def configure(self, time_series):
"""
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 = CrossCorrelate()
def configure(self, time_series):
"""
Store the input shape to be later used to estimate memory usage. Also create the algorithm instance.
:param time_series: the input time-series for which cross correlation should be computed
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = CrossCorrelate()
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 = CrossCorrelate()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
tree[0]['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
class CrossCorrelateAdapter(ABCAsynchronous):
""" TVB adapter for calling the CrossCorrelate algorithm. """
_ui_name = "Cross-correlation of nodes"
_ui_description = "Cross-correlate two one-dimensional arrays."
_ui_subsection = "crosscorr"
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 = CrossCorrelate()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
tree[0]['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
def get_output(self):
return [CrossCorrelation]
def configure(self, time_series):
"""
Store the input shape to be later used to estimate memory usage. Also create the algorithm instance.
:param time_series: the input time-series for which cross correlation should be computed
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
self.algorithm = CrossCorrelate()
def get_required_memory_size(self, **kwargs):
"""
Returns the required memory to be able to run the adapter.
"""
#Not all the data is loaded into memory at one time here.
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):
"""
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
