Repository: UXARRAY/uxarray

Issue:
Further Optimize Bounds Counstriction
Taken from https://github.com/UXARRAY/uxarray/pull/1180#issuecomment-2781997490

Timings were taken on a single NCAR Derecho CPU node. 

| Resolution | Nodes      | Faces      | Edges       | Grid Load Time (s) | Bounds Construction Time (s) | Total Time (s) |
|------------|------------|------------|-------------|-------------------:|----------------------------:|---------------:|
| 30km       | 1,310,720  | 655,362    | 1,966,080   | 1.923              | 2.541                      | 4.464          |
| 15km       | 5,242,880  | 2,621,442  | 7,864,320   | 7.489              | 10.191                     | 17.68          |
| 7.5km      | 20,971,520 | 10,485,762 | 31,457,280  | 29.228             | 37.866                     | 67.095         |
| 3.75km     | 83,886,080 | 41,943,042 | 125,829,120 | 113.028            | 155.486                    | 268.514        |

Discussion:

Solution patch:
diff --git a/docs/examples/visualization/mpas_topology.ipynb b/docs/examples/visualization/mpas_topology.ipynb
index 6a3becf42..155d9e2e3 100644
--- a/docs/examples/visualization/mpas_topology.ipynb
+++ b/docs/examples/visualization/mpas_topology.ipynb
@@ -17,71 +17,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
    "id": "69e63dca15b3d77b",
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-06-24T08:26:11.405882Z",
-     "start_time": "2024-06-24T08:26:11.403370900Z"
-    }
+    "collapsed": false
    },
-   "outputs": [],
    "source": [
     "import uxarray as ux"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
    "id": "3e4b0e39e371277b",
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-06-24T08:26:11.627188800Z",
-     "start_time": "2024-06-24T08:26:11.582503300Z"
-    }
+    "collapsed": false
    },
-   "outputs": [],
    "source": [
     "base_path = \"../../../test/meshfiles/mpas/QU/\"\n",
     "grid_path = base_path + \"mesh.QU.1920km.151026.nc\"\n",
     "primal_grid = ux.open_grid(grid_path, use_dual=False)\n",
     "dual_grid = ux.open_grid(grid_path, use_dual=True)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
    "id": "df9e6c984a6b005e",
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-06-24T08:26:12.154619300Z",
-     "start_time": "2024-06-24T08:26:11.819808600Z"
-    }
+    "collapsed": false
    },
-   "outputs": [
-    {
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-    },
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-      "application/vnd.holoviews_exec.v0+json": "",
-      "text/plain": ":Overlay\n   .Path.I    :Path   [x,y]\n   .Points.I  :Points   [x,y]\n   .Path.II   :Path   [x,y]\n   .Points.II :Points   [x,y]"
-     },
-     "execution_count": 16,
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "p8376"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "(\n",
     "    primal_grid.plot.edges(color=\"Blue\", line_width=2)\n",
@@ -95,7 +61,17 @@
     "    width=700,\n",
     "    height=350,\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "",
+   "id": "a4fa03742c6850ae",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {
diff --git a/uxarray/core/zonal.py b/uxarray/core/zonal.py
index 4535af36d..2b36fe0aa 100644
--- a/uxarray/core/zonal.py
+++ b/uxarray/core/zonal.py
@@ -2,7 +2,7 @@
 import numpy as np
 
 from uxarray.grid.integrate import _zonal_face_weights, _zonal_face_weights_robust
-from uxarray.grid.utils import _get_cartesian_face_edge_nodes
+from uxarray.grid.utils import _get_cartesian_face_edge_nodes_array
 
 
 def _compute_non_conservative_zonal_mean(uxda, latitudes, use_robust_weights=False):
@@ -17,7 +17,7 @@ def _compute_non_conservative_zonal_mean(uxda, latitudes, use_robust_weights=Fal
         # Create a NumPy array for storing results
         result = np.zeros(shape, dtype=uxda.dtype)
 
-    faces_edge_nodes_xyz = _get_cartesian_face_edge_nodes(
+    faces_edge_nodes_xyz = _get_cartesian_face_edge_nodes_array(
         uxgrid.face_node_connectivity.values,
         uxgrid.n_face,
         uxgrid.n_max_face_nodes,
diff --git a/uxarray/grid/bounds.py b/uxarray/grid/bounds.py
new file mode 100644
index 000000000..877b9510d
--- /dev/null
+++ b/uxarray/grid/bounds.py
@@ -0,0 +1,559 @@
+import numpy as np
+import pandas as pd
+import xarray as xr
+from numba import njit, prange
+
+from uxarray.constants import ERROR_TOLERANCE, INT_FILL_VALUE
+from uxarray.grid.arcs import (
+    extreme_gca_latitude,
+    point_within_gca,
+)
+from uxarray.grid.geometry import pole_point_inside_polygon
+from uxarray.grid.utils import (
+    _get_cartesian_face_edge_nodes,
+    _get_spherical_face_edge_nodes,
+    all_elements_nan,
+    any_close_lat,
+)
+
+
+def _populate_face_bounds(
+    grid,
+    is_latlonface: bool = False,
+    is_face_GCA_list=None,
+    return_array=False,
+    process_intervals=False,
+):
+    """Populates the spherical bounds for each face in a grid.
+
+
+    Parameters
+    ----------
+    is_latlonface : bool, optional
+        A global flag that indicates if faces are latlon faces. If True, all faces
+        are treated as latlon faces, meaning that all edges are either longitude or
+        constant latitude lines. If False, all edges are considered as Great Circle Arcs (GCA).
+        Default is False.
+
+    is_face_GCA_list : list or np.ndarray, optional
+        A list or an array of boolean values for each face, indicating whether each edge
+        in that face is a GCA. The shape of the list or array should be (n_faces, n_edges),
+        with each sub-list or sub-array like [True, False, True, False] indicating the
+        nature of each edge (GCA or constant latitude line) in a face. This parameter allows
+        for mixed face types within the grid by specifying the edge type at the face level.
+        If None, all edges are considered as GCA. This parameter, if provided, will overwrite
+        the `is_latlonface` attribute for specific faces. Default is None.
+    return_array: bool, optional
+        Whether to return the bounds array, instead of populating the grid. Default is False
+    process_intervals: bool, optional
+        Whether to process the latitude intervals. Default is False
+
+    Returns
+    -------
+    xr.DataArray
+        A DataArray containing the latitude and longitude bounds for each face in the grid,
+        expressed in radians. The array has dimensions ["n_face", "Two", "Two"], where "Two"
+        is a literal dimension name indicating two bounds (min and max) for each of latitude
+        and longitude. The DataArray includes attributes detailing its purpose and the mapping
+        of latitude intervals to face indices.
+
+        Attributes include:
+        - `cf_role`: Describes the role of the DataArray, here indicating face latitude bounds.
+        - `_FillValue`: The fill value used in the array, indicating uninitialized or missing data.
+        - `long_name`: A descriptive name for the DataArray.
+        - `start_index`: The starting index for face indices in the grid.
+        - `latitude_intervalsIndex`: An IntervalIndex indicating the latitude intervals, only if
+        - `latitude_intervals_name_map`: A DataFrame mapping the latitude intervals to face indices.
+
+    Example
+    -------
+    Consider a scenario where you have four faces on a grid, each defined by vertices in longitude and latitude degrees:
+
+        face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]]
+        face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]]
+        face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]]
+        face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]]
+
+    After defining these faces, you can create a grid and populate its bounds by treating all faces as latlon faces:
+
+        grid = ux.Grid.from_face_vertices([face_1, face_2, face_3, face_4], latlon=True)
+        bounds_dataarray = grid._populate_bounds(is_latlonface=True)
+
+    This will calculate and store the bounds for each face within the grid, adjusting for any special conditions such as crossing the antimeridian, and return them as a DataArray.
+    """
+    grid.normalize_cartesian_coordinates()
+
+    bounds_array = _construct_face_bounds_array(
+        grid.face_node_connectivity.values,
+        grid.n_nodes_per_face.values,
+        grid.node_x.values,
+        grid.node_y.values,
+        grid.node_z.values,
+        grid.node_lon.values,
+        grid.node_lat.values,
+        is_latlonface,
+        is_face_GCA_list,
+    )
+
+    bounds_da = xr.DataArray(
+        bounds_array,
+        dims=["n_face", "lon_lat", "min_max"],
+        attrs={
+            "cf_role": "face_latlon_bounds",
+            "_FillValue": INT_FILL_VALUE,
+            "long_name": "Latitude and longitude bounds for each face in radians.",
+        },
+    )
+
+    if process_intervals:
+        intervals_tuple_list = []
+        intervals_name_list = []
+        for face_idx in range(grid.n_face):
+            assert bounds_array[face_idx][0][0] != bounds_array[face_idx][0][1]
+            assert bounds_array[face_idx][1][0] != bounds_array[face_idx][1][1]
+            lat_array = bounds_array[face_idx][0]
+            intervals_tuple_list.append((lat_array[0], lat_array[1]))
+            intervals_name_list.append(face_idx)
+
+        intervalsIndex = pd.IntervalIndex.from_tuples(
+            intervals_tuple_list, closed="both"
+        )
+        df_intervals_map = pd.DataFrame(
+            index=intervalsIndex, data=intervals_name_list, columns=["face_id"]
+        )
+        bounds_da.assign_attrs(
+            {
+                "latitude_intervalsIndex": intervalsIndex,
+                "latitude_intervals_name_map": df_intervals_map,
+            }
+        )
+
+    if return_array:
+        return bounds_da
+    else:
+        grid._ds["bounds"] = bounds_da
+
+
+@njit(cache=True, parallel=True)
+def _construct_face_bounds_array(
+    face_node_connectivity,
+    n_nodes_per_face,
+    node_x,
+    node_y,
+    node_z,
+    node_lon,
+    node_lat,
+    is_latlonface: bool = False,
+    is_face_GCA_list=None,
+):
+    """Computes an array of face bounds."""
+    n_face = face_node_connectivity.shape[0]
+    bounds_array = np.empty((n_face, 2, 2), dtype=np.float64)
+
+    node_lon = np.deg2rad(node_lon)
+    node_lat = np.deg2rad(node_lat)
+
+    # Iterate over each face in parallel
+    for face_idx in prange(n_face):
+        # 1) Create the Cartesian Face Edge Nodes for the current face
+        cur_face_edge_nodes_cartesian = _get_cartesian_face_edge_nodes(
+            face_idx, face_node_connectivity, n_nodes_per_face, node_x, node_y, node_z
+        )
+        # 2) Create the Spherical Face Edge Nodes for the current face
+        cur_face_edge_nodes_spherical = _get_spherical_face_edge_nodes(
+            face_idx, face_node_connectivity, n_nodes_per_face, node_lon, node_lat
+        )
+
+        # TODO: This isn't currently used for the user API, consider updating in the future
+        if is_face_GCA_list is not None:
+            is_GCA_list = is_face_GCA_list[face_idx]
+        else:
+            is_GCA_list = None
+
+        # 3) Populate the bounds for the current face
+        bounds_array[face_idx] = _construct_face_bounds(
+            cur_face_edge_nodes_cartesian,
+            cur_face_edge_nodes_spherical,
+            is_latlonface=is_latlonface,
+            is_GCA_list=is_GCA_list,
+        )
+
+    return bounds_array
+
+
+@njit(cache=True)
+def _construct_face_bounds(
+    face_edges_xyz,
+    face_edges_lonlat,
+    is_latlonface=False,
+    is_GCA_list=None,
+):
+    """Compute the bounds of a single face."""
+    # Check if face_edges contains pole points
+    has_north_pole = pole_point_inside_polygon(1, face_edges_xyz, face_edges_lonlat)
+    has_south_pole = pole_point_inside_polygon(-1, face_edges_xyz, face_edges_lonlat)
+
+    # Initialize face_latlon_array with INT_FILL_VALUE
+    face_latlon_array = np.full((2, 2), np.nan, dtype=np.float64)
+
+    if has_north_pole or has_south_pole:
+        # Initial assumption that the pole point is inside the face
+        is_center_pole = True
+
+        pole_point_xyz = np.zeros(3, dtype=np.float64)
+        pole_point_lonlat = np.zeros(2, dtype=np.float64)
+        new_pt_latlon = np.zeros(2, dtype=np.float64)
+
+        if has_north_pole:
+            pole_point_xyz[2] = 1.0  # [0.0, 0.0, 1.0]
+            pole_point_lonlat[1] = np.pi / 2  # [0.0, pi/2]
+            new_pt_latlon[0] = np.pi / 2  # [pi/2, nan]
+            new_pt_latlon[1] = np.nan
+        else:
+            pole_point_xyz[2] = -1.0  # [0.0, 0.0, -1.0]
+            pole_point_lonlat[1] = -np.pi / 2  # [0.0, -pi/2]
+            new_pt_latlon[0] = -np.pi / 2  # [-pi/2, nan]
+            new_pt_latlon[1] = np.nan
+
+        for i in range(face_edges_xyz.shape[0]):
+            edge_xyz = face_edges_xyz[i]
+            edge_lonlat = face_edges_lonlat[i]
+
+            # Skip processing if the edge is marked as a dummy with a fill value
+            if np.any(edge_xyz == INT_FILL_VALUE):
+                continue
+
+            # Extract cartesian coordinates of the edge's endpoints
+            n1_cart = edge_xyz[0]
+            n2_cart = edge_xyz[1]
+            n1_lonlat = edge_lonlat[0]
+            n2_lonlat = edge_lonlat[1]
+
+            # Extract latitudes and longitudes of the nodes
+            node1_lon_rad = n1_lonlat[0]
+            node1_lat_rad = n1_lonlat[1]
+
+            # Determine if the edge's extreme latitudes need to be considered
+            if is_GCA_list is not None:
+                is_GCA = is_GCA_list[i]
+            else:
+                is_GCA = not is_latlonface or n1_cart[2] != n2_cart[2]
+
+            # Check if the node matches the pole point or if the pole point is within the edge
+            max_abs_diff = np.max(np.abs(n1_cart - pole_point_xyz))
+            if max_abs_diff <= ERROR_TOLERANCE or point_within_gca(
+                pole_point_xyz,
+                n1_cart,
+                n2_cart,
+            ):
+                is_center_pole = False
+                face_latlon_array = insert_pt_in_latlonbox(
+                    face_latlon_array, new_pt_latlon
+                )
+
+            # Insert the current node's lat/lon into the latlonbox
+            face_latlon_array = insert_pt_in_latlonbox(
+                face_latlon_array, np.array([node1_lat_rad, node1_lon_rad])
+            )
+
+            # Create n1n2_cart and n1n2_lonlat arrays using preallocation
+            n1n2_cart = np.empty((2, 3), dtype=np.float64)
+            n1n2_cart[0, :] = n1_cart
+            n1n2_cart[1, :] = n2_cart
+
+            n1n2_lonlat = np.empty((2, 2), dtype=np.float64)
+            n1n2_lonlat[0, :] = n1_lonlat
+            n1n2_lonlat[1, :] = n2_lonlat
+
+            # Determine extreme latitudes for GCA edges
+            if is_GCA:
+                lat_max = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "max")
+                lat_min = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "min")
+            else:
+                lat_max = node1_lat_rad
+                lat_min = node1_lat_rad
+
+            # Insert latitudinal extremes based on pole presence
+            if has_north_pole:
+                face_latlon_array = insert_pt_in_latlonbox(
+                    face_latlon_array, np.array([lat_min, node1_lon_rad])
+                )
+                face_latlon_array[0, 1] = np.pi / 2  # Upper latitude bound
+            else:
+                face_latlon_array = insert_pt_in_latlonbox(
+                    face_latlon_array, np.array([lat_max, node1_lon_rad])
+                )
+                face_latlon_array[0, 0] = -np.pi / 2  # Lower latitude bound
+
+        # Adjust longitude bounds globally if the pole is centrally inside the polygon
+        if is_center_pole:
+            face_latlon_array[1, 0] = 0.0
+            face_latlon_array[1, 1] = 2 * np.pi
+
+    else:
+        # Normal Face
+        for i in range(face_edges_xyz.shape[0]):
+            edge_xyz = face_edges_xyz[i]
+            edge_lonlat = face_edges_lonlat[i]
+
+            # Skip processing if the edge is marked as a dummy with a fill value
+            if np.any(edge_xyz == INT_FILL_VALUE):
+                continue
+
+            # Extract cartesian coordinates of the edge's endpoints
+            n1_cart = edge_xyz[0]
+            n2_cart = edge_xyz[1]
+            n1_lonlat = edge_lonlat[0]
+            n2_lonlat = edge_lonlat[1]
+
+            # Extract latitudes and longitudes of the nodes
+            node1_lon_rad = n1_lonlat[0]
+            node1_lat_rad = n1_lonlat[1]
+            # node2_lon_rad = n2_lonlat[0]
+            node2_lat_rad = n2_lonlat[1]
+
+            # Determine if the edge's extreme latitudes need to be considered
+            if is_GCA_list is not None:
+                is_GCA = is_GCA_list[i]
+            else:
+                is_GCA = not is_latlonface or n1_cart[2] != n2_cart[2]
+
+            # Create n1n2_cart and n1n2_lonlat arrays using preallocation
+            n1n2_cart = np.empty((2, 3), dtype=np.float64)
+            n1n2_cart[0, :] = n1_cart
+            n1n2_cart[1, :] = n2_cart
+
+            n1n2_lonlat = np.empty((2, 2), dtype=np.float64)
+            n1n2_lonlat[0, :] = n1_lonlat
+            n1n2_lonlat[1, :] = n2_lonlat
+
+            if is_GCA:
+                lat_max = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "max")
+                lat_min = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "min")
+            else:
+                lat_max = node1_lat_rad
+                lat_min = node1_lat_rad
+
+            # Insert extreme latitude points into the latlonbox
+            if (
+                abs(node1_lat_rad - lat_max) > ERROR_TOLERANCE
+                and abs(node2_lat_rad - lat_max) > ERROR_TOLERANCE
+            ):
+                face_latlon_array = insert_pt_in_latlonbox(
+                    face_latlon_array, np.array([lat_max, node1_lon_rad])
+                )
+            elif (
+                abs(node1_lat_rad - lat_min) > ERROR_TOLERANCE
+                and abs(node2_lat_rad - lat_min) > ERROR_TOLERANCE
+            ):
+                face_latlon_array = insert_pt_in_latlonbox(
+                    face_latlon_array, np.array([lat_min, node1_lon_rad])
+                )
+            else:
+                face_latlon_array = insert_pt_in_latlonbox(
+                    face_latlon_array, np.array([node1_lat_rad, node1_lon_rad])
+                )
+
+    return face_latlon_array
+
+
+@njit(cache=True)
+def insert_pt_in_latlonbox(old_box, new_pt, is_lon_periodic=True):
+    """Update the latitude-longitude box to include a new point in radians.
+
+    Parameters
+    ----------
+    old_box : np.ndarray
+        The original latitude-longitude box in radians, a 2x2 array:
+        [[min_lat, max_lat],
+         [left_lon, right_lon]].
+    new_pt : np.ndarray
+        The new latitude-longitude point in radians, an array: [lat, lon].
+    is_lon_periodic : bool, optional
+        Flag indicating if the latitude-longitude box is periodic in longitude (default is True).
+
+    Returns
+    -------
+    np.ndarray
+        Updated latitude-longitude box including the new point in radians.
+
+    Raises
+    ------
+    Exception
+        If logic errors occur in the calculation process.
+    """
+    # Check if the new point is a fill value
+    all_fill = all_elements_nan(new_pt)
+    if all_fill:
+        return old_box
+
+    # Create a copy of the old box
+    latlon_box = np.copy(old_box)
+
+    # Extract latitude and longitude from the new point
+    lat_pt = new_pt[0]
+    lon_pt = new_pt[1]
+
+    # Normalize the longitude if it's not a fill value
+    if not np.isnan(lon_pt):
+        lon_pt = lon_pt % (2.0 * np.pi)
+
+    # Check if the latitude range is uninitialized and update it
+    if np.isnan(old_box[0, 0]) and np.isnan(old_box[0, 1]):
+        latlon_box[0, 0] = lat_pt
+        latlon_box[0, 1] = lat_pt
+    else:
+        # Update latitude range
+        if lat_pt < latlon_box[0, 0]:
+            latlon_box[0, 0] = lat_pt
+        if lat_pt > latlon_box[0, 1]:
+            latlon_box[0, 1] = lat_pt
+
+    # Check if the longitude range is uninitialized and update it
+    if np.isnan(old_box[1, 0]) and np.isnan(old_box[1, 1]):
+        latlon_box[1, 0] = lon_pt
+        latlon_box[1, 1] = lon_pt
+    else:
+        # Validate longitude point
+        if not np.isnan(lon_pt) and (lon_pt < 0.0 or lon_pt > 2.0 * np.pi):
+            raise Exception("Longitude point out of range")
+
+        # Check for pole points
+        is_pole_point = False
+        if np.isnan(lon_pt):
+            if any_close_lat(lat_pt, ERROR_TOLERANCE):
+                is_pole_point = True
+
+        if is_pole_point:
+            # Update latitude for pole points
+            if np.isclose(lat_pt, 0.5 * np.pi, ERROR_TOLERANCE):
+                latlon_box[0, 1] = 0.5 * np.pi  # Update max_lat for North Pole
+            elif np.isclose(lat_pt, -0.5 * np.pi, ERROR_TOLERANCE):
+                latlon_box[0, 0] = -0.5 * np.pi  # Update min_lat for South Pole
+        else:
+            # Update longitude range based on periodicity
+            if not is_lon_periodic:
+                # Non-periodic: straightforward min and max updates
+                if lon_pt < latlon_box[1, 0]:
+                    latlon_box[1, 0] = lon_pt
+                if lon_pt > latlon_box[1, 1]:
+                    latlon_box[1, 1] = lon_pt
+            else:
+                # Periodic longitude handling
+                # Determine if the new point extends the current longitude range
+                # considering the periodic boundary at 2*pi
+                left_lon = latlon_box[1, 0]
+                right_lon = latlon_box[1, 1]
+
+                # Check if the current box wraps around
+                wraps_around = left_lon > right_lon
+
+                if wraps_around:
+                    # If the box wraps around, check if the new point is outside the current range
+                    if not (left_lon <= lon_pt or lon_pt <= right_lon):
+                        # Decide to extend either the left or the right
+                        # Calculate the new width for both possibilities
+                        # Option 1: Extend the left boundary to lon_pt
+                        box_a = np.copy(latlon_box)
+                        box_a[1, 0] = lon_pt
+                        d_width_a = _get_latlonbox_width(box_a)
+
+                        # Option 2: Extend the right boundary to lon_pt
+                        box_b = np.copy(latlon_box)
+                        box_b[1, 1] = lon_pt
+                        d_width_b = _get_latlonbox_width(box_b)
+
+                        # Ensure widths are non-negative
+                        if (d_width_a < 0.0) or (d_width_b < 0.0):
+                            raise Exception(
+                                "Logic error in longitude box width calculation"
+                            )
+
+                        # Choose the box with the smaller width
+                        if d_width_a < d_width_b:
+                            latlon_box = box_a
+                        else:
+                            latlon_box = box_b
+                else:
+                    # If the box does not wrap around, simply update min or max longitude
+                    if lon_pt < left_lon or lon_pt > right_lon:
+                        # Calculate the new width for both possibilities
+                        # Option 1: Extend the left boundary to lon_pt
+                        box_a = np.copy(latlon_box)
+                        box_a[1, 0] = lon_pt
+                        d_width_a = _get_latlonbox_width(box_a)
+
+                        # Option 2: Extend the right boundary to lon_pt
+                        box_b = np.copy(latlon_box)
+                        box_b[1, 1] = lon_pt
+                        d_width_b = _get_latlonbox_width(box_b)
+
+                        # Ensure widths are non-negative
+                        if (d_width_a < 0.0) or (d_width_b < 0.0):
+                            raise Exception(
+                                "Logic error in longitude box width calculation"
+                            )
+
+                        # Choose the box with the smaller width
+                        if d_width_a < d_width_b:
+                            latlon_box = box_a
+                        else:
+                            latlon_box = box_b
+
+    return latlon_box
+
+
+@njit(cache=True)
+def _get_latlonbox_width(latlonbox_rad):
+    """Calculate the width of a latitude-longitude box in radians. The box
+    should be represented by a 2x2 array in radians and lon0 represent the
+    "left" side of the box. while lon1 represent the "right" side of the box.
+
+    This function computes the width of a given latitude-longitude box. It
+    accounts for periodicity in the longitude direction.
+
+    Non-Periodic Longitude: This is the usual case where longitude values are considered within a fixed range,
+            typically between -180 and 180 degrees, or 0 and 360 degrees.
+            Here, the longitude does not "wrap around" when it reaches the end of this range.
+
+    Periodic Longitude: In this case, the longitude is considered to wrap around the globe.
+            This means that if you have a longitude range from 350 to 10 degrees,
+            it is understood to cross the 0-degree meridian and actually represents a 20-degree span
+            (350 to 360 degrees, then 0 to 10 degrees).
+
+    Parameters
+    ----------
+    latlonbox_rad : np.ndarray
+        A latitude-longitude box represented by a 2x2 array in radians and lon0 represent the "left" side of the box.
+        while lon1 represent the "right" side of the box:
+        [[lat_0, lat_1], [lon_0, lon_1]].
+
+    Returns
+    -------
+    float
+        The width of the latitude-longitude box in radians.
+
+    Raises
+    ------
+    Exception
+        If the input longitude range is invalid.
+
+    Warning
+        If the input longitude range is flagged as periodic but in the form [lon0, lon1] where lon0 < lon1.
+        The function will automatically use the is_lon_periodic=False instead.
+    """
+    lon0, lon1 = latlonbox_rad[1]
+
+    if lon0 != INT_FILL_VALUE:
+        lon0 = np.mod(lon0, 2 * np.pi)
+    if lon1 != INT_FILL_VALUE:
+        lon1 = np.mod(lon1, 2 * np.pi)
+    if (lon0 < 0.0 or lon0 > 2.0 * np.pi) and lon0 != INT_FILL_VALUE:
+        # -1 used for exception
+        return -1
+
+    if lon0 <= lon1:
+        return lon1 - lon0
+    else:
+        # Adjust for periodicity
+        return 2 * np.pi - lon0 + lon1
diff --git a/uxarray/grid/coordinates.py b/uxarray/grid/coordinates.py
index 176d536b4..dd1d0f2c1 100644
--- a/uxarray/grid/coordinates.py
+++ b/uxarray/grid/coordinates.py
@@ -791,22 +791,6 @@ def _xyz_to_lonlat_rad_no_norm(
     return lon, lat
 
