diff --git a/MSUtils/voronoi/VoronoiGBErosion.py b/MSUtils/voronoi/VoronoiGBErosion.py
index 719f78c..4ca4d88 100644
--- a/MSUtils/voronoi/VoronoiGBErosion.py
+++ b/MSUtils/voronoi/VoronoiGBErosion.py
@@ -8,6 +8,7 @@
 from scipy.spatial import Delaunay
 
 from MSUtils.voronoi import VoronoiImage, VoronoiTessellation
+import json
 
 
 class PeriodicVoronoiImageErosion:
@@ -38,19 +39,21 @@ def __init__(
         self.N = np.array(voroImg.resolution)
         self.voroTess = voroTess
 
-        # Initialize arrays to store grain boundary information
-        self.coords_array = None
-        self.elem_xyz_array = None
-        self.materials_array = None
-        self.normal_array = None
+        self.num_crystals = len(np.unique(self.image))
+        self.ridge_metadata = {}  # tag  →  (normal, ia, ib)
 
         self._precompute_polyhedrons(voroTess)
         self._shrink_and_analyze()
 
     def _periodic_thickness_consistent_erosion(self, crystal) -> npt.ArrayLike:
+        """
+        Returns a Boolean mask the same shape as self.image:
+        - True  → voxel still belongs to `crystal` after erosion
+        - False → voxel was removed (belongs to a grain boundary)
+        """
         crystal_mask = self.image == crystal
-        crystal_mask_indices = np.array(np.where(crystal_mask)).T
-        voxel_coords = (crystal_mask_indices + 0.5) * self.voxel_scale
+        crystal_idx = np.array(np.where(crystal_mask)).T
+        voxel_coords = (crystal_idx + 0.5) * self.voxel_scale
 
         for poly_index, side_flag in self.polytrack.get(crystal, []):
             delaunay = (
@@ -58,232 +61,224 @@ def _periodic_thickness_consistent_erosion(self, crystal) -> npt.ArrayLike:
                 if side_flag == 0
                 else self.polylist_B[poly_index]
             )
-            # Identify normal
-            ridge_pts = self.polyinfo[poly_index]  # Seed indices
+
+            # outward-pointing normal for this crystal
+            i_idx, j_idx = self.polyinfo[poly_index]
             diff = (
-                self.voroTess.voronoi.points[ridge_pts[1]]
-                - self.voroTess.voronoi.points[ridge_pts[0]]
+                self.voroTess.voronoi.points[j_idx]
+                - self.voroTess.voronoi.points[i_idx]
             )
-            norm = np.linalg.norm(diff)
-            if norm == 0:
-                continue  # Avoid division by zero
-            normal = diff / norm
-
-            points = delaunay.points
-
-            # Bounding box filtering
-            min_coords = np.min(points, axis=0)
-            max_coords = np.max(points, axis=0)
+            normal = diff / np.linalg.norm(diff)
+            if side_flag:  # current crystal is j ⇒ flip
+                normal = -normal
+
+            # bounding-box pre-filter
+            min_xyz, max_xyz = (self.bbox_A if side_flag == 0 else self.bbox_B)[
+                poly_index
+            ]
             bbox_mask = np.all(
-                (voxel_coords >= min_coords) & (voxel_coords <= max_coords), axis=1
-            )
-            candidate_voxel_coords = voxel_coords[bbox_mask]
-            candidate_indices_in_voxel_coords = np.where(bbox_mask)[0]
-
-            if candidate_voxel_coords.size == 0:
-                continue  # Skip if no candidate voxels
-
-            #################################
-            # Identify grain boundary voxels
-            ###
-            # STEP 1: Keep all voxels where the distance to interface plane is not larger than extrusion factor (projection onto normal vector)
-            ###
-            inside = (
-                np.abs((candidate_voxel_coords - points[0][None, :]) @ normal)
-                <= self.extrusion_factor
+                (voxel_coords >= min_xyz) & (voxel_coords <= max_xyz), axis=1
             )
+            cand_ids = np.where(bbox_mask)[0]
+            if cand_ids.size == 0:
+                continue
+            cand_coords = voxel_coords[cand_ids]
 
-            ###
-            # STEP 2: Keep all voxels where the in-plane projection lies within the ridge (2D polygon), i.e. voxel is 'above'  or 'below the ridge area
-            # Idea: when walking along the edges of the ridge a point always needs to be on the same side of yours to be inside of the polygon
-            # ===> if the cross products of the candidate voxel (wrt to ridge points) with each edge of the ridge are all of same sign,
-            # the voxel should be kept
-            ###
-            # Define ridge (shifted to origin)
-            # IMPORTANT: Last TWO components of points are crystal seeds used for the construction of the polyhedron (DO NOT CONSIDER!)
-            polygon = points[:-1] - points[0][None, :]
-            polygon_rolled = np.roll(polygon, -1, axis=0)
-            edges = (polygon_rolled - polygon)[np.newaxis, :, :]
-
-            # Project points onto ridge plane: ONLY CONSIDER VOXELS THAT HAVE BEEN IDENTIFIED IN STEP 1 TO SAVE TIME!
-            coord_projections = (
-                candidate_voxel_coords[inside] - points[0][None, :]
-            ) @ (np.eye(3, 3) - np.outer(normal, normal))
-            # Vector from ridge vertices to each voxel position to be checked
-            coord_vectors = coord_projections[:, np.newaxis, :] - polygon[None, :, :]
-
-            # Cross product for each point with each edge
-            cross = np.cross(edges, coord_vectors, axis=-1)
-
-            # Check whether they all have same direction by checking dot products of the cross products
-            ref = cross[:, 0:1, :]
-            same_direction = np.sum(cross * ref, axis=-1)
-            all_positive = np.all(same_direction >= 0, axis=1)
-            all_negative = np.all(same_direction <= 0, axis=1)
-
-            inside[inside] &= np.logical_or(all_positive, all_negative)
-
-            #################################
-
-            if not np.any(inside):
-                continue  # Skip if no inside voxels
-
-            inside_indices_in_voxel_coords = candidate_indices_in_voxel_coords[inside]
-            inside_indices = crystal_mask_indices[inside_indices_in_voxel_coords]
+            # STEP-1: keep voxels within ±extrusion_factor of ridge plane
+            d = (cand_coords - delaunay.points[0]) @ normal
+            band_mask = np.abs(d) <= self.extrusion_factor
+            if not np.any(band_mask):
+                continue
+            band_ids = cand_ids[band_mask]
+            band_coords = cand_coords[band_mask]
+
+            # STEP-2: 2-D point-in-polygon test in the ridge plane
+            polygon = delaunay.points[:-1] - delaunay.points[0]
+            poly_roll = np.roll(polygon, -1, axis=0)
+            edges = (poly_roll - polygon)[np.newaxis, :, :]
+
+            proj = band_coords - delaunay.points[0]
+            proj -= np.outer((proj @ normal), normal)  # project
+            vecs = proj[:, np.newaxis, :] - polygon[np.newaxis, :, :]
+            cross = np.cross(edges, vecs, axis=-1)
+            ref = cross[:, :1, :]
+            same_dir = np.sum(cross * ref, axis=-1)
+            inside_poly = np.logical_or(
+                np.all(same_dir >= 0, 1), np.all(same_dir <= 0, 1)
+            )
+            if not np.any(inside_poly):
+                continue
 
