CORR.py

def Correspondence(df,df2,hist_dist_Ga,hist_dist_Gb):
    import networkx as nx
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import operator
    import random as rd
    import networkx as nx
    from matplotlib import pyplot as plt


    aa=hist_dist_Ga
    bb=hist_dist_Gb

    #ns=10
    #nb=0.5

    df_np=np.array(df)
    #np.shape(df_np)[0]

    adj = np.zeros([np.shape(df_np)[0],np.shape(df_np)[0]]) 
    dist1=adj.copy()
    for i in range((np.shape(df_np)[0])):
        for j in range((np.shape(df_np)[0])):
            dist=np.linalg.norm(df_np[i] - df_np[j])
            dist1[i,j]=dist
            if dist < aa:
                adj[i,j]=1


    import sys
    np.set_printoptions(threshold=sys.maxsize)
    adj

    dist2=dist1.flatten()

    plt.hist(dist2)
    plt.show()


    adj_df=pd.DataFrame(data=adj)
    G = nx.from_pandas_adjacency(adj_df)
    G.name = "Graph from pandas adjacency matrix"
    print(nx.info(G))
    nx.draw(G,with_labels=True,font_color='red',font_size=0,node_size=70,node_color='red')
    #plot a graph
    plt.show()
    ######

    df_np_2=np.array(df2)
    #np.shape(df_np_2)[0]

    adj_2 = np.zeros([np.shape(df_np_2)[0],np.shape(df_np_2)[0]]) 
    dist1_2=adj.copy()
    for i in range((np.shape(df_np_2)[0])):
        for j in range((np.shape(df_np_2)[0])):
            dist_2=np.linalg.norm(df_np_2[i] - df_np_2[j])
            dist1_2[i,j]=dist_2
            if dist_2 < bb:
                adj_2[i,j]=1

    import sys
    np.set_printoptions(threshold=sys.maxsize)
    adj_2

    dist2_2=dist1_2.flatten()

    plt.hist(dist2_2)
    plt.show()


    import networkx as nx
    from matplotlib import pyplot as plt
    adj_df_2=pd.DataFrame(data=adj_2)
    G_2 = nx.from_pandas_adjacency(adj_df_2)
    G.name_2 = "Graph from pandas adjacency matrix"
    print(nx.info(G_2))
    nx.draw(G_2,with_labels=True,font_color='green',font_size=0,node_size=65,node_color='green')
    #plot a graph
    plt.show()


        # -*- coding: utf-8 -*-
    """Page Rank Algorithm.ipynb
    Automatically generated by Colaboratory.
    Original file is located at
        https://colab.research.google.com/drive/1oUC_418I6e2nv_2xBQ0sgXZtDfA98zuH
    """
    graph=G
    graph1=G_2



    # created a directed graph
    #graph=nx.gnp_random_graph(ns,nb,directed=False)
    #draw a graph
    #nx.draw(graph,with_labels=True,font_color='red',font_size=10,node_color='yellow')
    #plot a graph
    #plt.show()
    #number of nodes for graph
    count=graph.number_of_nodes()
    # #Page Rank Algorithm-Calculating random walk score
    rank_dict={}
    #Page rank by networkx library

    pagerank=nx.pagerank(graph)
    #pagerank= nx.closeness_centrality(graph)  #close_centrality
    #sorting both dictionaries based on items
    pagerank_sorted=sorted(pagerank.items(),key=lambda v:(v[1],v[0]),reverse=True)
    #sorting the rank_dict based on values
    rank_dict_sorted=sorted(rank_dict.items(),key=lambda v:(v[1],v[0]),reverse=True)

    # created a directed graph
    #graph1=nx.gnp_random_graph(ns,nb,directed=False)
    #draw a graph
    #nx.draw(graph1,with_labels=True,font_color='black',font_size=10,node_color='yellow')
    #plot a graph
    #plt.show()
    #number of nodes for graph
    count1=graph1.number_of_nodes()
    # #Page Rank Algorithm-Calculating random walk score
    rank_dict1={}
    #Page rank by networkx library

    pagerank1=nx.pagerank(graph1)
    #pagerank1= nx.closeness_centrality(graph1)  #close_centrality

    #sorting both dictionaries based on items
    pagerank_sorted1=sorted(pagerank1.items(),key=lambda v1:(v1[1],v1[0]),reverse=True)
    #sorting the rank_dict based on values
    rank_dict_sorted1=sorted(rank_dict1.items(),key=lambda v1:(v1[1],v1[0]),reverse=True)
    # print("\n\nThe order for Graph is\n")
    # for i in pagerank_sorted:
    #   print(i[0],end=" ")

    # print("\n\nThe order Graph1 is\n")
    # for j in pagerank_sorted:
    #   print(j[0],end=" ")

    #def pgr_sort(self,rank_dict_sorted,rank_dict_sorted1):

    pr=[]
    for i in pagerank_sorted:
      pr.append(i[0])

    pr1=[]
    for j in pagerank_sorted1:
      pr1.append(j[0])


    cm1=np.shape(pr)[0]
    cm2=np.shape(pr1)[0]
    cm = np.zeros([cm1,cm2]) 
    #np.shape(cm)

    cmm=min(cm1,cm2)
    for i in range(0,cmm):
            cm[pr[i],pr1[i]]=1


    from matplotlib import pyplot as plt
    fig = plt.figure(figsize=(8,8))

    ax = fig.add_subplot(111)
    ax.set_title('colorMap')
    plt.imshow(cm)
    ax.set_aspect('equal')

    cax = fig.add_axes([0.3, 0.1, 0.78, 0.8])
    cax.get_xaxis().set_visible(False)
    cax.get_yaxis().set_visible(False)
    cax.patch.set_alpha(0)
    cax.set_frame_on(False)
    plt.colorbar(orientation='vertical')
    plt.show()

    return cm