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ECE-C301 Programming for Engineers

Programming Assignment 1
1 The Big Idea
In this assignment you will be programming a simulation known as Conway’s Game of Life. As shown in
Fig. 1, the simulation consists of a world defined by an N ×M pixel grid. In this world, pixels represent cells
that can take on one of two different states: living cells are drawn as black pixels and dead cells are drawn
as white pixels. The state of each cell within the world may change as the simulation runs forward in time.
We determine if a particular cell will be alive or dead in the next frame by examining the current frame and
applying an update rule. In Conway’s Game of Life, this update rule consists of inspecting the state of the
eight cells immediately surrounding the cell being updated. Living cells with too many neighbors die due to
starvation, living cells with too few neighbors die due to under population, and dead cells with neither too
many nor too few neighbors come to life as a result of reproduction within a hospitable environment.
(a) Frame n (b) Frame n + 1 (c) Frame n + 2

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ECE-C301 Programming for Engineers

Programming Assignment 1
1 The Big Idea
In this assignment you will be programming a simulation known as Conway’s Game of Life. As shown in
Fig. 1, the simulation consists of a world defined by an N ×M pixel grid. In this world, pixels represent cells
that can take on one of two different states: living cells are drawn as black pixels and dead cells are drawn
as white pixels. The state of each cell within the world may change as the simulation runs forward in time.
We determine if a particular cell will be alive or dead in the next frame by examining the current frame and
applying an update rule. In Conway’s Game of Life, this update rule consists of inspecting the state of the
eight cells immediately surrounding the cell being updated. Living cells with too many neighbors die due to
starvation, living cells with too few neighbors die due to under population, and dead cells with neither too
many nor too few neighbors come to life as a result of reproduction within a hospitable environment.
(a) Frame n (b) Frame n + 1 (c) Frame n + 2
Fig. 1: Three frames from a running simulation of Conway’s Game of Life. Each pixel represents a living cell (black)
or a dead cell (white). The number of living cells immediately adjacent to a cell determines if it is alive or dead in
the next frame of the simulation. Each cell has eight immediately adjacent neighbors. The particular pattern shown
here oscillates between two states.
These simple biologically inspired rules for determining if a single cell should be alive or dead lead to some
pretty astounding results when simulated. You will notice that certain stable and oscillatory stable patterns
that tend to behave like cellular organisms emerge from the chaos.
The process of building a world grid, implementing an update rule, and managing boundary conditions are
all fundamental elements of designing and implementing a broad class of computer simulations you will
encounter within engineering. This project will expose you to these concepts and their implementation.
1
2 The Update Rule
Simulations step through simulation time (usually at a rate that is different than real time) and produce a
result at each step. The results produced at each step in the simulation are generated by applying an update
rule. The update rule is applied to the results of the previous step in order to generate the results at the
next step. The update rule for Conway’s Game of Life is simple:
• If a cell is alive and has less than 2 living neighbors, it dies due to underpopulation. See Fig. 2(a)
• If a cell is alive and has more than 3 living neighbors, it dies due to overpopulation. See Fig. 2(b)
• If a cell is dead and has exactly 3 living neighbors, it comes to life due to reproduction. See Fig. 2(c)
• Otherwise, cells maintain their current state. See Fig. 2(d)
A
B
1
(a) Under Population – Both cells A and B will
be dead in the next frame because they have less
than 2 living neighbors.
B A
1
2
3
4
1 2 3
5 4
(b) Over Population – Both cells A and B will be
dead in the next frame because they have more than
3 living neighbors.
A
B
1 2
3 1
2
3
(c) Reproduction – Both cells A and B become
alive in the next frame because they have exactly 3
living neighbors.
B A
1 2 1
2
3
(d) Stable Population – Both cells A and B will
stay alive in the next frame because the number of
neighbors is either 2 or 3.
Fig. 2: Update Rule Examples – The number of living cells surrounding any given living cell determines if it will
be dead (a,b) or alive (c) in the next frame. Likewise, the number of living cells surrounding a dead cell determines
if it will come back to life (d) in the next frame.
2
3 Getting Ready: Basic Program Organization
The simulation program begins by running the function main(), which performs 5 simple operations:
1. Gets the command line options provided by the user
2. Generates the simulation world
3. Prints the user supplied options to the screen
4. Adds a known pattern to the simulation (in this case a glider )
5. Starts the simulation loop
1 def main():
2 “””
3 The main function — everything starts here!
4 “””
5 opts = get_commandline_options()
6 world = generate_world(opts)
7 report_options(opts)
8
9 blit(world, patterns.glider, 20, 20)
10
11 run_simulation(opts, world)
12
13 if __name__ == ’__main__’:
14 main()
Let’s first focus on getting command line options from the user. We will take a look at each of the other
operations in later sections.
