Mark Janikas wrote:
> I don't think I can do that because I have heterogeneous rows of
> data.... I.e. the columns in each row are different in length.
like I said, show us your whole problem...
But you don't have to write.read all the data at once with from/tofile()
anyway. Each of your "rows" has to be in a separate array anyway, as
numpy arrays don't support "ragged" arrays, but each row can be written
with tofile()
> Furthermore, when reading it back in, I want to read only bytes of the
> info at a time so I can save memory. In this case, I only want to have
> one record in mem at once.
you can make multiple calls to fromfile(), thou you'll have to know how
long each record is.
> Another issue has arisen from taking this routine cross-platform....
> namely, if I write the file on Windows I cant read it on Solaris. I
> assume the big-little endian is at hand here.
yup.
> I know using the struct
> module that I can pack using either one.
so can numpy. see the "byteswap" method, and you can specify a
particular endianess with a datatype when you read with fromfile():
a = N.fromfile(DataFile, dtype=N.dtype("<d"), count=20)
reads 20 little-endian doubles from DataFile, regardless of the native
endianess of the machine you're on.
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
[EMAIL PROTECTED]
_______________________________________________
Numpy-discussion mailing list
[email protected]
http://projects.scipy.org/mailman/listinfo/numpy-discussion
