Code as a Liberal Art, Spring 2025

Unit 1, Lesson 3 — Thursday, February 6

Table of contents

Topics today: (Click each to jump down to the corresponding section.)

1. Background: Experiments in digital formalism
2. Some comments on theory & practice
3. Homework review
   1. A bug in our code
   2. A test case
   3. One solution
   4. Another solution
   5. Debugging with a variable value table [Added after class]
4. Variability: User input via the command line
5. Images in Python
6. Algorithms and Images: combination
7. Wrapping up & homework

I. Background: Experiments in digital formalism

Today's lesson builds really nicely on our Tuesday discussion. Recall that for Tuesday's class, we read and discussed texts that considered what we're calling formal properties of digital media. Today we will be working with coding techniques that afford you the opportunity to experiment with these formal properties by manipulating digital objects with computer program code.

In particular, we're going to experiment with code that illustrates a few ideas from what Manovich called the five principles of digital media: digital objects as numerically encoded, modularity in the form of re-usable files and chunks of code, transcoding from one file format to another, and the easy variability of digital objects. In the background will be ideas from Kittler about encoding, and ideas about glitch techniques.

This week and next will also offer you opportunities for creative expression through code. With these techniques you'll be creating code to generate a variable range of creative possibilities, rather than producing digital objects yourself. In this sense, we can start thinking about algorithms as automating some aspect of creative production.

II. Some comments on theory & practice

Working with this material in the context of a hands-on theory/practice class such as this affords a unique opportunity to think carefully and critically about what the digital is on a conceptual level, and then hold up those theories to the scrutiny of hands-on work with these objects of conceptual inquiry.

The hands-on work this week will manifest in relation to our conceptual work in a few ways. First we might think of it as a kind of "test" of the theory. We might ask: do those theoretical ideas actually apply when we start looking closely at digital objects and lines of code? Perhaps you'll find that your own hands-on work offers you critical insights into blindspots in the theoretical ideas that we read this week. Notably, some of the scholars who we read this week themselves have technical backgrounds and have been known to conduct their own hands-on experiments in the process of their research and scholarship (cf. the coding work of Friedrich Kittler). Second, we might think of our hands-on work as examples that illustrate some of the theoretical ideas with concrete cases. And finally, I hope that this hands-on work shapes and informs your thinking about the theoretical work this week, allowing you develop even more sophisticated conceptual understanding by virtue of the hands-on exercises.

III. Homework review

But first let's start with some review and go over how to complete last week's homework.

I think the most interesting part of our review discussion was in regards to part c of the homework assignment: Using the modulo operator (%) to write code that finds the smallest even number in a list.

The initial solution we dicussed (which is the same or similar to what most of you came up with) is the following:

from unit1_lesson2_data import *

smallest_even_number = number_list[0]

for n in number_list:
    if n < smallest_even_number and n % 2 == 0:
            smallest_even_number = n

print("The smallest even number in this list is: " + str(smallest_even_number) )

a. A bug in our code

But as we discussed in class, while this code works for the data list that I provided (in unit1_lesson2_data) it has a potential error if run on a different input.

b. A test case

Here is a shorter sample list of numbers to illustrate this:

 number_list = [ 3, 6, 8, 10, 7, 5, 9 ]

The key thing here is that the first number is an odd number, and it is smaller than all the even numbers. In a case like this, the above algorithm will not work because when it initially sets smallest_even_number to number_list[0], it will set it to 3, and when the code loops through the list, as it encounters small even numbers, those numbers will never be smaller than 3, so the code will never consider them to be smallest even number, and so when the loop finishes running, smallest_even_number will still be 3, which is wrong because this is an odd number.

c. One solution

One solution is to start by initially setting smallest_even_number to a number guaranteed to be larger than all the numbers in the list. As I wrote in the homework assignment, number_list consists of "a list of 1,000 numbers between 0 and 1000". So this would be:

from unit1_lesson2_data import *

# A solution
smallest_even_number = 1000

for n in number_list:
    if n < smallest_even_number and n % 2 == 0:
            smallest_even_number = n

print("The smallest even number in this list is: " + str(smallest_even_number) )

