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Source code for tvb.adapters.visualizers.eeg_monitor

# -*- 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
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# (c) 2012-2020, Baycrest Centre for Geriatric Care ("Baycrest") and others
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# This program is free software: you can redistribute it and/or modify it under the
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# program.  If not, see <http://www.gnu.org/licenses/>.
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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)
#
#
"""
.. moduleauthor:: Ionel Ortelecan <ionel.ortelecan@codemart.ro>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
.. moduleauthor:: Bogdan Neacsa <bogdan.neacsa@codemart.ro>
"""
import json
import numpy
from tvb.adapters.visualizers.time_series import ABCSpaceDisplayer
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesH5
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.core.adapters.abcadapter import ABCAdapterForm
from tvb.core.adapters.abcdisplayer import URLGenerator
from tvb.core.adapters.exceptions import LaunchException
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neocom import h5
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr, replace_nan_values
from tvb.datatypes.time_series import TimeSeries


[docs]class EegMonitorModel(ViewModel): input_data = DataTypeGidAttr( linked_datatype=TimeSeries, label='Input Data', doc='Time series to display.' ) data_2 = DataTypeGidAttr( linked_datatype=TimeSeries, required=False, label='Input Data 2', doc='Time series to display.' ) data_3 = DataTypeGidAttr( linked_datatype=TimeSeries, required=False, label='Input Data 3', doc='Time series to display.' )
[docs]class EegMonitorForm(ABCAdapterForm): def __init__(self, prefix='', project_id=None): super(EegMonitorForm, self).__init__(prefix, project_id) self.input_data = TraitDataTypeSelectField(EegMonitorModel.input_data, self, name='input_data') self.data_2 = TraitDataTypeSelectField(EegMonitorModel.data_2, self, name='data_2') self.data_3 = TraitDataTypeSelectField(EegMonitorModel.data_3, self, name='data_3') @staticmethod
[docs] def get_view_model(): return EegMonitorModel
@staticmethod
[docs] def get_required_datatype(): return TimeSeriesIndex
@staticmethod
[docs] def get_input_name(): return 'input_data'
@staticmethod
[docs] def get_filters(): return None
[docs]class EegMonitor(ABCSpaceDisplayer): """ This viewer takes as inputs at least one ArrayWrapper and at most 3 ArrayWrappers, and returns the needed parameters for a 2D representation of the values from these arrays, in EEG form. So far arrays of at most 3 dimensions are supported. """ has_nan = False _ui_name = "Animated Time Series Visualizer" _ui_subsection = "animated_timeseries" page_size = 4000 preview_page_size = 250 current_page = 0
[docs] def get_form_class(self): return EegMonitorForm
[docs] def get_required_memory_size(self, view_model): # type: (EegMonitorModel) -> int """ Return the required memory to run this algorithm. """ return -1
@staticmethod def _get_input_time_series(input_data, data_2=None, data_3=None, is_preview=False): """ Returns a list of the distinct time series to be viewed Convert Original ArrayWrappers into a 2D list. :param input_data: Time series to display :type input_data: `TimeSeriesEEG` :param data_2: additional input data :param data_3: additional input data """ original_timeseries = [input_data] error_sample = "The input TimeSeries have different sample periods. You cannot view them in the same time !" if data_2 is not None and data_2.gid != input_data.gid and is_preview is False: if data_2.sample_period != input_data.sample_period: raise LaunchException(error_sample) original_timeseries.append(data_2) if (data_3 is not None and data_3.gid != input_data.gid and (data_2 is None or data_2.gid != data_3.gid) and is_preview is False): if data_3.sample_period != input_data.sample_period: raise LaunchException(error_sample) original_timeseries.append(data_3) return original_timeseries def _compute_ag_settings(self, original_timeseries, is_preview, graph_labels, no_of_channels, total_time_length, points_visible, is_extended_view, measure_points_sel_gids): # Compute distance between channels step, translations, channels_per_set = self.compute_required_info(original_timeseries) base_urls, page_size, total_pages, time_set_urls = self._get_data_set_urls(original_timeseries, is_preview) return dict(channelsPerSet=channels_per_set, channelLabels=graph_labels, noOfChannels=no_of_channels, translationStep=step, normalizedSteps=translations, nan_value_found=self.has_nan, baseURLS=base_urls, pageSize=page_size, nrOfPages=total_pages, timeSetPaths=time_set_urls, totalLength=total_time_length, number_of_visible_points=points_visible, extended_view=is_extended_view, measurePointsSelectionGIDs=measure_points_sel_gids)
