I have a function f(u,v,w) which I would like to interpolate using a scipy function (with linear interpolation). This is easy enough.
When I run the interpolation step, I simply do the following (interpolating over a u,v,w grid):
u = np.linspace(-1,1,100)
v = np.linspace(-2,2,50)
w = np.linspace(3,8,30)
values_grid = np.zeros((len(u),len(v),len(w)))
count = 0
for i in range(len(u)):
for j in range(len(w)):
for k in range(len(w)):
values_grid[i,j,k] = f(u[i],v[j],w[k])
from scipy.interpolate import RegularGridInterpolator
my_interpolating_function = RegularGridInterpolator((u, v, w), values_grid, method='linear',bounds_error=False,fill_value=-999)
This is fine for many cases. However, when I want to evaluate this interpolation function it seems like I am required to use inputs which have shape [(Number of input samples) x (Dimension of Samples)]. E.g:
func_input = np.vstack([u_samps,v_samps,w_samps].T # E.g. shape is 500,3
output = my_interpolating_function(func_input)) # Has output shape 500
This works fine. The issue is that I would like to evaluate this function over a grid where the samples have the following shape
shape(u_samps) = 500
shape(v_samps) = (100,100)
shape(w_samps) = (100,100)
Meaning I would like to evaluate
my_interpolating_function([u_samps, v_samps, w_samps])
and get out an array which has shape (500,100,100) (so the interpolation is evaluated for all 500 u_samps over the v_samps and w_samps grids). I can flatten the v_samps and w_samps array, but then I have to make several (hundreds) copies of u_samps to get the inputs into the correct format. So is there any way to have an interpolation function that can take the inputs above (u_samps, v_samps, w_samps with the specified shapes) and get out an array with shape (500,100,100) efficiently?
Any help greatly appreciated, I have been stuck on this problem and it's really holding up my progress! The end goal is to use this function in a statistical likelihood which needs to be sampled with MCMC, so speed is pretty important (and making hundreds of copies of massive arrays is very slow)
