Just a guess out of my hat: there might be a buffer class in the standard python library... I'm thinking of a class that implements file-I/O and collects input up to a maximum buffer size before it copies the same byte stream to it's output. Since I/O is more efficient if larger chunks are written this should improve the overall performance. How large are your data-chunks per write ? (IOW: what is len(temp.data)) HTH, Sebastian Haase On 7/23/07, Lars Friedrich <lfriedri@imtek.de> wrote:

Hello everyone,

I am using array.tofile successfully for a data-acqusition-streaming application. I mean that I do the following:

for a long time: temp = dataAcquisisionDevice.getData() temp.tofile(myDataFile)

temp is a numpy array that is used for storing the data temporarily. The data acquisition device is acquiring continuously and writing the data to a buffer from which I can read with .getData(). This works fine, but of course, when I turn the sample rate higher, there is a point when temp.toFile is too slow. The dataAcquisitionDevice's buffer will run full before I can fetch the data again.

(temp has a size of ~Mbyte, and the for loop has a period of ~0.5 seconds so that increasing the chunk size won't help)

I have no idea how efficient array.tofile() is. Maybe it is terribly efficient and what I see is just the limitation of my hardware (harddisk). Currently I can stream with roughly 4 Mbyte/s, which is quite fast, I guess. However, if anyone can point me to a way to write my data to harddisk faster, I would be very happy!

Thanks

Lars

-- Dipl.-Ing. Lars Friedrich

Photonic Measurement Technology Department of Microsystems Engineering -- IMTEK University of Freiburg Georges-Köhler-Allee 102 D-79110 Freiburg Germany

phone: +49-761-203-7531 fax: +49-761-203-7537 room: 01 088 email: lfriedri@imtek.de _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion