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, 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):
"""
Launch algorithm and build results.
:param time_series: the input time series for which the correlation should be computed
:returns: the cross correlation for the given time series
:rtype: `CrossCorrelation`
"""
##--------- Prepare a CrossCorrelation object for result ------------##
cross_corr = CrossCorrelation(source=time_series,
storage_path=self.storage_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(use_storage=False)
small_ts.sample_period = time_series.sample_period
partial_cross_corr = 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_cross_corr = self.algorithm.evaluate()
cross_corr.write_data_slice(partial_cross_corr)
cross_corr.time = partial_cross_corr.time
cross_corr.labels_ordering[1] = time_series.labels_ordering[2]
cross_corr.labels_ordering[2] = time_series.labels_ordering[2]
cross_corr.close_file()
return cross_corr
def launch(self, time_series, dt=None, bold_model=None, RBM=None, neural_input_transformation=None):
"""
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`
"""
time_line = time_series.read_time_page(0, self.input_shape[0])
bold_signal = TimeSeriesRegion(storage_path=self.storage_path,
sample_period=time_series.sample_period,
start_time=time_series.start_time,
connectivity=time_series.connectivity)
##---------- 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(use_storage=False, sample_period=time_series.sample_period, time=time_line)
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_bold = self.algorithm.evaluate()
bold_signal.write_data_slice(partial_bold.data, grow_dimension=2)
bold_signal.write_time_slice(time_line)
bold_signal.close_file()
return bold_signal
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, 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, time_series, algorithms=None):
"""
Launch algorithm and build results.
:param time_series: the time series on which the algorithms are run
:param algorithms: the algorithms to be run for computing measures on the time series
:type algorithms: any subclass of BaseTimeseriesMetricAlgorithm (KuramotoIndex, \
GlobalVariance, VarianceNodeVariance)
:rtype: `DatatypeMeasure`
"""
if algorithms is None:
algorithms = self.available_algorithms.keys()
shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
metrics_results = {}
for algorithm_name in algorithms:
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [
slice(shape[0]),
slice(shape[1]),
slice(shape[2]),
slice(shape[3])
]
##---------- Iterate over slices and compose final result ------------##
unstored_ts = TimeSeries(use_storage=False)
unstored_ts.data = time_series.read_data_slice(tuple(node_slice))
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = self.available_algorithms[algorithm_name](
time_series=unstored_ts)
## Validate that current algorithm's filter is valid.
if (algorithm.accept_filter is not None and not algorithm.
accept_filter.get_python_filter_equivalent(time_series)):
LOG.warning(
'Measure algorithm will not be computed because of incompatibility on input. '
'Filters failed on algo: ' + str(algorithm_name))
continue
else:
LOG.debug("Applying measure: " + str(algorithm_name))
unstored_result = algorithm.evaluate()
##----------------- Prepare a Float object for result ----------------##
metrics_results[algorithm_name] = unstored_result
result = DatatypeMeasure(analyzed_datatype=time_series,
storage_path=self.storage_path,
data_name=self._ui_name,
metrics=metrics_results)
return result
def launch(self, time_series, algorithms=None, start_point=None, segment=None):
"""
Launch algorithm and build results.
:param time_series: the time series on which the algorithms are run
:param algorithms: the algorithms to be run for computing measures on the time series
:type algorithms: any subclass of BaseTimeseriesMetricAlgorithm
(KuramotoIndex, GlobalVariance, VarianceNodeVariance)
:rtype: `DatatypeMeasure`
"""
if algorithms is None:
algorithms = self.available_algorithms.keys()
shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
metrics_results = {}
for algorithm_name in algorithms:
##------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
node_slice = [slice(shape[0]), slice(shape[1]), slice(shape[2]), slice(shape[3])]
##---------- Iterate over slices and compose final result ------------##
unstored_ts = TimeSeries(use_storage=False)
unstored_ts.data = time_series.read_data_slice(tuple(node_slice))
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = self.available_algorithms[algorithm_name](time_series=unstored_ts)
if segment is not None:
algorithm.segment = segment
if start_point is not None:
