Address SciPy interp2d deprecation

Gallery/Vectors/NCL_vector_5.py

@@ -119,19 +119,15 @@
 
 # We attempt to recreate curly vectors with Matplotlib's streamplot function.
 # streamplot requires the input parameter x, y to be evenly spaced strictly increasing arrays.
-# Therefore we interpolate the original dataset onto an manually set, evenly spaced grid.
-# There are probably more suitable interpolation routines than scipy's interp2d,
-# but we do not have a standardized procedure for interpolation for streamplot as of this point.
+# Therefore we interpolate the original dataset onto an evenly spaced grid that we generate below.
 
 # regularly spaced grid spanning the domain of x and y
 xi = np.linspace(T['lat'].min(), T['lat'].max(), T['lat'].size)
 yi = np.linspace(T['plev'].min(), T['plev'].max(), T['plev'].size)
 
-# interp2d function creates interpolator classes
-u_func = interp2d(T['lat'], T['plev'], V)
-v_func = interp2d(T['lat'], T['plev'], wscale)
-uCi = u_func(xi, yi)
-vCi = v_func(xi, yi)
+# use xarray's interp to interpolate onto the new grid
+uCi = V.interp(lat=xi, plev=yi)
+vCi = wscale.interp(lat=xi, plev=yi)
 
 # Use streamplot to match curly vector
 ax.streamplot(xi,

conda_environment.yml

@@ -19,13 +19,12 @@ dependencies:
   - pillow
   - pip
   - scikit-learn
-  - scipy<1.14
+  - scipy
   - sphinx<7
   - sphinx-book-theme
   - sphinx-design
   - sphinx-gallery
   - xarray
   - dask
   - wrf-python
-  - pip:
-      - pre-commit
+  - pre-commit