# -*- 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
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (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
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program. If not, see <http://www.gnu.org/licenses/>.
#
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
#
#
"""
.. 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
from tvb.core.utils import TVBJSONEncoder
[docs]@add_metaclass(ABCMeta)
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] @staticmethod
def compute_raw_matrix_params(matrix):
"""
Serializes matrix data, shape and stride metadata to json
"""
matrix = ABCDisplayer.handle_infinite_values(matrix)
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, has_infinite_values=False):
# 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,
has_infinite_values=has_infinite_values,
viewer_title=title_suffix,
title=dtm_index.display_name + " - " + title_suffix,
matrix_labels=json.dumps(labels, cls=TVBJSONEncoder))
return view_pars
[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 MappedArrayVisualizer(ABCMappedArraySVGVisualizer):
_ui_name = "Matrix Visualizer"
_ui_subsection = "matrix"
[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, not dtm_index.array_is_finite)
return self.build_display_result("matrix/svg_view", params)