algorithm.start_point = start_point
## Validate that current algorithm's filter is valid.
if (algorithm.accept_filter is not None and
not algorithm.accept_filter.get_python_filter_equivalent(time_series)):
LOG.warning('Measure algorithm will not be computed because of incompatibility on input. '
'Filters failed on algo: ' + str(algorithm_name))
continue
else:
LOG.debug("Applying measure: " + str(algorithm_name))
unstored_result = algorithm.evaluate()
##----------------- Prepare a Float object(s) for result ----------------##
if isinstance(unstored_result, dict):
metrics_results.update(unstored_result)
else:
metrics_results[algorithm_name] = unstored_result
result = DatatypeMeasure(analyzed_datatype=time_series, storage_path=self.storage_path,
data_name=self._ui_name, metrics=metrics_results)
return result
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
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):
# 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.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 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.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: (NodeCovarianceAdapterModel) -> [CovarianceIndex]
"""
Launch algorithm and build results.
:returns: the `CovarianceIndex` built with the given time_series index as source
"""
# Create an index for the computed covariance.
covariance_index = CovarianceIndex()
covariance_h5_path = h5.path_for(self.storage_path, CovarianceH5,
covariance_index.gid)
covariance_h5 = CovarianceH5(covariance_h5_path)
# NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries()
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cov = self._compute_node_covariance(
small_ts, ts_h5)
covariance_h5.write_data_slice(partial_cov.array_data)
array_metadata = covariance_h5.array_data.get_cached_metadata()
covariance_index.fk_source_gid = self.input_time_series_index.gid
covariance_index.subtype = type(covariance_index).__name__
covariance_index.array_has_complex = array_metadata.has_complex
if not covariance_index.array_has_complex:
covariance_index.array_data_min = float(array_metadata.min)
covariance_index.array_data_max = float(array_metadata.max)
covariance_index.array_data_mean = float(array_metadata.mean)
covariance_index.array_is_finite = array_metadata.is_finite
covariance_index.shape = json.dumps(covariance_h5.array_data.shape)
covariance_index.ndim = len(covariance_h5.array_data.shape)
# TODO write this part better, by moving into the Model fill_from...
covariance_h5.gid.store(uuid.UUID(covariance_index.gid))
covariance_h5.source.store(view_model.time_series)
covariance_h5.close()
return covariance_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 launch(self, view_model):
# type: (PearsonCorrelationCoefficientAdapterModel) -> [CorrelationCoefficientsIndex]
"""
Launch algorithm and build results.
Compute the node-pairwise pearson correlation coefficient of the given input 4D TimeSeries datatype.
The result will contain values between -1 and 1, inclusive.
:param time_series: the input time-series for which correlation coefficient should be computed
:param t_start: the physical time interval start for the analysis
:param t_end: physical time, interval end
:returns: the correlation coefficient for the given time series
:rtype: `CorrelationCoefficients`
"""
with h5.h5_file_for_index(self.input_time_series_index) as ts_h5:
ts_labels_ordering = ts_h5.labels_ordering.load()
result = self._compute_correlation_coefficients(ts_h5, view_model.t_start, view_model.t_end)
if isinstance(self.input_time_series_index, TimeSeriesEEGIndex) \
or isinstance(self.input_time_series_index, TimeSeriesMEGIndex) \
or isinstance(self.input_time_series_index, TimeSeriesSEEGIndex):
labels_ordering = ["Sensor", "Sensor", "1", "1"]
else:
labels_ordering = list(CorrelationCoefficients.labels_ordering.default)
labels_ordering[0] = ts_labels_ordering[2]
labels_ordering[1] = ts_labels_ordering[2]
corr_coef = CorrelationCoefficients()
corr_coef.array_data = result
corr_coef.source = TimeSeries(gid=view_model.time_series)
corr_coef.labels_ordering = labels_ordering
return h5.store_complete(corr_coef, self.storage_path)
def test_store_load_region_mapping(session, connectivity_factory,
surface_factory, region_mapping_factory,