This program uses a standard Python module called argparse. The gameoflife.py skeleton code provided
to you dedicates an entire function for dealing with getting commandline options that have been supplied
by the user: get commandline options():
1 def get_commandline_options():
2 parser = argparse.ArgumentParser()
3
5 help=’type of world to generate’,
6 action=’store’,
7 type=str,
8 dest=’world_type’,
9 default=’empty’)
10
11 opts = parser.parse_args()
12
13 return opts
The code shown above is an abridged version of the actual function in the gameoflife.py skeleton in that
it only defines a single option, which is used to set the type of world to be used in the simulation. This
option allows the user to set the string stored in a variable named world type. For example, if the user
wanted your program to start with a world populated by randomly set pixels, they might supply the following
argument at the commandline:
3
1 \$ python gameoflife.py –world random
Once your program executed the line:
opts = parser.parse_args()
the value supplied by the user, in this case: ’random’, would be stored in an attribute of the opts object
named world type. So, if you read the value of opts.world type, you would get the value of ’random’
supplied by the user at the command line. Later in your program were the world is generated, you would
first read opts.world type to determine the type of world the user asked for and generate accordingly. This
4 Task 1: Creating the World
As mentioned previsouly, for this assignment you are provided with incomplete skeleton code for Conway’s
game of life. Your first task will be to generate the 2D matrix that will hold the state of the world. This is
done in the function generate world():
1 def generate_world(opts):
2 “””
3 Accepts: opts — parsed command line options
4 Returns: world — a list of lists that forms a 2D pixel buffer
5
6 Description: This function generates a 2D pixel buffer with dimensions
7 opts.cols x opts.rows (in pixels). The initial contents
8 of the generated world is determined by the value provided
9 by opts.world_type: either ’random’ or ’empty’ A ’random’
10 world has 10% ’living’ pixels and 90% ’dead’ pixels. An
11 ’empty’ world has 100% ’dead’ pixels.
12 “””
13 world = []
14
16 #
17 # [ YOUR CODE GOES HERE ]
18 #
19 #######################################################################
20
21 return world
As you can see by inspecting the main() function of the simulation, the function generate world() will only
be called once at the start of the simulation. This same world will be used throughout the entire simulation
and will be updated at every simulation step.
For this task your program should generate an empty 2D world if the user specifies the commandline option
–world empty and a world with a randomly distributed 10% of the pixels being alive and 90% dead if the
user specifies –world random
Upon sucessfully completing this task, you should be able to run gameoflife.py and the simulation world
will be displayed.
4
5 Task 2: Writing a Blitter
A blitter (short for block image transferer) copies a smaller block of pixel data into a larger pixel buffer at
a particular x, y coordinate. You are going to write a blitting function in order to help you test out the
simulation – this will allow you place specific patterns of cells into the simulation, which will make testing
the correctness of your update rule implementation (i.e. Task 3) much easier.
Again refer to the code in the function main(). You will see that once the world is created, we attempt to
copy in the pattern of a glider into the world at coordinates (20, 20):
blit(world, patterns.glider, 20, 20)
Notice that patterns is the name space given to our imported file patterns.py. In this file, I have provided
some common patterns with known behaviors. If you open this file, you will see that I have divided the
different patterns into groups:
• Stills – These are patterns that do not change when the update rule is ran. They are statically stable.
• Oscillators – These are patterns that don’t move around within the world (i.e. their coordinates don’t
change) but they are in some way animated with a set period.
• Spaceshipts – These are patterns that do move around within the world.
• Generators – These are patterns that are capable of producing other independent patterns.
In the gameoflife.py skeleton provided, the function blit() doesn’t do anything:
1 def blit(world, sprite, x, y):
2 “””
3 Accepts: world — a 2D world pixel buffer generated by generate_world()
4 sprite — a 2D matrix containing a pattern of 1s and 0s
5 x — x world coord where left edge of sprite will be placed
6 y — y world coord where top edge of sprite will be placed
7
8 Returns: (Nothing)
9
10 Description: Copies a 2D pixel pattern (i.e sprite) into the larger 2D
11 world. The sprite will be copied into the 2D world with
12 its top left corner being located at world coordinate (x,y)
13 “””
15 #
16 # [ YOUR CODE GOES HERE ]
17 #
18 #######################################################################
It is your job to populate this function. Test your implementation by generating an empty simulation world
and placing a glider pattern at coordinates (20, 20).
