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derivative in numpy

P: n/a
Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.

Cheers,
Kim
Jul 28 '08 #1
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6 Replies

P: n/a
kn********@gmail.com wrote:
Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.
numpy.diff() handles the discrete difference. All you need to do is supply the
scaling factor(s) to interpret it as a derivative.

Again, numpy questions are best asked on the numpy mailing list.

http://www.scipy.org/Mailing_Lists

--
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco

Jul 28 '08 #2

P: n/a
kn********@gmail.com wrote:
Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.
Derivatives are a property of functions. Since numpy provides
representations of arrays not functions, how would you expect this to work?

To be more concrete, what you you expect the derivative of
[ [ 1 2 ]
[ 3 4 ]
]
to be?

Or do you have in mind some array representation of the coefficients of
a function of some pre-defined type - like a polynomial?

Gary Herron
Cheers,
Kim
--
http://mail.python.org/mailman/listinfo/python-list
Jul 28 '08 #3

P: n/a
kn********@gmail.com wrote:
Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.

Cheers,
Kim
numpy and much more are wrapped together in 'sage' and you should get
the functionality you need there.

For a review, see:

http://vnoel.wordpress.com/2008/05/0...n-thanks-sage/
Jul 28 '08 #4

P: n/a
Gary Herron wrote:
kn********@gmail.com wrote:
>Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.

Derivatives are a property of functions. Since numpy provides
representations of arrays not functions, how would you expect this to work?

To be more concrete, what you you expect the derivative of
[ [ 1 2 ]
[ 3 4 ]
]
to be?

Or do you have in mind some array representation of the coefficients of
a function of some pre-defined type - like a polynomial?
Typically, when people ask questions like this, they have a uniform, discretely
sampled version of the function, and they want the
(forward,central,backward)-difference approximation of the derivative.

--
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco

Jul 28 '08 #5

P: n/a
Ken Starks wrote:
kn********@gmail.com wrote:
>Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.

Cheers,
Kim
numpy and much more are wrapped together in 'sage' and you should get
the functionality you need there.

For a review, see:

http://vnoel.wordpress.com/2008/05/0...n-thanks-sage/
Sorry, the review isn't very useful. for something more focused on your
request, see:

http://www.sagemath.org/doc/tut/node13.html
Jul 28 '08 #6

P: n/a
On Jul 28, 5:34*pm, knielse...@gmail.com wrote:
Hi,

I am looking to do a simple derivative. I would expect such a function
to be available in numpy, but can't find it. I have written my own,
but just curious if anybody knows of such function in numpy.

Cheers,
*Kim
I presume you are taking the numerical derivative, not the symbolic
one.
Jul 29 '08 #7

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