Source code for tvb.adapters.uploaders.zip_connectivity_importer

# -*- 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
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# (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,
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# program.  If not, see <http://www.gnu.org/licenses/>.
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#   CITATION:
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"""
.. moduleauthor:: Calin Pavel <calin.pavel@codemart.ro>
.. moduleauthor:: Lia Domide <lia.domide@codemart.ro>
"""
import numpy
from tvb.adapters.datatypes.db.connectivity import ConnectivityIndex
from tvb.core.adapters.abcuploader import ABCUploader, ABCUploaderForm
from tvb.core.adapters.exceptions import LaunchException
from tvb.core.neotraits.uploader_view_model import UploaderViewModel
from tvb.core.neotraits.view_model import Str
from tvb.core.neotraits.forms import TraitUploadField, SelectField
from tvb.datatypes.connectivity import Connectivity

NORMALIZATION_OPTIONS = {'Region (node)': 'region', 'Absolute (max weight)': 'tract'}




class ZIPConnectivityImporterModel(UploaderViewModel):
    uploaded = Str(
        label='Connectivity file (zip)'
    )

    normalization = Str(
        required=False,
        choices=tuple(NORMALIZATION_OPTIONS.values()),
        label='Weights Normalization',
        doc='Normalization mode for weights'
    )





class ZIPConnectivityImporterForm(ABCUploaderForm):

    def __init__(self):
        super(ZIPConnectivityImporterForm, self).__init__()

        self.uploaded = TraitUploadField(ZIPConnectivityImporterModel.uploaded, '.zip', 'uploaded')
        self.normalization = SelectField(ZIPConnectivityImporterModel.normalization, name='normalization')



    @staticmethod
    def get_view_model():
        return ZIPConnectivityImporterModel




    @staticmethod
    def get_upload_information():
        return {
            'uploaded': '.zip'
        }






class ZIPConnectivityImporter(ABCUploader):
    """
    Handler for uploading a Connectivity archive, with files holding 
    text export of connectivity data from Numpy arrays.
    """
    _ui_name = "Connectivity ZIP"
    _ui_subsection = "zip_connectivity_importer"
    _ui_description = "Import a Connectivity from ZIP"

    WEIGHT_TOKEN = "weight"
    CENTRES_TOKEN = "centres"
    CENTRES_TOKEN2 = "centers"
    TRACT_TOKEN = "tract"
    ORIENTATION_TOKEN = "orientation"
    AREA_TOKEN = "area"
    CORTICAL_INFO = "cortical"
    HEMISPHERE_INFO = "hemisphere"



    def get_form_class(self):
        return ZIPConnectivityImporterForm




    def get_output(self):
        return [ConnectivityIndex]




    def launch(self, view_model):
        # type: (ZIPConnectivityImporterModel) -> [ConnectivityIndex]
        """
        Execute import operations: unpack ZIP and build Connectivity object as result.
        :raises LaunchException: when `uploaded` is empty or nonexistent
        :raises Exception: when
                    * weights or tracts matrix is invalid (negative values, wrong shape)
                    * any of the vector orientation, areas, cortical or hemisphere is \
                      different from the expected number of nodes
        """
        if view_model.uploaded is None:
            raise LaunchException("Please select ZIP file which contains data to import")

        files = self.storage_interface.unpack_zip(view_model.uploaded, self.get_storage_path())

        weights_matrix = None
        centres = None
        labels_vector = None
        tract_matrix = None
        orientation = None
        areas = None
        cortical_vector = None
        hemisphere_vector = None

        for file_name in files:
            file_name_low = file_name.lower()
            if self.WEIGHT_TOKEN in file_name_low:
                weights_matrix = self.read_list_data(file_name)
            elif self.CENTRES_TOKEN in file_name_low or self.CENTRES_TOKEN2 in file_name_low:
                centres = self.read_list_data(file_name, usecols=[1, 2, 3])
                labels_vector = self.read_list_data(file_name, dtype=numpy.str_, usecols=[0])
            elif self.TRACT_TOKEN in file_name_low:
                tract_matrix = self.read_list_data(file_name)
            elif self.ORIENTATION_TOKEN in file_name_low:
                orientation = self.read_list_data(file_name)
            elif self.AREA_TOKEN in file_name_low:
                areas = self.read_list_data(file_name)
            elif self.CORTICAL_INFO in file_name_low:
                cortical_vector = self.read_list_data(file_name, dtype=numpy.bool_)
            elif self.HEMISPHERE_INFO in file_name_low:
                hemisphere_vector = self.read_list_data(file_name, dtype=numpy.bool_)

        # Clean remaining text-files.
        self.storage_interface.remove_files(files, True)

        result = Connectivity()

        # Set attributes
        expected_number_of_nodes = len(centres)
        result.set_centres(centres, expected_number_of_nodes)
        result.set_region_labels(labels_vector)
        result.set_weights(weights_matrix, expected_number_of_nodes)
        if view_model.normalization:
            result.weights = result.scaled_weights(view_model.normalization)
        result.set_tract_lengths(tract_matrix, expected_number_of_nodes)
        result.set_orientations(orientation, expected_number_of_nodes)
        result.set_areas(areas, expected_number_of_nodes)
        result.set_cortical(cortical_vector, expected_number_of_nodes)
        result.set_hemispheres(hemisphere_vector, expected_number_of_nodes)

        result.configure()
        return self.store_complete(result)