[Matplotlib-users] performance... plot_date Vs plot

[Peter G. <pgr...@ge...>]

Hi:

I was wondering whether anyone else has noticed a performance difference 
between using plot_date() and plot().
Here is a dummy script:

#!/usr/bin/env python

import time
from matplotlib.dates import EpochConverter
from matplotlib.matlab import *
from matplotlib.ticker import  DateFormatter, DayLocator, HourLocator

useDates=0

now=time.time()
weekAgo=now-60*60*24*7

dates=arange(weekAgo, now, 10)   #Say have a point every 10 secs..
vals=dates

if useDates:
   #Date plot

   fmt=DateFormatter('%D')
   days=DayLocator(1)
   hours=HourLocator(12)

   converter = EpochConverter()

   ax = subplot(111)
   plot_date(dates, vals, converter)

   ax.xaxis.set_major_locator(days)
   ax.xaxis.set_major_formatter(fmt)
   ax.xaxis.set_minor_locator(hours)

   #What will this do??
   #ax.autoscale_view()

else:
   #Regular plot
   plot(dates, vals)

ylabel('Number of points: '+str(len(dates)))
xlabel('time')
grid(True)
show()



I use python2.2 running on a 3.2 PIV with 2GB or ram  and when I use 
regular plotting, this takes ~1sec. With plot_date() it takes ~8sec.

I really like the flexibility of the way dates are handled but this 
seems to be too much of a performance hit for me to use (I ofter have 
data sets of  500 000 points). So my question is whether I am perhaps 
missing something trivial and not setting things up right. I cannot run 
any of the date_demo scripts because don't have python2.3 so cannot test 
how fast they run.

I will need to setup the x-axis labeling as dates, but now it seems it 
would be faster to just update the xticslabels of a "regular" plot.

Any thoughts?
Thanks.

--
Peter Groszkowski         Gemini Observatory
Tel: +1 808 974-2509      670 N. A'ohoku Place
Fax: +1 808 935-9235      Hilo, Hawai'i 96720, USA