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model_patchcog.py
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model_patchcog.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Apr 17 13:42:23 2019
@author: mahi
"""
from mesa import Agent, Model
from mesa.space import SingleGrid
import random
from mesa.time import RandomActivation
import matplotlib.pyplot as plt
#import matplotlib.animation as plt
import numpy as np
from mesa.space import Grid
from mesa.datacollection import DataCollector
import scipy.stats as st
from matplotlib import colors
from mesa.time import BaseScheduler
import math
import pandas as pd
import sys
# add colors to graphics
# add step number to graphics
class A2(Agent): # second level agent-- predator agent
""" animal agent functions
"""
def __init__(self, unique_id, model, start_energy, cognition, disp_rate):
super().__init__(unique_id, model) # creates an agent in the world with a unique id
self.energy = self.model.start_energy
self.eat_energy = self.model.eat_energy
self.tire_energy = self.model.tire_energy
self.reproduction_energy = self.model.reproduction_energy
if self.model.cog_fixed:
self.cognition = (self.model.dist, self.model.det)
else:
self.cognition = cognition
self.cognition_energy = self.model.cognition_energy
self.dead = False
self.identity = "A2"
self.age = 0
if self.model.evolve_disp == True:
self.disp_rate = disp_rate
disp = np.power(self.disp_rate, range(0,100))
self.disp = disp/sum(disp)
else:
self.disp_rate = self.model.disp_rate
self.disp = self.model.disp
def step(self): # this iterates at every step
if not self.dead: # the agent moves on ev ery step
self.cognition_and_move()
if not self.dead:
self.tire_die()
if not self.dead:
self.reproduce()
self.age+=1
self.model.age.append(self.age)
def introduce(self, x, y, energy, cog, disp):
a = A2(self.model.unique_id, self.model, start_energy = energy, cognition = cog, disp_rate = disp)
self.model.unique_id += 1
self.model.grid.place_agent(a, (x,y))
self.model.schedule.add(a)
self.model.agentgrid[x][y] = 2
self.model.coggrid[:, x, y] = a.cognition
self.model.dispgrid[1,x,y] = a.disp_rate
def kill(self):
self.dead=True
x,y = self.pos
self.model.grid.remove_agent(self)
self.model.schedule.remove(self)
self.model.agentgrid[x][y] = 0
self.model.coggrid[:, x, y] = tuple([101] * 2)#self.model.nCogPar)
self.model.dispgrid[1, x, y] = 101
self.model.death += 1
def move(self, coord):
x,y = self.pos
newx, newy = coord
self.model.grid.move_agent(self, coord)
self.model.agentgrid[newx][newy] = 2
self.model.coggrid[:, newx, newy] = self.cognition
self.model.dispgrid[1, newx, newy ] = self.disp_rate
self.model.agentgrid[x][y] = 0
self.model.coggrid[:, x, y] = tuple([101] * 2) #self.model.nCogPar)
self.model.dispgrid[1, x, y] = 101
def eat(self, coord, eat_now = True):
die = self.model.grid.get_cell_list_contents([coord])[0]
food = die.energy
self.model.food += food
die.dead = True
self.model.grid.remove_agent(die)
self.model.schedule.remove(die)
x,y = coord
self.model.agentgrid[x][y]= 0
self.model.dispgrid[0,x,y]=101
if eat_now:
self.energy += food
else:
return(food)
def reproduce(self): # reproduce function
if self.energy >= self.reproduction_energy:
