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plot.py
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plot.py
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import numpy as np
import pandas as pd
import random
import sys
import os
import math
import copy
import pickle
import pyclustering
from pyclustering.cluster import xmeans
from matplotlib import pyplot as plt
import seaborn as sns
sns.set_style(style="whitegrid")
import warnings
warnings.filterwarnings('ignore')
if int(sys.argv[1])==0:
if not os.path.exists("./figs"):
os.mkdir("./figs")
cluster_ls=[]
for i in range(50):
cluster_ls.append(i*10)
def cluster(data):
res=0
structure=0
data=np.array(data)
clans=[]
clusters=[]
structure_path=[]
for descent in [0,1]:
try:
init_center = xmeans.kmeans_plusplus_initializer(data[data[:,0]==descent], 2).initialize()
xm = xmeans.xmeans(data[data[:,0]==descent], init_center, ccore=False)
xm.process()
sizes = [len(cluster) for cluster in xm.get_clusters()]
centers=xm.get_centers()
clusters_candidate=xm.get_clusters()
for i in range(len(sizes)):
if sizes[i]>num_lineage/10:
clans.append(centers[i])
clusters.append(clusters_candidate[i])
# clans=xm.get_centers()
except:
continue
if len(clans)>0:
num_clans=len(clans)
clan_ls=[]
for i in range(num_clans):
mate=0
child=0
cur_mate=100
cur_child=100
for j in range(num_clans):
mate_cur=(clans[i][3]-clans[j][1])**2+(clans[i][4]-clans[j][2])**2
if mate_cur<cur_mate:
mate=j
cur_mate=mate_cur
if clans[i][0]==0:
clan_ls.append([i,mate,[i]])
else:
clan_ls.append([i,mate,[]])
for i in range(len(clan_ls)):
if clan_ls[i][2]==[]:
mates=[mate for mate in clan_ls if mate[1]==i]
children=[]
for mate in mates:
cur_child=100
for j in range(num_clans):
if clans[j][0]==1:
child_cur=(clans[i][1]-clans[j][1])**2+(clans[mate[0]][2]-clans[j][2])**2
if child_cur<cur_child:
child=j
cur_child=child_cur
children.append(child)
clan_ls[i][2]=children
counter=1
for clan in clan_ls:
if len(clan[2])==1:
clan[2]=clan[2][0]
elif len(clan[2])>1:
counter=counter*len(clan[2])
clan_ls_ls=[]
clan_ls_ori=copy.deepcopy(clan_ls[:])
for i in range(counter):
clan_ls=copy.deepcopy(clan_ls_ori[:])
for clan in clan_ls:
if type(clan[2])!=type(0):
if len(clan[2])==0:
clan[2]=-1
else:
clan[2]=clan[2][counter%len(clan[2])]
clan_ls_ls.append(clan_ls)
cur_man_cycle=0
cur_cycle=0
cur_woman_cycle=0
num_clans=0
for clan_ls in clan_ls_ls:
candidate=list(range(len(clans)))
while len(candidate)>0:
marriage_path=[]
cur=candidate[-1]
man_path=[cur]
vill_ls=[]
while True:
next=clan_ls[cur][2]
if next in man_path:
man_path=man_path[man_path.index(next):]
break
elif next == -1:
break
else:
man_path.append(next)
cur=next
cur_woman_cycle_cur=0
for clan in man_path:
if clan not in marriage_path:
cur_path=[clan]
cur=clan
while True:
next=clan_ls[clan_ls[cur][1]][2]
if next in cur_path:
marriage_path.extend(cur_path[cur_path.index(next):])
if len(cur_path[cur_path.index(next):])>cur_woman_cycle_cur:
cur_woman_cycle_cur=len(cur_path[cur_path.index(next):])
break
elif next == -1:
break
else:
cur_path.append(next)
cur=next
marriage_path=list(set(marriage_path))
candidate.pop()
for man in man_path:
if man not in marriage_path:
man_path.remove(man)
if man in candidate:
candidate.remove(man)
if len(marriage_path)>cur_cycle:
structure_path=marriage_path
cur_cycle=len(marriage_path)
cur_man_cycle=len(man_path)
cur_woman_cycle=cur_woman_cycle_cur
elif len(marriage_path)==cur_cycle and len(man_path)>cur_man_cycle:
structure_path=marriage_path
cur_cycle=len(marriage_path)
cur_man_cycle=len(man_path)
cur_woman_cycle=cur_woman_cycle_cur
