Re: new to mac OS10

2005-04-15 Thread Jorl Shefner
If you simply want the latest python to run from the command line, then compiling the source code works fine on the mac. I just installed 2.4.1 on Os 10.3.8 last week without any problems. http://www.python.org/ftp/python/2.4.1/Python-2.4.1.tgz "./configure" "make" "make install" J.S. -- http

Minor, but annoying legend problem in matplotlib

2005-02-25 Thread Jorl Shefner
I have a problem that I run into a lot with the 'legend' command's default behavior. I've found a work-around but I wonder if there's a better way. For a simple example, take the following: x= [1,2,3,4,5,6,7,8] a= [5,3,2,4,6,5,8,7] b= [4,1,3,

Re: Reinstall python 2.3 on OSX 10.3.5?

2005-02-03 Thread Jorl Shefner
In article <[EMAIL PROTECTED]>, Robert Kern <[EMAIL PROTECTED]> wrote: > Christian Dieterich wrote: > > On Dé Céadaoin, Feabh 2, 2005, at 17:48 America/Chicago, > > [EMAIL PROTECTED] wrote: > > > >> Hi there > >> > >> I started a very long and roundabout process of attempting to install > >> py

Legend problems in MatPlotLib

2004-12-16 Thread Jorl Shefner
I've only been able to plot data with both symbols and lines by issuing two plot commands, one for markers and one for lines. That's perfectly fine, but it creates a problem when I try to create a legend for it. For some reason, the legend command by default alternates between using symbo

Re: Numeric: 'where' function conditions

2004-11-30 Thread Jorl Shefner
In article <[EMAIL PROTECTED]>, Robert Kern <[EMAIL PROTECTED]> wrote: > > Right. "3 < data" creates an array of 0s and 1s where the condition is > false and true, respectively. You don't need where() at all. > > Try > > mask = logical_and(3 < data, data <= 7) Great. That's exactly what I n

Numeric: 'where' function conditions

2004-11-29 Thread Jorl Shefner
Could anyone tell me the efficient way to do this? Extracting values from an array for a single condition (say all values greater than 'x') using 'where' and 'compress' is simple enough. >>> from Numeric import arange,where,compress >>> data= arange(10) >>> data= [0, 1, 2, 3, 4, 5, 6, 7, 8,