The Virtual Brain Project

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
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# (c) 2012-2017, 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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#   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 <mw@eml.cc>

"""

import json
import tvb.datatypes.time_series as tsdata
from tvb.basic.filters.chain import FilterChain
from tvb.core.adapters.abcdisplayer import ABCDisplayer



[docs]class TimeSeries(ABCDisplayer): _ui_name = "Time Series Visualizer (SVG/d3)" _ui_subsection = "timeseries" MAX_PREVIEW_DATA_LENGTH = 200
[docs] def get_input_tree(self): """ Inform caller of the data we need as input. """ return [{"name": "time_series", "type": tsdata.TimeSeries, "label": "Time series to be displayed in a 2D form.", "required": True, "conditions": FilterChain(fields=[FilterChain.datatype + '.type'], operations=["!="], values=["TimeSeriesVolume"]) }]
[docs] def get_required_memory_size(self, **kwargs): """Return required memory.""" return -1
[docs] def launch(self, time_series, preview=False, figsize=None): """Construct data for visualization and launch it.""" ts = time_series.get_data('time') shape = list(time_series.read_data_shape()) ## 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 state_variables = time_series.labels_dimensions.get(time_series.labels_ordering[1], []) labels = time_series.get_space_labels() # 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': ABCDisplayer.VISUALIZERS_URL_PREFIX + time_series.gid, 'labels': labels, 'labels_json': json.dumps(labels), 'ts_title': time_series.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': range(shape[3]) } pars.update(self.build_template_params_for_subselectable_datatype(time_series)) return self.build_display_result("time_series/view", pars, pages=dict(controlPage="time_series/control"))
[docs] def generate_preview(self, time_series, figure_size): return self.launch(time_series, preview=True, figsize=figure_size)