study/2019-ics-malariafreek
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fix nets, improve drawing

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Sijmen 2019-03-08 15:40:50 +01:00
parent 8cdfa7b878
commit 9c467e1878
1 changed files with 85 additions and 60 deletions
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145
malaria.py
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@ -2,13 +2,16 @@ import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
import random
from collections import defaultdict
from enum import IntEnum
from sys import argv
from matplotlib.patches import Patch
class Model:
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,
hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-6,
hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-5,
mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000,
graphical=True):
@ -47,15 +50,7 @@ class Model:
self.nets = self.gen_nets()
# statistics
self.stats = {
"natural deaths": 0,
"malaria deaths": 0,
"total deaths": 0,
"mosquitos fed": 0,
"humans infected": 0,
"mosquitos infected": 0,
"net count": 0
}
self.stats = defaultdict(int)
if self.graphical:
self.init_draw()
@ -63,54 +58,61 @@ class Model:
def init_draw(self):
plt.ion()
self.colors = matplotlib.colors.ListedColormap(
["black", "green", "red", "yellow"])
["white", "green", "red", "yellow"])
def make_babies(self, n):
if n == 0:
return
self.stats["humans born"] += n
births = np.transpose(random.sample(
list(np.transpose(np.where(self.grid == Human.DEAD))), n))
self.grid[births[0], births[1]] = \
np.random.choice((Human.HEALTHY, Human.IMMUNE), size=n,
p=(1 - self.immunepct, self.immunepct))
# Randomly distribute a net
nets = births.T[np.random.rand(len(births.T)) < self.mosqnetdens].T
self.nets[nets[0], nets[1]] = True
def recycle_human(self):
"""
Determine if a human dies of natural causes and then replace them by a
new human.
"""
# Get all living humans
# Find living humans, determine if they die, and if so, kill them
humans = np.transpose(np.where(self.grid != Human.DEAD))
# Get a mask of humans to kill
deaths = np.random.rand(len(humans)) < self.humandiepct
# Kill them.
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
locations = humans[deaths].T
self.grid[locations[0], locations[1]] = Human.DEAD
self.nets[locations[0], locations[1]] = False
death_count = len(np.where(deaths)[0])
self.stats["natural deaths"] += death_count
# Pick a random, unpopulated spot
births = np.array(random.sample(
list(np.transpose(np.where(self.grid == Human.DEAD))),
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))
# Replace the dead humans
self.make_babies(death_count)
def do_malaria(self):
"""
This function determines who of the infected dies from their illness
"""
# Get all infected humans
# Find infected humans, determine if they die, and if so, kill them
infected = np.transpose(np.where(self.grid == Human.INFECTED))
# Decide which infected people die
deaths = np.random.rand(len(infected)) < self.hinfdiepct
# Now let's kill them
self.grid[infected[deaths][:, 0], infected[deaths][:, 1]] = Human.DEAD
locs = infected[deaths].T
self.grid[locs[0], locs[1]] = Human.DEAD
self.nets[locs[0], locs[1]] = False
self.stats["malaria deaths"] += len(np.where(deaths)[0])
death_count = len(np.where(deaths)[0])
self.stats["malaria deaths"] += death_count
# Replace the dead humans
self.make_babies(death_count)
def feed(self):
"""
@ -120,23 +122,26 @@ class Model:
for mos in self.mosquitos:
if not mos.hungry:
continue
# state of current place on the grid where mosquito lives
state = self.grid[mos.x, mos.y]
if state != Human.DEAD:
self.stats["mosquitos fed"] += 1
mos.hungry = False
if state == Human.DEAD:
continue
# check if healthy human needs to be infected or mosquito
# 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.stats["humans infected"] += 1
elif state == Human.INFECTED and not mos.infected \
and random.uniform(0, 1) < self.hm_infpct:
self.stats["mosquitos infected"] += 1
mos.infected = True
self.stats["mosquitos fed"] += 1
mos.hungry = False
# check if healthy human needs to be infected or mosquito
# 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.stats["humans infected"] += 1
elif state == Human.INFECTED and not mos.infected \
and random.uniform(0, 1) < self.hm_infpct:
mos.infected = True
self.stats["mosquitos infected"] += 1
def determine_hunger(self):
"""
@ -224,18 +229,22 @@ class Model:
"""
Generates the grid of nets
"""
humans = np.transpose(np.where(self.grid != Human.DEAD))
positions = humans[np.random.choice(
len(humans), size=round(self.mosqnetdens * len(humans)))].T
return np.random.choice([False, True],
p=[1-self.mosqnetdens, self.mosqnetdens],
size=(self.width, self.height))
grid = np.zeros((self.width, self.height), dtype=bool)
grid[positions[0], positions[1]] = True
return grid
def run(self):
"""
This functions runs the simulation
"""
print(chr(27) + "[2J")
# Actual simulation runs inside try except to catch keyboard interrupts
# and always print stats
self.stats["humans alive before simulation"] = \
np.count_nonzero(self.grid != Human.DEAD)
try:
for t in range(self.time_steps):
print("Simulating timestep: {}".format(t), end='\r')
@ -244,8 +253,10 @@ class Model:
self.draw(t)
except KeyboardInterrupt:
pass
self.stats["humans alive after simulation"] = \
np.count_nonzero(self.grid != Human.DEAD)
print(chr(27) + "[2J")
print()
self.compile_stats()
self.print_stats()
@ -256,14 +267,13 @@ class Model:
self.stats["total deaths"] = \
self.stats["malaria deaths"] + self.stats["natural deaths"]
# print(np.where(self.nets))
self.stats["net count"] = len(np.where(self.nets)[0])
def print_stats(self):
"""
Prints the gathered statistics from the simulation
"""
for stat in self.stats:
for stat, value in sorted(self.stats.items()):
print(f"{stat}: {self.stats[stat]}")
def step(self):
@ -285,8 +295,11 @@ class Model:
"""
Draws the grid of humans, tents and mosquitos
"""
# this function draws the humans
if t % 10 > 0:
return
plt.title("t={}".format(t))
# draw the grid
plt.imshow(self.grid, cmap=self.colors)
@ -296,7 +309,14 @@ class Model:
# draw mosquitos
for mos in self.mosquitos:
plt.plot(mos.x, mos.y, mos.get_color()+mos.get_shape())
plt.plot(mos.y, mos.x, mos.get_color()+mos.get_shape())
# draw the legend
dead_patch = Patch(color="green", label="Healthy human")
immune_patch = Patch(color="yellow", label="Immune human")
infected_patch = Patch(color="red", label="Infected human")
plt.legend(handles=[dead_patch, immune_patch, infected_patch],
loc=9, bbox_to_anchor=(0.5, -0.03), ncol=5)
plt.pause(0.0001)
plt.clf()
@ -327,6 +347,11 @@ class Human(IntEnum):
if __name__ == "__main__":
model = Model(graphical=True)
try:
graphical = argv[1] == "-g"
except IndexError:
graphical = False
model = Model(graphical=graphical)
model.run()