-            crystal_mask[*inside_indices.T] = 0
+            # remove voxels
+            kill_ids = band_ids[inside_poly]
+            kill_idx = crystal_idx[kill_ids]
+            crystal_mask[tuple(kill_idx.T)] = False
 
         return crystal_mask
 
     def _shrink_and_analyze(self) -> None:
-        unique_crystals = np.unique(self.image)
+        """
+        Builds:
+        • self.eroded_image        – crystals (0…N-1) and ridge IDs (N…)
+        • self.coords_array …      – unchanged from your previous implementation
+        • self.ridge_metadata[tag] – (normal, crystal_a, crystal_b)
+        """
+        N = self.num_crystals
+        unique_crystals = np.arange(N)  # 0 … N-1
         eroded_image = -1 * np.ones_like(self.image)
         Nx, Ny, Nz = self.image.shape
         hx, hy, hz = self.L / np.array([Nx, Ny, Nz])
-
-        # Initialize lists for concatenation
-        coords_list = []
-        elem_xyz_list = []
-        materials_list = []
-        normal_list = []
-
-        # Precompute voxel scales
         self.voxel_scale = np.array([hx, hy, hz])
 
         for crystal in unique_crystals:
             crystal_mask = self.image == crystal
-            eroded_mask = self._periodic_thickness_consistent_erosion(crystal)
-
-            boundary_mask = np.logical_and(crystal_mask, np.logical_not(eroded_mask))
-            boundary_mask_indices = np.array(
-                np.where(boundary_mask)
-            ).T  # Shape: (num_voxels, 3)
-            voxel_coords = (
-                boundary_mask_indices + 0.5
-            ) * self.voxel_scale  # Shape: (num_voxels, 3)
-
-            if boundary_mask_indices.size == 0:
-                continue  # Skip if no boundary voxels
-
-            # Initialize a boolean array to keep track of checked voxels
-            voxel_checked = np.full(len(voxel_coords), False, dtype=bool)
+            # interior voxels that survive the erosion step
+            interior_mask = self._periodic_thickness_consistent_erosion(crystal)
+
+            # voxels that WERE removed => potential grain-boundary voxels
+            boundary_mask = crystal_mask & np.logical_not(interior_mask)
+            boundary_idx = np.array(np.where(boundary_mask)).T
+            if boundary_idx.size == 0:
+                # crystal disappeared completely (rare)
+                continue
+            voxel_coords = (boundary_idx + 0.5) * self.voxel_scale
+            voxel_checked = np.zeros(len(voxel_coords), dtype=bool)
 
             for poly_index, side_flag in self.polytrack.get(crystal, []):
+                tag = self.ridge_tags[poly_index]  # N + poly_index
                 delaunay = (
                     self.polylist_A[poly_index]
                     if side_flag == 0
                     else self.polylist_B[poly_index]
                 )
-                points = delaunay.points
 
-                # Bounding box filtering
-                min_coords = np.min(points, axis=0)
-                max_coords = np.max(points, axis=0)
+                min_xyz, max_xyz = (self.bbox_A if side_flag == 0 else self.bbox_B)[
+                    poly_index
+                ]
                 bbox_mask = np.all(
-                    (voxel_coords >= min_coords) & (voxel_coords <= max_coords), axis=1
+                    (voxel_coords >= min_xyz) & (voxel_coords <= max_xyz), axis=1
                 )
-                candidate_voxel_coords = voxel_coords[bbox_mask]
-                candidate_indices_in_voxel_coords = np.where(bbox_mask)[0]
-
-                if candidate_voxel_coords.size == 0:
-                    continue  # Skip if no candidate voxels
-
-                # Perform point-in-polyhedron test
-                inside = delaunay.find_simplex(candidate_voxel_coords) >= 0
-                inside &= ~voxel_checked[candidate_indices_in_voxel_coords]
+                cand_ids = np.where(bbox_mask & ~voxel_checked)[0]
+                if cand_ids.size == 0:
+                    continue
+                cand_coords = voxel_coords[cand_ids]
 
