[Numpy-discussion] fromstring, tostring slow?

Mark Janikas mjanikas@esri....
Tue Feb 13 17:44:37 CST 2007

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.
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.

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.  I know using the struct
module that I can pack using either one.  Perhaps I will have to go back
to the drawing board.  I actually love these methods now because I get
back out directly what I put in.  Great kudos to the developers....


-----Original Message-----
From: numpy-discussion-bounces@scipy.org
[mailto:numpy-discussion-bounces@scipy.org] On Behalf Of Christopher
Sent: Tuesday, February 13, 2007 1:39 PM
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] fromstring, tostring slow?

Mark Janikas wrote:
> I am finding that directly packing numpy arrays into binary using the 
> tostring and fromstring methods

For starters, use fromfile and tofile, to save the overhead of creating 
an entire extra string.

fromfile is a function (as it is an alternate constructor for arrays):


ndarray.tofile() is an array method.

Enclosed is your test, including a test for tofile(), I needed to make 
the arrays much larger, and use time.time() rather than time.clock() to 
get enough time resolution to see anything, though if you really want to

be accurate, you need to use the timeit module.

My results:
Using lists 0.457561016083
Using tostring 0.00922703742981
Using tofile 0.00431108474731

Another note: where is the data coming from -- there may be ways to 
optimize this whole process if we saw that.


Christopher Barker, Ph.D.

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