On Wed, Oct 28, 2009 at 14:31, Peter Schmidtke pschmidtke@mmb.pcb.ub.es wrote:

Dear Numpy Mailing List Readers,

I have a quite simple problem, for what I did not find a solution for now. I have a gzipped file lying around that has some numbers stored in it and I want to read them into a numpy array as fast as possible but only a bunch of data at a time. So I would like to use numpys fromfile funtion.

For now I have somehow the following code :

f=gzip.open( "myfile.gz", "r" ) xyz=npy.fromfile(f,dtype="float32",count=400)

So I would read 400 entries from the file, keep it open, process my data, come back and read the next 400 entries. If I do this, numpy is complaining that the file handle f is not a normal file handle : OError: first argument must be an open file

but in fact it is a zlib file handle. But gzip gives access to the normal filehandle through f.fileobj.

np.fromfile() requires a true file object, not just a file-like object. np.fromfile() works by grabbing the FILE* pointer underneath and using C system calls to read the data, not by calling the .read() method.

So I tried  xyz=npy.fromfile(f.fileobj,dtype="float32",count=400)

But there I get just meaningless values (not the actual data) and when I specify the sep=" " argument for npy.fromfile I get just .1 and nothing else.

This is reading the compressed data, not the data that you want.

Can you tell me why and how to fix this problem? I know that I could read everything to memory, but these files are rather big, so I simply have to avoid this.

Read in reasonably-sized chunks of bytes at a time, and use np.fromstring() to create arrays from them.