Project 3: Limning L-Systems

Deliverables: (1) Bring in to class on Friday, 26 February a stapled turn-in containing your answers to all of the questions (which should all be in your file). (2) Post an image in the cs1120 slack group showing your most awesomest fractal.



Before starting this project, you should complete Udacity cs101 Lesson 4: Responding to Queries. It is not necessary to do the Lesson 4: Problem Set.

Collaboration Policy

For this assignment, you may work alone or work with a partner. If you work with a partner, you and your partner should submit a single assignment with both of your names and UVA ids on it. You and your partner should work on all the problems together, and both of you must completely understand everything you turn in. You should also take turns driving (typing) when you are at the computer.

Regardless of whether you work alone or with a partner, you may discuss the assignment with anyone you want and get help from others, so long as it is help in the spirit of learning how to do things yourself not getting answers you don't understand. You should understand everything you turn in for the assignment well enough to be able to produce it completely on your own.

Remember to follow the course pledge you read and signed at the beginning of the semester. For this assignment, you may consult any outside resources, including books, papers, web sites and people, you wish except for materials from previous cs1120 courses or direct solutions to the given problems. You may consult anyone you want, but that person cannot type anything in for you and all work must remain your own and outside sources should never give you specific answers to problem set questions.

If you use resources other than the class materials, lectures and course staff, you should document this in your turn-in.

You are strongly encouraged to take advantage of the scheduled help hours and office hours for this course.

Project 3 Repository

Set up your project3 repository in the same way as you did for project 1 and project 2.

The project3 repository contains these files:

Warning: This project expects you to write considerably more code than Project 2. We recommend you start early and take advantage of the scheduled help hours.

Background: L-System Fractals

In this problem set, you will explore a method of creating fractals known as the Lindenmayer system (or L-system).
Aristid Lindemayer, a theoretical biologist at the University of Utrecht, developed the L-system in 1968 as a mathematical theory of plant development. In the late 1980s, he collaborated with Przemyslaw Prusinkiewicz, a computer scientist at the University of Regina, to explore computational properties of the L-system and developed many of the ideas on which this problem set is based.

The idea behind L-system fractals is that we can describe a curve as a list of lines and turns, and create new curves by rewriting old curves. Everything in an L-system curve is either a forward line (denoted by F), or a right turn (denoted by Ra where a is an angle in degrees clockwise). We denote left turns by using negative angles.

We create fractals by recursively replacing all forward lines in a curve list with the original curve list. Lindemayer found that many objects in nature could be described using regularly repeating patterns. For example, the way some tree branches sprout from a trunk can be described using the pattern: F O(R30 F) F O(R-60 F) F.

This is interpreted as: the trunk goes up one unit distance, a branch sprouts at an angle 30 degrees to the trunk and grows for one unit. The O means an offshoot — we draw the curve in the following parentheses, and then return to where we started before the offshoot. The trunk grows another unit and now another branch, this time at -60 degrees relative to the trunk grows for one units. Finally the trunk grows for one more unit. The branches continue to sprout in this manner as they get smaller and smaller, and eventually we reach the leaves.

We can describe this process using replacement rules. The rules are similar to the BNF replacement rules we have used previously, with one big difference: instead of just picking one nonterminal to replace for each step, with the L-System rules we replace all occurences of the nonterminal with its replacement.

For example:

Start: [F]
Rule:  F ::= [F, O[R30, F], F, O[R-60, F], F]

Here are the commands this produces after two iterations:

Iteration 0: [F]
Iteration 1: [F, O[R30, F], F, O[R-60, F], F]
Iteration 2:
[F, O[R30, F], F, O[R-60, F], F, O[R30, F, O[R30, F], F, O[R-60, F], F],
 F, O[R30, F], F, O[R-60, F], F, O[R-60, F, O[R30, F], F, O[R-60, F], F],
 F, O[R30, F], F, O[R-60, F], F]

Here's what that looks like graphically:

Iteration 0

Iteration 1

Iteration 2

Iteration 5

Iteration 5 (with color)
The Great Lambda Tree of
Infinite Knowledge and Ultimate Power

Breaking down the Problem

We can divide the problem of producing an L-system fractal into two main parts:

  1. Produce a list of L-system commands that represents the fractal by rewriting according to the L-system rule; and

  2. Drawing a list of L-system commands.

We will first work on producing the list of L-system commands, and then work on how to draw a list of L-system commands.

