The Virtual Brain Project

Source code for tvb.datatypes.fcd

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#   Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
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"""
Adapter that uses the traits module to generate interfaces for ... Analyzer.

.. moduleauthor:: Francesca Melozzi <france.melozzi@gmail.com>
.. moduleauthor:: Marmaduke Woodman <mmwoodman@gmail.com>

"""

from tvb.basic.logger.builder import get_logger

import tvb.basic.traits.core as core
import tvb.basic.traits.types_basic as basic
import tvb.datatypes.arrays as arrays
import tvb.datatypes.time_series as time_series



LOG = get_logger(__name__)

[docs]class Fcd(arrays.MappedArray): array_data = arrays.FloatArray(file_storage=core.FILE_STORAGE_DEFAULT) source = time_series.TimeSeries( label="Source time-series", doc="Links to the time-series on which FCD is calculated.") sw = basic.Float( label="Sliding window length (ms)", default=120000, doc="""Length of the time window used to divided the time series. FCD matrix is calculated in the following way: the time series is divided in time window of fixed length and with an overlapping of fixed length. The datapoints within each window, centered at time ti, are used to calculate FC(ti) as Pearson correlation. The ij element of the FCD matrix is calculated as the Pearson correlation between FC(ti) and FC(tj) arranged in a vector.""") sp = basic.Float( label="Spanning between two consecutive sliding window (ms)", default=2000, doc="""Spanning= (time windows length)-(overlapping between two consecutive time window). FCD matrix is calculated in the following way: the time series is divided in time window of fixed length and with an overlapping of fixed length. The datapoints within each window, centered at time ti, are used to calculate FC(ti) as Pearson correlation. The ij element of the FCD matrix is calculated as the Pearson correlation between FC(ti) and FC(tj) arranged in a vector""") labels_ordering = basic.List( label="Dimension Names", default=["Time", "Time", "State Variable", "Mode"], doc="""List of strings representing names of each data dimension""") __generate_table__ = True def configure(self): """After populating few fields, compute the rest of the fields""" # Do not call super, because that accesses data not-chunked self.nr_dimensions = len(self.read_data_shape()) for i in range(self.nr_dimensions): setattr(self, 'length_%dd' % (i + 1), int(self.read_data_shape()[i])) def _find_summary_info(self): """ Gather scientifically interesting summary information from an instance of this datatype. """ summary = {"FCD type": self.__class__.__name__, "Source": self.source.title, "Dimensions": self.labels_ordering} summary.update(self.get_info_about_array('array_data')) return summary