Mercurial > repos > guerler > springsuite
annotate planemo/lib/python3.7/site-packages/networkx/algorithms/threshold.py @ 1:56ad4e20f292 draft
"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author | guerler |
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date | Fri, 31 Jul 2020 00:32:28 -0400 |
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1 # Copyright (C) 2004-2019 by |
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2 # Aric Hagberg <hagberg@lanl.gov> |
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3 # Dan Schult <dschult@colgate.edu> |
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4 # Pieter Swart <swart@lanl.gov> |
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5 # All rights reserved. |
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6 # BSD license. |
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7 # |
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8 # Authors: Aric Hagberg (hagberg@lanl.gov) |
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9 # Pieter Swart (swart@lanl.gov) |
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10 # Dan Schult (dschult@colgate.edu) |
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11 """ |
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12 Threshold Graphs - Creation, manipulation and identification. |
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13 """ |
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14 from math import sqrt |
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15 import networkx as nx |
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16 from networkx.utils import py_random_state |
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17 |
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18 __all__ = ['is_threshold_graph', 'find_threshold_graph'] |
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19 |
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20 |
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21 def is_threshold_graph(G): |
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22 """ |
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23 Returns True if G is a threshold graph. |
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24 """ |
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25 return is_threshold_sequence(list(d for n, d in G.degree())) |
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26 |
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27 |
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28 def is_threshold_sequence(degree_sequence): |
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29 """ |
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30 Returns True if the sequence is a threshold degree seqeunce. |
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31 |
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32 Uses the property that a threshold graph must be constructed by |
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33 adding either dominating or isolated nodes. Thus, it can be |
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34 deconstructed iteratively by removing a node of degree zero or a |
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35 node that connects to the remaining nodes. If this deconstruction |
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36 failes then the sequence is not a threshold sequence. |
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37 """ |
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38 ds = degree_sequence[:] # get a copy so we don't destroy original |
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39 ds.sort() |
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40 while ds: |
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41 if ds[0] == 0: # if isolated node |
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42 ds.pop(0) # remove it |
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43 continue |
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44 if ds[-1] != len(ds) - 1: # is the largest degree node dominating? |
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45 return False # no, not a threshold degree sequence |
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46 ds.pop() # yes, largest is the dominating node |
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47 ds = [d - 1 for d in ds] # remove it and decrement all degrees |
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48 return True |
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49 |
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50 |
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51 def creation_sequence(degree_sequence, with_labels=False, compact=False): |
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52 """ |
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53 Determines the creation sequence for the given threshold degree sequence. |
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54 |
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55 The creation sequence is a list of single characters 'd' |
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56 or 'i': 'd' for dominating or 'i' for isolated vertices. |
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57 Dominating vertices are connected to all vertices present when it |
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58 is added. The first node added is by convention 'd'. |
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59 This list can be converted to a string if desired using "".join(cs) |
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60 |
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61 If with_labels==True: |
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62 Returns a list of 2-tuples containing the vertex number |
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63 and a character 'd' or 'i' which describes the type of vertex. |
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64 |
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65 If compact==True: |
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66 Returns the creation sequence in a compact form that is the number |
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67 of 'i's and 'd's alternating. |
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68 Examples: |
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69 [1,2,2,3] represents d,i,i,d,d,i,i,i |
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70 [3,1,2] represents d,d,d,i,d,d |
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71 |
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72 Notice that the first number is the first vertex to be used for |
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73 construction and so is always 'd'. |
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74 |
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75 with_labels and compact cannot both be True. |
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76 |
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77 Returns None if the sequence is not a threshold sequence |
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78 """ |
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79 if with_labels and compact: |
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80 raise ValueError("compact sequences cannot be labeled") |
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81 |
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82 # make an indexed copy |
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83 if isinstance(degree_sequence, dict): # labeled degree seqeunce |
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84 ds = [[degree, label] for (label, degree) in degree_sequence.items()] |
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85 else: |
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86 ds = [[d, i] for i, d in enumerate(degree_sequence)] |
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87 ds.sort() |
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88 cs = [] # creation sequence |
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89 while ds: |
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90 if ds[0][0] == 0: # isolated node |
