Merge branch 'main' into rajeeja/yac

Author: rajeeja

ci/asv.yml

@@ -13,7 +13,7 @@ dependencies:
   - healpix
   - netcdf4
   - numba
-  - numpy<2.4
+  - numpy
   - pandas
   - pathlib
   - pre_commit

ci/docs.yml

@@ -7,7 +7,7 @@ dependencies:
     - cmocean
     - netcdf4
     - numba
-    - numpy<2.4
+    - numpy
     - pathlib
     - pre_commit
     - pytest

ci/environment.yml

@@ -18,7 +18,7 @@ dependencies:
   - matplotlib-inline
   - netcdf4
   - numba
-  - numpy<2.4
+  - numpy
   - pandas
   - pathlib
   - pre_commit

ci/install-upstream.sh

@@ -23,18 +23,17 @@ pip uninstall -y \
 conda list
 
 # if available install from scientific-python nightly wheels
-# constrain numpy to <2.4 until numba supports newer versions
 # use stable pandas (not nightly) due to geopandas incompatibility with pandas nightly internals
 # (see: https://github.com/UXARRAY/uxarray/issues/1414)
 python -m pip install \
-    'numpy<2.4' \
     'pandas>=2.0.0'
 
 python -m pip install \
     -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple \
     --no-deps \
     --pre \
     --upgrade \
+    numpy \
     scikit-learn \
     scipy \
     xarray

docs/conf.py

@@ -237,6 +237,8 @@ def __getattr__(cls, name):
     r"https://docs.xarray.dev/*",
     r"https://seatstandards.org/*",
     r"https://healpix.sourceforge.io/*",
+    r"https://zenodo.org/*",
+    r"https://doi.org/10.5281/zenodo*",
     # More URLs as needed
 ]
 

pyproject.toml

@@ -31,7 +31,7 @@ dependencies = [
   "matplotlib-inline",
   "netcdf4",
   "numba",
-  "numpy<2.4",
+  "numpy",
   "pandas",
   "pyarrow",
   "requests",

uxarray/remap/bilinear.py

@@ -128,7 +128,7 @@ def _barycentric_weights(point_xyz, dual, data_size, source_grid):
         # there were rare cases where counts > 0, hence the newer condition at the end
         elif counts == 1:
             all_weights[i, 0] = 1.0
-            all_indices[i, 0] = int(cur_inds[0])
+            all_indices[i, 0] = int(cur_inds[0].squeeze())
         # 2/2/2026: For some `remap_to="nodes"` cases, `counts` happen to be bigger than 1. Not sure
         # if the best way to add weights from multiple faces is to take only the first face's though
         else: