def test_fetch_abide_pcp(tmp_path, request_mocker): ids = list(range(800)) filenames = ['no_filename'] * 800 filenames[::2] = ['filename'] * 400 pheno = pd.DataFrame({"subject_id": ids, "FILE_ID": filenames}, columns=["subject_id", "FILE_ID"]) request_mocker.url_mapping["*rocessed1.csv"] = pheno.to_csv(index=False) # All subjects dataset = func.fetch_abide_pcp(data_dir=str(tmp_path), quality_checked=False, verbose=0) assert len(dataset.func_preproc) == 400 assert dataset.description != '' # Smoke test using only a string, rather than a list of strings dataset = func.fetch_abide_pcp(data_dir=str(tmp_path), quality_checked=False, verbose=0, derivatives='func_preproc')
def test_fetch_abide_pcp(): local_url = "file://" + tst.datadir ids = [('50%03d' % i).encode() for i in range(800)] filenames = ['no_filename'] * 800 filenames[::2] = ['filename'] * 400 pheno = np.asarray(list(zip(ids, filenames)), dtype=[('subject_id', int), ('FILE_ID', 'U11')]) # pheno = pheno.T.view() tst.mock_fetch_files.add_csv('Phenotypic_V1_0b_preprocessed1.csv', pheno) # All subjects dataset = func.fetch_abide_pcp(data_dir=tst.tmpdir, url=local_url, quality_checked=False, verbose=0) assert_equal(len(dataset.func_preproc), 400) assert_not_equal(dataset.description, '') # Smoke test using only a string, rather than a list of strings dataset = func.fetch_abide_pcp(data_dir=tst.tmpdir, url=local_url, quality_checked=False, verbose=0, derivatives='func_preproc')
def test_fetch_abide_pcp(): local_url = "file://" + datadir ids = [("50%03d" % i).encode() for i in range(800)] filenames = ["no_filename"] * 800 filenames[::2] = ["filename"] * 400 pheno = np.asarray(list(zip(ids, filenames)), dtype=[("subject_id", int), ("FILE_ID", "U11")]) # pheno = pheno.T.view() mock_fetch_files.add_csv("Phenotypic_V1_0b_preprocessed1.csv", pheno) # All subjects dataset = func.fetch_abide_pcp(data_dir=tmpdir, url=local_url, quality_checked=False, verbose=0) assert_equal(len(dataset.func_preproc), 400)
def test_fetch_abide_pcp(): local_url = "file://" + datadir ids = [('50%03d' % i).encode() for i in range(800)] filenames = ['no_filename'] * 800 filenames[::2] = ['filename'] * 400 pheno = np.asarray(list(zip(ids, filenames)), dtype=[('subject_id', int), ('FILE_ID', 'U11')]) # pheno = pheno.T.view() mock_fetch_files.add_csv('Phenotypic_V1_0b_preprocessed1.csv', pheno) # All subjects dataset = func.fetch_abide_pcp(data_dir=tmpdir, url=local_url, quality_checked=False, verbose=0) assert_equal(len(dataset.func_preproc), 400)
def test_fetch_abide_pcp(tmp_path, request_mocker, quality_checked): n_subjects = 800 ids = list(range(n_subjects)) filenames = ['no_filename'] * n_subjects filenames[::2] = ['filename'] * int(n_subjects / 2) qc_rater_1 = ['OK'] * n_subjects qc_rater_1[::4] = ['fail'] * int(n_subjects / 4) pheno = pd.DataFrame( { "subject_id": ids, "FILE_ID": filenames, "qc_rater_1": qc_rater_1, "qc_anat_rater_2": qc_rater_1, "qc_func_rater_2": qc_rater_1, "qc_anat_rater_3": qc_rater_1, "qc_func_rater_3": qc_rater_1 }, columns=[ "subject_id", "FILE_ID", "qc_rater_1", "qc_anat_rater_2", "qc_func_rater_2", "qc_anat_rater_3", "qc_func_rater_3" ]) request_mocker.url_mapping["*rocessed1.csv"] = pheno.to_csv(index=False) # All subjects dataset = func.fetch_abide_pcp(data_dir=tmp_path, quality_checked=quality_checked, verbose=0) div = 4 if quality_checked else 2 assert len(dataset.func_preproc) == n_subjects / div assert dataset.description != '' # Smoke test using only a string, rather than a list of strings dataset = func.fetch_abide_pcp(data_dir=tmp_path, quality_checked=quality_checked, verbose=0, derivatives='func_preproc')