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graphkit-learn/notebooks/utils/plot_all_graphs.py

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  1. #!/usr/bin/env python3

  2. # -*- coding: utf-8 -*-

  3. """

  4. Created on Tue Jan  7 15:25:36 2020

  5. 

  6. @author: ljia

  7. """

  8. 

  9. # draw all the praphs

  10. import matplotlib.pyplot as plt

  11. 

  12. import networkx as nx

  13. import numpy as np

  14. from gklearn.utils.graphfiles import loadDataset, loadGXL

  15. 

  16. 

  17. def main(): 

  18.     # MUTAG dataset.

  19.     dataset, y = loadDataset("../../datasets/MUTAG/MUTAG_A.txt")

  20.     for idx in [6]: #[65]:#

  21.         G = dataset[idx]

  22.         ncolors= []

  23.         for node in G.nodes:

  24.             if G.nodes[node]['atom'] == '0':

  25.                 G.nodes[node]['atom'] = 'C'

  26.                 ncolors.append('#bd3182')

  27.             elif G.nodes[node]['atom'] == '1':

  28.                 G.nodes[node]['atom'] = 'N'

  29.                 ncolors.append('#3182bd')

  30.             elif G.nodes[node]['atom'] == '2':

  31.                 G.nodes[node]['atom'] = 'O'

  32.                 ncolors.append('#82bd31')

  33.             elif G.nodes[node]['atom'] == '3':

  34.                 G.nodes[node]['atom'] = 'F'

  35.             elif G.nodes[node]['atom'] == '4':

  36.                 G.nodes[node]['atom'] = 'I'

  37.             elif G.nodes[node]['atom'] == '5':

  38.                 G.nodes[node]['atom'] = 'Cl'

  39.             elif G.nodes[node]['atom'] == '6':

  40.                 G.nodes[node]['atom'] = 'Br'

  41.         ecolors = []

  42.         for edge in G.edges:

  43.             if G.edges[edge]['bond_type'] == '0':

  44.                 ecolors.append('#bd3182')

  45.             elif G.edges[edge]['bond_type'] == '1':

  46.                 ecolors.append('#3182bd')

  47.             elif G.edges[edge]['bond_type'] == '2':

  48.                 ecolors.append('#82bd31')

  49.             elif G.edges[edge]['bond_type'] == '3':

  50.                 ecolors.append('orange')

  51. 

  52.         print(idx)

  53.         print(nx.get_node_attributes(G, 'atom'))

  54.         edge_labels = nx.get_edge_attributes(G, 'bond_type')

  55.         print(edge_labels)

  56.         pos=nx.spring_layout(G)

  57.         nx.draw(G, 

  58.                 pos,

  59.                 node_size=500,

  60.                 labels=nx.get_node_attributes(G, 'atom'), 

  61.                 node_color=ncolors, 

  62.                 font_color='w', 

  63.                 edge_color=ecolors,

  64.                 width=3,

  65.                 with_labels=True)

  66. #        edge_labels = nx.draw_networkx_edge_labels(G, pos, 

  67. #                                                   edge_labels=edge_labels,

  68. #                                                   font_color='pink')

  69.         plt.savefig('mol1_graph.svg', format='svg', dpi=300)

  70.         plt.show()

  71.         plt.clf()

  72.     

  73.     

  74. #    # monoterpenoides dataset.

  75. #    dataset, y = loadDataset("../../datasets/monoterpenoides/dataset_10+.ds")

  76. #    for idx in [12,22,29,74]:

  77. #        print(idx)

  78. #        print(nx.get_node_attributes(dataset[idx], 'atom'))

  79. #        edge_labels = nx.get_edge_attributes(dataset[idx], 'bond_type')

  80. #        print(edge_labels)

  81. #        pos=nx.spring_layout(dataset[idx])

  82. #        nx.draw(dataset[idx], pos, labels=nx.get_node_attributes(dataset[idx], 'atom'), with_labels=True)

  83. #        edge_labels = nx.draw_networkx_edge_labels(dataset[idx], pos, 

  84. #                                                   edge_labels=edge_labels,

  85. #                                                   font_color='pink')

  86. #        plt.show()

  87.     

  88.     

  89. #    # Fingerprint dataset.

  90. #    dataset = '/media/ljia/DATA/research-repo/codes/Linlin/gedlib/data/collections/Fingerprint.xml'

  91. #    graph_dir = '/media/ljia/DATA/research-repo/codes/Linlin/gedlib/data/datasets/Fingerprint/data/' 

  92. #    Gn, y_all = loadDataset(dataset, extra_params=graph_dir)

  93. ##    dataset = '/media/ljia/DATA/research-repo/codes/Linlin/graphkit-learn/datasets/Fingerprint/Fingerprint_A.txt'

  94. ##    Gn, y_all = loadDataset(dataset)

  95. #    

  96. #    idx_no_node = []

  97. #    idx_no_edge = []

  98. #    idx_no_both = []

  99. #    for idx, G in enumerate(Gn):

  100. #        if nx.number_of_nodes(G) == 0:

  101. #            idx_no_node.append(idx)

  102. #            if nx.number_of_edges(G) == 0:

  103. #                idx_no_both.append(idx)

  104. #        if nx.number_of_edges(G) == 0:

  105. #            idx_no_edge.append(idx)

  106. ##        file_prefix = '../results/graph_images/Fingerprint/' + G.graph['name']

  107. ##        draw_Fingerprint_graph(Gn[idx], file_prefix=file_prefix, save=True)

  108. #    print('nb_no_node: ', len(idx_no_node))

  109. #    print('nb_no_edge: ', len(idx_no_edge))

  110. #    print('nb_no_both: ', len(idx_no_both))

  111. #    print('idx_no_node: ', idx_no_node)

  112. #    print('idx_no_edge: ', idx_no_edge)

  113. #    print('idx_no_both: ', idx_no_both)

  114. #    

  115. #    for idx in [0, 10, 100, 1000]:

  116. #        print(idx)

  117. #        print(Gn[idx].nodes(data=True))

  118. #        print(Gn[idx].edges(data=True))

  119. #        draw_Fingerprint_graph(Gn[idx], file_prefix='')

  120.     

