The Virtual Brain Project

Source code for tvb.adapters.analyzers.ica_adapter

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Adapter that uses the traits module to generate interfaces for ICA Analyzer.

.. moduleauthor:: Paula Sanz Leon


import numpy
from tvb.analyzers.ica import fastICA
from tvb.core.adapters.abcadapter import ABCAsynchronous
from tvb.datatypes.time_series import TimeSeries
from tvb.datatypes.mode_decompositions import IndependentComponents
from tvb.basic.traits.util import log_debug_array
from tvb.basic.filters.chain import FilterChain
from tvb.basic.logger.builder import get_logger
LOG = get_logger(__name__)

[docs]class ICAAdapter(ABCAsynchronous): """ TVB adapter for calling the ICA algorithm. """ _ui_name = "Independent Component Analysis" _ui_description = "ICA for a TimeSeries input DataType." _ui_subsection = "ica"
[docs] 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 = fastICA() 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
[docs] def get_output(self): return [IndependentComponents]
[docs] def configure(self, time_series, n_components=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 -------------------## algorithm = fastICA() if n_components is not None: algorithm.n_components = n_components else: ## It will only work for Simulator results. algorithm.n_components = self.input_shape[2] self.algorithm = algorithm
[docs] 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 = * 8.0 output_size = self.algorithm.result_size(used_shape) return input_size + output_size
[docs] 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))
[docs] 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) = 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