interpolate linear array to non linear array using python numpy or scipy -

i have arrays:

a linear one;

x = array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. , 1.1,  1.2,  1.3,  1.4]) 

and corresponding result non-linear one;

y = array([ 13.07,  13.7 ,  14.35,  14.92,  15.5 ,  16.05,  16.56,  17.12,         17.62,  18.08,  18.55,  19.02,  19.45,  19.88,  20.25]) 

now: want convert y linearly spaced array , find corresponding interpolated values of x.

i.e. find x when

y = array([ 13. ,  13.5,  14. ,  14.5,  15. ,  15.5,  16. ,  16.5,  17. , 17.5,  18. ,  18.5,  19. ,  19.5,  20. ]) 

thanks in advance.

i use following method using interp function in numpy:

ynew = np.linspace(np.min(y), np.max(y), len(y)) xnew = np.interp(ynew, y, x) 

i.e. exchanging x , y in np.interp function.

is correct ? or break down condition.

unless i'm missing something, case calls simple invocation of numpy.interp. want predict x y reverse of how people variable definitions, other wrinkle, need is:

import numpy np x = np.array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. , 1.1,  1.2,  1.3,  1.4]) y = np.array([ 13.07,  13.7 ,  14.35,  14.92,  15.5 ,  16.05,  16.56,  17.12,         17.62,  18.08,  18.55,  19.02,  19.45,  19.88,  20.25]) ynew = np.array([ 13. ,  13.5,  14. ,  14.5,  15. ,  15.5,  16. ,  16.5,  17. , 17.5,  18. ,  18.5,  19. ,  19.5,  20. ]) xnew = np.interp(ynew, y, x) print xnew 

which gives ouput:

[ 0.          0.06825397  0.14615385  0.22631579  0.3137931   0.4   0.49090909  0.58823529  0.67857143  0.776       0.8826087   0.9893617   1.09574468  1.21162791  1.33243243]