fix more positionnal arguments

scilpy/denoise/asym_filtering.py

@@ -78,7 +78,7 @@ def unified_filtering(sh_data, sh_order, sh_basis, is_legacy, full_basis,
         raise ValueError('Option use_opencl must be enabled '
                          'to use device \'gpu\'.')
 
-    sphere = get_sphere(sphere_str)
+    sphere = get_sphere(name=sphere_str)
 
     if sigma_spatial is not None:
         if sigma_spatial <= 0.0:

scilpy/image/volume_b0_synthesis.py

@@ -73,16 +73,20 @@ def compute_b0_synthesis(t1_data, t1_bet_data, b0_data, b0_bet_data,
     t1_bet_to_b0, t1_bet_to_b0_transform = register_image(
         b0_bet_data, b0_affine, t1_bet_data, t1_affine, fine=True)
     affine_map = AffineMap(t1_bet_to_b0_transform,
-                           b0_data.shape, b0_affine,
-                           t1_data.shape, t1_affine)
+                           domain_grid_shape=b0_data.shape,
+                           domain_grid2world=b0_affine,
+                           codomain_grid_shape=t1_data.shape,
+                           codomain_grid2world=t1_affine)
     t1_skull_to_b0 = affine_map.transform(t1_data.astype(np.float64))
 
     # Then register to MNI (using the BET again)
     _, t1_bet_to_b0_to_mni_transform = register_image(
         template_data, template_affine, t1_bet_to_b0, b0_affine, fine=True)
     affine_map = AffineMap(t1_bet_to_b0_to_mni_transform,
-                           template_data.shape, template_affine,
-                           b0_data.shape, b0_affine)
+                           domain_grid_shape=template_data.shape,
+                           domain_grid2world=template_affine,
+                           codomain_grid_shape=b0_data.shape,
+                           codomain_grid2world=b0_affine)
 
     # But for prediction, we want the skull
     b0_skull_to_mni = affine_map.transform(b0_data.astype(np.float64))

scilpy/image/volume_operations.py

@@ -155,8 +155,10 @@ def apply_transform(transfo, reference, moving,
     if moving_data.ndim == 3:
         orig_type = moving_data.dtype
         affine_map = AffineMap(np.linalg.inv(transfo),
-                               dim, grid2world,
-                               moving_data.shape, moving_affine)
+                               domain_grid_shape=dim,
+                               domain_grid2world=grid2world,
+                               codomain_grid_shape=moving_data.shape,
+                               codomain_grid2world=moving_affine)
         resampled = affine_map.transform(moving_data.astype(np.float64),
                                          interpolation=interp)
     elif moving_data.ndim == 4:
@@ -168,8 +170,10 @@ def apply_transform(transfo, reference, moving,
                             'bvecs with scil_gradients_apply_transform.py')
 
         affine_map = AffineMap(np.linalg.inv(transfo),
-                               dim[0:3], grid2world,
-                               moving_data.shape[0:3], moving_affine)
+                               domain_grid_shape=dim[0:3],
+                               domain_grid2world=grid2world,
+                               codomain_grid_shape=moving_data.shape[0:3],
+                               codomain_grid2world=moving_affine)
 
         orig_type = moving_data.dtype
         resampled = transform_dwi(affine_map, static_data, moving_data,
@@ -268,7 +272,7 @@ def register_image(static, static_grid2world, moving, moving_grid2world,
 
     sigmas = [8.0, 4.0, 2.0, 1.0] if fine else [8.0, 4.0, 2.0]
     factors = [8, 4, 2, 1.0] if fine else [8, 4, 2]
-    metric = MutualInformationMetric(nbins, sampling_prop)
+    metric = MutualInformationMetric(nbins=nbins, sampling_proportion=sampling_prop)
     reg_obj = AffineRegistration(metric=metric, level_iters=level_iters,
                                  sigmas=sigmas, factors=factors, verbosity=0)
 

scilpy/reconst/bingham.py

@@ -170,7 +170,7 @@ def bingham_fit_sh(sh, max_lobes=5, abs_th=0.,
     order, full_basis = get_sh_order_and_fullness(sh.shape[-1])
     shape = sh.shape
 
-    sphere = get_sphere('symmetric724').subdivide(2)
+    sphere = get_sphere(name='symmetric724').subdivide(n=2)
     B_mat = sh_to_sf_matrix(sphere, order,
                             full_basis=full_basis,
                             return_inv=False)

scilpy/reconst/fodf.py

@@ -61,7 +61,7 @@ def get_ventricles_max_fodf(data, fa, md, zoom, sh_basis,
     """
 
     order = find_order_from_nb_coeff(data)
-    sphere = get_sphere('repulsion100')
+    sphere = get_sphere(name='repulsion100')
     b_matrix, _ = sh_to_sf_matrix(sphere, order, sh_basis, legacy=is_legacy)
     mask = np.zeros(data.shape[:-1])
 

scilpy/reconst/frf.py

@@ -82,7 +82,7 @@ def compute_ssst_frf(data, bvals, bvecs, b0_threshold=DEFAULT_B0_THRESHOLD,
         logging.warning("Your b-vectors do not seem normalized... Normalizing")
         bvecs = normalize_bvecs(bvecs)
 
-    gtab = gradient_table(bvals, bvecs, b0_threshold=b0_threshold)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=b0_threshold)
 
     if mask is not None:
         data = applymask(data, mask)
@@ -236,7 +236,7 @@ def compute_msmt_frf(data, bvals, bvecs, btens=None, data_dti=None,
     # Note. Using the tolerance here because currently, the gtab.b0s_mask is
     # not used. Below, we use the tolerance only (in dipy).
     # An issue has been added in dipy too.
-    gtab = gradient_table(bvals, bvecs, btens=btens, b0_threshold=tol)
+    gtab = gradient_table(bvals, bvecs=bvecs, btens=btens, b0_threshold=tol)
 
     if data_dti is None and bvals_dti is None and bvecs_dti is None:
         logging.warning(
@@ -257,7 +257,7 @@ def compute_msmt_frf(data, bvals, bvecs, btens=None, data_dti=None,
             logging.warning('Your b-vectors do not seem normalized...')
             bvecs_dti = normalize_bvecs(bvecs_dti)
 
-        gtab_dti = gradient_table(bvals_dti, bvecs_dti, btens=btens_dti)
+        gtab_dti = gradient_table(bvals_dti, bvecs=bvecs_dti, btens=btens_dti)
 
         wm_frf_mask, gm_frf_mask, csf_frf_mask \
             = mask_for_response_msmt(gtab_dti, data_dti,

scilpy/reconst/sh.py

@@ -212,9 +212,9 @@ def _peaks_from_sh_loop(shm_coeff, B, sphere, relative_peak_threshold,
             odf[odf < absolute_threshold] = 0.
 
             dirs, peaks, ind = peak_directions(odf, sphere,
-                                               relative_peak_threshold,
-                                               min_separation_angle,
-                                               is_symmetric)
+                                               relative_peak_threshold=relative_peak_threshold,
+                                               min_separation_angle=min_separation_angle,
+                                               is_symmetric=is_symmetric)
 
             if peaks.shape[0] != 0:
                 n = min(npeaks, peaks.shape[0])
@@ -287,7 +287,8 @@ def peaks_from_sh(shm_coeff, sphere, mask=None, relative_peak_threshold=0.5,
     tuple of np.ndarray
         peak_dirs, peak_values, peak_indices
     """
-    sh_order = order_from_ncoef(shm_coeff.shape[-1], full_basis)
+    sh_order = order_from_ncoef(shm_coeff.shape[-1],
+                                full_basis=full_basis)
     B, _ = sh_to_sf_matrix(sphere, sh_order, sh_basis_type,
                            full_basis, legacy=is_legacy)
 

scilpy/reconst/tests/test_fodf.py

@@ -29,7 +29,7 @@ def test_get_ventricles_max_fodf():
 
     # Reconstruct SF values same as in method.
     order = find_order_from_nb_coeff(fodf_3x3_order8_descoteaux07)
-    sphere = get_sphere('repulsion100')
+    sphere = get_sphere(name='repulsion100')
     b_matrix, _ = sh_to_sf_matrix(sphere, order, sh_basis, legacy=True)
 
     sf1 = np.dot(fodf_3x3_order8_descoteaux07[1, 0, 0], b_matrix)

scilpy/segment/recobundlesx.py

@@ -180,9 +180,9 @@ def _register_model_to_neighb(self, select_model=1000, select_target=1000,
         possible_slr_transform_type = {'translation': 0, 'rigid': 1,
                                        'similarity': 2, 'scaling': 3}
         static = select_random_set_of_streamlines(self.model_centroids,
-                                                  select_model, self.rng)
+                                                  select_model, rng=self.rng)
         moving = select_random_set_of_streamlines(self.neighb_centroids,
-                                                  select_target, self.rng)
+                                                  select_target, rng=self.rng)
 
