Hi Christian, Thank you for your input! I prefer np.gradient because it takes mid-point finite difference estimation instead of one-sided estimates, but np.diff() is also a good idea. Just wondering why np.gradient does not have something similar, being curious :)
Shawn On Thu, May 1, 2014 at 6:42 PM, Christian K. <ckk...@hoc.net> wrote: > Am 01.05.14 18:45, schrieb Yuxiang Wang: >> Hi all, >> >> I am trying to calculate the 2nd-order gradient numerically of an >> array in numpy. >> >> import numpy as np >> a = np.sin(np.arange(0, 10, .01)) >> da = np.gradient(a) >> dda = np.gradient(da) > > It looks like you are looking for the derivative rather than the > gradient. Have a look at: > > np.diff(a, n=1, axis=-1) > > n is the order if the derivative. > > Christian > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Yuxiang "Shawn" Wang Gerling Research Lab University of Virginia yw...@virginia.edu +1 (434) 284-0836 https://sites.google.com/a/virginia.edu/yw5aj/ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