[docs] def compute_parameters(self, input_data, data_2=None, data_3=None, is_preview=False, is_extended_view=False, selected_dimensions=None): """ Start the JS visualizer, similar to EEG-lab :param is_preview: Boolean True wne shown on Burst page :param selected_dimensions: from GUI int :param is_extended_view: Boolean when to display as dual-viewer :param input_data: Time series to display :type input_data: `TimeSeriesEEG` :param data_2: additional input data :param data_3: additional input data :returns: the needed parameters for a 2D representation :rtype: dict :raises LaunchException: when at least two input data parameters are provided and they sample periods differ """ original_timeseries = self._get_input_time_series(input_data, data_2, data_3) self.selected_dimensions = selected_dimensions or [0, 2] # Hardcoded now 1st dimension is time h5_timeseries = [] for timeseries in original_timeseries: if timeseries is None: continue ts_h5 = h5.h5_file_for_index(timeseries) assert isinstance(ts_h5, TimeSeriesH5) h5_timeseries.append(ts_h5) if not is_preview: max_chunck_length = max([ts_h5.read_data_shape()[0] for ts_h5 in h5_timeseries]) else: max_chunck_length = min(self.preview_page_size, h5_timeseries[0].read_data_shape()[0]) # compute how many elements will be visible on the screen points_visible = min(max_chunck_length, 500) (no_of_channels, ts_names, grouped_labels, total_time_length, graph_labels, initial_selections, measure_points_selectionGIDs, modes, state_vars) = self._pre_process(h5_timeseries) # ts_names : a string representing the time series # labels, modes, state_vars are maps ts_name -> list(...) # The label values must reach the client in ascending ordered. ts_names preserves the # order created by _pre_process if is_preview: total_time_length = max_chunck_length ag_settings = self._compute_ag_settings(h5_timeseries, is_preview, graph_labels, no_of_channels, total_time_length, points_visible, is_extended_view, measure_points_selectionGIDs) parameters = dict(title=self._get_sub_title(original_timeseries), tsNames=ts_names, groupedLabels=grouped_labels, tsModes=modes, tsStateVars=state_vars, longestChannelLength=max_chunck_length, label_x=self._get_label_x(original_timeseries[0]), entities=h5_timeseries, page_size=min(self.page_size, max_chunck_length), number_of_visible_points=points_visible, extended_view=is_extended_view, initialSelection=initial_selections, ag_settings=json.dumps(ag_settings)) for ts_h5 in h5_timeseries: ts_h5.close() return parameters
def _load_input_indexes(self, view_model): main_time_series_index = self.load_entity_by_gid(view_model.input_data) time_series_index2 = None time_series_index3 = None if view_model.data_2: time_series_index2 = self.load_entity_by_gid(view_model.data_2) if view_model.data_3: time_series_index3 = self.load_entity_by_gid(view_model.data_3) return main_time_series_index, time_series_index2, time_series_index3
[docs] def generate_preview(self, view_model, figure_size=None): # type: (EegMonitorModel, list) -> dict main_time_series_index, time_series_index2, time_series_index3 = self._load_input_indexes(view_model) params = self.compute_parameters(main_time_series_index, time_series_index2, time_series_index3, is_preview=True) pages = dict(channelsPage=None) return self.build_display_result("eeg/preview", params, pages)
[docs] def launch(self, view_model): # type: (EegMonitorModel) -> dict """ Compute visualizer's page """ main_time_series_index, time_series_index2, time_series_index3 = self._load_input_indexes(view_model) params = self.compute_parameters(main_time_series_index, time_series_index2, time_series_index3) pages = dict(controlPage="eeg/controls", channelsPage="commons/channel_selector.html") return self.build_display_result("eeg/view", params, pages=pages)
