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 <[EMAIL PROTECTED]> 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: [EMAIL PROTECTED] > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion