Merge pull request #470 from arnaudbore/fix_numpy_warning

Warning numpy fixed

scilpy/io/image.py

@@ -42,7 +42,7 @@ def get_data_as_mask(in_img, dtype=np.uint8):
     Return
     ------
     data: numpy.ndarray
-        Data (dtype : np.uint8 or np.bool).
+        Data (dtype : np.uint8 or bool).
     """
     if not (issubclass(np.dtype(dtype).type, np.uint8) or
             issubclass(np.dtype(dtype).type, np.dtype(bool).type)):

scilpy/preprocessing/distortion_correction.py

@@ -46,7 +46,7 @@ def create_index(bvals):
     -------
     index: np.array
     """
-    index = np.ones(len(bvals), dtype=np.int).tolist()
+    index = np.ones(len(bvals), dtype=int).tolist()
 
     return index
 

scilpy/segment/streamlines.py

@@ -31,7 +31,7 @@ def streamlines_in_mask(sft, target_mask, all_in=False):
     sft.to_corner()
     # Copy-Paste from Dipy to get indices
     if all_in:
-        target_mask = np.array(target_mask, dtype=np.bool, copy=True)
+        target_mask = np.array(target_mask, dtype=bool, copy=True)
         target_mask = np.invert(target_mask)
         tractogram_mask = compute_tract_counts_map(sft.streamlines,
                                                    target_mask.shape)

scilpy/segment/voting_scheme.py

@@ -486,4 +486,4 @@ def single_recognize(args):
                                          round(time() - recognize_timer, 2)))
     if recognized_indices is None:
         recognized_indices = []
-    return bundle_id, np.asarray(recognized_indices, dtype=np.int)
+    return bundle_id, np.asarray(recognized_indices, dtype=int)

scilpy/tractanalysis/streamlines_metrics.pyx

@@ -40,18 +40,18 @@ def compute_tract_counts_map(streamlines, vol_dims):
     flags = np.seterr(divide="ignore", under="ignore")
 
     # Inspired from Dipy track_counts
-    vol_dims = np.asarray(vol_dims).astype(np.int)
+    vol_dims = np.asarray(vol_dims).astype(int)
     n_voxels = np.prod(vol_dims)
 
     # This array counts the number of different tracks going through each voxel.
     # Need to keep both the array and the memview on it to be able to
     # reshape and return in the end.
-    traversal_tags = np.zeros((n_voxels,), dtype=np.int)
+    traversal_tags = np.zeros((n_voxels,), dtype=int)
     cdef np.int_t[:] traversal_tags_v = traversal_tags
 
     # This array keeps track of whether the current track has already been
     # flagged in a specific voxel.
-    cdef np.int_t[:] touched_tags_v = np.zeros((n_voxels,), dtype=np.int)
+    cdef np.int_t[:] touched_tags_v = np.zeros((n_voxels,), dtype=int)
 
     cdef int streamlines_len = len(streamlines)
 
@@ -71,7 +71,7 @@ def compute_tract_counts_map(streamlines, vol_dims):
     cdef np.double_t[:] cur_edge = np.zeros(3, dtype=np.double)
 
     # Memview for the coordinates of the current voxel
-    cdef np.int_t[:] cur_voxel_coords = np.zeros(3, dtype=np.int)
+    cdef np.int_t[:] cur_voxel_coords = np.zeros(3, dtype=int)
 
