# -*- 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
# documentation-folder for more details. See also http://www.thevirtualbrain.org
#
# (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,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program. If not, see <http://www.gnu.org/licenses/>.
#
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as explained here:
# https://www.thevirtualbrain.org/tvb/zwei/neuroscience-publications
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"""
Import Brain Tumor dataset
.. moduleauthor:: Bogdan Valean <bogdan.valean@codemart.ro>
.. moduleauthor:: Robert Vincze <robert.vincze@codemart.ro>
"""
import csv
import os
import uuid
import numpy as np
import json
from tvb.adapters.datatypes.db.graph import CorrelationCoefficientsIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.datatypes.h5.graph_h5 import CorrelationCoefficientsH5
from tvb.adapters.datatypes.h5.time_series_h5 import TimeSeriesRegionH5
from tvb.adapters.uploaders.csv_connectivity_importer import CSVDelimiterOptionsEnum
from tvb.adapters.uploaders.zip_connectivity_importer import ZIPConnectivityImporter, ZIPConnectivityImporterModel
from tvb.config.algorithm_categories import DEFAULTDATASTATE_RAW_DATA
from tvb.core.adapters.abcadapter import ABCAdapter
from tvb.core.adapters.abcuploader import ABCUploader, ABCUploaderForm
from tvb.core.entities.generic_attributes import GenericAttributes
from tvb.core.entities.storage import dao
from tvb.core.neocom import h5
from tvb.core.neotraits.forms import TraitUploadField
from tvb.core.neotraits.view_model import Str, ViewModel
from tvb.datatypes.graph import CorrelationCoefficients
from tvb.datatypes.time_series import TimeSeriesRegion, TimeSeries
WARNING_MSG = "File {} does not exist."
[docs]class TumorDatasetImporterModel(ViewModel):
data_file = Str(
label='Tumor Dataset (BIDS + zip)'
)
[docs]class TumorDatasetImporter(ABCAdapter):
_ui_name = "Tumor Dataset"
_ui_description = "Download manually Tumor Dataset from the EBRAINS KG and import it into TVB."
MAXIMUM_DOWNLOAD_RETRIES = 3
SLEEP_TIME = 3
CONN_ZIP_FILE = "SC.zip"
FC_MAT_FILE = "FC.mat"
FC_DATASET_NAME = "FC_cc_DK68"
TIME_SERIES_CSV_FILE = "HRF.csv"
[docs] def get_output(self):
return []
[docs] def get_required_disk_size(self, view_model):
return -1
[docs] def get_required_memory_size(self, view_model):
return -1
def __import_tumor_connectivity(self, conn_folder, patient, user_tag):
connectivity_zip = os.path.join(conn_folder, self.CONN_ZIP_FILE)
if not os.path.exists(connectivity_zip):
self.log.warning(WARNING_MSG.format(connectivity_zip))
return
import_conn_adapter = self.build_adapter_from_class(ZIPConnectivityImporter)
operation = dao.get_operation_by_id(self.operation_id)
import_conn_adapter.extract_operation_data(operation)
import_conn_model = ZIPConnectivityImporterModel()
import_conn_model.uploaded = connectivity_zip
import_conn_model.data_subject = patient
import_conn_model.generic_attributes.user_tag_1 = user_tag
connectivity_index = import_conn_adapter.launch(import_conn_model)
self.generic_attributes.subject = patient
self.generic_attributes.user_tag_1 = user_tag
self._capture_operation_results([connectivity_index])
connectivity_index.fk_from_operation = self.operation_id
dao.store_entity(connectivity_index)
return connectivity_index.gid
def __import_time_series_csv_datatype(self, hrf_folder, connectivity_gid, patient, user_tag):
path = os.path.join(hrf_folder, self.TIME_SERIES_CSV_FILE)
if not os.path.exists(path):
self.log.warning(WARNING_MSG.format(path))
return
with open(path) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=CSVDelimiterOptionsEnum.COMMA.value)
ts = list(csv_reader)
ts_data = np.array(ts, dtype=np.float64).reshape((len(ts), 1, len(ts[0]), 1))
ts_time = np.random.rand(ts_data.shape[0], )
project = dao.get_project_by_id(self.current_project_id)
ts_gid = uuid.uuid4()
h5_path = "TimeSeries_{}.h5".format(ts_gid.hex)
operation_folder = self.storage_interface.get_project_folder(project.name, str(self.operation_id))
h5_path = os.path.join(operation_folder, h5_path)
conn = h5.load_from_gid(connectivity_gid)
ts = TimeSeriesRegion()
ts.data = ts_data
ts.time = ts_time
ts.gid = ts_gid
ts.connectivity = conn
