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Source code for tvb.adapters.visualizers.matrix_viewer

# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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# documentation-folder for more details. See also http://www.thevirtualbrain.org
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# (c) 2012-2020, Baycrest Centre for Geriatric Care ("Baycrest") and others
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#   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)
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"""
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Paula Popa <paula.popa@codemart.ro>
.. moduleauthor:: Andrei Mihai <mihai.andrei@codemart.ro>
"""

import json
import numpy
from six import add_metaclass
from abc import ABCMeta
from tvb.adapters.visualizers.time_series import ABCSpaceDisplayer
from tvb.adapters.datatypes.db.spectral import DataTypeMatrix
from tvb.basic.neotraits.api import Attr
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.adapters.arguments_serialisation import parse_slice, slice_str
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.core.neotraits.forms import TraitDataTypeSelectField, StrField
from tvb.core.neocom import h5
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr


@add_metaclass(ABCMeta)
[docs]class ABCMappedArraySVGVisualizer(ABCSpaceDisplayer): """ To be inherited by visualizers for DataTypeMatrix subclasses """
[docs] def get_required_memory_size(self, view_model): # type: (MatrixVisualizerModel) -> float """Return required memory.""" dtm_index = self.load_entity_by_gid(view_model.datatype) input_size = dtm_index.parsed_shape return numpy.prod(input_size) * 8.0
[docs] def generate_preview(self, view_model, **kwargs): # type: (MatrixVisualizerModel, dict) -> dict result = self.launch(view_model) result["isPreview"] = True return result
@staticmethod
[docs] def compute_raw_matrix_params(matrix): """ Serializes matrix data, shape and stride metadata to json """ matrix_data = ABCDisplayer.dump_with_precision(matrix.flat) matrix_shape = json.dumps(matrix.shape) return dict(matrix_data=matrix_data, matrix_shape=matrix_shape)
[docs] def compute_2d_view(self, dtm_index, slice_s): # type: (DataTypeMatrix, str) -> (numpy.array, str, bool) """ Create a 2d view of the matrix using the suggested slice If the given slice is invalid or fails to produce a 2d array the default is used which selects the first 2 dimensions. If the matrix is complex the real part is shown :param dtm_index: main input. It can have more then 2D :param slice_s: a string representation of a slice :return: (a 2d array, the slice used to make it, is_default_returned) """ default = (slice(None), slice(None)) + tuple(0 for _ in range(dtm_index.ndim - 2)) # [:,:,0,0,0,0 etc] slice_used = default try: if slice_s is not None and slice_s != "": slice_used = parse_slice(slice_s) except ValueError: # if the slice could not be parsed self.log.warning("failed to parse the slice") try: with h5.h5_file_for_index(dtm_index) as h5_file: result_2d = h5_file.array_data[slice_used] result_2d = result_2d.astype(float) if result_2d.ndim > 2: # the slice did not produce a 2d array, treat as error raise ValueError(str(dtm_index.shape)) except (ValueError, IndexError, TypeError): # if the slice failed to produce a 2d array self.log.warning("failed to produce a 2d array") return self.compute_2d_view(dtm_index, "") return result_2d, slice_str(slice_used), slice_used == default
[docs] def compute_params(self, dtm_index, matrix2d, title_suffix, labels=None, given_slice=None, slice_used=None, is_default_slice=True): # type: (DataTypeMatrix, numpy.array, str, list, str, str, bool) -> dict view_pars = self.compute_raw_matrix_params(matrix2d) view_pars.update(original_matrix_shape=dtm_index.shape, show_slice_info=True, given_slice=given_slice, slice_used=slice_used, is_default_slice=is_default_slice, has_complex_numbers=dtm_index.array_has_complex, viewer_title=title_suffix, title=dtm_index.display_name + " - " + title_suffix, matrix_labels=json.dumps(labels)) return view_pars
[docs] def extract_source_labels(self, datatype_matrix): # type: (DataTypeMatrix) -> list if hasattr(datatype_matrix, "fk_connectivity_gid"): conn_idx = self.load_entity_by_gid(datatype_matrix.fk_connectivity_gid) with h5.h5_file_for_index(conn_idx) as conn_h5: labels = list(conn_h5.region_labels.load()) return labels if hasattr(datatype_matrix, "fk_source_gid"): source_index = self.load_entity_by_gid(datatype_matrix.fk_source_gid) with h5.h5_file_for_index(source_index) as source_h5: labels = self.get_space_labels(source_h5) return labels return None
[docs]class MatrixVisualizerModel(ViewModel): datatype = DataTypeGidAttr( linked_datatype=DataTypeMatrix, label='Array data type' ) slice = Attr( field_type=str, required=False, label='slice indices in numpy syntax' )
[docs]class MatrixVisualizerForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(MatrixVisualizerForm, self).__init__(prefix, project_id, False) self.datatype = TraitDataTypeSelectField(MatrixVisualizerModel.datatype, self, name='datatype', conditions=self.get_filters()) self.slice = StrField(MatrixVisualizerModel.slice, self, name='slice') @staticmethod
[docs] def get_view_model(): return MatrixVisualizerModel
@staticmethod
[docs] def get_input_name(): return 'datatype'
@staticmethod
[docs] def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.ndim'], operations=[">="], values=[2])
@staticmethod
[docs] def get_required_datatype(): return DataTypeMatrix
[docs]class MappedArrayVisualizer(ABCMappedArraySVGVisualizer): _ui_name = "Matrix Visualizer" _ui_subsection = "matrix"
[docs] def get_form_class(self): return MatrixVisualizerForm
[docs] def launch(self, view_model): # type: (MatrixVisualizerModel) -> dict dtm_gid = view_model.datatype dtm_index = self.load_entity_by_gid(dtm_gid) labels = self.extract_source_labels(dtm_index) matrix2d, slice_used, is_default_slice = self.compute_2d_view(dtm_index, view_model.slice) if matrix2d is None or labels is None or len(labels) != matrix2d.shape[0] or len(labels) != matrix2d.shape[1]: labels = None else: labels = [labels, labels] params = self.compute_params(dtm_index, matrix2d, "Matrix Plot", labels, view_model.slice, slice_used, is_default_slice) return self.build_display_result("matrix/svg_view", params)