+                inside = delaunay.find_simplex(cand_coords) >= 0
                 if not np.any(inside):
-                    continue  # Skip if no inside voxels
-
-                inside_indices_in_voxel_coords = candidate_indices_in_voxel_coords[
-                    inside
-                ]
-                inside_indices = boundary_mask_indices[inside_indices_in_voxel_coords]
-
-                # Compute element indices
-                elem_xyz_array = np.ravel_multi_index(inside_indices.T, (Nx, Ny, Nz))
-
-                ridge_pts = self.polyinfo[poly_index]  # Seed indices
-                diff = (
-                    self.voroTess.voronoi.points[ridge_pts[1]]
-                    - self.voroTess.voronoi.points[ridge_pts[0]]
-                )
-                norm = np.linalg.norm(diff)
-                if norm == 0:
-                    continue  # Avoid division by zero
-                normal = diff / norm
-
-                # Map extended seed indices to original seed indices (crystal labels)
-                materials = [
-                    self.voroTess.crystal_index_map[pt_idx] for pt_idx in ridge_pts
-                ]
-
-                num_voxels = inside_indices.shape[0]
-                coords_list.append(inside_indices)
-                elem_xyz_list.append(elem_xyz_array)
-                materials_array = np.tile(materials, (num_voxels, 1))
-                materials_list.append(materials_array)
-                normal_array = np.tile(normal, (num_voxels, 1))
-                normal_list.append(normal_array)
-
-                # Mark checked voxels
-                voxel_checked[inside_indices_in_voxel_coords] = True
-
-            print(
-                f"In crystal number-{crystal}, number of marked voxels-{voxel_checked.sum()} out of {len(voxel_checked)} voxels"
-            )
-            eroded_image[eroded_mask] = crystal
-
+                    continue
+                in_ids = cand_ids[inside]
+                in_idx = boundary_idx[in_ids]
+
+                # write ridge tag into eroded_image (skip if already tagged)
+                new_vox = eroded_image[tuple(in_idx.T)] == -1
+                in_idx = in_idx[new_vox]
+                if in_idx.size == 0:
+                    continue
+                eroded_image[tuple(in_idx.T)] = tag
+
+                # ---------- outward normal (store once per tag) ---------------
+                if tag not in self.ridge_metadata:
+                    i_idx, j_idx = self.polyinfo[poly_index]
+                    diff = (
+                        self.voroTess.voronoi.points[j_idx]
+                        - self.voroTess.voronoi.points[i_idx]
+                    )
+                    normal = diff / np.linalg.norm(diff)
+                    cA = self.voroTess.crystal_index_map[i_idx]
+                    cB = self.voroTess.crystal_index_map[j_idx]
+                    self.ridge_metadata[tag] = (normal, cA, cB)
+                # ----------------------------------------------------------------
+
+                voxel_checked[in_ids] = True
+
+            # crystal interior stays labelled with the crystal index
+            eroded_image[(crystal_mask & interior_mask)] = crystal
+
+        # ----------------------------------------------------------------------
+        # Renumber ridge tags so they are contiguous: N, N+1, N+2, ...
+        # ----------------------------------------------------------------------
+        active_tags = sorted(t for t in self.ridge_metadata.keys())
+        mapping = {old: self.num_crystals + i for i, old in enumerate(active_tags)}
+
+        if active_tags:  # nothing to do for pure bulk
+            # 1) remap eroded_image in-place  (vectorised boolean mask per tag)
+            for old, new in mapping.items():
+                if old == new:
+                    continue
+                eroded_image[eroded_image == old] = new
+
+            # 2) rebuild ridge_metadata with new keys
+            self.ridge_metadata = {
+                mapping[old]: value for old, value in self.ridge_metadata.items()
+            }
+        # ----------------------------------------------------------------------
         self.eroded_image = eroded_image
 
-        # Concatenate arrays
-        if coords_list:
-            self.coords_array = np.vstack(coords_list)
-            self.elem_xyz_array = np.concatenate(elem_xyz_list)
-            self.materials_array = np.vstack(materials_list)
-            self.normal_array = np.vstack(normal_list)
-        else:
-            self.coords_array = np.empty((0, 3), dtype=int)
-            self.elem_xyz_array = np.empty(0, dtype=int)
-            self.materials_array = np.empty((0, 2), dtype=int)
-            self.normal_array = np.empty((0, 3), dtype=float)
-
     def _precompute_polyhedrons(self, voroTess: VoronoiTessellation) -> None:
-        self.polylist_A = []  # ridge + seed_i
-        self.polylist_B = []  # ridge + seed_j
-        self.polyinfo = []
+        self.polylist_A, self.polylist_B = [], []
+        self.polyinfo, self.ridge_tags = [], []
+        self.bbox_A, self.bbox_B = [], []
         self.polytrack = defaultdict(list)
 
-        for _i, (ridge, ridge_pts) in enumerate(
-            zip(
-                voroTess.voronoi.ridge_vertices,
-                voroTess.voronoi.ridge_points,
-                strict=False,
-            )
+        # helper maps
+        self._key2tag = {}  # (cA,cB,n_key) → tag
+        self.next_tag = self.num_crystals  # first ridge label
+        Q = 1_000_000.0  # 1 µrad normal quantisation
+
+        for ridge, ridge_pts in zip(
+            voroTess.voronoi.ridge_vertices, voroTess.voronoi.ridge_points, strict=False
         ):
-            if -1 in ridge:
-                # Skip infinite ridges
+            if -1 in ridge:  # infinite ridge
                 continue
 
-            verts = voroTess.voronoi.vertices[ridge]  # k×3
+            cA = self.voroTess.crystal_index_map[ridge_pts[0]]
+            cB = self.voroTess.crystal_index_map[ridge_pts[1]]
+
+            # ---------- canonical, orientation-agnostic normal ---------------
             seed_i, seed_j = voroTess.voronoi.points[ridge_pts]
+            n = seed_j - seed_i
+            n /= np.linalg.norm(n)
+            if (
+                (n[0] < 0)
+                or (np.isclose(n[0], 0) and n[1] < 0)
+                or (np.isclose(n[0], 0) and np.isclose(n[1], 0) and n[2] < 0)
+            ):
+                n = -n
+            n_key = tuple((n * Q).round().astype(int))
+            # -----------------------------------------------------------------
+
+            tag = self._key2tag.setdefault(
+                (min(cA, cB), max(cA, cB), n_key), self.next_tag
+            )
+            if tag == self.next_tag:
+                self.next_tag += 1
 
