CS 1112: Spring 2020

Class 33 — Friday April 10

Chrestomatics and patterning

TBD

Look both ways

Back or ahead

Zoom

Link

Agenda

Problem solving

Image manipulation

Admire past artistry

Prepare for image manipulation

To do

Consider practice modules

Homework 28: image drawing

Homework 29: problem solving

Downloads

Practice module luna.py

Practice module calc.py

Practice module eval.py

Example module uate.py

Example module sigma.py

Homework module trio.py

Homework module flat.py

Practice module `luna.py`

Defines a function h(). The function has a single numeric parameter x. The function returns the number of hours it takes to get the moon while traveling at a speed of x miles per hour. For your information:

elapsed time = distance / speed

For your convenience the module defines the constant

DISTANCE_IN_MILES_TO_MOON = 238900.0

Observation: the function makes a straight-forward calculation and returns the result.

The output of its built-in testing should be

h( 119.45 ) = 2000.0
h( 597.25 ) = 400.0

Practice module `calc.py`

Defines a function e(). The function has three parameters x, y, and s. Parameters x and y are decimals; parameter s is a string. If s is either '+', '-', '*', or '/', the function returns respectively x + y, x - y, x * y, or x / y. Otherwise, the functions None.

Observation: an if-elif-...else structure is needed to determine which of five possibile cases applies based on parameter s. Knowing which case applies, allows a straight-forward calculation and then the immediate return of the calculation

The output of its built-in testing should be

19.5 + 5.25 = 24.75
12.5 - 6.5 = 6.0
12.5 * 4.5 = 56.25
10.0 / 2.25 = 4.444444444444445
1.0 @ 5.0 = None

Practice module `eval.py`

Defines a functions f(). The function has two list parameters x and y. The function returns a new list whose elements are the elements of x followed by the elements of y. The function does not change its list parameters.

The built-in testing makes use of the following lists.

x1 = [ ]; y1 = [ ]
x2 = [ 3, 1, 4 ]; y2 = [ ]
x3 = [ ]; y3 = [2, 7, 8]
x4 = [ 3, 1, 4 ]; y4 = [1, 5, 1, 9]

The output of its built-in testing should be

f( x1, y1 ) = [ ]
f( x2, y2 ) = [ 3, 1, 4 ]
f( x3, y3 ) = [ 2, 7, 8 ]
f( x4, y4 ) = [ 3, 1, 4, 1, 5, 1, 9 ]

Example module `uate.py`

Defines a function g(). The function has one list parameter x. The function returns a new list whose elements are the element values of x without duplication. The function does not change its list parameter.

Observation: an accumulator is needed. The elements of x need to be added to the accumulator one-at-a-time if they are not already there. Afterwards, the accumulator is returned.

The built-in testing makes use of the following lists.

x1 = [ 0, 1, 2 ]
x2 = [ 0, 4, 1, 2, 2, 1, 3, 6, 3, 3, 4 ]
x3 = [ ]

The output of its built-in testing should be

g( x1 ) = [ 0, 1, 2 ]
g( x2 ) = [ 0, 4, 1, 2, 3, 6 ]
g( x3 ) = [ ]

Example module `sigma.py`

Defines a function s(). The function has one parameter d. Parameter d is an already initialized integer dataset; that is, it is a list of integer lists. The function returns the sum of the dataset values. The function does not change its list parameter.

Observation: an accumulator is needed to store the sum of the dataset values. The sum of the values for each row of the dataset needs to be added to the accumulator. Afterwards, the accumulator is returned.

The built-in testing makes use of the following lists.

d1 = [ [ 0 ], [ 1, 2 ], [ 1, 2, 3 ], [ 0 ] ]
d2 = [ [ 1, 0, 1, 2, 2 ], [ 3, 0, 1, 1, 1, 0 ], [ 2 ], [ 0, 0, 1 ] ]
d3 = [ [ 3, 0, 3], [ 3, 0, 3, 0, 1], [ 1, 0, 2 ] ]
d4 = [ ]

The output of its built-in testing should be

s( d1 ) = 9
s( d2 ) = 15
s( d3 ) = 16
s( d4 ) = 0

Homework module `trio.py`

Defines a function t(). The function has one list parameter x of numeric values. The function does not change its list parameter. The function returns a new three-element list whose values are respectively the number of negative, zero, and positive values in x.