generic_attributes = GenericAttributes()
generic_attributes.user_tag_1 = user_tag
generic_attributes.state = DEFAULTDATASTATE_RAW_DATA
with TimeSeriesRegionH5(h5_path) as ts_h5:
ts_h5.store(ts)
ts_h5.nr_dimensions.store(4)
ts_h5.subject.store(patient)
ts_h5.store_generic_attributes(generic_attributes)
ts_index = TimeSeriesIndex()
ts_index.gid = ts_gid.hex
ts_index.fk_from_operation = self.operation_id
ts_index.time_series_type = "TimeSeriesRegion"
ts_index.data_length_1d = ts_data.shape[0]
ts_index.data_length_2d = ts_data.shape[1]
ts_index.data_length_3d = ts_data.shape[2]
ts_index.data_length_4d = ts_data.shape[3]
ts_index.data_ndim = len(ts_data.shape)
ts_index.sample_period_unit = 'ms'
ts_index.sample_period = TimeSeries.sample_period.default
ts_index.sample_rate = 1024.0
ts_index.subject = patient
ts_index.state = DEFAULTDATASTATE_RAW_DATA
ts_index.labels_ordering = json.dumps(list(TimeSeries.labels_ordering.default))
ts_index.labels_dimensions = json.dumps(TimeSeries.labels_dimensions.default)
ts_index.visible = False # we don't want to show these TimeSeries because they are dummy
dao.store_entity(ts_index)
return ts_gid
def __import_pearson_coefficients_datatype(self, fc_folder, patient, user_tag, ts_gid):
path = os.path.join(fc_folder, self.FC_MAT_FILE)
if not os.path.exists(path):
self.log.warning(WARNING_MSG.format(path))
return
result = ABCUploader.read_matlab_data(path, self.FC_DATASET_NAME)
result = result.reshape((result.shape[0], result.shape[1], 1, 1))
project = dao.get_project_by_id(self.current_project_id)
pearson_gid = uuid.uuid4()
h5_path = "CorrelationCoefficients_{}.h5".format(pearson_gid.hex)
operation_folder = self.storage_interface.get_project_folder(project.name, str(self.operation_id))
h5_path = os.path.join(operation_folder, h5_path)
generic_attributes = GenericAttributes()
generic_attributes.user_tag_1 = user_tag
generic_attributes.state = DEFAULTDATASTATE_RAW_DATA
with CorrelationCoefficientsH5(h5_path) as pearson_correlation_h5:
pearson_correlation_h5.array_data.store(result)
pearson_correlation_h5.gid.store(pearson_gid)
pearson_correlation_h5.source.store(ts_gid)
pearson_correlation_h5.labels_ordering.store(CorrelationCoefficients.labels_ordering.default)
pearson_correlation_h5.subject.store(patient)
pearson_correlation_h5.store_generic_attributes(generic_attributes)
pearson_correlation_index = CorrelationCoefficientsIndex()
pearson_correlation_index.gid = pearson_gid.hex
pearson_correlation_index.fk_from_operation = self.operation_id
pearson_correlation_index.subject = patient
pearson_correlation_index.state = DEFAULTDATASTATE_RAW_DATA
pearson_correlation_index.ndim = 4
pearson_correlation_index.fk_source_gid = ts_gid.hex # we need a random gid here to store the index
pearson_correlation_index.has_valid_time_series = False
dao.store_entity(pearson_correlation_index)
def __import_from_folder(self, datatype_folder, patient, user_tag):
conn_gid = self.__import_tumor_connectivity(datatype_folder, patient, user_tag)
# The Time Series are invisible in the UI and are imported
# just so we can link them with the Pearson Coefficients
ts_gid = self.__import_time_series_csv_datatype(datatype_folder, conn_gid, patient, user_tag)
self.__import_pearson_coefficients_datatype(datatype_folder, patient, user_tag, ts_gid)
[docs] def launch(self, view_model):
# type: (TumorDatasetImporterModel) -> []
"""
Download the Tumor Dataset and then import its data
(currently only the connectivities and pearson coefficients (FC) are imported).
"""
structure = self.storage_interface.unpack_zip(view_model.data_file, self.get_storage_path())
subject_folders = {}
for name in structure:
if os.path.isdir(name):
user_tag = os.path.split(name)[1]
if user_tag.startswith("sub-"):
subject_folders[user_tag] = name
for patient, patient_path in subject_folders.items():
root_folder_imported = False
for user_tag in os.listdir(patient_path):
datatype_folder = os.path.join(patient_path, user_tag)
if os.path.isdir(datatype_folder):
self.__import_from_folder(datatype_folder, patient, user_tag)
elif not root_folder_imported:
root_folder_imported = True
self.__import_from_folder(patient_path, patient, "")
self.log.debug("Importing Tumor Dataset has been successfully completed!")
