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Source code for tvb.adapters.analyzers.pca_adapter

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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 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 PCAAdapterForm(ABCAdapterForm): def __init__(self): super(PCAAdapterForm, self).__init__() self.time_series = TraitDataTypeSelectField(PCAAdapterModel.time_series, name=self.get_input_name(), conditions=self.get_filters(), has_all_option=True) @staticmethod
[docs] def get_view_model(): return PCAAdapterModel
@staticmethod
[docs] def get_required_datatype(): return TimeSeriesIndex
@staticmethod
[docs] def get_filters(): return FilterChain(fields=[FilterChain.datatype + '.data_ndim'], operations=["=="], values=[4])
@staticmethod
[docs] def get_input_name(): return "time_series"
[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_form_class(self): return PCAAdapterForm
[docs] def get_output(self): return [PrincipalComponentsIndex]
[docs] def configure(self, view_model): # type: (PCAAdapterModel) -> 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) self.log.debug("Time series shape is %s" % str(self.input_shape))
[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 = h5.path_for(self.storage_path, 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
@staticmethod
[docs] 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]