def configure(self, time_series, nfft=None):
"""
Store the input shape to be later used to estimate memory usage.
Also create the algorithm instance.
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = NodeCoherence()
if nfft is not None:
self.algorithm.nfft = nfft
def configure(self, time_series, nfft=None):
"""
Store the input shape to be later used to estimate memory usage.
Also create the algorithm instance.
"""
self.input_time_series_index = time_series
self.input_shape = (self.input_time_series_index.data_length_1d,
self.input_time_series_index.data_length_2d,
self.input_time_series_index.data_length_3d,
self.input_time_series_index.data_length_4d)
LOG.debug("Time series shape is %s" % str(self.input_shape))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = NodeCoherence()
if nfft is not None:
self.algorithm.nfft = nfft
def configure(self, view_model):
# type: (NodeCoherenceModel) -> None
"""
Store the input shape to be later used to estimate memory usage.
Also create the algorithm instance.
"""
self.input_time_series_index = self.load_entity_by_gid(view_model.time_series.hex)
self.input_shape = (self.input_time_series_index.data_length_1d,
self.input_time_series_index.data_length_2d,
self.input_time_series_index.data_length_3d,
self.input_time_series_index.data_length_4d)
self.log.debug("Time series shape is %s" % str(self.input_shape))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = NodeCoherence()
if view_model.nfft is not None:
self.algorithm.nfft = view_model.nfft
def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
algorithm = NodeCoherence()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
for node in tree:
if node['name'] == 'time_series':
node['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
class NodeCoherenceAdapter(ABCAsynchronous):
""" TVB adapter for calling the NodeCoherence algorithm. """
_ui_name = "Cross coherence of nodes"
_ui_description = "Compute Node Coherence for a TimeSeries input DataType."
_ui_subsection = "coherence"
def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
algorithm = NodeCoherence()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
for node in tree:
if node['name'] == 'time_series':
node['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
def get_output(self):
return [CoherenceSpectrum]
def configure(self, time_series, nfft=None):
"""
Store the input shape to be later used to estimate memory usage.
Also create the algorithm instance.
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = NodeCoherence()
if nfft is not None:
self.algorithm.nfft = nfft
def get_required_memory_size(self, **kwargs):
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], self.input_shape[3])
input_size = numpy.prod(used_shape) * 8.0
output_size = self.algorithm.result_size(used_shape)
return input_size + output_size
def get_required_disk_size(self, **kwargs):
"""
Returns the required disk size to be able to run the adapter (in kB).
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], self.input_shape[3])
return self.array_size2kb(self.algorithm.result_size(used_shape))
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
class NodeCoherenceAdapter(ABCAdapter):
""" TVB adapter for calling the NodeCoherence algorithm. """
_ui_name = "Cross coherence of nodes"
_ui_description = "Compute Node Coherence for a TimeSeries input DataType."
_ui_subsection = "coherence"
def get_form_class(self):
return NodeCoherenceForm
def get_output(self):
return [CoherenceSpectrumIndex]
def configure(self, view_model):
# type: (NodeCoherenceModel) -> None
"""
Store the input shape to be later used to estimate memory usage.
Also create the algorithm instance.
"""
self.input_time_series_index = self.load_entity_by_gid(
view_model.time_series)
self.input_shape = (self.input_time_series_index.data_length_1d,
self.input_time_series_index.data_length_2d,
self.input_time_series_index.data_length_3d,
self.input_time_series_index.data_length_4d)
self.log.debug("Time series shape is %s" % str(self.input_shape))
# -------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = NodeCoherence()
if view_model.nfft is not None:
self.algorithm.nfft = view_model.nfft
def get_required_memory_size(self, view_model):
# type: (NodeCoherenceModel) -> int
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2],
self.input_shape[3])
input_size = numpy.prod(used_shape) * 8.0
output_size = self.algorithm.result_size(used_shape)
return input_size + output_size
def get_required_disk_size(self, view_model):
# type: (NodeCoherenceModel) -> int
"""
Returns the required disk size to be able to run the adapter (in kB).
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2],
self.input_shape[3])
return self.array_size2kb(self.algorithm.result_size(used_shape))
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 get_traited_datatype(self):
return NodeCoherence()
