interp2d Depreciated

[RFingAdam]

https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp2d.html

The recommended alternatives are RegularGridInterpolator for regularly spaced data or griddata for scattered data. Here's how these alternatives work in your case:

1. Using RegularGridInterpolator for Regular Grids
   If your data grid is regularly spaced (i.e., your unique_phi and unique_theta values are evenly spaced), RegularGridInterpolator is the best choice and will work similarly to interp2d but is faster and more flexible.

Here’s how you might use it:

from scipy.interpolate import RegularGridInterpolator

# Define your interpolator
f_interp = RegularGridInterpolator((unique_theta, unique_phi), data_grid, method='linear')

# To get interpolated values at new theta and phi coordinates
# Provide points as an array of (theta, phi) pairs
interpolated_values = f_interp((new_theta, new_phi))

2. Using griddata for Scattered or Irregular Grids
   If your grid is not evenly spaced, griddata is a good option. It allows interpolation over scattered data points.

from scipy.interpolate import griddata

# Flatten the data and coordinates if needed
theta_flat = unique_theta.ravel()
phi_flat = unique_phi.ravel()
data_flat = data_grid.ravel()

# Define new grid of points where you want to interpolate
new_points = np.array([[t, p] for t in new_theta for p in new_phi])

# Interpolate
interpolated_values = griddata(
    (theta_flat, phi_flat), data_flat, new_points, method='linear'
)

Summary
Use RegularGridInterpolator if your data is on a regular grid.
Use griddata if your data points are scattered or irregular.
These approaches give more flexibility and efficiency than interp2d while ensuring compatibility with future SciPy versions.