self.model.reprod += 1
if self.model.disp_rate == 1:
x = random.randrange(self.model.grid.width)
y = random.randrange(self.model.grid.height)
new_position = (x,y)
elif self.disp_rate == 0:
possible_locs = self.model.grid.get_neighborhood( # position of new ofspring
self.pos,
moore=True,
include_center=False)
new_position = random.choice(possible_locs)
else:
xnum = abs(100-self.pos[0])
if self.pos[0]>100:
xnum = 200-xnum
ynum = abs(100-self.pos[1])
if self.pos[1]>100:
ynum = 200-ynum
p = self.model.positions
p = np.concatenate((p[xnum:, :], p[:xnum, :]))
positions = np.concatenate((p[:, ynum:], p[:, :ynum]), 1)
dist = random.choices( range(100), k=1, weights = self.disp )[0]
new_position = tuple(random.choice(list(np.argwhere( positions == (dist+1) )) ))
energy_own = math.ceil(self.energy/2)
energy_off = self.energy - energy_own
self.energy = energy_own
cog = self.cognition
if not self.model.cog_fixed: # if model is 0 cogtype, don't evolve
p = random.choice([0,1])
random_ = np.random.normal(0,0.05,1)[0]
new = max(min(cog[p] + random_, 1), 0) #max value goes in the inner bracket, min goes in the outr bracket#
cog = ( *cog[0:p], new, *cog[p+1:])
new_disp = self.disp_rate
if self.model.evolve_disp == True:
random_ = np.random.normal(0,0.025,1)[0]
new_disp = max(min(self.disp_rate + random_, 1), 0)
x,y = new_position
if self.model.agentgrid[x][y] == 1:
food = self.eat((x,y), eat_now = False)
self.introduce(x,y, energy_off + food, cog, new_disp)
elif self.model.agentgrid[x][y] == 0:
self.introduce(x,y,energy_off, cog, new_disp)
def tire_die(self):
x,y = self.pos
self.energy-=self.tire_energy # + (self.cognition[0]/10)
if self.energy<=0:
self.kill()
def cogdecision(self):
neighbors = self.model.grid.get_neighborhood(
self.pos,
moore=True,
include_center=False)
if (self.cognition[1]==0):
new_pos = random.choice(neighbors)
else:
(a1weights, a2weights) = (np.array([]), np.array([]))
weight = self.cognition[0]
for n in neighbors:
weights__ = np.power(weight, self.model.positions_food)
food__ = self.toroidal( (n[0]-10)%200, (n[0]+10+1)%200, (n[1]-10)%200, (n[1]+10+1)%200 )
a1weights = np.append(a1weights, np.sum(weights__*food__))
a2weights = np.array([0]*8)
(a1wt_, a2wt_, k ) = (self.cognition[1], 0, 4)
a1wt = a1wt_ * a1weights
a2wt = a2wt_ * a2weights
wt = a1wt + a2wt
wtexp = np.exp(wt*k)
inf_check = np.argwhere(np.isinf(wtexp))
if len(inf_check)==1:
idx = int(inf_check[0])
print(idx)
return(neighbors[idx])
if len(inf_check)>1:
wtexp = np.exp(wt*1.6)
wtfinal = wtexp/np.sum(wtexp)
new_pos = random.choices( neighbors, k=1, weights = wtfinal )[0]
return (new_pos)
def cognition_and_move(self):
self.energy-=self.cognition_energy
new_pos = self.cogdecision()
newx, newy = new_pos
x,y = self.pos
if self.model.agentgrid[newx][newy] == 1:
self.eat(new_pos)
self.move(new_pos)
elif self.model.agentgrid[newx][newy] == 0:
self.move(new_pos)
elif self.model.agentgrid[newx][newy] >= 2:
self.model.combat += 1
combat = self.model.grid.get_cell_list_contents([new_pos])[0]
coin = random.random()
if combat.energy>self.energy or (combat.energy==self.energy and coin<0.5):
self.kill()
else:
combat.kill()
self.move(new_pos)
