Hi Mathieu,
As Anthony indicates, it's hard to discern the exact issue when you don't
provide much in the way of code to look at.
If it helps, here is an example of creating a HDF5 file with a float32 array of
the dimensions you specified. The shape value should be a tuple.
import numpy as
On Jun 06, 2013, at 04:19 AM, Anthony Scopatz scop...@gmail.com wrote:Thanks Antonio and Tim!These are great. I think that one of these should definitely make it into the examples/ dir.Be WellAnthonyOK. I have put up a pull request with the code
I was playing around with in-memory HDF5 prior to the 3.0 release. Here's an example based on what I was doing.I looked over the docs and it does mention that there is an option to throw away the 'file' rather than write it to disk.Not sure how to do that and can't actually think of a use case
My thoughts are:- try it without any compression. Assuming 32 bit floats, your monthly 5760 x 2880 is only about 65MB. Uncompressed data may perform well and at the least it will give you a baseline to work from - and will help if you are investigating IO tuning.- I have found with CArray that the
and for the record...yes, it should be much faster than 4 seconds. foo = np.empty([5760,2880,150],dtype=np.float32) idx = ((5000,600,800,900),(1000,2000,500,1)) import time t0 = time.time();bar=np.vstack([foo[i,j] for i,j in zip(*idx)]);t1=time.time(); print t1-t00.000144004821777On Jun 03, 2013,
The netCDF library gives me a masked array so I have to explicitly transform that into a regular numpy array.Ahh interesting. Depending on the netCDF version the file was made with, you should be able to read the file directly from PyTables. You could thus directly get a normal numpy array. This
%4.1f' % (np.nanmax(sst[0,qual_indices[1],qual_indices[2]]), np.nanmax(sst_node[day]))Would value any comments on this :-)Thanks,Tim Burgess--
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