Hi,
I am trying to use my own data type with NumPy, but get some funny
result when creating a NumPy array with it.
My data type is indexable and sliceable and what happens now is when I
create an array, NumPy is adding the instance as a list of the indexed
values. How can I force NumPy to handle
I want to change the status of a numpy array.
I mean this array was created by a server application using
PyArray_FromDimsAndData that sets the NPY_OWNDATA flag to False.
The server application believes the client would free the memory.
But there are more than one client application and none
Hi there,
I am personnally a bit annoyed by the way trac handle bug reports,
and would like to know if there is space for improvement. I do not know
much about bug tracking systems, so maybe I just don't know how to use
it, though. The main thing I dislike is the status of tickets and
A related question, just out of curiosity: is there a technical
reason why Numpy has been coded in C rather than C++?
Joris
On 05 Sep 2007, at 02:24, David Goldsmith wrote:
Anyone have a well-tested SWIG-based C++ STL valarray = numpy.array
typemap to share? Thanks!
DG
--
I'm using the numpy C API (PyArray_SimpleNewFromData) to perform the
conversion but my code is written by hands. I would like to simplify it
using SWIG but I also would like to see a good typemap valarray =
numpy.array :)
Joris : Historical ones? Maybe also the fact that distutils has some
Have you seen this?
http://www.scipy.org/Cookbook/SWIG_and_NumPy
Also, the numpy/doc/swig directory has the simple typemaps.
Travis
On Sep 5, 2007, at 7:08 AM, Xavier Gnata wrote:
I'm using the numpy C API (PyArray_SimpleNewFromData) to perform the
conversion but my code is written by
I was surprised to see that an in-place modification of a 2-d array
turns out to be slower from the respective non-mutating operation on 1-
d arrays, although the latter creates new array objects. Here is the
benchmarking code:
import timeit
for n in 10,100,1000,1:
setup = 'from
I have been considering adding some C++ STL support to numpy/doc/swig/
numpy.i. Probably std::vectorTYPE = PyArrayObject (and some
std::complexTYPE support as well). Is this what you had in mind?
On Sep 4, 2007, at 6:24 PM, David Goldsmith wrote:
Anyone have a well-tested SWIG-based C++
Point of clarification: below well-tested = well-use-tested, not
(necessarily) well-unit-tested.
DG
David Goldsmith wrote:
Anyone have a well-tested SWIG-based C++ STL valarray = numpy.array
typemap to share? Thanks!
DG
--
ERD/ORR/NOS/NOAA
No I hadn't - thanks! (Probably should have Google-d first, huh. :-[ )
DG
Travis Vaught wrote:
Have you seen this?
http://www.scipy.org/Cookbook/SWIG_and_NumPy
Also, the numpy/doc/swig directory has the simple typemaps.
Travis
On Sep 5, 2007, at 7:08 AM, Xavier Gnata wrote:
I'm
Not presently, as the C++ code I need to wrap now is using the valarray
class template (largely at my behest), though I (and I imagine others)
might find this useful in the future.
DG
Bill Spotz wrote:
I have been considering adding some C++ STL support to
numpy/doc/swig/numpy.i. Probably
A Wednesday 05 September 2007, George Sakkis escrigué:
I was surprised to see that an in-place modification of a 2-d array
turns out to be slower from the respective non-mutating operation on
1- d arrays, although the latter creates new array objects. Here is
the benchmarking code:
import
Hi,
I have two questions:
1) Is there any way in numpy to represent vectors? Currently I'm using
'array' for vectors.
2) Is there a way to calculate the magnitude (length) of a vector in numpy?
Thanks.
___
Numpy-discussion mailing list
On Wed, Sep 05, 2007 at 11:55:36AM -0500, Robert Dailey wrote:
1) Is there any way in numpy to represent vectors? Currently I'm using
'array' for vectors.
What do you call a vector ? For me a vector is an element of an linear
space. In numerical methods what is comonly called a vector is
2007/9/5, Robert Dailey [EMAIL PROTECTED]:
Hi,
I have two questions:
1) Is there any way in numpy to represent vectors? Currently I'm using
'array' for vectors.
