def launch(self, matfile):
mat = scipy.io.loadmat(matfile)
hdr = mat['hdr']
fs, ns = [hdr[key][0, 0][0, 0] for key in ['Fs', 'nSamples']]
# the entities to populate
#ch = Sensors(storage_path=self.storage_path)
ts = TimeSeries(storage_path=self.storage_path)
# (nchan x ntime) -> (t, sv, ch, mo)
dat = mat['dat'].T[:, numpy.newaxis, :, numpy.newaxis]
# write data
ts.write_data_slice(dat)
# fill in header info
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = 'Time 1 Channel 1'.split()
ts.write_time_slice(numpy.r_[:ns] * 1.0 / fs)
ts.start_time = 0.0
ts.sample_period_unit = 's'
ts.sample_period = 1.0 / float(fs)
ts.close_file()
# setup sensors information
# ch.labels = numpy.array(
# [str(l[0]) for l in hdr['label'][0, 0][:, 0]])
# ch.number_of_sensors = ch.labels.size
return ts #, ch
def launch(self, matfile):
mat = scipy.io.loadmat(matfile)
hdr = mat['hdr']
fs, ns = [hdr[key][0, 0][0, 0] for key in ['Fs', 'nSamples']]
# the entities to populate
#ch = Sensors(storage_path=self.storage_path)
ts = TimeSeries(storage_path=self.storage_path)
# (nchan x ntime) -> (t, sv, ch, mo)
dat = mat['dat'].T[:, numpy.newaxis, :, numpy.newaxis]
# write data
ts.write_data_slice(dat)
# fill in header info
ts.length_1d, ts.length_2d, ts.length_3d, ts.length_4d = dat.shape
ts.labels_ordering = 'Time 1 Channel 1'.split()
ts.write_time_slice(numpy.r_[:ns] * 1.0 / fs)
ts.start_time = 0.0
ts.sample_period_unit = 's'
ts.sample_period = 1.0 / float(fs)
ts.close_file()
# setup sensors information
# ch.labels = numpy.array(
# [str(l[0]) for l in hdr['label'][0, 0][:, 0]])
# ch.number_of_sensors = ch.labels.size
return ts #, ch
def launch(self, view_model):
# type: (NodeComplexCoherenceModel) -> [ComplexCoherenceSpectrumIndex]
"""
Launch algorithm and build results.
:returns: the `ComplexCoherenceSpectrum` built with the given time-series
"""
# ------- Prepare a ComplexCoherenceSpectrum object for result -------##
complex_coherence_spectrum_index = ComplexCoherenceSpectrumIndex()
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
dest_path = h5.path_for(self.storage_path, ComplexCoherenceSpectrumH5,
complex_coherence_spectrum_index.gid)
spectra_h5 = ComplexCoherenceSpectrumH5(dest_path)
spectra_h5.gid.store(uuid.UUID(complex_coherence_spectrum_index.gid))
spectra_h5.source.store(time_series_h5.gid.load())
# ------------------- NOTE: Assumes 4D TimeSeries. -------------------##
input_shape = time_series_h5.data.shape
node_slice = [
slice(input_shape[0]),
slice(input_shape[1]),
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()
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_result = self.algorithm.evaluate()
self.log.debug("got partial_result")
self.log.debug("partial segment_length is %s" %
(str(partial_result.segment_length)))
self.log.debug("partial epoch_length is %s" %
(str(partial_result.epoch_length)))
self.log.debug("partial windowing_function is %s" %
(str(partial_result.windowing_function)))
# LOG.debug("partial frequency vector is %s" % (str(partial_result.frequency)))
spectra_h5.write_data_slice(partial_result)
spectra_h5.segment_length.store(partial_result.segment_length)
spectra_h5.epoch_length.store(partial_result.epoch_length)
spectra_h5.windowing_function.store(partial_result.windowing_function)
# spectra.frequency = partial_result.frequency
spectra_h5.close()
time_series_h5.close()
complex_coherence_spectrum_index.fk_source_gid = self.input_time_series_index.gid
complex_coherence_spectrum_index.epoch_length = partial_result.epoch_length
complex_coherence_spectrum_index.segment_length = partial_result.segment_length
complex_coherence_spectrum_index.windowing_function = partial_result.windowing_function
complex_coherence_spectrum_index.frequency_step = partial_result.freq_step
complex_coherence_spectrum_index.max_frequency = partial_result.max_freq
return complex_coherence_spectrum_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(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, 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
def launch(self, view_model):
# type: (CrossCorrelateAdapterModel) -> [CrossCorrelationIndex]
"""
Launch algorithm and build results.