This code will give us the correct answer because every number we encounter will be smaller than the initial value, so when we first encounter an even number, that will be set to smallest_even_number (and as we continue scanning through the list, we may find smaller even numbers).

d. Another solution

Another solution entails first looping over the list to find an even number, without checking whether it is greater or less than anything. Once we find the first even number in the list, set that to smallest_even_number, and then go on with our code, looking for potentially smaller even numbers. My version of that is here:

from unit1_lesson2_data import *

# Another solution:
for n in number_list:
    if n % 2 == 0:
        smallest_even_number = n
        break

for n in number_list:
    if n < smallest_even_number and n % 2 == 0:
            smallest_even_number = n

print("The smallest even number in this list is: " +
str(smallest_even_number) )

This uses a new bit of syntax with the break command. This command tells Python to exit out of any loop it might be currently in. So this code finds the first even number in the list, and then proceeds as before.

e. Debugging with a variable value table [Added after class]

We also talked about how you might try to understand or debug an algorithm by stepping through it line-by-line and keeping a variable value table. Returning for a moment to the exercise from the homework that asked you to find the smallest number in a list that was great than some value (in this case 500), we can try to understand the logic of the below solution by creating a table that keeps track of the variable values at each stage of execution. Let's assume we're working with the below input in the number_list variable, and step through this code:

number_list = [ 100, 653, 501, 408, 520 ]

smallest_number = number_list[0]
for n in number_list:
    if smallest_number < 500 or (n < smallest_number and n > 500):
        smallest_number = n

Keeping track of each value as we step through each iteration of the for loop would yield something like this:

      smallest_number      n
      100                  -
      100                  100
      100
      100                  653
      653
      653                  501
      501
      501                  408
      501
      501                  520
      501                  

Hopefully that might help shed some light on how this algorithm is operating, and how it gets the right answer.

(jump back up to table of contents)

IV. Variability: User input via the command line

To start with some new material, let's look at how you can leverage the power of variability within the command line interface to make your programs more dynamic, and able to respond to user input.

In the context of the command line, we process user input as arguments: bits of text that follow after the command that you type. You have already seen and used arugments on the command line. For example, cd Coding. In this case, the string Coding is an argument to the cd command. It tells the cd command what to do — which directory to change to. Without the argument, the cd command would only ever be able to do one thing. To be useful, we want to be able to indicate to this command which directory we want to change to. That is what command line arguments are for.

Python makes it very easy to work with command line arguments. By using the sys library, which is short for "system". This is a library that contains various utilities for dealing with the computer and operating system within which your code is running.

To get started, make a new folder for this week. I recommend calling it Unit 1 Lesson 3. Create a new file in VS Code, name it arguments.py, save it into this new folder, and put the below code into it.

Start with these few lines:

import sys

print( sys.argv )

print( len(sys.argv) )

Now run this new program by clicking the 'Play' button in VS Code. You should see something like this in the terminal:

$ /usr/local/bin/python3 "/Users/rory/Code as a Liberal Art/Coding/Unit 1 Lesson 3/arguments.py"
['/Users/rory/Code as a Liberal Art/Coding/Unit 1 Lesson 3/arguments.py']
1

That is showing you the command that VS Code just ran, and the results of the two print statements. In this case, sys.argv (a list of all the arguments specified on the command line) contains only the name of your Python file. And you can see that the length of this list is 1.

Now, instead of clicking 'Play' in VS Code, try running the program by typing the Python command on your system (the thing underlined in orange above), then the name of your file (probably arguments.py) and then a few other words separated by spaces. You should see something like this:

$ python3 arguments.py gritty sharkie gnash
['arguments.py', 'gritty', 'sharkie', 'gnash']
4

This is showing the sys.argv list, consisting of the name of my Python file plus the three additional arguments that I've typed. And the next line is showing that the length of sys.argv is now 4.

