Tridiagonal Matrix Algorithm solver in Python. I've modified the code from cbellei so, it works with python 3.0+ and implemented the use of jit to increase the speed.

TDMAsolver.py

import numpy as np
from numba import jit, f8

## Tri Diagonal Matrix Algorithm(a.k.a Thomas algorithm) solver
@jit(f8[:] (f8[:],f8[:],f8[:],f8[:] ))
def TDMAsolver(a, b, c, d):
    '''
    TDMA solver, a b c d can be NumPy array type or Python list type.
    refer to http://en.wikipedia.org/wiki/Tridiagonal_matrix_algorithm
    and to http://www.cfd-online.com/Wiki/Tridiagonal_matrix_algorithm_-_TDMA_(Thomas_algorithm)
    '''
    nf = len(d) # number of equations
    ac, bc, cc, dc = map(np.array, (a, b, c, d)) # copy arrays
    for it in range(1, nf):
        mc = ac[it-1]/bc[it-1]
        bc[it] = bc[it] - mc*cc[it-1] 
        dc[it] = dc[it] - mc*dc[it-1]
        	    
    xc = bc
    xc[-1] = dc[-1]/bc[-1]

    for il in range(nf-2, -1, -1):
        xc[il] = (dc[il]-cc[il]*xc[il+1])/bc[il]

    return xc

I run your code, and got different speed results.

Test results:
[ 0.14877589 0.75612053 -1.00188324 2.25141243]
[ 0.14877589 0.75612053 -1.00188324 2.25141243]
Speed results:
jit_new 6.220152854919434
control 0.7551865577697754

I am not familiar with the numba or jit. So I don't know if it is caused by this. Anyway, thank you for your improvement.

Indeed the code gives wrong answers. I tried to compare the results also.