-            poly_i = Delaunay(np.vstack((verts, seed_i)))  # half-space toward i
-            poly_j = Delaunay(np.vstack((verts, seed_j)))  # half-space toward j
+            # -------------- build the two half-polyhedra ---------------------
+            verts = voroTess.voronoi.vertices[ridge]
+            poly_i = Delaunay(np.vstack((verts, seed_i)))
+            poly_j = Delaunay(np.vstack((verts, seed_j)))
 
             idx = len(self.polylist_A)
             self.polylist_A.append(poly_i)
             self.polylist_B.append(poly_j)
-            self.polyinfo.append(ridge_pts)
+            self.polyinfo.append(tuple(ridge_pts))
+            self.ridge_tags.append(tag)
 
-            # Update polytrack with the correct index
-            for pt_idx in ridge_pts:
-                crystal_label = self.voroTess.crystal_index_map[pt_idx]
-                self.polytrack[crystal_label].append(
-                    (idx, 0 if pt_idx == ridge_pts[0] else 1)
-                )
+            pts_i = np.vstack((verts, seed_i))
+            pts_j = np.vstack((verts, seed_j))
+            self.bbox_A.append((pts_i.min(0), pts_i.max(0)))
+            self.bbox_B.append((pts_j.min(0), pts_j.max(0)))
 
-    def write_h5(self, filepath: Path, grp_name: str, order: str = "zyx") -> None:
+            self.polytrack[cA].append((idx, 0))  # hull A faces cA
+            self.polytrack[cB].append((idx, 1))  # hull B faces cB)
+
+    def write_h5(
+        self,
+        filepath: Path,
+        grp_name: str,
+        order: str = "zyx",
+        save_normals: bool = False,
+    ) -> None:
         """
         Write this eroded periodic voronoi image into a h5-file.
 
@@ -291,65 +286,42 @@ def write_h5(self, filepath: Path, grp_name: str, order: str = "zyx") -> None:
             filepath (Path): Path to h5 file.
             grp_name (str): Name of h5py group.
             order (str, optional): Either one of 'xyz' or 'zyx'. Defaults to "zyx".
+            save_normals (bool, optional): Whether to save the normals field. Defaults to False.
         """
         with h5py.File(filepath, "a") as h5_file:
             grp = h5_file.require_group(grp_name)
             compression_opts = 6
 
-            # Define dtype for the structured numpy array for GBVoxelInfo
-            voxel_info_dtype = np.dtype(
+            # Define dtype for the ridge metadata to include ridge tag
+            GBVoxelInfo_dtype = np.dtype(
                 [
-                    ("coords", "i8", (3,)),
-                    ("elem_xyz", "i8"),
-                    ("materials", "i8", (2,)),
-                    ("normal", "f8", (3,)),
+                    ("GB_tag", "i8"),
+                    ("GB_normal", "f8", (3,)),
+                    ("crystalA", "i8"),
+                    ("crystalB", "i8"),
                 ]
             )
-            # Convert lists to a structured numpy array
-            voxel_info_array = np.array(
-                list(
-                    zip(
-                        self.coords_array,
-                        self.elem_xyz_array,
-                        self.materials_array,
-                        self.normal_array,
-                        strict=False,
+            # Convert ridge_metadata dictionary to structured array
+            ridge_tags = sorted(self.ridge_metadata.keys())
+            GBVoxelInfo = np.array(
+                [
+                    (
+                        tag,
+                        self.ridge_metadata[tag][0],
+                        self.ridge_metadata[tag][1],
+                        self.ridge_metadata[tag][2],
                     )
-                ),
-                dtype=voxel_info_dtype,
+                    for tag in ridge_tags
+                ],
+                dtype=GBVoxelInfo_dtype,
             )
 
-            # Create a new field for normals, taking shape from the original image
-            Nx, Ny, Nz = self.image.shape
-            normals_field = np.zeros((Nx, Ny, Nz, 3))
-
-            for idx, voxel in enumerate(self.coords_array):
-                x, y, z = voxel
-                normals_field[x, y, z] = self.normal_array[idx]
-
-            # Optionally permute the eroded_image before saving, based on the order parameter
+            # Prepare permutated eroded image
             if order == "xyz":
-                #################################
-                # Image is in order of x, y, z
-                # Vector field is in order of x, y, z
-                #################################
                 permuted_eroded_image = self.eroded_image
-                permuted_voxel_info_array = voxel_info_array.copy()
-                permuted_normals_field = normals_field
             elif order == "zyx":
-                #################################
-                # Image is in order of z, y, x
-                # Vector field is in order of x, y, z
-                #################################
                 permuted_eroded_image = self.eroded_image.transpose(2, 1, 0)
 
-                permuted_voxel_info_array = voxel_info_array.copy()
-                permuted_voxel_info_array["coords"] = voxel_info_array["coords"]
-                permuted_voxel_info_array["normal"] = voxel_info_array["normal"]
-
-                permuted_normals_field = normals_field
-                permuted_normals_field = normals_field.transpose(2, 1, 0, 3)
-
             # Save eroded image to .h5 file
             if "eroded_image" in grp:
                 del grp["eroded_image"]
@@ -367,27 +339,56 @@ def write_h5(self, filepath: Path, grp_name: str, order: str = "zyx") -> None:
             image_dataset.attrs.create(
                 "VoronoiSeeds_xyz", np.array(self.seeds, dtype=np.float64)
             )
-
-            # Save GBVoxelInfo to .h5 file
+            # Create GBVoxelInfo attribute with ridge_tag and normal information as key-value pairs
+            gb_voxel_info = {}
+            for tag in sorted(self.ridge_metadata.keys()):
+                normal, _, _ = self.ridge_metadata[tag]
+                gb_voxel_info[str(tag)] = {
+                    "GB_tag": int(tag),
+                    "GB_normal": normal.tolist(),
+                }
+            # Store as string attribute (JSON format)
+            image_dataset.attrs["GBVoxelInfo"] = json.dumps(gb_voxel_info)
+            image_dataset.attrs["num_crystals"] = self.num_crystals
+            image_dataset.attrs["num_GB"] = len(self.ridge_metadata)
+
+            # Save ridge metadata to .h5 file
             if "GBVoxelInfo" in grp:
                 del grp["GBVoxelInfo"]
                 print("Overwriting existing 'GBVoxelInfo' dataset.")
             grp.create_dataset(
                 "GBVoxelInfo",
-                data=permuted_voxel_info_array,
+                data=GBVoxelInfo,
                 compression="gzip",
                 compression_opts=compression_opts,
             )
 