elif len(marriage_path)==cur_cycle and len(man_path)==cur_man_cycle and cur_woman_cycle_cur>cur_woman_cycle:
structure_path=marriage_path
cur_cycle=len(marriage_path)
cur_man_cycle=len(man_path)
cur_woman_cycle=cur_woman_cycle_cur
else:
continue
rest=0
for man in marriage_path:
if len(clan_ls[man])==len(set(clan_ls[man])):
rest+=1
if rest>=cur_cycle/2:
rest=1
else:
rest=0
clan_ls=np.array(clan_ls)
ind_ls=[clan_ls[i,2] for i in list(set(clan_ls[:,1]))]
clan_ls=[list(clan) for clan in clan_ls if clan[0] in ind_ls]
if len(clan_ls)>num_clans:
num_clans=len(clan_ls)
if cur_cycle*cur_man_cycle*cur_woman_cycle!=0:
if cur_woman_cycle>1 and cur_man_cycle>1 and cur_cycle>3:
structure=4
elif cur_cycle==1:
structure=1
elif cur_cycle<=num_clans/3:
structure=5
elif cur_cycle==2:
structure=2
elif cur_woman_cycle>2 or cur_man_cycle>2:
structure=3
else:
structure=6
# structures=["dead","incest", "dual", "generalized", "restricted", "vill division", "others"]
res=[cur_cycle,cur_man_cycle,cur_woman_cycle,rest]
return [structure,res,np.array(clusters)[structure_path],np.array(clans)[structure_path]]
class Village:
def __init__(self):
self.lineages=[]
self.population=0
class Lineage:
def __init__(self,trait,preference,descent,num_couple):
self.trait=trait
self.preference=preference
self.couple=num_couple
self.man=0
self.woman=0
self.father=trait
self.descent=descent
self.candidate=[]
def year(vill):
vill.population=0
lineages=[lineage for lineage in vill.lineages if lineage.couple>0]
traits=np.array([lineage.trait for lineage in lineages])
fathers=np.array([lineage.father for lineage in lineages])
preferences=np.array([lineage.preference for lineage in lineages])
for lineage in lineages:
distance=np.array([np.sum((traits-lineage.trait)**2,axis=1),np.sum((traits-lineage.preference)**2,axis=1),np.sum((preferences-lineage.trait)**2,axis=1),np.sum((fathers-lineage.trait)**2,axis=1),np.sum((traits-lineage.father)**2,axis=1)]).min(axis=0)
friend=np.sum(np.exp(-distance))/len(lineages)
rate=1/(1+coop*(1-friend))
couple=birth*lineage.couple
lineage.man=round(np.random.poisson(lam=couple)*rate)
lineage.woman=round(np.random.poisson(lam=couple)*rate)
lineage.couple=0
for lineage in lineages:
if min(lineage.man,lineage.woman)>2*initial_pop:
n=math.floor(math.log2(min(lineage.man,lineage.woman)/initial_pop))
lineage.man=round(lineage.man/2**n)
lineage.woman=round(lineage.woman/2**n)
for i in [0]*(2**n-1):
lineages.append(Lineage(np.copy(lineage.trait),np.copy(lineage.preference),lineage.descent,0))
lineages[-1].man=lineage.man
lineages[-1].woman=lineage.woman
lineages=[lineage for lineage in lineages if lineage.man*lineage.woman>0]
for lineage in lineages:
if random.random()<descent_mut:
lineage.descent=(lineage.descent+1)%2
lineage.trait+=np.random.uniform(-mutation,mutation,2)
lineage.preference+=np.random.uniform(-mutation,mutation,2)
preferences=np.array([lineage.preference for lineage in lineages])
for lineage in lineages:
enemy=np.sum(np.exp(-np.sum((preferences-lineage.preference)**2,axis=1)))/len(lineages)
rate=1/(1+conflict*enemy)
lineage.man=round(lineage.man*rate)
lineage.woman=round(lineage.woman*rate)
vill.population+=lineage.man+lineage.woman
vill.lineages=lineages[:]
def mating(vill):
lineages=vill.lineages
mates=np.array([mate.preference for mate in lineages])
for lineage in lineages:
if lineage.man>0:
dist=np.exp(-np.sum((mates-lineage.trait)**2,axis=1))
dist=dist/np.sum(dist)
mate = np.random.choice(lineages, p=dist)
mate.candidate.append(lineage)
for mate in lineages:
if mate.woman<1 or len(mate.candidate)==0:
mate.candidate=[]
continue
random.shuffle(mate.candidate)
for lineage in mate.candidate:
if mate.woman<1:
break
