Hi, I read a file with array data (one per line), and send the arrays to a
module in C. In the C module, I need to store pointers to the arrays, so I
don't have to make a copy for each array it receives from python.
I found that if I reuse the same variable name to create the array, the
Hi
I'm trying to make a python class to be used in object arrays
(specifically, an mpfr type for multiprecision). Numpy lets me create
member functions like 'cos' which get called elemenwise when I call cos(a)
on an object array a. However, this doesn't work for some functions, like
isnan.
I believe in your current setup there is no better way. But you should
seriously consider changing the way of using array data. Storing bare
pointers in the C side and not holding a reference to the object
providing the data in the C side is really error prone.
On 6/3/08, Jose Martin [EMAIL
Lisandro Dalcin wrote:
I believe in your current setup there is no better way. But you should
seriously consider changing the way of using array data. Storing bare
pointers in the C side and not holding a reference to the object
providing the data in the C side is really error prone.
exactly.
On Tue, Jun 3, 2008 at 3:21 AM, Hanni Ali [EMAIL PROTECTED] wrote:
Hi David,
I compiled numpy with MSVC 9.0 (vs 2008), I am just using the inbuilt LA
libs to minimise complexity.
Although I have hacked it such that I can compile and all but one of the
regression tests passed:
Thanks for the fast responses!
data. Storing bare pointers in the C side and not holding a
reference to the object providing the data in the C side is really
error prone.
It's true, I don't do it because I have to process a large number of arrays,
and each has thousands of elements; so I
Its probably something simple I don't understand but...
I've written a dummy function which takes an array m. I'd like it to
return a changed array m_i, and not change the initial array m. I
call it with mm = dummy(m);
3 from numpy import *;
4 def dummy(m):
5 m_o =
Hi,
m is a variable.
m_o refers to m.
m_i refers to m_o which is m.
dummy refers to m.
dummy2 and dummy3 refer to m_o which is m.
So when you modify m_i, you are modifying the variable refered by m_i, m and
also m_o, dummy, dummy2 and dummy3.
It's always the same object, with different names.
On Tue, Jun 3, 2008 at 4:19 PM, Payton Gardner [EMAIL PROTECTED] wrote:
Its probably something simple I don't understand but...
I've written a dummy function which takes an array m. I'd like it to return
a changed array m_i, and not change the initial array m. I call it with mm
= dummy(m);
I have just tried to run the 1.1.0 OSX installer on a MacBookAir
running 10.5.3 and the installer fails with
You cannot install numpy 1.1.0 on this volume. numpy requires System
Python 2.5 to install.
The system python version reports as
jaroslav$ python
Python 2.5.1 (r251:54863, Apr 15 2008,
Payton,
In your example, use
m_o = m.copy()
or
m_o = m + 0
or
m_o = numpy.array(m, copy=True)
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Where is your python located I have installed numpy 1.1.0 using the
binary installer successfully on 10.5.3 but I am using ActiveState
python. I think the problem might be that the installer looks in
/Library/Frameworks/Python.framework/ for python, while the standard
python is located somewhere
2008/6/3 Robert Kern [EMAIL PROTECTED]:
Python does not copy data when you assign something to a new variable.
Python simply points the new name to the same object. If you modify
the object using the new name, all of the other names pointing to that
object will see the changes. If you want a
Robert Kern wrote:
The presence of these functions should have been detected by the
configuration process of numpy. HAVE_FREXPF and HAVE_LDEXPF would have
been #define'd if we had detected them correctly. It is possible that
our configuration process for this does not work correctly with VS
On Tue, Jun 3, 2008 at 9:49 PM, David Cournapeau
[EMAIL PROTECTED] wrote:
Robert Kern wrote:
The presence of these functions should have been detected by the
configuration process of numpy. HAVE_FREXPF and HAVE_LDEXPF would have
been #define'd if we had detected them correctly. It is possible
On Tue, Jun 3, 2008 at 8:49 PM, David Cournapeau
[EMAIL PROTECTED] wrote:
Robert Kern wrote:
The presence of these functions should have been detected by the
configuration process of numpy. HAVE_FREXPF and HAVE_LDEXPF would have
been #define'd if we had detected them correctly. It is
Jaroslav,
The installer works with the MacPython from python.org, not Apple's python
(the one that ships with Leopard).
The MacPython is installed in the /Library/Frameworks... It should work if
your python is here:
cburns$ python -c import sys; print sys.prefix
Hi,
I thought it might be useful to summarize the different ways to use
numpy's indexing, slice and fancy. The document so far is here:
http://www.scipy.org/Cookbook/Indexing
While writing it I ran into some puzzling issues. The first of them
is, how is Boolean indexing supposed to work when
Pierre,
I believe if you rename your TimingTests they'll work. Nose looks for
functions starting with test, and runs those. So your 'utility' functions
like testta, testtb... should not begin with test, but the function
calling them, timingTest, should. Probably want to use more meaningful
Is there a way to get the range of a numpy type? I'd like to clamp a
parameter to be within the range of a numpy type, np.uint8, np.uint32...
Something like:
if x max_value_of(np.uint8):
x = max_value_of(np.uint8)
--
Christopher Burns
Computational Infrastructure for Research Labs
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