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KinshipMap_UpUp.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Dec 6 13:29:43 2017 This is a copy for Python 3.x
@author: Jianqiu Lu, Hanyu Zhang
@email: [email protected],[email protected]
"""
import pandas as pd
import networkx as nx
import numpy as np
import queue
import codecs
import re
###############################################################################
# readData: input the file name of three data files: kindata, biogmain and
# kincodefile and read them into the program and transform to pandas dataframe
def readData():
KinDataFile = input("Filename of kindata(*.csv): ")
BioMainFile = input("Filename of biography(*.csv): ")
KinCodeFile = input("Filename of kindatacode(*.csv): ")
kindata = pd.DataFrame.from_csv(KinDataFile)
biogmain = pd.DataFrame.from_csv(BioMainFile)
kindatacode = pd.DataFrame.from_csv(KinCodeFile)
reslist = [kindata,biogmain,kindatacode]
return(reslist)
###############################################################################
# preProcess: read the pandas dataframe file and do basis pre processing of the
# data
def preProcess(reslist):
kindata = reslist[0]
biogmain = reslist[1]
kindatacode = reslist[2]
allkindatacode = set(kindatacode['c_kincode'])-{0,-1}
biogmain.index = biogmain["c_personid"].tolist()
kindatacode.index = kindatacode["c_kincode"].tolist()
people = set(kindata['c_personid']) & set(biogmain["c_personid"])
n = len(people)
kindata.index = kindata["c_kin_id"].tolist()
m = max(kindata.index)
datalist = [kindata,biogmain,kindatacode,allkindatacode,people,n,m]
return datalist
###############################################################################
# readFile: read data from a txt file and construct the graphs list
def readFile(inGraphFile):
#if(not inGraphFile):
# inGraphFile = input('Input Graph File Name(If No More File, Input 0): ')
with open(inGraphFile,'r') as f:
lines = f.readlines()
graphs = []
for k in range(int((len(lines)-1)/3)):
G = nx.MultiDiGraph()
node = [int(x) for x in re.findall(r'\d+',lines[1+3*k])]
G.add_nodes_from(node)
edge = [int(x) for x in re.findall(r'\d+',lines[2+3*k])]
for l in range(int((len(edge)-1)/3)):
G.add_edge(edge[3*l],edge[3*l+1],weight = edge[3*l+2])
graphs.append(G)
return graphs
###############################################################################
# Divide: Transform the datafiles into compact components of the network. Using
# specific rules to divide large graphs to small graphs and save the result into
# a txt file named as OutPutFile.
def Divide():
reslist = readData()
datalist = preProcess(reslist)
OutPutFile = input("Output Filename: ")
kindata = datalist[0]
biogmain = datalist[1]
kindatacode = datalist[2]
allkindatacode = datalist[3]
people = datalist[4]
n = datalist[5]
unvisited = people - set([0])
m = datalist[6]
graphs = []
pairs = []
roots = []
crash = 0
status = np.zeros(m+1,np.int32)
cnterrid = 0
cnterrrel = 0
badrel = 0
Alphabet = set("QWERTYUIOPASDFGHJKLZXCVBNM")
while(len(unvisited)>0):
k = min(unvisited)
while biogmain.loc[k,"c_female"] and len(unvisited)>0:
unvisited.remove(k)
k = min(unvisited)
G = nx.MultiDiGraph()
Q = queue.Queue(maxsize=0)
Q.put(k)
unvisited.remove(k)
G.add_node(k)
print("root:",k)
roots.append(k)
last = len(unvisited)
while(not Q.empty()):
k = Q.get()
if (last-len(unvisited)>1000): #防止一个家族过大程序假死
last = len(unvisited)
print("current:",len(unvisited))
kin4k = kindata.loc[kindata["c_personid"]==k,("c_kin_id","c_kin_code")]
for key,value in kin4k.iterrows():
if value[0]==k:
continue;
if (value[0] not in biogmain.index): #人物实际没有出现
cnterrid += 1
continue;
if value[1] not in allkindatacode: #关系代码找不到
