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 to download
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# documentation-folder for more details. See also http://www.thevirtualbrain.org
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# (c) 2012-2023, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# This program is free software: you can redistribute it and/or modify it under the
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# PARTICULAR PURPOSE. See the GNU General Public License for more details.
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"""
.. 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.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.core.entities.filters.chain import FilterChain
from tvb.adapters.datatypes.db.graph import ConnectivityMeasureIndex
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.core.utils import TVBJSONEncoder
from tvb.datatypes.graph import ConnectivityMeasure
[docs]class HistogramViewerModel(ViewModel):
input_data = DataTypeGidAttr(
linked_datatype=ConnectivityMeasure,
label='Connectivity Measure',
doc='A BCT computed measure for a Connectivity'
)
[docs]class HistogramViewer(ABCDisplayer):
"""
The viewer takes as input a result DataType as computed by BCT analyzers.
"""
_ui_name = "Histogram Visualizer"
[docs] def launch(self, view_model):
# type: (HistogramViewerModel) -> dict
"""
Prepare input data for display.
:param input_data: A BCT computed measure for a Connectivity
:type input_data: `ConnectivityMeasureIndex`
"""
params = self.prepare_parameters(view_model.input_data)
return self.build_display_result("histogram/view", params, pages=dict(controlPage="histogram/controls"))
[docs] def get_required_memory_size(self, view_model):
# type: (HistogramViewerModel) -> numpy.ndarray
"""
Return the required memory to run this algorithm.
"""
input_data = self.load_entity_by_gid(view_model.input_data)
return numpy.prod(input_data.shape) * 2
[docs] @staticmethod
def gather_params_dict(labels_list, values_list, title):
params = dict(title=title, labels=json.dumps(labels_list, cls=TVBJSONEncoder), isSingleMode=True,
data=json.dumps(values_list), colors=json.dumps(values_list),
xposition='center' if min(values_list) < 0 else 'bottom',
minColor=min(values_list), maxColor=max(values_list))
return params
[docs] def prepare_parameters(self, connectivity_measure_gid):
"""
Prepare all required parameters for a launch.
"""
conn_measure = self.load_with_references(connectivity_measure_gid)
assert isinstance(conn_measure, ConnectivityMeasure)
labels_list = conn_measure.connectivity.region_labels.tolist()
values_list = conn_measure.array_data.tolist()
# A gradient of colors will be used for each node
params = self.gather_params_dict(labels_list, values_list, "Connectivity Measure - " + conn_measure.title)
return params