Representing L-System Commands

Here is a BNF grammar for L-system commands:

  1. CommandSequence ::= [ CommandList ]
  2. CommandList ::= Command, CommandList
  3. CommandList ::= Command
  4. Command ::= F
  5. Command ::= RAngle
  6. Command ::= O CommandSequence
  7. Angle ::= OptSign Number
  8. OptSign ::= -
  9. OptSign ::= ε
  10. Number ::= Digit Digits
  11. Digits ::= Digit Digits
  12. Digits ::= ε
  13. Digit ::= 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9

We need to find a way to turn strings in this grammar into lists and objects we can manipulate in a Python program. We will use a list of lists to represent a CommandList and a list of strings to represent different commands. For example, the list ['F'] will denote forward, ['R', angle] will denote a rotation, and ['O', commandsequence] will denote an offshoot.

def make_forward_command():
    """Returns an object that represents a forward command, Command ::= F"""
    return ['F'] # creates a list of one element, 'F'

def make_rotate_command(angle): 
    """Returns an object that represents a right turn of angle degrees, Command ::= R Angle"""
    return ['R', angle] # creates a list of two elements, 'R' and a number

def make_offshoot_command(commandsequence): 
    """Returns an object that represents an offshoot commnd of the input sequence, 
       Command ::= O CommandSequence"""
    return ['O', commandsequence] # creates a list of two elements, 'O' and another list 

It will be useful to have functions that take L-System commands as parameters, and return information about those commands. The next problem is to define functions for manipulating L-System commands.

Exercise 1. Define the following functions in (note that these are not in, and you do not need to turn in these definitions; but they do need to be correct for the rest of the code to work):

is_forward(lcommand) ‐ returns True if the input is a forward command (indicated by its first element being an 'F'); otherwise, returns False.
is_rotate(lcommand) — returns True if the input is a rotate command; otherwise, returns False.
is_offshoot(lcommand) — returns True if the input is an offshoot command; otherwise, returns False.
get_angle(lcommand) — returns the angle of the input command, which must be a rotate command. Produces an error if the command is not a rotate command (see for how to produce an error).
get_offshoot_commands(lcommand) — returns the offshoot command list associated with the input offshoot command. Produces an error if the input is not an offshoot command.

The provided code in the project3 repository actually already includes the definitions requested in Exercise 1. We forgot to remove these before putting the code into the public repository. So, you don't need to do anything for Exercise 1, but should make sure you understand all this code (and feel free to change the error messages to be more creative!)

If you define these functions correctly, you should produce these evaluations (after running the module):

>>> is_forward(make_forward_command())
>>> is_forward(make_rotate_command(90))
>>> get_angle(make_rotate_command(90))
>>> get_angle(make_forward_command())
# Error (specifics of error are up to you)

You should be able to make up similar test cases yourself to check that all the functions you defined work correctly.

Else-If Statements

Before getting into fractals, we'll introduce one new Python programming construct. When we have if else statements, there are two branches we can take: the if-branch and the else-branch. We can use elif (short for "else if") statements if we want to have more than just two branches in our program. For example, nucleotide_complement from Project 2 might be written:

def nucleotide_complement(base):
    if   base == 'G': return 'C'
    elif base == 'C': return 'G'
    elif base == 'A': return 'T'
    elif base == 'T': return 'A'
    else: # not a valid base
        assert False

Exercise 2. Demonstrate that using elif statements is never necessary. That is, show that we can use always nested if-else statements to do the same thing any elif statements.

Answer this question by providing a convincing written argument. Strive to make your answer as general as and formal as possible. For example, just showing how the example above (one example) could be written does not necessarily handle all possible cases. A good answer would show how to mechanically transform any elif construct into one with the same meaning that does not use elif. (It is not necessary to turn anything in for this, but you should make sure you understand it, and ask questions if not.)

We don't need to know how to use elif statements, but using them can make our code shorter and more readable.

Rewriting Curves

The power of the L-System commands comes from the rewriting mechanism. Recall how we described the tree fractal:

Start: [F]
Rule:  F ::= [F, O[R30, F], F, O[R-60, F], F]

To produce levels of the tree fractal, we need a procedure that takes a list of L-system commands and replaces each F command with the list of L-system commands given by the rule.

So, for every command in the list:

  • If the command is a forward (F) command, replace it with the replacement commands.
  • If the command is a turn (Rangle) command, keep it as is.
  • If the command is an offshoot (O[CommandSequence]) command, recursively rewrite every command in the offshoot's command list the same way.

One slight complication is that the replacement commands are a list of L-system commands, and we want to end up with a flat list of L-System commands.

For example, consider a simple L-System rewriting:

Start: [F]
Rule:  F ::= [F, R30, F]

We want to get:

Iteration1: [F, R30, F]
Iteration2: [F, R30, F, R30, F, R30, F]
Iteration3: [F, R30, F, R30, F, R30, F, R30, F, R30, F, R30, F, R30, F]

However, if we naively replace each F with the list [F, R30, F], we would instead get something a little different:

Iteration1: [F, R30, F]
Iteration2: [[F, R30, F], R30, [F, R30, F]]
Iteration2: [[[F, R30, F], R30, [F, R30, F]], R30, [[F, R30, F], R30, [F, R30, F]]]

There are too many [ ]'s!.

To fix this problem, we need to flatten the result. Flattening a list take a list which make contain nested lists (which themsleves may contain nested lists) and returns a list which is just all the (non-list) elements in all the lists in order.

Problem 1. Define a function, flatten_commands, that takes as input a possibly nested list of L-System commands and returns a flattened list containing the same commands in the same order, but without any nested lists.

Hint: flatten_commands is very similar to the list_flatten example from class, but that code won't work here since it would end up breaking into the L-System commands themselves since they are represented as lists. If you understand that code, though, you should be able to adapt it to define flatten_commands.

Problem 2. Define a function rewrite_lcommands that takes a list of L-system commands as its first parameter. The second parameter is a list of L-system commands that should replace every forward command in the first list of commands in the result.

Hint: You'll have to handle both forward commands and also offshoot commands specially.

If you define these procedures correctly, you should produce these evaluations:

>>> rewrite_lcommands(['F'], [['F'],['R', 30], ['F']])
[['F'], ['R', 30], ['F']]
>>> rewrite_lcommands([['F'],['R', 30], ['F']], [['F'], ['F'], ['R', 30]])
[['F'], ['F'], ['R', 30], ['R', 30], ['F'], ['F'], ['R', 30]]

To make interesting L-system curves, we will need to apply rewrite_lcommands many times. We will leave that until the last problem since it will be a lot more fun to test this if we can actually draw our L-System fractals.

Parsing and Unparsing. One thing that is very useful for debugging and testing code is to have easy ways to turn strings into objects (parsing), and to turn objects into strings (unparsing).

We have defined functions that parse and unprase L-System commands (in

unparse_lsystem_command(command) returns a string representation of the L-System command.
unparse_lsystem_sequence(commands) returns a string representation of an L-System command sequence.
parse_lsystem_command(command) returns an L-System command object that corresponds to an input string.
parse_lsystem_sequence(sequence) returns an L-System command sequence (list of L-System commands) corresponding to the input string.