Beyond just printing out the arguments, you can actually use them in your code. Modify argments.py so it includes this code, and run this to see that in action:

import sys

data = [ 643, 452, 230, 219, 962, 532 ]

index = int(sys.argv[1])

print( data[index] )

Output:

$ python3 arguments.py 1
452
$ python3 arguments.py 2
230

This code is taking the first argument, converting it to a number (with int()), and using that as the index to access a value in my list. Now your code can change its behavior based on some user input.

Of course, if the user specifies an invalid value, you may get an error. In the first example below, I'm specifying a number that's too large, and in the second example below I'm omitting any argument entirely. These are the error messages you would see in those situations.

$ python3 arguments.py 9
Traceback (most recent call last):
  File "arguments.py", line 7, in <module>
    print( data[index] )
IndexError: list index out of range

$ python3 arguments.py 
Traceback (most recent call last):
  File "arguments.py", line 5, in <module>
    index = int(sys.argv[1])

You can improve on this with some error handling code that checks for valid input and gives the user a helpful message if there's a problem:

import sys

data = [ 643, 452, 230, 219, 962, 532 ]

# check if the user did not enter an argument
if len(sys.argv) < 2:
    print("You forgot to type an argument")
    exit()

index = int(sys.argv[1])
# check if the user entered an argument that is too large
if index > len(data) - 1:
    print("You typed a number that's too large")
    exit()

print( data[index] )

$ python3 arguments.py 
You forgot to type an argument

$ python3 arguments.py 10
You typed a number that's too large

Note that in the above error handling example, if the user has entered an invalid value, the code uses the exit() command. This tells Python to stop running your program with executing any other commands.

We'll use this technique in more interesting ways later.

(jump back up to table of contents)

V. Images in Python

One of the discussion points this week was about numerical encoding: the idea that all digital objects, in some sense or another, are represented within digital machines with numerical values. One way we can see this is by examining digital images.

In Python, probably the easiest way to work with digital images is to work with PIL (the Python Image Library). In coding, a library is a collection of commands bundled together under one name, that all work together. There are libraries for doing networking operations (cf. Code Toolkit), for working with human languages (cd. Coding Natural Language), and for other things like working with sounds, encryption, etc.

PIL is currently implemented by a project called Pillow, so that is the name we need to reference when installing this library. All of us should have this installed on our computers at this point. (If not, see the Homework 0 for how to do that.)

Now, let's try to use some command line arguments to access a digital image file from Python.

Create a new file in the same folder as above. Let's name it images.py. Add the following lines.

import sys
from PIL import Image

img = Image.open( sys.argv[1] )

print("You typed the filename: " + sys.argv[1] )
print("This is a " + img.format)
print(img.format_description)
print("Size: " + str(img.size) )

Now, add an image file to this directory. You can copy any image from your computer into this folder. If you need an image to work with, you can use this one: fire.jpg. Just right-click on that, click "Save Link As" (or your browser's equivalent) and save the image into your Unit 1 Lesson 3 folder, or whatever you called it.

Now run your Python program, but as above, don't click the "Play" button. Instead, click into the Terminal (if you don't have a terminal window open, from the menu click Terminal > New Terminal) and then run the program manually by typing your system's Python command and the name of the above file (probably images.py). For me that generates the following output:

$ python3 arguments.py fire.jpg 
You typed the filename: fire.jpg
This is a JPEG
JPEG (ISO 10918)
Size: (720, 480)

This isn't too interesting but it is using the Python Image Library to access the image file and tell us some basic information about it.

What if the user forgets to specify an argument? You would see an error like the below:

 python3 arguments_image.py 
Traceback (most recent call last):
  File "arguments_image.py", line 4, in <module>
    img = Image.open( sys.argv[1] )
IndexError: list index out of range

Let's add some error handling for this situation:

import sys
from PIL import Image

if len(sys.argv) != 2:
    exit("This command requires one argument: the name of an image file")

img = Image.open( sys.argv[1] )

print("You typed the filename: " + sys.argv[1] )
print("This is a " + img.format)
print(img.format_description)
print("Size: " + str(img.size) )

Of course, if the user types a filename that doesn't exist, you will still receive an error. Or if you type a file that is not an image, you will also receive an error. (Something like No such file or directory.) We can talk about how to handle this error later.

