return [experiment] + np.mean(results, axis=0).tolist()
if __name__ == "__main__":
reset()
arguments = docopt(__doc__)
pd.set_option("display.expand_frame_repr", False)
pheno_path = "./data/phenotypes/Phenotypic_V1_0b_preprocessed949.csv"
pheno = load_phenotypes(pheno_path)
hdf5 = hdf5_handler("./data/abide_cc200_tichu.hdf5", "a")
valid_derivatives = ["cc200", "aal", "ez", "ho", "tt", "dosenbach160"]
derivatives = [
derivative for derivative in arguments["<derivative>"]
if derivative in valid_derivatives
]
experiments = []
for derivative in derivatives:
config = {"derivative": derivative}
if arguments["--whole"]:
experiments += [format_config("{derivative}_whole", config)],
help='folder containing the stim times')
#-----------------------------------------------------------------------------------------------
args = parser.parse_args()
visits = ['visit1','visit2']#args.nifti_folder
in_rois = args.in_rois
subjects_ids_csv = args.subjects_ids
output_file = args.output_file
nifti_type = args.nifti_type
stims_folder = args.stims_folder
if not isinstance(visits,list):
visits = [visits]
# Instance output file
output_file = hdf5_handler(output_file,'a')
# Load clinical data
subjects_ids = pd.read_csv(subjects_ids_csv,header=None)
# Create roi and correlation object
masker = NiftiLabelsMasker(labels_img=in_rois, standardize=True)
correlation_measure = ConnectivityMeasure(kind='correlation')
ids_nifts = subjects_ids.values.astype(str)
# ids_nifts = ids_nifts[ids_nifts != 'nan']
for visit in visits:
if nifti_type == 'errts':
nifti_files = glob.glob('../'+visit+'/*+tlrc.BRIK')
patient_storage.attrs["sex"] = record["SEX"]
patient_storage.create_dataset(derivative, data=func_data[pid])
if __name__ == "__main__":
random.seed(19)
np.random.seed(19)
arguments = docopt(__doc__)
folds = int(arguments["--folds"])
pheno_path = "./data/phenotypes/Phenotypic_V1_0b_preprocessed949.csv"
pheno = load_phenotypes(pheno_path)
hdf5 = hdf5_handler("./data/abide_aal_tichu.hdf5", "a")
#hdf5 = hdf5_handler("./data/abide_cc200_tichu.hdf5", "a")
#hdf5 = hdf5_handler("./data/abide_dosenbach160_tichu.hdf5", "a")
valid_derivatives = ["cc200", "aal", "ez", "ho", "tt", "dosenbach160"]
derivatives = [
derivative for derivative in arguments["<derivative>"]
if derivative in valid_derivatives
]
if "patients" not in hdf5:
load_patients_to_file(hdf5, pheno, derivatives)
if arguments["--whole"]:
print
print "Preparing whole dataset"
prev_model_2_path=ae2_model_path,
code_size_1=code_size_1,
code_size_2=code_size_2)
if __name__ == "__main__":
reset()
arguments = docopt(__doc__)
pheno_path = "./data/phenotypes/Phenotypic_V1_0b_preprocessed1.csv"
pheno = load_phenotypes(pheno_path)
# hdf5 = hdf5_handler("./data/abide.hdf5", "a")
hdf5 = hdf5_handler(bytes("./data/abide.hdf5", encoding="utf8"), 'a')
valid_derivatives = ["cc200", "aal", "ez", "ho", "tt", "dosenbach160"]
derivatives = [
derivative for derivative in arguments["<derivative>"]
if derivative in valid_derivatives
]
experiments = []
for derivative in derivatives:
config = {"derivative": derivative}
if arguments["--whole"]:
experiments += [format_config("{derivative}_whole", config)],
patient_storage.attrs["sex"] = record["SEX"]
patient_storage.create_dataset(derivative, data=func_data[pid])
if __name__ == "__main__":
random.seed(19)
np.random.seed(19)
arguments = docopt(__doc__)
folds = int(arguments["--folds"])
pheno_path = "./data/phenotypes/Phenotypic_V1_0b_preprocessed1.csv"
pheno = load_phenotypes(pheno_path)
hdf5 = hdf5_handler("./data/abide.hdf5", "a")
valid_derivatives = ["cc200", "aal", "ez", "ho", "tt", "dosenbach160"]
derivatives = [
derivative for derivative in arguments["<derivative>"]
if derivative in valid_derivatives
]
if "patients" not in hdf5:
load_patients_to_file(hdf5, pheno, derivatives)
if arguments["--whole"]:
print
print "Preparing whole dataset"
prepare_folds(hdf5,
folds,