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A slightly different approach to live plotting. I have a numerical
program that calls a status function every run through the main loop. It
allows the user to define a status function and pass it that function
(defaulting to just a one-line report on sys.err).
Using GTK idle slowed things down way too much, because GTK was always
idle, even though the number cruncher wasn't. GTK timeout didn't work
either. Using threads was too complicated and error prone, for no gain.
What I wanted was a meter that updated every second or so.
Here is my status function. It shows traces for the best score so far, and
the current score. If enough time has elapsed, it plots (thus scheduling
GTK events), otherwise no. So the gtk.events_pending() call near the end
is usually false.
It rescales the graph too, as required. You can ignore the "interrupted"
stuff -- that allows me to send a "stop" signal to the numerical routine,
saying "Ok, it looks to me like you've converged, stop now."
	-Charles
----------
def my_status_func(*args, **kwargs):
 """ Gather info from a running snob, and plot it.
 It expects the final arg to be a dictionary with
 the following keyword arguments:
 fig -- the matplot figure for drawing to
 ax -- the matplot axes in that figure
 t1 -- a list of length 1, with timestamp of last refresh
 iteratns -- sequence of all iteration #s to date (x axis)
 currlens -- sequence of all current lengths to date
 bestlens -- sequence of all best lenghts to date
 All of these kwargs will be modified, at least sometimes.
 """
 #global currlens, bestlens, iteratns, t
 UPDATE_INTERVAL = 1 # how long in s to wait before updating graph
 itr = args[0] + 1
 max_itr = args[1]
 cl = int (args[2][0] / N.log(2))
 bl = int (args[3][0] / N.log(2))
 #cl = int (curr[0] / N.log(2))
 #bl = int (best[0] / N.log(2))
 # Extract variables from kwargs
 t1 = kwargs['t1']
 iteratns = kwargs['iteratns']
 currlens = kwargs['currlens']
 bestlens = kwargs['bestlens']
 iteratns.append (itr)
 currlens.append (cl)
 bestlens.append (bl)
 if itr == 1:
 print "Original length: ", cl
 delta = time.time() - t1[0]
 if (itr > 1) and ( (delta > UPDATE_INTERVAL) or (itr >= max_itr) ):
 # matplot has trouble if we plot just a single data point
 # so we wait for itr > 1
# if not ShowOn().get():
# ShowOn().set(True)
 fig = kwargs['fig']
 ax = kwargs['ax']
 xlo, xhi = ax.get_xlim()
 xlo = max ( 0, xlo) # Check bc original call is ignored
 xhi = max (50, xhi) # Likewise
 if itr > xhi:
 xhi *= 2
 ax.set_xlim ([xlo, xhi])
 ax.set_ylim ( [ min(currlens), max(currlens) ] )
 plot(iteratns, currlens, 'b',
 iteratns, bestlens, 'r')
 fig.draw() # schedule a refresh
 t1[0] = time.time()
 # Display any updates to the figure
 while gtk.events_pending():
 gtk.mainiteration() # if running in normal gtk thread
 global interrupted
 if interrupted:
 return 1
 return snob.default_status_func(*args)

Pretty!