sensors_factory):
connectivity = connectivity_factory(2)
conn_idx = ConnectivityIndex()
conn_idx.fill_from_has_traits(connectivity)
session.add(conn_idx)
surface = surface_factory(5)
surf_idx = SurfaceIndex()
surf_idx.fill_from_has_traits(surface)
session.add(surf_idx)
region_mapping = region_mapping_factory(surface, connectivity)
rm_idx = RegionMappingIndex()
rm_idx.fill_from_has_traits(region_mapping)
rm_idx.connectivity = conn_idx
rm_idx.surface = surf_idx
session.add(rm_idx)
sensors = sensors_factory("SEEG", 3)
sensors_seeg_idx = SensorsIndex()
sensors_seeg_idx.fill_from_has_traits(sensors)
session.add(sensors_seeg_idx)
sensors_eeg = sensors_factory("EEG", 3)
sensors_eeg_idx = SensorsIndex()
sensors_eeg_idx.fill_from_has_traits(sensors_eeg)
session.add(sensors_eeg_idx)
time_series = TimeSeries(data=numpy.arange(5))
fcd = Fcd(
array_data=numpy.arange(5),
source=time_series,
)
ts_index = TimeSeriesIndex()
ts_index.fill_from_has_traits(time_series)
session.add(ts_index)
fcd_index = FcdIndex()
fcd_index.fill_from_has_traits(fcd)
fcd_index.source = ts_index
session.add(fcd_index)
session.commit()
res = session.query(ConnectivityIndex)
assert res.count() == 1
assert res[0].number_of_regions == 2
assert res[0].number_of_connections == 4
assert res[0].undirected is True
assert res[0].weights_min == 0
res = session.query(SurfaceIndex)
assert res.count() == 1
res = session.query(RegionMappingIndex)
assert res.count() == 1
def _import(self, import_file_path=None):
"""
This method is used for importing data in NIFIT format
:param import_file_path: absolute path of the file to be imported
"""
### Retrieve Adapter instance
group = dao.find_group('tvb.adapters.uploaders.nifti_importer',
'NIFTIImporter')
importer = ABCAdapter.build_adapter(group)
importer.meta_data = {
DataTypeMetaData.KEY_SUBJECT: "",
DataTypeMetaData.KEY_STATE: "RAW"
}
args = {'data_file': import_file_path}
### Launch import Operation
FlowService().fire_operation(importer, self.test_user,
self.test_project.id, **args)
time_series = TimeSeries()
data_types = FlowService().get_available_datatypes(
self.test_project.id, time_series.module + "." + time_series.type)
self.assertEqual(1, len(data_types),
"Project should contain only one data type.")
time_series = ABCAdapter.load_entity_by_gid(data_types[0][2])
self.assertTrue(time_series is not None,
"TimeSeries should not be none")
return time_series
def build():
ts_index = time_series_factory()
ts_h5 = h5_file_for_index(ts_index)
ts = TimeSeries()
ts_h5.load_into(ts)
ts_h5.close()
data_shape = ts.data.shape
result_shape = (data_shape[2], data_shape[2], data_shape[1],
data_shape[3])
result = numpy.zeros(result_shape)
for mode in range(data_shape[3]):
for var in range(data_shape[1]):
data = ts_h5.data[:, var, :, mode]
data = data - data.mean(axis=0)[numpy.newaxis, 0]
result[:, :, var, mode] = numpy.cov(data.T)
covariance = Covariance(source=ts, array_data=result)
op = operation_factory()
covariance_db = CovarianceIndex()
covariance_db.fk_from_operation = op.id
covariance_db.fill_from_has_traits(covariance)
covariance_h5_path = h5.path_for_stored_index(covariance_db)
with TimeSeriesH5(covariance_h5_path) as f:
f.store(ts)
session.add(covariance_db)
session.commit()
return covariance_db
def create_time_series(self,
storage_path,
connectivity=None,
surface=None,
region_map=None,
region_volume_map=None):
"""
Create a time series instance that will be populated by this monitor
:param surface: if present a TimeSeriesSurface is returned
:param connectivity: if present a TimeSeriesRegion is returned
Otherwise a plain TimeSeries will be returned
"""
if surface is not None:
return TimeSeriesSurface(storage_path=storage_path,
surface=surface,
sample_period=self.period,
title='Surface ' +
self.__class__.__name__,
**self._transform_user_tags())
if connectivity is not None:
return TimeSeriesRegion(storage_path=storage_path,
connectivity=connectivity,
region_mapping=region_map,
region_mapping_volume=region_volume_map,
sample_period=self.period,
title='Regions ' + self.__class__.__name__,
**self._transform_user_tags())
return TimeSeries(storage_path=storage_path,
sample_period=self.period,
title=' ' + self.__class__.__name__,
**self._transform_user_tags())
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 create_ICA(self, timeseries):
