study/2019-ics-malariafreek
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people now die randomly

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Chris Bras 2019-03-07 19:53:26 +01:00
parent 8a7eb62af4
commit 89e9b4f2f8
2 changed files with 21 additions and 10 deletions
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malaria.py
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@ -8,7 +8,7 @@ from enum import IntEnum
class Model: class Model:
def __init__(self, width=32, height=32, humandens=0.15, mosquitodens=0.10, def __init__(self, width=32, height=32, humandens=0.15, mosquitodens=0.10,
immunepct=0.1, mosqinfpct=0.1, hm_infpct=0.5, mh_infpct=0.5, immunepct=0.1, mosqinfpct=0.1, hm_infpct=0.5, mh_infpct=0.5,
hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-6, hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-3,
mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000): mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000):
plt.ion() plt.ion()
@ -40,7 +40,7 @@ class Model:
# The number of timesteps to run te simulation for # The number of timesteps to run te simulation for
self.time_steps = time_steps self.time_steps = time_steps
self.humans = self.gen_humans() self.grid = self.gen_humans()
self.mosquitos = self.gen_mosquitos() self.mosquitos = self.gen_mosquitos()
self.init_draw() self.init_draw()
@ -52,31 +52,41 @@ class Model:
def recycle_human(self): def recycle_human(self):
# Get all living humans # Get all living humans
humans = np.transpose(np.where(self.humans != Human.DEAD)) humans = np.transpose(np.where(self.grid != Human.DEAD))
# Get a mask of humans to kill # Get a mask of humans to kill
deaths = np.random.rand(len(humans)) < self.humandiepct deaths = np.random.rand(len(humans)) < self.humandiepct
# Kill them. # Kill them.
self.humans[humans[deaths][:, 0], humans[deaths][:, 1]] = Human.DEAD self.grid[humans[deaths][:, 0], humans[deaths][:, 1]] = Human.DEAD
# get num humans after killing
humans_survive = len(np.transpose(np.where(self.grid != Human.DEAD)))
death_count = len(humans) - humans_survive
# Pick a random, unpopulated spot # Pick a random, unpopulated spot
x, y = random.choice(np.transpose(np.where(self.humans == Human.DEAD)), births = np.array(random.sample(list(np.transpose(np.where(self.grid == Human.DEAD))),
size=np.count_nonzero(deaths)) death_count))
# Deliver the newborns
for birth in births:
self.grid[birth[0]][birth[1]] = np.random.choice([Human.HEALTHY, Human.IMMUNE],
p=[1-self.immunepct, self.immunepct])
def do_malaria(self): def do_malaria(self):
""" """
This function determines who of the infected dies from their illness This function determines who of the infected dies from their illness
""" """
# Get all infected humans # Get all infected humans
infected = np.transpose(np.where(self.humans == Human.INFECTED)) infected = np.transpose(np.where(self.grid == Human.INFECTED))
# Decide which infected people die # Decide which infected people die
deaths = np.random.rand(len(infected)) < self.hinfdiepct deaths = np.random.rand(len(infected)) < self.hinfdiepct
# Now let's kill them # Now let's kill them
self.humans[infected[deaths][:, 0], infected[deaths][:, 1]] = Human.DEAD self.grid[infected[deaths][:, 0], infected[deaths][:, 1]] = Human.DEAD
def gen_humans(self): def gen_humans(self):
# Calculate the probabilities # Calculate the probabilities
@ -98,12 +108,13 @@ class Model:
self.draw(t) self.draw(t)
def step(self): def step(self):
self.do_malaria() # self.do_malaria()
self.recycle_human()
def draw(self, t): def draw(self, t):
# this function draws the humans # this function draws the humans
plt.title("t={}".format(t)) plt.title("t={}".format(t))
plt.imshow(self.humans, cmap=self.colors, norm=self.norm) plt.imshow(self.grid, cmap=self.colors, norm=self.norm)
plt.pause(0.0001) plt.pause(0.0001)
plt.clf() plt.clf()