[SciPy-User] Derivative in scipy?

[Warren Weckesser]

On Sun, Oct 30, 2011 at 12:55 PM, Warren Weckesser <
warren.weckesser at enthought.com> wrote:

>
>
> On Sun, Oct 30, 2011 at 11:03 AM, <josef.pktd at gmail.com> wrote:
>
>> 2011/10/30 François Boulogne <boulogne.f at gmail.com>:
>> >
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>> > Hash: SHA1
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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
>


Correction:  I put this in the wrong list;  central_difference_weights is
just a utility function for computing weights (as the name says). It does
not compute derivatives of a callable function.

Warren



> 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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