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Source code for tvb.adapters.visualizers.time_series

# -*- 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)
#
#

"""
A Javascript displayer for time series, using SVG.

.. moduleauthor:: Marmaduke Woodman <marmaduke.woodman@univ-amu.fr>

"""

import json
from abc import ABCMeta
from six import add_metaclass
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesRegionH5, TimeSeriesSensorsH5, TimeSeriesH5
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import ABCDisplayer, URLGenerator
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neocom import h5
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.connectivity import Connectivity
from tvb.datatypes.time_series import TimeSeries


[docs]class TimeSeriesModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label="Time series to be displayed in a 2D form." )
[docs]class TimeSeriesForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(TimeSeriesForm, self).__init__(prefix, project_id, False) self.time_series = TraitDataTypeSelectField(TimeSeriesModel.time_series, self, name='time_series', conditions=self.get_filters()) @staticmethod
[docs] def get_view_model(): return TimeSeriesModel
@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'], operations=["in"], values=[['TimeSeriesEEG', 'TimeSeriesSEEG', 'TimeSeriesMEG', 'TimeSeriesRegion', 'TimeSeriesSurface']])
@add_metaclass(ABCMeta)
[docs]class ABCSpaceDisplayer(ABCDisplayer): @staticmethod
[docs] def build_params_for_selectable_connectivity(connectivity): # type: (Connectivity) -> dict return {'measurePointsSelectionGID': connectivity.gid, 'initialSelection': connectivity.saved_selection or list(range(len(connectivity.region_labels))), 'groupedLabels': connectivity.get_grouped_space_labels()}
[docs] def build_params_for_subselectable_ts(self, ts_h5): """ creates a template dict with the initial selection to be displayed in a time series viewer """ return {'measurePointsSelectionGID': ts_h5.get_measure_points_selection_gid(), 'initialSelection': ts_h5.get_default_selection(), 'groupedLabels': self.get_grouped_space_labels(ts_h5)}
[docs] def get_grouped_space_labels(self, ts_h5): """ :return: A structure of this form [('left', [(idx, lh_label)...]), ('right': [(idx, rh_label) ...])] """ if isinstance(ts_h5, TimeSeriesSensorsH5): sensors_gid = ts_h5.sensors.load() sensors_idx = self.load_entity_by_gid(sensors_gid) with h5.h5_file_for_index(sensors_idx) as sensors_h5: labels = sensors_h5.labels.load() # TODO uncomment this when the UI component will be able to scale for many groups # if isinstance(ts_h5, TimeSeriesSEEGH5): # return SensorsInternal.group_sensors_to_electrodes(labels) return [('', list(enumerate(labels)))] if isinstance(ts_h5, TimeSeriesRegionH5): connectivity_gid = ts_h5.connectivity.load() conn = self.load_traited_by_gid(connectivity_gid) assert isinstance(conn, Connectivity) return conn.get_grouped_space_labels() return ts_h5.get_grouped_space_labels()
[docs] def get_space_labels(self, ts_h5): """ :return: An array of strings with the connectivity node labels. """ if type(ts_h5) is TimeSeriesRegionH5: connectivity_gid = ts_h5.connectivity.load() if connectivity_gid is None: return [] conn_idx = self.load_entity_by_gid(connectivity_gid) with h5.h5_file_for_index(conn_idx) as conn_h5: return list(conn_h5.region_labels.load()) if isinstance(ts_h5, TimeSeriesSensorsH5): sensors_gid = ts_h5.sensors.load() if sensors_gid is None: return [] sensors_idx = self.load_entity_by_gid(sensors_gid) with h5.h5_file_for_index(sensors_idx) as sensors_h5: return list(sensors_h5.labels.load()) return ts_h5.get_space_labels()
[docs]class TimeSeriesDisplay(ABCSpaceDisplayer): _ui_name = "Time Series Visualizer (SVG/d3)" _ui_subsection = "timeseries" MAX_PREVIEW_DATA_LENGTH = 200
[docs] def get_form_class(self): return TimeSeriesForm
[docs] def get_required_memory_size(self, view_model): # type: (TimeSeriesModel) -> int """Return required memory.""" return -1
def _launch(self, view_model, figsize, preview=False): time_series_index = self.load_entity_by_gid(view_model.time_series) h5_file = h5.h5_file_for_index(time_series_index) assert isinstance(h5_file, TimeSeriesH5) shape = list(h5_file.read_data_shape()) ts = h5_file.storage_manager.get_data('time') state_variables = time_series_index.get_labels_for_dimension(1) labels = self.get_space_labels(h5_file) # Assume that the first dimension is the time since that is the case so far if preview and shape[0] > self.MAX_PREVIEW_DATA_LENGTH: shape[0] = self.MAX_PREVIEW_DATA_LENGTH # when surface-result, the labels will be empty, so fill some of them, # but not all, otherwise the viewer will take ages to load. if shape[2] > 0 and len(labels) == 0: for n in range(min(self.MAX_PREVIEW_DATA_LENGTH, shape[2])): labels.append("Node-" + str(n)) pars = {'baseURL': URLGenerator.build_base_h5_url(time_series_index.gid), 'labels': labels, 'labels_json': json.dumps(labels), 'ts_title': time_series_index.title, 'preview': preview, 'figsize': figsize, 'shape': repr(shape), 't0': ts[0], 'dt': ts[1] - ts[0] if len(ts) > 1 else 1, 'labelsStateVar': state_variables, 'labelsModes': list(range(shape[3])) } pars.update(self.build_params_for_subselectable_ts(h5_file)) h5_file.close() return self.build_display_result("time_series/view", pars, pages=dict(controlPage="time_series/control"))
[docs] def launch(self, view_model): # type: (TimeSeriesModel) -> dict """Construct data for visualization and launch it.""" return self._launch(view_model, None)
[docs] def generate_preview(self, view_model, figure_size=None): # type: (TimeSeriesModel, (int, int)) -> dict return self._launch(view_model, figsize=figure_size, preview=True)