[SciPy-User] Derivative in scipy?

Warren Weckesser warren.weckesser@enthought....
Sun Oct 30 12:55:03 CDT 2011


On Sun, Oct 30, 2011 at 11:03 AM, <josef.pktd@gmail.com> wrote:

> 2011/10/30 François Boulogne <boulogne.f@gmail.com>:
> >
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> > Dear all,
> >
> > I was wondering if a piece of code has been developped for derivative
> > calculations, espacially for a sample (array of points) like for
> > integration:
> > http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html with
> > different methods (right or left first derivatives, second
> derivatives...)
> > I didn't succeed in finding this in the documentation. Does it exist? If
> > not, is it planned by someone?
>
> There is one helper function in scipy.optimize
> (scipy.optimize.optimize), nothing else in scipy.
>


Well, not exactly "nothing else"...
(eat's email arrived as I was typing this, so it will echo some of what he
said.)

Functions that operate on a discrete sample:

numpy.diff
    This can be used to compute a derivative by dividing by the appropriate
    power of dx.

numpy.ediff1d
    Like numpy.diff, but strictly for 1D arrays.  It also provides the
option
    for specifying values to append to the ends of the array before
computing
    the difference.

numpy.gradient
    Return the gradient of an n-d array.

scipy.fftpack.diff
    Derivative of a periodic sequence.
    See http://www.scipy.org/Cookbook/KdV for an example.


Functions that operate on a callable function:

scipy.misc.derivative
    Find the n-th derivative of a function at point x0.

scipy.misc.central_difference_weights
    Return weights for an Np-point central derivative

scipy.optimize.approx_fprime
    No docstring (sigh), but from the source code (use approx_fprime?? in
    ipython), it is pretty easy to figure out what it does.


Having said that, I think a module specifically for computing derivatives
(with good docs and tests), as being discussed in the ticket #1510 (
http://projects.scipy.org/scipy/ticket/1510) would be a nice addition.


Warren



>
> My standard recommendation for finite differences is numdifftools,
> it's on pypi. There is a ticket that asks for it's inclusion in scipy,
> IIRC.
>

> There are some packages on automatic differentiation.
>
> (and we have our own hacked together numdiff in statsmodels, just for
> optimization and Hessian calculations.)
>
> Josef
>
>
> >
> > Thanks.
> > Cheers,
> >
> > - --
> > François Boulogne.
> >
> > Membre de l'April - Promouvoir et défendre le logiciel libre
> > http://www.april.org
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