cnterrrel += 1
continue;
if abs(kindatacode.loc[value[1],'代差'])>=90:
badrel += 1
continue
'''
k为男/女性时的亲属加入
'''
if (not biogmain.loc[k,'c_female']):
if not biogmain.loc[value[0],'c_female']: #亲属同样为男性
if set("WMCHAPZ") & set(kindatacode.loc[value[1],'c_kinrel'])!=set():
continue #二者间的关系不存在联姻
if kindatacode.loc[value[1],'c_kinrel'][0]=='D' and kindatacode.loc[value[1],'代差']<-1:
continue #女儿的后代不应计入家谱
if (value[0] in unvisited): #未访问过的节点
Q.put(value[0])
unvisited.remove(value[0])
G.add_node(value[0])
G.add_edge(k,value[0],weight=value[1])
else: #已访问/出现过的节点
if not G.has_edge(k,value[0]):
G.add_edge(k,value[0],weight=value[1])
else: #女性亲属的情况
UsefulRel = Alphabet & set(kindatacode.loc[value[1],'c_kinrel'])
if len(UsefulRel)>1:
continue
if UsefulRel.difference(set("MWCDZ"))==set():#仅考虑五种简单关系之一
if status[value[0]]==0:
Q.put(value[0])
if UsefulRel.difference(set("MWC"))==set():#婆家
if status[value[0]]!=2:
status[value[0]]=status[value[0]]+2
else: #娘家
if status[value[0]]!=1:
status[value[0]]=status[value[0]]+1
if status[value[0]]==3:
Q.put(value[0])
G.add_node(value[0])
G.add_edge(k,value[0],weight=value[1])
if value[0] in unvisited:
unvisited.remove(value[0])
else:
if status[k]==1:
ForbidList={"H","S","D","P","A","G-","W"} #娘家里不宜引入的关系，过于严格
elif status[k]==2:
ForbidList={"B","Z","G+","F","M","K","P"}
else:
ForbidList={"P"} #同时属于婆家和娘家的情形
if ForbidList & set(kindatacode.loc[value[1],'c_kinrel'])!=set():
continue
if not biogmain.loc[value[0],'c_female']: #加入男性亲属
if set("WMCHAPZ") & set(kindatacode.loc[value[1],'c_kinrel'])!=set():
continue #二者间的关系不存在联姻
if kindatacode.loc[value[1],'c_kinrel'][0]=='D' and kindatacode.loc[value[1],'代差']<-1:
continue #女儿的后代不应计入家谱
if (value[0] in unvisited): #未访问过的节点
Q.put(value[0])
unvisited.remove(value[0])
G.add_node(value[0])
G.add_edge(k,value[0],weight=value[1])
else: #已访问/出现过的节点
if not G.has_edge(k,value[0]):
G.add_edge(k,value[0],weight=value[1])
else: #女性亲属的情况
UsefulRel = Alphabet & set(kindatacode.loc[value[1],'c_kinrel'])
if len(UsefulRel)>1:
continue
if UsefulRel.difference(set("MDZ"))==set():#仅考虑五种简单关系之一
if status[value[0]]==0:
Q.put(value[0])
if UsefulRel.difference(set("M"))==set():#婆家
if status[value[0]]!=2:
status[value[0]]=status[value[0]]+2
else: #娘家
if status[value[0]]!=1:
status[value[0]]=status[value[0]]+1
if status[value[0]]==3:
Q.put(value[0])
if not G.has_node(value[0]):
G.add_node(value[0])
G.add_edge(k,value[0],weight=value[1])
elif not G.has_edge(k,value[0]):
G.add_edge(k,value[0],weight=value[1])
if value[0] in unvisited:
unvisited.remove(value[0])
for k in G.nodes():
status[k]=0
#print("New Graph")
#print(G.edges())
graphs.append(G)
# save the results
f = codecs.open(OutPutFile+".cut","w",encoding="utf8")
for g in range(len(graphs)):
f.write("Family%d\n"%g)
f.write(str(graphs[g].nodes()))
f.write("\n")
Edge = []
for k in graphs[g].nodes():
for t in graphs[g][k]:
Edge.append(str((k,t,graphs[g][k][t][0]['weight'])))
for s in Edge:
f.write(s)
f.write(" ")
f.write("\n")
f.write("#")
f.close()
graphs = Combine(graphs,m,biogmain)
return graphs
###############################################################################
# Merge: given two graphs list file and merge them into one single larger graph
# list
def Merge():
BioMainFile = input("Filename of biography(*.csv): ")
biogmain = pd.DataFrame.from_csv(BioMainFile)
biogmain.index = biogmain["c_personid"].tolist()
inGraphFile = input('Input Graph File Name: ')
print('Loading... Please wait...')
graphs1 = readFile(inGraphFile)
graphs2=0
while True:
inGraphFile = input('Input Graph File Name(If No More File, Input 0): ')
if inGraphFile=="0":
break
print('Processing......Please wait......')
