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[Matplotlib-users] Re: [SciPy-user] Numerical gradient approximation on matrix

 [Matplotlib-users] Re: [SciPy-user] Numerical gradient approximation on matrix From: Alan G Isaac - 2005-07-29 16:06:55 ```On Fri, 29 Jul 2005, Dimitri D'Or apparently wrote: > I have a two-dimensional array from which I wish to > compute the gradient (i.e. the slope against the first and > second dimension). With Matlab, I can do it easily using > the gradient.m function. Is there something similar in > Scipy or matplotlib? I've browsed the documentation but > couldn't found anything but approximate gradient > computations on functions in the optimize module. Nothing > about computations on matrices. Look at scipy.diff. E.g., for the two dimensions grad0=scipy.diff(x,axis=0) grad1=scipy.diff(x,axis=1) hth, Alan Isaac ```

 [Matplotlib-users] Numerical gradient approximation on matrix From: Dimitri D'Or - 2005-07-29 08:57:53 Attachments: Message as HTML ```Hi all, I have a two-dimensional array from which I wish to compute the gradient (i.e. the slope against the first and second dimension). With Matlab, I can do it easily using the gradient.m function. Is there something similar in Scipy or matplotlib? I've browsed the documentation but couldn't found anything but approximate gradient computations on functions in the optimize module. Nothing about computations on matrices. Thank you for your help, Dimitri ```
 [Matplotlib-users] Re: [SciPy-user] Numerical gradient approximation on matrix From: Alan G Isaac - 2005-07-29 16:06:55 ```On Fri, 29 Jul 2005, Dimitri D'Or apparently wrote: > I have a two-dimensional array from which I wish to > compute the gradient (i.e. the slope against the first and > second dimension). With Matlab, I can do it easily using > the gradient.m function. Is there something similar in > Scipy or matplotlib? I've browsed the documentation but > couldn't found anything but approximate gradient > computations on functions in the optimize module. Nothing > about computations on matrices. Look at scipy.diff. E.g., for the two dimensions grad0=scipy.diff(x,axis=0) grad1=scipy.diff(x,axis=1) hth, Alan Isaac ```