The Virtual Brain Project

Source code for tvb.adapters.analyzers.fmri_balloon_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
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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:
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#   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 BalloonModel Analyzer.

.. moduleauthor:: Paula Sanz Leon <Paula@tvb.invalid>

"""

import numpy
from tvb.analyzers.fmri_balloon import BalloonModel
from tvb.datatypes.time_series import TimeSeries
from tvb.datatypes.time_series import TimeSeriesRegion
from tvb.core.adapters.abcadapter import ABCAsynchronous
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 BalloonModelAdapter(ABCAsynchronous): """ TVB adapter for calling the BalloonModel algorithm. """ _ui_name = "Balloon Model " _ui_description = "Compute BOLD signals for a TimeSeries input DataType." _ui_subsection = "balloon"
[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 current analysis. """ algorithm = BalloonModel() 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 [TimeSeriesRegion]
[docs] def configure(self, time_series, dt=None, bold_model=None, RBM=None, neural_input_transformation=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 = BalloonModel() if dt is not None: algorithm.dt = dt else: algorithm.dt = time_series.sample_period / 1000. if bold_model is not None: algorithm.bold_model = bold_model if RBM is not None: algorithm.RBM = RBM if neural_input_transformation is not None: algorithm.neural_input_transformation = neural_input_transformation self.algorithm = algorithm self.algorithm.time_series = time_series
[docs] def get_required_memory_size(self, **kwargs): """ Return the required memory to run this algorithm. """ used_shape = self.input_shape 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], self.input_shape[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, dt=None, bold_model=None, RBM=None, neural_input_transformation=None): """ Launch algorithm and build results. :param time_series: the input time-series used as neural activation in the Balloon Model :returns: the simulated BOLD signal :rtype: `TimeSeries` """ time_line = time_series.read_time_page(0, self.input_shape[0]) bold_signal = TimeSeriesRegion(storage_path=self.storage_path, sample_period=time_series.sample_period, start_time=time_series.start_time, connectivity=time_series.connectivity) ##---------- Iterate over slices and compose final result ------------## node_slice = [slice(self.input_shape[0]), slice(self.input_shape[1]), None, slice(self.input_shape[3])] small_ts = TimeSeries(use_storage=False, sample_period=time_series.sample_period, time=time_line) for node in range(self.input_shape[2]): node_slice[2] = slice(node, node + 1) small_ts.data = time_series.read_data_slice(tuple(node_slice)) self.algorithm.time_series = small_ts partial_bold = self.algorithm.evaluate() bold_signal.write_data_slice(partial_bold.data, grow_dimension=2) bold_signal.write_time_slice(time_line) bold_signal.close_file() return bold_signal