The Virtual Brain Project

Source code for tvb.adapters.analyzers.node_covariance_adapter

# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and 
# Web-UI helpful to run brain-simulations. To use it, you also need do download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
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# (c) 2012-2017, Baycrest Centre for Geriatric Care ("Baycrest") and others
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# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
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# program.  If not, see <http://www.gnu.org/licenses/>.
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#   CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
#   Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
#       The Virtual Brain: a simulator of primate brain network dynamics.
#   Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
#
#

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

.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>

"""

import numpy
from tvb.analyzers.node_covariance import NodeCovariance
from tvb.core.adapters.abcadapter import ABCAsynchronous
from tvb.datatypes.time_series import TimeSeries
from tvb.datatypes.graph import Covariance
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 NodeCovarianceAdapter(ABCAsynchronous): """ TVB adapter for calling the NodeCovariance algorithm. """ _ui_name = "Temporal covariance of nodes" _ui_description = "Compute Temporal Node Covariance for a TimeSeries input DataType." _ui_subsection = "covariance"
[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 = NodeCovariance() 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
[docs] def get_output(self): return [Covariance]
[docs] 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 = NodeCovariance()
[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], 1) input_size = numpy.prod(used_shape) * 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], 1) return self.array_size2kb(self.algorithm.result_size(used_shape))
[docs] 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