-            # Save normals to .h5 file
-            if "normals" in grp:
-                del grp["normals"]
-                print("Overwriting existing 'normals' dataset.")
-            normals_dataset = grp.create_dataset(
-                "normals",
-                data=permuted_normals_field,
-                dtype="f8",
-                compression="gzip",
-                compression_opts=compression_opts,
-            )
-            normals_dataset.attrs["permute_order"] = order
+            # Only save normals if explicitly requested
+            if save_normals:
+                # Create a new field for normals, taking shape from the original image
+                Nx, Ny, Nz = self.eroded_image.shape
+                normals_field = np.zeros((Nx, Ny, Nz, 3))
+                for i, j, k in np.ndindex(self.eroded_image.shape):
+                    mat_index = self.eroded_image[i, j, k]
+                    if mat_index >= self.num_crystals:
+                        normal, _, _ = self.ridge_metadata[mat_index]
+                        normals_field[i, j, k] = normal
+
+                # Apply permutation to normals field if needed
+                if order == "xyz":
+                    permuted_normals_field = normals_field
+                elif order == "zyx":
+                    permuted_normals_field = normals_field.transpose(2, 1, 0, 3)
+
+                # Save normals to .h5 file
+                if "normals" in grp:
+                    del grp["normals"]
+                    print("Overwriting existing 'normals' dataset.")
+                normals_dataset = grp.create_dataset(
+                    "normals",
+                    data=permuted_normals_field,
+                    dtype="f8",
+                    compression="gzip",
+                    compression_opts=compression_opts,
+                )
+                normals_dataset.attrs["permute_order"] = order
diff --git a/MSUtils/voronoi/main.py b/MSUtils/voronoi/main.py
index 9b84a24..34c7c3d 100644
--- a/MSUtils/voronoi/main.py
+++ b/MSUtils/voronoi/main.py
@@ -9,39 +9,39 @@
 def main():
     num_crystals = 8
     L = [1, 1, 1]
-    Nx, Ny, Nz = 128, 128, 128
+    Nx, Ny, Nz = 256, 256, 256
     permute_order = "zyx"
 
     # Generate Voronoi seeds and tessellation
-    SeedInfo = VoronoiSeeds(num_crystals, L, "diamond", BitGeneratorSeed=42)
+    SeedInfo = VoronoiSeeds(num_crystals, L, "sobol", BitGeneratorSeed=42)
     voroTess = PeriodicVoronoiTessellation(L, SeedInfo.seeds)
-    voroTess.write_to_vtu("data/voroTess.vtu")
 
     # Generate Voronoi image
     voroImg = PeriodicVoronoiImage([Nx, Ny, Nz], SeedInfo.seeds, L)
-    voroImg.write(
-        h5_filename="data/voroImg.h5", dset_name="/dset_0", order=permute_order
-    )
-    write_xdmf("data/voroImg.h5", "data/voroImg.xdmf", microstructure_length=[1, 1, 1])
 
     # Generate Voronoi image with grain boundaries of a specific thickness
-    interface_thickness = (1.0 / 256) * 6
+    interface_thickness = (1.0 / 256) * 8
     voroErodedImg = PeriodicVoronoiImageErosion(
         voroImg, voroTess, interface_thickness=interface_thickness
     )
-    voroErodedImg.write_h5("data/voroImg_eroded.h5", "/dset_0", order=permute_order)
+    voroErodedImg.write_h5(
+        "data/voroImg_eroded.h5", "/dset_0", order=permute_order, save_normals=True
+    )
     write_xdmf(
         "data/voroImg_eroded.h5",
         "data/voroImg_eroded.xdmf",
         microstructure_length=[1, 1, 1],
     )
 
-    # Calculate and print volume fraction of grain boundary
+    # Calculate and print volume fraction of all grain boundary (all tags >= num_crystals)
     msimage = MicrostructureImage(image=voroErodedImg.eroded_image, L=L)
-    phase_volume_fraction = msimage.volume_fractions.get(-1, 0) * 100
-    print(
-        f"Volume fraction of grain boundary (phase: -1): {phase_volume_fraction:.4f}%"
-    )
+    gb_volume_fraction = 0
+    for phase, fraction in msimage.volume_fractions.items():
+        if phase >= num_crystals:
+            gb_volume_fraction += fraction
+
+    gb_volume_fraction_percent = gb_volume_fraction * 100
+    print(f"Volume fraction of all grain boundaries: {gb_volume_fraction_percent:.8f}%")
     print(f"Interface thickness: {interface_thickness:.10f}")
 