couple=min(lineage.man,mate.woman)
lineage.man-=couple
mate.woman-=couple
if lineage.descent==1:
lineages.append(Lineage(np.array([lineage.trait[0],mate.trait[1]]),np.array([lineage.preference[0],mate.preference[1]]),lineage.descent,couple))
lineages[-1].father=np.copy(lineage.trait)
else:
lineage.couple+=couple
lineage.father=np.copy(lineage.trait)
mate.candidate=[]
def main():
global num
num=0
vills=[]
cycles=[]
man_cycles=[]
woman_cycles=[]
restricts=[]
num_clans_ls=[]
structures=[]
initial_population=initial_pop*num_lineage*2
for i in range(num_trial):
vills.append(Village())
for j in range(num_lineage):
vills[i].lineages.append(Lineage(np.random.normal(0,friendship,2),np.random.normal(0,friendship,2),1*(random.random()<0.5),initial_pop))
while num <500:
remove_ls=[]
duplicate_ls=[]
for vill in vills:
year(vill)
if vill.population<initial_population/10:
remove_ls.append(vill)
elif vill.population>initial_population*2:
duplicate_ls.append(vill)
for vill in remove_ls:
vills.remove(vill)
for vill in duplicate_ls:
random.shuffle(vill.lineages)
n=math.floor(math.log2(vill.population/initial_population))
k=round(len(vill.lineages)/2**n)
for i in [0]*(2**n-1):
lineages=vill.lineages[:k]
vill.lineages=vill.lineages[k:]
vills.append(Village())
vills[-1].lineages=copy.deepcopy(lineages)
while len(vills)>num_trial:
random.shuffle(vills)
vills=vills[:num_trial]
for vill in vills:
mating(vill)
if len(vills)==0:
break
num+=1
j=0
for vill in vills:
data=[[lineage.descent,lineage.trait[0],lineage.trait[1],lineage.preference[0],lineage.preference[1]] for lineage in vill.lineages]
res=cluster(data)
data=np.array(data)
ls=res[2][:res[0]]
clans=res[3][:res[0]]
if res[0]==3:
structure="generalized"
elif res[0]==4:
structure="restricted"
else:
j+=1
continue
if len(ls)<6:
fig, (axL,axC, axR) = plt.subplots(ncols=3, figsize=(12,4))
for i in range(len(ls)):
try:
axL.scatter(data[:,1][ls[i]],data[:,3][ls[i]],s=100-20*i,c=current_palette[i])
except:
pass
axL.set_xlabel(r"$t_1$",fontsize=36)
axL.set_ylabel(r"$p_1$",fontsize=36)
axL.tick_params(labelsize=24)
axL.set_aspect('equal', 'datalim')
for i in range(len(ls)):
try:
axC.scatter(data[:,2][ls[i]],data[:,4][ls[i]],s=100-20*i,c=current_palette[i])
except:
pass
axC.set_xlabel(r"$t_2$",fontsize=36)
axC.set_ylabel(r"$p_2$",fontsize=36)
axC.tick_params(labelsize=24)
axC.set_aspect('equal', 'datalim')
for i in range(len(ls)):
try:
axR.scatter(data[:,1][ls[i]],data[:,2][ls[i]],s=100-20*i,c=current_palette[i])
except:
pass
axR.set_xlabel(r"$t_1$",fontsize=36)
axR.set_ylabel(r"$t_2$",fontsize=36)
axR.tick_params(labelsize=24)
axR.set_aspect('equal', 'datalim')
fig.tight_layout()
fig.savefig("./figs/structure_emerge_{}_coop{}pc_conf{}pc_mutation{}pc_{}.eps".format(structure,round(coop*100),round(conflict*100),round(mutation*100),j))
j+=1
if len(vills)==0:
cycles=0
return [cycles,man_cycles,woman_cycles,restricts,structures]
def run():
df=pd.DataFrame(index=list(range(50)))
k=0
for l in range(10):
try:
res=main()
if res[0]==0:
continue
else:
df[k]=np.array(res).T.tolist()
k+=1
except:
pass
df.to_pickle("./res/{}regions_{}lineages_coop{}pc_conflict{}pc_mutation{}pm_descentmut{}pm_marry{}_friendship{}_initial{}_birth{}.pkl".format(num_trial,num_lineage,round(coop*100),round(conflict*100),round(mutation*1000),round(descent_mut*1000),marry,friendship,initial,birth))
#settings
num_lineage=50
initial_pop=5
num_trial=100
friendship=1
descent_mut=0.01
marry=1
mutation=0.3
coop=0.5
conflict=2.0
initial=1
birth=4
epsilon=1
current_palette = sns.color_palette("Set1", 4)
if int(sys.argv[1])==0:
mutation=0.1
main()
elif int(sys.argv[1])==1:
mutation=0.3
main()