Source code for tvb.adapters.visualizers.time_series

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A Javascript displayer for time series, using SVG.

.. moduleauthor:: Marmaduke Woodman <>


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.core.utils import TVBJSONEncoder
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): super(TimeSeriesForm, self).__init__() self.time_series = TraitDataTypeSelectField(TimeSeriesModel.time_series, name='time_series', conditions=self.get_filters())
[docs] @staticmethod def get_view_model(): return TimeSeriesModel
[docs] @staticmethod def get_required_datatype(): return TimeSeriesIndex
[docs] @staticmethod def get_input_name(): return 'time_series'
[docs] @staticmethod def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.time_series_type'], operations=["in"], values=[['TimeSeriesEEG', 'TimeSeriesSEEG', 'TimeSeriesMEG', 'TimeSeriesRegion', 'TimeSeriesSurface']])
[docs]@add_metaclass(ABCMeta) class ABCSpaceDisplayer(ABCDisplayer):
[docs] @staticmethod 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() with h5.h5_file_for_gid(sensors_gid) 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 [] with h5.h5_file_for_gid(connectivity_gid) 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 [] with h5.h5_file_for_gid(sensors_gid) 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.time.load() 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, cls=TVBJSONEncoder), '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)