TheVirtualBrain:

TheDocumentationwebsite.

Source code for tvb.adapters.visualizers.brain

# -*- 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 do download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2020, 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 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)
#
#

"""
.. moduleauthor:: Ionel Ortelecan <ionel.ortelecan@codemart.ro>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Bogdan Neacsa <bogdan.neacsa@codemart.ro>
"""

import numpy
from tvb.adapters.datatypes.h5.surface_h5 import SurfaceH5
from tvb.adapters.visualizers.eeg_monitor import EegMonitor
from tvb.adapters.visualizers.surface_view import ensure_shell_surface, SurfaceURLGenerator, ABCSurfaceDisplayer
from tvb.adapters.visualizers.sensors import prepare_sensors_as_measure_points_params, function_sensors_to_surface
from tvb.adapters.visualizers.sensors import prepare_mapped_sensors_as_measure_points_params
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesH5
from tvb.core.adapters.abcdisplayer import URLGenerator
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.adapters.datatypes.db.time_series import *
from tvb.core.entities.storage import dao
from tvb.core.neotraits.view_model import DataTypeGidAttr, ViewModel
from tvb.datatypes.surfaces import CORTICAL, EEG_CAP, Surface
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neocom import h5

MAX_MEASURE_POINTS_LENGTH = 600


[docs]class BrainViewerModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label='Time Series (Region or Surface)' ) shell_surface = DataTypeGidAttr( linked_datatype=Surface, required=False, label='Shell Surface', doc='Surface to be displayed semi-transparently as overlay, for visual navigation purposes only.' )
[docs]class BrainViewerForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(BrainViewerForm, self).__init__(prefix, project_id) self.time_series = TraitDataTypeSelectField(BrainViewerModel.time_series, self, name='time_series', conditions=self.get_filters()) self.shell_surface = TraitDataTypeSelectField(BrainViewerModel.shell_surface, self, name='shell_surface') @staticmethod
[docs] def get_view_model(): return BrainViewerModel
@staticmethod
[docs] def get_required_datatype(): return TimeSeriesIndex
@staticmethod
[docs] def get_input_name(): return 'time_series'
@staticmethod
[docs] def get_filters(): return FilterChain( fields=[FilterChain.datatype + '.time_series_type', FilterChain.datatype + '.has_surface_mapping'], operations=["in", "=="], values=[['TimeSeriesRegion', 'TimeSeriesSurface'], True])
[docs]class BrainViewer(ABCSurfaceDisplayer): """ Interface between the 3D view of the Brain Cortical Surface and TVB framework. This viewer will build the required parameter dictionary that will be sent to the HTML / JS for further processing, having as end result a brain surface plus activity that will be displayed in 3D. """ _ui_name = "Brain Activity Visualizer" PAGE_SIZE = 500
[docs] def get_form_class(self): return BrainViewerForm
[docs] def get_required_memory_size(self, view_model): # type: (BrainViewerModel) -> numpy.ndarray """ Assume one page doesn't get 'dumped' in time and it is highly probably that two consecutive pages will be in the same time in memory. """ time_series = self.load_entity_by_gid(view_model.time_series) overall_shape = time_series.get_data_shape() used_shape = (overall_shape[0] / (self.PAGE_SIZE * 2.0), overall_shape[1], overall_shape[2], overall_shape[3]) return numpy.prod(used_shape) * 8.0
[docs] def generate_preview(self, view_model, figure_size=None): """ Generate the preview for the burst page """ time_series = self.load_entity_by_gid(view_model.time_series) self.populate_surface_fields(time_series) url_vertices, url_normals, url_lines, url_triangles, url_region_map = \ SurfaceURLGenerator.get_urls_for_rendering(self.surface_h5, self.region_map_gid) params = self.retrieve_measure_points_params(time_series) base_adapter_url, time_urls = self._prepare_data_slices(time_series) with h5.h5_file_for_index(time_series) as time_series_h5: assert isinstance(time_series_h5, TimeSeriesH5) min_val, max_val = time_series_h5.get_min_max_values() if self.surface_gid and self.region_map_gid: boundary_url = SurfaceURLGenerator.get_url_for_region_boundaries(self.surface_gid, self.region_map_gid, self.stored_adapter.id) else: boundary_url = '' params.update(urlVertices=json.dumps(url_vertices), urlTriangles=json.dumps(url_triangles), urlLines=json.dumps(url_lines), urlNormals=json.dumps(url_normals), urlRegionMap=json.dumps(url_region_map), urlRegionBoundaries=boundary_url, base_adapter_url=base_adapter_url, isOneToOneMapping=self.one_to_one_map, minActivity=min_val, maxActivity=max_val) normalization_factor = figure_size[0] / 800 if figure_size[1] / 600 < normalization_factor: normalization_factor = figure_size[1] / 600 params['width'] = figure_size[0] * normalization_factor params['height'] = figure_size[1] * normalization_factor if self.surface_h5: self.surface_h5.close() return self.build_display_result("brain/portlet_preview", params)
