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Apr 11, 2025
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Add conda-forge badge, install note #59

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Add conda-forge badge, install note
BSchilperoort Apr 9, 2025
bd28a6a
Avoid making mypy unhappy
BSchilperoort Apr 11, 2025
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5 changes: 4 additions & 1 deletion README.md
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Expand Up @@ -15,6 +15,7 @@ All regridding methods can operate lazily on [Dask arrays](https://docs.xarray.d
Note that "Most common value" is designed to regrid categorical data to a coarse resolution. For regridding categorical data to a finer resolution, please use "nearest-neighbor" regridder.

[![PyPI](https://img.shields.io/pypi/v/xarray-regrid.svg?style=flat)](https://pypi.python.org/pypi/xarray-regrid/)
[![conda-forge](https://anaconda.org/conda-forge/xarray-regrid/badges/version.svg)](https://anaconda.org/conda-forge/xarray-regrid)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10203304.svg)](https://doi.org/10.5281/zenodo.10203304)
[![Docs](https://readthedocs.org/projects/xarray-regrid/badge/?version=latest&style=flat)](https://xarray-regrid.readthedocs.org/)

Expand All @@ -29,6 +30,8 @@ For a minimal install:
pip install xarray-regrid
```

*Note: xarray-regrid is also [available on conda-forge](https://anaconda.org/conda-forge/xarray-regrid).*

To improve performance in certain cases:
```console
pip install xarray-regrid[accel]
Expand All @@ -39,7 +42,7 @@ which includes optional extras such as:
- `sparse`: for performing conservative regridding using sparse weight matrices
- `opt-einsum`: optimized einsum routines used in conservative regridding

Benchmarking varies across different hardware specifications, but the inclusion of these extras can often provide significant speedups.
Benchmarking varies across different hardware specifications, but the inclusion of these extras can often provide significant speedups.

## Usage
The xarray-regrid routines are accessed using the "regrid" accessor on an xarray Dataset:
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6 changes: 3 additions & 3 deletions src/xarray_regrid/utils.py
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Expand Up @@ -311,7 +311,7 @@ def format_lat(
# TODO: with cos(90) = 0 weighting, these weights might be 0?

polar_lat = 90
dy = obj.coords[lat_coord].diff(lat_coord).max().values.item()
dy: Any = obj.coords[lat_coord].diff(lat_coord).max().values.item()

# Only pad if global but don't have edge values directly at poles
# NOTE: could use xr.pad here instead of xr.concat, but none of the
Expand Down Expand Up @@ -369,8 +369,8 @@ def format_lon(
# Only pad if domain is global in lon
source_lon = obj.coords[lon_coord]
target_lon = target.coords[lon_coord]
dx_s = source_lon.diff(lon_coord).max().values.item()
dx_t = target_lon.diff(lon_coord).max().values.item()
dx_s: Any = source_lon.diff(lon_coord).max().values.item()
dx_t: Any = target_lon.diff(lon_coord).max().values.item()
is_global_lon = source_lon.max().values - source_lon.min().values >= 360 - dx_s

if is_global_lon:
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