def toroidal(self, start1, end1, start2, end2, gridsize=200):
array = self.model.agentgrid
if start1<end1 and start2<end2:
array = array[start1:end1, start2:end2]
elif start1>end1 and start2<end2:
array1 = array[start1:gridsize, start2: end2]
array2 = array[0:end1, start2: end2]
array = np.concatenate((array1, array2))
elif start1<end1 and start2>end2:
array1 = array[start1:end1, start2:gridsize]
array2 = array[start1:end1, 0:end2]
array = np.concatenate((array1, array2), 1 )
else:
array1 = array[start1:gridsize, start2:gridsize]
array2 = array[start1:gridsize, 0:end2]
array3 = array[0:end1, start2:gridsize]
array4 = array[0:end1, 0:end2]
array = np.concatenate(( np.concatenate((array1, array2), 1 ), \
np.concatenate((array3, array4), 1 )))
return(array == 1)
class A1(Agent):
""" plants agent functions
"""
def __init__(self, unique_id, model, energy, disp_rate):
super().__init__(unique_id, model)
self.energy = self.model.start_energy # agent starts at energy level 10
self.eat_energy = self.model.eat_energy
self.tire_energy = self.model.tire_energy
self.reproduction_energy = self.model.reproduction_energy
self.dead = False
self.identity = "A1"
if self.model.evolve_disp == True:
self.disp_rate = disp_rate
disp = np.power(self.disp_rate, range(0,100))
self.disp = disp/sum(disp)
else:
self.disp_rate = self.model.disp_rate
self.disp = self.model.disp
def step(self): # this iterates at every step
self.eat()
self.tire_die()
if not self.dead:
self.reproduce()
def reproduce(self):
if self.energy>=self.reproduction_energy:
if self.disp_rate == 1:
x = random.randrange(self.model.grid.width)
y = random.randrange(self.model.grid.height)
new_position = (x,y)
elif self.disp_rate == 0:
possible_locs = self.model.grid.get_neighborhood( # position of new ofspring
self.pos,
moore=True,
include_center=False)
new_position = random.choice(possible_locs)
else:
xnum = abs(100-self.pos[0])
if self.pos[0]>100:
xnum = 200-xnum
ynum = abs(100-self.pos[1])
if self.pos[1]>100:
ynum = 200-ynum
p = self.model.positions
p = np.concatenate((p[xnum:, :], p[:xnum, :]))
positions = np.concatenate((p[:, ynum:], p[:, :ynum]), 1)
dist = random.choices( range(100), k=1, weights = self.disp )[0]
new_position = tuple(random.sample(list(np.argwhere( positions == (dist+1) )), k=1)[0])
energy_own = math.ceil(self.energy/2)
energy_off = self.energy - energy_own
self.energy = energy_own
new_disp = self.disp_rate
if self.model.evolve_disp == True:
random_ = np.random.normal(0,0.025,1)[0]
new_disp = max(min(self.disp_rate + random_, 1), 0)
if ( self.model.grid.is_cell_empty(new_position)):
a = A1(self.model.unique_id, self.model, energy_off, new_disp)
self.model.unique_id += 1
self.model.grid.place_agent(a, new_position)
self.model.schedule.add(a)
x,y = new_position
self.model.agentgrid[x][y] = 1
self.model.dispgrid[0,x,y] = new_disp
def eat(self): # agent eats at every step and thus depeletes resources
self.energy += self.eat_energy # nutrition is added to agent's nutrition
def tire_die(self): # agent loses energy at every step. if it fails to eat regularly, it dies due to energy loss
x,y = self.pos
self.energy-=self.tire_energy
if self.energy<=0:
self.dead=True
