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

# ### Overview
# Analyze the interdependcies between classes as output from Roaster data. 
# 
# This notebook is authored by Paul Bastide. 
# No warranty.  Provided as-is. 
# 
# ### Documentation
# https://networkx.github.io/documentation/networkx-1.9/reference/generated/networkx.drawing.layout.spring_layout.html
# https://networkx.github.io/documentation/latest/auto_examples/drawing/plot_unix_email.html 

# In[1]:


get_ipython().system('pip install plotly networkx')


# In[2]:


import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import networkx as nx


# In[3]:


df = pd.read_csv('diagram.csv', header = None) 


# In[4]:


df.head()


# In[5]:


G = nx.MultiDiGraph()


# In[6]:


for input0, output0, in zip(df[0],df[1]):
    output0 = "" + str(output0)
    if output0:
        outputs = output0.split(',')
        for output in outputs: 
            G.add_edge(input0,output, message=input0)


# In[8]:


plt.figure(3,figsize=(10,10)) 
pos = nx.spring_layout(G, iterations=50)

nx.draw(G, pos, node_size=0, alpha=0.4, edge_color='b', font_size=10, with_labels=True)

plt.draw()
plt.savefig("diagram-network.png")