This is just a very brief sample of working with images using the Python Image Library, and all we had time for today. Next week we'll pick up right here and see some more sophisticated (and fun!) techniques for manipulating and generating images in Python.

(jump back up to table of contents)

VI. Algorithms and Images: combination

This is as far as we got in class. I encourage you to play with the below examples while doing the homework for this week, and we'll pick up here next week in class.

Example VI.1: Combining images

import sys
from PIL import Image 

if len(sys.argv) != 3:
    exit("This program requires two arguments: the name of two image files to combine.")


# open both images
img1 = Image.open( sys.argv[1] )
img2 = Image.open( sys.argv[2] )

# resize both images so they are no bigger than 400x400
# but preserve the original aspect ratio
img1.thumbnail( (400,400) )
img2.thumbnail( (400,400) )

# make a new image 600x600, with a white background
new_image = Image.new( "RGB", (600,600), "white" )

# paste in the first image to the upper-left corner (0,0)
new_image.paste(img1, (0,0) )

# paste in the second image, to (200,200)
new_image.paste(img2, (200,200) )

# save the resulting image
new_image.save("new.jpg")

Example VI.2: Combining images with transparency

import sys
from PIL import Image 

if len(sys.argv) != 3:
    exit("This program requires two arguments: the name of two image files to combine.")


# open both images
img1 = Image.open( sys.argv[1] )
img2 = Image.open( sys.argv[2] )

# resize both images so they are no bigger than 400x400
# but preserve the original aspect ratio
img1.thumbnail( (400,400) )
img2.thumbnail( (400,400) )

# make a new image 600x600, with a white background
# Note that this image now has an "alpha" component
new_image = Image.new( "RGBA", (600,600), "white" )

# paste in the first image to the upper-left corner (0,0)
new_image.paste(img1, (0,0) )

# add some transparency (alpha) to the second image
img2.putalpha(128)

# paste in the second image, preserving its new transparency
new_image.alpha_composite(img2, (200,200) )

# save the resulting image
# Note that we must convert it to RGB with no alpha to save it as a JPEG
new_image.convert("RGB").save("new.jpg")

# Alternatively, we could have avoided converting by saving it to a
# PNG like this (since PNGs allow alpha):
# new_image.save("new.png")

Example VI.3: Combining images with transparency based on pixel values of the source image

import sys
from PIL import Image 

if len(sys.argv) != 3:
    exit("This program requires two arguments: the name of two image files to combine.")


# open both images
img1 = Image.open( sys.argv[1] )
img2 = Image.open( sys.argv[2] )

# resize both images so they are no bigger than 400x400
# but preserve the original aspect ratio
img1.thumbnail( (400,400) )
img2.thumbnail( (400,400) )

# make a new image 600x600, with a white background
# Note that this image now has an "alpha" component
new_image = Image.new( "RGBA", (600,600), "white" )

# paste in the first image to the upper-left corner (0,0)
new_image.paste(img1, (0,0) )

# convert the second image to a new image with transparency (alpha)
img2_alpha = img2.convert("RGBA")

# modify the second image, make all bluish pixels totally transparent
# (meaning that alpha the fourth argument will be 0)
(width,height) = img2_alpha.size
for x in range(width):
    for y in range(height):
        (red,green,blue,alpha) = img2_alpha.getpixel((x,y))
        if blue > red and blue > green:
            img2_alpha.putpixel( (x,y), (0,0,0,0) )


# paste in the second image, preserving its new transparency.
# Note that this time I'm placing it at 0,0 to show the transparent overlay
new_image.alpha_composite(img2_alpha, (0,0) )

# save the resulting image
# Note that we must convert it to RGB with no alpha to save it as a JPEG
new_image.convert("RGB").save("new.jpg")

# Alternatively, we could have avoided converting by saving it to a
# PNG like this (since PNGs allow alpha):
# new_image.save("new.png")

(jump back up to table of contents)

VII. Wrapping up & homework

The homework for this week builds on the above with some exercises that also will lead in to next week.

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