Implements `Grid.compute_face_node_angles()` by Sevans711 · Pull Request #1672 · UXARRAY/uxarray

Sevans711 · 2026-08-12T15:08:25Z

Overview

Implements new function: Grid.compute_face_node_angles(), which computes the internal angles at each node, for each face in a grid. Intentionally excludes many options initially suggested in #1566, as the discussion there revealed:

There is no need to support non-spherical geometry or non-convex faces (see also Clarify in docs: UXarray supports only spherical 2D grids with convex faces #1626).
The implementation here is extremely fast (e.g., roughly 3 seconds for 3.75km MPAS grid, even when numba has only 4 threads), and the result's size is nontrivial (size=n_faces*n_max_face_nodes), so it doesn't really make sense to include a caching option right now.

Also adds corresponding tests, checking that the angle computations give correct values in a roughly 30,60,90 degree triangle and a hexagon grid. Also checks nan handling in a grid with both pentagons and hexagons (result should be nan wherever n_max_face_nodes dimension index is larger than n_nodes_per_face). For all three cases, checks that the generalized spherical excess formula holds, by comparing to Grid.face_areas results.

Within scope but possibly unexpected change included this PR: refactored _small_angle_of_2_vectors to improve efficiency when inputs are not numpy arrays, and rewrote docstring accordingly. (See also: #1648.) This was necessary to improve efficiency for compute_face_node_angles() without inlining all of the _small_angle_of_2_vectors logic. This is unlikely to affect efficiency elsewhere in the code, because other regions of the code seem to always construct tiny numpy arrays before calling _small_angle_of_2_vectors. Running ASV benchmarks here to check, just in case (but it would be acceptable if benchmarks show no improvements).

Also related (may help visualize face node angle results, if implemented): #1578

Expected Usage

importuxarrayasuxgrid_path="/path/to/grid.nc"data_path="/path/to/data.nc"uxds=ux.open_dataset(grid_path, data_path)
grid=uxds.uxgrid# or just do: grid = ux.open_grid(grid_path)# internal angle [in radians] at each node, for each face, as an xr.DataArrayangles_rad=grid.compute_face_node_angles()
# internal angle [in degrees] at each node, for each face, as an xr.DataArrayangles_deg=grid.compute_face_node_angles(degrees=True)
# internal angle [in radians] at each node, for each face, as a ux.UxDataArrayangles_rad_uxarr=grid.compute_face_node_angles(as_uxarray=True)

PR Checklist

General

An issue is created and linked
Added appropriate labels (if your uxarray repo permissions allow it)
Filled out Overview and Expected Usage (if applicable) sections

Testing & Benchmarking

Adequate tests are created if there is new functionality
Tests are not too basic (such as simply calling a function and nothing else)
Tests cover all major paths in your new functions
If this PR could affect performance, ran ASV benchmarks and confirmed they show expected behavior (add a new benchmark if necessary)

Documentation

Docstrings have been added to all new functions
Docstrings have been updated with any function changes
User (public) functions have been added to docs/api.rst
Internal (private) function names start with an underscore (_)

AI Disclosure

AI Usage: discussions with Claude and ChatGPT, inline code suggestions from GitHub Copilot. Also (not in this PR, but contributed to decisions, see original issue for details) used Claude to make a numpy-only method for efficiency comparisons, and other people used Claude to iterate and run tests for more efficiency improvements.

I take responsibility for all AI-generated content in my PR.
I have tested all AI-generated content in my PR.

This provides an initial implementation which seems to be working. See #1566 TODO: - support assume_convex=False - type-hint possible UxDataArray return type? - tests - examples

removes "geometry" (always use spherical) removes "assume_convex" (always assume convex) Also, improves type-hinting for compute_face_node_angles(). Also, formats using pre-commit ruff formatting.

use tuples instead of tiny arrays in numba! Cleans up docstring in grid.py (forgot to change in previous commit).

