Source code for tvb.adapters.analyzers.ica_adapter

# -*- coding: utf-8 -*-
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"""
Adapter that uses the traits module to generate interfaces for ICA Analyzer.

.. moduleauthor:: Paula Sanz Leon

"""

import uuid

import numpy
from tvb.adapters.datatypes.db.mode_decompositions import IndependentComponentsIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.mode_decompositions_h5 import IndependentComponentsH5
from tvb.analyzers.ica import compute_ica_decomposition
from tvb.basic.neotraits.api import Int
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, IntField
from tvb.core.neotraits.view_model import ViewModel, DataTypeGidAttr
from tvb.datatypes.time_series import TimeSeries


class ICAAdapterModel(ViewModel):
    time_series = DataTypeGidAttr(
        linked_datatype=TimeSeries,
        label="Time Series",
        required=True,
        doc="The timeseries to which the ICA is to be applied."
    )

    n_components = Int(
        label="Number of principal components to unmix.",
        required=False,
        default=None,
        doc="Number of principal components to unmix.")


class ICAAdapterForm(ABCAdapterForm):

    def __init__(self):
        super(ICAAdapterForm, self).__init__()
        self.time_series = TraitDataTypeSelectField(ICAAdapterModel.time_series, name='time_series',
                                                    conditions=self.get_filters(), has_all_option=True)
        self.n_components = IntField(ICAAdapterModel.n_components)

    @staticmethod
    def get_view_model():
        return ICAAdapterModel

    @staticmethod
    def get_required_datatype():
        return TimeSeriesIndex

    @staticmethod
    def get_filters():
        return FilterChain(fields=[FilterChain.datatype + '.data_ndim'], operations=["=="], values=[4])

    @staticmethod
    def get_input_name():
        return "time_series"


class ICAAdapter(ABCAdapter):
    """ TVB adapter for calling the ICA algorithm. """

    _ui_name = "Independent Component Analysis"
    _ui_description = "ICA for a TimeSeries input DataType."
    _ui_subsection = "ica"

    def get_form_class(self):
        return ICAAdapterForm

    def get_output(self):
        return [IndependentComponentsIndex]

    def configure(self, view_model):
        # type: (ICAAdapterModel) -> None
        """
        Store the input shape to be later used to estimate memory usage. Also
        create the algorithm instance.
        """
        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))
        self.log.debug("Provided number of components is %s" % view_model.n_components)

        if view_model.n_components is None:
            view_model.n_components = self.input_time_series_index.data_length_3d

    def get_required_memory_size(self, view_model):
        # type: (ICAAdapterModel) -> 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(self.input_shape, view_model.n_components)
        return input_size + output_size

    def get_required_disk_size(self, view_model):
        # type: (ICAAdapterModel) -> 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, view_model.n_components))

    def launch(self, view_model):
        # type: (ICAAdapterModel) -> [IndependentComponentsIndex]
        """
        Launch algorithm and build results.
        :param view_model: the ViewModel keeping the algorithm inputs
        :return: the ica index for the specified time series
        """
        # --------------------- Prepare result entities ---------------------##
        ica_index = IndependentComponentsIndex()
        result_path = self.path_for(IndependentComponentsH5, ica_index.gid)
        ica_h5 = IndependentComponentsH5(path=result_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))
            partial_ica = compute_ica_decomposition(small_ts, view_model.n_components)
            ica_h5.write_data_slice(partial_ica)

        time_series_h5.close()

        partial_ica.source.gid = view_model.time_series
        partial_ica.gid = uuid.UUID(ica_index.gid)

        ica_h5.store(partial_ica, scalars_only=True)
        ica_h5.close()

        ica_index.fill_from_has_traits(partial_ica)

        return ica_index

    @staticmethod
    def result_shape(input_shape, n_components):
        """Returns the shape of the mixing matrix."""
        n = n_components or input_shape[2]
        return n, n, input_shape[1], input_shape[3]

    def result_size(self, input_shape, n_components):
        """Returns the storage size in bytes of the mixing matrix of the ICA analysis, assuming 64-bit float."""
        return numpy.prod(self.result_shape(input_shape, n_components)) * 8