xyzt: compute and plot time cost scaling for functions with numpy array input
I often implement functions on atom positions in terms of Nx3 numpy arrays,
and need to compare speed of different implementations.
This small library is a thin wrapper over timeit for that purpose.
xyzt.timethem(functions_to_check, nmin=10, nmax=1e5, number=1e5) -> (n, times)
xyzt.plot(n, times, labels=None)
for fi in functions:
for n in valid_range:
generate random n-dimensional input vector
timeit fi(v_rand)
example: scipy.spatial.distance.cdist+min versus scipy.spatial.cKDTree
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