# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Framework Package. This package holds all Data Management, and
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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.mode_decompositions import PrincipalComponentsIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.mode_decompositions_h5 import PrincipalComponentsH5
from tvb.analyzers.pca import compute_pca
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.time_series import TimeSeries
[docs]class PCAAdapterModel(ViewModel):
time_series = DataTypeGidAttr(
linked_datatype=TimeSeries,
label="Time Series",
required=True,
doc="""The timeseries to which the PCA is to be applied. NOTE: The
TimeSeries must be longer(more time-points) than the number of nodes
-- Mostly a problem for surface times-series, which, if sampled at
1024Hz, would need to be greater than 16 seconds long."""
)
[docs]class PCAAdapter(ABCAdapter):
""" TVB adapter for calling the PCA algorithm. """
_ui_name = "Principal Component Analysis"
_ui_description = "PCA for a TimeSeries input DataType."
_ui_subsection = "components"
[docs] def get_output(self):
return [PrincipalComponentsIndex]
[docs] def get_required_memory_size(self, view_model):
# type: (PCAAdapterModel) -> int
"""
Return the required memory to run this algorithm.
"""
used_shape = (self.input_shape[0], 1, self.input_shape[2], self.input_shape[3])
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: (PCAAdapterModel) -> 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], self.input_shape[3])
return self.array_size2kb(self.result_size(used_shape))
[docs] def launch(self, view_model):
# type: (PCAAdapterModel) -> [PrincipalComponentsIndex]
"""
Launch algorithm and build results.
:param view_model: the ViewModel keeping the algorithm inputs
:return: the `PrincipalComponentsIndex` object built with the given timeseries as source
"""
# --------------------- Prepare result entities ----------------------##
principal_components_index = PrincipalComponentsIndex()
dest_path = self.path_for(PrincipalComponentsH5, principal_components_index.gid)
pca_h5 = PrincipalComponentsH5(path=dest_path)
# ------------- NOTE: Assumes 4D, Simulator timeSeries. --------------##
time_series_h5 = h5.h5_file_for_index(self.input_time_series_index)
input_shape = time_series_h5.data.shape
node_slice = [slice(input_shape[0]), None, slice(input_shape[2]), slice(input_shape[3])]
# ---------- Iterate over slices and compose final result ------------##
small_ts = TimeSeries()
for var in range(input_shape[1]):
node_slice[1] = slice(var, var + 1)
small_ts.data = time_series_h5.read_data_slice(tuple(node_slice))
self.time_series = small_ts.gid
partial_pca = compute_pca(small_ts)
pca_h5.write_data_slice(partial_pca)
time_series_h5.close()
partial_pca.source.gid = view_model.time_series
partial_pca.gid = uuid.UUID(principal_components_index.gid)
principal_components_index.fill_from_has_traits(partial_pca)
pca_h5.store(partial_pca, scalars_only=True)
pca_h5.close()
return principal_components_index
[docs] def result_size(self, input_shape):
"""
Returns the storage size in Bytes of the results of the PCA analysis.
"""
result_size = numpy.sum(list(map(numpy.prod,
self.result_shape(input_shape)))) * 8.0 # Bytes
return result_size
[docs] @staticmethod
def result_shape(input_shape):
"""
Returns the shape of the main result of the PCA analysis -- compnnent
weights matrix and a vector of fractions.
"""
weights_shape = (input_shape[2], input_shape[2], input_shape[1],
input_shape[3])
fractions_shape = (input_shape[2], input_shape[1], input_shape[3])
return [weights_shape, fractions_shape]