[docs] def launch(self, view_model): # type: (BrainViewerModel) -> dict """ Build visualizer's page. """ time_series_index = self.load_entity_by_gid(view_model.time_series) shell_surface_index = None if view_model.shell_surface: shell_surface_index = self.load_entity_by_gid(view_model.shell_surface) params = self.compute_parameters(time_series_index, shell_surface_index) return self.build_display_result("brain/view", params, pages=dict(controlPage="brain/controls"))
[docs] def populate_surface_fields(self, time_series_index): """ To be overwritten for populating fields: one_to_one_map/connectivity/region_map/surface fields """ self.one_to_one_map = isinstance(time_series_index, TimeSeriesSurfaceIndex) if self.one_to_one_map: self.PAGE_SIZE /= 10 surface_gid = time_series_index.fk_surface_gid surface_index = dao.get_datatype_by_gid(surface_gid) region_map_indexes = dao.get_generic_entity(RegionMappingIndex, surface_gid, 'fk_surface_gid') if len(region_map_indexes) < 1: region_map_index = None connectivity_index = None else: region_map_index = region_map_indexes[0] connectivity_index = dao.get_datatype_by_gid(region_map_index.fk_connectivity_gid) else: connectivity_index = dao.get_datatype_by_gid(time_series_index.fk_connectivity_gid) if time_series_index.fk_region_mapping_gid: region_map_index = dao.get_datatype_by_gid(time_series_index.fk_region_mapping_gid) else: region_map_indexes = dao.get_generic_entity(RegionMappingIndex, connectivity_index.gid, 'fk_connectivity_gid') region_map_index = region_map_indexes[0] surface_index = dao.get_datatype_by_gid(region_map_index.fk_surface_gid) self.connectivity_index = connectivity_index self.region_map_gid = None if region_map_index is None else region_map_index.gid self.surface_gid = None if surface_index is None else surface_index.gid self.surface_h5 = None if surface_index is None else h5.h5_file_for_index(surface_index)
[docs] def retrieve_measure_points_params(self, time_series): """ To be overwritten method, for retrieving the measurement points (region centers, EEG sensors). """ if self.connectivity_index is None: self.measure_points_no = 0 return {'urlMeasurePoints': [], 'urlMeasurePointsLabels': [], 'noOfMeasurePoints': 0} connectivity_gid = self.connectivity_index.gid measure_points = SurfaceURLGenerator.build_h5_url(connectivity_gid, 'get_centres') measure_points_labels = SurfaceURLGenerator.build_h5_url(connectivity_gid, 'get_region_labels') self.measure_points_no = self.connectivity_index.number_of_regions return {'urlMeasurePoints': measure_points, 'urlMeasurePointsLabels': measure_points_labels, 'noOfMeasurePoints': self.measure_points_no}
[docs] def compute_parameters(self, time_series, shell_surface=None): """ Create the required parameter dictionary for the HTML/JS viewer. :rtype: `dict` :raises Exception: when * number of measure points exceeds the maximum allowed * a Face object cannot be found in database """ self.populate_surface_fields(time_series) url_vertices, url_normals, url_lines, url_triangles, url_region_map = SurfaceURLGenerator.get_urls_for_rendering( self.surface_h5, self.region_map_gid) hemisphere_chunk_mask = self.surface_h5.get_slices_to_hemisphere_mask() params = self.retrieve_measure_points_params(time_series) if not self.one_to_one_map and self.measure_points_no > MAX_MEASURE_POINTS_LENGTH: raise Exception("Max number of measure points " + str(MAX_MEASURE_POINTS_LENGTH) + " exceeded.") time_series_h5 = h5.h5_file_for_index(time_series) assert isinstance(time_series_h5, TimeSeriesH5) base_adapter_url, time_urls = self._prepare_data_slices(time_series) min_val, max_val = time_series_h5.get_min_max_values() legend_labels = self._compute_legend_labels(min_val, max_val) state_variables = time_series.get_labels_for_dimension(1) if self.surface_gid and self.region_map_gid: boundary_url = SurfaceURLGenerator.get_url_for_region_boundaries(self.surface_gid, self.region_map_gid, self.stored_adapter.id) else: boundary_url = '' shell_surface = ensure_shell_surface(self.current_project_id, shell_surface) shelf_object = None if shell_surface: with h5.h5_file_for_index(shell_surface) as shell_h5: shell_vertices, shell_normals, _, shell_triangles, _ = SurfaceURLGenerator.get_urls_for_rendering( shell_h5) shelf_object = json.dumps([shell_vertices, shell_normals, shell_triangles]) params.update(dict(title="Cerebral Activity: " + time_series.title, isOneToOneMapping=self.one_to_one_map, urlVertices=json.dumps(url_vertices), urlTriangles=json.dumps(url_triangles), urlLines=json.dumps(url_lines), urlNormals=json.dumps(url_normals), urlRegionMap=json.dumps(url_region_map), base_adapter_url=base_adapter_url, time=json.dumps(time_urls), minActivity=min_val, maxActivity=max_val, legendLabels=legend_labels, labelsStateVar=state_variables, labelsModes=list(range(time_series.data_length_4d)), extended_view=False, shelfObject=shelf_object, biHemispheric=self.surface_h5.bi_hemispheric.load(), hemisphereChunkMask=json.dumps(hemisphere_chunk_mask), pageSize=self.PAGE_SIZE, urlRegionBoundaries=boundary_url, measurePointsLabels=self.get_space_labels(time_series_h5), measurePointsTitle=time_series.title)) params.update(self.build_params_for_subselectable_ts(time_series_h5)) time_series_h5.close() if self.surface_h5: self.surface_h5.close() return params
@staticmethod def _prepare_mappings(mappings_dict): """ Get full mapping dictionary between the original vertices and multiple slices (for WebGL compatibility). """ prepared_mappings = [] for key in mappings_dict: this_mappings = [] vert_map_dict = mappings_dict[key] vertices_indexes = vert_map_dict['indices'] this_mappings.append(vertices_indexes[0].tolist()) for i in range(1, len(vertices_indexes)): if vertices_indexes[i][0] == vertices_indexes[i][1]: this_mappings.append(vertices_indexes[i][0]) else: for index in range(vertices_indexes[i][0], vertices_indexes[i][1] + 1): this_mappings.append(index) prepared_mappings.append(this_mappings) return prepared_mappings @staticmethod def _compute_legend_labels(min_val, max_val, nr_labels=5, min_nr_dec=3): """ Compute rounded labels for MIN and MAX values such that decimals will show a difference between them. """ if len(str(min_val).split('.')) == 2: min_integer, min_decimals = str(min_val).split('.') else: min_integer, min_decimals = [str(int(min_val)), ""] if len(str(max_val).split('.')) == 2: max_integer, max_decimals = str(max_val).split('.') else: max_integer, max_decimals = [str(int(max_val)), ""] idx = min_nr_dec if len(min_decimals) < min_nr_dec or len(max_decimals) < min_nr_dec: processed_min_val = float(min_val) processed_max_val = float(max_val) elif min_integer != max_integer: processed_min_val = float(min_integer + '.' + min_decimals[:min_nr_dec]) processed_max_val = float(max_integer + '.' + max_decimals[:min_nr_dec]) else: for idx, val in enumerate(min_decimals): if idx < len(max_decimals) or val != max_decimals[idx]: break processed_min_val = float(min_integer + '.' + min_decimals[:idx]) processed_max_val = float(max_integer + '.' + max_decimals[:idx]) value_diff = (processed_max_val - processed_min_val) / (nr_labels + 1) inter_values = [round(processed_min_val + value_diff * i, idx) for i in range(nr_labels, 0, -1)] return [processed_max_val] + inter_values + [processed_min_val] def _prepare_data_slices(self, time_series_index): """ Prepare data URL for retrieval with slices of timeSeries activity and Time-Line. :returns: [activity_urls], [timeline_urls] Currently timeline_urls has just one value, as on client is loaded entirely anyway. """ time_series_gid = time_series_index.gid activity_base_url = URLGenerator.build_url(self.stored_adapter.id, 'read_data_page_split', time_series_gid, "") time_urls = [SurfaceURLGenerator.build_h5_url(time_series_gid, 'read_time_page', parameter="current_page=0;page_size=" + str(time_series_index.data_length_1d))] return activity_base_url, time_urls
[docs] def read_data_page_split(self, time_series_gid, from_idx, to_idx, step=None, specific_slices=None): time_series_index = self.load_entity_by_gid(time_series_gid) with h5.h5_file_for_index(time_series_index) as time_series_h5: assert isinstance(time_series_h5, TimeSeriesH5) basic_result = time_series_h5.read_data_page(from_idx, to_idx, step, specific_slices) if not isinstance(time_series_index, TimeSeriesSurfaceIndex): return basic_result.tolist() result = [] surface_index = self.load_entity_by_gid(time_series_index.fk_surface_gid) surface_h5 = h5.h5_file_for_index(surface_index) assert isinstance(surface_h5, SurfaceH5) number_of_split_slices = surface_h5.number_of_split_slices.load() if number_of_split_slices <= 1: result.append(basic_result.tolist()) else: for slice_number in range(surface_h5.number_of_split_slices): start_idx, end_idx = surface_h5.get_slice_vertex_boundaries(slice_number) result.append(basic_result[:, start_idx:end_idx].tolist()) surface_h5.close() return result
