def egg_generate(egg_size):
egg_start_pos = [
random.randint(0, gv.display_width),
random.randint(0, gv.display_height)
]
# egg_start_pos = [int(gv.display_width/2), int(gv.display_height/2)]
return Egg.Egg(egg_size, egg_start_pos)
def layeggs(self, batchsize) -> None:
# Adds normal distribution with avgeggbatch mean number of eggs to the model or fixed number from avgeggbatch
# TODO: Optimize by doing total survival calc in one go
for i in range(batchsize):
# Each offspring has same starting location as egg-laying location
a = Egg.Egg(self.model.getid(), self.model)
self.model.schedule.add(a)
self.model.grid.place_agent(a, self.pos)
def __init__(self,
NumMidges,
NumDeer,
width=100,
height=100,
mapfile=None,
trapfile=None,
dps=0.5):
Midge.Midge.setdps(dps)
Midge.Midge.createdpsarray(dps)
self.NumMidges = NumMidges # Starting number of midges
self.NumDeer = NumDeer # Starting number of deer
self.idcounter = 0 # Global counter for id of agents, increments with every new agent creation
self.mapfile = mapfile # Argument passed to decide on which map data to use (must be in CSV format)
self.trapfile = trapfile # File used to decide where the traps will be placed, used to replicate data in paper
self.running = True # Used for running model in batches
self.midges = [
] # List of all current midges, can set to update every step
self.traps = [] # List of all trap agents
self.deer = [] # List of all deer agents
self.dps = dps # Daily probability of survival for all midges
# Time (days) since simulation has begun
self.day = 0
# Activates the step function for midges sequentially (no order needed)
self.schedule = BaseScheduler(self)
# Create a grid model with potential for multiple agents on one cell
self.grid = ContinuousSpace(width, height, torus=False)
# Open the map file and create a csvreader object to be used by every midge
self.mapdata = open(self.mapfile, 'r')
self.csvreader = csv.reader(self.mapdata, delimiter=',')
# Create a blank map array filled with the generic 'lightgreen' value
self.maparray = np.full(shape=(width, height),
fill_value=BiomeCell.BiomeCell('lightgreen'),
dtype=BiomeCell.BiomeCell)
# Add all biome types from the mapfile to the map array
with open(self.mapfile, 'r', encoding='utf-8-sig') as csvfile:
csvreader = csv.reader(csvfile, delimiter=',')
for l in csvreader:
x, y = int(l[0]), int(l[1])
self.maparray[x][y] = BiomeCell.BiomeCell(l[2])
# # Adds midges to random location in grid and to queue in scheduler
for i in range(self.NumMidges):
x = self.random.random() * self.grid.width
y = self.random.random() * self.grid.height
a = Midge.Midge(self.getid(), self)
# Gives midges random probabilities of having fed and are in stages of gonotrophic cycle
a.fed = True
a.timesincefed = np.random.randint(0, Midge.Midge.gtrclength)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
# Adds some eggs to the simulation as well, just to even out the population variability
for i in range(10000):
x = self.random.random() * (self.grid.width)
y = self.random.random() * (self.grid.height)
a = Egg.Egg(self.getid(), self)
# Gives eggs random age
a.age = random.randint(-Egg.Egg.growthtime, Egg.Egg.growthtime)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
# # Adds traps to random locations in the grid from the trap location file and to the queue in scheduler
# with open(self.trapfile, 'r', encoding='utf-8-sig') as csvfile:
# csvreader = csv.reader(csvfile, delimiter=',')
# for l in csvreader:
# x, y = int(l[0]), int(l[1])
# a = Trap.Trap(self.getid(), self)
# self.schedule.add(a)
# self.grid.place_agent(a, (x, y))
# Adds deer to random locations in the grid and to queue in scheduler
for i in range(self.NumDeer):
x = self.random.random() * (self.grid.width)
y = self.random.random() * (self.grid.height)
a = Deer.Deer(self.getid(), self)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
# Add 10 deer with BTV as a case study
for i in range(10):
a = Deer.Deer(self.getid(), self)
a.hasbtv = True
self.schedule.add(a)
self.grid.place_agent(a, (self.random.random() * self.grid.width,
self.random.random() * self.grid.height))
self.traps = [i for i in self.schedule.agents if type(i) == Trap.Trap
] # Fills list of traps (only once)
self.deer = [i for i in self.schedule.agents
if type(i) == Deer.Deer] # Fills list of deer (only once)
self.targets = np.array(
combinelists(self.traps, self.deer), dtype=Target.Target
) # Combines all subclasses of the target class into one list
self.deerbites = 0 # Global counter of the total number of deer bites
# TODO: Implement datacollector that can collect data from different agent types (gonna be a pain in my ass)
self.datacollector = DataCollector(
model_reporters={
"Day": "day",
"MidgePop": "NumMidges",
"DeerBites": "deerbites",
"DeerwBTV": "DeerwBTV",
"NumVectors": "NumVectors",
"DPS": "DPS",
"MidgeAges": "MidgeAges",
"AvgAge": "AvgAge"
})