Here are some examples using these functions (instead of the much harder to read example above):

>> rewrite_lcommands(parse_lsystem_sequence('[F, R30, F]'), 
                     parse_lsystem_sequence('[F, F, R30]'))
[['F'], ['F'], ['R', 30], ['R', 30], ['F'], ['F'], ['R', 30]]
>>> unparse_lsystem_sequence(_)  
'[F, F, R30, R30, F, F, R30]'

(Note that we use _ in the second expression as a shortcut to getting the value of the last expression evaluated in the Python shell.)

Drawing L-System Fractals

To draw our L-system fractals, we need procedures for drawing curves. We consider a curve any set of points in a two-dimensional space, so a straight line is a kind of curve as is a circle, or a complex drawing like a fractal.

There are many different ways of thinking about curves. Mathematicians sometimes think about curves as functions from an x-coordinate value to a y-coordinate value. For example, y = 3_x_, describes a simple slanted line.

The problem with this way of thinking about curves is there can only be one y point for a given x point. This makes it impossible to make simple curves like a circle where there are two y points for every x value on the curve.

So, a more useful way of thinking about curves is as functions that map numbers to [x, y] points. We can produce infinitely many different points on the curve by evaluating the curve function with the (infinitely many different) real numbers between 0 and 1 inclusive.

You can think of the number between 0 and 1 as time — the first point of the curve you draw will be the point returned by curve(0.0), the middle part of the curve you draw will be the point returned by curve(0.5), and the last part of the curve you draw will be the point returned by curve(1.0).

Of course, we can't really evaluate the curve function at every real number between 0 and 1 because there are infinitely many of them — it would take too long (even with an infinitely fast computer)! Instead, we will evaluate it at a large (but finite) number of values distributed evenly between 0 and 1 to display an approximation of the curve.

Representing Points

We need a way to represent the points on our curves. A point is a pair of two values, x and y representing the horizontal and vertical location of the point.

We will use a coordinate system from (0, 0) to (1, 1):

(0.0, 1.0) (1.0, 1.0)
(0.0, 0.0) (1.0, 0.0)

A point has an x and y coordinate. So, to represent points we define procedures make_point, point_x and point_y. We use lists to represent points.

Our pictures will be more interesting if points can have color too. So, we represent a colored point using a list of three values: x, y and color:

def make_point(x, y): 
    return [x, y, BLACK] # default color is

def make_colored_point(x, y, c):
    return [x, y, c]

def point_x(point):
    return point[0]

def point_y(point):
    return point[1]

def point_color(point):
    return point[2]

We have provided some procedures for drawing on the window in including:

window_draw_point(point) — Draw the point on the window.
For example, window_draw_point(make_point(0.5, 0.5)) puts a black dot in the center of the window. (The point is only one pixel, so it is hard to see.)
window_draw_line(pointa, pointb) — Draw a black line from pointa to pointb.
For example, window_draw_line(make_point(0.0, 0.0), make_point(1.0, 1.0)) draws a diagonal line from the bottom left corner to the top right corner.

Read through for other useful drawing functions.

Drawing Curves

Building upon points, we can make curves and lines (straight lines are just a special kind of curve).

Curves are procedures from values to points. One way to represent a curve is as a function that returns a point for every input value between 0.0 and 1.0. Ideally, there would be infinitely many points to draw on the curve, but in practice, we will need to select a fininte number of points to evaluate that we hope provides a good approximation of the curve to draw.

For example, the following here is a function that defines a curve that is a horizontal line accross the window:

def mid_line(t):
    return make_point(t, 0.5) 

If we call mid_line with an input value x, it returns the point (x, 0.5). Hence, if we apply mid_line to all the values between 0.0 and 1.0, we get a horizontal line.

Of course, we can't really apply it to all the values, but can draw enough of them that it will look like the full curve, a solid horizontal line. So, to draw a curve we need to apply the curve procedure to many values in the range from 0.0 to 1.0 and draw each point it evaluates to.

