Please, stop reporting carray problems here. Let's communicate
privately if you want.
Thanks,
Francesc
On 12/7/12 8:22 PM, Alvaro Tejero Cantero wrote:
> Thanks Francesc, that solved it. Having the disk datastructures load
> compressed in memory can be a deal-breaker when you got daily 50Gb+
Thanks Francesc, that solved it. Having the disk datastructures load
compressed in memory can be a deal-breaker when you got daily 50Gb+
datasets to process!
The carray google group (I had not noticed it) seems unreachable at the
moment. That's why I am going to report a problem here for the mome
Hmm, perhaps cythonizing by hand is your best bet:
$ cython carray/carrayExtension.pyx
If you continue having problems, please write to the carray mailing list.
Francesc
On 12/7/12 5:29 PM, Alvaro Tejero Cantero wrote:
> I have now similar dependencies as you, except for Numpy 1.7 beta 2.
>
> I
I have now similar dependencies as you, except for Numpy 1.7 beta 2.
I wish I could help with the carray flavor.
--
Running setup.py install for carray
* Found Cython 0.17.2 package installed.
* Found numpy 1.6.2 package installed.
* Found numexpr 2.0.1 package installed.
buildi
On 12/6/12 1:42 PM, Alvaro Tejero Cantero wrote:
> Thank you for the comprehensive round-up. I have some ideas and
> reports below.
>
> What about ctables? The documentation says that it is specificly
> column-access optimized, which is what I need in this scenario
> (sometimes sequential, somet
I'll answer myself on the size-checking: the right attributes are
Leaf.size_in_memory and Leaf.size_on_disk (per
http://pytables.github.com/usersguide/libref/hierarchy_classes.html)
-รก.
On 6 December 2012 12:42, Alvaro Tejero Cantero wrote:
> Thank you for the comprehensive round-up. I have
Thank you for the comprehensive round-up. I have some ideas and reports
below.
What about ctables? The documentation says that it is specificly
column-access optimized, which is what I need in this scenario (sometimes
sequential, sometimes random).
Unfortunately I could not get the rootdir parame
On 12/5/12 7:55 PM, Alvaro Tejero Cantero wrote:
> My system was benched for reads and writes with Blosc[1]:
>
> with pt.openFile(paths.braw(block), 'r') as handle:
> pt.setBloscMaxThreads(1)
> %timeit a = handle.root.raw.c042[:]
> pt.setBloscMaxThreads(6)
> %timeit a = handle.root.raw.c0
My system was benched for reads and writes with Blosc[1]:
with pt.openFile(paths.braw(block), 'r') as handle:
pt.setBloscMaxThreads(1)
%timeit a = handle.root.raw.c042[:]
pt.setBloscMaxThreads(6)
%timeit a = handle.root.raw.c042[:]
pt.setBloscMaxThreads(11)
%timeit a = h