 
-def _normalize_xyz(
-    x: Union[np.ndarray, float],
-    y: Union[np.ndarray, float],
-    z: Union[np.ndarray, float],
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Normalizes a set of Cartesiain coordinates."""
-    denom = np.linalg.norm(
-        np.asarray(np.array([x, y, z]), dtype=np.float64), ord=2, axis=0
-    )
-
-    x_norm = x / denom
-    y_norm = y / denom
-    z_norm = z / denom
-    return x_norm, y_norm, z_norm
-
-
 @njit(cache=True)
 def _lonlat_rad_to_xyz(
     lon: Union[np.ndarray, float],
diff --git a/uxarray/grid/geometry.py b/uxarray/grid/geometry.py
index 74b388374..5f0e2a2ba 100644
--- a/uxarray/grid/geometry.py
+++ b/uxarray/grid/geometry.py
@@ -4,12 +4,10 @@
 import cartopy.crs as ccrs
 import geopandas
 import numpy as np
-import pandas as pd
 import shapely
 import spatialpandas
-import xarray as xr
 from matplotlib.collections import LineCollection, PolyCollection
-from numba import njit, prange
+from numba import njit
 from shapely import Polygon
 from shapely import polygons as Polygons
 from spatialpandas.geometry import MultiPolygonArray, PolygonArray
@@ -20,18 +18,13 @@
     INT_FILL_VALUE,
 )
 from uxarray.grid.arcs import (
-    extreme_gca_latitude,
     point_within_gca,
 )
 from uxarray.grid.coordinates import _xyz_to_lonlat_rad
 from uxarray.grid.intersections import (
     gca_gca_intersection,
 )
-from uxarray.grid.utils import (
-    _get_cartesian_face_edge_nodes,
-    _get_lonlat_rad_face_edge_nodes,
-)
-from uxarray.utils.computing import allclose, isclose
+from uxarray.utils.computing import allclose
 
 POLE_POINTS_XYZ = {
     "North": np.array([0.0, 0.0, 1.0]),
@@ -919,560 +912,6 @@ def _check_intersection(ref_edge_xyz, edges_xyz):
     return unique_count
 
 
-@njit(cache=True)
-def _get_latlonbox_width(latlonbox_rad):
-    """Calculate the width of a latitude-longitude box in radians. The box
-    should be represented by a 2x2 array in radians and lon0 represent the
-    "left" side of the box. while lon1 represent the "right" side of the box.
-
-    This function computes the width of a given latitude-longitude box. It
-    accounts for periodicity in the longitude direction.
-
-    Non-Periodic Longitude: This is the usual case where longitude values are considered within a fixed range,
-            typically between -180 and 180 degrees, or 0 and 360 degrees.
-            Here, the longitude does not "wrap around" when it reaches the end of this range.
-
-    Periodic Longitude: In this case, the longitude is considered to wrap around the globe.
-            This means that if you have a longitude range from 350 to 10 degrees,
-            it is understood to cross the 0-degree meridian and actually represents a 20-degree span
-            (350 to 360 degrees, then 0 to 10 degrees).
-
-    Parameters
-    ----------
-    latlonbox_rad : np.ndarray
-        A latitude-longitude box represented by a 2x2 array in radians and lon0 represent the "left" side of the box.
-        while lon1 represent the "right" side of the box:
-        [[lat_0, lat_1], [lon_0, lon_1]].
-
-    Returns
-    -------
-    float
-        The width of the latitude-longitude box in radians.
-
-    Raises
-    ------
-    Exception
-        If the input longitude range is invalid.
-
-    Warning
-        If the input longitude range is flagged as periodic but in the form [lon0, lon1] where lon0 < lon1.
-        The function will automatically use the is_lon_periodic=False instead.
-    """
-    # TODO: ensure this function works properly
-    lon0, lon1 = latlonbox_rad[1]
-
-    if lon0 != INT_FILL_VALUE:
-        lon0 = np.mod(lon0, 2 * np.pi)
-    if lon1 != INT_FILL_VALUE:
-        lon1 = np.mod(lon1, 2 * np.pi)
-    if (lon0 < 0.0 or lon0 > 2.0 * np.pi) and lon0 != INT_FILL_VALUE:
-        # -1 used for exception
-        return -1
-
-    if lon0 <= lon1:
-        return lon1 - lon0
-    else:
-        # Adjust for periodicity
-        return 2 * np.pi - lon0 + lon1
-
-
-@njit(cache=True)
-def all_elements_nan(arr):
-    """Check if all elements in an array are np.nan."""
-    for i in range(arr.shape[0]):
-        if not np.isnan(arr[i]):
-            return False
-    return True
-
-
-@njit(cache=True)
-def any_close_lat(lat_pt, atol):
-    """Check if the latitude point is close to either the North or South Pole."""
-    return isclose(lat_pt, 0.5 * math.pi, atol) or isclose(lat_pt, -0.5 * math.pi, atol)
-
-
-@njit(cache=True)
-def insert_pt_in_latlonbox(old_box, new_pt, is_lon_periodic=True):
-    """Update the latitude-longitude box to include a new point in radians.
-
-    Parameters
-    ----------
-    old_box : np.ndarray
-        The original latitude-longitude box in radians, a 2x2 array:
-        [[min_lat, max_lat],
-         [left_lon, right_lon]].
-    new_pt : np.ndarray
-        The new latitude-longitude point in radians, an array: [lat, lon].
-    is_lon_periodic : bool, optional
-        Flag indicating if the latitude-longitude box is periodic in longitude (default is True).
-
-    Returns
-    -------
-    np.ndarray
-        Updated latitude-longitude box including the new point in radians.
-
-    Raises
-    ------
-    Exception
-        If logic errors occur in the calculation process.
-    """
-    # Check if the new point is a fill value
-    all_fill = all_elements_nan(new_pt)
-    if all_fill:
-        return old_box
-
-    # Create a copy of the old box
-    latlon_box = np.copy(old_box)
-
-    # Extract latitude and longitude from the new point
-    lat_pt = new_pt[0]
-    lon_pt = new_pt[1]
-
-    # Normalize the longitude if it's not a fill value
-    if not np.isnan(lon_pt):
-        lon_pt = lon_pt % (2.0 * math.pi)
-
-    # Check if the latitude range is uninitialized and update it
-    if np.isnan(old_box[0, 0]) and np.isnan(old_box[0, 1]):
-        latlon_box[0, 0] = lat_pt
-        latlon_box[0, 1] = lat_pt
-    else:
-        # Update latitude range
-        if lat_pt < latlon_box[0, 0]:
-            latlon_box[0, 0] = lat_pt
-        if lat_pt > latlon_box[0, 1]:
-            latlon_box[0, 1] = lat_pt
-
-    # Check if the longitude range is uninitialized and update it
-    if np.isnan(old_box[1, 0]) and np.isnan(old_box[1, 1]):
-        latlon_box[1, 0] = lon_pt
-        latlon_box[1, 1] = lon_pt
-    else:
-        # Validate longitude point
-        if not np.isnan(lon_pt) and (lon_pt < 0.0 or lon_pt > 2.0 * math.pi):
-            raise Exception("Longitude point out of range")
-
-        # Check for pole points
-        is_pole_point = False
-        if np.isnan(lon_pt):
-            if any_close_lat(lat_pt, ERROR_TOLERANCE):
-                is_pole_point = True
-
-        if is_pole_point:
-            # Update latitude for pole points
-            if isclose(lat_pt, 0.5 * math.pi, ERROR_TOLERANCE):
-                latlon_box[0, 1] = 0.5 * math.pi  # Update max_lat for North Pole
-            elif isclose(lat_pt, -0.5 * math.pi, ERROR_TOLERANCE):
-                latlon_box[0, 0] = -0.5 * math.pi  # Update min_lat for South Pole
-        else:
-            # Update longitude range based on periodicity
-            if not is_lon_periodic:
-                # Non-periodic: straightforward min and max updates
-                if lon_pt < latlon_box[1, 0]:
-                    latlon_box[1, 0] = lon_pt
-                if lon_pt > latlon_box[1, 1]:
-                    latlon_box[1, 1] = lon_pt
-            else:
-                # Periodic longitude handling
-                # Determine if the new point extends the current longitude range
-                # considering the periodic boundary at 2*pi
-                left_lon = latlon_box[1, 0]
-                right_lon = latlon_box[1, 1]
-
-                # Check if the current box wraps around
-                wraps_around = left_lon > right_lon
-
-                if wraps_around:
-                    # If the box wraps around, check if the new point is outside the current range
-                    if not (left_lon <= lon_pt or lon_pt <= right_lon):
-                        # Decide to extend either the left or the right
-                        # Calculate the new width for both possibilities
-                        # Option 1: Extend the left boundary to lon_pt
-                        box_a = np.copy(latlon_box)
-                        box_a[1, 0] = lon_pt
-                        d_width_a = _get_latlonbox_width(box_a)
-
-                        # Option 2: Extend the right boundary to lon_pt
-                        box_b = np.copy(latlon_box)
-                        box_b[1, 1] = lon_pt
-                        d_width_b = _get_latlonbox_width(box_b)
-
-                        # Ensure widths are non-negative
-                        if (d_width_a < 0.0) or (d_width_b < 0.0):
-                            raise Exception(
-                                "Logic error in longitude box width calculation"
-                            )
-
-                        # Choose the box with the smaller width
-                        if d_width_a < d_width_b:
-                            latlon_box = box_a
-                        else:
-                            latlon_box = box_b
-                else:
-                    # If the box does not wrap around, simply update min or max longitude
-                    if lon_pt < left_lon or lon_pt > right_lon:
-                        # Calculate the new width for both possibilities
-                        # Option 1: Extend the left boundary to lon_pt
-                        box_a = np.copy(latlon_box)
-                        box_a[1, 0] = lon_pt
-                        d_width_a = _get_latlonbox_width(box_a)
-
-                        # Option 2: Extend the right boundary to lon_pt
-                        box_b = np.copy(latlon_box)
-                        box_b[1, 1] = lon_pt
-                        d_width_b = _get_latlonbox_width(box_b)
-
-                        # Ensure widths are non-negative
-                        if (d_width_a < 0.0) or (d_width_b < 0.0):
-                            raise Exception(
-                                "Logic error in longitude box width calculation"
-                            )
-
-                        # Choose the box with the smaller width
-                        if d_width_a < d_width_b:
-                            latlon_box = box_a
-                        else:
-                            latlon_box = box_b
-
-    return latlon_box
-
-
-@njit(cache=True)
-def _populate_face_latlon_bound(
-    face_edges_xyz,
-    face_edges_lonlat,
-    is_latlonface=False,
-    is_GCA_list=None,
-):
-    # Check if face_edges contains pole points
-    has_north_pole = pole_point_inside_polygon(1, face_edges_xyz, face_edges_lonlat)
-    has_south_pole = pole_point_inside_polygon(-1, face_edges_xyz, face_edges_lonlat)
-
-    # Initialize face_latlon_array with INT_FILL_VALUE
-    face_latlon_array = np.full((2, 2), np.nan, dtype=np.float64)
-
-    if has_north_pole or has_south_pole:
-        # Initial assumption that the pole point is inside the face
-        is_center_pole = True
-
-        pole_point_xyz = np.zeros(3, dtype=np.float64)
-        pole_point_lonlat = np.zeros(2, dtype=np.float64)
-        new_pt_latlon = np.zeros(2, dtype=np.float64)
-
-        if has_north_pole:
-            pole_point_xyz[2] = 1.0  # [0.0, 0.0, 1.0]
-            pole_point_lonlat[1] = math.pi / 2  # [0.0, pi/2]
-            new_pt_latlon[0] = math.pi / 2  # [pi/2, INT_FILL_VALUE]
-            new_pt_latlon[1] = np.nan
-        else:
-            pole_point_xyz[2] = -1.0  # [0.0, 0.0, -1.0]
-            pole_point_lonlat[1] = -math.pi / 2  # [0.0, -pi/2]
-            new_pt_latlon[0] = -math.pi / 2  # [-pi/2, INT_FILL_VALUE]
-            new_pt_latlon[1] = np.nan
-
-        for i in range(face_edges_xyz.shape[0]):
-            edge_xyz = face_edges_xyz[i]
-            edge_lonlat = face_edges_lonlat[i]
-
-            # Skip processing if the edge is marked as a dummy with a fill value
-            if np.any(edge_xyz == INT_FILL_VALUE):
-                continue
-
-            # Extract cartesian coordinates of the edge's endpoints
-            n1_cart = edge_xyz[0]
-            n2_cart = edge_xyz[1]
-            n1_lonlat = edge_lonlat[0]
-            n2_lonlat = edge_lonlat[1]
-
-            # Extract latitudes and longitudes of the nodes
-            node1_lon_rad = n1_lonlat[0]
-            node1_lat_rad = n1_lonlat[1]
-
-            # Determine if the edge's extreme latitudes need to be considered
-            if is_GCA_list is not None:
-                is_GCA = is_GCA_list[i]
-            else:
-                is_GCA = not is_latlonface or n1_cart[2] != n2_cart[2]
-
-            # Check if the node matches the pole point or if the pole point is within the edge
-            max_abs_diff = np.max(np.abs(n1_cart - pole_point_xyz))
-            if max_abs_diff <= ERROR_TOLERANCE or point_within_gca(
-                pole_point_xyz,
-                n1_cart,
-                n2_cart,
-            ):
-                is_center_pole = False
-                face_latlon_array = insert_pt_in_latlonbox(
-                    face_latlon_array, new_pt_latlon
-                )
-
-            # Insert the current node's lat/lon into the latlonbox
-            face_latlon_array = insert_pt_in_latlonbox(
-                face_latlon_array, np.array([node1_lat_rad, node1_lon_rad])
-            )
-
-            # Create n1n2_cart and n1n2_lonlat arrays using preallocation
-            n1n2_cart = np.empty((2, 3), dtype=np.float64)
-            n1n2_cart[0, :] = n1_cart
-            n1n2_cart[1, :] = n2_cart
-
-            n1n2_lonlat = np.empty((2, 2), dtype=np.float64)
-            n1n2_lonlat[0, :] = n1_lonlat
-            n1n2_lonlat[1, :] = n2_lonlat
-
-            # Determine extreme latitudes for GCA edges
-            if is_GCA:
-                lat_max = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "max")
-                lat_min = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "min")
-            else:
-                lat_max = node1_lat_rad
-                lat_min = node1_lat_rad
-
-            # Insert latitudinal extremes based on pole presence
-            if has_north_pole:
-                face_latlon_array = insert_pt_in_latlonbox(
-                    face_latlon_array, np.array([lat_min, node1_lon_rad])
-                )
-                face_latlon_array[0, 1] = math.pi / 2  # Upper latitude bound
-            else:
-                face_latlon_array = insert_pt_in_latlonbox(
-                    face_latlon_array, np.array([lat_max, node1_lon_rad])
-                )
-                face_latlon_array[0, 0] = -math.pi / 2  # Lower latitude bound
-
-        # Adjust longitude bounds globally if the pole is centrally inside the polygon
-        if is_center_pole:
-            face_latlon_array[1, 0] = 0.0
-            face_latlon_array[1, 1] = 2 * math.pi
-
-    else:
-        # Normal Face
-        for i in range(face_edges_xyz.shape[0]):
-            edge_xyz = face_edges_xyz[i]
-            edge_lonlat = face_edges_lonlat[i]
-
-            # Skip processing if the edge is marked as a dummy with a fill value
-            if np.any(edge_xyz == INT_FILL_VALUE):
-                continue
-
-            # Extract cartesian coordinates of the edge's endpoints
-            n1_cart = edge_xyz[0]
-            n2_cart = edge_xyz[1]
-            n1_lonlat = edge_lonlat[0]
-            n2_lonlat = edge_lonlat[1]
-
-            # Extract latitudes and longitudes of the nodes
-            node1_lon_rad = n1_lonlat[0]
-            node1_lat_rad = n1_lonlat[1]
-            # node2_lon_rad = n2_lonlat[0]
-            node2_lat_rad = n2_lonlat[1]
-
-            # Determine if the edge's extreme latitudes need to be considered
-            if is_GCA_list is not None:
-                is_GCA = is_GCA_list[i]
-            else:
-                is_GCA = not is_latlonface or n1_cart[2] != n2_cart[2]
-
-            # Create n1n2_cart and n1n2_lonlat arrays using preallocation
-            n1n2_cart = np.empty((2, 3), dtype=np.float64)
-            n1n2_cart[0, :] = n1_cart
-            n1n2_cart[1, :] = n2_cart
-
-            n1n2_lonlat = np.empty((2, 2), dtype=np.float64)
-            n1n2_lonlat[0, :] = n1_lonlat
-            n1n2_lonlat[1, :] = n2_lonlat
-
-            if is_GCA:
-                lat_max = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "max")
-                lat_min = extreme_gca_latitude(n1n2_cart, n1n2_lonlat, "min")
-            else:
-                lat_max = node1_lat_rad
-                lat_min = node1_lat_rad
-
-            # Insert extreme latitude points into the latlonbox
-            if (
-                abs(node1_lat_rad - lat_max) > ERROR_TOLERANCE
-                and abs(node2_lat_rad - lat_max) > ERROR_TOLERANCE
-            ):
-                face_latlon_array = insert_pt_in_latlonbox(
-                    face_latlon_array, np.array([lat_max, node1_lon_rad])
-                )
-            elif (
-                abs(node1_lat_rad - lat_min) > ERROR_TOLERANCE
-                and abs(node2_lat_rad - lat_min) > ERROR_TOLERANCE
-            ):
-                face_latlon_array = insert_pt_in_latlonbox(
-                    face_latlon_array, np.array([lat_min, node1_lon_rad])
-                )
-            else:
-                face_latlon_array = insert_pt_in_latlonbox(
-                    face_latlon_array, np.array([node1_lat_rad, node1_lon_rad])
-                )
-
-    return face_latlon_array
-
-
-@njit(cache=True, parallel=True)
-def compute_temp_latlon_array(
-    face_node_connectivity,
-    faces_edges_cartesian,
-    faces_edges_lonlat_rad,
-    n_nodes_per_face,
-    is_latlonface,
-    is_face_GCA_list,
-    INT_FILL_VALUE,
-):
-    n_face = face_node_connectivity.shape[0]
-    temp_latlon_array = np.full((n_face, 2, 2), INT_FILL_VALUE, dtype=np.float64)
-    for face_idx in prange(n_face):
-        cur_face_edges_cartesian = faces_edges_cartesian[
-            face_idx, 0 : n_nodes_per_face[face_idx]
-        ]
-        cur_faces_edges_lonlat_rad = faces_edges_lonlat_rad[
-            face_idx, 0 : n_nodes_per_face[face_idx]
-        ]
-        if is_face_GCA_list is not None:
-            is_GCA_list = is_face_GCA_list[face_idx]
-        else:
-            is_GCA_list = None
-
-        temp_latlon_array[face_idx] = _populate_face_latlon_bound(
-            cur_face_edges_cartesian,
-            cur_faces_edges_lonlat_rad,
-            is_latlonface=is_latlonface,
-            is_GCA_list=is_GCA_list,
-        )
-    return temp_latlon_array
-
-
-def _populate_bounds(
-    grid, is_latlonface: bool = False, is_face_GCA_list=None, return_array=False
-):
-    """Populates the bounds of the grid based on the geometry of its faces,
-    taking into account special conditions such as faces crossing the
-    antimeridian or containing pole points. This method updates the grid's
-    internal representation to include accurate bounds for each face, returned
-    as a DataArray with detailed attributes.
-
-    Parameters
-    ----------
-    is_latlonface : bool, optional
-        A global flag that indicates if faces are latlon faces. If True, all faces
-        are treated as latlon faces, meaning that all edges are either longitude or
-        constant latitude lines. If False, all edges are considered as Great Circle Arcs (GCA).
-        Default is False.
-
-    is_face_GCA_list : list or np.ndarray, optional
-        A list or an array of boolean values for each face, indicating whether each edge
-        in that face is a GCA. The shape of the list or array should be (n_faces, n_edges),
-        with each sub-list or sub-array like [True, False, True, False] indicating the
-        nature of each edge (GCA or constant latitude line) in a face. This parameter allows
-        for mixed face types within the grid by specifying the edge type at the face level.
-        If None, all edges are considered as GCA. This parameter, if provided, will overwrite
-        the `is_latlonface` attribute for specific faces. Default is None.
-
-    Returns
-    -------
-    xr.DataArray
-        A DataArray containing the latitude and longitude bounds for each face in the grid,
-        expressed in radians. The array has dimensions ["n_face", "Two", "Two"], where "Two"
-        is a literal dimension name indicating two bounds (min and max) for each of latitude
-        and longitude. The DataArray includes attributes detailing its purpose and the mapping
-        of latitude intervals to face indices.
-
-        Attributes include:
-        - `cf_role`: Describes the role of the DataArray, here indicating face latitude bounds.
-        - `_FillValue`: The fill value used in the array, indicating uninitialized or missing data.
-        - `long_name`: A descriptive name for the DataArray.
-        - `start_index`: The starting index for face indices in the grid.
-        - `latitude_intervalsIndex`: An IntervalIndex indicating the latitude intervals.
-        - `latitude_intervals_name_map`: A DataFrame mapping the latitude intervals to face indices.
-
-    Example
-    -------
-    Consider a scenario where you have four faces on a grid, each defined by vertices in longitude and latitude degrees:
-
-        face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]]
-        face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]]
-        face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]]
-        face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]]
-
-    After defining these faces, you can create a grid and populate its bounds by treating all faces as latlon faces:
-
-        grid = ux.Grid.from_face_vertices([face_1, face_2, face_3, face_4], latlon=True)
-        bounds_dataarray = grid._populate_bounds(is_latlonface=True)
-
-    This will calculate and store the bounds for each face within the grid, adjusting for any special conditions such as crossing the antimeridian, and return them as a DataArray.
-    """
-
-    # Ensure grid's cartesian coordinates are normalized
-    grid.normalize_cartesian_coordinates()
-
-    # Prepare data for Numba functions
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
-        grid.face_node_connectivity.values,
-        grid.n_face,
-        grid.n_max_face_edges,
-        grid.node_x.values,
-        grid.node_y.values,
-        grid.node_z.values,
-    )
-
-    faces_edges_lonlat_rad = _get_lonlat_rad_face_edge_nodes(
-        grid.face_node_connectivity.values,
-        grid.n_face,
-        grid.n_max_face_edges,
-        grid.node_lon.values,
-        grid.node_lat.values,
-    )
-
-    n_nodes_per_face = grid.n_nodes_per_face.values
-
-    # Call the Numba-compiled function
-    temp_latlon_array = compute_temp_latlon_array(
-        grid.face_node_connectivity.values,
-        faces_edges_cartesian,
-        faces_edges_lonlat_rad,
-        n_nodes_per_face,
-        is_latlonface,
-        is_face_GCA_list,
-        INT_FILL_VALUE,
-    )
-
-    # Process results outside Numba
-    intervals_tuple_list = []
-    intervals_name_list = []
-    for face_idx in range(grid.n_face):
-        assert temp_latlon_array[face_idx][0][0] != temp_latlon_array[face_idx][0][1]
-        assert temp_latlon_array[face_idx][1][0] != temp_latlon_array[face_idx][1][1]
-        lat_array = temp_latlon_array[face_idx][0]
-        intervals_tuple_list.append((lat_array[0], lat_array[1]))
-        intervals_name_list.append(face_idx)
-
-    intervalsIndex = pd.IntervalIndex.from_tuples(intervals_tuple_list, closed="both")
-    df_intervals_map = pd.DataFrame(
-        index=intervalsIndex, data=intervals_name_list, columns=["face_id"]
-    )
-
-    bounds = xr.DataArray(
-        temp_latlon_array,
-        dims=["n_face", "lon_lat", "min_max"],
-        attrs={
-            "cf_role": "face_latlon_bounds",
-            "_FillValue": INT_FILL_VALUE,
-            "long_name": "Provides the latitude and longitude bounds for each face in radians.",
-            "start_index": INT_DTYPE(0),
-            "latitude_intervalsIndex": intervalsIndex,
-            "latitude_intervals_name_map": df_intervals_map,
-        },
-    )
-
-    if return_array:
-        return bounds
-    else:
-        grid._ds["bounds"] = bounds
-
-
 def _construct_boundary_edge_indices(edge_face_connectivity):
     """Index the missing edges on a partial grid with holes, that is a region
     of the grid that is not covered by any geometry."""
diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py
index 244c144e1..dab9bab31 100644
--- a/uxarray/grid/grid.py
+++ b/uxarray/grid/grid.py
@@ -22,6 +22,7 @@
 from uxarray.cross_sections import GridCrossSectionAccessor
 from uxarray.formatting_html import grid_repr
 from uxarray.grid.area import get_all_face_area_from_coords
+from uxarray.grid.bounds import _construct_face_bounds_array, _populate_face_bounds
 from uxarray.grid.connectivity import (
     _populate_edge_face_connectivity,
     _populate_edge_node_connectivity,
@@ -32,7 +33,6 @@
 )
 from uxarray.grid.coordinates import (
     _lonlat_rad_to_xyz,
-    _normalize_xyz,
     _populate_edge_centroids,
     _populate_face_centerpoints,
     _populate_face_centroids,
@@ -50,9 +50,7 @@
     _grid_to_matplotlib_polycollection,
     _grid_to_polygon_geodataframe,
     _populate_antimeridian_face_indices,
-    _populate_bounds,
     _populate_max_face_radius,
-    compute_temp_latlon_array,
 )
 from uxarray.grid.intersections import (
     constant_lat_intersections_face_bounds,
@@ -69,7 +67,7 @@
     _populate_edge_face_distances,
     _populate_edge_node_distances,
 )
-from uxarray.grid.utils import _get_cartesian_face_edge_nodes
+from uxarray.grid.utils import _get_cartesian_face_edge_nodes_array
 from uxarray.grid.validation import (
     _check_area,
     _check_connectivity,
@@ -1426,15 +1424,17 @@ def bounds(self):
         """Latitude Longitude Bounds for each Face in radians.
 