(it was making a type error in numba when trying to run the method)

compute_face_node_angles() seems to be really fast, so including a cache option in initial implementation isn't worthwhile.

github-actions · 2026-08-12T15:48:25Z

ASV Benchmarking

Benchmark Comparison Results

Benchmarks that have improved:

Change	Before [`fda71bb`]	After [`de36c56`]	Ratio	Benchmark (Parameter)
-	3.45±0.01s	2.49±0.01s	0.72	mpas_ocean.ConstructFaceLatLon.time_welzl('120km')
-	224±0.7ms	162±1ms	0.72	mpas_ocean.ConstructFaceLatLon.time_welzl('480km')

Benchmarks that have stayed the same:

Before [`fda71bb`]	After [`de36c56`]	Ratio	Benchmark (Parameter)
200±0.2ms	204±1ms	1.02	bench_connectivity.Connectivity.time_edge_face('120km')
12.2±0.1ms	12.3±0.08ms	1.01	bench_connectivity.Connectivity.time_edge_face('480km')
200±1ms	203±0.4ms	1.01	bench_connectivity.Connectivity.time_edge_node('120km')
11.3±0.03ms	11.3±0.06ms	1.00	bench_connectivity.Connectivity.time_edge_node('480km')
199±1ms	203±0.6ms	1.02	bench_connectivity.Connectivity.time_face_edge('120km')
11.4±0.1ms	12.1±0.4ms	1.06	bench_connectivity.Connectivity.time_face_edge('480km')
898±6ms	921±5ms	1.03	bench_connectivity.Connectivity.time_face_face('120km')
57.7±0.6ms	58.0±4ms	1.01	bench_connectivity.Connectivity.time_face_face('480km')
70.6±6μs	69.2±3μs	0.98	bench_connectivity.Connectivity.time_face_node('120km')
68.0±2μs	67.5±1μs	0.99	bench_connectivity.Connectivity.time_face_node('480km')
426±5μs	431±8μs	1.01	bench_connectivity.Connectivity.time_n_nodes_per_face('120km')
355±10μs	360±8μs	1.01	bench_connectivity.Connectivity.time_n_nodes_per_face('480km')
200±0.6ms	204±0.6ms	1.02	bench_connectivity.Connectivity.time_node_edge('120km')
11.6±0.09ms	11.6±0.08ms	1.00	bench_connectivity.Connectivity.time_node_edge('480km')
82.4±0.9ms	80.2±0.6ms	0.97	bench_connectivity.Connectivity.time_node_face('120km')
5.35±0.07ms	5.29±0.06ms	0.99	bench_connectivity.Connectivity.time_node_face('480km')
8.52±0.09ms	8.51±0.07ms	1.00	face_bounds.FaceBounds.time_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/mpas/QU/oQU480.231010.nc'))
2.76±0.1ms	2.79±0.04ms	1.01	face_bounds.FaceBounds.time_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/scrip/outCSne8/outCSne8.nc'))
10.5±10s	10.3±10ms	~0.00	face_bounds.FaceBounds.time_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/geoflow-small/grid.nc'))
2.26±0.01ms	2.18±0.04ms	0.97	face_bounds.FaceBounds.time_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/quad-hexagon/grid.nc'))
57.3k	57.3k	1.00	face_bounds.FaceBounds.track_nbytes_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/mpas/QU/oQU480.231010.nc'))
12.3k	12.3k	1.00	face_bounds.FaceBounds.track_nbytes_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/scrip/outCSne8/outCSne8.nc'))
123k	123k	1.00	face_bounds.FaceBounds.track_nbytes_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/geoflow-small/grid.nc'))
128	128	1.00	face_bounds.FaceBounds.track_nbytes_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/quad-hexagon/grid.nc'))
1.27M	1.27M	1.00	face_bounds.FaceBounds.track_nbytes_grid_with_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/mpas/QU/oQU480.231010.nc'))