The draw_curve_points procedure does that:

def draw_curve_points(curve, npoints):
    for step in range(npoints + 1):
        window_draw_point(curve(step / npoints))

The procedure draw_curve_points takes a function representing a curve, and npoints, the number of points to draw. It draws npoints + 1 evenly spaced points for the curve.

For example, if npoints is 5, draw_curve_points will draw the curve with time inputs 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. Note that this is actually 6 points, since the loop uses range(npoints + 1) to include both the 0.0 and 1.0 points. (Improving draw_curve_points to draw n points including the 0.0 and 1.0 times, with the others evenly divided is left as a challenge exercise!)

Problem 3. Define a vertical_mid_line curve function that can used to draw a vertical line in the middle of the window. So, draw_curve_points(vertical_mid_line, 1000) draws a vertical line in the middle of the window as shown below.

Problem 4. Define a function, make_vertical_line that takes one number as its input and returns a function that produces a vertical line at that the horizontal location given by the input. For example, draw_curve_points(make_vertical_line(0.5), 1000) should produce a vertical line in the middle of the window (identical to draw_curve_points(vertical_mid_line, 1000)) and draw_curve_points(make_vertical_line(0.2), 1000) should produce a vertical line near the left side of the window (shown in the image below).

Manipulating Curves

The good thing about defining curves as functions is that it is now possible to combine then in interesting ways.

For example, the procedure rotate_ccw takes a curve and rotates it 90 degrees counter-clockwise by simply swapping the x and y points:

def rotate_ccw(curve):
    def new_curve(t):
        ct = curve(t)
        return make_point(point_y(ct), point_x(ct))
    return new_curve

Note that rotate_ccw(curve) returns a function that is a new curve that is just like the input curve, but rotated.

The function rotate_ccw is a higher-order function: it takes a function (representing a curve) as its input, and returns a new function (that represents the rotated curve).

Inside rotate_ccw, the new_curve function is defined. Within its definition, we can use curve, the name of the parameter to rotate_ccw to refer to the input curve. The new local variable, ct stores the result of curve(t) so it is not necessary to call it twice, and then we create a new point using the point_y(ct) and point_x(ct) to flip the x and y coordinates. (For simplicity, the definition here does not include color; see for a definition of rotate_ccw that preserves the colors of points.)

Predict what draw_curve_points(rotate_ccw(mid_line), 1000) and draw_curve_points(rotate_ccw(rotate_ccw(mid_line)), 1000) will do. Confirm your predictions by trying them in your Python interpreter.

You may have just read over that paragraph and not actually done the prediction. The instructions are not just there to waste time: experience suggests that students who do not do them become confused on subsequent problems and then come back. Its really important to understand what is going on here, before attempting the more complex problems. Thus you can save time by doing them now! If you already did them, great work!

Here's another example:

def scale(curve, ratio):
    def new_curve(t):
        ct = curve(t)
        return make_point(ratio * point_x(ct), ratio * point_y(ct))
    return new_curve

Predict what draw_curve_points(scale(mid_line, 0.5), 1000) will do, and then try it in your interpreter.

The scale procedure doesn't produce quite what we want because in addition to changing the size of the curve, it moves it around. Why does this happen? Try scaling a few different curves to make sure you understand why the curve moves.

One way to fix this problem is to center our curves around (0,0) and then translate them to the middle of the screen. In mathematics and geometry, translation means moving a shape without rotating or flipping it. That is, to translate a curve is to "slide" it.

We can do this by adding or subtracting constants to the points they produce:

def translate(curve, delta_x, delta_y):
    def new_curve(t):
        ct = curve(t)
        return make_point(delta_x + point_x(ct), delta_y + point_y(ct))
    return new_curve

Using translate, we can now define mid_line by translating a horizontal line (at the bottom of the window) to the middle:

def horiz_line(t):
    return make_point(t, 0)

mid_line = translate(horiz_line, 0, 0.5) 

Problem 5. To check you understand everything so far, define a curve function half_line(t) that uses translate, horiz_line and scale to define a horizontal line half the width of the window that starts in the middle of the display window. Executing draw_curve_points(half_line, 1000) should produce a horizontal line that starts in the middle of the window and extends to the right boundary (as shown below).

