The module I'm working with, which uses Boost, doesn't have a function
"initcvisual". Rather there's a section headed with
BOOST_PYTHON_MODULE( cvisual). Placing the import_array macro directly
in this section causes an unwanted return.
I guess it doesn't matter, since what I've done works okay. A
Hi Bruce and Chris,
This was a user build and install of Python (particularly 2.6.6 since
2.7.1 has build troubles on CentOS 5). The original python 2.4 in the
system is ignored for this effort because I can't get to it. Since I
was unfamiliar with building Python from source I didn't know i
On Mon, Dec 27, 2010 at 13:09, Bruce Sherwood wrote:
> Thanks for the good suggestion. I now see that it was purely
> historical that import_array was driven (indirectly through
> init_numpy) from the pure Python component of the module rather than
> in the import of the C++ component, and I've ch
Hi,
I have a strange problem with h5py or with numpy.
I try to read a bunch of hdf files in a loop. The problem is that I get
an error at the second file because the file handle is of type
It seems the file is opened and instantaneous closed again. Meanwhile I
found the root of the evil: Firs
Hi Mr. Goodman
Thanks a lot. Works Fine
Reagards
Mario Moura
2010/12/27 Keith Goodman :
> On Mon, Dec 27, 2010 at 10:36 AM, Mario Moura wrote:
>> Hi Folks
>>
>> a = np.zeros((4,3,5,55,5),dtype='|S8')
>> myLen = 4 # here I use myLen = len(something)
>> li = [3,2,4] # li from a list.append(somet
On Mon, Dec 27, 2010 at 10:36 AM, Mario Moura wrote:
> Hi Folks
>
> a = np.zeros((4,3,5,55,5),dtype='|S8')
> myLen = 4 # here I use myLen = len(something)
> li = [3,2,4] # li from a list.append(something)
> sl = slice(0,myLen)
> tmpIndex = tuple(li) + sl + 4 # <== Here my problem
> a[tmpIndex]
>
Hi Folks
a = np.zeros((4,3,5,55,5),dtype='|S8')
myLen = 4 # here I use myLen = len(something)
li = [3,2,4] # li from a list.append(something)
sl = slice(0,myLen)
tmpIndex = tuple(li) + sl + 4 # <== Here my problem
a[tmpIndex]
# So What I want is:
fillMe = np.array(['foo','bar','hello','world'])
Thanks for the good suggestion. I now see that it was purely
historical that import_array was driven (indirectly through
init_numpy) from the pure Python component of the module rather than
in the import of the C++ component, and I've changed that. However,
I'm still curious as to whether there's a
On Sun, Dec 26, 2010 at 17:26, Bruce Sherwood wrote:
> In my Python code I have
>
> import cvisual
> cvisual.init_numpy()
>
> and in my C++ code I have
>
> void
> init_numpy()
> {
> import_array();
> }
The import_array() call goes into the initialization function for your
module, e.g. initcvis
Many thanks to Josef and Justin for their replies.
Josef's hint sounds like a good way of reducing peak memory allocation
especially when the row size is large, which makes the "for" overhead for
each iteration comparatively lower. However, time is still spent in
back-and-forth conversions between
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