         Dimensions ``(n_face", two, two)``
+
+
         """
         if "bounds" not in self._ds:
-            if not is_numba_function_cached(compute_temp_latlon_array):
+            if not is_numba_function_cached(_construct_face_bounds_array):
                 warn(
                     "Necessary functions for computing the bounds of each face are not yet compiled with Numba. "
                     "This initial execution will be significantly longer.",
                     RuntimeWarning,
                 )
-            _populate_bounds(self)
+            _populate_face_bounds(self)
         return self._ds["bounds"]
 
     @bounds.setter
@@ -1952,30 +1952,20 @@ def normalize_cartesian_coordinates(self):
             # check if coordinates are already normalized
             return
 
-        if "node_x" in self._ds:
-            # normalize node coordinates
-            node_x, node_y, node_z = _normalize_xyz(
-                self.node_x.values, self.node_y.values, self.node_z.values
-            )
-            self.node_x.data = node_x
-            self.node_y.data = node_y
-            self.node_z.data = node_z
-        if "edge_x" in self._ds:
-            # normalize edge coordinates
-            edge_x, edge_y, edge_z = _normalize_xyz(
-                self.edge_x.values, self.edge_y.values, self.edge_z.values
-            )
-            self.edge_x.data = edge_x
-            self.edge_y.data = edge_y
-            self.edge_z.data = edge_z
-        if "face_x" in self._ds:
-            # normalize face coordinates
-            face_x, face_y, face_z = _normalize_xyz(
-                self.face_x.values, self.face_y.values, self.face_z.values
-            )
-            self.face_x.data = face_x
-            self.face_y.data = face_y
-            self.face_z.data = face_z
+        for prefix in ("node", "edge", "face"):
+            x_var = f"{prefix}_x"
+            if x_var not in self._ds:
+                continue
+
+            dx = self._ds[f"{prefix}_x"]
+            dy = self._ds[f"{prefix}_y"]
+            dz = self._ds[f"{prefix}_z"]
+
+            # normalize
+            norm = (dx**2 + dy**2 + dz**2) ** 0.5
+            self._ds[x_var] = dx / norm
+            self._ds[f"{prefix}_y"] = dy / norm
+            self._ds[f"{prefix}_z"] = dz / norm
 
     def to_xarray(self, grid_format: Optional[str] = "ugrid"):
         """Returns an ``xarray.Dataset`` with the variables stored under the
@@ -2603,7 +2593,7 @@ def get_faces_containing_point(
             return np.empty(0, dtype=np.int64)
 
         # Get the faces in terms of their edges
-        face_edge_nodes_xyz = _get_cartesian_face_edge_nodes(
+        face_edge_nodes_xyz = _get_cartesian_face_edge_nodes_array(
             subset.face_node_connectivity.values,
             subset.n_face,
             subset.n_max_face_nodes,
diff --git a/uxarray/grid/utils.py b/uxarray/grid/utils.py
index b8b1d68af..c1071416f 100644
--- a/uxarray/grid/utils.py
+++ b/uxarray/grid/utils.py
@@ -138,7 +138,7 @@ def _swap_first_fill_value_with_last(arr):
     return arr
 
 
-def _get_cartesian_face_edge_nodes(
+def _get_cartesian_face_edge_nodes_array(
     face_node_conn, n_face, n_max_face_edges, node_x, node_y, node_z
 ):
     """Construct an array to hold the edge Cartesian coordinates connectivity
@@ -179,7 +179,7 @@ def _get_cartesian_face_edge_nodes(
     >>> node_x = np.array([0, 1, 1, 0, 1, 0])
     >>> node_y = np.array([0, 0, 1, 1, 2, 2])
     >>> node_z = np.array([0, 0, 0, 0, 1, 1])
-    >>> _get_cartesian_face_edge_nodes(
+    >>> _get_cartesian_face_edge_nodes_array(
     ...     face_node_conn, n_face, n_max_face_edges, node_x, node_y, node_z
     ... )
     array([[[[    0,     0,     0],
@@ -259,7 +259,7 @@ def _get_cartesian_face_edge_nodes(
     return face_edges_cartesian.reshape(n_face, n_max_face_edges, 2, 3)
 
 
-def _get_lonlat_rad_face_edge_nodes(
+def _get_lonlat_rad_face_edge_nodes_array(
     face_node_conn, n_face, n_max_face_edges, node_lon, node_lat
 ):
     """Construct an array to hold the edge latitude and longitude in radians
@@ -320,3 +320,114 @@ def _get_lonlat_rad_face_edge_nodes(
     face_edges_lonlat_rad[valid_mask, 1] = node_lat_rad[valid_edges]
 
     return face_edges_lonlat_rad.reshape(n_face, n_max_face_edges, 2, 2)
+
+
+@njit(cache=True)
+def _get_cartesian_face_edge_nodes(
+    face_idx, face_node_connectivity, n_edges_per_face, node_x, node_y, node_z
+):
+    """Computes the Cartesian Coordinates of the edge nodes that make up a given face.
+
+    Parameters
+    ----------
+    face_idx : int
+        The index of the face to construct the edge nodes
+    face_node_connectivity : np.ndarray
+        Face Node Connectivity array
+    n_edges_per_face : np.ndarray
+        Number of non-fill-value edges for each face
+    node_x : np.ndarray
+        Cartesian x coordinates
+    node_y : np.ndarray
+        Cartesian y coordinates
+    node_z : np.ndarray
+        Cartesian z coordinates
+
+    Returns
+    -------
+    face_edge_nodes: np.ndarray
+        Cartesian coordinates of the edge nodes that make up a given face
+    """
+    # Number non-fill-value edges
+    n_edges = n_edges_per_face[face_idx]
+
+    # Allocate data for face_edge_nodes
+    face_edge_nodes = np.empty((n_edges, 2, 3), dtype=np.float64)
+
+    start_nodes = face_node_connectivity[face_idx, 0:n_edges]
+    end_nodes = np.roll(start_nodes, -1)
+
+    # Assign x coordinates of start and end nodes
+    face_edge_nodes[0:n_edges, 0, 0] = node_x[start_nodes]
+    face_edge_nodes[0:n_edges, 1, 0] = node_x[end_nodes]
+
+    # Assign y coordinates of start and end nodes
+    face_edge_nodes[0:n_edges, 0, 1] = node_y[start_nodes]
+    face_edge_nodes[0:n_edges, 1, 1] = node_y[end_nodes]
+
+    # Assign z coordinates of start and end nodes
+    face_edge_nodes[0:n_edges, 0, 2] = node_z[start_nodes]
+    face_edge_nodes[0:n_edges, 1, 2] = node_z[end_nodes]
+
+    return face_edge_nodes
+
+
+@njit(cache=True)
+def _get_spherical_face_edge_nodes(
+    face_idx, face_node_connectivity, n_edges_per_face, node_lon, node_lat
+):
+    """Computes the Spherical Coordinates of the edge nodes that make up a given face.
+
+    Parameters
+    ----------
+    face_idx : int
+        The index of the face to construct the edge nodes
+    face_node_connectivity : np.ndarray
+        Face Node Connectivity array
+    n_edges_per_face : np.ndarray
+        Number of non-fill-value edges for each face
+    node_lon : np.ndarray
+        Longitude coordinates
+    node_lat : np.ndarray
+        Latitude coordinates
+
+    Returns
+    -------
+    face_edge_nodes: np.ndarray
+        Spherical coordinates of the edge nodes that make up a given face
+    """
+    # Number non-fill-value edges
+    n_edges = n_edges_per_face[face_idx]
+
+    # Allocate data for face_edge_nodes
+    face_edge_nodes = np.empty((n_edges, 2, 2), dtype=np.float64)
+
+    start_nodes = face_node_connectivity[face_idx, 0:n_edges]
+    end_nodes = np.roll(start_nodes, -1)
+
+    # Assign longitude coordinates of start and end nodes
+    face_edge_nodes[0:n_edges, 0, 0] = node_lon[start_nodes]
+    face_edge_nodes[0:n_edges, 1, 0] = node_lon[end_nodes]
+
+    # Assign latitude coordinates of start and end nodes
+    face_edge_nodes[0:n_edges, 0, 1] = node_lat[start_nodes]
+    face_edge_nodes[0:n_edges, 1, 1] = node_lat[end_nodes]
+
+    return face_edge_nodes
+
+
+@njit(cache=True)
+def all_elements_nan(arr):
+    """Check if all elements in an array are np.nan."""
+    for i in range(arr.shape[0]):
+        if not np.isnan(arr[i]):
+            return False
+    return True
+
+
+@njit(cache=True)
+def any_close_lat(lat_pt, atol):
+    """Check if the latitude point is close to either the North or South Pole."""
+    return np.isclose(lat_pt, 0.5 * np.pi, atol) or np.isclose(
+        lat_pt, -0.5 * np.pi, atol
+    )
diff --git a/uxarray/grid/validation.py b/uxarray/grid/validation.py
index d4c3d33f6..36c05b119 100644
--- a/uxarray/grid/validation.py
+++ b/uxarray/grid/validation.py
@@ -104,44 +104,23 @@ def _find_duplicate_nodes(grid):
 
