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Hi,
I don't know if this is of importance, but when I compile code using the
numpy C API, I get the warning:
site-packages/numpy/core/include/numpy/__multiarray_api.h:1532: warning:
'int _import_array()' defined but not used
Might be worth cleaning it up.
Best regards,
Mads
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.
However, if I do:
V = numpy.array(U.transpose()).transpose()
and call the C++ routine, everything is perfectly fine, ie. the data
structure is as expected.
What went wrong?
Best regards,
Mads
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. PyArray_DIMS and
PyArray_DATA could become functions that are looked up in an API-table
that must be loaded by import_array() ).
Best regards,
-Travis
On Feb 14, 2012, at 3:03 AM, Mads Ipsen wrote:
Hi,
I have C++ module (OpenGL) that extracts data from numpy arrays. The
interface is pure
On 14/02/2012 10:30, Pauli Virtanen wrote:
14.02.2012 10:20, Mads Ipsen kirjoitti:
[clip]
* Should import_array() only be called one time, namely when the main
application is started?
It should be called once when the application is started, before you do
any other Numpy-using operations
readable (and there's
certainly space enough to add it)?
Best regards,
Mads
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, is there some nifty numpy function that can
generate the above slices for me (or their start and stop values)?
Best regards,
Mads
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On 11/04/2012 13:42, Warren Weckesser wrote:
On Wed, Apr 11, 2012 at 4:28 AM, Mads Ipsen madsip...@gmail.com
mailto:madsip...@gmail.com wrote:
Hi,
Suppose a have an array of indices, say
indices = [0,1,2,3,5,7,8,9,10,12,13,14]
Then the following slices
a = slice
that.)
___
NumPy-Discussion mailing list
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Thanks - very helpful!
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Sebastian - thanks - very helpful.
Best regards,
Mads
On 10/01/2013 12:06, Sebastian Berg wrote:
On Thu, 2013-01-10 at 11:32 +0100, Mads Ipsen wrote:
Hi,
I find this to be a little strange:
x = numpy.arange(10)
isinstance(x[0],int)
gives True
y = numpy.where(x 5)[0
]
[11 6]]
[[ 8 9]
[23 7]]
[[11 6]
[ 8 9]]
[[23 7]
[ 1 12]]]
I thought this should produce a sorted version of the indices array.
Any help is appreciated.
Best regards,
Mads
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towards the
correct solution.
Best regards,
Mads
On 01/15/2013 09:53 PM, eat wrote:
Hi,
On Tue, Jan 15, 2013 at 1:50 PM, Mads Ipsen madsip...@gmail.com
mailto:madsip...@gmail.com wrote:
Hi,
I simply can't understand this. I'm trying to use argsort to
produce indices that can
this whole thing
is explained in detail. I must admit, its somewhat hard to grasp
what's going on.
Best regards,
Mads
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, 2, 2, 2, 3, 5, 6],
[2, 3, 5, 2, 1, 6, 0, 0, 1, 2]]
The Python for loop in (*) may easily contain 50.000 iteration. Is there
a smart way to utilize numpy functionality to avoid this?
Thanks and best regards,
Mads
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= np.where(keepdata[b[0]] keepdata[b[1]])
newindex = keepdata.cumsum()-1
c = newindex[b[:,w[0]]]
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= numpy.array([a1,a2,a3])
How can open up the doors to the array data of b on the C-side?
Best regards,
Mads
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On 02/07/14 12:46, Julian Taylor wrote:
On Wed, Jul 2, 2014 at 12:15 PM, Mads Ipsen mads.ip...@gmail.com wrote:
Hi,
If you setup an M x N array like this
a = 1.0*numpy.arange(24).reshape(8,3)
you can access the data from a C function like this
void foo(PyObject * numpy_data
4.3.2-1ubuntu12)
Ubuntu 8.10
Best regards,
Mads
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Hey,
I recently posted a bug related to a compile error in the header file
'npy_common.h' but have received no responses so far.
Am I posting this in the wrong mailing list?
Best regards,
Mads
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a single comma, which will make the source less
sensitive to compilers and compiler flags.
Mads
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Is there a numpy operation on two arrays, say [1,2,3] and [4,5,6], that
will yield:
[[(1,4),(1,5),(1,6)],[(2,4),(2,5),(2,6)],[(3,4),(3,5),(3,6)]]
Any suggestions are most welcome.
Mads
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Gökhan Sever wrote:
On Fri, Sep 25, 2009 at 12:45 PM, Mads Ipsen m...@comxnet.dk
mailto:m...@comxnet.dk wrote:
Is there a numpy operation on two arrays, say [1,2,3] and [4,5,6],
that
will yield:
[[(1,4),(1,5),(1,6)],[(2,4),(2,5),(2,6)],[(3,4),(3,5),(3,6)]]
Any
).reshape(n,m)
b = numpy.repeat(b,n).reshape(m,n).transpose()
ab = numpy.dstack((a,b))
print ab.tolist()
[[[1, 4], [1, 5], [1, 6]], [[2, 4], [2, 5], [2, 6]], [[3, 4], [3, 5],
[3, 6]]]
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Alan G Isaac wrote:
On 9/25/2009 4:01 PM, Mads Ipsen wrote:
a = numpy.array([1,2,3])
b = numpy.array([4,5,6])
(n,m) = (a.shape[0],b.shape[0])
a = numpy.repeat(a,m).reshape(n,m)
b = numpy.repeat(b,n).reshape(m,n).transpose()
ab = numpy.dstack((a,b))
print ab.tolist()
That's
Robert Kern wrote:
On Fri, Sep 25, 2009 at 17:38, Mads Ipsen m...@comxnet.dk wrote:
Yes, but it should also work for [2.1,3.2,4.5] combined with
[4.6,-2.3,5.6] - forgot to tell that.
In [5]: np.transpose(np.meshgrid([2.1,3.2,4.5], [4.6,-2.3,5.6]))
Out[5]:
array([[[ 2.1, 4.6
in advance.
Best regards,
Mads
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+0200, Mads Ipsen wrote:
Hi,
I am trying to inspect the reference count of numpy arrays
generated by
my application.
Initially, I thought I could inspect the tracked objects
using
gc.get_objects
been invoked on the matrix, and thereby only do the copy operation
when it really is needed? For example
if a_has_transposed_data:
foo(a.copy())
else:
foo(a)
Best regards,
Mads
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On 13/10/14 01:18, Nathaniel Smith wrote:
On Mon, Oct 13, 2014 at 12:07 AM, Pauli Virtanen p...@iki.fi wrote:
12.10.2014, 22:16, Eric Firing kirjoitti:
On 2014/10/12, 8:29 AM, Pauli Virtanen wrote:
12.10.2014, 20:19, Mads Ipsen kirjoitti:
Is there any way for me to detect (on the Python side
the
script, the evaluation of the 'dot' increases the memory by app. 450 MB.
Is the expected?
Best regards,
Mads
specs:
Ubuntu 12.04
ifort (IFORT) 14.0.1 2013100
gcc version 4.6.3 (Ubuntu/Linaro 4.6.3-1ubuntu5)
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