50.1k	50.1k	1.00	face_bounds.FaceBounds.track_nbytes_grid_with_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/scrip/outCSne8/outCSne8.nc'))
1.48M	1.48M	1.00	face_bounds.FaceBounds.track_nbytes_grid_with_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/geoflow-small/grid.nc'))
712	712	1.00	face_bounds.FaceBounds.track_nbytes_grid_with_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/quad-hexagon/grid.nc'))
1.98M	1.98M	1.00	face_bounds.FaceBounds.track_peakmem_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/mpas/QU/oQU480.231010.nc'))
1.99M	1.99M	1.00	face_bounds.FaceBounds.track_peakmem_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/scrip/outCSne8/outCSne8.nc'))
2.15M	2.15M	1.00	face_bounds.FaceBounds.track_peakmem_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/geoflow-small/grid.nc'))
38.3k	38.3k	1.00	face_bounds.FaceBounds.track_peakmem_face_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/quad-hexagon/grid.nc'))
336M	336M	1.00	face_bounds.FaceBoundsColdStartRss.peakmem_open_and_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/mpas/QU/oQU480.231010.nc'))
365M	367M	1.01	face_bounds.FaceBoundsColdStartRss.peakmem_open_and_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/scrip/outCSne8/outCSne8.nc'))
338M	338M	1.00	face_bounds.FaceBoundsColdStartRss.peakmem_open_and_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/geoflow-small/grid.nc'))
337M	337M	1.00	face_bounds.FaceBoundsColdStartRss.peakmem_open_and_bounds(PosixPath('/home/runner/work/uxarray/uxarray/test/meshfiles/ugrid/quad-hexagon/grid.nc'))
1.20±0.06μs	1.22±0.03μs	1.02	geometry_kernels.AccucrossKernels.time_accucross
2.79±0.04μs	2.75±0.04μs	0.99	geometry_kernels.AccucrossKernels.time_accucross_pair
471±20ns	451±10ns	0.96	geometry_kernels.EFTPrimitives.time_acc_sqrt_re
446±20ns	441±30ns	0.99	geometry_kernels.EFTPrimitives.time_diff_of_products
390±20ns	426±20ns	1.09	geometry_kernels.EFTPrimitives.time_two_prod
411±30ns	400±20ns	0.97	geometry_kernels.EFTPrimitives.time_two_sum
1.68±0.06μs	1.59±0.06μs	0.95	geometry_kernels.GCAConstLatIntersection.time_accux_constlat_kernel
1.13±0.02μs	1.13±0.01μs	1.00	geometry_kernels.GCAConstLatIntersection.time_gca_const_lat_intersection
1.92±0.03μs	1.94±0.02μs	1.01	geometry_kernels.GCAConstLatIntersection.time_try_gca_const_lat_intersection
1.72±0.02μs	1.68±0.05μs	0.98	geometry_kernels.GCAGCAIntersection.time_accux_gca_kernel
1.44±0.04μs	1.41±0.03μs	0.98	geometry_kernels.GCAGCAIntersection.time_gca_gca_intersection
2.19±0.02μs	2.22±0.02μs	1.02	geometry_kernels.GCAGCAIntersection.time_try_gca_gca_intersection
52.9±0.8μs	53.1±1μs	1.00	geometry_kernels.OrientPredicates.time_on_minor_arc
1.09±0.03μs	1.12±0.04μs	1.03	geometry_kernels.OrientPredicates.time_orient3d_on_sphere
2.72±0.1ms	2.63±0.02ms	0.97	geometry_samebody.SameBodyConstLat.time_accux_dispatch
1.17±0.01ms	1.17±0.01ms	1.00	geometry_samebody.SameBodyConstLat.time_accux_kernel
1.73±0.01ms	1.72±0.01ms	1.00	geometry_samebody.SameBodyConstLat.time_fp64_dispatch
147±0.7μs	147±0.3μs	1.00	geometry_samebody.SameBodyConstLat.time_fp64_kernel
32.3±0ms	32.5±0.2ms	1.01	geometry_samebody_gcagca.SameBodyGcaGca.time_accux_dispatch
10.3±0ms	11.9±0.8ms	~1.16	geometry_samebody_gcagca.SameBodyGcaGca.time_accux_kernel