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91 (d, v) = ds.pop(0) |
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92 if len(ds) > 0: # make sure we start with a d |
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93 cs.insert(0, (v, 'i')) |
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94 else: |
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95 cs.insert(0, (v, 'd')) |
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96 continue |
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97 if ds[-1][0] != len(ds) - 1: # Not dominating node |
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98 return None # not a threshold degree sequence |
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99 (d, v) = ds.pop() |
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100 cs.insert(0, (v, 'd')) |
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101 ds = [[d[0] - 1, d[1]] for d in ds] # decrement due to removing node |
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102 |
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103 if with_labels: |
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104 return cs |
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105 if compact: |
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106 return make_compact(cs) |
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107 return [v[1] for v in cs] # not labeled |
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108 |
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109 |
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110 def make_compact(creation_sequence): |
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111 """ |
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112 Returns the creation sequence in a compact form |
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113 that is the number of 'i's and 'd's alternating. |
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114 |
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115 Examples |
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116 -------- |
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117 >>> from networkx.algorithms.threshold import make_compact |
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118 >>> make_compact(['d', 'i', 'i', 'd', 'd', 'i', 'i', 'i']) |
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119 [1, 2, 2, 3] |
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120 >>> make_compact(['d', 'd', 'd', 'i', 'd', 'd']) |
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121 [3, 1, 2] |
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122 |
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123 Notice that the first number is the first vertex |
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124 to be used for construction and so is always 'd'. |
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125 |
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126 Labeled creation sequences lose their labels in the |
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127 compact representation. |
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128 |
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129 >>> make_compact([3, 1, 2]) |
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130 [3, 1, 2] |
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131 """ |
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132 first = creation_sequence[0] |
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133 if isinstance(first, str): # creation sequence |
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134 cs = creation_sequence[:] |
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135 elif isinstance(first, tuple): # labeled creation sequence |
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136 cs = [s[1] for s in creation_sequence] |
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137 elif isinstance(first, int): # compact creation sequence |
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138 return creation_sequence |
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139 else: |
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140 raise TypeError("Not a valid creation sequence type") |
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141 |
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142 ccs = [] |
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143 count = 1 # count the run lengths of d's or i's. |
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144 for i in range(1, len(cs)): |
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145 if cs[i] == cs[i - 1]: |
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146 count += 1 |
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147 else: |
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148 ccs.append(count) |
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149 count = 1 |
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150 ccs.append(count) # don't forget the last one |
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151 return ccs |
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152 |
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153 |
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154 def uncompact(creation_sequence): |
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155 """ |
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156 Converts a compact creation sequence for a threshold |
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157 graph to a standard creation sequence (unlabeled). |
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158 If the creation_sequence is already standard, return it. |
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159 See creation_sequence. |
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160 """ |
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161 first = creation_sequence[0] |
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162 if isinstance(first, str): # creation sequence |
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163 return creation_sequence |
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164 elif isinstance(first, tuple): # labeled creation sequence |
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165 return creation_sequence |
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166 elif isinstance(first, int): # compact creation sequence |
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167 ccscopy = creation_sequence[:] |
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168 else: |
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169 raise TypeError("Not a valid creation sequence type") |
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170 cs = [] |
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171 while ccscopy: |
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172 cs.extend(ccscopy.pop(0) * ['d']) |
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173 if ccscopy: |
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174 cs.extend(ccscopy.pop(0) * ['i']) |
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175 return cs |
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176 |
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177 |
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178 def creation_sequence_to_weights(creation_sequence): |
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179 """ |
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180 Returns a list of node weights which create the threshold |
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181 graph designated by the creation sequence. The weights |
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182 are scaled so that the threshold is 1.0. The order of the |
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183 nodes is the same as that in the creation sequence. |
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184 """ |
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185 # Turn input sequence into a labeled creation sequence |
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186 first = creation_sequence[0] |
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187 if isinstance(first, str): # creation sequence |
