Functions (organizing inline code)

[bratiskovci]

1.How do I get functions to help me simplify access to the data structure.
2. How do I get functions to encapsulate the various calculations that I have to make.
3. How do I get functions to print out the computed information.

import sys
subjects = ["A", "B", "L", "Z", "Q", "T", "V"]
 
# Trial Data
trials = [["Trial 1", [ 178, 206, 271, 254, 261, 218, 255]],
          ["Trial 2", [ 206, 215, 221, 244, 218, 271, 215]],
          ["Trial 3", [ 237, 298, 215, 233, 224, 216, 195]],
          ["Trial 4", [ 198, 273, 219, 241, 218, 279, 234]],
          ["Trial 5", [ 234, 226, 302, 263, 217, 275, 216]],
          ["Trial 6", [ 217, 256, 227, 227, 299, 234, 229]]
         ]
 
 
# Print out trial averages
for trial in trials:
    trialSum = 0.0
 
    for measures in trial[1]:
        trialSum = trialSum + measures
    print "Average for %s is %.3f mg/dL." % (trial[0], trialSum/len(trial[1]))
 
 
# Add spacing between data groups for better visual appearance.
print ""
 
# print out subject averages
for subject in subjects:
    subjectIndex = subjects.index(subject)
    subjectSum = 0.0
 
    # compute average for subject over all trials
    for trial in trials:
        subjectSum = subjectSum + trial[1][subjectIndex]
 
    # Print out average
    sys.stdout.write( "Average for subject %s is %.3f mg/dL.  " %
                      (subject, subjectSum/len(trials)))
 
    # Classify subject
    if subjectSum/len(trials)>240:
        print "Subject %s is high risk." % subject
    elif subjectSum/len(trials)>200:
        print "Subject %s is borderline high risk." % subject
    else:
        print "Subject %s is low risk." % subject

[bartonc]

This is a great question! With your code organized like this, you will be able to change the way you get your data more easily.

import sys
def GetSubjects():
    return ["A", "B", "L", "Z", "Q", "T", "V"]
def GetTrialData():
    # Trial Data
    return [["Trial 1", [ 178, 206, 271, 254, 261, 218, 255]],
             ["Trial 2", [ 206, 215, 221, 244, 218, 271, 215]],
             ["Trial 3", [ 237, 298, 215, 233, 224, 216, 195]],
             ["Trial 4", [ 198, 273, 219, 241, 218, 279, 234]],
             ["Trial 5", [ 234, 226, 302, 263, 217, 275, 216]],
             ["Trial 6", [ 217, 256, 227, 227, 299, 234, 229]]
             ]
 
def PrintTrailAverage(trials):
    # Print out trial averages
    for trial in trials:
        trialSum = 0.0
        for measures in trial[1]:
            trialSum = trialSum + measures
        print "Average for %s is %.3f mg/dL." % (trial[0], trialSum/len(trial[1]))
    # Add spacing after this data group for better visual appearance.
    print
def PrintSubjectAverage(subjects, trials):
    # print out subject averages
    for subject in subjects:
        subjectIndex = subjects.index(subject)
        subjectSum = 0.0
        # compute average for subject over all trials
        for trial in trials:
            subjectSum = subjectSum + trial[1][subjectIndex]
        # Print out average
        sys.stdout.write( "Average for subject %s is %.3f mg/dL. " %
                         (subject, subjectSum/len(trials)))
        # Classify subject
        if subjectSum/len(trials)>240:
            print "Subject %s is high risk." % subject
        elif subjectSum/len(trials)>200:
            print "Subject %s is borderline high risk." % subject
        else:
            print "Subject %s is low risk." % subject
    # Add spacing after this data group for better visual appearance.
    print
def MainProgram():
    s = GetSubjects()
    td = GetTrialData()
    PrintTrailAverage(td)
    PrintSubjectAverage(s, td)
MainProgram()
 

Now that you see how it's done, I'll bet you can move the calculations to fuctions... Have fun and keep posting,
Barton