Compute the node-pairwise cross-correlation of the source 4D TimeSeries represented by the index given as input.
Return a CrossCorrelationIndex. Create a CrossCorrelationH5 that contains the cross-correlation
sequences for all possible combinations of the nodes.
See: http://www.scipy.org/doc/api_docs/SciPy.signal.signaltools.html#correlate
:param time_series: the input time series index for which the correlation should be computed
:returns: the cross correlation index for the given time series
:rtype: `CrossCorrelationIndex`
"""
# --------- Prepare CrossCorrelationIndex and CrossCorrelationH5 objects for result ------------##
cross_corr_index = CrossCorrelationIndex()
cross_corr_h5_path = h5.path_for(self.storage_path, CrossCorrelationH5, cross_corr_index.gid)
cross_corr_h5 = CrossCorrelationH5(cross_corr_h5_path)
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()
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
small_ts.sample_period = ts_h5.sample_period.load()
small_ts.sample_period_unit = ts_h5.sample_period_unit.load()
partial_cross_corr = None
labels_ordering = ts_h5.labels_ordering.load()
for var in range(self.input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cross_corr = self._compute_cross_correlation(small_ts, ts_h5)
cross_corr_h5.write_data_slice(partial_cross_corr)
ts_array_metadata = cross_corr_h5.array_data.get_cached_metadata()
cross_corr_h5.time.store(partial_cross_corr.time)
cross_corr_labels_ordering = list(partial_cross_corr.labels_ordering)
cross_corr_labels_ordering[1] = labels_ordering[2]
cross_corr_labels_ordering[2] = labels_ordering[2]
cross_corr_h5.labels_ordering.store(json.dumps(tuple(cross_corr_labels_ordering)))
cross_corr_h5.source.store(uuid.UUID(self.input_time_series_index.gid))
cross_corr_h5.gid.store(uuid.UUID(cross_corr_index.gid))
cross_corr_index.fk_source_gid = self.input_time_series_index.gid
cross_corr_index.labels_ordering = cross_corr_h5.labels_ordering.load()
cross_corr_index.type = type(cross_corr_index).__name__
cross_corr_index.array_data_min = ts_array_metadata.min
cross_corr_index.array_data_max = ts_array_metadata.max
cross_corr_index.array_data_mean = ts_array_metadata.mean
cross_corr_h5.close()
return cross_corr_index
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):
# type: (BalloonModelAdapterModel) -> [TimeSeriesRegionIndex]
"""
Launch algorithm and build results.