def _pre_process(self, timeseries_list): """From input, Compute no of lines and labels.""" multiple_inputs = len(timeseries_list) > 1 no_of_lines, max_length = 0, 0 modes, state_vars = {}, {} # all these arrays are consistently indexed. At index idx they all refer to the same time series initial_selections, measures_sel_gids = [], [] ts_names, graph_labels, grouped_labels = [], [], [] for idx, timeseries in enumerate(timeseries_list): shape = timeseries.read_data_shape() no_of_lines += shape[self.selected_dimensions[1]] max_length = max(max_length, shape[0]) self._fill_graph_labels(timeseries, graph_labels, multiple_inputs, idx) ts_name = timeseries.title.load() ts_names.append(ts_name) if multiple_inputs: # for multiple inputs the default selections might be too big: select the first few # warn: assumes that the selection values are a range initial_selections.append(list(range(4))) else: initial_selections.append(timeseries.get_default_selection()) if isinstance(timeseries.get_measure_points_selection_gid(), str): measures_sel_gids.append(timeseries.get_measure_points_selection_gid()) else: measures_sel_gids.append(timeseries.get_measure_points_selection_gid().hex) grouped_labels.append(self.get_grouped_space_labels(timeseries)) state_vars[ts_name] = timeseries.labels_dimensions.load().get(timeseries.labels_ordering.load()[1], []) modes[ts_name] = list(range(shape[3])) return (no_of_lines, ts_names, grouped_labels, max_length, graph_labels, initial_selections, measures_sel_gids, modes, state_vars) def _fill_graph_labels(self, timeseries, graph_labels, mult_inp, idx): """ Fill graph labels in the graph_labels parameter """ shape = timeseries.read_data_shape() space_labels = self.get_space_labels(timeseries) for j in range(shape[self.selected_dimensions[1]]): if space_labels: if j >= len(space_labels): # for surface time series get_space_labels will return labels up to a limit, # not a label for each signal. # to honor that behaviour we break the loop if we run out of labels. # todo a robust cap on signals. break this_label = str(space_labels[j]) else: this_label = "channel_" + str(j) if mult_inp: this_label = str(idx + 1) + '.' + this_label graph_labels.append(this_label)
[docs] def compute_required_info(self, list_of_timeseries): """Compute average difference between Max and Min.""" # The values computed by this function will be serialized to json and passed to the client. # The time series might be of numpy.float32 a data type that is not serializable. # To overcome this we convert numpy scalars to python floats step = [] translations = [] channels_per_set = [] for timeseries in list_of_timeseries: data_shape = timeseries.read_data_shape() resulting_shape = [] for idx, shape in enumerate(data_shape): if idx in self.selected_dimensions: resulting_shape.append(shape) page_chunk_data = timeseries.read_data_page(self.current_page * self.page_size, (self.current_page + 1) * self.page_size) channels_per_set.append(int(resulting_shape[1])) for idx in range(resulting_shape[1]): self.has_nan = self.has_nan or replace_nan_values(page_chunk_data[:, idx]) array_max = numpy.max(page_chunk_data[:, idx]) array_min = numpy.min(page_chunk_data[:, idx]) translations.append(float((array_max + array_min) / 2)) if array_max == array_min: array_max += 1 step.append(abs(array_max - array_min)) return float(max(step)), translations, channels_per_set
@staticmethod def _get_sub_title(datatype_list): """ Compute sub-title for current page""" return "_".join(d.display_name for d in datatype_list) @staticmethod def _get_label_x(original_timeseries): """ Compute the label displayed on the x axis """ return "Time(%s)" % original_timeseries.sample_period_unit def _get_data_set_urls(self, list_of_timeseries, is_preview=False): """ Returns a list of lists. Each list contains the urls to the files containing the data for a certain array wrapper. """ base_urls = [] time_set_urls = [] total_pages_set = [] if is_preview is False: page_size = self.page_size for timeseries in list_of_timeseries: overall_shape = timeseries.read_data_shape() total_pages = overall_shape[0] // self.page_size if overall_shape[0] % self.page_size > 0: total_pages += 1 timeline_urls = [] ts_gid = timeseries.gid.load().hex for i in range(total_pages): current_max_size = min((i + 1) * self.page_size, overall_shape[0]) - i * self.page_size params = "current_page=" + str(i) + ";page_size=" + str(self.page_size) + \ ";max_size=" + str(current_max_size) timeline_urls.append(URLGenerator.build_h5_url(ts_gid, 'read_time_page', parameter=params)) base_urls.append(URLGenerator.build_base_h5_url(ts_gid)) time_set_urls.append(timeline_urls) total_pages_set.append(total_pages) else: ts_gid = list_of_timeseries[0].gid.load().hex base_urls.append(URLGenerator.build_base_h5_url(ts_gid)) total_pages_set.append(1) page_size = self.preview_page_size params = "current_page=0;page_size=" + str(self.preview_page_size) + ";max_size=" + \ str(min(self.preview_page_size, list_of_timeseries[0].read_data_shape()[0])) time_set_urls.append([URLGenerator.build_h5_url(ts_gid, 'read_time_page', parameter=params)]) return base_urls, page_size, total_pages_set, time_set_urls