def test_all_predict_gen():
# Test the general k-fold cross-validation where one of the inputs is a model class.
actual_vals, predicted_vals = pn.kfold_xval_gen(sfm.SparseDeconvolutionModelMultiB,
data_pv, bvecs_pv, bvals_pv, 20,
mask = mask_pv, fODF_mode = "single",
axial_diffusivity = ad,
radial_diffusivity = rd,
mean = "empirical",
solver = "nnls")
npt.assert_equal(np.mean(predicted_vals[1][predicted_vals[1] >1])>800,1)
npt.assert_equal(np.mean(actual_vals[1][actual_vals[1] >1])>800,1)
# Let's see if the RMSE is reasonable.
this_rmse = np.sqrt(np.mean((actual_vals - predicted_vals)**2))
npt.assert_equal(this_rmse<350, 1)
if __name__ == "__main__":
sid = "103414"
hcp_path = "/biac4/wandell/data/klchan13/hcp_data_q3"
data_path = os.path.join(hcp_path, "%s/T1w/Diffusion" % sid)
data_file = nib.load(os.path.join(data_path, "data.nii.gz"))
wm_data_file = nib.load(os.path.join(data_path, "wm_mask_no_vent.nii.gz"))
data = data_file.get_data()
wm_data = wm_data_file.get_data()
wm_idx = np.where(wm_data == 1)
bvals = np.loadtxt(os.path.join(data_path, "bvals"))
bvecs = np.loadtxt(os.path.join(data_path, "bvecs"))
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
ad_rd = np.loadtxt(os.path.join(data_path, "ad_rd_%s.txt" % sid))
ad = {1000: ad_rd[0, 0], 2000: ad_rd[0, 1], 3000: ad_rd[0, 2]}
rd = {1000: ad_rd[1, 0], 2000: ad_rd[1, 1], 3000: ad_rd[1, 2]}
b_inds_w0 = np.concatenate((b_inds[0], b_inds[3]))
actual, predicted = pn.kfold_xval_gen(
dti.TensorModel, data[..., b_inds_w0], bvecs[:, b_inds_w0], bvals[b_inds_w0], 10, mask=wm_data
)
cod = ozu.coeff_of_determination(actual, predicted)
np.save(os.path.join(data_path, "dtm_predict_b3k.npy"), predicted)
np.save(os.path.join(data_path, "dtm_cod_b3k.npy"), cod)
hcp_path = '/biac4/wandell/data/klchan13/hcp_data_q3'
data_path = os.path.join(hcp_path, "%s/T1w/Diffusion" % sid)
data_file = nib.load(os.path.join(data_path, "data.nii.gz"))
wm_data_file = nib.load(os.path.join(data_path, "wm_mask_no_vent.nii.gz"))
data = data_file.get_data()
wm_data = wm_data_file.get_data()
wm_idx = np.where(wm_data == 1)
bvals = np.loadtxt(os.path.join(data_path, "bvals"))
bvecs = np.loadtxt(os.path.join(data_path, "bvecs"))
bval_list, b_inds, unique_b, bvals_scaled = ozu.separate_bvals(bvals)
all_b_idx = np.where(bvals_scaled != 0)
ad_rd = np.loadtxt(os.path.join(data_path, "ad_rd_%s.txt" % sid))
ad = {1000: ad_rd[0, 0], 2000: ad_rd[0, 1], 3000: ad_rd[0, 2]}
rd = {1000: ad_rd[1, 0], 2000: ad_rd[1, 1], 3000: ad_rd[1, 2]}
b_inds_w0 = np.concatenate((b_inds[0], b_inds[3]))
actual, predicted = pn.kfold_xval_gen(dti.TensorModel,
data[..., b_inds_w0],
bvecs[:, b_inds_w0],
bvals[b_inds_w0],
10,
mask=wm_data)
cod = ozu.coeff_of_determination(actual, predicted)
np.save(os.path.join(data_path, "dtm_predict_b3k.npy"), predicted)
np.save(os.path.join(data_path, "dtm_cod_b3k.npy"), cod)