         # Tuple 0,1,2 are the min & max bound in x,y,z for translation
         # Tuple 3,4,5 are the min & max bound in x,y,z for rotation

scilpy/tracking/propagator.py

@@ -278,7 +278,7 @@ def __init__(self, datavolume, step_size, rk_order, dipy_sphere,
         """
         super().__init__(datavolume, step_size, rk_order, space, origin)
 
-        self.sphere = dipy.data.get_sphere(dipy_sphere).subdivide(sub_sphere)
+        self.sphere = dipy.data.get_sphere(dipy_sphere).subdivide(n=sub_sphere)
         self.dirs = np.zeros(len(self.sphere.vertices), dtype=np.ndarray)
         for i in range(len(self.sphere.vertices)):
             self.dirs[i] = TrackingDirection(self.sphere.vertices[i], i)

scilpy/tracking/utils.py

@@ -322,7 +322,7 @@ def get_direction_getter(in_img, algo, sphere, sub_sphere, theta, sh_basis,
     img_data = nib.load(in_img).get_fdata(dtype=np.float32)
 
     sphere = HemiSphere.from_sphere(
-        get_sphere(sphere)).subdivide(sub_sphere)
+        get_sphere(sphere)).subdivide(n=sub_sphere)
 
     # Theta depends on user choice and algorithm
     theta = get_theta(theta, algo)

scilpy/tractanalysis/afd_along_streamlines.py

@@ -84,7 +84,7 @@ def afd_and_rd_sums_along_streamlines(sft, fodf, fodf_basis,
 
     fodf_data = fodf.get_fdata(dtype=np.float32)
     order = find_order_from_nb_coeff(fodf_data)
-    sphere = get_sphere('repulsion724')
+    sphere = get_sphere(name='repulsion724')
     b_matrix, _ = sh_to_sf_matrix(sphere, order, fodf_basis, legacy=is_legacy)
     _, n = sph_harm_ind_list(order)
     legendre0_at_n = lpn(order, 0)[0][n]

scilpy/tractanalysis/reproducibility_measures.py

@@ -657,7 +657,7 @@ def tractogram_pairwise_comparison(sft_one, sft_two, mask, nbr_cpu=1,
     sft_2.to_center()
 
     global B
-    B, _ = sh_to_sf_matrix(get_sphere('repulsion724'), 8, 'descoteaux07')
+    B, _ = sh_to_sf_matrix(get_sphere(name='repulsion724'), 8, 'descoteaux07')
 
     diff_data, acc_data = _compare_tractogram_wrapper(
         mask, nbr_cpu, skip_streamlines_distance)

scilpy/tractograms/tractogram_operations.py

@@ -656,7 +656,7 @@ def transform_warp_sft(sft, linear_transfo, target, inverse=False,
         streamlines = transform_streamlines(streamlines, linear_transfo)
 
     streamlines._data = streamlines._data.astype(dtype)
-    new_sft = StatefulTractogram(streamlines, target, Space.RASMM,
+    new_sft = StatefulTractogram(streamlines, target, space=Space.RASMM,
                                  data_per_point=sft.data_per_point,
                                  data_per_streamline=sft.data_per_streamline)
     if cut_invalid:

scilpy/viz/gradients.py

@@ -43,7 +43,7 @@ def plot_each_shell(ms, centroids, plot_sym_vecs=True, use_sphere=True,
     _colors = generate_n_colors(len(ms))
 
     if use_sphere:
-        sphere = get_sphere('symmetric724')
+        sphere = get_sphere(name='symmetric724')
         shape = (1, 1, 1, sphere.vertices.shape[0])
         _, fname = mkstemp(suffix='_odf_slicer.mmap')
         odfs = np.memmap(fname, dtype=np.float64, mode='w+', shape=shape)
@@ -108,7 +108,7 @@ def plot_proj_shell(ms, use_sym=True, use_sphere=True, same_color=False,
     scene = window.Scene()
     scene.SetBackground(1, 1, 1)
     if use_sphere:
-        sphere = get_sphere('symmetric724')
+        sphere = get_sphere(name='symmetric724')
         shape = (1, 1, 1, sphere.vertices.shape[0])
         _, fname = mkstemp(suffix='_odf_slicer.mmap')
         odfs = np.memmap(fname, dtype=np.float64, mode='w+', shape=shape)

scilpy/viz/slice.py

@@ -246,7 +246,7 @@ def create_odf_slicer(sh_fodf, orientation, slice_index, sphere, sh_order,
 
