#!/usr/bin/env python
# coding: utf-8

# In[102]:


import pandas as pd, numpy as np, json, os
import networkx as nx
import operator
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[103]:


plt.style.use('seaborn-whitegrid')


# In[104]:


m=json.loads(open('ignore/member_timelines.json','r').read())


# In[105]:


set([i['Csoportok'] for i in m if i['Típus']=='Párt'])


# In[1]:


set([i['Csoportok'] for i in m if i['Típus']=='Kamara'])


# In[133]:


set([i['Csoportok'] for i in m if i['Típus']=='Bizottság'])


# In[106]:


def replace_all(text, dic):
    for i in dic:
        text = text.replace(i, dic[i])
    return text


# In[75]:


medals={'🥈':2,'🥇':3,'🥉':1.5,'🏅':1.25,'🚩':1.5}
medals_none={i:'' for i in medals}


# In[111]:


data={'Ország':{},'Bizottság':{},'Párt':{},'Megye':{}}
attention={}
for k,i in enumerate(m):
    if k%1000==0: print(k/len(m)*100,'%')
    name=i['Simple']
    if i['Típus'] in list(data.keys()):
        csoport=i['Csoportok']
        csoport=replace_all(csoport,medals_none)
        if csoport not in data[i['Típus']]:data[i['Típus']][csoport]={}
        for t in pd.date_range(i['start'],i['end']):
            st=str(t)[:10]
            if st not in data[i['Típus']][csoport]:data[i['Típus']][csoport][st]=set()
            data[i['Típus']][csoport][st].add(name)            
            if name not in attention:attention[name]={}
            if csoport not in attention[name]:attention[name][csoport]={}
            if st not in attention[name][csoport]:attention[name][csoport][st]=[]
            for j in medals:
                if j in i['Csoportok']:
                    attention[name][csoport][st].append(j)


# In[124]:


m[8]


# In[123]:


set([i['Medals'] for i in m])


# In[77]:


def anneal(t,p=7):
    #pd.to_datetime('2020-01-01')-pd.to_datetime('1989-01-01')=11322
    return (2-(pd.to_datetime('2020-01-01')-pd.to_datetime(t)).days/11322)**p
    
tanneal={}
anneal2020=anneal(pd.to_datetime('2020'))
for t in pd.date_range('1990','2020'):
    tanneal[t]=anneal(t)/anneal2020+0.1
    tanneal[str(t)[:10]]=tanneal[t]
plt.subplots(1,1,figsize=(5,3))
plt.plot(tanneal.keys(),tanneal.values())
plt.ylabel('Elavulási együttható')
plt.show()


# In[78]:


edges={}
edges2={}
points={'Ország':1.5,'Bizottság':4,'Párt':2,'Megye':1}
for tipus in data:
    for csoport in data[tipus]:
        print(csoport)
        for t in data[tipus][csoport]:
            names=list(data[tipus][csoport][t])
            for r in range(len(names)):
                name=names[r]
                for p in range(r+1,len(names)):
                    name2=names[p]
                    nset=tuple(np.sort([name,name2]))
                    v=points[tipus]
                    
                    #symmetric
                    if nset not in edges:edges[nset]={'anneal':0,'values':0}
                    edges[nset]['values']+=v
                    edges[nset]['anneal']+=v*tanneal[t]
                    
                    #asymmetric
                    nset=tuple(np.sort([name,name2]))
                    v=points[tipus]*attention[name][csoport][t]
                    if nset not in edges2:edges2[nset]={'weight':0}
                    edges2[nset]['weight']+=v*tanneal[t]
                    
                    nset=tuple(np.sort([name,name2]))
                    v=points[tipus]*attention[name2][csoport][t]
                    nset=tuple(np.sort([name,name2])[::-1])
                    if nset not in edges2:edges2[nset]={'weight':0}
                    edges2[nset]['weight']+=v*tanneal[t]


# Save

# In[86]:


import pickle


# In[87]:


def save_obj(obj, name ):
    with open('ignore/'+ name + '.pkl', 'wb') as f:
        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)

def load_obj(name ):
    with open('ignore/' + name + '.pkl', 'rb') as f:
        return pickle.load(f)


# In[88]:


save_obj(edges,'plotter_name_edges')


# In[82]:


#edges=load_obj('plotter_name_edges')


# Weighted

# In[100]:


edges2l=[(i[0],i[1],edges2[i]) for i in edges2]
G = nx.DiGraph()
G.add_edges_from(edges2l)
nodes={}

r = nx.pagerank(G,weight='weight')
mr=np.median(list(r.values()))
for i in r:
    nodes[i]={'pr':r[i]/mr}
    
links={}
for i in G.edges(data=True):
    name=i[0]
    name2=i[1]
    z=i[2]['weight']
    if name not in links:links[name]={}
    if name2 not in links:links[name2]={}
    links[name][name2]=z


# In[101]:


#save 
network={'nodes':nodes,'links':links,'size':len(edges2l)}
print(len(nodes),'nodes and',len(G.edges()),'edges')
open('ignore/plotter_name_weighted.json','w').write(json.dumps(network))
import zipfile

with zipfile.ZipFile("json/plotter_name_weighted.zip", "w", compression=zipfile.ZIP_DEFLATED) as zf:
    zf.write("ignore/plotter_name_weighted.json",'plotter_name_weighted.json')


# Prune

# In[97]:


min_t=365*4*10
edges2l=[(i[0],i[1],edges2[i]) for i in edges2 if edges2[i]['weight']>min_t]
G = nx.DiGraph()
G.add_edges_from(edges2l)
nodes={}

r = nx.pagerank(G,weight='weight')
mr=np.median(list(r.values()))
for i in r:
    nodes[i]={'pr':r[i]/mr}
    
links={}
for i in G.edges(data=True):
    name=i[0]
    name2=i[1]
    z=i[2]['weight']
    if name not in links:links[name]={}
    if name2 not in links:links[name2]={}
    links[name][name2]=z


# In[99]:


#save 
network={'nodes':nodes,'links':links,'size':len(edges2l)}
print(len(nodes),'nodes and',len(G.edges()),'edges')
open('ignore/plotter_name_weighted_pruned.json','w').write(json.dumps(network))
import zipfile

with zipfile.ZipFile("json/plotter_name_weighted_pruned.zip", "w", compression=zipfile.ZIP_DEFLATED) as zf:
    zf.write("ignore/plotter_name_weighted_pruned.json",'plotter_name_weighted_pruned.json')