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

Force-directed graphs in Lightning # ##
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') # ##
Random binary network # A random graph where every node has the same degree, with default styling. #
# This and other graph plots take matrices (e.g. adjacency matrices) as inputs. #
# We use the `networkx` library to generate the graph, then get its adjancency matrix. #
# Double click on a point to see its neighbors (all should have 3). #
# 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) # ##
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) # ##
Lobster network # The lobster graph, a backbone with some leaves, is colored here by node ordering. #
# 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) # ##
Coloring by degree # Here we color points (and also change their size) to indicate their degree. #
# 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) # ##
Graph of clustering # Graphs can be a useful way to look at data that doesn't neccessarily come from a graph. #
# Here we create a graph from a thresholded correlation matrix on data drawn from a set of clusters. #
# 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)