     # Subdivide the spheres if nb_subdivide is provided
     if nb_subdivide is not None:
-        sphere = sphere.subdivide(nb_subdivide)
+        sphere = sphere.subdivide(n=nb_subdivide)
 
     fodf = sh_to_sf(sh_fodf, sphere, sh_order, sh_basis,
                     full_basis=full_basis, legacy=is_legacy)

scripts/scil_bundle_clean_qbx_clusters.py

@@ -280,7 +280,7 @@ def save_clusters(cluster_lists, indexes_list, directory, basenames_list,
         if directory:
             tmp_sft = StatefulTractogram(streamlines,
                                          cluster_lists[0],
-                                         Space.RASMM)
+                                         space=Space.RASMM)
             tmp_filename = os.path.join(directory,
                                         basenames_list[idx])
             save_tractogram(tmp_sft, tmp_filename,

scripts/scil_bundle_generate_priors.py

@@ -129,7 +129,7 @@ def main():
 
     # Memory friendly saving, as soon as possible saving then delete
     priors_3d = np.zeros(sh_shape)
-    sphere = get_sphere('repulsion724')
+    sphere = get_sphere(name='repulsion724')
     priors_3d[sub_mask_3d] = sf_to_sh(todi_sf, sphere,
                                       sh_order_max=sh_order,
                                       basis_type=sh_basis,

scripts/scil_dki_metrics.py

@@ -200,7 +200,7 @@ def main():
     _ = check_b0_threshold(bvals.min(), b0_thr=args.tolerance,
                            skip_b0_check=args.skip_b0_check,
                            overwrite_with_min=False)
-    gtab = gradient_table(bvals, bvecs, b0_threshold=args.tolerance)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=args.tolerance)
 
     # Processing
 

scripts/scil_dti_metrics.py

@@ -207,7 +207,7 @@ def main():
     args.b0_threshold = check_b0_threshold(bvals.min(),
                                            b0_thr=args.b0_threshold,
                                            skip_b0_check=args.skip_b0_check)
-    gtab = gradient_table(bvals, bvecs, b0_threshold=args.b0_threshold)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=args.b0_threshold)
 
     # Processing
 
@@ -220,7 +220,7 @@ def main():
         tenmodel = TensorModel(gtab, fit_method=args.method,
                                min_signal=np.min(data[data > 0]))
 
-    tenfit = tenmodel.fit(data, mask)
+    tenfit = tenmodel.fit(data, mask=mask)
 
     # Save all metrics.
     if args.tensor:
@@ -345,12 +345,12 @@ def main():
 
         for i in range(data.shape[0]):
             if args.mask is not None:
-                tenfit2 = tenmodel.fit(data[i, :, :, :], mask[i, :, :])
+                tenfit2 = tenmodel.fit(data[i, :, :, :], mask=mask[i, :, :])
             else:
                 tenfit2 = tenmodel.fit(data[i, :, :, :])
 
             S0_i = np.maximum(S0[i, :, :], tenfit2.model.min_signal)
-            tenfit2_predict[i, :, :, :] = tenfit2.predict(gtab, S0_i)
+            tenfit2_predict[i, :, :, :] = tenfit2.predict(gtab, S0=S0_i)
 
         R, data_diff = compute_residuals(
             predicted_data=tenfit2_predict.astype(np.float32),

scripts/scil_dwi_extract_b0.py

@@ -98,7 +98,7 @@ def main():
     args.b0_threshold = check_b0_threshold(bvals.min(),
                                            b0_thr=args.b0_threshold,
                                            skip_b0_check=args.skip_b0_check)
-    gtab = gradient_table(bvals, bvecs, b0_threshold=args.b0_threshold)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=args.b0_threshold)
     b0_idx = np.where(gtab.b0s_mask)[0]
 
     logger.info('Number of b0 images in the data: {}'.format(len(b0_idx)))

scripts/scil_dwi_powder_average.py

@@ -111,7 +111,7 @@ def main():
                 len(pwd_avg_idx), shell))
 
