def test_normalize_denormalize_3():
"""Test the data normalization and denormalization with shift > 0."""
# Load the data with only a single serie
currdir = os.path.dirname(os.path.abspath(__file__))
path_data = os.path.join(currdir, 'data', 'full_dce')
# Create an object to handle the data
dce_mod = DCEModality()
# Read the data
dce_mod.read_data_from_path(path_data)
# Load the GT data
path_gt = [os.path.join(currdir, 'data', 'full_gt', 'prostate')]
label_gt = ['prostate']
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, path_gt)
# Create the object to make the normalization
stn = StandardTimeNormalization(dce_mod)
# Simulate that we fitted the data
stn.model_ = np.array([30., 30., 32., 31., 31., 30., 35., 55., 70., 80.])
stn.is_model_fitted_ = True
stn.fit_params_ = {'scale-int': 1.2296657327848537,
'shift-time': 3.0,
'shift-int': np.array([191.29, 193.28, 195.28, 195.28,
195.28, 197.28, 213.25, 249.18,
283.12, 298.10])}
stn.is_fitted_ = True
# Store the data somewhere
data_gt_cp = dce_mod.data_.copy()
# Normalize the data
dce_mod_norm = stn.normalize(dce_mod)
# Check if the data are properly normalized
dce_mod_norm.data_.flags.writeable = False
data = np.load(os.path.join(currdir, 'data', 'data_normalized_dce_3.npy'))
assert_equal(hash(dce_mod_norm.data_.data), data)
dce_mod_norm.data_.flags.writeable = True
dce_mod_2 = stn.denormalize(dce_mod_norm)
dce_mod_2.data_.flags.writeable = False
assert_equal(hash(dce_mod_2.data_.data), -3781160829709175881)