:param time_series: the input time-series used as neural activation in the Balloon Model
:returns: the simulated BOLD signal
:rtype: `TimeSeries`
"""
input_time_series_h5 = h5.h5_file_for_index(
self.input_time_series_index)
time_line = input_time_series_h5.read_time_page(0, self.input_shape[0])
bold_signal_index = TimeSeriesRegionIndex()
bold_signal_h5_path = h5.path_for(self.storage_path,
TimeSeriesRegionH5,
bold_signal_index.gid)
bold_signal_h5 = TimeSeriesRegionH5(bold_signal_h5_path)
bold_signal_h5.gid.store(uuid.UUID(bold_signal_index.gid))
self._fill_result_h5(bold_signal_h5, input_time_series_h5)
# ---------- Iterate over slices and compose final result ------------##
node_slice = [
slice(self.input_shape[0]),
slice(self.input_shape[1]), None,
slice(self.input_shape[3])
]
small_ts = TimeSeries()
small_ts.sample_period = self.input_time_series_index.sample_period
small_ts.sample_period_unit = self.input_time_series_index.sample_period_unit
small_ts.time = time_line
for node in range(self.input_shape[2]):
node_slice[2] = slice(node, node + 1)
small_ts.data = input_time_series_h5.read_data_slice(
tuple(node_slice))
self.algorithm.time_series = small_ts
partial_bold = self.algorithm.evaluate()
bold_signal_h5.write_data_slice_on_grow_dimension(
partial_bold.data, grow_dimension=2)
bold_signal_h5.write_time_slice(time_line)
bold_signal_shape = bold_signal_h5.data.shape
bold_signal_h5.nr_dimensions.store(len(bold_signal_shape))
bold_signal_h5.close()
input_time_series_h5.close()
self._fill_result_index(bold_signal_index, bold_signal_shape)
return bold_signal_index
def _create_timeseries(self):
"""Launch adapter to persist a TimeSeries entity"""
storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id))
time_series = TimeSeries()
time_series.sample_period = 10.0
time_series.start_time = 0.0
time_series.storage_path = storage_path
time_series.write_data_slice(numpy.array([1.0, 2.0, 3.0]))
time_series.close_file()
time_series.sample_period_unit = 'ms'
self._store_entity(time_series, "TimeSeries", "tvb.datatypes.time_series")
count_ts = self.count_all_entities(TimeSeries)
self.assertEqual(1, count_ts, "Should be only one TimeSeries")
def _create_timeseries(self):
"""Launch adapter to persist a TimeSeries entity"""
storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id))
time_series = TimeSeries()
time_series.sample_period = 10.0
time_series.start_time = 0.0
time_series.storage_path = storage_path
time_series.write_data_slice(numpy.array([1.0, 2.0, 3.0]))
time_series.close_file()
time_series.sample_period_unit = 'ms'
self._store_entity(time_series, "TimeSeries", "tvb.datatypes.time_series")
count_ts = self.count_all_entities(TimeSeries)
self.assertEqual(1, count_ts, "Should be only one TimeSeries")
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 launch(self, view_model):
# type: (CrossCorrelateAdapterModel) -> [CrossCorrelationIndex]
"""
Launch algorithm and build results.
Compute the node-pairwise cross-correlation of the source 4D TimeSeries represented by the index given as input.
Return a CrossCorrelationIndex. Create a CrossCorrelationH5 that contains the cross-correlation
sequences for all possible combinations of the nodes.
See: http://www.scipy.org/doc/api_docs/SciPy.signal.signaltools.html#correlate
:param view_model: the ViewModel keeping the algorithm inputs
:return: the cross correlation index for the given time series
:rtype: `CrossCorrelationIndex`
"""
# --------- Prepare CrossCorrelationIndex and CrossCorrelationH5 objects for result ------------##
cross_corr_index = CrossCorrelationIndex()
cross_corr_h5_path = h5.path_for(self.storage_path, CrossCorrelationH5, cross_corr_index.gid)
cross_corr_h5 = CrossCorrelationH5(cross_corr_h5_path)
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()
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
small_ts.sample_period = ts_h5.sample_period.load()
small_ts.sample_period_unit = ts_h5.sample_period_unit.load()
partial_cross_corr = None
for var in range(self.input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cross_corr = self._compute_cross_correlation(small_ts, ts_h5)
cross_corr_h5.write_data_slice(partial_cross_corr)
partial_cross_corr.source.gid = view_model.time_series
partial_cross_corr.gid = uuid.UUID(cross_corr_index.gid)
cross_corr_index.fill_from_has_traits(partial_cross_corr)
self.fill_index_from_h5(cross_corr_index, cross_corr_h5)
cross_corr_h5.store(partial_cross_corr, scalars_only=True)
cross_corr_h5.close()
return cross_corr_index
def launch(self, view_model):
# type: (FFTAdapterModel) -> [FourierSpectrumIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the fourier spectrum for the specified time series
"""
block_size = int(math.floor(self.input_shape[2] / self.memory_factor))
blocks = int(math.ceil(self.input_shape[2] / block_size))
input_time_series_h5 = h5.h5_file_for_index(
self.input_time_series_index)
# --------------------- Prepare result entities ----------------------
fft_index = FourierSpectrumIndex()
dest_path = self.path_for(FourierSpectrumH5, fft_index.gid)
spectra_file = FourierSpectrumH5(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 = input_time_series_h5.sample_period.load()
small_ts.sample_period_unit = input_time_series_h5.sample_period_unit.load(
)
for block in range(blocks):
node_slice[2] = slice(
block * block_size,
min([(block + 1) * block_size, self.input_shape[2]]), 1)
small_ts.data = input_time_series_h5.read_data_slice(
tuple(node_slice))
partial_result = compute_fast_fourier_transform(
small_ts, view_model.segment_length,
view_model.window_function, view_model.detrend)
if blocks <= 1 and len(partial_result.array_data) == 0:
self.add_operation_additional_info(
"Fourier produced empty result (most probably due to a very short input TimeSeries)."