 
 def _check_normalization(grid):
-    """Checks whether all the cartesiain coordinates are normalized."""
-
-    if grid._normalized is True:
-        # grid is already normalized, no need to run extra checks
-        return grid._normalized
-
-    if "node_x" in grid._ds:
-        if not (
-            np.isclose(
-                (grid.node_x**2 + grid.node_y**2 + grid.node_z**2),
-                1.0,
-                atol=ERROR_TOLERANCE,
-            )
-        ).all():
-            grid._normalized = False
-            return False
-    if "edge_x" in grid._ds:
-        if not (
-            np.isclose(
-                (grid.node_x**2 + grid.node_y**2 + grid.node_z**2),
-                1.0,
-                atol=ERROR_TOLERANCE,
-            )
-        ).all():
-            grid._normalized = False
-            return False
-    if "face_x" in grid._ds:
-        if not (
-            np.isclose(
-                (grid.node_x**2 + grid.node_y**2 + grid.node_z**2),
-                1.0,
-                atol=ERROR_TOLERANCE,
-            )
-        ).all():
+    if grid._normalized:
+        return True
+
+    for name in ("node", "edge", "face"):
+        if f"{name}_x" not in grid._ds:
+            continue
+
+        x = grid._ds[f"{name}_x"]
+        y = grid._ds[f"{name}_y"]
+        z = grid._ds[f"{name}_z"]
+
+        # compute the max deviation from 1.0
+        max_dev = abs((x**2 + y**2 + z**2 - 1.0).max().compute())
+
+        if max_dev > ERROR_TOLERANCE:
             grid._normalized = False
             return False
 
-    # set the grid as normalized
     grid._normalized = True
-
     return True


Test patch:
diff --git a/test/test_geometry.py b/test/test_geometry.py
index c066f612a..f300639d9 100644
--- a/test/test_geometry.py
+++ b/test/test_geometry.py
@@ -12,11 +12,13 @@
 from uxarray.grid.coordinates import _populate_node_latlon, _lonlat_rad_to_xyz, _normalize_xyz, _xyz_to_lonlat_rad, \
     _xyz_to_lonlat_deg, _xyz_to_lonlat_rad_scalar
 from uxarray.grid.arcs import extreme_gca_latitude, extreme_gca_z
-from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _get_lonlat_rad_face_edge_nodes
+from uxarray.grid.utils import _get_cartesian_face_edge_nodes_array, _get_lonlat_rad_face_edge_nodes_array
 
-from uxarray.grid.geometry import _populate_face_latlon_bound, _populate_bounds, _pole_point_inside_polygon_cartesian, \
+from uxarray.grid.geometry import _pole_point_inside_polygon_cartesian, \
     stereographic_projection, inverse_stereographic_projection, point_in_face, haversine_distance
 
+
+from uxarray.grid.bounds import _populate_face_bounds, _construct_face_bounds_array, _construct_face_bounds
 from sklearn.metrics.pairwise import haversine_distances
 
 current_path = Path(os.path.dirname(os.path.realpath(__file__)))
@@ -364,11 +366,11 @@ def test_extreme_gca_latitude_min():
 
 def test_get_latlonbox_width():
     gca_latlon = np.array([[0.0, 0.0], [0.0, 3.0]])
-    width = ux.grid.geometry._get_latlonbox_width(gca_latlon)
+    width = ux.grid.bounds._get_latlonbox_width(gca_latlon)
     assert width == 3.0
 
     gca_latlon = np.array([[0.0, 0.0], [2 * np.pi - 1.0, 1.0]])
-    width = ux.grid.geometry._get_latlonbox_width(gca_latlon)
+    width = ux.grid.bounds._get_latlonbox_width(gca_latlon)
     assert width == 2.0
 
 
@@ -376,7 +378,7 @@ def test_insert_pt_in_latlonbox_non_periodic():
     old_box = np.array([[0.1, 0.2], [0.3, 0.4]])  # Radians
     new_pt = np.array([0.15, 0.35])
     expected = np.array([[0.1, 0.2], [0.3, 0.4]])
-    result = ux.grid.geometry.insert_pt_in_latlonbox(old_box, new_pt, False)
+    result = ux.grid.bounds.insert_pt_in_latlonbox(old_box, new_pt, False)
     np.testing.assert_array_equal(result, expected)
 
 
@@ -384,7 +386,7 @@ def test_insert_pt_in_latlonbox_periodic():
     old_box = np.array([[0.1, 0.2], [6.0, 0.1]])  # Radians, periodic
     new_pt = np.array([0.15, 6.2])
     expected = np.array([[0.1, 0.2], [6.0, 0.1]])
-    result = ux.grid.geometry.insert_pt_in_latlonbox(old_box, new_pt, True)
+    result = ux.grid.bounds.insert_pt_in_latlonbox(old_box, new_pt, True)
     np.testing.assert_array_equal(result, expected)
 
 
@@ -392,7 +394,7 @@ def test_insert_pt_in_latlonbox_pole():
     old_box = np.array([[0.1, 0.2], [0.3, 0.4]])
     new_pt = np.array([np.pi / 2, np.nan])  # Pole point
     expected = np.array([[0.1, np.pi / 2], [0.3, 0.4]])
-    result = ux.grid.geometry.insert_pt_in_latlonbox(old_box, new_pt)
+    result = ux.grid.bounds.insert_pt_in_latlonbox(old_box, new_pt)
     np.testing.assert_array_equal(result, expected)
 
 
@@ -401,7 +403,7 @@ def test_insert_pt_in_empty_state():
                         [np.nan, np.nan]])  # Empty state
     new_pt = np.array([0.15, 0.35])
     expected = np.array([[0.15, 0.15], [0.35, 0.35]])
-    result = ux.grid.geometry.insert_pt_in_latlonbox(old_box, new_pt)
+    result = ux.grid.bounds.insert_pt_in_latlonbox(old_box, new_pt)
     np.testing.assert_array_equal(result, expected)
 
 
@@ -481,7 +483,7 @@ def test_populate_bounds_normal_latlon_bounds_gca():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -509,7 +511,7 @@ def test_populate_bounds_antimeridian_latlon_bounds_gca():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -523,7 +525,7 @@ def test_populate_bounds_equator_latlon_bounds_gca():
     face_edges_lonlat = np.array(
         [[_xyz_to_lonlat_rad(*edge[0]), _xyz_to_lonlat_rad(*edge[1])] for edge in face_edges_cart])
 
-    bounds = _populate_face_latlon_bound(face_edges_cart, face_edges_lonlat)
+    bounds = _construct_face_bounds(face_edges_cart, face_edges_lonlat)
     expected_bounds = np.array([[-0.05235988, 0], [6.23082543, 0]])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
@@ -539,7 +541,7 @@ def test_populate_bounds_south_sphere_latlon_bounds_gca():
     face_edges_lonlat = np.array(
         [[_xyz_to_lonlat_rad(*edge[0]), _xyz_to_lonlat_rad(*edge[1])] for edge in face_edges_cart])
 
-    bounds = _populate_face_latlon_bound(face_edges_cart, face_edges_lonlat)
+    bounds = _construct_face_bounds(face_edges_cart, face_edges_lonlat)
     expected_bounds = np.array([[-1.51843645, -1.45388627], [3.14159265, 3.92699082]])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
@@ -555,7 +557,7 @@ def test_populate_bounds_near_pole_latlon_bounds_gca():
     face_edges_lonlat = np.array(
         [[_xyz_to_lonlat_rad(*edge[0]), _xyz_to_lonlat_rad(*edge[1])] for edge in face_edges_cart])
 
-    bounds = _populate_face_latlon_bound(face_edges_cart, face_edges_lonlat)
+    bounds = _construct_face_bounds(face_edges_cart, face_edges_lonlat)
     expected_bounds = np.array([[-1.20427718, -1.14935491], [0, 0.13568803]])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
@@ -571,7 +573,7 @@ def test_populate_bounds_near_pole2_latlon_bounds_gca():
     face_edges_lonlat = np.array(
         [[_xyz_to_lonlat_rad(*edge[0]), _xyz_to_lonlat_rad(*edge[1])] for edge in face_edges_cart])
 
-    bounds = _populate_face_latlon_bound(face_edges_cart, face_edges_lonlat)
+    bounds = _construct_face_bounds(face_edges_cart, face_edges_lonlat)
     expected_bounds = np.array([[-1.20427718, -1.14935491], [6.147497, 4.960524e-16]])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
@@ -595,7 +597,7 @@ def test_populate_bounds_long_face_latlon_bounds_gca():
     face_edges_lonlat = np.array(
         [[_xyz_to_lonlat_rad(*edge[0]), _xyz_to_lonlat_rad(*edge[1])] for edge in face_edges_cart])
 
-    bounds = _populate_face_latlon_bound(face_edges_cart, face_edges_lonlat)
+    bounds = _construct_face_bounds(face_edges_cart, face_edges_lonlat)
 