5
6 Task 3: Implementing the Update Rule
Once the world has been generated and you have added a pattern or two using the blitter, you are ready to
start the simulation—this is done by calling run simulation().
run_simulation(opts, world)
This function creates a figure using matplotlib and plots each pixel in the world grid using the imshow()
function. Subsequently, FuncAnimation() is called:
1 fig = plt.figure()
2 img = plt.imshow(world, interpolation=’none’, cmap=’Greys’, vmax=1, vmin=0)
3 ani = animation.FuncAnimation(fig,
4 update_frame,
5 fargs=(opts, world, img),
6 interval=opts.framedelay)
7
8 plt.show()
The fourth argument supplied to FuncAnimation() specifies how frequently the plot should be automatically
updated (in milliseconds). As you can see, this is set on the command line using the –framedelay option
(See Section 3 and get commandline options() in gameoflife.py). The second argument supplied to
FuncAnimation() specifies the function that is called for each frame update. The third argument allows you
to pass function parameters to the update function in the form of a tuple. In this case we are passing the
opts, world, and img parameters to the update frame() function1
. This means that all you need to do to
implement the update rule is implement the function update frame():
1 def update_frame(frame_num, opts, world, img):
2 “””
3 Accepts: frame_num — (automatically passed in) current frame number
4 opts — a populated command line options instance
5 world — the 2D world pixel buffer
6 img — the plot image
7 “””
8
9 img.set_array(world)
10
11 new_world = []
12 for row in world:
13 new_world.append(row[:])
14
16 #
17 # [ YOUR CODE GOES HERE ]
18 #
19 #######################################################################
20
21 # Copy the contents of the new_world into the world
22 # (i.e. make the future the present)
23 world[:] = new_world[:]
24 return img,
1
If you look at the definition of the update function update frame(), you will notice that it accepts 4 parameters instead of
3. This is because the frame number is automatically passed in as the first argument when the update function is called. Any
arguments passed in as a tuple through the third argument of FuncAnimation() are passed in after the frame number argument.
6
Compute
Next Frame
O!screen
Frame Bu!er
(Contains World Frame n)
O!screen Bu!er
(Write Update Rule Results Here) Copy to Frame Bu!er
Frame Bu!er
(Contains World Frame n + 1)
Notes:
— A “Frame Bu!er” is simply a matrix that
contains values that are being displayed
on the screen. Updating this matrix
changes what is drawn to the screen.
— The O!screen Bu!er is used because
perfoming the update ‘in-place’ will
result in the wrong answer. Why?
Fig. 3: Graphical depiction of the frame update process.
Here, world contains the current world state, and new world will be used to hold the solution to applying
the update rule. Once the next frame has been generated, the solution is copied back into world for display.
This process is visually depicted in Fig. 3.
6.1 Imposing Boundary Conditions
In Conway’s Game of Life, the number of neighbors surrounding a cell determine if it lives or dies. Generally,
cells have 8 surrounding neighbors. However, cells lying on the border of the image only have 5 neighbors,
and corner cells only have 3 neighbors.
We can make all cells have 8 neighbors by implementing the boundary condition that the “world wraps
around.” In other words, all cells on the left edge of the world should be considered to be neighbors to the
right of all cells on the right edge of the world. A similar condition should be implemented for the top and
bottom of the world.
6.2 How to Test it
Generate an empty world using –world empty and place a static pattern into the world using your blit()
function. The static pattern should not change if your update function is correct.
7
Next, test your update function using a more complex oscillating pattern. Check the Game of Life entry
on Wikipedia for animations that show what the oscillating patterns should look like if your update rule is
correct.
Finally, test your update function using the glider spaceship pattern. If your update rule and boundary
conditions are correct, the glider should move diagonally towards the bottom right of the world and wrap
around to the top left of the world when it hits the edge.
Once your simulation if finally verified as working, try out the gosper pattern.
7 Deliverables
Your deliverables for this project are a working implementation of Conway’s Game of Life that you wrote
as well as a report detailing the project, the theory, your implementation, and your testing results including
screenshots. Please do not submit other students work.
Submit your report in PDF format.
Submit your complete source code as a zip file. (Do NOT include your report in this zip file.)
NOTE: In order for the matplotlib windows to open, you must enable X11 tunneling! The
TA will explain how to do this in the beginning of lab in Week 1. If you are connecting to
thanos.ece.drexel.edu on Windows, make sure X-Win32 is running (it renders the graphical
windows for you!). Additionally, using X11 tunneling to display windows will probably be
incredibly slow if you are working from home. I suggest that you work on this assignment
in the labs. You can also install Python 2.7 and matplotlib on your personal computer as an
alternative, which doesn’t require you to connect to thanos.
8