TheVirtualBrain:

TheDocumentationwebsite.

Source code for tvb.adapters.analyzers.metrics_group_timeseries

# -*- 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) 2012-2022, 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)
#
#

"""
Adapter that uses the traits module to generate interfaces for group of 
Analyzer used to calculate a single measure for TimeSeries.

.. moduleauthor:: Paula Sanz Leon <pau.sleon@gmail.com>
.. moduleauthor:: Bogdan Neacsa <bogdan.neacsa@codemart.ro>
.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>

"""
import numpy
from tvb.adapters.datatypes.db.mapped_value import DatatypeMeasureIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.basic.neotraits.api import Int, Float
from tvb.basic.neotraits.api import List
from tvb.config import ALGORITHMS
from tvb.core.adapters.abcadapter import ABCAdapterForm, ABCAdapter
from tvb.core.entities.file.simulator.datatype_measure_h5 import DatatypeMeasure
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField, MultiSelectField, FloatField, IntField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.time_series import TimeSeries


[docs]class TimeseriesMetricsAdapterModel(ViewModel): time_series = DataTypeGidAttr( linked_datatype=TimeSeries, label="Time Series", required=True, doc="The TimeSeries for which the metric(s) will be computed." ) algorithms = List( of=str, choices=tuple(ALGORITHMS.keys()), label='Selected metrics to be applied', doc='The selected algorithms will all be applied on the input TimeSeries' ) start_point = Float( label="Start point (ms)", default=500.0, required=False, doc=""" The start point determines how many points of the TimeSeries will be discarded before computing the metric. By default it drops the first 500 ms.""") segment = Int( label="Segmentation factor", default=4, required=False, doc=""" Divide the input time-series into discrete equally sized sequences and use the last segment to compute the metric. It is only used when the start point is larger than the time-series length.""")
[docs]class TimeseriesMetricsAdapterForm(ABCAdapterForm): @staticmethod
[docs] def get_extra_algorithm_filters(): return {"KuramotoIndex": FilterChain(fields=[FilterChain.datatype + '.data_length_2d'], operations=[">="], values=[2])}
def __init__(self): super(TimeseriesMetricsAdapterForm, self).__init__() self.time_series = TraitDataTypeSelectField(TimeseriesMetricsAdapterModel.time_series, name="time_series") self.start_point = FloatField(TimeseriesMetricsAdapterModel.start_point) self.segment = IntField(TimeseriesMetricsAdapterModel.segment) self.algorithms = MultiSelectField(TimeseriesMetricsAdapterModel.algorithms, name="algorithms") @staticmethod
[docs] def get_view_model(): return TimeseriesMetricsAdapterModel
@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 + '.data_ndim'], operations=["=="], values=[4])
[docs]class TimeseriesMetricsAdapter(ABCAdapter): """ TVB adapter for exposing as a group the measure algorithm. """ _ui_name = "TimeSeries Metrics" _ui_description = "Compute a single number for a TimeSeries input DataType." _ui_subsection = "timeseries" input_shape = ()
[docs] def get_form_class(self): return TimeseriesMetricsAdapterForm
[docs] def get_output(self): return [DatatypeMeasureIndex]
[docs] def configure(self, view_model): # type: (TimeseriesMetricsAdapterModel) -> None """ Store the input shape to be later used to estimate memory usage. """ self.input_time_series_index = self.load_entity_by_gid(view_model.time_series) self.input_shape = (self.input_time_series_index.data_length_1d, self.input_time_series_index.data_length_2d, self.input_time_series_index.data_length_3d, self.input_time_series_index.data_length_4d)
[docs] def get_required_memory_size(self, view_model): # type: (TimeseriesMetricsAdapterModel) -> int """ Return the required memory to run this algorithm. """ input_size = numpy.prod(self.input_shape) * 8.0 return input_size
[docs] def get_required_disk_size(self, view_model): # type: (TimeseriesMetricsAdapterModel) -> int """ Returns the required disk size to be able to run the adapter (in kB). """ return 0
[docs] def launch(self, view_model): # type: (TimeseriesMetricsAdapterModel) -> [DatatypeMeasureIndex] """ Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs """ algorithms = view_model.algorithms if algorithms is None or len(algorithms) == 0: algorithms = list(ALGORITHMS) self.log.debug("time_series shape is %s" % str(self.input_shape)) dt_timeseries = self.load_traited_by_gid(self.input_time_series_index.gid) metrics_results = {} for algorithm_name in algorithms: algorithm_func = ALGORITHMS[algorithm_name] # Validate that current algorithm's filter is valid. algorithm_filter = TimeseriesMetricsAdapterForm.get_extra_algorithm_filters().get(algorithm_name) if algorithm_filter is not None \ and not algorithm_filter.get_python_filter_equivalent(self.input_time_series_index): self.log.warning('Measure algorithm will not be computed because of incompatibility on input. ' 'Filters failed on algo: ' + str(algorithm_name)) continue else: self.log.debug("Applying measure: " + str(algorithm_name)) unstored_result = algorithm_func({'time_series': dt_timeseries, 'start_point': view_model.start_point, 'segment': view_model.segment}) # ----------------- Prepare a Float object(s) for result ----------------## if isinstance(unstored_result, dict): metrics_results.update(unstored_result) else: metrics_results[algorithm_name] = unstored_result dt_metric = DatatypeMeasure(analyzed_datatype=dt_timeseries, metrics=metrics_results) result = h5.store_complete(dt_metric, self._get_output_path()) return result