Python multiprocessing plots

Often I need to create lots of plots. Wouldn't it be great if you could create all the plots at the same time rather than one by one. Summon the multiprocessing module! 

##---- Multiprocessing -------------------------------------------------------------------##

## There are a lot of data files for the two days of TDWR data that need to be plotted.

## We want to make these plots really fast, so use the multiprocessing module

## to create a separate plot on each available processor.

##      1) Place the plotting script in a function. 

##         The input (can only take one) is the file name of the data to be plotted

##      2) In the main program (bottom section of this script) 

##         a) creat a list of the file names (this simple loop is very fast)

##         b) count the number

##         c) creat the pool object p = multiprocessing.Pool(num_proc)

##         d) send each list item to the plot function with the pool function

## This method makes 475 plots in a few minutes rather than over an hour!!!

##---- Multiprocessing -------------------------------------------------------------------##

import multiprocessing #:)

import matplotlib.pyplot as plt

import numpy as np

#etc.

def make_plot(i):    

    # i is the name of the file we will open

    print i

    radar_time = i[8:23]

    

    DATETIME = datetime.datetime.strptime(radar_time,"%Y%m%d_%H%M%S")

    mesowest_time = DATETIME.strftime("%Y%m%d%H%M")

    

    string_time = DATETIME.strftime('%d %b %Y  %H:%M UTC')

    

    

    ### TDWR Radar Data (converted from .nids to .asc grid)

    text = 'SLC_TV0_'+radar_time+'.asc'

    this = np.flipud(np.genfromtxt(text,skip_header=6)) #flip the array

    # etc. plotting functions

## This is the good stuff...utilizing multiprocessors

if __name__ == '__main__':

    # List of file names we want to make plots for

    somelist = []

    for i in os.listdir('./'):

        if i[0:3]=='SLC' and i[-3:]=='asc': # get the file name of a TDWR file for all files

            somelist.append(i)      

    # Count number of processors

    num_proc = multiprocessing.cpu_count()

    p = multiprocessing.Pool(num_proc)

    p.map(make_plot,somelist)