def test_fit():
"""Test the routine to fit the parameters of the dce normalization."""
# 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)
# Create a synthetic model to fit on
stn.model_ = np.array([30., 30., 32., 31., 31., 30., 35., 55., 70., 80.])
stn.is_model_fitted_ = True
# Fit the parameters on the model
stn.fit(dce_mod, gt_mod, label_gt[0])
assert_almost_equal(stn.fit_params_['scale-int'], 1.2296657327848537,
decimal=PRECISION_DECIMAL)
assert_equal(stn.fit_params_['shift-time'], 0.0)
data = np.array([191.29, 193.28, 195.28, 195.28, 195.28, 197.28, 213.25,
249.18, 283.12, 298.10])
assert_array_almost_equal(stn.fit_params_['shift-int'], data,
decimal=PRECISION_DECIMAL)
def find_normalization_params(pat_dce, pat_gt, label, pat_model):
# Create the normalization object and load the model
dce_norm = StandardTimeNormalization(DCEModality())
dce_norm.load_model(pat_model)
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label, pat_gt)
# Find the normalization parameters
dce_norm.fit(dce_mod, ground_truth=gt_mod, cat=label[0])
return dce_norm
def find_normalization_params(pat_dce, pat_gt, label, pat_model):
# Create the normalization object and load the model
dce_norm = StandardTimeNormalization(DCEModality())
dce_norm.load_model(pat_model)
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(pat_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label, pat_gt)
# Find the normalization parameters
dce_norm.fit(dce_mod, ground_truth=gt_mod, cat=label[0])
return dce_norm
path_dce = '/data/prostate/experiments/Patient 387/DCE'
# Define the list of path for the GT
path_gt = ['/data/prostate/experiments/Patient 387/GT_inv/prostate']
# Define the associated list of label for the GT
label_gt = ['prostate']
# Read the DCE
dce_mod = DCEModality()
dce_mod.read_data_from_path(path_dce)
# Read the GT
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, path_gt)
# Fit the data to get the normalization parameters
dce_norm.fit(dce_mod, ground_truth=gt_mod,
cat='prostate')
dce_mod_norm = dce_norm.normalize(dce_mod)
# Plot the figure
plt.figure()
heatmap, bins_heatmap = dce_mod.build_heatmap(np.nonzero(gt_mod.data_[0, :, :, :]))
sns.heatmap(heatmap, cmap='jet')
# plt.plot(dce_norm.shift_idx_, np.arange(0, dce_mod.n_serie_)[::-1] + .5 ,'ro')
# plt.plot(dce_norm.shift_idx_ + dce_norm.rmse,
# np.arange(0, dce_mod.n_serie_)[::-1] + .5 ,'go')
# plt.plot(dce_norm.shift_idx_ - dce_norm.rmse,
# np.arange(0, dce_mod.n_serie_)[::-1] + .5 ,'go')
plt.savefig('heatmap.png')