27.0±0.2ms	26.7±0.1ms	0.99	geometry_samebody_gcagca.SameBodyGcaGca.time_fp64_dispatch
4.90±0ms	4.96±0.07ms	1.01	geometry_samebody_gcagca.SameBodyGcaGca.time_fp64_kernel
833±3ms	830±2ms	1.00	import.Imports.timeraw_import_uxarray
293M	293M	1.00	import.Imports.track_peakmem_import_uxarray
2.76±0.02ms	2.85±0.04ms	1.03	mpas_ocean.CheckNorm.time_check_norm('120km')
2.27±0.03ms	2.31±0.01ms	1.02	mpas_ocean.CheckNorm.time_check_norm('480km')
858±10ms	851±9ms	0.99	mpas_ocean.ConnectivityConstruction.time_face_face_connectivity('120km')
55.2±0.3ms	56.0±0.5ms	1.01	mpas_ocean.ConnectivityConstruction.time_face_face_connectivity('480km')
678±10μs	677±9μs	1.00	mpas_ocean.ConnectivityConstruction.time_n_nodes_per_face('120km')
587±10μs	606±10μs	1.03	mpas_ocean.ConnectivityConstruction.time_n_nodes_per_face('480km')
5.46±0.03ms	5.47±0.04ms	1.00	mpas_ocean.ConstructFaceLatLon.time_cartesian_averaging('120km')
3.99±0.02ms	3.96±0.04ms	0.99	mpas_ocean.ConstructFaceLatLon.time_cartesian_averaging('480km')
18.2±0.02ms	18.2±0.01ms	1.00	mpas_ocean.ConstructTreeStructures.time_ball_tree('120km')
1.05±0.01ms	1.04±0.02ms	0.99	mpas_ocean.ConstructTreeStructures.time_ball_tree('480km')
10.6±0.03ms	10.6±0.03ms	1.00	mpas_ocean.ConstructTreeStructures.time_kd_tree('120km')
730±7μs	732±10μs	1.00	mpas_ocean.ConstructTreeStructures.time_kd_tree('480km')
587±4ms	591±2ms	1.01	mpas_ocean.CrossSections.time_const_lat('120km', 1)
297±0.7ms	296±1ms	1.00	mpas_ocean.CrossSections.time_const_lat('120km', 2)
155±0.5ms	156±1ms	1.01	mpas_ocean.CrossSections.time_const_lat('120km', 4)
534±0.7ms	536±3ms	1.00	mpas_ocean.CrossSections.time_const_lat('480km', 1)
270±0.8ms	270±1ms	1.00	mpas_ocean.CrossSections.time_const_lat('480km', 2)
139±0.3ms	139±0.6ms	1.00	mpas_ocean.CrossSections.time_const_lat('480km', 4)
356M	356M	1.00	mpas_ocean.CrossSectionsPeakMem.peakmem_const_lat('120km', 1)
356M	356M	1.00	mpas_ocean.CrossSectionsPeakMem.peakmem_const_lat('120km', 2)
355M	356M	1.00	mpas_ocean.CrossSectionsPeakMem.peakmem_const_lat('120km', 4)
341M	339M	0.99	mpas_ocean.CrossSectionsPeakMem.peakmem_const_lat('480km', 1)
339M	339M	1.00	mpas_ocean.CrossSectionsPeakMem.peakmem_const_lat('480km', 2)
339M	339M	1.00	mpas_ocean.CrossSectionsPeakMem.peakmem_const_lat('480km', 4)
25.0±0.4ms	24.5±0.07ms	0.98	mpas_ocean.DualMesh.time_dual_mesh_construction('120km')
3.21±0.07ms	3.25±0.06ms	1.01	mpas_ocean.DualMesh.time_dual_mesh_construction('480km')
61.6±1ms	61.7±0.6ms	1.00	mpas_ocean.FaceAreas.time_face_areas('120km')
4.90±5s	7.88±5ms	~0.00	mpas_ocean.FaceAreas.time_face_areas('480km')
229k	229k	1.00	mpas_ocean.FaceAreas.track_nbytes_face_areas('120km')
14.3k	14.3k	1.00	mpas_ocean.FaceAreas.track_nbytes_face_areas('480km')
2.12M	2.12M	1.00	mpas_ocean.FaceAreas.track_peakmem_face_areas('120km')
823k	823k	1.00	mpas_ocean.FaceAreas.track_peakmem_face_areas('480km')
949±9ms	943±10ms	0.99	mpas_ocean.GeoDataFrame.time_to_geodataframe('120km', False)
54.5±0.8ms	55.2±2ms	1.01	mpas_ocean.GeoDataFrame.time_to_geodataframe('120km', True)
84.5±0.3ms	84.6±0.5ms	1.00	mpas_ocean.GeoDataFrame.time_to_geodataframe('480km', False)
5.76±0.05ms	5.98±0.1ms	1.04	mpas_ocean.GeoDataFrame.time_to_geodataframe('480km', True)