self.model.grid[x][y].remove(self)
self.model.schedule.remove(self)
self.model.agentgrid[x][y] -= 1
self.model.dispgrid[0,x,y] =101
class modelSim(Model):
"""
details of the world
introduce time is when animal agents first get introduced into the wrold
disp_rate is the dispersal rate for experiment 3
dist is perceptual strength for animals if fixed
det is decision determinacy of animals if fixed
cog_fixed determines if cognition of animals is fixed to particular values or is allowed to evolve
if skip_300 is True, patchiness values are not calculated for the first 300 steps-- this makes the model run faster
collect_cog_dist creates a seperate dataframe for all cognition values for agents at every timestep
if evolve_disp is true, dispersion rate of plants is free to evolve
"""
def __init__(self, introduce_time, disp_rate, dist, det, cog_fixed = False, \
skip_300 = True, collect_cog_dist = False, evolve_disp = False):
self.skip_300 = skip_300
self.cog_fixed = cog_fixed
self.evolve_disp = evolve_disp
self.collect_cog_dist = collect_cog_dist
self.dist = dist
self.det = det
self.disp_rate = disp_rate
self.intro_time = introduce_time
(self.a1num, self.a2num) = (20, 20)
self.schedule = RandomActivation(self) # agents take a step in random order
self.grid = SingleGrid(200, 200, True) # the world is a grid with specified height and width
self.initialize_perception()
disp = np.power(self.disp_rate, range(0,100))
self.disp = disp/sum(disp)
self.grid_ind = np.indices((200,200))
positions = np.maximum(abs(100-self.grid_ind[0]),
abs(100-self.grid_ind[1]) )
self.positions = np.minimum(positions, 200-positions)
self.agentgrid = np.zeros((self.grid.width, self.grid.height)) # allows for calculation of patchiness of both agents
self.coggrid = np.full((self.nCogPar, self.grid.width, self.grid.height), 101.0)
self.dispgrid = np.full((2, self.grid.width, self.grid.height), 101.0 )
self.age = []
(self.nstep, self.unique_id, self.reprod, self.food, self.death, self.combat) = (0, 0, 0, 0, 0, 0)
self.cmap = colors.ListedColormap(['midnightblue', 'mediumseagreen', 'white', 'white', 'white', 'white', 'white'])#'yellow', 'orange', 'red', 'brown'])
bounds=[0,1,2,3,4,5,6,7]
self.norm = colors.BoundaryNorm(bounds, self.cmap.N)
self.expect_NN = []
self.NN = [5, 10]
for i in self.NN:
self.expect_NN.append((math.factorial(2*i) * i)/(2**i * math.factorial(i))**2)
grid_ind_food = np.indices((21,21))
positions_food = np.maximum(abs(10-grid_ind_food[0]), abs(10-grid_ind_food[1]) )
self.positions_food = np.minimum(positions_food, 21 - positions_food)
if self.collect_cog_dist:
self.cog_dist_dist = pd.DataFrame(columns = [])
self.cog_dist_det = pd.DataFrame(columns = [])
for i in range(self.a1num): # initiate a1 agents at random locations
self.introduce_agents("A1")
self.nA1 = self.a1num
self.nA2 = 0
# self.agent_steps = {}
def initialize_perception(self):
self.history = pd.DataFrame(columns = ["nA1", "nA2", "age", "LIP5", "LIP10", "LIPanim5", "LIPanim10", "Morsita5", "Morsita10", "Morsitaanim5", "Morsitaanim10", "NN5","NN10","NNanim5", "NNanim10", "reprod", "food", "death",
"combat", "dist", "det", "dist_lower", "det_lower", "dist_upper", "det_upper", "dist_ci", "det_ci"])