Hint: If you do not see anything when you are drawing a curve, it may be that you haven't yet applied translate and the points are being drawn along the bottom edge of the screen. You may find it helpful to modify draw_curve_points to print out the points as it draws them so you can see from the printed points if there are any points off the screen (if you do this, you'll want to reduce the number of points to print less on the screen).

In addition to altering the points a curve produces, we can alter a curve by changing the time values it will see. For example,

def first_half(curve):
   def new_curve(t):
      return curve(t / 2)
   return new_curve
is a function that takes a curve and produces a new curve that only draws the first half of the input curve.

Predict what each of these expressions will do:

  • draw_curve_points(first_half(mid_line), 1000)
  • draw_curve_points(first_half(first_half(mid_line)), 1000)

Try evaluating them in your interpreter to check if you were right.

The provided code in includes several other functions that transform curves including:

scale_x_y(curve, x_scale, y_scale) — returns a curve function that is the input curve stretched along the x and y axis using the given scale factors.
scale(curve, ratio) — returns a curve function that is the input curve stretched along both the x and y axis by the same ratio.
rotate_around_origin(curve, degrees) — returns a curve function that is the input curve rotated counterclockwise by the given number of degrees.

You should be able to understand the code in that defines these functions.

Connecting Curves

It is also useful to have curve transforms that combine multiple input curves into a new curve.

An example is connect_rigidly(curve1, curve2) which returns a curve that consists of curve1 followed by curve2. The starting point of the new curve is the starting point of curve1, and the final point of curve2 is the final point of the new curve.

Here's how connect_rigidly is defined (try to figure this out from just the code here, but look at the version in to see more explanations in the comments):

def connect_rigidly(curve1, curve2):
    def new_curve(t):
        if (t < 0.5):
            return curve1(2 * t)
            return curve2(2 * t - 1)
    return new_curve

Note that connect_rigidly gives half of its points to drawing curve1 and half to curve2, so when we call draw_curve_points(..., n) we'll may want to double the value of n to ensure there are enough points in each curve. This becomes a big problem when many curves are connected (as will be necessary to draw interesting fractals); we'll return to it soon.

Predict what the expression below will do:    draw_curve_points(connect_rigidly(vertical_mid_line, mid_line), 1000)
Is there any difference between that and this one:    draw_curve_points(connect_rigidly(mid_line, vertical_mid_line), 1000)

Check your predictions in the interpreter, and try your own experiments connecting and transforming different curves to make sure you understand how everything works so far.

Distributing Points

The way connect_rigidly is defined above, we use all the t-values below 0.5 on the first curve, and use the t-values between 0.5 and 1.0 on the second curve.

If the second curve is the result of connecting two other curves, like connect_rigidly(c1, connect_rigidly (c2, c3)) then 1/2 of the points will be used to draw c1, 1/4 to draw c2, and 1/4 to draw c3. (If you're not sure why, take another look at the connect_rigidly code.) That means that c2 and c3 will look dotty or grainy compared to c1.

This isn't too big a problem when only a few curves are combined; we can just increase the number of points passed to draw_curve_points to have enough points to make a smooth curve. However, you will be drawing fractal curves made up of thousands of connected curves. Just increasing the number of points won't help much, as you'll see in the next problem.

Problem 6. Define a function num_points(t, n) that determines the approximate number of points that will be used for the nth curve when drawing

   connect_rigidly(c1, connect_rigidly(c2, connect_rigidly(c3, ... cn)))

The first argument to num_points is the number of points (t-values) total to draw. The second argument is the number of curves.

Think about this yourself first, but look in for a hint if you are stuck. There are mathematical ways to calculate this efficiently, but the simplest way to calculate it is to define a procedure that keeps halving the number of points n times to find out how many are left for the nth curve.

