On Sun, Oct 30, 2011 at 1:57 PM, Warren Weckesser <warren.weckesser@enthought.com> wrote:
On Sun, Oct 30, 2011 at 12:55 PM, Warren Weckesser <warren.weckesser@enthought.com> wrote:
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
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
Ok. I take back the "nothing else" for the original question since in reading to fast, I misinterpreted the initial question with what I usually need, derivatives of a function in several arguments. Josef
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.
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