"""
:returns: persisted entity IndependentComponents
"""
operation, _, storage_path = self.__create_operation()
partial_ts = TimeSeries(use_storage=False)
partial_ts.data = numpy.random.random((10, 10, 10, 10))
partial_ica = IndependentComponents(source=partial_ts,
component_time_series=numpy.random.random((10, 10, 10, 10)),
prewhitening_matrix=numpy.random.random((10, 10, 10, 10)),
unmixing_matrix=numpy.random.random((10, 10, 10, 10)),
n_components=10, use_storage=False)
ica = IndependentComponents(source=timeseries, n_components=10, storage_path=storage_path)
ica.write_data_slice(partial_ica)
adapter_instance = StoreAdapter([ica])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return ica
def create_ICA(self, timeseries):
"""
:returns: persisted entity IndependentComponents
"""
operation, _, storage_path = self.__create_operation()
partial_ts = TimeSeries(use_storage=False)
partial_ts.data = numpy.random.random((10, 10, 10, 10))
partial_ica = IndependentComponents(source=partial_ts,
component_time_series=numpy.random.random((10, 10, 10, 10)),
prewhitening_matrix=numpy.random.random((10, 10, 10, 10)),
unmixing_matrix=numpy.random.random((10, 10, 10, 10)),
n_components=10, use_storage=False)
ica = IndependentComponents(source=timeseries, n_components=10, storage_path=storage_path)
ica.write_data_slice(partial_ica)
adapter_instance = StoreAdapter([ica])
OperationService().initiate_prelaunch(operation, adapter_instance, {})
return ica
def launch(self, view_model):
# type: (FooDataImporterModel) -> TimeSeriesIndex
array_data = numpy.loadtxt(view_model.array_data)
ts = TimeSeries(data=array_data)
ts.configure()
ts_index = TimeSeriesIndex()
ts_index.fill_from_has_traits(ts)
ts_h5_path = h5.path_for(self.storage_path, TimeSeriesH5, ts_index.gid)
with TimeSeriesH5(ts_h5_path) as ts_h5:
ts_h5.store(ts, scalars_only=True)
ts_h5.store_generic_attributes(GenericAttributes())
ts_h5.write_data_slice(array_data)
return ts_index
def plot_timeseries_interactive(self, timeseries, first_n=-1, **kwargs):
if isinstance(timeseries, TimeSeries):
self.plot_tvb_timeseries_interactive(timeseries, first_n, **kwargs)
elif isinstance(timeseries, Timeseries):
self.plot_tvb_timeseries_interactive(timeseries._tvb, first_n, **kwargs)
elif isinstance(timeseries, numpy.ndarray):
self.plot_tvb_timeseries_interactive(Timeseries(data=timeseries), first_n, **kwargs)
elif isinstance(timeseries, (list, tuple)):
self.plot_tvb_timeseries_interactive(Timeseries(data=TimeSeries(data=numpy.stack(timeseries, axis=1))),
first_n, **kwargs)
elif isinstance(timeseries, dict):
ts = numpy.stack(timeseries.values(), axis=1)
timeseries = TimeSeries(data=ts, labels_dimensions={"State Variable": timeseries.keys()})
self.plot_tvb_timeseries_interactive(timeseries, first_n, **kwargs)
else:
raise_value_error("Input timeseries: %s \n" "is not on of one of the following types: "
"[Timeseries (tvb-scripts), TimeSeries (TVB), numpy.ndarray, dict, list, tuple]" %
str(timeseries))
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, 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 _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, 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, 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
def launch(self, view_model):
# type: (ICAAdapterModel) -> [IndependentComponentsIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the ica index for the specified time series
"""
# --------------------- Prepare result entities ---------------------##
ica_index = IndependentComponentsIndex()
result_path = h5.path_for(self.storage_path, IndependentComponentsH5,
ica_index.gid)
ica_h5 = IndependentComponentsH5(path=result_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()
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_ica = compute_ica_decomposition(small_ts,
view_model.n_components)
ica_h5.write_data_slice(partial_ica)
time_series_h5.close()
partial_ica.source.gid = view_model.time_series
partial_ica.gid = uuid.UUID(ica_index.gid)
ica_h5.store(partial_ica, scalars_only=True)
ica_h5.close()
ica_index.fill_from_has_traits(partial_ica)
return ica_index
def launch(self, view_model):
# type: (ICAAdapterModel) -> [IndependentComponentsIndex]
"""
Launch algorithm and build results.