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188 if isinstance(creation_sequence, list): |
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189 wseq = creation_sequence[:] |
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190 else: |
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191 wseq = list(creation_sequence) # string like 'ddidid' |
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192 elif isinstance(first, tuple): # labeled creation sequence |
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193 wseq = [v[1] for v in creation_sequence] |
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194 elif isinstance(first, int): # compact creation sequence |
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195 wseq = uncompact(creation_sequence) |
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196 else: |
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197 raise TypeError("Not a valid creation sequence type") |
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198 # pass through twice--first backwards |
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199 wseq.reverse() |
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200 w = 0 |
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201 prev = 'i' |
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202 for j, s in enumerate(wseq): |
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203 if s == 'i': |
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204 wseq[j] = w |
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205 prev = s |
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206 elif prev == 'i': |
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207 prev = s |
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208 w += 1 |
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209 wseq.reverse() # now pass through forwards |
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210 for j, s in enumerate(wseq): |
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211 if s == 'd': |
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212 wseq[j] = w |
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213 prev = s |
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214 elif prev == 'd': |
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215 prev = s |
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216 w += 1 |
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217 # Now scale weights |
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218 if prev == 'd': |
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219 w += 1 |
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220 wscale = 1. / float(w) |
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221 return [ww * wscale for ww in wseq] |
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222 # return wseq |
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223 |
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224 |
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225 def weights_to_creation_sequence(weights, threshold=1, with_labels=False, compact=False): |
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226 """ |
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227 Returns a creation sequence for a threshold graph |
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228 determined by the weights and threshold given as input. |
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229 If the sum of two node weights is greater than the |
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230 threshold value, an edge is created between these nodes. |
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231 |
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232 The creation sequence is a list of single characters 'd' |
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233 or 'i': 'd' for dominating or 'i' for isolated vertices. |
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234 Dominating vertices are connected to all vertices present |
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235 when it is added. The first node added is by convention 'd'. |
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236 |
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237 If with_labels==True: |
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238 Returns a list of 2-tuples containing the vertex number |
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239 and a character 'd' or 'i' which describes the type of vertex. |
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240 |
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241 If compact==True: |
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242 Returns the creation sequence in a compact form that is the number |
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243 of 'i's and 'd's alternating. |
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244 Examples: |
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245 [1,2,2,3] represents d,i,i,d,d,i,i,i |
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246 [3,1,2] represents d,d,d,i,d,d |
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247 |
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248 Notice that the first number is the first vertex to be used for |
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249 construction and so is always 'd'. |
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250 |
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251 with_labels and compact cannot both be True. |
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252 """ |
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253 if with_labels and compact: |
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254 raise ValueError("compact sequences cannot be labeled") |
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255 |
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256 # make an indexed copy |
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257 if isinstance(weights, dict): # labeled weights |
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258 wseq = [[w, label] for (label, w) in weights.items()] |
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259 else: |
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260 wseq = [[w, i] for i, w in enumerate(weights)] |
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261 wseq.sort() |
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262 cs = [] # creation sequence |
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263 cutoff = threshold - wseq[-1][0] |
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264 while wseq: |
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265 if wseq[0][0] < cutoff: # isolated node |
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266 (w, label) = wseq.pop(0) |
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267 cs.append((label, 'i')) |
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268 else: |
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269 (w, label) = wseq.pop() |
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270 cs.append((label, 'd')) |
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271 cutoff = threshold - wseq[-1][0] |
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272 if len(wseq) == 1: # make sure we start with a d |
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273 (w, label) = wseq.pop() |
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274 cs.append((label, 'd')) |
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275 # put in correct order |
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276 cs.reverse() |
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277 |
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278 if with_labels: |
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279 return cs |
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280 if compact: |
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281 return make_compact(cs) |
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282 return [v[1] for v in cs] # not labeled |
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283 |
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284 |
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285 # Manipulating NetworkX.Graphs in context of threshold graphs |
