On Thu, Aug 25, 2011 at 23:27, Kibeom Kim wrote:
> Hello,
>
> Q1. Is lazy loading ndarray from user defined data supplying function
> possible?
No, not really.
> Q2. If possible, how can I implement it?
>
>
> The closest method I can think of is, (which requires c++ posix)
>
> 1. create a memor
Hello,
Q1. Is lazy loading ndarray from user defined data supplying function possible?
Q2. If possible, how can I implement it?
The closest method I can think of is, (which requires c++ posix)
1. create a memory region using mmap and protect read operation by mprotect.
2. add SIGSEGV signal han
On 2011-08-25, at 2:42 PM, Chris.Barker wrote:
> On 8/24/11 9:22 AM, Anthony Scopatz wrote:
>>You can use Python pickling, if you do *not* have a requirement for:
>
> I can't recall why, but it seem pickling of numpy arrays has been
> fragile and not very performant.
>
> I like the npy / np
Paul Menzel wrote:
> Dear NumPy folks,
>
>
> is there an easy way to also save the indexes of an array (columns, rows
> or both) when outputting it to a text file. For saving an array to a
> file I only found `savetxt()` [1] which does not seem to have such an
> option. Adding indexes manually is d
Dear NumPy folks,
is there an easy way to also save the indexes of an array (columns, rows
or both) when outputting it to a text file. For saving an array to a
file I only found `savetxt()` [1] which does not seem to have such an
option. Adding indexes manually is doable but I would like to avoid
On 8/24/11 9:22 AM, Anthony Scopatz wrote:
> You can use Python pickling, if you do *not* have a requirement for:
I can't recall why, but it seem pickling of numpy arrays has been
fragile and not very performant.
I like the npy / npz format, built in to numpy, if you don't need:
> - acc
I've written some introductory documentation for the NA-masked arrays. The
patch is here:
https://github.com/m-paradox/numpy/commit/227e39c34b0e5d9dfde2bbce054b5a8ac088fd64
This is approaching the end of what I will implement for NA masks at the
moment. I think the system is quite usable as is, t
Hi,
I would like to know a bit about how the installation process works. Could
you point me to a resource. In particular I want to know how the site.cfg
configuration works. Is it numpy/scipy specific or is it standard with
distutils. I googled for site.cfg and distutils but did not find any
auth