Your num_points procedure should produce results similar to:

>>> num_points(1000, 10)
>>> num_points(1000, 20)
>>> num_points(1000000, 20)

This means if we connected just 20 curves using connect_rigidly, and passed the result to draw_curve_points with one million as the number of points, there would still be only one or two points drawn for the 20th curve. If we are drawing thousands of curves, for most of them, not even a single point would be drawn!

To fix this, we need to distribute the points between our curves more fairly. We have provided a procedure connect_curves_evenly in that connects a list of curves in a way that distributes the range of t-values evenly between the curves.

The definition is a bit complicated, so don't worry if you don't understand it completely. You should, however, be able to figure out the basic idea for how it distributes the t-values evenly between every curve in a list of curves. (See for a version with lots of comments explaining what is going on, but try to understand it from just the code here first.)

def connect_curves_evenly(curvelist):
    ncurves = len(curvelist)
    def new_curve(t):
        which_curve = min(int(math.floor(t * ncurves)), ncurves - 1)
        chosen_curve = curvelist[which_curve]
        adjusted_t = ncurves * (t - which_curve * 1.0 / ncurves)
        return chosen_curve(adjusted_t)
    return new_curve

It will also be useful to connect curves so that the next curve begins where the first curve ends. We can do this by translating the second curve to begin where the first curve ends.

To do this for a list of curves, we translate each curve in the list the same way using a list comprehension:

def construct_simple_curvelist(curve, curvelist):
    endpoint = curve(1.0)
    delta_x = point_x(endpoint)
    delta_y = point_y(endpoint)
    return [curve] + [translate(tcurve, delta_x, delta_y) for tcurve in curvelist]

Now that we have defined all of these procedures to manipulate, connect and draw listscurves, we just have to turn L-System fractals into lists of curves and then we can draw them!

Drawing L-System Curves

To draw an L-system fractal, we need to convert a sequence of L-system commands into a curve.

We defined the connect_curves_evenly procedure to take a list of curves, and produce a single curve that connects all the curves. So, to draw an L-System curve, we need a procedure that turns an L-System Curve into a list of curve procedures.

The convert_lcommands_to_curvelist procedure converts a list of L-System commands into a curve. Here is a template for convert_lcommands_to_curvelist (with some missing parts that you will need to complete). It will be explained later, but try to understand it yourself first.

def convert_lcommands_to_curvelist(lcommands):
    if len(lcommands) == 0:
        # We need to make a leaf with just a single point of green
        return [lambda t: make_colored_point(0, 0, GREEN)]
    elif is_forward(lcommands[0]):
        # Make a vertical_line and then recurse on the rest of the list
        return construct_simple_curvelist(vertical_line, 
    elif is_rotate(lcommands[0]):
        rotate_angle = -1 * get_angle(lcommands[0])
        # Every curve in the rest of the list should be rotated
        # Problem 7: replace with code for doing rotation
        raise RuntimeError("Rotate not yet implemented!")
    elif is_offshoot(lcommands[0]):
        # Problem 8: replace with code for doing offshoots
        raise RuntimeError("Rotate not yet implemented!")
        # This branch should never happen, but let's raise an error if it does.
        raise RuntimeError("Bad Command")

We define convert_lcommands_to_curvelist recursively.

The base case is when there are no more commands (the lcommands parameter is the empty list). It evaluates to the leaf curve (for now, we just make a point of green — you may want to replace this with something more interesting to make a better fractal). Since convert_lcommands_to_curvelist evaluates to a list of curves, we need to make a singleton list of curves containing only one curve.

Otherwise, we need to do something different depending on what the first command in the command list is. If it is a forward command we draw a vertical line, and connect the rest of the fractal is connected to the end of the vertical line. The recursive call to convert_lcommands_to_curve produces the curve list corresponding to the rest of the L-system commands. Note how we pass lcommands[1:] in the recursive call to get the rest of the command list.

