Add conda-forge badge, install note (#59)

* Add conda-forge badge, install note

* Avoid making mypy unhappy

Author: BSchilperoort

README.md

@@ -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/)
 
@@ -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]
@@ -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:

src/xarray_regrid/utils.py

@@ -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
@@ -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: