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Finding a similarity

mark

(10 pts)

In this problem, you’re going to play with some computer code to define a similarity transformation that accomplishes a specific task. To get the details, choose your name from the menu below:

adrienne

Well, my code is not:

S = shift([-1, 0])
  .compose(rotate(120 * degree))
  .compose(scale(1 / 2))

Since the picture looks like:

mreyeslo

Here’s the code I used to get to the red figure

S = reflect([1, 0])
  .compose(shift([2, 1]))
  .compose(rotate(30 * degree))
  .compose(scale(0.9))

pikenber

My code is:

S = shift([-2, 0.5])
  .compose(scale(0.8))
  .compose(rotate(15*degree))
  .compose(reflect([0,1]))

And this is what it looks like:

ofeldman

My code is:

S = shift([-2, 0])
    .compose(rotate(-60 * degree))
    .compose(scale(0.8))
    .compose(reflect([1, 0]))

The picture looks like:

scowart

My code is:

 S = shift([-2, -0.5])
   .compose(rotate(-120 * degree))
   .compose(scale(0.8))

Which corresponds to this picture:

badler

Here is the code I used to get the red figure starting with the green figure:

S = shift([-2,-1.5])
.compose(rotate(45*degree))
.compose(scale(.3))
.compose(reflect([0,1]))

The corresponding visual:

lee7

My code is:

S = shift([-2,.5])
.compose(rotate(-105*degree))
.compose(scale(8/10))

To produce:

mark