Here is a complete example of an analysis script. The data file consists of lines like these with the prompt to type, the hand to use, and the time it took separated by spaces. The lines look like this:

xbafkn 0 1.2505370144
ugqrsmx 1 1.37932912815
jegzf 0 1.09309074306
hrpi 1 0.842401438712
bezud 0 1.10971105873

These data are stored in a folder named a7data in files with names like subjaa, subjab, etc.

This script shows how I might read those data, analyze them, and produce a simple graph.

import numpy as np
import pylab
import os

lengths = [] # lengths of each prompt
hands = []   # hand used to type it
times = []   # time required

# os.listdir will return a list of the names of the files in a folder
# for each filename in the folder
for fname in os.listdir('a7data'):
    # for mac users
    if fname.startswith('.'):
        continue
    # open the file for reading
    fp = file('a7data/' + fname, 'r')
    # for each line in the file
    for line in fp:
        # split it into the three fields
        prompt, hand, time = line.split()
        # convert the hand to an int
        hand = int(hand)
        # and the time to a float
        time = float(time)
        # I'm only interested in the length of the prompt for my analysis
        length = len(prompt)
        # append them to the lists
        lengths.append(length)
        hands.append(hand)
        times.append(time)
# make numpy arrays out of the lists
lengths = np.array(lengths)
hands = np.array(hands)
times = np.array(times)

# create Boolean arrays for the hands
left = hands == 0
right = hands == 1

# plot the times for left and right as points
pylab.plot(lengths[left], times[left], 'bo',
    lengths[right], times[right], 'ro')

# fit a line equation to all the data, should have done the left and right separately
A = np.array([lengths, np.ones_like(lengths)]).T
B = np.array([times]).T

soln = np.linalg.lstsq(A,B)
X = soln[0]

# plot the best fit line
R = np.arange(3,8)
pylab.plot(R, X[0]*R + X[1], 'r')

# print the coefficients of the best fit line
print X

pylab.show()

It has come to my attention that some of you wrote lists of tuples to your data files. These files are pretty hard to take apart yourself but python has some help for you. The eval function will take a string and treat it like python source code. To make it more concrete, suppose my file is named data.txt and the content looks like this:

[('cow',1.34),('dog',1.5)]

I could read it like this:

# open the file, of course I could do this in a loop as in the above example
fp = file('data.txt', 'r')
# read all the content of the file into a string variable named bytes
bytes = fp.read()
# convert that string into a python list
data = eval(bytes)

Now data is a list of tuples of strings and floats.