graphs2 = readFile(inGraphFile)
m1 = 0
m2 = 0
for g in graphs2:
graphs1.append(g)
mt = max(g.nodes())
if mt > m2:
m2 = mt
graphs = graphs1
for g in graphs1:
mt = max(g.nodes())
if mt > m1:
m1 = mt
m = max([m1,m2])
graphs = Combine(graphs,m,biogmain)
graphs1 = graphs
if graphs2==0:
m1=0
for g in graphs1:
mt = max(g.nodes())
if mt > m1:
m1 = mt
graphs = Combine(graphs1,m1,biogmain)
return graphs
###############################################################################
# Combine: given a graphs file and the corresponding parameter m (largest index
# of nodes in all graphs) and eliminate repetition of same male family members
# in different families aka different connected components
def Combine(graphs,m,biogmain):
first = np.zeros(m+1,np.int32)-1
f = codecs.open("Result1.txt","w",encoding="utf8")
cnt = 0
for g in range(len(graphs)):
comb = []
for k in graphs[g].nodes():
if not biogmain.loc[k,'c_female']:
if first[k]!=-1:
comb.append(first[k])
else:
first[k]=cnt
if comb!=[]:
comb.append(cnt)
combto = min(comb)
G = nx.MultiDiGraph()
for k in comb:
for n1 in graphs[k].nodes():
for n2 in graphs[k][n1]:
if not G.has_edge(n1,n2):
G.add_edge(n1,n2,weight=graphs[k][n1][n2][0]['weight'])
graphs[k]=nx.MultiDiGraph()
graphs[combto]=G
for n in G.nodes():
first[n]=combto
cnt += 1
return graphs
###############################################################################
# ForbidList: generate the forbidlist for different status of female members
def ForbidList(k):
if k==1:
return {"H","S","D","P","A","G-","W"}
if k==2:
return {"B","Z","G+","F","M","K","P"}
return {"P"}
###############################################################################
# Generate: function to calculate generations of a family member, deal with crashes
# and generate graphViz code to visualize family size.
def Generate():
reslist = readData()
datalist = preProcess(reslist)
kindata = datalist[0]
biogmain = datalist[1]
kindatacode = datalist[2]
allkindatacode = datalist[3]
people = datalist[4]
n = datalist[5]
m = datalist[6]
graphs = pickle.load('graphs.pk','')
Alphabet = set("QWERTYUIOPASDFGHJKLZXCVBNM")
not0 = 0
num_node = []
num_edge = []
for g in graphs:
num_node.append(g.nodes())
Edge = []
for k in g.nodes():
for t in g[k]:
Edge.append((k,t,g[k][t][0]['weight']))
num_edge.append(Edge)
if len(g.nodes())>9000:
TMZ = g
if len(g.nodes())>0:
not0+=1
gen = np.zeros(m+1,np.int32)-1000
gen_pj = np.zeros(m+1,np.int32)-1000
status = np.zeros(m+1,np.int32)
PjFlag = np.zeros(m+1,np.int32)
Nodes = TMZ.nodes()
gen[min(Nodes)]=0
gen_pj[min(Nodes)]=0
unvisited = set(Nodes)-{min(Nodes)}
cnt = 0
Q = queue.Queue(maxsize=0)
Q.put(min(Nodes))
while(not Q.empty()):
k = Q.get()
if (not biogmain.loc[k,'c_female']):
for val in TMZ[k]:
if val in unvisited:
Q.put(val)
unvisited.remove(val)
if (not biogmain.loc[val,'c_female']):
gen[val] = gen[k]+ kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
else:
UsefulRel = set(kindatacode.loc[TMZ[k][val][0]['weight'],'c_kinrel']) & Alphabet
if UsefulRel.difference(set("MWC"))==set():
gen_pj[val] = gen[k]+ kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
if status[val]!=2:
status[val]=status[val]+2
else:
gen[val] = gen[k]+ kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
if status[val]!=1:
status[val]=status[val]+1
for val in TMZ.in_edges(k):
if (not biogmain.loc[val[0],'c_female']):
gen[val[0]] = gen[k] - kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
else:
UsefulRel = set(kindatacode.loc[TMZ[val[0]][k][0]['weight'],'c_kinrel']) & Alphabet
if UsefulRel.difference(set("DSH"))==set():
gen_pj[val[0]] = gen[k]- kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
if status[val[0]]!=2:
status[val[0]]=status[val[0]]+2
else:
gen[val[0]] = gen[k]- kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
if status[val[0]]!=1:
status[val[0]]=status[val[0]]+1
if val[0] in unvisited:
Q.put(val[0])
unvisited.remove(val[0])
else:
Flag = False
Flag2 = False
if status[k]==1:
gk=gen[k]
elif status[k]==2:
gk=gen_pj[k]
else:
Flag2 = True
for val in TMZ[k]:
if ForbidList(status[k]) & set(kindatacode.loc[TMZ[k][val][0]['weight'],'c_kinrel'])!=set():
Flag = True
continue
if (val in unvisited): #未访问过的节点
Q.put(val)
unvisited.remove(val)
if not biogmain.loc[val,'c_female']: #加入男性亲属
if not Flag2:
gen[val] = gk + kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
else:
if ForbidList(1) & set(kindatacode.loc[TMZ[k][val][0]['weight'],'c_kinrel'])!=set():
gen[val] = gen[k] + kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
else:
gen[val] = gen_pj[k] + kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
else: #女性亲属的情况
UsefulRel = set(kindatacode.loc[TMZ[k][val][0]['weight'],'c_kinrel']) & Alphabet
if UsefulRel==set("M"):
gen_pj[val] = gen[k]+ kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
if status[val]!=2:
status[val]=status[val]+2
elif UsefulRel==set("D"):
gen[val] = gen_pj[k] + kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
if status[val]!=1:
status[val]=status[val]+1
else:
gen[val] = gen[k] + kindatacode.loc[TMZ[k][val][0]['weight'],'代差']
if status[val]!=1:
status[val]=status[val]+1
for val in TMZ.in_edges(k):
UsefulRel = set(kindatacode.loc[TMZ[val[0]][k][0]['weight'],'c_kinrel']) & Alphabet
if not biogmain.loc[val[0],'c_female']: #加入男性亲属
if UsefulRel.difference(set("MWC"))==set():
if status[k]==1:
Flag = True
continue
else:
gen[val[0]] = gen_pj[k] - kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
else:
if status[k]==2:
Flag = True
continue
else:
gen[val[0]] = gen[k] - kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
else: #女性亲属的情况
if UsefulRel==set("M"):
if status[k]==1:
Flag = True
continue
gen[val[0]] = gen_pj[k]- kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
if status[val[0]]!=1:
status[val[0]]=status[val[0]]+1
elif status[k]==2:
Flag = True
continue
elif UsefulRel==set("D"):
gen_pj[val[0]] = gen[k] - kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
if status[val[0]]!=2:
status[val[0]]=status[val[0]]+2
else:
gen[val[0]] = gen[k] - kindatacode.loc[TMZ[val[0]][k][0]['weight'],'代差']
if status[val[0]]!=1:
status[val[0]]=status[val[0]]+1
if (val[0] in unvisited): #未访问过的节点
Q.put(val[0])
unvisited.remove(val[0])
#if Flag:
#Q.put(k)
#print(biogmain.loc[k,"c_name_chn"])
#input("")
gen0 = gen
def genn(i):
return gen0[i]
def gennn(i):
return gen0[i[0]]
GraphVizPrint = ["digraph TMZ{",'rankdir="LR";','ranksep="3 equally"', 'node [shape=record,fontname="Fangsong"];','edge[fontname="Fangsong"]']
Nodes = TMZ.nodes()
for node in Nodes:
gen0[node]=max(gen_pj[node],gen[node])
maxdepth = max(gen0[Nodes])
mindepth = min(gen0[Nodes])
Nodes = sorted(Nodes,key=genn,reverse=True)
i = 0
for j in sorted(range(mindepth,maxdepth+1),reverse=True):
GraphVizPrint.append("{")
GraphVizPrint.append('rank="same";')
i0=i
while gen0[Nodes[i]]==j:
if biogmain.loc[Nodes[i],"c_female"]:
GraphVizPrint.append('%d[label="(%s)%d",style="filled", fillcolor="red"];'% (Nodes[i],biogmain.loc[Nodes[i],"c_name_chn"],gen0[Nodes[i]]))
else:
GraphVizPrint.append('%d[label="(%s)%d"];'% (Nodes[i],biogmain.loc[Nodes[i],"c_name_chn"],gen0[Nodes[i]]))
i = i + 1
if i==len(Nodes):
break
if i0==i:
GraphVizPrint.pop()
GraphVizPrint.pop()
else:
GraphVizPrint.append("}")
#print(biogmain.loc[name,"c_name_chn"])
#if biogmain.loc[name,"c_female"]:
# GraphVizPrint.append('%s [shape="circle"]'% biogmain.loc[name,"c_name_chn"])
#else:
# GraphVizPrint.append(biogmain.loc[name,"c_name"])
Edges = sorted(TMZ.edges(),key=gennn,reverse=True)
for edge in Edges:
while Edges.count((edge[1],edge[0]))>0:
Edges.remove((edge[1],edge[0]))
GraphVizPrint.append('%d -> %d[label="%s"];'% (edge[0],edge[1],kindatacode.loc[TMZ[edge[0]][edge[1]][0]['weight'],"c_kinrel_chn"]))
GraphVizPrint.append("}")
f = codecs.open("map1203.dot","w",encoding="utf8")
for sentense in GraphVizPrint:
f.write(sentense)
f.write("\n")
f.close()
print("CBDB To Kinship Map\n")
print("1. Divide\n")
print("2. Merge two graphs files\n")
print("3. Generate Graphviz Code\n")
print("0. exit\n")
while True:
S = input("Please Choose: ")
if S=="1":
Divide()
elif S=="2":
Merge()
elif S=="3":
Generate()
elif S=="0":
break
else:
input("Wrong Input! Please Choose:")