The Virtual Brain Project

Source code for tvb.datatypes.graph

# -*- coding: utf-8 -*-
#
#
#  TheVirtualBrain-Scientific Package. This package holds all simulators, and 
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2017, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# 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,
# either version 3 of the License, or (at your option) any later version.
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# PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program.  If not, see <http://www.gnu.org/licenses/>.
#
#
#   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)
#
#

"""

The Graph datatypes. This brings together the scientific and framework methods
that are associated with the Graph datatypes.

.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
.. moduleauthor:: Paula Sanz Leon <paula.sanz-leon@univ-amu.fr>

"""

from tvb.basic.traits import core, types_basic as basic
from tvb.basic.logger.builder import get_logger
from tvb.datatypes import arrays, time_series, connectivity

LOG = get_logger(__name__)


[docs]class Covariance(arrays.MappedArray): """Covariance datatype.""" array_data = arrays.ComplexArray(file_storage=core.FILE_STORAGE_EXPAND) source = time_series.TimeSeries( label="Source time-series", doc="Links to the time-series on which NodeCovariance is applied.") __generate_table__ = True def configure(self): """After populating few fields, compute the rest of the fields""" # Do not call super, because that accesses data not-chunked self.nr_dimensions = len(self.read_data_shape()) for i in range(self.nr_dimensions): setattr(self, 'length_%dd' % (i + 1), int(self.read_data_shape()[i])) def write_data_slice(self, partial_result): """ Append chunk. """ self.store_data_chunk('array_data', partial_result, grow_dimension=2, close_file=False) def _find_summary_info(self): summary = {"Graph type": self.__class__.__name__, "Source": self.source.title} summary.update(self.get_info_about_array('array_data')) return summary
[docs]class CorrelationCoefficients(arrays.MappedArray): """Correlation coefficients datatype.""" # Extreme values for pearson Correlation Coefficients PEARSON_MIN = -1 PEARSON_MAX = 1 array_data = arrays.FloatArray(file_storage=core.FILE_STORAGE_DEFAULT) source = time_series.TimeSeries( label="Source time-series", doc="Links to the time-series on which Correlation (coefficients) is applied.") labels_ordering = basic.List( label="Dimension Names", default=["Node", "Node", "State Variable", "Mode"], doc="""List of strings representing names of each data dimension""") __generate_table__ = True def configure(self): """After populating few fields, compute the rest of the fields""" # Do not call super, because that accesses data not-chunked self.nr_dimensions = len(self.read_data_shape()) for i in range(self.nr_dimensions): setattr(self, 'length_%dd' % (i + 1), int(self.read_data_shape()[i])) def _find_summary_info(self): summary = {"Graph type": self.__class__.__name__, "Source": self.source.title, "Dimensions": self.labels_ordering} summary.update(self.get_info_about_array('array_data')) return summary def get_correlation_data(self, selected_state, selected_mode): matrix_to_display = self.array_data[:, :, int(selected_state), int(selected_mode)] return list(matrix_to_display.flat)
[docs]class ConnectivityMeasure(arrays.MappedArray): """Measurement of based on a connectivity.""" connectivity = connectivity.Connectivity def _find_summary_info(self): summary = {"Graph type": self.__class__.__name__} # summary["Source"] = self.connectivity.title summary.update(self.get_info_about_array('array_data')) return summary __generate_table__ = True