     # various temporary loop and working variables
     #cdef:

scilpy/tractanalysis/todi.py

@@ -172,7 +172,7 @@ def smooth_todi_spatial(self, sigma=0.5):
             Gaussian blurring factor (default 0.5).
         """
         # This operation changes the mask as well as the TODI
-        mask_3d = self.reshape_to_3d(self.mask).astype(np.float)
+        mask_3d = self.reshape_to_3d(self.mask).astype(float)
         mask_3d = gaussian_filter(
             mask_3d, sigma, truncate=GAUSSIAN_TRUNCATE).flatten()
         new_mask = mask_3d > MINIMUM_TODI_EPSILON
@@ -336,7 +336,7 @@ def compute_distance_to_peak(self, peak_img, normalize_count=True,
             error_map = np.arccos(
                 np.clip(np.abs(np.sum(avg_dir * peak_img, axis=1)), 0.0, 1.0))
         else:
-            error_map = np.zeros((len(peak_img)), dtype=np.float)
+            error_map = np.zeros((len(peak_img)), dtype=float)
             for i in range(self.nb_sphere_vts):
                 count_i = self.todi[:, i]
                 error_i = np.dot(peak_img, self.sphere.vertices[i])
@@ -345,7 +345,7 @@ def compute_distance_to_peak(self, peak_img, normalize_count=True,
                 error_map[mask] += count_i[mask] * arccos_i
 
             if normalize_count:
-                tdi = self.get_tdi().astype(np.float)
+                tdi = self.get_tdi().astype(float)
                 tdi_zero = tdi < MINIMUM_TODI_EPSILON
                 error_map[tdi_zero] = 0.0
                 error_map[~tdi_zero] /= tdi[~tdi_zero]
@@ -366,7 +366,7 @@ def compute_average_dir(self):
         avg_dir : numpy.ndarray (4D)
             Volume containing a single 3-vector (peak) per voxel.
         """
-        avg_dir = np.zeros((len(self.todi), 3), dtype=np.float)
+        avg_dir = np.zeros((len(self.todi), 3), dtype=float)
 
         sym_dir_index = self.nb_sphere_vts // 2
         for i in range(sym_dir_index):

scilpy/tractanalysis/todi_util.py

@@ -95,13 +95,13 @@ def psf_from_sphere(sphere_vertices):
 
 # Mask functions
 def generate_mask_indices_1d(nb_voxel, indices_1d):
-    mask_1d = np.zeros(nb_voxel, dtype=np.bool)
+    mask_1d = np.zeros(nb_voxel, dtype=bool)
     mask_1d[indices_1d] = True
     return mask_1d
 
 
 def get_indices_1d(volume_shape, pts):
-    return np.ravel_multi_index(pts.T.astype(np.int), volume_shape)
+    return np.ravel_multi_index(pts.T.astype(int), volume_shape)
 
 
 def get_dir_to_sphere_id(vectors, sphere_vertices):

scilpy/utils/metrics_tools.py

@@ -109,9 +109,9 @@ def get_bundle_metrics_profiles(sft, metrics_files):
     streamlines = sft.streamlines
 
     def _get_profile_one_streamline(streamline, metrics_files):
-        x_ind = np.floor(streamline[:, 0]).astype(np.int)
-        y_ind = np.floor(streamline[:, 1]).astype(np.int)
-        z_ind = np.floor(streamline[:, 2]).astype(np.int)
+        x_ind = np.floor(streamline[:, 0]).astype(int)
+        y_ind = np.floor(streamline[:, 1]).astype(int)
+        z_ind = np.floor(streamline[:, 2]).astype(int)
 
         return list(map(lambda metric_file: metric_file[x_ind, y_ind, z_ind],
                         metrics_files))

scilpy/utils/streamlines.py

@@ -514,7 +514,7 @@ def filter_tractogram_data(tractogram, streamline_ids):
         and data
     """
 
-    streamline_ids = np.asarray(streamline_ids, dtype=np.int)
+    streamline_ids = np.asarray(streamline_ids, dtype=int)
 
     assert np.all(
         np.in1d(streamline_ids, np.arange(len(tractogram.streamlines)))

scripts/scil_analyse_lesions_load.py

@@ -81,7 +81,7 @@ def main():
                                    args.out_streamlines_stats])
 
     lesion_img = nib.load(args.in_lesion)
-    lesion_data = get_data_as_mask(lesion_img, dtype=np.bool)
+    lesion_data = get_data_as_mask(lesion_img, dtype=bool)
 
