Source code for tvb.adapters.visualizers.pearson_cross_correlation

# -*- coding: utf-8 -*-
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"""
.. moduleauthor:: Paula Popa <paula.popa@codemart.ro>
.. moduleauthor:: Dan Pop <dan.pop@codemart.ro>
.. 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


class PearsonCorrelationCoefficientVisualizerModel(ViewModel):
    datatype = DataTypeGidAttr(
        linked_datatype=CorrelationCoefficients,
        label='Correlation Coefficients'
    )


class PearsonCorrelationCoefficientVisualizerForm(ABCAdapterForm):

    def __init__(self):
        super(PearsonCorrelationCoefficientVisualizerForm, self).__init__()
        self.datatype = TraitDataTypeSelectField(PearsonCorrelationCoefficientVisualizerModel.datatype,
                                                 name='datatype', conditions=self.get_filters())

    @staticmethod
    def get_view_model():
        return PearsonCorrelationCoefficientVisualizerModel

    @staticmethod
    def get_required_datatype():
        return CorrelationCoefficientsIndex

    @staticmethod
    def get_input_name():
        return 'datatype'

    @staticmethod
    def get_filters():
        return FilterChain(fields=[FilterChain.datatype + '.has_valid_time_series'], operations=['=='], values=[True])


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"

    def get_form_class(self):
        return PearsonCorrelationCoefficientVisualizerForm

    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)