 
diff --git a/pixi.lock b/pixi.lock
index 70b913a..48868a8 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -27,12 +27,12 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.2-h3122c55_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.17.1-h3122c55_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.1.31-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.4.26-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py312h06ac9bb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.2-h59595ed_0.conda
@@ -51,7 +51,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.9-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.0-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.18.0-pyhd8ed1ab_0.conda
@@ -62,9 +62,9 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.57.0-py312h178313f_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.0-py312h178313f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.5.0-py312h178313f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.6.0-py312hb9e946c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gl2ps-1.4.2-hae5d5c5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda
@@ -72,7 +72,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.13.0-nompi_py312h01d377b_101.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.1.0-h3beb420_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.2.1-h3beb420_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf4-4.2.15-h2a13503_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.6-nompi_h2d575fe_101.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
@@ -81,7 +81,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/id-1.5.0-pyh29332c3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.10-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.12.30-py312h097a544_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.12.30-py312hbc2ce58_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/imageio-2.37.0-pyhfb79c49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.5.2-pyhd8ed1ab_0.conda
@@ -109,7 +109,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.2.1-hbb36593_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.3.0-h766b0b6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda
@@ -119,7 +119,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.4-default_he06ed0a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.13.0-h332b0f4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h86f0d12_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.124-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda
@@ -128,15 +128,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h767d61c_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hf1ad2bd_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.84.1-h2ff4ddf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h767d61c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.11.2-default_h0d58e46_1001.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.2.0-hf40a0c7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda
@@ -144,7 +144,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.11.1-h7b0646d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.4-he9d0ab4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.9.2-nompi_h0134ee8_117.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
@@ -154,31 +154,31 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-17.4-h27ae623_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.1-hee588c1_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.2-hee588c1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-h8f9b012_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libtheora-1.1.1-h4ab18f5_1006.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hd9ff511_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hf01ce69_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.9.1-h65c71a3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.7-h4bc477f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.9.2-h65c71a3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzip-1.11.2-h6991a6a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/loguru-0.7.2-py312h7900ff3_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/loguru-0.7.3-pyh707e725_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lxml-5.4.0-py312h68d7fa5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.1-py312h7900ff3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.1-py312hd3ec401_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.3-py312h7900ff3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.3-py312hd3ec401_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.7-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda
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   - brunsli >=0.1,<1.0a0
   - bzip2 >=1.0.8,<2.0a0
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+  - c-blosc2 >=2.17.1,<2.18.0a0
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   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
-  - lcms2 >=2.16,<3.0a0
+  - lcms2 >=2.17,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.1.1,<2.0a0
+  - libavif16 >=1.3.0,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
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   - libcxx >=18
-  - libdeflate >=1.23,<1.24.0a0
-  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libdeflate >=1.24,<1.25.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
   - libjxl >=0.11,<0.12.0a0
-  - liblzma >=5.6.3,<6.0a0
-  - libpng >=1.6.45,<1.7.0a0
+  - liblzma >=5.8.1,<6.0a0
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   - libtiff >=4.7.0,<4.8.0a0
   - libwebp-base >=1.5.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
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-  - zlib-ng >=2.2.3,<2.3.0a0
-  - zstd >=1.5.6,<1.6.0a0
+  - zlib-ng >=2.2.4,<2.3.0a0
+  - zstd >=1.5.7,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
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-  size: 1932393
-  timestamp: 1736603790358
-- conda: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.12.30-py313h152d89a_0.conda
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-  md5: adaa273bc777a8305a13a44ec334bc51
+  size: 1703602
+  timestamp: 1747074822902
+- conda: https://conda.anaconda.org/conda-forge/win-64/imagecodecs-2024.12.30-py313h5c5effa_1.conda
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   - bzip2 >=1.0.8,<2.0a0
