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

# # <img style='float: left' src="http://lightning-viz.github.io/images/logo.png"> <br> <br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Force-directed graphs in <a href='http://lightning-viz.github.io/'><font color='#9175f0'>Lightning</font></a>

# ## <hr> Setup

# In[1]:


import os
from lightning import Lightning

from numpy import random, asarray, linspace, corrcoef
from colorsys import hsv_to_rgb
from sklearn import datasets
import networkx as nx


# ## Connect to server

# In[2]:


lgn = Lightning(ipython=True, host='http://public.lightning-viz.org')


# ## <hr> Random binary network

# A random graph where every node has the same degree, with default styling.
# <br>
# This and other graph plots take matrices (e.g. adjacency matrices) as inputs.
# <br>
# We use the `networkx` library to generate the graph, then get its adjancency matrix.
# <br>
# Double click on a point to see its neighbors (all should have 3).
# <br>
# Drag points to try and move the graph, it should remain tight because of the degree structure.

# In[3]:


n = 100
G = nx.random_regular_graph(3,n)
mat = nx.adjacency_matrix(G).todense()
lgn.force(mat)


# ## <hr> Random weighted network

# The previous network was binary (all links either 0 or 1).
# Here the links are weighted, which is reflected in the line widths.

# In[4]:


G = nx.random_regular_graph(3,100)
mat = asarray(nx.adjacency_matrix(G).todense()) * (((random.rand(100,100))*5) ** 2)
lgn.force(mat)


# ## <hr> Lobster network

# The lobster graph, a backbone with some leaves, is colored here by node ordering.
# <br>
# We also set all nodes with degree less than 2 to gray.

# In[5]:


G = nx.random_lobster(60, 0.5, 0.0, seed=44)
mat = nx.adjacency_matrix(G).todense()
n = len(G.nodes())
c = [list(asarray(hsv_to_rgb(float(x) / n , 0.8, 1.0))*255) if y > 1 else [150,150,150] for (x,y) in G.degree_iter()]
lgn.force(mat, color=c)


# ## <hr> Coloring by degree

# Here we color points (and also change their size) to indicate their degree.
# <br>
# Click to confirm bigger points have more connections.

# In[6]:


G = nx.random_geometric_graph(50, 0.1)
mat = nx.adjacency_matrix(G).todense()
g = G.degree().values()
lgn.force(mat, group=g, size=(asarray(g) + 1.5)*3)


# ## <hr> Graph of clustering

# Graphs can be a useful way to look at data that doesn't neccessarily come from a graph.
# <br>
# Here we create a graph from a thresholded correlation matrix on data drawn from a set of clusters.
# <br>
# The cluster identities are clear in the resulting graph.

# In[7]:


d, g = datasets.make_blobs(n_features=5, n_samples=50, centers=5, cluster_std=2.0, random_state=100)
c = corrcoef(d)
c[c<0.9] = 0
lgn.force(c, group=g)