On 27/02/15 17:50, Charles R Harris wrote:
>
>
> On Fri, Feb 27, 2015 at 9:02 AM, Mads Ipsen <mailto:mads.ip...@gmail.com>> wrote:
>
> Hi,
>
> If I build Python 2.7.2 and numpy-1.9.1 and run the following script
>
> import numpy
>
nd run 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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On 13/10/14 01:18, Nathaniel Smith wrote:
> On Mon, Oct 13, 2014 at 12:07 AM, Pauli Virtanen 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
has 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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| Mads
f isinstance(o, T)]
>
> In [18]: objs
> Out[18]: [()]
>
> In [19]: a = 123.
>
> In [20]: objs = [o for o in gc.get_objects() if isinstance(o, float)]
>
> In [21]: objs
> Out[21]: []
>
> So I guess nothing is tracked, unless it con
advance.
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 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 l
= 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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5, 3, 4, 11, 0, 0, 1, 2, 3, 4, 5]
> ])
>
> a = [1,2,3,7,8]
>
> keepdata = np.ones(12, dtype=np.bool)
> keepdata[a] = False
> w = np.where(keepdata[b[0]] & keepdata[b[1]])
> newindex =
b = [[0, 0, 1, 1, 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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Is there a doc somewhere where 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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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 <mailto:madsip...@gmail.com>> wrote:
Hi,
I simply can't understand this. I'm trying to use argsort to
produce indices that can be us
[[ 4 3]
[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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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(
ads
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| Mads Ipsen |
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remember
that.)
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Thanks - very helpful!
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On 11/04/2012 13:42, Warren Weckesser wrote:
On Wed, Apr 11, 2012 at 4:28 AM, Mads Ipsen <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(0,4
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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| Mads Ipsen
, wouldn't '0.0' instead of '0.' be more readable (and there's
certainly space enough to add it)?
Best regards,
Mads
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| Mads Ipsen |
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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
&g
(i.e. 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
ally nasty?
Best regards,
Mads
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oiding copies when it can, for instance -- this give efficiency and
flexibility, but you do need to be careful, particularly when
interfacing with the binary data directly.
-Chris
Thanks for all the answers to my question. Helped a lot.
Mads
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= 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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Thanks - very cool!
On 10/01/2012 21:45, Val Kalatsky wrote:
A - np.digitize(A, S)
Should do the trick, just make sure that S is sorted and A and S do
not overlap,
if they do remove those items from A using set operations.
Val
On Tue, Jan 10, 2012 at 2:14 PM, Mads Ipsen <mailto:mad
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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
__
ards
Mads
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| Mads Ipsen |
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| Denmark | email
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| Mads Ipsen |
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| Denmark | email: mads.ip
On 11/08/2011 12:14 PM, David Cournapeau wrote:
On Tue, Nov 8, 2011 at 9:20 AM, Mads Ipsen wrote:
Yup, that fixes it. For now, we can apply a temporary fix on our build
system. Is this something that'll go into, say, 1.6.2?
That's more of a workaround than a fix. We need to decide
On 11/08/2011 10:01 AM, David Cournapeau wrote:
Hi Mads,
On Tue, Nov 8, 2011 at 8:40 AM, Mads Ipsen wrote:
Hi,
I am trying to build numpy-1.6.1 with the following gcc compiler specs:
Reading specs from /usr/lib/gcc/x86_64-redhat-linux/3.4.6/specs
Configured with: ../configure --prefix=/usr
l/quantumsource/external-libs/src/numpy-1.6.1/make-stamp]
Error 1
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Robert Kern wrote:
> On Fri, Sep 25, 2009 at 17:38, Mads Ipsen 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]))
&
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()
>>
])
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()
[[[1, 4], [1, 5], [1, 6]], [[2, 4], [2, 5], [2, 6]], [[3, 4], [3, 5],
[3, 6]]]
--
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| Mads Ipsen, Scientific developer |
+
Gökhan Sever wrote:
>
>
> On Fri, Sep 25, 2009 at 12:45 PM, Mads Ipsen <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,
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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| Mads Ipsen, Scientific developer
current SVN, because apparently the configuration
> detection code is not strict C89.
>
>
Well, I don't know you consider this as a valid argument, but to me its
a matter of removing a single comma, which will make the source les
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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| Mads Ipsen,
4.3.2-1ubuntu12)
Ubuntu 8.10
Best regards,
Mads
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