def launch(self, view_model):
# type: (WaveletAdapterModel) -> (WaveletCoefficientsIndex)
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the wavelet coefficients for the specified time series
"""
frequencies_array = numpy.array([])
if view_model.frequencies is not None:
frequencies_array = view_model.frequencies.to_array()
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
assert isinstance(time_series_h5, TimeSeriesH5)
# --------------------- Prepare result entities ----------------------##
wavelet_index = WaveletCoefficientsIndex()
dest_path = self.path_for(WaveletCoefficientsH5, wavelet_index.gid)
wavelet_h5 = WaveletCoefficientsH5(path=dest_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()
small_ts.sample_period = time_series_h5.sample_period.load()
small_ts.sample_period_unit = time_series_h5.sample_period_unit.load()
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
partial_wavelet = compute_continuous_wavelet_transform(
small_ts, view_model.frequencies, view_model.sample_period,
view_model.q_ratio, view_model.normalisation,
view_model.mother)
wavelet_h5.write_data_slice(partial_wavelet)
time_series_h5.close()
partial_wavelet.source.gid = view_model.time_series
partial_wavelet.gid = uuid.UUID(wavelet_index.gid)
wavelet_index.fill_from_has_traits(partial_wavelet)
self.fill_index_from_h5(wavelet_index, wavelet_h5)
wavelet_h5.store(partial_wavelet, scalars_only=True)
wavelet_h5.frequencies.store(frequencies_array)
wavelet_h5.close()
return wavelet_index
def launch(self, view_model):
"""
Launch algorithm and build results.
"""
# --------- Prepare a WaveletCoefficients object for result ----------##
frequencies_array = numpy.array([])
if self.algorithm.frequencies is not None:
frequencies_array = self.algorithm.frequencies.to_array()
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
assert isinstance(time_series_h5, TimeSeriesH5)
wavelet_index = WaveletCoefficientsIndex()
dest_path = h5.path_for(self.storage_path, WaveletCoefficientsH5,
wavelet_index.gid)
wavelet_h5 = WaveletCoefficientsH5(path=dest_path)
wavelet_h5.gid.store(uuid.UUID(wavelet_index.gid))
wavelet_h5.source.store(time_series_h5.gid.load())
wavelet_h5.mother.store(self.algorithm.mother)
wavelet_h5.q_ratio.store(self.algorithm.q_ratio)
wavelet_h5.sample_period.store(self.algorithm.sample_period)
wavelet_h5.frequencies.store(frequencies_array)
wavelet_h5.normalisation.store(self.algorithm.normalisation)
# ------------- 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()
small_ts.sample_period = time_series_h5.sample_period.load()
small_ts.sample_period_unit = time_series_h5.sample_period_unit.load()
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_wavelet = self.algorithm.evaluate()
wavelet_h5.write_data_slice(partial_wavelet)
wavelet_h5.close()
time_series_h5.close()
wavelet_index.fk_source_gid = self.input_time_series_index.gid
wavelet_index.mother = self.algorithm.mother
wavelet_index.normalisation = self.algorithm.normalisation
wavelet_index.q_ratio = self.algorithm.q_ratio
wavelet_index.sample_period = self.algorithm.sample_period
wavelet_index.number_of_scales = frequencies_array.shape[0]
wavelet_index.frequencies_min, wavelet_index.frequencies_max, _ = from_ndarray(
frequencies_array)
return wavelet_index