
dest_path = h5.path_for(self.storage_path, CoherenceSpectrumH5,
coherence_spectrum_index.gid)
coherence_h5 = CoherenceSpectrumH5(dest_path)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
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))
partial_coh = calculate_cross_coherence(small_ts, view_model.nfft)
coherence_h5.write_data_slice(partial_coh)
time_series_h5.close()
partial_coh.source.gid = view_model.time_series
partial_coh.gid = uuid.UUID(coherence_spectrum_index.gid)
coherence_spectrum_index.fill_from_has_traits(partial_coh)
self.fill_index_from_h5(coherence_spectrum_index, coherence_h5)
coherence_h5.store(partial_coh, scalars_only=True)
coherence_h5.frequency.store(partial_coh.frequency)
coherence_h5.close()
return coherence_spectrum_index
def build():
time_series_index = time_series_index_factory()
time_series = h5.load_from_index(time_series_index)
cross_coherence = spectral.CoherenceSpectrum(source=time_series,
nfft=4,
array_data=numpy.random.random((10, 10)),
frequency=numpy.random.random((10,)))
op = operation_factory()
cross_coherence_index = CoherenceSpectrumIndex()
cross_coherence_index.fk_from_operation = op.id
cross_coherence_index.fill_from_has_traits(cross_coherence)
cross_coherence_h5_path = h5.path_for_stored_index(cross_coherence_index)
with CoherenceSpectrumH5(cross_coherence_h5_path) as f:
f.store(cross_coherence)
cross_coherence_index = dao.store_entity(cross_coherence_index)
return cross_coherence_index
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(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.fk_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