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