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286 def threshold_graph(creation_sequence, create_using=None): |
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287 """ |
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288 Create a threshold graph from the creation sequence or compact |
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289 creation_sequence. |
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290 |
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291 The input sequence can be a |
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292 |
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293 creation sequence (e.g. ['d','i','d','d','d','i']) |
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294 labeled creation sequence (e.g. [(0,'d'),(2,'d'),(1,'i')]) |
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295 compact creation sequence (e.g. [2,1,1,2,0]) |
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296 |
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297 Use cs=creation_sequence(degree_sequence,labeled=True) |
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298 to convert a degree sequence to a creation sequence. |
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299 |
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300 Returns None if the sequence is not valid |
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301 """ |
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302 # Turn input sequence into a labeled creation sequence |
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303 first = creation_sequence[0] |
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304 if isinstance(first, str): # creation sequence |
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305 ci = list(enumerate(creation_sequence)) |
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306 elif isinstance(first, tuple): # labeled creation sequence |
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307 ci = creation_sequence[:] |
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308 elif isinstance(first, int): # compact creation sequence |
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309 cs = uncompact(creation_sequence) |
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310 ci = list(enumerate(cs)) |
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311 else: |
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312 print("not a valid creation sequence type") |
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313 return None |
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314 |
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315 G = nx.empty_graph(0, create_using) |
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316 if G.is_directed(): |
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317 raise nx.NetworkXError("Directed Graph not supported") |
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318 |
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319 G.name = "Threshold Graph" |
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320 |
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321 # add nodes and edges |
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322 # if type is 'i' just add nodea |
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323 # if type is a d connect to everything previous |
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324 while ci: |
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325 (v, node_type) = ci.pop(0) |
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326 if node_type == 'd': # dominating type, connect to all existing nodes |
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327 # We use `for u in list(G):` instead of |
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328 # `for u in G:` because we edit the graph `G` in |
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329 # the loop. Hence using an iterator will result in |
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330 # `RuntimeError: dictionary changed size during iteration` |
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331 for u in list(G): |
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332 G.add_edge(v, u) |
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333 G.add_node(v) |
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334 return G |
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335 |
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336 |
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337 def find_alternating_4_cycle(G): |
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338 """ |
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339 Returns False if there aren't any alternating 4 cycles. |
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340 Otherwise returns the cycle as [a,b,c,d] where (a,b) |
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341 and (c,d) are edges and (a,c) and (b,d) are not. |
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342 """ |
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343 for (u, v) in G.edges(): |
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344 for w in G.nodes(): |
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345 if not G.has_edge(u, w) and u != w: |
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346 for x in G.neighbors(w): |
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347 if not G.has_edge(v, x) and v != x: |
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348 return [u, v, w, x] |
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349 return False |
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350 |
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351 |
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352 def find_threshold_graph(G, create_using=None): |
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353 """ |
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354 Return a threshold subgraph that is close to largest in G. |
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355 The threshold graph will contain the largest degree node in G. |
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356 |
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357 """ |
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358 return threshold_graph(find_creation_sequence(G), create_using) |
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359 |
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360 |
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361 def find_creation_sequence(G): |
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362 """ |
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363 Find a threshold subgraph that is close to largest in G. |
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364 Returns the labeled creation sequence of that threshold graph. |
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365 """ |
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366 cs = [] |
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367 # get a local pointer to the working part of the graph |
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368 H = G |
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369 while H.order() > 0: |
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370 # get new degree sequence on subgraph |
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371 dsdict = dict(H.degree()) |
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372 ds = [(d, v) for v, d in dsdict.items()] |
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373 ds.sort() |
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374 # Update threshold graph nodes |
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375 if ds[-1][0] == 0: # all are isolated |
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376 cs.extend(zip(dsdict, ['i'] * (len(ds) - 1) + ['d'])) |
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377 break # Done! |
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378 # pull off isolated nodes |
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379 while ds[0][0] == 0: |
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380 (d, iso) = ds.pop(0) |
