def testDieOldSync(self):
env = environment.environment(5,'sync')
#Create an old fox
fox= agents.fox(age=1000, food=10, pos=[5.5,5.5], speed=2, last_breed=7)
fox.die(env)
self.failUnless(fox.messages.dead)
def testDieStarveSync(self):
env = environment.environment(5,'sync')
#Create a starving fox
fox= agents.fox(age=2, food=-1, pos=[5.5,5.5], speed=2, last_breed=7)
#Ensure that it knows that its time to die
fox.die(env)
self.failUnless(fox.messages.dead)
def testBreed4(self):
#Esnure that a fox that is not ready to breed, doesn't
env = environment.environment(5)
#Create a fox that's not ready to breed doesn't
fox= agents.fox(age=30, food=100, pos=[5.5,5.5], speed=2, last_breed=10)
new_fox = fox.breed(env)
self.failUnless(new_fox is None)
def testBreed3(self):
#Esnure that a breeding fox correctly zeros last_breed
env = environment.environment(5)
#Create a fox that's ready to breed
fox= agents.fox(age=30, food=100, pos=[5.5,5.5], speed=2, last_breed=21)
new_fox = fox.breed(env)
self.failUnless(new_fox.last_breed == 0 and new_fox.last_breed == 0)
def testBreed1(self):
#Esnure that a fox that can breed, does breed
env = environment.environment(5)
#Create a fox that's ready to breed
fox= agents.fox(age=30, food=100, pos=[5.5,5.5], speed=2, last_breed=21)
new_fox = fox.breed(env)
self.failUnless(isinstance(new_fox,agents.fox))
def create_agents(self, nr, nf, list_type='joined'):
"""
environment.create_agents(nr, nf) - Creates a list of agents
within the environment
Input Parameters
----------------
nr : integer
Number of rabbits
nf : integer
Number of foxes
list_type : string (default='joined')
Determines how the agents are stored.
'joined' - maintains a single list containing both
rabbits and foxes.
'seprate' - maintains separate lists of rabbits and foxes
(THIS WONT WORK WITH THE REST OF ECOLAB YET)
Output:
-------
agent_list : list
List(s) of generated agents
"""
# We create the transpose here in order to generate matrices
# that are identical to MATLAB's
# generate random initial positions for rabbits and foxes
rloc = np.transpose((self.bm_size-1)*rand(2, nr))+1
floc = np.transpose((self.bm_size-1)*rand(2, nf))+1
rabbits = []
foxes = []
for r in range(nr):
pos = rloc[r, :]
age = np.ceil(rand()*10)
food = np.ceil(rand()*20)+20
lbreed = np.round(rand()*rabbit.brdfq)
rabbits.append(rabbit(age, food, pos, rabbit.speed, lbreed))
for f in range(nf):
pos = floc[f, :]
age = np.ceil(rand()*10)
food = np.ceil(rand()*20)+20
lbreed = np.round(rand()*fox.brdfq)
foxes.append(fox(age, food, pos, fox.speed, lbreed))
if list_type == 'separate':
self.foxes = foxes
self.rabbits = rabbits
elif list_type == 'joined':
self.agents = rabbits
self.agents.extend(foxes)
def testEat1Async(self):
env = environment.environment(5,'async')
# Create a very fast fox
fox = agents.fox(age=2, food=10, pos=[1.5,1.5], speed=10, last_breed=7)
# create a rabbit very close to it
env.agents = [agents.rabbit(age=2, food=10, pos=[1.52,1.52], speed=10, last_breed=7)]
#Set the previous position of rabbit to be [1.52,1.52]
env.agents[0].messages.pos = [1.52, 1.52]
# Its almost certain that this fox ate this rabbit
fox.eat(env)
# Doex rabbit know that its been eaten?
self.failUnless(env.agents[0].has_been_eaten)
def testEat2(self):
# Ensure that the same rabbit doesn't get eaten multiple times in async mode
env = environment.environment(5,mode='async')
env.agents = []
# Surround a rabbit by 4 foxes in eating range
env.agents.append(agents.rabbit(20 ,30, [1.5, 1.5], 2, 1))
env.agents.append(agents.fox(20 ,30, [1.5, 1.25], 10, 10))
env.agents.append(agents.fox(20 ,30, [1.25, 1.5], 10, 10))
env.agents.append(agents.fox(20 ,30, [1.25, 1.25], 10, 10))
env.agents.append(agents.fox(20 ,30, [1.3, 1.25], 10, 10))
eaten1 = env.agents[1].eat(env)
eaten2 = env.agents[2].eat(env)
eaten3 = env.agents[3].eat(env)
eaten4 = env.agents[4].eat(env)
self.failUnless(eaten1, 'Fox 1 should have eaten but didn''t')
self.failUnless(not eaten2, 'Fox 2 should not have eaten but did')
self.failUnless(not eaten3, 'Fox 3 should not have eaten but did')
self.failUnless(not eaten4, 'Fox 4 should not have eaten but did')
self.failUnlessEqual(agents.rabbit.num_rabbits,0)
def testBreed2(self):
#Ensure that an eaten rabbit doesn't breed in async mode
env = environment.environment(5,mode='async')
env.agents = []
# Put a hungry fox next to a rabbit
env.agents.append(agents.fox(20,30,[1.5,1.5],5,10))
env.agents.append(agents.rabbit(20,30,[1.5,1.25],2,10))
eaten = env.agents[0].eat(env)
self.failUnless(eaten,'Fox should have eaten rabbit')
new = env.agents[1].breed(env)
self.failUnless(new is None,'Dead rabbit bred')
def testDieDoubleCount(self):
# Ensure that we don't decrement rabbit counter twice
# If an eaten rabbit was also ready to die
env = environment.environment(10,'async')
env.agents = []
# Put an old rabbit next to a hungry fox
env.agents.append(agents.rabbit(1000,30,[1.5,1.5],2,1))
env.agents.append(agents.fox(20,30,[1.5,1.25],10,10))
# Fox eats rabbit
eaten = env.agents[1].eat(env)
self.failUnless(eaten,'Fox didn''t eat rabbit')
# Apply die rule to the dead rabbit
env.agents[0].die(env)
self.failUnlessEqual(agents.rabbit.num_rabbits,0,'Should be 0 rabbits')
def testFoxMigrate(self):
numpy.random.seed(1)
fox= agents.fox(age=2, food=10, pos=[5.5,5.5], speed=2, last_breed=7)
fox.migrate(self.env)
self.failUnlessAlmostEqual(fox.pos[0], 3.765721, places=4, msg='Migrating fox has given different results from MATLAB')
self.failUnlessAlmostEqual(fox.pos[1], 6.496130, places=4, msg='Migrating fox has given different results from MATLAB')