     # The expected bounds should not contain the south pole [0,-0.5*np.pi]
     assert bounds[1][0] != 0.0
@@ -624,7 +626,7 @@ def test_populate_bounds_node_on_pole_latlon_bounds_gca():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -651,7 +653,7 @@ def test_populate_bounds_edge_over_pole_latlon_bounds_gca():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -678,7 +680,7 @@ def test_populate_bounds_pole_inside_latlon_bounds_gca():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -755,8 +757,8 @@ def test_populate_bounds_normal_latlon_bounds_latlonface():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_latlonface=True)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_latlonface=True)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -781,8 +783,8 @@ def test_populate_bounds_antimeridian_latlon_bounds_latlonface():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_latlonface=True)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_latlonface=True)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -807,8 +809,8 @@ def test_populate_bounds_node_on_pole_latlon_bounds_latlonface():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_latlonface=True)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_latlonface=True)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -833,8 +835,8 @@ def test_populate_bounds_edge_over_pole_latlon_bounds_latlonface():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_latlonface=True)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_latlonface=True)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -859,8 +861,8 @@ def test_populate_bounds_pole_inside_latlon_bounds_latlonface():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_latlonface=True)
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_latlonface=True)
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -937,8 +939,8 @@ def test_populate_bounds_normal_latlon_bounds_gca_list():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_GCA_list=[True, False, True, False])
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_GCA_list=[True, False, True, False])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -963,8 +965,8 @@ def test_populate_bounds_antimeridian_latlon_bounds_gca_list():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_GCA_list=[True, False, True, False])
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_GCA_list=[True, False, True, False])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -989,8 +991,8 @@ def test_populate_bounds_node_on_pole_latlon_bounds_gca_list():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_GCA_list=[True, False, True, False])
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_GCA_list=[True, False, True, False])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -1015,8 +1017,8 @@ def test_populate_bounds_edge_over_pole_latlon_bounds_gca_list():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_GCA_list=[True, False, True, False])
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_GCA_list=[True, False, True, False])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -1041,8 +1043,8 @@ def test_populate_bounds_pole_inside_latlon_bounds_gca_list():
         grid.edge_node_connectivity.values, grid.node_lon.values,
         grid.node_lat.values)
     expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]])
-    bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
-                                         is_GCA_list=[True, False, True, False])
+    bounds = _construct_face_bounds(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat,
+                                    is_GCA_list=[True, False, True, False])
     np.testing.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE)
 
 
@@ -1079,7 +1081,7 @@ def test_populate_bounds_LatlonFace_mix_latlon_bounds_mix():
                        [[np.deg2rad(60.0), np.pi / 2], [0., 2 * np.pi]]]
 
     grid = ux.Grid.from_face_vertices(faces, latlon=True)
-    bounds_xarray = _populate_bounds(grid, is_latlonface=True, return_array=True)
+    bounds_xarray = _populate_face_bounds(grid, is_latlonface=True, return_array=True)
     face_bounds = bounds_xarray.values
     for i in range(len(faces)):
         np.testing.assert_allclose(face_bounds[i], expected_bounds[i], atol=ERROR_TOLERANCE)
@@ -1099,7 +1101,7 @@ def test_populate_bounds_GCAList_mix_latlon_bounds_mix():
                        [[np.deg2rad(60.0), np.pi / 2], [0., 2 * np.pi]]]
 
     grid = ux.Grid.from_face_vertices(faces, latlon=True)
-    bounds_xarray = _populate_bounds(grid, is_face_GCA_list=np.array([[True, False, True, False],
+    bounds_xarray = _populate_face_bounds(grid, is_face_GCA_list=np.array([[True, False, True, False],
                                                                       [True, False, True, False],
                                                                       [True, False, True, False],
                                                                       [True, False, True, False]]), return_array=True)
@@ -1184,7 +1186,7 @@ def test_point_inside():
     grid = ux.open_grid(grid_mpas_2)
 
     # Get the face edges of all faces in the grid
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1209,7 +1211,7 @@ def test_point_outside():
     grid = ux.open_grid(grid_mpas_2)
 
     # Get the face edges of all faces in the grid
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1232,7 +1234,7 @@ def test_point_on_node():
     grid = ux.open_grid(grid_mpas_2)
 
     # Get the face edges of all faces in the grid
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1263,7 +1265,7 @@ def test_point_inside_close():
 
     # Create the grid and face edges
     grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True)
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1288,7 +1290,7 @@ def test_point_outside_close():
 
     # Create the grid and face edges
     grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True)
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1312,7 +1314,7 @@ def test_face_at_pole():
 
     # Create the grid and face edges
     grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True)
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1334,7 +1336,7 @@ def test_face_at_antimeridian():
 
     # Create the grid and face edges
     grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True)
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
@@ -1357,7 +1359,7 @@ def test_face_normal_face():
 
     # Create the grid and face edges
     grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True)
-    faces_edges_cartesian = _get_cartesian_face_edge_nodes(
+    faces_edges_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,
diff --git a/test/test_helpers.py b/test/test_helpers.py
index 9f4bd571e..076d6b7cb 100644
--- a/test/test_helpers.py
+++ b/test/test_helpers.py
@@ -11,7 +11,7 @@
 from uxarray.constants import INT_DTYPE, INT_FILL_VALUE
 from uxarray.grid.coordinates import _lonlat_rad_to_xyz, _normalize_xyz, _xyz_to_lonlat_rad
 from uxarray.grid.arcs import point_within_gca, _angle_of_2_vectors, in_between
-from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _get_lonlat_rad_face_edge_nodes
+from uxarray.grid.utils import _get_cartesian_face_edge_nodes_array, _get_lonlat_rad_face_edge_nodes_array
 from uxarray.grid.geometry import pole_point_inside_polygon, _pole_point_inside_polygon_cartesian
 
 try:
@@ -273,7 +273,7 @@ def test_get_cartesian_face_edge_nodes_pipeline():
     node_y = grid.node_y.values
     node_z = grid.node_z.values
 
-    face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes(
+    face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes_array(
         face_node_conn, n_face, n_max_face_edges, node_x, node_y, node_z
     )
 
@@ -298,7 +298,7 @@ def test_get_cartesian_face_edge_nodes_filled_value():
     node_y = grid.node_y.values
     node_z = grid.node_z.values
 
-    face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes(
+    face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes_array(
         face_node_conn, n_face, n_max_face_edges, node_x, node_y, node_z
     )
 
@@ -335,7 +335,7 @@ def test_get_cartesian_face_edge_nodes_filled_value2():
     node_y = np.array([v0_cart[1],v1_cart[1],v2_cart[1],v3_cart[1],v4_cart[1]])
     node_z = np.array([v0_cart[2],v1_cart[2],v2_cart[2],v3_cart[2],v4_cart[2]])
 
-    face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes(
+    face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes_array(
         face_node_conn, n_face, n_max_face_edges, node_x, node_y, node_z
     )
 
@@ -368,7 +368,7 @@ def test_get_lonlat_face_edge_nodes_pipeline():
     node_lon = grid.node_lon.values
     node_lat = grid.node_lat.values
 
-    face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes(
+    face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes_array(
         face_node_conn, n_face, n_max_face_edges, node_lon, node_lat
     )
 
@@ -398,7 +398,7 @@ def test_get_lonlat_face_edge_nodes_filled_value():
     node_lon = grid.node_lon.values
     node_lat = grid.node_lat.values
 
-    face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes(
+    face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes_array(
         face_node_conn, n_face, n_max_face_edges, node_lon, node_lat
     )
 
@@ -434,7 +434,7 @@ def test_get_lonlat_face_edge_nodes_filled_value2():
     node_lon = np.array([v0_rad[0],v1_rad[0],v2_rad[0],v3_rad[0],v4_rad[0]])
     node_lat = np.array([v0_rad[1],v1_rad[1],v2_rad[1],v3_rad[1],v4_rad[1]])
 
-    face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes(
+    face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes_array(
         face_node_conn, n_face, n_max_face_edges, node_lon, node_lat
     )
 
diff --git a/test/test_integrate.py b/test/test_integrate.py
index ad9881c04..6dc8e16f8 100644
--- a/test/test_integrate.py
+++ b/test/test_integrate.py
@@ -19,7 +19,7 @@
     _get_faces_constLat_intersection_info, _zonal_face_weights, \
     _zonal_face_weights_robust
 
-from uxarray.grid.utils import _get_cartesian_face_edge_nodes
+from uxarray.grid.utils import _get_cartesian_face_edge_nodes_array
 
 current_path = Path(os.path.dirname(os.path.realpath(__file__)))
 
@@ -1037,7 +1037,7 @@ def test_compare_zonal_weights():
     for gridfile in gridfiles:
         uxgrid = ux.open_grid(gridfile)
         n_nodes_per_face = uxgrid.n_nodes_per_face.values
-        face_edge_nodes_xyz =  _get_cartesian_face_edge_nodes(
+        face_edge_nodes_xyz =  _get_cartesian_face_edge_nodes_array(
                 uxgrid.face_node_connectivity.values,
                 uxgrid.n_face,
                 uxgrid.n_max_face_edges,
diff --git a/test/test_intersections.py b/test/test_intersections.py
index f2fbfe1ee..09a56d41f 100644
--- a/test/test_intersections.py
+++ b/test/test_intersections.py
@@ -161,14 +161,14 @@ def test_GCA_GCA_north_pole_angled():
 
 def test_GCA_edge_intersection_count():
 
-    from uxarray.grid.utils import _get_cartesian_face_edge_nodes
+    from uxarray.grid.utils import _get_cartesian_face_edge_nodes_array
 
     # Generate a normal face that is not crossing the antimeridian or the poles
     vertices_lonlat = [[29.5, 11.0], [29.5, 10.0], [30.5, 10.0], [30.5, 11.0]]
     vertices_lonlat = np.array(vertices_lonlat)
 
     grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True)
-    face_edge_nodes_cartesian = _get_cartesian_face_edge_nodes(
+    face_edge_nodes_cartesian = _get_cartesian_face_edge_nodes_array(
         grid.face_node_connectivity.values,
         grid.n_face,
         grid.n_max_face_edges,