Source code for tvb.adapters.visualizers.pearson_cross_correlation

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.. moduleauthor:: Paula Popa <>
.. moduleauthor:: Dan Pop <>
.. moduleauthor:: Paula Sanz Leon <Paula@tvb.invalid>


import json
from tvb.adapters.datatypes.db.graph import CorrelationCoefficientsIndex
from tvb.adapters.visualizers.matrix_viewer import ABCMappedArraySVGVisualizer
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.neocom import h5
from tvb.core.adapters.abcdisplayer import URLGenerator
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 CorrelationCoefficients

[docs]class PearsonCorrelationCoefficientVisualizerModel(ViewModel): datatype = DataTypeGidAttr( linked_datatype=CorrelationCoefficients, label='Correlation Coefficients' )
[docs]class PearsonCorrelationCoefficientVisualizerForm(ABCAdapterForm): def __init__(self): super(PearsonCorrelationCoefficientVisualizerForm, self).__init__() self.datatype = TraitDataTypeSelectField(PearsonCorrelationCoefficientVisualizerModel.datatype, name='datatype', conditions=self.get_filters())
[docs] @staticmethod def get_view_model(): return PearsonCorrelationCoefficientVisualizerModel
[docs] @staticmethod def get_required_datatype(): return CorrelationCoefficientsIndex
[docs] @staticmethod def get_input_name(): return 'datatype'
[docs] @staticmethod def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.has_valid_time_series'], operations=['=='], values=[True])
[docs]class PearsonCorrelationCoefficientVisualizer(ABCMappedArraySVGVisualizer): """ 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_form_class(self): return PearsonCorrelationCoefficientVisualizerForm
[docs] def launch(self, view_model): """Construct data for visualization and launch it.""" cc_gid = view_model.datatype cc_index = self.load_entity_by_gid(cc_gid) assert isinstance(cc_index, CorrelationCoefficientsIndex) matrix_shape = cc_index.parsed_shape[0:2] ts_gid = cc_index.fk_source_gid ts_index = self.load_entity_by_gid(ts_gid) state_list = ts_index.get_labels_for_dimension(1) mode_list = list(range(ts_index.data_length_4d)) with h5.h5_file_for_index(ts_index) as ts_h5: labels = self.get_space_labels(ts_h5) if not labels: labels = None pars = dict(matrix_labels=json.dumps([labels, labels], cls=TVBJSONEncoder), matrix_shape=json.dumps(matrix_shape), viewer_title='Cross Correlation Matrix Plot', url_base=URLGenerator.build_h5_url(cc_gid, '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)