The Virtual Brain Project

Source code for tvb.adapters.visualizers.matrix_viewer

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.. moduleauthor:: Andrei Mihai <>


import json
import numpy
from tvb.basic.filters.chain import FilterChain
from tvb.basic.arguments_serialisation import parse_slice, slice_str
from tvb.datatypes.arrays import MappedArray
from tvb.core.adapters.abcdisplayer import ABCDisplayer
from tvb.datatypes.time_series import TimeSeriesRegion

[docs]def compute_2d_view(matrix, slice_s): """ 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 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(matrix.ndim - 2)) # [:,:,0,0,0,0 etc] try: if slice_s is not None: matrix_slice = parse_slice(slice_s) else: matrix_slice = slice(None) m = matrix[matrix_slice] if m.ndim > 2: # the slice did not produce a 2d array, treat as error raise ValueError(str(matrix.shape)) except (IndexError, ValueError): # if the slice could not be parsed or it failed to produce a 2d array matrix_slice = default slice_used = slice_str(matrix_slice) return matrix[matrix_slice].astype(float), slice_used, matrix_slice == default
[docs]class MappedArraySVGVisualizerMixin(object): """ To be mixed in a ABCDisplayer """
[docs] def get_required_memory_size(self, datatype): input_size = datatype.read_data_shape() return / input_size[0] * 8.0
[docs] def generate_preview(self, datatype, **kwargs): result = self.launch(datatype) result["isPreview"] = True return result
[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_params(self, matrix, viewer_title, given_slice=None, labels=None): """ Prepare a 2d matrix to display :param matrix: input matrix :param given_slice: a string representation of a slice. This slice should cut a 2d view from matrix If the matrix is not 2d and the slice will not make it 2d then a default slice is used """ matrix2d, slice_used, is_default_slice = compute_2d_view(matrix, given_slice) view_pars = self.compute_raw_matrix_params(matrix2d) view_pars.update(original_matrix_shape=str(matrix.shape), show_slice_info=given_slice is not None, given_slice=given_slice, slice_used=slice_used, is_default_slice=is_default_slice, viewer_title=viewer_title, title=viewer_title, matrix_labels=json.dumps(labels)) return view_pars
def _get_associated_connectivity_labeling(self, datatype): """ If datatype has a source attribute of type TimeSeriesRegion then the labels of the associated connectivity are returned. Else None """ source = self.load_entity_by_gid(datatype.source.gid) # function exists in the mixin target if isinstance(source, TimeSeriesRegion): # todo should we use connectivity.ordered_labels? # If so also permute the matrix to be consistent with the conn views labels = source.connectivity.region_labels.tolist() return [labels, labels]
[docs]class MappedArrayVisualizer(MappedArraySVGVisualizerMixin, ABCDisplayer): _ui_name = "Matrix Visualizer" _ui_subsection = "matrix"
[docs] def get_input_tree(self): return [{'name': 'datatype', 'label': 'Array data type', 'type': MappedArray, 'required': True, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=[">="], values=[2])}, {'name': 'slice', 'label': 'slice indices in numpy syntax', 'type': 'str', 'required': False}]
[docs] def launch(self, datatype, slice=''): matrix = datatype.get_data('array_data') matrix2d, _, _ = compute_2d_view(matrix, slice) title = datatype.display_name + " matrix plot" pars = self.compute_params(matrix, title, slice) return self.build_display_result("matrix/svg_view", pars)