Problem 7. Fill in the missing code for handling rotate commands (marked in You will want to use rotate_around_origin(curve, rotate_angle) somewhere in your code to rotate every curve after the rotate command by the rotate_angle. The rotate_around_origin function was introduced briefly above and is defined in

You can test your code by drawing the curve that results from any list of L-system commands that does not use offshoots. For example, evaluating the code below should produce a "V":

lcommands = parse_lsystem_sequence("[R150, F, R-120, F]")
curvelist = convert_lcommands_to_curvelist(lcommands)
c1 = connect_curves_evenly(curvelist)
c2 = translate(c1, 0.3, 0.7)
c3 = position_curve(c2, 0, 0.5)
draw_curve_points(c3, 10000)

Problem 8. Fill in the missing code for handling offshoot commands (marked in (See hints below for help.)

We have provided the position_curve function (defined in to make it easier to fit fractals into the graphics window:

`position_curve(curve, startx, starty)` returns a curve that translates `curve` to start at `(startx, starty)` and scales it to fit into the graphics window maintaining the aspect ratio (the x and y dimensions are both scaled the same amount).

Now, you should be able to draw any L-System command list using position_curve and the convert_lcommands_to_curvelist function you have now completed.

Try drawing a few simple L-System command lists before moving on to the next part. For example, given this input:

    convert_lcommands_to_curvelist(TREE_COMMANDS)), 0.5, 0.1), 5000)

Your output should look like this:

Problem 9. Define a function, make_lsystem_fractal that takes three parameters: replace_commands, a list of L-System commands that replace forward commands in the rewriting; start, a list of L-system commands that describes the starting curve; and level, the number of iterations to apply the rewrite rule. It should return a sequence of L-System commands that results from repeating the replacement rule level times, starting from the start commands. (Hint: You should use the rewrite_lcommands you defined in Problem 5.)

You should now be able to draw a tree fractal using make_tree_fractal and draw_lsystem_fractal (these and the TREE_COMMANDS list of L-system commands are defined in

def make_tree_fractal(level):
    make_lsystem_fractal(TREE_COMMANDS, [make_forward_command()], level)

def draw_lsystem_fractal(lcommands):
            0.5, 0.1), 

For example, draw_lsystem_fractal(make_tree_fractal(3)) should produce a tree fractal with 3 levels of branching.

Draw some fractals by playing with the L-system commands. Try changing the rewrite rule, the starting commands, level and leaf curve (in convert_lcommands_to_curvelist) to draw an interesting fractal. You might want to make the branches colorful, or even include curves that change color along the points.

Try and make a fractal picture that will make a better course logo than the Great Lambda Tree of Infinite Knowledge and Ultimate Power.

Your last-displayed fractal image file is saved in fractal.svg in your current directory. It is overwritten every time, so make sure to save any interesting fractals with different filenames.

Problem 10. Submit your best fractal image to the #general channel on slack. You can modify any of the code you want, as well as do extra drawing in addition to the fractal to make a cool image. If your image is interesting or beautiful enough, you may receive untold fame and eternal glory by having it be used as the course logo, or at least on a lecture slide! Include all the code you used to generate the image in your submission. If you want to use another tool to convert the .svg image to another image format like .png or .jpg that will display in-line in slack that's fine (but not necessary).

Credits: This problem set was originally created for CS200 Spring 2002 by Dante Guanlao, Jon Erdman and David Evans, and revised for CS200 Spring 2003 by Jacques Fournier and David Evans, and revised for CS200 Spring 2004, CS150 Fall 2005, and CS150 Spring 2007 by David Evans. A version of this assignment was also used at the University of Georgia. Most recently, it was modified for CS 150 in Spring 2009 by Westley Weimer (including adding the Extra Credit section), and updated for cs1120 Fall 2009 by David Evans. Jonathan Burket and Lenny Li adapted it for Python in 2012, and Yuchi Tian revised it for Python 3 in 2016.