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+  - c-blosc2 >=2.17.1,<2.18.0a0
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   - giflib >=5.2.2,<5.3.0a0
   - jxrlib >=1.1,<1.2.0a0
-  - lcms2 >=2.16,<3.0a0
+  - lcms2 >=2.17,<3.0a0
   - lerc >=4.0.0,<5.0a0
   - libaec >=1.1.3,<2.0a0
-  - libavif16 >=1.1.1,<2.0a0
+  - libavif16 >=1.3.0,<2.0a0
   - libbrotlicommon >=1.1.0,<1.2.0a0
   - libbrotlidec >=1.1.0,<1.2.0a0
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-  - libdeflate >=1.23,<1.24.0a0
-  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libdeflate >=1.24,<1.25.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
   - libjxl >=0.11,<0.12.0a0
-  - liblzma >=5.6.3,<6.0a0
-  - libpng >=1.6.45,<1.7.0a0
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   - libwebp-base >=1.5.0,<2.0a0
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-  - zlib-ng >=2.2.3,<2.3.0a0
-  - zstd >=1.5.6,<1.6.0a0
+  - zlib-ng >=2.2.4,<2.3.0a0
+  - zstd >=1.5.7,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
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-  size: 1656774
-  timestamp: 1736603847710
+  size: 1528660
+  timestamp: 1747075135937
 - conda: https://conda.anaconda.org/conda-forge/noarch/imageio-2.37.0-pyhfb79c49_0.conda
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   - libgcc >=13
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+  - rav1e >=0.7.1,<0.8.0a0
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+  - rav1e >=0.7.1,<0.8.0a0
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+  - rav1e >=0.7.1,<0.8.0a0
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+  size: 111817
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libavif16-1.3.0-hf2698fe_0.conda
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+  md5: cd552166ea3c57d74c797d68e643a659
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+  - _libavif_api >=1.3.0,<1.3.1.0a0
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   - dav1d >=1.2.1,<1.2.2.0a0
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+  - rav1e >=0.7.1,<0.8.0a0
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   - vc >=14.2,<15
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   license: MIT
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-  - libgcc-ng ==14.2.0=*_2
+  - libgcc-ng ==15.1.0=*_2
+  - libgomp 15.1.0 h767d61c_2
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   - pcre2 >=10.44,<10.45.0a0
   - zstd >=1.5.7,<1.6.0a0
@@ -12137,19 +12034,19 @@ packages:
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 44513633
-  timestamp: 1744200982990
-- conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.0-h83cda92_1.conda
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-  md5: 412f970fc305449b6ee626fe9c6638a8
+  size: 44444863
+  timestamp: 1746636100316
+- conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.0-h1ab902a_2.conda
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+  md5: 99a8af7791ea42b4994cea09dd858ca8
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   - harfbuzz >=11.0.1
   - icu >=75.1,<76.0a0
   - krb5 >=1.21.3,<1.22.0a0
-  - libclang13 >=20.1.2
+  - libclang13 >=20.1.4
   - libglib >=2.84.1,<3.0a0
-  - libjpeg-turbo >=3.0.0,<4.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
   - libpng >=1.6.47,<1.7.0a0
   - libsqlite >=3.49.1,<4.0a0
   - libtiff >=4.7.0,<4.8.0a0
@@ -12166,8 +12063,8 @@ packages:
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 94780444
-  timestamp: 1744204470975
+  size: 93587777
+  timestamp: 1746640460607
 - conda: https://conda.anaconda.org/conda-forge/linux-64/quaternion-2024.0.8-py312hc0a28a1_0.conda
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@@ -12228,35 +12125,46 @@ packages:
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   timestamp: 1743502194434
-- conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda
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-  md5: 77d9955b4abddb811cb8ab1aa7d743e4
+- conda: https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.7.1-h8fae777_3.conda
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+  md5: 2c42649888aac645608191ffdc80d13a
   depends:
-  - libgcc-ng >=12
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  constrains:
+  - __glibc >=2.17
   license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 15423721
-  timestamp: 1694329261357
-- conda: https://conda.anaconda.org/conda-forge/osx-64/rav1e-0.6.6-h7205ca4_2.conda
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-  md5: ab03527926f8ce85f84a91fd35520ef2
+  size: 5176669
+  timestamp: 1746622023242
+- conda: https://conda.anaconda.org/conda-forge/osx-64/rav1e-0.7.1-h371c88c_3.conda
+  sha256: d86b9631d6237f5a62957f9461d321d9bd2fef0807fb60de823b8dea2028501b
+  md5: 30e2344bbe29f60bb535ec0bfff31008
+  depends:
+  - __osx >=10.13
+  constrains:
+  - __osx >=10.13
   license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 1767853
-  timestamp: 1694329738983
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rav1e-0.6.6-h69fbcac_2.conda
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-  md5: e309ae86569b1cd55a0285fa4e939844
+  size: 1093718
+  timestamp: 1746622590144
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rav1e-0.7.1-h0716509_3.conda
+  sha256: 65f862b2b31ef2b557990a82015cbd41e5a66041c2f79b4451dd14b4595d4c04
+  md5: 7b37f30516100b86ea522350c8cab44c
+  depends:
+  - __osx >=11.0
+  constrains:
+  - __osx >=11.0
   license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 1526706
-  timestamp: 1694329743011
-- conda: https://conda.anaconda.org/conda-forge/win-64/rav1e-0.6.6-h975169c_2.conda
-  sha256: 3193451440e5ac737b7d5d2a79f9e012d426c0c53e41e60df4992150bfc39565
-  md5: bd32cc2ed62374932f9d57a2e3eb2863
+  size: 856271
+  timestamp: 1746622200646
+- conda: https://conda.anaconda.org/conda-forge/win-64/rav1e-0.7.1-ha073cba_3.conda
+  sha256: d19a58b882a0387c7c8efbfce4e67a0df4b19d8da6cf6cec3011b6079e5bc743
+  md5: 3bd3626822633688691ed41d661c2b2e
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
@@ -12264,8 +12172,8 @@ packages:
   license: BSD-2-Clause
   license_family: BSD
   purls: []
-  size: 1523119
-  timestamp: 1694330157594
+  size: 4122383
+  timestamp: 1746622805379
 - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda
   sha256: 2d6d0c026902561ed77cd646b5021aef2d4db22e57a5b0178dfc669231e06d2c
   md5: 283b96675859b20a825f8fa30f311446
@@ -12832,17 +12740,17 @@ packages:
   - pkg:pypi/snowballstemmer?source=hash-mapping
   size: 58824
   timestamp: 1637143137377
-- conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda
-  sha256: 54ae221033db8fbcd4998ccb07f3c3828b4d77e73b0c72b18c1d6a507059059c
-  md5: 3f144b2c34f8cb5a9abd9ed23a39c561
+- conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.7-pyhd8ed1ab_0.conda
+  sha256: 7518506cce9a736042132f307b3f4abce63bf076f5fb07c1f4e506c0b214295a
+  md5: fb32097c717486aa34b38a9db57eb49e
   depends:
-  - python >=3.8
+  - python >=3.9
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/soupsieve?source=hash-mapping
-  size: 36754
-  timestamp: 1693929424267
+  size: 37773
+  timestamp: 1746563720271
 - conda: https://conda.anaconda.org/conda-forge/noarch/sphinx-8.2.3-pyhd8ed1ab_0.conda
   sha256: 995f58c662db0197d681fa345522fd9e7ac5f05330d3dff095ab2f102e260ab0
   md5: f7af826063ed569bb13f7207d6f949b0
@@ -12955,58 +12863,58 @@ packages:
   - pkg:pypi/sphinxcontrib-serializinghtml?source=hash-mapping
   size: 28669
   timestamp: 1733750596111
-- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.49.1-h9eae976_2.conda
-  sha256: 85c5b96686a900411ad8bdd424dcc2efb2044f15488bb89e57dff3bfcb74cc65
-  md5: 1894a9d3a8c3ffa53f8ca09fcd2fac25
+- conda: https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.49.2-h9eae976_0.conda
+  sha256: 4abeb399fadf62b2cd3e9e58c6e8a64e924ba9593c1fd935a864e46541d42267
+  md5: 02161fcc0f832baf517750700ae1d6f6
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  - libsqlite 3.49.1 hee588c1_2
+  - libsqlite 3.49.2 hee588c1_0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
   - readline >=8.2,<9.0a0
   license: Unlicense
   purls: []
-  size: 859553
-  timestamp: 1742083683966
-- conda: https://conda.anaconda.org/conda-forge/osx-64/sqlite-3.49.1-h2e4c9dc_2.conda
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-  md5: 775febaf021c23b27b8b04b2fa428388
+  size: 860062
+  timestamp: 1746637017338
+- conda: https://conda.anaconda.org/conda-forge/osx-64/sqlite-3.49.2-h2e4c9dc_0.conda
+  sha256: 69ee2f8923bc225bf23689779588682ee224358c39a5b43360f26d9ed2b8c763
+  md5: 8a9400a450bd055764cf73c053a57d03
   depends:
   - __osx >=10.13
-  - libsqlite 3.49.1 hdb6dae5_2
+  - libsqlite 3.49.2 hdb6dae5_0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
   - readline >=8.2,<9.0a0
   license: Unlicense
   purls: []
-  size: 941074
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.49.1-hd7222ec_2.conda
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-  md5: 69dd1428d6a7d068bfc1d2ad70ab6701
+  size: 941411
+  timestamp: 1746637108837
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlite-3.49.2-hd7222ec_0.conda
+  sha256: 779771c384d872d8f5391c01c7d36c4ca72f55f43c83c94aa7adb4161e5de693
+  md5: 77dd98d863ce0590fcb91ef5ccc03f44
   depends:
   - __osx >=11.0
-  - libsqlite 3.49.1 h3f77e49_2
+  - libsqlite 3.49.2 h3f77e49_0
   - libzlib >=1.3.1,<2.0a0
   - ncurses >=6.5,<7.0a0
   - readline >=8.2,<9.0a0
   license: Unlicense
   purls: []
-  size: 883664
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-- conda: https://conda.anaconda.org/conda-forge/win-64/sqlite-3.49.1-h2466b09_2.conda
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-  md5: e30b7cd408f01cc06073d9e68bfc1710
+  size: 883692
+  timestamp: 1746637250671
+- conda: https://conda.anaconda.org/conda-forge/win-64/sqlite-3.49.2-h2466b09_0.conda
+  sha256: 6b37c360b808ecc8e40a60d091ade8f932ac1e50f3bae91e41d5c4e70a9647fa
+  md5: 0f8cff11f83df8b545dd1f011be8a702
   depends:
-  - libsqlite 3.49.1 h67fdade_2
+  - libsqlite 3.49.2 h67fdade_0
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: Unlicense
   purls: []
-  size: 1105027
-  timestamp: 1742083977188
+  size: 1105619
+  timestamp: 1746637431686
 - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda
   sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41
   md5: b1b505328da7a6b246787df4b5a49fbc
@@ -13144,22 +13052,21 @@ packages:
   purls: []
   size: 151460
   timestamp: 1732982860332
-- conda: https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.3.30-pyhd8ed1ab_0.conda
-  sha256: faffc031f313d5738291639aeeb3a5b0ee191451924393754b9039731dd52aac
-  md5: 14f46147fae19bb867f82a787c7059e9
+- conda: https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.5.10-pyhd8ed1ab_0.conda
+  sha256: 3ea3854eb8a41bbb128598a5d5bc9aed52446d20d2f1bd6e997c2387074202e4
+  md5: 1fdb801f28bf4987294c49aaa314bf5e
   depends:
   - imagecodecs >=2024.12.30
   - numpy >=1.19.2
   - python >=3.10
   constrains:
   - matplotlib-base >=3.3
-  - zarr <3
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/tifffile?source=hash-mapping
-  size: 180495
-  timestamp: 1743428184701
+  size: 179592
+  timestamp: 1746986641678
 - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda
   sha256: cad582d6f978276522f84bd209a5ddac824742fe2d452af6acf900f8650a73a2
   md5: f1acf5fdefa8300de697982bcb1761c9
@@ -13521,9 +13428,9 @@ packages:
   purls: []
   size: 750733
   timestamp: 1743195092905
-- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.30.0-pyhd8ed1ab_0.conda
-  sha256: 1dbb24b144f7b8400b30cca760cdee1b7de61716cd7f06d7ea82b741645823ce
-  md5: c0e0b4a09aa5a698a1bdd4ebfe28be38
+- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.31.2-pyhd8ed1ab_0.conda
+  sha256: 763dc774200b2eebdf5437b112834c5455a1dd1c9b605340696950277ff36729
+  md5: c0600c1b374efa7a1ff444befee108ca
   depends:
   - distlib >=0.3.7,<1
   - filelock >=3.12.2,<4
@@ -13533,8 +13440,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=hash-mapping
-  size: 3635535
-  timestamp: 1743474070226
+  size: 4133755
+  timestamp: 1746781585998
 - conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.42.34438-h7142326_26.conda
   sha256: 432f2937206f1ad4a77e39f84fabc1ce7d2472b669836fb72bd2bfd19a2defc9
   md5: 3357e4383dbce31eed332008ede242ab
diff --git a/pyproject.toml b/pyproject.toml
index 08cc22e..d36f7b0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ pyrecest = {git = "https://github.com/FlorianPfaff/pyRecEst.git"}
 numpy = ">=2.2.5,<3"
 h5py = ">=3.13.0,<4"
 scipy = ">=1.15.2,<2"
-matplotlib = ">=3.10.1,<4"
+matplotlib = ">=3.10.3,<4"
 scikit-image = ">=0.25.2,<0.26"
 lxml = ">=5.4.0,<6"
 plotly = ">=6.0.1,<7"
@@ -42,7 +42,7 @@ nbclient = ">=0.10.2,<0.11"
 pre-commit = ">=4.2.0,<5"
 quaternion = ">=2024.0.8,<2025"
 beartype = ">=0.20.2,<0.21"
-pyvista = ">=0.45.0,<0.46"
+pyvista = ">=0.45.2,<0.46"
 
 [tool.pixi.feature.build.dependencies]
 twine = "*"