The Virtual Brain Project

Source code for tvb.adapters.visualizers.pearson_cross_correlation

# -*- coding: utf-8 -*-
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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)
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"""
.. moduleauthor:: Dan Pop <dan.pop@codemart.ro>
.. moduleauthor:: Paula Sanz Leon <Paula@tvb.invalid>

"""

import json
import numpy
from tvb.adapters.visualizers.matrix_viewer import MappedArrayVisualizer
from tvb.datatypes.graph import CorrelationCoefficients


[docs]class PearsonCorrelationCoefficientVisualizer(MappedArrayVisualizer): """ Viewer for Pearson CorrelationCoefficients. Very similar to the CrossCorrelationVisualizer - this one done with Matplotlib """ _ui_name = "Pearson Correlation Coefficients" _ui_subsection = "correlation_pearson"
[docs] def get_input_tree(self): """ Inform caller of the data we need as input """ return [{"name": "datatype", "type": CorrelationCoefficients, "label": "Correlation Coefficients", "required": True}]
[docs] def get_required_memory_size(self, datatype): """Return required memory.""" input_size = datatype.read_data_shape() return numpy.prod(input_size) * 8.0
[docs] def launch(self, datatype): """Construct data for visualization and launch it.""" matrix_shape = datatype.array_data.shape[0:2] parent_ts = datatype.source parent_ts = self.load_entity_by_gid(parent_ts.gid) labels = parent_ts.get_space_labels() state_list = datatype.source.labels_dimensions.get(datatype.source.labels_ordering[1], []) mode_list = range(datatype.source._length_4d) if not labels: labels = None pars = dict(matrix_labels=json.dumps([labels, labels]), matrix_shape=json.dumps(matrix_shape), viewer_title='Cross Corelation Matrix plot', url_base=MappedArrayVisualizer.paths2url(datatype, "get_correlation_data", parameter=""), state_variable=state_list[0], mode=mode_list[0], state_list=state_list, mode_list=mode_list, pearson_min=CorrelationCoefficients.PEARSON_MIN, pearson_max=CorrelationCoefficients.PEARSON_MAX) return self.build_display_result("pearson_correlation/view", pars)