self.nCogPar = 2
(self.start_energy, self.eat_energy, self.tire_energy, self.reproduction_energy, self.cognition_energy) \
= (10, 5, 3, 20, 1)
def introduce_agents(self, which_agent):
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
if which_agent == "A1":
if self.grid.is_cell_empty((x,y)):
a = A1(self.unique_id, self, self.start_energy, disp_rate = 0)
self.unique_id += 1
self.grid.position_agent(a, x, y)
self.schedule.add(a)
self.agentgrid[x][y] = 1
else:
self.introduce_agents(which_agent)
elif which_agent == "A2":
if self.cog_fixed:
c = (self.dist, self.det)
else:
c = tuple([0]*self.nCogPar)
a = A2(self.unique_id, self, self.start_energy, cognition = c, disp_rate = 0)
self.unique_id += 1
if self.agentgrid[x][y] == 1:
die = self.grid.get_cell_list_contents([(x,y)])[0]
die.dead = True
self.grid.remove_agent(die)
self.schedule.remove(die)
self.grid.place_agent(a, (x,y))
self.schedule.add(a)
self.agentgrid[x][y] = 2
self.coggrid[:, x, y] = c
elif self.agentgrid[x][y] == 0:
self.grid.place_agent(a, (x,y))
self.schedule.add(a)
self.agentgrid[x][y] = 2
self.coggrid[:, x, y] = c
def flatten_(self, n, grid, full_grid = False, mean = True, range_ = False):
if full_grid:
return(grid[n].flatten())
i = grid[n].flatten()
if mean:
i = np.delete(i, np.where(i == 101))
if len(i) == 0:
# if range_:
return([0]*4)
#else:
# return(0)
if range_:
if self.cog_fixed:
return([np.mean(i)]*4)
return( np.concatenate( ( [np.mean(i)], np.percentile(i, [2.5, 97.5]), self.calculate_ci(i) )) )
return([np.mean(i), 0, 0, 0])
else:
return(i)
def calculate_ci(self, data):
if np.min(data) ==np.max(data):
return( [ 0.0])
return ( [np.mean(data) - st.t.interval(0.95, len(data)-1, loc=np.mean(data), scale=st.sem(data))[0]])
def return_zero(self, num, denom):
if self.nstep == 1:
# print("whaaat")
return(0)
if denom == "old_nA2":
denom = self.history["nA2"][self.nstep-2]
if denom == 0.0:
return 0
return(num/denom)
def nearest_neighbor(self, agent): # fix this later
if agent == "a1":
x = np.argwhere(self.agentgrid==1)
if len(x)<=10:
return([-1]*len(self.NN))
elif len(x) > 39990:
return([0.97, 0.99])
# if self.nstep<300 and self.skip_300:
# return([-1,-1] )
else:
x = np.argwhere(self.agentgrid==2)
if len(x)<=10:
return([-1]*len(self.NN))
density = len(x)/ (self.grid.width)**2
expect_NN_ = self.expect_NN
expect_dist = np.array(expect_NN_) /(density ** 0.5)
distances = [0, 0]
for i in x:
distx = abs(x[:,0]-i[0])
distx[distx>100] = 200-distx[distx>100]
disty = abs(x[:,1]-i[1])
disty[disty>100] = 200-disty[disty>100]
dist = (distx**2+disty**2)**0.5
distances[0] += (np.partition(dist, 5)[5])
distances[1] += (np.partition(dist, 10)[10])
mean_dist = np.array(distances)/len(x)
out = mean_dist/expect_dist
return(out)
def quadrant_patch(self, agent): # function to calculate the patchiness index of agents at every step
if agent == "a1":
x = self.agentgrid == 1
else:
x = self.agentgrid == 2
gsize = np.array([5,10])
gnum = 200/gsize
qcs = []
for i in range(2):
x_ = x.reshape(int(gnum[i]), gsize[i], int(gnum[i]), gsize[i]).sum(1).sum(2)
mean = np.mean(x_)
var = np.var(x_)
if mean==0.0:
return([-1]*4)
lip = 1 + (var-mean) / (mean**2)