Observation: three accumulators are needed to store the three wanted counts. Each element of the list needs to be examined to determine which count should be incremented based on its numeric property. Afterwards, the return is a list of three elements (i.e, the computed counts).

The built-in testing makes use of the following lists.

x1 = [ 0, -3, 0, -4, -2 ]
x2 = [ -3, 1, -2, 1, -3, -3, -2, -4, -1, -4 ]
x3 = [ 2, -1, 0, 3, 0, 3, -2, -2, -1, -4, 3, -4, 3, -1, 3 ]
x4 = [ ]

The output of its built-in testing should be

t( x1 ) = [ 3, 2, 0 ]
t( x2 ) = [ 8, 0, 2 ]
t( x3 ) = [ 7, 2, 6 ]
t( x4 ) = [ 0, 0, 0 ]

Homework module `flat.py`

Defines a function n(). The function has a single dataset parameter d.

The function returns a simple list whose elements are the elements of first row of dataset d, followed by the elements of the second row of the dataset, followed by the elements of the third row of the dataset, and so on.

The built-in tester makes use of the following definitions.

d1 = [ [ 0 ], [ 1, 2 ], [ 1, 2, 3 ], [ 0 ] ]
d2 = [ [ 1, 0, 1, 2, 2 ], [ 3, 0, 1, 1, 1, 0 ], [ 2 ], [ 0, 0, 1 ] ]
d3 = [ [ 3, 0, 3], [ 3, 0, 3, 0, 1], [ 1, 0, 2 ] ]
d4 = [ ]

The output of its built-in testing should be

n( d1 ): [0, 1, 2, 1, 2, 3, 0]
n( d2 ): [1, 0, 1, 2, 2, 3, 0, 1, 1, 1, 0, 2, 0, 0, 1]
n( d3 ): [3, 0, 3, 3, 0, 3, 0, 1, 1, 0, 2]
n( d4 ): []

Some possible image transformations

Duplicated image

Mirrored image

Flipped image

Clockwise rotation

Basic photo-manipulation problem-solving pattern

# get dimensions of the original
ow, oh = ...

# set dimensions of the new image
nw, nh = ...

# get a new appropriately sized image
new_image = Image.new( 'RGB', ( nw, nh ) )

# fill in every pixel of the new image
for nx in range( 0, nw ) : # consider every x value for the new image
for ny in range( 0, nh ) : # in tandem with every y value for the image

# set the spot to be filled in the new image
nspot = (nx ,ny )

# determine the corresponding spot of interest in the original
ospot = ...

# get the pixel at the ospot
opixel = ...

# determine the pixel for the new image
npixel = ...

# set the nspot in the new image
...

# return the filled in new image
return new_image

Basic photo-manipulation problem-solving Python function

def manip( original, size, color, where ) :
''' Provide a pattern for image manipulation
'''

# set dimensions of the new image
nw, nh = size( original )

# get a new appropriately sized image
new_image = Image.new( 'RGB', ( nw, nh ) )

# fill in every pixel of the new image
for nx in range( 0, nw ) : # consider every x value for the new image
for ny in range( 0, nh ) : # in tandem with every y value for the image
# set the spot to be filled in the new image
nspot = ( nx, ny )

# determine the corresponding spot of interest in the original
ospot = where( nspot, original )

# get the pixel at the ospot
opixel = original.getpixel( ospot )

# determine the pixel for the new image
npixel = color( opixel )

# set the nspot in the new image
new_image.putpixel( nspot, npixel )

# return the filled in new image
return new_image

Resources from previous semesters are available.

	Duplicated image

	Mirrored image

	Flipped image

	Clockwise rotation

Class 33 — Friday April 10

Chrestomatics and patterning

Look both ways

Zoom

Agenda

To do

Downloads

Practice module luna.py

Practice module calc.py

Practice module eval.py

Example module uate.py

Example module sigma.py

Homework module trio.py

Homework module flat.py

Some possible image transformations

Basic photo-manipulation problem-solving pattern

Basic photo-manipulation problem-solving Python function

Practice module `luna.py`

Practice module `calc.py`

Practice module `eval.py`

Example module `uate.py`

Example module `sigma.py`

Homework module `trio.py`

Homework module `flat.py`