Mon, 15 Jun 2009 09:52:05 -0700, David Goldsmith kirjoitti:
[clip]
Is there a protocol for making sure these things get done? (Just don't
want to reinvent the wheel.)
I don't think so.
The current way is that people forget to do it, and then someone fixes it
afterwards :)
I'm not sure how
Hi! I'm looking at trying to bind a rather large (150K lines of code)
crystallography library to python and would like to know what the state of
F2py is. Are allocatable arrays supported? Derived types? Modules,
Pointers, etc.? Is there a list somewhere? Has anyone else looked into
wrapping
Good day.
I have this array:
a = array([[u'0', u'0', u'0', u'0', u'0', u' '],
[u'1', u'1', u'1', u'1', u'1', u' '],
[u'2', u'2', u'2', u'2', u'2', u' '],
[u'3', u'3', u'3', u'3', u'3', u' '],
[u'4', u'4', u'4', u'4', u'4', u'']],
dtype='U1')
I want to resize
I would be interested in testing your GSOC project and will do what I can in
the mean time. I do develop on windows, but the library lives on linux,
macos, and windows, so we can test on anyg--it also binds with ifort,
gfortran, etc. so seems rather robust.
Cheers,
William
On Tue, Jun 16, 2009
http://projects.scipy.org/numpy/ticket/1096
Is the fix to this to check if (line 95 of
trunk/numpy/core/src/umathmodule.c.srchttp://projects.scipy.org/numpy/changeset/6000/trunk/numpy/core/src/umathmodule.c.src
)
const @type@ tmp = x - y;
is -inf or not. And if it is, just to return -inf.
On 2009-06-16, Brian Lewis brian.lewi...@gmail.com wrote:
http://projects.scipy.org/numpy/ticket/1096
Is the fix to this to check if (line 95 of
trunk/numpy/core/src/umathmodule.c.srchttp://projects.scipy.org/numpy/changeset/6000/trunk/numpy/core/src/umathmodule.c.src
)
const @type@ tmp
Ian Mallett wrote:
n = #blah
testlist = []
for x in xrange(n):
for y in xrange(n):
testlist.append([x,y])
testlist.append([x+1,y])
If testlist is an array (i.e., I could go: array(testlist)), it
works nicely. However, my Python method is certainly improveable
On 06/16/2009 02:18 PM, Robert wrote:
n = 10
xx = np.ones(n)
yy = np.arange(n)
aa = np.column_stack((xx,yy))
bb = np.column_stack((xx+1,yy))
aa
array([[ 1., 0.],
[ 1., 1.],
[ 1., 2.],
[ 1., 3.],
[ 1., 4.],
[ 1.,
Neil Martinsen-Burrell wrote:
On 06/16/2009 02:18 PM, Robert wrote:
n = 10
xx = np.ones(n)
yy = np.arange(n)
aa = np.column_stack((xx,yy))
bb = np.column_stack((xx+1,yy))
aa
array([[ 1., 0.],
[ 1., 1.],
[ 1., 2.],
[ 1., 3.],
On 2009-06-16 16:05 , Robert wrote:
Neil Martinsen-Burrell wrote:
On 06/16/2009 02:18 PM, Robert wrote:
n = 10
xx = np.ones(n)
yy = np.arange(n)
aa = np.column_stack((xx,yy))
bb = np.column_stack((xx+1,yy))
aa
array([[ 1., 0.],
[ 1., 1.],
I'm not sure it's worth having a function to replace a one-liner
(column_stack followed by reshape). But if you're going to implement
this with slice assignment, you should take advantage of the
flexibility this method allows and offer the possibility of
interleaving raggedly, that is, where the
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