A vector is an array with one dimension, it's OK. You could use a matrix of
dimension 1xn or nx1 as well.
2) Is there a way
Günter Dannoritzer wrote:
My data type is indexable and sliceable and what happens now is when I
create an array, NumPy is adding the instance as a list of the indexed
values. How can I force NumPy to handle my data type as an 'Object'
Object arrays are tricky, 'cause it's hard for numpy to
In article [EMAIL PROTECTED],
Bill Spotz [EMAIL PROTECTED] wrote:
I have been considering adding some C++ STL support to numpy/doc/swig/
numpy.i. Probably std::vectorTYPE = PyArrayObject (and some
std::complexTYPE support as well). Is this what you had in mind?
That sounds very useful,
Joris De Ridder wrote:
A related question, just out of curiosity: is there a technical
reason why Numpy has been coded in C rather than C++?
There was a fair bit of discussion about this back when the numarray
project started, which was a re-implementation of the original Numeric.
IIRC, one
On 9/5/07, Vincent Broman [EMAIL PROTECTED] wrote:
Harris asked about long doubles.
On my YDL, SIZEOF_LONG_DOUBLE and sizeof( long double) were both 8.
Hmm, so long doubles are just doubles, I kinda suspected that. I'm not
really familiar with this code, but what happens if you go to
David Cournapeau wrote:
Hi there,
I am personnally a bit annoyed by the way trac handle bug reports,
and would like to know if there is space for improvement. I do not know
much about bug tracking systems, so maybe I just don't know how to use
it, though. The main thing I dislike is
On Sep 5, 2007, at 11:19 AM, Christopher Barker wrote:
Bill Spotz wrote:
I have been considering adding some C++ STL support to numpy/doc/
swig/
numpy.i. Probably std::vectorTYPE = PyArrayObject (and some
std::complexTYPE support as well). Is this what you had in mind?
well,
On Sep 5, 2007, at 11:38 AM, Christopher Barker wrote:
Of course, it should be possible to write C++ wrappers around the core
ND-array object, if anyone wants to take that on!
boost::python has done this for Numeric, but last I checked, they
have not upgraded to numpy.
** Bill Spotz
Bill Spotz wrote:
Yes, this resizing memory management issue is the main reason I haven't
tried to implement it in numpy.i yet.
thinking out loud
A possibly better solution would be to develop a class that inherits
from std::valarrayTYPE but also implements the array interface
attributes
Hello,
'len' is a (pretty basic) python builtin function for getting the
length of anything with a list-like interface. (Or more generally,
getting the size of anything that is sized, e.g. a set or dictionary.)
Numpy arrays offer a list-like interface allowing you to iterate
along their
2007/9/5, Robert Dailey [EMAIL PROTECTED]:
Thanks for your response.
I was not able to find len() in the numpy documentation at the following
link:
http://www.scipy.org/doc/numpy_api_docs/namespace_index.html
Perhaps I'm looking in the wrong location?
Yes, it's a Python function ;)
Oh I think I get it.
You mean the built-in len() function? This isn't what I am looking for.
len() returns the number of components in the vector (e.g. whether it is a
2D, 3D, etc vector). I found that magnitude can be calculated using hypot()
in the math module that comes with python. However,
He goes on to suggest that Blitz++ might have more of a future. (though
it looks like he's involved with the Boost project now)
Blitz++ is more or less avandoned. It uses indexes than can be not-portable
between 32bits platforms and 64bits ones.
Is there another alternative? At the moment,
On 9/6/07, Christopher Barker [EMAIL PROTECTED] wrote:
Bill Spotz wrote:
However, I'm beginning to have my doubts about valarrays. I'm reading:
Josuttis, Nicolai M. 1999. The C+= Standard Library: A Tutorial and
Reference
It's 8 years old now, but he writes:
The valarray classes were not
Robert Dailey wrote:
Thanks for your response.
