fix nets, improve drawing
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8cdfa7b878
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9c467e1878
1 changed files with 85 additions and 60 deletions
123
malaria.py
123
malaria.py
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@ -2,13 +2,16 @@ import matplotlib.pyplot as plt
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import matplotlib.colors
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import numpy as np
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import random
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from collections import defaultdict
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from enum import IntEnum
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from sys import argv
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from matplotlib.patches import Patch
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class Model:
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def __init__(self, width=32, height=32, humandens=0.15, mosquitodens=0.10,
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immunepct=0.1, mosqinfpct=0.1, hm_infpct=0.5, mh_infpct=0.5,
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hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-6,
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hinfdiepct=0.01, mhungrypct=0.1, humandiepct=10**-5,
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mosqdiepct=10**-3, mosqnetdens=0.05, time_steps=2000,
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graphical=True):
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@ -47,15 +50,7 @@ class Model:
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self.nets = self.gen_nets()
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# statistics
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self.stats = {
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"natural deaths": 0,
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"malaria deaths": 0,
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"total deaths": 0,
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"mosquitos fed": 0,
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"humans infected": 0,
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"mosquitos infected": 0,
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"net count": 0
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}
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self.stats = defaultdict(int)
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if self.graphical:
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self.init_draw()
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@ -63,54 +58,61 @@ class Model:
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def init_draw(self):
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plt.ion()
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self.colors = matplotlib.colors.ListedColormap(
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["black", "green", "red", "yellow"])
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["white", "green", "red", "yellow"])
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def make_babies(self, n):
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if n == 0:
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return
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self.stats["humans born"] += n
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births = np.transpose(random.sample(
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list(np.transpose(np.where(self.grid == Human.DEAD))), n))
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self.grid[births[0], births[1]] = \
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np.random.choice((Human.HEALTHY, Human.IMMUNE), size=n,
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p=(1 - self.immunepct, self.immunepct))
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# Randomly distribute a net
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nets = births.T[np.random.rand(len(births.T)) < self.mosqnetdens].T
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self.nets[nets[0], nets[1]] = True
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def recycle_human(self):
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"""
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Determine if a human dies of natural causes and then replace them by a
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new human.
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"""
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# Get all living humans
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# Find living humans, determine if they die, and if so, kill them
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humans = np.transpose(np.where(self.grid != Human.DEAD))
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# Get a mask of humans to kill
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deaths = np.random.rand(len(humans)) < self.humandiepct
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# Kill them.
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self.grid[humans[deaths][:, 0], humans[deaths][:, 1]] = Human.DEAD
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# get num humans after killing
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humans_survive = len(np.transpose(np.where(self.grid != Human.DEAD)))
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death_count = len(humans) - humans_survive
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locations = humans[deaths].T
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self.grid[locations[0], locations[1]] = Human.DEAD
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self.nets[locations[0], locations[1]] = False
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death_count = len(np.where(deaths)[0])
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self.stats["natural deaths"] += death_count
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# Pick a random, unpopulated spot
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births = np.array(random.sample(
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list(np.transpose(np.where(self.grid == Human.DEAD))),
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death_count))
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# Deliver the newborns
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for birth in births:
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self.grid[birth[0]][birth[1]] = \
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np.random.choice((Human.HEALTHY, Human.IMMUNE),
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p=(1 - self.immunepct, self.immunepct))
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# Replace the dead humans
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self.make_babies(death_count)
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def do_malaria(self):
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"""
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This function determines who of the infected dies from their illness
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"""
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# Get all infected humans
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# Find infected humans, determine if they die, and if so, kill them
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infected = np.transpose(np.where(self.grid == Human.INFECTED))
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# Decide which infected people die
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deaths = np.random.rand(len(infected)) < self.hinfdiepct
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# Now let's kill them
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self.grid[infected[deaths][:, 0], infected[deaths][:, 1]] = Human.DEAD
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locs = infected[deaths].T
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self.grid[locs[0], locs[1]] = Human.DEAD
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self.nets[locs[0], locs[1]] = False
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self.stats["malaria deaths"] += len(np.where(deaths)[0])
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death_count = len(np.where(deaths)[0])
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self.stats["malaria deaths"] += death_count
