Scipy interpolation on a numpy array
- by dassouki
I have a lookup table that is defined the following way:
TR_ua1 = np.array([ [3.6, 6.5, 9.1, 11.5, 13.8],
[3.9, 7.3, 10.0, 13.1, 15.9],
[4.5, 9.2, 12.2, 14.8, 18.2] ])
The header row elements are (hh) <1,2,3,4,5+
The header column (inc) elements are <10000, 20000, 20001+
The user will input a value ex (1.3, 25,000) or (0.2, 50,000). Scipy.interpolate() should interpolate to determine the correct value.
Currently, the only way i can do this is with a bunch of if/elifs as exemplified below. I'm pretty sure there is a better, more efficient way of doing this
Here's what i've got so far
import numpy as np
from scipy import interplate
if (ua == 1):
if (inc <= low_inc): #low_inc = 10,000
if (hh <= 1):
return TR_ua1[0][0]
elif (hh >= 1 & hh < 2):
return interpolate( (1,2), (TR_ua1[0][1], TR_ua1[0][2]) )
