On Tue, Sep 8, 2009 at 6:41 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
More precisely, 2GB for windows and 3GB for (non-PAE enabled) linux.
And just to further clarify, even with PAE enabled on linux, any
individual process has about a 3 GB address limit (there are hacks to
raise
On 9-Sep-09, at 4:48 AM, Francesc Alted wrote:
Yes, this later is supported in PyTables as long as the underlying
filesystem
supports files 2 GB, which is very usual in modern operating
systems.
I think the OP said he was on Win32, in which case it should be noted:
FAT32 has its
Kim Hansen wrote:
On 9-Sep-09, at 4:48 AM, Francesc Alted wrote:
Yes, this later is supported in PyTables as long as the underlying
filesystem
supports files 2 GB, which is very usual in modern operating
systems.
I think the OP said he was on Win32, in which
A Wednesday 09 September 2009 07:22:33 David Cournapeau escrigué:
On Wed, Sep 9, 2009 at 2:10 PM, Sebastian Haaseseb.ha...@gmail.com wrote:
Hi,
you can probably use PyTables for this. Even though it's meant to
save/load data to/from disk (in HDF5 format) as far as I understand,
it can be
On 9-Sep-09, at 4:48 AM, Francesc Alted wrote:
Yes, this later is supported in PyTables as long as the underlying
filesystem
supports files 2 GB, which is very usual in modern operating
systems.
I think the OP said he was on Win32, in which case it should be noted:
FAT32 has its upper
On Wed, Sep 9, 2009 at 9:30 AM, Daniel
Platzmail.to.daniel.pl...@googlemail.com wrote:
Hi,
I have a numpy newbie question. I want to store a huge amount of data
in an array. This data come from a measurement setup and I want to
write them to disk later since there is nearly no time for this
On Tue, Sep 8, 2009 at 7:30 PM, Daniel Platz
mail.to.daniel.pl...@googlemail.com wrote:
Hi,
I have a numpy newbie question. I want to store a huge amount of data
in an array. This data come from a measurement setup and I want to
write them to disk later since there is nearly no time for
Daniel Platz skrev:
data1 = numpy.zeros((256,200),dtype=int16)
data2 = numpy.zeros((256,200),dtype=int16)
This works for the first array data1. However, it returns with a
memory error for array data2. I have read somewhere that there is a
2GB limit for numpy arrays on a 32 bit
Hi,
you can probably use PyTables for this. Even though it's meant to
save/load data to/from disk (in HDF5 format) as far as I understand,
it can be used to make your task solvable - even on a 32bit system !!
It's free (pytables.org) -- so maybe you can try it out and tell me if
I'm right
Or
On Wed, Sep 9, 2009 at 2:10 PM, Sebastian Haaseseb.ha...@gmail.com wrote:
Hi,
you can probably use PyTables for this. Even though it's meant to
save/load data to/from disk (in HDF5 format) as far as I understand,
it can be used to make your task solvable - even on a 32bit system !!
It's free
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