I was not able to find len() in the numpy documentation at the following
link:
http://www.scipy.org/doc/numpy_api_docs/namespace_index.html
http://www.scipy.org/doc/numpy_api_docs/namespace_index.html
Perhaps I'm looking in the wrong
maybe numpy.vdot is good for you.
In [3]: x = numpy.random.rand(4)
In [4]: x
Out[4]: array([ 0.45426898, 0.22369238, 0.98731244, 0.7758774 ])
In [5]: numpy.sqrt(numpy.vdot(x,x))
Out[5]: 1.35394615117
hth,
lorenzo
On 9/5/07, Robert Dailey [EMAIL PROTECTED] wrote:
Oh I think I get it.
--- Robert Kern [EMAIL PROTECTED] wrote:
Besides constructing the Euclidean norm itself (as
shown by others here), you
can also use numpy.linalg.norm() to calculate any of
several different norms of
a vector or a matrix:
Right. linalg.norm also gives the proper magnitude of
complex
Hi,
I have a scalar value S. I want to perform the following math on vectors A
and B (both of type array):
A + B * S
By order of operations, B * S should be done first. This is a vector
multiplied by a real number and should be valid. However, the interpreter
outputs:
ValueError: shape
Robert Dailey wrote:
Hi,
I have a scalar value S. I want to perform the following math on vectors
A and B (both of type array):
A + B * S
By order of operations, B * S should be done first. This is a vector
multiplied by a real number and should be valid. However, the
interpreter
Christopher Barker wrote:
[...]
The solution is to make an empty object array first, then populate it.
[...]
Does that help?
Robert, Chris, thanks for that explanation. I understand that now.
The purpose of my (Python) class is to model a fixed point data type. So
I can specify how many
Günter Dannoritzer wrote:
Christopher Barker wrote:
[...]
The solution is to make an empty object array first, then populate it.
[...]
Does that help?
Robert, Chris, thanks for that explanation. I understand that now.
The purpose of my (Python) class is to model a fixed point data
Günter Dannoritzer wrote:
The purpose of my (Python) class is to model a fixed point data type. So
I can specify how many bits are used for integer and how many bits are
used for fractional representation.
it would be possible to create a
list of my FixedPoint instances and then assign that
Matthieu Brucher wrote:
Blitz++ is more or less avandoned. It uses indexes than can be
not-portable between 32bits platforms and 64bits ones.
Oh well -- that seems remarkably short sited, but would I have done better?
The Boost.Array is a fixed-size array, determined at compile-time,
Ah, I
Christopher Barker writes:
I've seen that -- it does look like all we'd need is the header.
So, can one:
- create a Multiarray from an existing data pointer?
- get the data pointer for an existing Multiarray?
I think that's what I'd need to make the numpy array -
Philip Austin wrote:
Albert Strasheim has done some work on this:
http://thread.gmane.org/gmane.comp.python.c++/11559/focus=11560
Thanks for the pointer. Not a lot of docs, and it looks like he's using
boost::python, and I want to use SWIG, but I'm sure there's something
useful in there.
Travis Vaught wrote:
Have you seen this?
http://www.scipy.org/Cookbook/SWIG_and_NumPy
Unclear (to me): precisely what does one get from running python
numpy/docs/swig/setup.py install, and is the product necessary, and if
so, which other components rely on the product? I ask 'cause I'm
Christopher Barker wrote:
[...]
Why would a FixPoint object have to look like a sequence, with a length
and a _getitem_? That's where the confusion is coming from.
That allows me to slice bits.
If I understand your needs, a FixPoint object is a number -- you'll want
to override __add__
The setup.py script in numpy/doc/swig is for compiling test code for
numpy.i. It is properly invoked by the Makefile, which will first
run swig to generate the wrapper code for the test classes. All a
developer, who is using swig to interface some code with numpy in
python, needs is
| Date: Wed, 05 Sep 2007 21:19:58 +0200
| From: G?nter Dannoritzer [EMAIL PROTECTED]
| Subject: Re: [Numpy-discussion] Use my own data type with NumPy
|
| The purpose of my (Python) class is to model a fixed point data
| type. So I can specify how many bits are used for integer and how
|
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