The Virtual Brain Project

Source code for tvb.adapters.visualizers.histogram

# -*- 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
#
# (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.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# 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)
#
#

"""
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Ionel Ortelecan <ionel.ortelecan@codemart.ro>
.. moduleauthor:: Bogdan Neacsa <bogdan.neacsa@codemart.ro>
"""

import json
import numpy
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.basic.filters.chain import FilterChain
from tvb.datatypes.graph import ConnectivityMeasure



[docs]class HistogramViewer(ABCDisplayer): """ The viewer takes as input a result DataType as computed by BCT analyzers. """ _ui_name = "Connectivity Measure Visualizer"
[docs] def get_input_tree(self): return [{'name': 'input_data', 'type': ConnectivityMeasure, 'label': 'Connectivity Measure', 'required': True, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'A BCT computed measure for a Connectivity'}]
[docs] def launch(self, input_data): """ Prepare input data for display. :param input_data: A BCT computed measure for a Connectivity :type input_data: `ConnectivityMeasure` """ params = self.prepare_parameters(input_data) return self.build_display_result("histogram/view", params, pages=dict(controlPage="histogram/controls"))
[docs] def get_required_memory_size(self, input_data, figure_size): """ Return the required memory to run this algorithm. """ return numpy.prod(input_data.shape) * 2
[docs] def generate_preview(self, input_data, figure_size): """ The preview for the burst page. """ params = self.prepare_parameters(input_data) return self.build_display_result("histogram/view", params)
[docs] def prepare_parameters(self, input_data): """ Prepare all required parameters for a launch. """ labels_list = input_data.connectivity.region_labels.tolist() values_list = input_data.array_data.tolist() # A gradient of colors will be used for each node colors_list = values_list params = dict(title="Connectivity Measure - " + input_data.title, labels=json.dumps(labels_list), data=json.dumps(values_list), colors=json.dumps(colors_list), xposition='center' if min(values_list) < 0 else 'bottom', minColor=min(colors_list), maxColor=max(colors_list)) return params