[SciPy-user] What is fastest load/save matrix methods?
cournape at atr.jp
Tue Dec 20 00:04:56 CST 2005
On Mon, 2005-12-19 at 20:27 -0800, Dave Kuhlman wrote:
> On Tue, Dec 20, 2005 at 10:49:14AM +0900, Cournapeau David wrote:
> > On Mon, 2005-12-19 at 11:01 +0100, Arnd Baecker wrote:
> > > On Mon, 19 Dec 2005, Hugo Gamboa wrote:
> > >
> > > > Hi there,
> > > >
> > > > I need to work with large matrixes that come in ascii format.
> > > >
> > pytables uses hdf5 file format as a storage model, with a high level
> > abstraction when you want to save a lot of data in one file.
> > http://pytables.sourceforge.net/html/WelcomePage.html
> Good suggestion.
> Am I right that PyTables does not *directly* support scipy arrays?
> That's not a big problem. You can write a scipy array to an HDF5
> file using PyTables with something like the following::
> filename = 'testpytables1.h5'
> dataset1 = [[1,2],[3,4],[5,6]]
> h5file = tables.openFile(filename, mode = "w",
> title = "PyTables test file"$
> datasets = h5file.createGroup(h5file.root, "datasets", "Test data sets")
> array1 = scipy.array(dataset1)
> # Write array after converting to a numarray array.
> h5file.createArray(datasets, 'dataset1',
> "Test data set #1")
> And, reading it back in:
> h5file = tables.openFile(filename, 'r')
> dataset1Obj = h5file.getNode('/datasets', 'dataset1')
> dataset1Array = scipy.array(dataset1Obj.read())
> Is there a more efficient way?
Is there no way to convert numarray to scipy more directly than
going through list ? Concerning implementing scipy array support, what
is the difference between scipy array and numarray ?
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