         # remove b0 indices
-        b0_idx = get_bval_indices(bvals, 0, args.b0_thr)
+        b0_idx = get_bval_indices(bvals, 0, tol=args.b0_thr)
         logging.debug('{} b0 volumes detected and not included'.format(
             len(b0_idx)))
         for val in b0_idx:

scripts/scil_dwi_to_sh.py

@@ -77,7 +77,7 @@ def main():
     args.b0_threshold = check_b0_threshold(bvals.min(),
                                            b0_thr=args.b0_threshold,
                                            skip_b0_check=args.skip_b0_check)
-    gtab = gradient_table(bvals, bvecs, b0_threshold=args.b0_threshold)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=args.b0_threshold)
 
     sh_basis, is_legacy = parse_sh_basis_arg(args)
 

scripts/scil_fibertube_score_tractogram.py

@@ -163,8 +163,9 @@ def main():
     our_origin = Origin('center')
 
     logging.debug('Loading centerline tractogram & diameters')
-    truth_sft = load_tractogram(args.in_fibertubes, 'same', our_space,
-                                our_origin)
+    truth_sft = load_tractogram(args.in_fibertubes, 'same',
+                                to_space=our_space,
+                                to_origin=our_origin)
     centerlines = truth_sft.get_streamlines_copy()
     centerlines, centerlines_length = get_streamlines_as_fixed_array(
         centerlines)
@@ -176,8 +177,9 @@ def main():
                            len(centerlines))
 
     logging.debug('Loading reconstructed tractogram')
-    in_sft = load_tractogram(args.in_tracking, 'same', our_space,
-                             our_origin)
+    in_sft = load_tractogram(args.in_tracking, 'same',
+                             to_space=our_space,
+                             to_origin=our_origin)
     streamlines = in_sft.get_streamlines_copy()
     streamlines, streamlines_length = get_streamlines_as_fixed_array(
         streamlines)

scripts/scil_fibertube_tracking.py

@@ -198,7 +198,9 @@ def main():
     our_origin = Origin('center')
 
     logging.debug('Loading tractogram & diameters')
-    in_sft = load_tractogram(args.in_fibertubes, 'same', our_space, our_origin)
+    in_sft = load_tractogram(args.in_fibertubes, 'same',
+                             to_space=our_space,
+                             to_origin=our_origin)
     centerlines = list(in_sft.get_streamlines_copy())
     diameters = np.reshape(in_sft.data_per_streamline['diameters'],
                            len(centerlines))

scripts/scil_fodf_memsmt.py

@@ -187,7 +187,7 @@ def main():
     wm_frf, gm_frf, csf_frf = verify_frf_files(wm_frf, gm_frf, csf_frf)
 
     # Loading spheres
-    reg_sphere = get_sphere('symmetric362')
+    reg_sphere = get_sphere(name='symmetric362')
 
     # Starting main process!
 

scripts/scil_fodf_msmt.py

@@ -160,10 +160,10 @@ def main():
     _ = check_b0_threshold(bvals.min(), b0_thr=args.tolerance,
                            skip_b0_check=args.skip_b0_check,
                            overwrite_with_min=False)
-    gtab = gradient_table(bvals, bvecs, b0_threshold=args.tolerance)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=args.tolerance)
 
     # Loading spheres
-    reg_sphere = get_sphere('symmetric362')
+    reg_sphere = get_sphere(name='symmetric362')
 
     # Starting main process!
 

scripts/scil_fodf_ssst.py

@@ -105,7 +105,7 @@ def main():
     args.b0_threshold = check_b0_threshold(bvals.min(),
                                            b0_thr=args.b0_threshold,
                                            skip_b0_check=args.skip_b0_check)
-    gtab = gradient_table(bvals, bvecs, b0_threshold=args.b0_threshold)
+    gtab = gradient_table(bvals, bvecs=bvecs, b0_threshold=args.b0_threshold)
 
     # Checking full_frf and separating it
     if not full_frf.shape[0] == 4:
@@ -115,7 +115,7 @@ def main():
     mean_b0_val = full_frf[3]
 
     # Loading the sphere
-    reg_sphere = get_sphere('symmetric362')
+    reg_sphere = get_sphere(name='symmetric362')
 
     # Computing CSD
     csd_model = ConstrainedSphericalDeconvModel(gtab, (frf, mean_b0_val),