"""
# --------- Prepare a IndependentComponents object for result ----------##
ica_index = IndependentComponentsIndex()
ica_index.fk_source_gid = view_model.time_series.hex
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
result_path = h5.path_for(self.storage_path, IndependentComponentsH5,
ica_index.gid)
ica_h5 = IndependentComponentsH5(path=result_path)
ica_h5.gid.store(uuid.UUID(ica_index.gid))
ica_h5.source.store(view_model.time_series)
ica_h5.n_components.store(self.algorithm.n_components)
# ------------- 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()
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_ica = self.algorithm.evaluate()
ica_h5.write_data_slice(partial_ica)
array_metadata = ica_h5.unmixing_matrix.get_cached_metadata()
ica_index.array_has_complex = array_metadata.has_complex
ica_index.shape = json.dumps(ica_h5.unmixing_matrix.shape)
ica_index.ndim = len(ica_h5.unmixing_matrix.shape)
ica_h5.close()
time_series_h5.close()
return ica_index
def build(data=None):
time = numpy.linspace(0, 1000, 4000)
if data is None:
data = numpy.zeros((time.size, 1, 3, 1))
data[:, 0, 0, 0] = numpy.sin(2 * numpy.pi * time / 1000.0 * 40)
data[:, 0, 1, 0] = numpy.sin(2 * numpy.pi * time / 1000.0 * 200)
data[:, 0, 2, 0] = numpy.sin(2 * numpy.pi * time / 1000.0 * 100) + numpy.sin(
2 * numpy.pi * time / 1000.0 * 300)
return TimeSeries(time=time, data=data, sample_period=1.0 / 4000, sample_period_unit="sec")
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `CovarianceIndex` built with the given time_series index as source
"""
# Create an index for the computed covariance.
covariance_index = CovarianceIndex()
covariance_h5_path = h5.path_for(self.storage_path, CovarianceH5,
covariance_index.gid)
covariance_h5 = CovarianceH5(covariance_h5_path)
# NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
with h5.h5_file_for_index(time_series) as ts_h5:
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries()
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cov = self._compute_node_covariance(
small_ts, ts_h5)
covariance_h5.write_data_slice(partial_cov.array_data)
ts_array_metadata = covariance_h5.array_data.get_cached_metadata()
covariance_index.source_gid = time_series.gid
covariance_index.subtype = type(covariance_index).__name__
covariance_index.array_data_min = ts_array_metadata.min
covariance_index.array_data_max = ts_array_metadata.max
covariance_index.array_data_mean = ts_array_metadata.mean
covariance_index.ndim = len(covariance_h5.array_data.shape)
covariance_h5.gid.store(uuid.UUID(covariance_index.gid))
covariance_h5.source.store(uuid.UUID(time_series.gid))
covariance_h5.close()
return covariance_index
def launch(self, view_model):
# type: (PCAAdapterModel) -> [PrincipalComponentsIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the `PrincipalComponentsIndex` object built with the given timeseries as source
"""
# --------------------- Prepare result entities ----------------------##
principal_components_index = PrincipalComponentsIndex()
dest_path = h5.path_for(self.storage_path, PrincipalComponentsH5,
principal_components_index.gid)
pca_h5 = PrincipalComponentsH5(path=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()
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.time_series = small_ts.gid
partial_pca = compute_pca(small_ts)
pca_h5.write_data_slice(partial_pca)
time_series_h5.close()
partial_pca.source.gid = view_model.time_series
partial_pca.gid = uuid.UUID(principal_components_index.gid)
principal_components_index.fill_from_has_traits(partial_pca)
pca_h5.store(partial_pca, scalars_only=True)
pca_h5.close()
return principal_components_index
def launch(self, array_data):
array_data = numpy.loadtxt(array_data)
ts = TimeSeries()
ts.storage_path = self.storage_path
#ts.configure()
ts.write_data_slice(array_data)
ts.close_file()
return ts
def launch(self,
time_series,
dt=None,
