[Solved] Fast Python Exponitial Function for curve_fit: Numpy Express gives slower results

scientificCompNoob Asks: Fast Python Exponitial Function for curve_fit: Numpy Express gives slower results
I am fitting exponential data with python in real time. Many millions of calls to my exponential decay function “fexp”, so speeding it up could greatly improve performance. I recently stumbled upon numexpr module but it seems to be tailored to larger arrays. Using it in my fit routine gives worse performance. Timeit results of the two fit functions:

Code:
import numexpr as ne
t=np.arange(0,1400)
a0,a1,a2=[-0.034913174979286636, 0.634038654906443, 0.010961607601301245]

%timeit (a0 + a1*np.exp(-t*a2))
25.5 µs ± 225 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit ne.evaluate('(a0 + a1*exp(-t*a2))')
33.1 µs ± 570 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

import numba as nb
@nb.njit(error_model="numpy",parallel=True)
def fexp_nb(t,a0,a1,a2):
    return a0 + a1*np.exp(-t*a2)

%timeit fexp_nb(t,a0,a1,a2)
26.4 µs ± 974 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Presumably this is due to numpy evaluates compilation overhead. Is there a way to precompile? Or another optimization I could make (change my chunksize to the length of my fit array? I didnt see any such options in the documentation.

I have also run across cpython and numba, would these be suitable for speeding up scipy fit? (Edit: Numba also doesnt seem to help, see results above) Here is the relevant fit code where y is raw data:

Code:
from scipy.optimize import curve_fit
...
x=np.arange(0,leny-syfd)
(M,pcov)=curve_fit(fexp, x, y[syfd:], p0=(M,), sigma=None, method='lm', ftol=0.001, xtol=0.001)

Also the format of the fexp functions:

Code:
def fexp(t,a0,a1,a2):
    return a0 + a1*np.exp(-t*a2)
def fexp_ne(t,a0,a1,a2):
    return ne.evaluate('a0 + a1*exp(-t*a2)')

This is already being run with a multiprocessing routine. The current limit is speed of fexp computation. Any help or insight optimizing exponential calls in python would be appreciated.

Edit: here is an example y for completeness:

Code:
array([ 6.77582760e-01,  6.71805572e-01,  6.62855412e-01,  6.54604329e-01,
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