On 27.06.2011, at 7:11PM, Nils Becker wrote:
>>> Finally, the former Scientific.IO NetCDF interface is now part of
>>> scipy.io, but I assume it only supports netCDF 3 (the documentation
>>> is not specific about that). This might be the easiest option for a
>>> portable data format (if Matlab sup
Hi,
>> Finally, the former Scientific.IO NetCDF interface is now part of
>> scipy.io, but I assume it only supports netCDF 3 (the documentation
>> is not specific about that). This might be the easiest option for a
>> portable data format (if Matlab supports it).
> Yes, it is NetCDF 3.
In recent
On 27.06.2011, at 6:36PM, Robert Kern wrote:
>> Some late comments on the note (I was a bit surprised that HDF5 installation
>> seems to be a serious hurdle to many - maybe I've just been profiting from
>> the fink build system for OS X here - but I also was not aware that the
>> current netCDF
On Mon, Jun 27, 2011 at 11:17, Derek Homeier
wrote:
> On 21.06.2011, at 8:35PM, Christopher Barker wrote:
>
>> Robert Kern wrote:
>>> https://raw.github.com/numpy/numpy/master/doc/neps/npy-format.txt
>>
>> Just a note. From that doc:
>>
>> """
>> HDF5 is a complicated format that more or less
On Tue, Jun 21, 2011 at 13:35, Christopher Barker wrote:
> Robert Kern wrote:
>> https://raw.github.com/numpy/numpy/master/doc/neps/npy-format.txt
>
> Just a note. From that doc:
>
> """
> HDF5 is a complicated format that more or less implements
> a hierarchical filesystem-in-a-file. Thi
On 21.06.2011, at 8:35PM, Christopher Barker wrote:
> Robert Kern wrote:
>> https://raw.github.com/numpy/numpy/master/doc/neps/npy-format.txt
>
> Just a note. From that doc:
>
> """
> HDF5 is a complicated format that more or less implements
> a hierarchical filesystem-in-a-file. This f
Robert Kern wrote:
> https://raw.github.com/numpy/numpy/master/doc/neps/npy-format.txt
Just a note. From that doc:
"""
HDF5 is a complicated format that more or less implements
a hierarchical filesystem-in-a-file. This fact makes satisfying
some of the Requirements difficult. To
Hi,
I have been using h5py a lot (both on windows and Mac OSX) and can only
recommend it- haven't tried the other options though
Cheers,
Simon
On Tue, Jun 21, 2011 at 8:24 PM, Derek Homeier <
de...@astro.physik.uni-goettingen.de> wrote:
> On 21.06.2011, at 7:58PM, Neal Becker wrote:
>
On 21.06.2011, at 7:58PM, Neal Becker wrote:
> I think, in addition, that hdf5 is the only one that easily interoperates
> with
> matlab?
>
> speaking of hdf5, I see:
>
> pyhdf5io 0.7 - Python module containing high-level hdf5 load and save
> functions.
> h5py 2.0.0 - Read and write HDF5 fi
Neal Becker wrote:
>> I'm wondering what are good choices for fast numpy array serialization?
>>
>> mmap: fast, but I guess not self-describing?
>> hdf5: ?
>> pickle: self-describing, but maybe not fast?
>> others?
>
> I think, in addition, that hdf5 is the only one that easily interoperates
> wi
Neal Becker wrote:
> I'm wondering what are good choices for fast numpy array serialization?
>
> mmap: fast, but I guess not self-describing?
> hdf5: ?
Should be pretty fast, and self describing -- advantage of being a
standard. Disadvantage is that it requires an hdf5 library, which can b
a pa
On Tue, Jun 21, 2011 at 12:49, Neal Becker wrote:
> I'm wondering what are good choices for fast numpy array serialization?
>
> mmap: fast, but I guess not self-describing?
> hdf5: ?
> pickle: self-describing, but maybe not fast?
> others?
NPY:
http://docs.scipy.org/doc/numpy/reference/generated/
Neal Becker wrote:
> I'm wondering what are good choices for fast numpy array serialization?
>
> mmap: fast, but I guess not self-describing?
> hdf5: ?
> pickle: self-describing, but maybe not fast?
> others?
I think, in addition, that hdf5 is the only one that easily interoperates with
matlab?
I'm wondering what are good choices for fast numpy array serialization?
mmap: fast, but I guess not self-describing?
hdf5: ?
pickle: self-describing, but maybe not fast?
others?
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