I am trying to interpolate some values contained in a list by first turning the lists into arrays and perform the calculations.

However, I need to write the formula three times and I need to specify the indexes manually.

Here is my code so far

import numpy as np
data1 = [(5,), (4,), (6,)]
data2 = [(2,), (8,), (9,)]
data3 = [(3,), (1,), (7,)]

x1 = [(4, 2, 1)]
x2 = [(6, 9, 7)]
y1 = [(1,)]
y2 = [(3,)]

data1 = np.array(data1)
x1 = np.array(x1)
x2 = np.array(x2)
y1 = np.array(y1)
y2 = np.array(y2)

new1 = ((data1-x1[0,0])/(x2[0,0]-x1[0,0]))*(y2-y1) y1
print(new1)

new2 = ((data2-x1[0,1])/(x2[0,1]-x1[0,1]))*(y2-y1) y1
print(new2)

new3 = ((data3-x1[0,2])/(x2[0,2]-x1[0,2]))*(y2-y1) y1
print(new3)

and the output is

[[2.]
 [1.]
 [3.]]
[[1.        ]
 [2.71428571]
 [3.        ]]
[[1.66666667]
 [1.        ]
 [3.        ]]

I was wondering if anyone has a better and faster way to do this automatically, without writing everything manually?

CodePudding user response：

Just put data1, data2 etc in an array and your expression will just work with element-wise and broadcasted computations.

>>> data = np.hstack([data1, data2, data3])
>>> new = (data - x1) / (x2 - x1) * (y2 - y1)   y1
>>> new
array([[2.        , 1.        , 1.66666667],
       [1.        , 2.71428571, 1.        ],
       [3.        , 3.        , 3.        ]])

If you want the results to be in column-like vectors of shape (3, 1) each, then do new = new[...,None] and then new[:,0] will give you the same exact result as your new1.