morsita = np.sum(x) * ( (np.sum(np.power(x_, 2)) - np.sum(x_))/( np.sum(x_)**2 - np.sum(x_)))
qcs += [lip, morsita]
return(qcs)
def l_function(self, agent):
if agent == "a1":
x = np.argwhere(self.agentgrid==1)
else:
x = np.argwhere(self.agentgrid==2)
if len(x)==0:
return(-1)
distances = np.array([])
for i in x:
distx = abs(x[:,0]-i[0])
distx[distx>100] = 200-distx[distx>100]
disty = abs(x[:,1]-i[1])
disty[disty>100] = 200-disty[disty>100]
dist = (distx**2 + disty**2)**0.5
distances = np.concatenate((distances, dist[dist!=0]))
l = np.array([])
for i in np.arange(5, 51, 5):
l = np.append(l, sum(distances<i))
k = (l * 200**2) / (len(x)**2)
l = (k/math.pi)**0.5
return(abs(l - np.arange(5, 51, 5)))
def collect_hist(self):
if self.nstep<300 and self.skip_300:
NNcalc = [-1, -1]#self.nearest_neighbor("a1")
NNanimcalc = [-1, -1]#self.nearest_neighbor("a2")
else:
NNcalc = self.nearest_neighbor("a1")
NNanimcalc = self.nearest_neighbor("a2")
quadrantcalc = self.quadrant_patch( "a1")
quadrantanimcalc = self.quadrant_patch( "a2")
dist_values = self.flatten_(0, grid = self.coggrid, mean = True, range_ = False)
det_values = self.flatten_(1, grid = self.coggrid, mean = True, range_ = False)
# l_f = 0#self.l_function("a1")
dat = { "nA1" : self.nA1, "nA2" : self.nA2,
"age" : self.return_zero(sum(self.age), self.nA2),
"LIP5" : quadrantcalc[0], "LIP10" : quadrantcalc[2],
"LIPanim5": quadrantanimcalc[0], "LIPanim10": quadrantanimcalc[2],
"Morsita5" : quadrantcalc[1], "Morsita10" : quadrantcalc[3],
"Morsitaanim5": quadrantanimcalc[1], "Morsitaanim10": quadrantanimcalc[3],
"NN5": NNcalc[0],"NN10": NNcalc[1],
"NNanim5": NNanimcalc[0],"NNanim10": NNanimcalc[1], #"l_ripley" : l_f,# self.nearest_neighbor("a2"),
"reprod" : self.return_zero(self.reprod, "old_nA2" ), "food": self.return_zero(self.food, self.nA2),
"death" : self.return_zero(self.death, "old_nA2"), "combat" : self.return_zero(self.combat, "old_nA2"),
"dist" : dist_values[0], "det" : det_values[0],
"dist_lower" : dist_values[1], "det_lower" : det_values[1],
"dist_upper" : dist_values[2], "det_upper" : det_values[2],
"dist_ci" : dist_values[3], "det_ci" : det_values[3],
"disp_a1" : self.flatten_(0, grid = self.dispgrid)[0], "disp_a2" : self.flatten_(1, grid = self.dispgrid)[0] }
self.history = self.history.append(dat, ignore_index = True)
self.age = []
(self.reprod, self.food, self.death, self.combat) = (0, 0, 0, 0)
if self.collect_cog_dist:
if (self.nstep %10) == 0:
self.cog_dist_dist[str(self.nstep-1)] = self.flatten_(0, grid = self.coggrid, full_grid = True, mean=False)
self.cog_dist_det[str(self.nstep-1)] = self.flatten_(1, grid = self.coggrid, full_grid = True, mean=False)
def step(self):
self.nstep +=1 # step counter
if self.nstep == self.intro_time:
for i in range(self.a2num):
self.introduce_agents("A2")
self.schedule.step()
self.nA1 = np.sum(self.agentgrid==1)
self.nA2 = np.sum(self.agentgrid==2)
self.collect_hist()
if self.nstep%10 == 0:
sys.stdout.write( (str(self.nstep) +" " +str(self.nA1) + " " + str(self.nA2) + "\n") )
def visualize(self):
f, ax = plt.subplots(1)
self.agentgrid = self.agentgrid.astype(int)
ax.imshow(self.agentgrid, interpolation='nearest', cmap=self.cmap, norm=self.norm)
# plt.axis("off")
return(f)