<jupyter_start><jupyter_text>Gaussian Approximation to B-Spline of order N<jupyter_code>cx = np.random.rand(int(1e8))
gx = cp.random.rand(int(1e8))
N = 1000
%%timeit
cspline = signal.gauss_spline(cx, N)
%%timeit
gspline = cusignal.gauss_spline(gx, N)<jupyter_output>CPU times: user 17.4 ms, sys: 5.72 ms, total: 23.1 ms
Wall time: 21.5 ms<jupyter_text>Cubic B-Spline<jupyter_code>%%timeit
ccspline = signal.cubic(cx)
%%timeit
gcspline = cusignal.cubic(gx)<jupyter_output>CPU times: user 850 ms, sys: 89.2 ms, total: 939 ms
Wall time: 940 ms<jupyter_text>Quadratic B-Spline<jupyter_code>%%timeit
cqspline = signal.quadratic(cx)
%%timeit
gqspline = cusignal.quadratic(gx)<jupyter_output>CPU times: user 146 ms, sys: 77 ms, total: 223 ms
Wall time: 221 ms
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