[docs]class DualBrainViewerModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label='Time Series' ) projection_surface = DataTypeGidAttr( linked_datatype=Surface, required=False, label='Projection Surface', doc='A surface on which to project the results. When missing, the first EEGCap is taken. ' 'This parameter is ignored when InternalSensors measures.' ) shell_surface = DataTypeGidAttr( linked_datatype=Surface, required=False, label='Shell Surface', doc='Wrapping surface over the internal sensors, to be displayed ' 'semi-transparently, for visual purposes only.' )
[docs]class DualBrainViewerForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(DualBrainViewerForm, self).__init__(prefix, project_id) self.time_series = TraitDataTypeSelectField(DualBrainViewerModel.time_series, self, name='time_series', conditions=self.get_filters()) self.projection_surface = TraitDataTypeSelectField(DualBrainViewerModel.projection_surface, self, name='projection_surface') self.shell_surface = TraitDataTypeSelectField(DualBrainViewerModel.shell_surface, self, name='shell_surface') @staticmethod
[docs] def get_view_model(): return DualBrainViewerModel
@staticmethod
[docs] def get_required_datatype(): return TimeSeriesIndex
@staticmethod
[docs] def get_input_name(): return 'time_series'
@staticmethod
[docs] def get_filters(): return FilterChain( fields=[FilterChain.datatype + '.time_series_type', FilterChain.datatype + '.has_surface_mapping'], operations=["in", "=="], values=[['TimeSeriesEEG', 'TimeSeriesSEEG', 'TimeSeriesMEG', 'TimeSeriesRegion'], True])
[docs]class DualBrainViewer(BrainViewer): """ Visualizer merging Brain 3D display and EEG lines display. """ _ui_name = "Brain Activity Viewer in 3D and 2D" _ui_subsection = "brain_dual"
[docs] def get_form_class(self): return DualBrainViewerForm
[docs] def populate_surface_fields(self, time_series_index): """ Prepares the urls from which the client may read the data needed for drawing the surface. """ if isinstance(time_series_index, TimeSeriesRegionIndex): BrainViewer.populate_surface_fields(self, time_series_index) return self.one_to_one_map = False self.region_map_gid = None self.connectivity_index = None if self.surface_index is None: eeg_cap = dao.get_generic_entity(SurfaceIndex, EEG_CAP, "surface_type") if len(eeg_cap) < 1: raise Exception("No EEG Cap Surface found for display!") self.surface_index = eeg_cap[0] self.surface_gid = self.surface_index.gid self.surface_h5 = h5.h5_file_for_index(self.surface_index)
[docs] def retrieve_measure_points_params(self, time_series): if isinstance(time_series, TimeSeriesRegionIndex): return BrainViewer.retrieve_measure_points_params(self, time_series) sensors_index = dao.get_datatype_by_gid(time_series.fk_sensors_gid) self.measure_points_no = sensors_index.number_of_sensors if isinstance(time_series, TimeSeriesEEGIndex): return prepare_mapped_sensors_as_measure_points_params(sensors_index, self.surface_index, self.stored_adapter.id) return prepare_sensors_as_measure_points_params(sensors_index)
[docs] def sensors_to_surface(self, sensors_gid, surface_to_map_gid): # Method needs to be defined on the adapter, to be called from JS return function_sensors_to_surface(sensors_gid, surface_to_map_gid)
[docs] def launch(self, view_model): # type: (DualBrainViewerModel) -> dict time_series_index = self.load_entity_by_gid(view_model.time_series) self.surface_index = None shell_surface_index = None if view_model.projection_surface: self.surface_index = self.load_entity_by_gid(view_model.projection_surface) if view_model.shell_surface: shell_surface_index = self.load_entity_by_gid(view_model.shell_surface) if isinstance(time_series_index, TimeSeriesSEEGIndex): shell_surface_index = ensure_shell_surface(self.current_project_id, shell_surface_index, CORTICAL) params = BrainViewer.compute_parameters(self, time_series_index, shell_surface_index) eeg_monitor = EegMonitor() eeg_monitor.storage_path = self.storage_path params.update(eeg_monitor.compute_parameters(time_series_index, is_extended_view=True)) params['isOneToOneMapping'] = False params['brainViewerTemplate'] = 'view.html' if isinstance(time_series_index, TimeSeriesSEEGIndex): params['brainViewerTemplate'] = "internal_view.html" # Mark as None since we only display shelf face and no point to load these as well params['urlVertices'] = None params['isSEEG'] = True return self.build_display_result("brain/extendedview", params, pages=dict(controlPage="brain/extendedcontrols", channelsPage="commons/channel_selector.html"))