Hi all, I recently noted that absolute values of complex arrays are very slow (about a factor of five compared to some straightforward implementations). I would like to understand the origin, but could not trace the code.
Please, consider these timings: In [67]: z = np.random.random(10000) + 1j*np.random.random(10000) In [68]: %timeit np.abs(z)**2 215 µs ± 2.09 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) In [69]: %timeit ((np.sqrt((z.real**2 + z.imag**2)))**2) 78.2 µs ± 193 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) In [70]: %timeit (z.real**2 + z.imag**2) 40.1 µs ± 196 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) In [71]: %timeit (z.conjugate()*z).real 43.7 µs ± 230 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) Even considering the square root and/or additional square, does not account for the time spent In [72]: %timeit np.abs(z) 206 µs ± 970 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) In [86]: %timeit np.sqrt(z.real**2 + z.imag**2) 70.1 µs ± 303 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) In [73]: %timeit np.sqrt((z.conjugate()*z).real) 105 µs ± 2.23 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) I could not follow the code to understand what calculations are taking place for `abs()`. Any ideas? Regards, Juan _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