  121.     

  122. #    # SYNTHETIC dataset.

  123. #    dataset = '/media/ljia/DATA/research-repo/codes/Linlin/graphkit-learn/datasets/SYNTHETICnew/SYNTHETICnew_A.txt'

  124. #    Gn, y_all = loadDataset(dataset)

  125. #    

  126. #    idx_no_node = []

  127. #    idx_no_edge = []

  128. #    idx_no_both = []

  129. #    for idx, G in enumerate(Gn):

  130. #        if nx.number_of_nodes(G) == 0:

  131. #            idx_no_node.append(idx)

  132. #            if nx.number_of_edges(G) == 0:

  133. #                idx_no_both.append(idx)

  134. #        if nx.number_of_edges(G) == 0:

  135. #            idx_no_edge.append(idx)

  136. ##        file_prefix = '../results/graph_images/SYNTHETIC/' + G.graph['name']

  137. ##        draw_SYNTHETIC_graph(Gn[idx], file_prefix=file_prefix, save=True)

  138. ##        draw_SYNTHETIC_graph(Gn[idx])

  139. #    print('nb_no_node: ', len(idx_no_node))

  140. #    print('nb_no_edge: ', len(idx_no_edge))

  141. #    print('nb_no_both: ', len(idx_no_both))

  142. #    print('idx_no_node: ', idx_no_node)

  143. #    print('idx_no_edge: ', idx_no_edge)

  144. #    print('idx_no_both: ', idx_no_both)

  145. #    

  146. #    for idx in [0, 10, 100]:

  147. #        print(idx)

  148. #        print(Gn[idx].nodes(data=True))

  149. #        print(Gn[idx].edges(data=True))

  150. #        draw_SYNTHETIC_graph(Gn[idx], save=None)

  151.         

  152.         

  153. def plot_a_graph(graph_filename):

  154.     graph = loadGXL(graph_filename)

  155.     print(nx.get_node_attributes(graph, 'atom'))

  156.     edge_labels = nx.get_edge_attributes(graph, 'bond_type')

  157.     print(edge_labels)

  158.     pos=nx.spring_layout(graph)

  159.     nx.draw(graph, pos, labels=nx.get_node_attributes(graph, 'atom'), with_labels=True)

  160.     edge_labels = nx.draw_networkx_edge_labels(graph, pos, 

  161.                                                edge_labels=edge_labels,

  162.                                                font_color='pink')

  163.     plt.show()

  164.     

  165.     

  166. #Dessin median courrant

  167. def draw_Fingerprint_graph(graph, file_prefix=None, save=None):

  168.     plt.figure()

  169.     pos = {}

  170.     for n in graph.nodes:

  171.         pos[n] = np.array([float(graph.node[n]['x']), float(graph.node[n]['y'])])

  172.     # set plot settings.

  173.     max_x = np.max([p[0] for p in pos.values()]) if len(pos) > 0 else 10

  174.     min_x = np.min([p[0] for p in pos.values()]) if len(pos) > 0 else 10

  175.     max_y = np.max([p[1] for p in pos.values()]) if len(pos) > 0 else 10

  176.     min_y = np.min([p[1] for p in pos.values()]) if len(pos) > 0 else 10

  177.     padding_x = (max_x - min_x + 10) * 0.1

  178.     padding_y = (max_y - min_y + 10) * 0.1

  179.     range_x = max_x + padding_x - (min_x - padding_x)

  180.     range_y = max_y + padding_y - (min_y - padding_y)

  181.     if range_x > range_y:

  182.         plt.xlim(min_x - padding_x, max_x + padding_x)

  183.         plt.ylim(min_y - padding_y - (range_x - range_y) / 2, 

  184.                  max_y + padding_y + (range_x - range_y) / 2)

  185.     else:

  186.         plt.xlim(min_x - padding_x - (range_y - range_x) / 2, 

  187.                  max_x + padding_x + (range_y - range_x) / 2)

  188.         plt.ylim(min_y - padding_y, max_y + padding_y)

  189.     plt.gca().set_aspect('equal', adjustable='box')

  190.     nx.draw_networkx(graph, pos)

  191.     if save is not None:

  192.         plt.savefig(file_prefix + '.eps', format='eps', dpi=300)

  193.     else:

  194.         plt.show()

  195.     plt.clf()

  196.     

  197.     

  198. def draw_SYNTHETIC_graph(graph, file_prefix=None, save=None):

  199.     plt.figure()

  200.     nx.draw_networkx(graph)

  201.     if save is not None:

  202.         plt.savefig(file_prefix + '.eps', format='eps', dpi=300)

  203.     else:

  204.         plt.show()

  205.     plt.clf()

  206.     

  207.     

  208. if __name__ == '__main__':

  209.     main()

  210. #    gfn = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/output/tmp_ged/set_median.gxl'

  211. #    plot_a_graph(gfn)

  212. #    gfn = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/output/tmp_ged/gen_median.gxl'

  213. #    plot_a_graph(gfn)