# -*- 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 to download
# TheVirtualBrain-Scientific Package (for simulators). See content of the
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (c) 2012-2023, 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.
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# PARTICULAR PURPOSE. See the GNU General Public License for more details.
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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 explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
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"""
Adapter that uses the traits module to generate interfaces for FFT Analyzer.
.. moduleauthor:: Stuart A. Knock <Stuart@tvb.invalid>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
"""
import uuid
import numpy
from tvb.adapters.datatypes.db.graph import CovarianceIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.graph_h5 import CovarianceH5
from tvb.basic.neotraits.info import narray_describe
from tvb.core.adapters.abcadapter import ABCAdapterForm, ABCAdapter
from tvb.core.entities.filters.chain import FilterChain
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitDataTypeSelectField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.graph import Covariance
from tvb.datatypes.time_series import TimeSeries
[docs]class NodeCovarianceAdapterModel(ViewModel):
time_series = DataTypeGidAttr(
linked_datatype=TimeSeries,
label="Time Series",
required=True,
doc="""The timeseries to which the NodeCovariance is to be applied."""
)
[docs]class NodeCovarianceAdapter(ABCAdapter):
""" TVB adapter for calling the NodeCovariance algorithm. """
_ui_name = "Temporal covariance of nodes"
_ui_description = "Compute Temporal Node Covariance for a TimeSeries input DataType."
_ui_subsection = "covariance"
[docs] def get_output(self):
return [CovarianceIndex]
[docs] def get_required_memory_size(self, view_model):
# type: (NodeCovarianceAdapterModel) -> int
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], 1)
input_size = numpy.prod(used_shape) * 8.0
output_size = self._result_size(used_shape)
return input_size + output_size
[docs] def get_required_disk_size(self, view_model):
# type: (NodeCovarianceAdapterModel) -> int
"""
Returns the required disk size to be able to run the adapter ( in kB).
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], 1)
return self.array_size2kb(self._result_size(used_shape))
[docs] def launch(self, view_model):
# type: (NodeCovarianceAdapterModel) -> [CovarianceIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the `CovarianceIndex` built with the given time_series index as source
"""
# -------------------- Prepare result entities ---------------------##
covariance_index = CovarianceIndex()
covariance_h5_path = self.path_for(CovarianceH5, covariance_index.gid)
covariance_h5 = CovarianceH5(covariance_h5_path)
# ------------ NOTE: Assumes 4D, Simulator timeSeries -------------##
node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), None]
ts_h5 = h5.h5_file_for_index(self.input_time_series_index)
for mode in range(self.input_shape[3]):
for var in range(self.input_shape[1]):
small_ts = TimeSeries()
node_slice[1] = slice(var, var + 1)
node_slice[3] = slice(mode, mode + 1)
small_ts.data = ts_h5.read_data_slice(tuple(node_slice))
partial_cov = self._compute_node_covariance(small_ts, ts_h5)
covariance_h5.write_data_slice(partial_cov.array_data)
ts_h5.close()
partial_cov.source.gid = view_model.time_series
partial_cov.gid = uuid.UUID(covariance_index.gid)
covariance_index.fill_from_has_traits(partial_cov)
self.fill_index_from_h5(covariance_index, covariance_h5)
covariance_h5.store(partial_cov, scalars_only=True)
covariance_h5.close()
return covariance_index
def _compute_node_covariance(self, small_ts, input_ts_h5):
"""
Compute the temporal covariance between nodes in a TimeSeries dataType.
A nodes x nodes matrix is returned for each (state-variable, mode).
"""
data_shape = small_ts.data.shape
# (nodes, nodes, state-variables, modes)
result_shape = (data_shape[2], data_shape[2], data_shape[1], data_shape[3])
self.log.info("result shape will be: %s" % str(result_shape))
result = numpy.zeros(result_shape)
# One inter-node temporal covariance matrix for each state-var & mode.
for mode in range(data_shape[3]):
for var in range(data_shape[1]):
data = input_ts_h5.data[:, var, :, mode]
data = data - data.mean(axis=0)[numpy.newaxis, 0]
result[:, :, var, mode] = numpy.cov(data.T)
self.log.debug("result")
self.log.debug(narray_describe(result))
covariance = Covariance(source=small_ts, array_data=result)
return covariance
@staticmethod
def _result_size(input_shape):
"""
Returns the storage size in Bytes of the NodeCovariance result.
"""
result_size = numpy.prod([input_shape[2], input_shape[2], input_shape[1], input_shape[3]]) * 8.0 # Bytes
return result_size
