I recently tried diff and gradient for some
medical time domain data, and the result nearly looked like pure noise.
I just found this after seeing John Agosta's post
https://gist.github.com/mblondel/487187
Find the solution for the second order differential equation
u'' = -u
with u(0) = 10
Hi Chris,
Thank you! This is useful information. Unfortunately, I am doing this
on data from a sensor and would be hard to fit to a simple polynomial
while avoiding overfitting.
Thanks again!
Shawn
On Thu, May 1, 2014 at 7:01 PM, Chris Barker chris.bar...@noaa.gov wrote:
On Thu, May 1, 2014
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
On Thu, May 1, 2014 at 6:00 PM, Yuxiang Wang yw...@virginia.edu wrote:
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
Shawn (Yuxiang) -
The right way to compute this is using Runga-Kutta approximations. I'm not
aware if numpy supports these. -jm
__
John Mark Agosta
650 465-4707
johnmark.ago...@gmail.com *Unpredictable consequences are the most
expected thing on earth.*
*
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)
This is what I come up. Is the the way it should be done?
I am asking this, because in numpy
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
On Thu, May 1, 2014 at 3:42 PM, Christian K. ckk...@hoc.net wrote:
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.
depending on your use case, you may want to use a polynomial fit for a
