study/2019-ics-malariafreek
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Sijmen 2019-03-08 12:20:59 +01:00
parent ff7cc3f8f3
commit 8cdfa7b878
1 changed files with 37 additions and 29 deletions
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malaria.py
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@ -11,7 +11,7 @@ class Model:
hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-6, hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-6,
mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000, mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000,
graphical=True): graphical=True):
self.width = width self.width = width
self.height = height self.height = height
@ -59,17 +59,16 @@ class Model:
if self.graphical: if self.graphical:
self.init_draw() self.init_draw()
def init_draw(self): def init_draw(self):
plt.ion() plt.ion()
self.colors = matplotlib.colors.ListedColormap( self.colors = matplotlib.colors.ListedColormap(
["black", "green", "red", "yellow"]) ["black", "green", "red", "yellow"])
bounds = [Human.DEAD, Human.HEALTHY, Human.INFECTED, Human.IMMUNE]
self.norm = matplotlib.colors.BoundaryNorm(bounds, self.colors.N)
def recycle_human(self): def recycle_human(self):
""" """
Determine if a human dies of natural causes and then replace them by a new human Determine if a human dies of natural causes and then replace them by a
new human.
""" """
# Get all living humans # Get all living humans
humans = np.transpose(np.where(self.grid != Human.DEAD)) humans = np.transpose(np.where(self.grid != Human.DEAD))
@ -88,13 +87,15 @@ class Model:
self.stats["natural deaths"] += death_count self.stats["natural deaths"] += death_count
# Pick a random, unpopulated spot # Pick a random, unpopulated spot
births = np.array(random.sample(list(np.transpose(np.where(self.grid == Human.DEAD))), births = np.array(random.sample(
death_count)) list(np.transpose(np.where(self.grid == Human.DEAD))),
death_count))
# Deliver the newborns # Deliver the newborns
for birth in births: for birth in births:
self.grid[birth[0]][birth[1]] = np.random.choice([Human.HEALTHY, Human.IMMUNE], self.grid[birth[0]][birth[1]] = \
p=[1-self.immunepct, self.immunepct]) np.random.choice((Human.HEALTHY, Human.IMMUNE),
p=(1 - self.immunepct, self.immunepct))
def do_malaria(self): def do_malaria(self):
""" """
@ -112,10 +113,10 @@ class Model:
self.stats["malaria deaths"] += len(np.where(deaths)[0]) self.stats["malaria deaths"] += len(np.where(deaths)[0])
def feed(self): def feed(self):
#TODO: dit refactoren?
""" """
Feed the mosquitos that want to and can be fed Feed the mosquitos that want to and can be fed
""" """
# TODO: dit refactoren?
for mos in self.mosquitos: for mos in self.mosquitos:
if not mos.hungry: if not mos.hungry:
continue continue
@ -125,12 +126,15 @@ class Model:
if state != Human.DEAD: if state != Human.DEAD:
self.stats["mosquitos fed"] += 1 self.stats["mosquitos fed"] += 1
mos.hungry = False mos.hungry = False
# check if healthy human needs to be infected or mosquito becomes infected from eating # check if healthy human needs to be infected or mosquito
if state == Human.HEALTHY and mos.infected and random.uniform(0, 1) < self.mh_infpct: # becomes infected from eating
if state == Human.HEALTHY and mos.infected \
and random.uniform(0, 1) < self.mh_infpct:
self.grid[mos.x, mos.y] = Human.INFECTED self.grid[mos.x, mos.y] = Human.INFECTED
self.stats["humans infected"] += 1 self.stats["humans infected"] += 1
elif state == Human.INFECTED and not mos.infected and random.uniform(0, 1) < self.hm_infpct: elif state == Human.INFECTED and not mos.infected \
and random.uniform(0, 1) < self.hm_infpct:
self.stats["mosquitos infected"] += 1 self.stats["mosquitos infected"] += 1
mos.infected = True mos.infected = True
@ -139,8 +143,9 @@ class Model:
Determines which mosquitos should get hungry Determines which mosquitos should get hungry
""" """
for mos in self.mosquitos: for mos in self.mosquitos:
mos.hungry = not mos.hungry and random.uniform(0, 1) < self.mhungrypct mos.hungry = not mos.hungry and \