     if args.bundle:
         bundle_name, _ = split_name_with_nii(os.path.basename(args.bundle))
@@ -112,7 +112,6 @@ def main():
         voxel_sizes=voxel_sizes, min_lesion_vol=args.min_lesion_vol)
 
     if args.out_lesion_atlas:
-        # lesion_atlas *= map_data.astype(np.bool)
         nib.save(nib.Nifti1Image(lesion_atlas, lesion_img.affine),
                  args.out_lesion_atlas)
 

scripts/scil_combine_labels.py

@@ -81,7 +81,7 @@ def main():
             used_indices_all = True
             indices_per_volume.append("all")
         else:
-            indices_per_volume.append(np.asarray(v_args[1:], dtype=np.int))
+            indices_per_volume.append(np.asarray(v_args[1:], dtype=int))
 
     if used_indices_all and args.out_labels_ids:
         logging.error("'all' indices cannot be used with 'out_labels_ids'")

scripts/scil_compute_bundle_voxel_label_map.py

@@ -131,7 +131,7 @@ def main():
     # with no neighbor. Remove isolated voxels to keep a single 'blob'
     binary_bundle = compute_tract_counts_map(sft_bundle.streamlines,
                                              sft_bundle.dimensions).astype(
-                                                 np.bool)
+                                                 bool)
 
     structure = ndi.generate_binary_structure(3, 1)
     if np.count_nonzero(binary_bundle) > args.min_voxel_count \
@@ -165,7 +165,7 @@ def main():
     # Map every streamlines points to the centroids
     binary_centroid = compute_tract_counts_map(sft_centroid.streamlines,
                                                sft_centroid.dimensions).astype(
-                                                   np.bool)
+                                                   bool)
     # TODO N^2 growth in RAM, should split it if we want to do nb_pts = 100
     min_dist_label, min_dist = min_dist_to_centroid(cut_sft.streamlines._data,
                                                     sft_centroid.streamlines._data)

scripts/scil_compute_connectivity.py

@@ -185,7 +185,7 @@ def _processing_wrapper(args):
                 lesion_img.set_filename('tmp.nii.gz')
                 lesion_atlas = get_data_as_label(lesion_img)
                 tmp_dict = compute_lesion_stats(
-                    density.astype(np.bool), lesion_atlas,
+                    density.astype(bool), lesion_atlas,
                     voxel_sizes=voxel_sizes, single_label=True,
                     min_lesion_vol=min_lesion_vol,
                     precomputed_lesion_labels=computed_lesion_labels)
@@ -329,7 +329,7 @@ def main():
     if args.lesion_load is not None:
         in_name = args.lesion_load[0]
         lesion_img = nib.load(in_name)
-        lesion_data = get_data_as_mask(lesion_img, dtype=np.bool)
+        lesion_data = get_data_as_mask(lesion_img, dtype=bool)
         lesion_atlas, _ = ndi.label(lesion_data)
         measures_to_compute.append(((in_name, np.unique(lesion_atlas)[1:]),
                                     nib.Nifti1Image(lesion_atlas,

scripts/scil_compute_kurtosis_metrics.py

@@ -178,7 +178,7 @@ def main():
         mask = None
     else:
         mask_img = nib.load(args.mask)
-        mask = get_data_as_mask(mask_img, dtype=np.bool)
+        mask = get_data_as_mask(mask_img, dtype=bool)
 
     # Validate bvals and bvecs
     bvals, bvecs = read_bvals_bvecs(args.in_bval, args.in_bvec)

scripts/scil_compute_local_tracking.py

@@ -229,7 +229,7 @@ def main():
         parser.error('Total number of seeds must be > 0.')
 
     mask_img = nib.load(args.in_mask)
-    mask_data = get_data_as_mask(mask_img, dtype=np.bool)
+    mask_data = get_data_as_mask(mask_img, dtype=bool)
 
