April 4th, 2013, 08:05 AM

Boids in python
Hello everyone,
In my study of the flocking of birds, i am interested in a pythoncode for boids to do a simulation. Because i am not a programming pro it would be very kind if someone could help me with a clear and relatively easy code.
Thanks
April 4th, 2013, 11:34 AM

I: please find an open source boids simulation as a start.
II: please hire someone.
III: looks interesting.
Originally Posted by wikipedia
separation: steer to avoid crowding local flockmates
alignment: steer towards the average heading of local flockmates
cohesion: steer to move toward the average position (center of mass) of local flockmates
A: how many dimensions is the simulation? Doesn't matter much but 1 dimension sounds stupid, and 4 or more dimensions is hard to display, and 2 dimensions is easiest.
B: I'd propose the inputs (volume can be specified as radius) are
i. starting volume and particle density.
ia. volume defining "local".
ii. particle speed. Could be a distribution in which case I suppose the slow particles would move more efficiently at the back of the flock. Easiest to assign them all the same speed.
iii. weights (importance) for separation, alignment, and cohesion.
iv. time step.
These are my initial thoughts having never heard (at least not within my memory) of a boids simulation.
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April 4th, 2013, 02:24 PM

Wow, thanks for the quick reply!
Unfortunately, as a simple student i can't afford to hire someone, so it's up to me and people who are so kind to help
This is what I already have:
WIDTH = 1000 # WIDTH OF SCREEN IN PIXELS
HEIGHT = 500 # HEIGHT OF SCREEN IN PIXELS
BOIDS = 20 # NUMBER OF BOIDS IN SIMULATION
SPEED_LIMIT = 500 # FOR BOID VELOCITY
BOID_EYESIGHT = 50 # HOW FAR A BOID CAN LOOK
WALL = 50 # FROM SIDE IN PIXELS
WALL_FORCE = 100 # ACCELERATION PER MOVE
################################################################################
########################
import random
boids = []
for a in range (BOIDS):
boid()
x_pos=boid.append(random.uniform(0,WIDTH))
y_pos=boid.append(random.uniform(0,HEIGHT)
x_vel=boid.append(random.uniform(0,SPEED_LIMIT)
y_vel=boid.append(random.uniform(0,SPEED_LIMIT)
boids.append(boid)
print boids
#afstand
for x_pos in range(0,WIDTH):
for y_pos in range (0,HEIGHT):
dist = sqrt( (x_pos  x_pos)**2 + (y_pos  y_pos)**2 )
########################
################################################################################
import show
show.start(boids, move)
I am also aware of the fact that i have to implement functions, that calculate the velocity, the new neighbours, combine the rules, limit the velocity, and important is the function 'move' that updates the boid, regarded all these rules.
For a handy link: google conrad pseudocode
It would be amazing if you could help me out!!!
April 4th, 2013, 02:35 PM

update position comes with these rules:
b(vel)=new(vel)
b(pos)=b(pos)+new(vel)/200
April 5th, 2013, 10:28 PM

nomenclature: I've used "boid" and "particle" interchangeably.
Complex numbers can represent position and velocity. The method works because you never have to multiply complex numbers. They'll add, they'll scale by real numbers, it all works. Your distance function can just be abs .
13 == abs(complex(5,12))
LABEL1234:
How do you compute local flockmates? (more efficient methods exist) The easiest thing that could work is to subtract every other position from the position under consideration. If the length (abs) of that vector is less than the vision distance the particle is in that particle's local set. These are the flockmates of the boid.
Let's see how to compute each term contributing to the heading.
separation: steer to avoid crowding local flockmates.
Find the vectors to each local flockmate as before, just a subtraction. Average these vectors, then negate and normalize the result. With complex numbers you don't need to worry about components, and you don't need numpy.
alignment: steer towards the average heading of local flockmates. The alignment contribution is the average of the directions of the flockmates. Normalize the speed per boid first.
Code:
>>> velocity = complex(3,4)
>>> normalized_velocity = velocity/abs(velocity)
>>> print(normalized_velocity)
(0.6+0.8j)
>>>
cohesion: steer to move toward the average position (center of mass) of local flockmates. That's (the sum divided by tally) of local flockmates. Normalize.
Use a weighted average to determine the new heading, and scale by the speed.
If there's another boid this time step go to LABEL1234.
I've ignored your wall acceleration.
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April 6th, 2013, 07:20 AM

ok, that's quite understandable! But my problem is the translation from the 'english' understanding of the problem to the python code. I had already numerous syntax errors in my first part of the code :s
And what do you mean with LABEL1234?
You're really helpful, thanks for the effort!!!!
April 6th, 2013, 08:54 AM

By replying to your message we see the your original indentation. Use an editor that matches parentheses and otherwise takes care of your python code. emacs.
Code:
import random
boids = []
for a in range (BOIDS):
boid()
x_pos=boid.append(random.uniform(0,WIDTH))
y_pos=boid.append(random.uniform(0,HEIGHT) ################ missing right )
x_vel=boid.append(random.uniform(0,SPEED_LIMIT)################ missing right )
y_vel=boid.append(random.uniform(0,SPEED_LIMIT)################ missing right )
boids.append(boid) ################ indentation certainly wrong
print boids
#afstand
for x_pos in range(0,WIDTH):
for y_pos in range (0,HEIGHT):
################IndentationError: expected an indented block
dist = sqrt( (x_pos  x_pos)**2 + (y_pos  y_pos)**2 )
Last edited by b49P23TIvg; April 6th, 2013 at 08:59 AM.
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