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Bug: structured data transpose changes face mapping in from_xarray

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#1,828 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
78/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
numpy, python
Área
data

Línea de trabajo

Read _map_dims_to_ugrid in uxarray/core/utils.py and face construction in uxarray/io/_structured.py, then inspect the existing structured mapping regression test. Reproduce the issue using the supplied (y, x) and (x, y) datasets and add a regression test showing both conversions map values identically to the same grid faces. Done when the test passes and the mapping remains consistent with the documented face ordering.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

bug
Version

Source main at 6d6c6dacd2472fda8c6a3f71af659e1eb1cbadc9; Python 3.12.14, NumPy 2.5.4, xarray 2026.9.0 on Linux.

How did you install UXarray?

Source

What happened?

Converting equivalent structured datasets with UxDataset.from_xarray(..., uxgrid=grid) associates different values with the same grid faces when the spatial dimension order is transposed. The conversion succeeds without a warning. This matters when normalizing structured member files into a shared unstructured representation for regional statistics or integration.

For the synthetic example below, (y, x) produces face values [0, 0, 0, 100, 100, 100], while the equivalent (x, y) input produces [0, 100, 0, 100, 0, 100]. Both retain exactly the same Grid object and its face connectivity. The integral changes from 67.08123535225863 to 79.37563993526874 for this example; this is not a general error bound.

What did you expect to happen?

Transposing named input dimensions should preserve the physical field and its association with grid faces, so both mapped values and integration results should agree.

Can you provide a MCVE to repoduce the bug?
import numpy as np
import xarray as xr
import uxarray as ux

original = xr.Dataset(
    {"temperature": (("y", "x"), [[0., 0., 0.], [100., 100., 100.]])},
    coords={"x": [0., 20., 40.], "y": [-10., 50.]},
)
transposed = original.transpose("x", "y")
xr.testing.assert_identical(original, transposed.transpose("y", "x"))
grid = ux.Grid.from_structured(lon=original.x, lat=original.y)

a = ux.UxDataset.from_xarray(original, uxgrid=grid)
b = ux.UxDataset.from_xarray(transposed, uxgrid=grid)
assert a.uxgrid is b.uxgrid is grid
print(a.temperature.values)  # [0., 0., 0., 100., 100., 100.]
print(b.temperature.values)  # [0., 100., 0., 100., 0., 100.]
print(float(a.temperature.integrate()))  # 67.08123535225863
print(float(b.temperature.integrate()))  # 79.37563993526874
np.testing.assert_array_equal(a.temperature.values, b.temperature.values)

The last assertion fails. I also reproduced the mapping after writing the two synthetic members as separate NetCDF files and reading them through xarray. Explicitly transposing the source back to (..., "y", "x") before conversion restores the correct values and integral.

Investigation and scope

_map_dims_to_ugrid chooses its stacking order from a data variable's final dimension, while _read_structured_grid constructs face connectivity in latitude-major, longitude-minor order. The existing structured mapping regression test also documents face k as (k // n_lon, k % n_lon).

This reproduction uses the existing from_xarray API on main, without the changes in #1827. It does not call weighted_mean, so it is distinct from #1800's reduction-axis issue. No implementation changes are included here. I would be happy to work on a focused fix and regression tests if this can be assigned to me, following the contribution guide.

AI assistance: OpenAI Codex assisted with investigation and synthetic reproduction; the recorded results were actually executed against the pinned source.

Lenguaje dominante
Python
Estrellas
230
Forks
57
Merge medio
4 d 14 h
PR fusionados (30 d)
7

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