175±0.7ms	175±0.2ms	1.00	mpas_ocean.Gradient.time_gradient('120km')
12.2±0.01ms	12.2±0.06ms	1.00	mpas_ocean.Gradient.time_gradient('480km')
457k	457k	1.00	mpas_ocean.Gradient.track_nbytes_gradient('120km')
28.7k	28.7k	1.00	mpas_ocean.Gradient.track_nbytes_gradient('480km')
5.08M	5.08M	1.00	mpas_ocean.Gradient.track_peakmem_gradient('120km')
328k	328k	1.00	mpas_ocean.Gradient.track_peakmem_gradient('480km')
351M	352M	1.00	mpas_ocean.GradientColdStartRss.peakmem_gradient('120km')
331M	331M	1.00	mpas_ocean.GradientColdStartRss.peakmem_gradient('480km')
371±10μs	370±10μs	1.00	mpas_ocean.HoleEdgeIndices.time_construct_hole_edge_indices('120km')
201±10μs	199±3μs	0.99	mpas_ocean.HoleEdgeIndices.time_construct_hole_edge_indices('480km')
566±9μs	579±6μs	1.02	mpas_ocean.Integrate.time_integrate('120km')
494±5μs	500±10μs	1.01	mpas_ocean.Integrate.time_integrate('480km')
18.4M	18.4M	1.00	mpas_ocean.Integrate.track_nbytes_integrate('120km')
1.2M	1.2M	1.00	mpas_ocean.Integrate.track_nbytes_integrate('480km')
184±1ms	183±1ms	1.00	mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection('120km', 'exclude')
187±6ms	184±1ms	0.98	mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection('120km', 'include')
183±2ms	182±1ms	1.00	mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection('120km', 'split')
13.7±0.09ms	13.7±0.08ms	1.00	mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection('480km', 'exclude')
14.0±0.08ms	13.7±0.03ms	0.98	mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection('480km', 'include')
13.6±0.09ms	13.7±0.2ms	1.01	mpas_ocean.MatplotlibConversion.time_dataarray_to_polycollection('480km', 'split')
430±20μs	433±10μs	1.01	mpas_ocean.PointInPolygon.time_face_search_lonlat('120km')
414±6μs	438±10μs	1.06	mpas_ocean.PointInPolygon.time_face_search_lonlat('480km')
407±9μs	402±10μs	0.99	mpas_ocean.PointInPolygon.time_face_search_xyz('120km')
408±10μs	383±7μs	0.94	mpas_ocean.PointInPolygon.time_face_search_xyz('480km')
244±1ms	239±0.7ms	0.98	mpas_ocean.RemapDownsample.time_bilinear_remapping
290±1ms	281±0.8ms	0.97	mpas_ocean.RemapDownsample.time_inverse_distance_weighted_remapping
15.6±0.1ms	15.7±0.1ms	1.00	mpas_ocean.RemapDownsample.time_nearest_neighbor_remapping
1.44±0.01s	1.39±0s	0.97	mpas_ocean.RemapUpsample.time_bilinear_remapping
36.0±0.3ms	37.0±0.4ms	1.03	mpas_ocean.RemapUpsample.time_inverse_distance_weighted_remapping
12.5±0.2ms	12.3±0.1ms	0.99	mpas_ocean.RemapUpsample.time_nearest_neighbor_remapping
9.29±0.2ms	9.12±0.04ms	0.98	mpas_ocean.ZonalAverage.time_zonal_average('120km')
4.82±0.06ms	4.79±0.04ms	1.00	mpas_ocean.ZonalAverage.time_zonal_average('480km')
358M	358M	1.00	mpas_ocean.ZonalAveragePeakMem.peakmem_zonal_average('120km')
341M	341M	1.00	mpas_ocean.ZonalAveragePeakMem.peakmem_zonal_average('480km')
6.83±0.03ms	6.84±0.01ms	1.00	quad_hexagon.QuadHexagon.time_open_dataset
5.86±0.01ms	5.87±0.01ms	1.00	quad_hexagon.QuadHexagon.time_open_grid
408	408	1.00	quad_hexagon.QuadHexagon.track_nbytes_open_dataset
392	392	1.00	quad_hexagon.QuadHexagon.track_nbytes_open_grid
73.5k	73.8k	1.00	quad_hexagon.QuadHexagon.track_peakmem_open_dataset
72.7k	73k	1.00	quad_hexagon.QuadHexagon.track_peakmem_open_grid