)
return None
spectra_file.write_data_slice(partial_result)
input_time_series_h5.close()
# ---------------------------- Fill results ----------------------------
partial_result.source.gid = view_model.time_series
partial_result.gid = uuid.UUID(fft_index.gid)
fft_index.fill_from_has_traits(partial_result)
self.fill_index_from_h5(fft_index, spectra_file)
spectra_file.store(partial_result, scalars_only=True)
spectra_file.windowing_function.store(view_model.window_function)
spectra_file.close()
self.log.debug("partial segment_length is %s" %
(str(partial_result.segment_length)))
return fft_index
def launch(self, view_model):
# type: (FFTAdapterModel) -> [FourierSpectrumIndex]
"""
Launch algorithm and build results.
:param time_series: the input time series to which the fft is to be applied
:param segment_length: the block size which determines the frequency resolution \
of the resulting power spectra
:param window_function: windowing functions can be applied before the FFT is performed
:type window_function: None; ‘hamming’; ‘bartlett’; ‘blackman’; ‘hanning’
:returns: the fourier spectrum for the specified time series
:rtype: `FourierSpectrumIndex`
"""
fft_index = FourierSpectrumIndex()
fft_index.fk_source_gid = view_model.time_series.hex
block_size = int(math.floor(self.input_shape[2] / self.memory_factor))
blocks = int(math.ceil(self.input_shape[2] / block_size))
input_time_series_h5 = h5.h5_file_for_index(
self.input_time_series_index)
dest_path = h5.path_for(self.storage_path, FourierSpectrumH5,
fft_index.gid)
spectra_file = FourierSpectrumH5(dest_path)
spectra_file.gid.store(uuid.UUID(fft_index.gid))
spectra_file.source.store(uuid.UUID(self.input_time_series_index.gid))
# ------------- 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 = input_time_series_h5.sample_period.load()
small_ts.sample_period_unit = input_time_series_h5.sample_period_unit.load(
)
for block in range(blocks):
node_slice[2] = slice(
block * block_size,
min([(block + 1) * block_size, self.input_shape[2]]), 1)
small_ts.data = input_time_series_h5.read_data_slice(
tuple(node_slice))
self.algorithm.time_series = small_ts
partial_result = self.algorithm.evaluate()
if blocks <= 1 and len(partial_result.array_data) == 0:
self.add_operation_additional_info(
"Fourier produced empty result (most probably due to a very short input TimeSeries)."
)
return None
spectra_file.write_data_slice(partial_result)
fft_index.ndim = len(spectra_file.array_data.shape)
input_time_series_h5.close()
fft_index.windowing_function = self.algorithm.window_function
fft_index.segment_length = self.algorithm.segment_length
fft_index.detrend = self.algorithm.detrend
fft_index.frequency_step = partial_result.freq_step
fft_index.max_frequency = partial_result.max_freq
spectra_file.segment_length.store(self.algorithm.segment_length)
spectra_file.windowing_function.store(
str(self.algorithm.window_function))
spectra_file.close()
self.log.debug("partial segment_length is %s" %
(str(partial_result.segment_length)))
return fft_index