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# Replace the dead humans
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self.make_babies(death_count)
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def feed(self):
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"""
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@ -120,10 +122,13 @@ class Model:
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for mos in self.mosquitos:
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if not mos.hungry:
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continue
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# state of current place on the grid where mosquito lives
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state = self.grid[mos.x, mos.y]
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if state != Human.DEAD:
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if state == Human.DEAD:
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continue
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self.stats["mosquitos fed"] += 1
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mos.hungry = False
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@ -135,8 +140,8 @@ class Model:
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self.stats["humans infected"] += 1
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elif state == Human.INFECTED and not mos.infected \
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and random.uniform(0, 1) < self.hm_infpct:
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self.stats["mosquitos infected"] += 1
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mos.infected = True
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self.stats["mosquitos infected"] += 1
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def determine_hunger(self):
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"""
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@ -224,18 +229,22 @@ class Model:
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"""
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Generates the grid of nets
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"""
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humans = np.transpose(np.where(self.grid != Human.DEAD))
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positions = humans[np.random.choice(
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len(humans), size=round(self.mosqnetdens * len(humans)))].T
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return np.random.choice([False, True],
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p=[1-self.mosqnetdens, self.mosqnetdens],
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size=(self.width, self.height))
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grid = np.zeros((self.width, self.height), dtype=bool)
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grid[positions[0], positions[1]] = True
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return grid
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def run(self):
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"""
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This functions runs the simulation
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"""
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print(chr(27) + "[2J")
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# Actual simulation runs inside try except to catch keyboard interrupts
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# and always print stats
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self.stats["humans alive before simulation"] = \
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np.count_nonzero(self.grid != Human.DEAD)
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try:
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for t in range(self.time_steps):
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print("Simulating timestep: {}".format(t), end='\r')
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@ -244,8 +253,10 @@ class Model:
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self.draw(t)
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except KeyboardInterrupt:
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pass
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self.stats["humans alive after simulation"] = \
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np.count_nonzero(self.grid != Human.DEAD)
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print(chr(27) + "[2J")
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print()
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self.compile_stats()
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self.print_stats()
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@ -256,14 +267,13 @@ class Model:
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self.stats["total deaths"] = \
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self.stats["malaria deaths"] + self.stats["natural deaths"]
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# print(np.where(self.nets))
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self.stats["net count"] = len(np.where(self.nets)[0])
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def print_stats(self):
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"""
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Prints the gathered statistics from the simulation
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"""
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for stat in self.stats:
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for stat, value in sorted(self.stats.items()):
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print(f"{stat}: {self.stats[stat]}")
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def step(self):
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@ -285,8 +295,11 @@ class Model:
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"""
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Draws the grid of humans, tents and mosquitos
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"""
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# this function draws the humans
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if t % 10 > 0:
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return
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plt.title("t={}".format(t))
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# draw the grid
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plt.imshow(self.grid, cmap=self.colors)
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@ -296,7 +309,14 @@ class Model:
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# draw mosquitos
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for mos in self.mosquitos:
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plt.plot(mos.x, mos.y, mos.get_color()+mos.get_shape())
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plt.plot(mos.y, mos.x, mos.get_color()+mos.get_shape())
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# draw the legend
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dead_patch = Patch(color="green", label="Healthy human")
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immune_patch = Patch(color="yellow", label="Immune human")
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infected_patch = Patch(color="red", label="Infected human")
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plt.legend(handles=[dead_patch, immune_patch, infected_patch],
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loc=9, bbox_to_anchor=(0.5, -0.03), ncol=5)
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plt.pause(0.0001)
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plt.clf()
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@ -327,6 +347,11 @@ class Human(IntEnum):
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if __name__ == "__main__":
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model = Model(graphical=True)
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try:
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graphical = argv[1] == "-g"
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except IndexError:
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graphical = False
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model = Model(graphical=graphical)
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model.run()
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