#!/usr/bin/env python
# Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import glob
import os
from WORC.tests import test_helpers as th
from WORC.addexceptions import WORCValueError
from WORC.featureprocessing.ICCThreshold import convert_features_ICC_threshold
from random import shuffle
[docs]def test_iccthreshold():
'''
Test ICC Thresholding statistical testing and computation.
'''
# Check if example data directory exists
example_data_dir = th.find_exampledatadir()
# Check if example data required exists
features = glob.glob(os.path.join(example_data_dir, 'examplefeatures_Patient*.hdf5'))
if len(features) < 6:
message = 'Too few example features for ICC testing not found!' +\
'Run the create_example_data script from the WORC exampledata ' +\
'directory!'
raise WORCValueError(message)
elif len(features) > 6:
message = 'Too many example features for ICC testing not found!' +\
'Run the create_example_data script from the WORC exampledata ' +\
'directory!'
raise WORCValueError(message)
# Pretend that features are from three observers
features_multi = list()
for i in range(0, 3):
shuffle(features)
features_multi.append(features[:])
features_out = [i.replace('examplefeatures_', 'examplefeatures_ICC_') for i in features]
features_out = [features_out, features_out, features_out]
# CSV to save ICC values to
csv_out = os.path.join(example_data_dir, 'ICCValues.csv')
# Run the ICC threshold function: only for training
convert_features_ICC_threshold(features_in=features_multi,
csv_out=csv_out,
features_out=features_out)
# Remove the feature files
for i in glob.glob(os.path.join(example_data_dir, '*examplefeatures_ICC_*.hdf5')):
os.remove(i)
if __name__ == "__main__":
test_iccthreshold()