bold_model=None,
RBM=None,
neural_input_transformation=None):
"""
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`
"""
time_line = time_series.read_time_page(0, self.input_shape[0])
bold_signal = TimeSeriesRegion(storage_path=self.storage_path,
sample_period=time_series.sample_period,
start_time=time_series.start_time,
connectivity=time_series.connectivity)
##---------- 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(use_storage=False,
sample_period=time_series.sample_period,
time=time_line)
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_bold = self.algorithm.evaluate()
bold_signal.write_data_slice(partial_bold.data, grow_dimension=2)
bold_signal.write_time_slice(time_line)
bold_signal.close_file()
return bold_signal
def launch(self, view_model):
# type: (NodeCovarianceAdapterModel) -> [CovarianceIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the `CovarianceIndex` built with the given time_series index as source
"""
# -------------------- Prepare result entities ---------------------##
covariance_index = CovarianceIndex()
covariance_h5_path = h5.path_for(self.storage_path, CovarianceH5,
covariance_index.gid)
covariance_h5 = CovarianceH5(covariance_h5_path)
# ------------ NOTE: Assumes 4D, Simulator timeSeries -------------##
node_slice = [
slice(self.input_shape[0]), None,
slice(self.input_shape[2]), None
]
ts_h5 = h5.h5_file_for_index(self.input_time_series_index)
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries()
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cov = self._compute_node_covariance(small_ts, ts_h5)
covariance_h5.write_data_slice(partial_cov.array_data)
ts_h5.close()
partial_cov.source.gid = view_model.time_series
partial_cov.gid = uuid.UUID(covariance_index.gid)
covariance_index.fill_from_has_traits(partial_cov)
self.fill_index_from_h5(covariance_index, covariance_h5)
covariance_h5.store(partial_cov, scalars_only=True)
covariance_h5.close()
return covariance_index
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `PrincipalComponents` object built with the given timeseries as source
"""
##--------- Prepare a PrincipalComponents object for result ----------##
pca_result = PrincipalComponents(source=time_series, 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)
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_pca = self.algorithm.evaluate()
pca_result.write_data_slice(partial_pca)
pca_result.close_file()
return pca_result
def launch(self, time_series, n_components=None):
"""
Launch algorithm and build results.
"""
##--------- Prepare a IndependentComponents object for result ----------##
ica_result = IndependentComponents(source=time_series,
n_components=int(self.algorithm.n_components),
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)
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_ica = self.algorithm.evaluate()
ica_result.write_data_slice(partial_ica)
ica_result.close_file()
return ica_result
def launch(self, time_series):
"""
Launch algorithm and build results.
:returns: the `Covariance` built with the given timeseries as source
"""
#Create a FourierSpectrum dataType object.
covariance = Covariance(source=time_series, storage_path=self.storage_path)
#NOTE: Assumes 4D, Simulator timeSeries.
node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), None]
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries(use_storage=False)
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = time_series.read_data_slice(tuple(node_slice))
self.algorithm.time_series = small_ts
partial_cov = self.algorithm.evaluate()
covariance.write_data_slice(partial_cov.array_data)
covariance.close_file()
return covariance
def launch(self, array_data):
array_data = numpy.loadtxt(array_data)
ts = TimeSeries()
ts.storage_path = self.storage_path
#ts.configure()
ts.write_data_slice(array_data)
ts.close_file()
return ts
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