random.uniform(0, 1) < self.mhungrypct
def get_movementbox(self, x: int, y: int): def get_movementbox(self, x: int, y: int):
""" """
Returns indices of a moore neighbourhood around the given index Returns indices of a moore neighbourhood around the given index
@ -151,8 +156,9 @@ class Model:
y_min = (y - 1) y_min = (y - 1)
y_max = (y + 1) y_max = (y + 1)
indices = [(i % self.width, j % self.height) for i in range(x_min, x_max + 1) indices = [(i % self.width, j % self.height)
for j in range(y_min, y_max + 1)] for i in range(x_min, x_max + 1)
for j in range(y_min, y_max + 1)]
# remove current location from the indices # remove current location from the indices
indices.remove((x, y)) indices.remove((x, y))
@ -168,8 +174,8 @@ class Model:
movement = self.get_movementbox(mosq.x, mosq.y) movement = self.get_movementbox(mosq.x, mosq.y)
# check for nets, and thus legal locations to go for the mosquito # check for nets, and thus legal locations to go for the mosquito
legal_moves = np.where(self.nets[tuple(movement.T)] == False)[0] legal_moves = np.where(~self.nets[tuple(movement.T)])[0]
# choose random new position # choose random new position
new_pos = random.choice(legal_moves) new_pos = random.choice(legal_moves)
@ -219,7 +225,7 @@ class Model:
Generates the grid of nets Generates the grid of nets
""" """
return np.random.choice([False, True], return np.random.choice([False, True],
p=[1-self.mosqnetdens, self.mosqnetdens], p=[1-self.mosqnetdens, self.mosqnetdens],
size=(self.width, self.height)) size=(self.width, self.height))
@ -228,7 +234,8 @@ class Model:
This functions runs the simulation This functions runs the simulation
""" """
print(chr(27) + "[2J") print(chr(27) + "[2J")
# actual simulation runs inside try except to catch keyboard interrupts and always print stats # Actual simulation runs inside try except to catch keyboard interrupts
# and always print stats
try: try:
for t in range(self.time_steps): for t in range(self.time_steps):
print("Simulating timestep: {}".format(t), end='\r') print("Simulating timestep: {}".format(t), end='\r')
@ -237,7 +244,7 @@ class Model:
self.draw(t) self.draw(t)
except KeyboardInterrupt: except KeyboardInterrupt:
pass pass
print(chr(27) + "[2J") print(chr(27) + "[2J")
self.compile_stats() self.compile_stats()
self.print_stats() self.print_stats()
@ -246,8 +253,9 @@ class Model:
""" """
Compiles a comprehensive list of statistics of the simulation Compiles a comprehensive list of statistics of the simulation
""" """
self.stats["total deaths"] = self.stats["malaria deaths"] + self.stats["natural deaths"] self.stats["total deaths"] = \
self.stats["malaria deaths"] + self.stats["natural deaths"]
# print(np.where(self.nets)) # print(np.where(self.nets))
self.stats["net count"] = len(np.where(self.nets)[0]) self.stats["net count"] = len(np.where(self.nets)[0])
@ -272,7 +280,7 @@ class Model:
self.feed() self.feed()
# make mosquitos hungry again # make mosquitos hungry again
self.determine_hunger() self.determine_hunger()
def draw(self, t: int): def draw(self, t: int):
""" """
Draws the grid of humans, tents and mosquitos Draws the grid of humans, tents and mosquitos
@ -280,8 +288,8 @@ class Model:
# this function draws the humans # this function draws the humans
plt.title("t={}".format(t)) plt.title("t={}".format(t))
# draw the grid # draw the grid
plt.imshow(self.grid, cmap=self.colors, norm=self.norm) plt.imshow(self.grid, cmap=self.colors)
# draw nets # draw nets
net_locations = np.where(self.nets) net_locations = np.where(self.nets)
plt.plot(net_locations[0], net_locations[1], 'w^') plt.plot(net_locations[0], net_locations[1], 'w^')
@ -301,7 +309,7 @@ class Mosquito:
self.infected = infected self.infected = infected
self.hungry = hungry self.hungry = hungry
def get_color(self): def get_color(self):
# returns the color for drawing, red if infected blue otherwise # returns the color for drawing, red if infected blue otherwise
return "r" if self.infected else "b" return "r" if self.infected else "b"