Sevans711 · 2026-08-12T16:41:46Z

Looks like this PR is having a nice side effect: the optimization to _small_angle_of_2_vectors did in fact improve the mpas_ocean.ConstructFaceLatLon.time_welzl benchmarks. Following the code reveals that it does in fact get called there, so this is probably a real improvement, not just a rounding error.

(The chain of calls is deep, so I'm putting it here for reference: construct_face_centers(method='welzl') --> _populate_face_centerpoints --> _construct_face_centerpoints --> _smallest_enclosing_circle --> _welzl_recursive --> _circle_from_two_points or _circle_from_three_points --> _small_angle_of_2_vectors)

Ignoring the apparent peakmem improvements (see #1605).

Most importantly, none of the benchmarks show performance degradation, which helps demonstrate that the _small_angle_of_2_vectors are at best an improvement, at worst causing no change.

erogluorhan

This looks great to me! Thanks for all the invaluable discussion throughout this!

cmdupuis3

LGTM, tests pass and the tests and numba parallelization look reasonable. The vector arithmetic will probably need to sync up with other incoming PRs in the future though

Sevans711and others added 9 commits July 15, 2026 13:45

Adds Grid.compute_face_node_angles (WIP)
ecba4f1
This provides an initial implementation which seems to be working. See #1566 TODO: - support assume_convex=False - type-hint possible UxDataArray return type? - tests - examples

Merge branch 'main' into face_node_angles
5ba343f

face_node_angles remove unneeded kwargs
f7866b4
removes "geometry" (always use spherical) removes "assume_convex" (always assume convex) Also, improves type-hinting for compute_face_node_angles(). Also, formats using pre-commit ruff formatting.

face_node_angles efficiency: tiny arrays in numba
f5967ac
use tuples instead of tiny arrays in numba! Cleans up docstring in grid.py (forgot to change in previous commit).

face_node_angles fix forgot an optimization
09bafd7
(it was making a type error in numba when trying to run the method)

remove face_node_angles cache
1e3932e
compute_face_node_angles() seems to be really fast, so including a cache option in initial implementation isn't worthwhile.

add face_node_angles tests
7d610ba

docs: add compute_face_node_angles to api.rst
74b1660

clean up comments related to face_node_angles
a6fa5bb

Sevans711 added new feature New feature or request run-benchmark Run ASV benchmark workflow labels Aug 12, 2026

Sevans711 requested review from cmdupuis3, erogluorhan, hongyuchen1030 and rajeeja August 12, 2026 15:09

Sevans711 mentioned this pull request Aug 12, 2026
Compute face_node_angles from uxarray.Grid? #1566
Closed

Sevans711and others added 2 commits August 13, 2026 14:25

Merge branch 'main' into face_node_angles
f5bcdb6

Merge branch 'main' into face_node_angles
e1b4d4e

erogluorhan approved these changes Aug 14, 2026
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erogluorhan reviewed Aug 14, 2026
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Comment threaduxarray/grid/utils.py

cmdupuis3 requested changes Aug 18, 2026
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Comment threadtest/grid/geometry/test_angles.py Outdated
Comment threadtest/grid/geometry/test_angles.py Outdated
Comment threadtest/grid/geometry/test_angles.py Outdated

Sevans711 added 3 commits August 18, 2026 13:22

test_angles style update: use np.isclose & floats
c43ec5b

fix test_angles: swapped rtol and atol
c57c594

Merge branch 'main' into face_node_angles
9c3c574

Sevans711 requested a review from cmdupuis3 August 18, 2026 17:42

cmdupuis3 approved these changes Aug 18, 2026
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Merge branch 'main' into face_node_angles
278a98f

Sevans711 merged commit ab39109 into mainAug 18, 2026
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Sevans711 deleted the face_node_angles branch August 18, 2026 18:38

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Implements `Grid.compute_face_node_angles()` - #1672

Implements `Grid.compute_face_node_angles()`#1672
Sevans711 merged 15 commits into
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