data_fiso = noddi_data.get_fiso().transpose(1, 0, 2)[::-1, ::-1]
data_ficvf = noddi_data.get_ficvf().transpose(1, 0, 2)[::-1, ::-1]
data_gfa = noddi_data.get_gfa().transpose(1, 0, 2)[::-1, ::-1]
# data_odi[data_odi>max_y[0]] = max_y[0]
# data_fiso[data_fiso>max_y[1]] = max_y[1]
# data_ficvf[data_ficvf>max_y[2]] = max_y[2]
# data_gfa[data_gfa>max_y[3]] = max_y[3]
slice_use = 22
prediction_1d = predict.golkov_multi(data_full,
n_directions).transpose(1, 0, 2,
3)[::-1, ::-1]
prediction_2d = predict.model_2d(data_full,
n_directions).transpose(1, 0, 2,
3)[::-1, ::-1]
prediction_raw = predict.model_raw(data_raw,
n_directions).transpose(1, 0, 2,
3)[::-1, ::-1]
prediction_raw_new = predict.model_raw_new(data_raw, n_directions).transpose(
1, 0, 2, 3)[::-1, ::-1]
montage_1 = np.concatenate(
(data_odi[:, :, slice_use], data_fiso[:, :, slice_use],
data_ficvf[:, :, slice_use], data_gfa[:, :, slice_use] / 0.5),
axis=1)
montage_2 = np.concatenate(
(prediction_1d[:, :, slice_use, 0], prediction_1d[:, :, slice_use, 1],
prediction_1d[:, :, slice_use,
data_fiso = noddi_data.get_fiso()
data_ficvf = noddi_data.get_ficvf()
data_gfa = noddi_data.get_gfa()
# data_md = noddi_data.get_md()
# data_ad = noddi_data.get_ad()
# data_fa = noddi_data.get_fa()
data_odi[data_odi > max_y[0]] = max_y[0]
data_fiso[data_fiso > max_y[1]] = max_y[1]
data_ficvf[data_ficvf > max_y[2]] = max_y[2]
data_gfa[data_gfa > max_y[3]] = max_y[3]
# data_md[data_md>max_y[4]] = max_y[4]
# data_ad[data_ad>max_y[5]] = max_y[5]
# data_fa[data_fa>max_y[6]] = max_y[6]
prediction = predict.model_2d(data_full, n_channels, loss_type=loss_type)
slice_use = 25
montage_top = np.concatenate(
(data_odi[:, :, slice_use], data_fiso[:, :, slice_use],
data_ficvf[:, :, slice_use]),
axis=1)
montage_bottom = np.concatenate(
(prediction[:, :, slice_use, 0], prediction[:, :, slice_use, 1],
prediction[:, :, slice_use, 2]),
axis=1)
montage_combine = np.concatenate(
(montage_top, montage_bottom, abs(montage_top - montage_bottom)), axis=0)
directions = [128, 64, 32, 24, 16, 8]
seeds = [100, 225, 300, 325, 400, 425, 500, 525, 600]
for patient_number in test_cases:
for n_directions in directions:
noddi_data = noddistudy.NoddiData(patient_number)
max_y_path = "/v/raid1b/egibbons/MRIdata/DTI/noddi/max_y_2d.h5"
max_y = readhdf5.read_hdf5(max_y_path, "max_y")
data_full = noddi_data.get_full()
prediction_2d = predict.model_2d(data_full,
n_directions,
random_seed=400)
hf = h5py.File(
"/v/raid1b/egibbons/MRIdata/DTI/noddi/processing/%s_%i_directions_2d.h5"
% (patient_number, n_directions), "w")
hf.create_dataset("predictions", data=prediction_2d)
hf.close
prediction_1d_res = predict.model_1d(data_full,
n_directions,
random_seed=400)
hf = h5py.File(
"/v/raid1b/egibbons/MRIdata/DTI/noddi/processing/%s_%i_directions_1d_res.h5"
% (patient_number, n_directions), "w")