     # Make sure the mask is isotropic. Else, the strategy used
     # when providing information to dipy (i.e. working as if in voxel space)

scripts/scil_compute_pft.py

@@ -245,7 +245,7 @@ def main():
     vox_step_size = args.step_size / voxel_size
     seed_img = nib.load(args.in_seed)
     seeds = track_utils.random_seeds_from_mask(
-        get_data_as_mask(seed_img, dtype=np.bool),
+        get_data_as_mask(seed_img, dtype=bool),
         np.eye(4),
         seeds_count=nb_seeds,
         seed_count_per_voxel=seed_per_vox,

scripts/scil_compute_seed_density_map.py

@@ -72,7 +72,7 @@ def main():
     seed_density = np.zeros(shape, dtype=np.int32)
     for seed in seeds:
         # Set value at mask, either binary or increment
-        seed_voxel = np.round(seed).astype(np.int)
+        seed_voxel = np.round(seed).astype(int)
         if args.binary is not None:
             seed_density[tuple(seed_voxel)] = args.binary
         else:

scripts/scil_compute_sh_from_signal.py

@@ -64,7 +64,7 @@ def main():
 
     mask = None
     if args.mask:
-        mask = get_data_as_mask(nib.load(args.mask), dtype=np.bool)
+        mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
 
     sh = compute_sh_coefficients(dwi, gtab, args.sh_order, args.sh_basis,
                                  args.smooth,

scripts/scil_compute_ssst_frf.py

@@ -110,11 +110,11 @@ def main():
 
     mask = None
     if args.mask:
-        mask = get_data_as_mask(nib.load(args.mask), dtype=np.bool)
+        mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
 
     mask_wm = None
     if args.mask_wm:
-        mask_wm = get_data_as_mask(nib.load(args.mask_wm), dtype=np.bool)
+        mask_wm = get_data_as_mask(nib.load(args.mask_wm), dtype=bool)
 
     full_response = compute_ssst_frf(
         data, bvals, bvecs, mask=mask,

scripts/scil_dilate_labels.py

@@ -91,7 +91,7 @@ def main():
                       np.asarray(args.label_not_to_dilate)[fill_and_not]))
 
     # Create background mask
-    is_background_mask = np.zeros(img_shape, dtype=np.bool)
+    is_background_mask = np.zeros(img_shape, dtype=bool)
     for i in args.label_to_fill:
         is_background_mask = np.logical_or(is_background_mask, data == i)
 
@@ -126,7 +126,7 @@ def main():
                           np.asarray(args.label_to_dilate)[dil_and_fill]))
 
         # Create new label to dilate list
-        new_label_mask = np.zeros_like(data, dtype=np.bool)
+        new_label_mask = np.zeros_like(data, dtype=bool)
         for i in args.label_to_dilate:
             new_label_mask = np.logical_or(new_label_mask, data == i)
 

scripts/scil_run_nlmeans.py

@@ -90,13 +90,13 @@ def main():
     vol = nib.load(args.in_image)
     data = vol.get_fdata(dtype=np.float32)
     if args.mask is None:
-        mask = np.zeros(data.shape[0:3], dtype=np.bool)
+        mask = np.zeros(data.shape[0:3], dtype=bool)
         if data.ndim == 4:
             mask[np.sum(data, axis=-1) > 0] = 1
         else:
             mask[data > 0] = 1
     else:
-        mask = get_data_as_mask(nib.load(args.mask), dtype=np.bool)
+        mask = get_data_as_mask(nib.load(args.mask), dtype=bool)
 
     sigma = args.sigma
 

scripts/scil_smooth_surface.py

@@ -74,7 +74,7 @@ def main():
     # Step size (zero for masked vertices)
     if args.vts_mask:
         mask = np.load(args.vts_mask)
-        step_size_per_vts = args.step_size * mask.astype(np.float)
+        step_size_per_vts = args.step_size * mask.astype(float)
     else:
         step_size_per_vts = args.step_size