Source code for tvb.adapters.uploaders.zip_connectivity_importer

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.. moduleauthor:: Calin Pavel <>
.. moduleauthor:: Lia Domide <>
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'}

[docs]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' )
[docs]class ZIPConnectivityImporterForm(ABCUploaderForm): def __init__(self): super(ZIPConnectivityImporterForm, self).__init__() self.uploaded = TraitUploadField(ZIPConnectivityImporterModel.uploaded, '.zip', 'uploaded') self.normalization = SelectField(ZIPConnectivityImporterModel.normalization, name='normalization')
[docs] @staticmethod def get_view_model(): return ZIPConnectivityImporterModel
[docs] @staticmethod def get_upload_information(): return { 'uploaded': '.zip' }
[docs]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"
[docs] def get_form_class(self): return ZIPConnectivityImporterForm
[docs] def get_output(self): return [ConnectivityIndex]
[docs] 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)