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381 cs.append((iso, 'i')) |
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382 # find new biggest node |
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383 (d, bigv) = ds.pop() |
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384 # add edges of star to t_g |
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385 cs.append((bigv, 'd')) |
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386 # form subgraph of neighbors of big node |
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387 H = H.subgraph(H.neighbors(bigv)) |
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388 cs.reverse() |
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389 return cs |
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390 |
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391 |
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392 # Properties of Threshold Graphs |
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393 def triangles(creation_sequence): |
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394 """ |
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395 Compute number of triangles in the threshold graph with the |
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396 given creation sequence. |
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397 """ |
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398 # shortcut algorithm that doesn't require computing number |
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399 # of triangles at each node. |
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400 cs = creation_sequence # alias |
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401 dr = cs.count("d") # number of d's in sequence |
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402 ntri = dr * (dr - 1) * (dr - 2) / 6 # number of triangles in clique of nd d's |
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403 # now add dr choose 2 triangles for every 'i' in sequence where |
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404 # dr is the number of d's to the right of the current i |
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405 for i, typ in enumerate(cs): |
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406 if typ == "i": |
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407 ntri += dr * (dr - 1) / 2 |
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408 else: |
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409 dr -= 1 |
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410 return ntri |
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411 |
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412 |
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413 def triangle_sequence(creation_sequence): |
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414 """ |
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415 Return triangle sequence for the given threshold graph creation sequence. |
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416 |
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417 """ |
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418 cs = creation_sequence |
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419 seq = [] |
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420 dr = cs.count("d") # number of d's to the right of the current pos |
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421 dcur = (dr - 1) * (dr - 2) // 2 # number of triangles through a node of clique dr |
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422 irun = 0 # number of i's in the last run |
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423 drun = 0 # number of d's in the last run |
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424 for i, sym in enumerate(cs): |
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425 if sym == "d": |
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426 drun += 1 |
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427 tri = dcur + (dr - 1) * irun # new triangles at this d |
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428 else: # cs[i]="i": |
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429 if prevsym == "d": # new string of i's |
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430 dcur += (dr - 1) * irun # accumulate shared shortest paths |
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431 irun = 0 # reset i run counter |
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432 dr -= drun # reduce number of d's to right |
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433 drun = 0 # reset d run counter |
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434 irun += 1 |
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435 tri = dr * (dr - 1) // 2 # new triangles at this i |
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436 seq.append(tri) |
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437 prevsym = sym |
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438 return seq |
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439 |
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440 |
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441 def cluster_sequence(creation_sequence): |
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442 """ |
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443 Return cluster sequence for the given threshold graph creation sequence. |
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444 """ |
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445 triseq = triangle_sequence(creation_sequence) |
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446 degseq = degree_sequence(creation_sequence) |
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447 cseq = [] |
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448 for i, deg in enumerate(degseq): |
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449 tri = triseq[i] |
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450 if deg <= 1: # isolated vertex or single pair gets cc 0 |
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451 cseq.append(0) |
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452 continue |
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453 max_size = (deg * (deg - 1)) // 2 |
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454 cseq.append(float(tri) / float(max_size)) |
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455 return cseq |
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456 |
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457 |
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458 def degree_sequence(creation_sequence): |
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459 """ |
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460 Return degree sequence for the threshold graph with the given |
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461 creation sequence |
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462 """ |
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463 cs = creation_sequence # alias |
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464 seq = [] |
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465 rd = cs.count("d") # number of d to the right |
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466 for i, sym in enumerate(cs): |
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467 if sym == "d": |
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468 rd -= 1 |
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469 seq.append(rd + i) |
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470 else: |
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471 seq.append(rd) |
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472 return seq |
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473 |
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474 |
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475 def density(creation_sequence): |
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476 """ |
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477 Return the density of the graph with this creation_sequence. |
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478 The density is the fraction of possible edges present. |
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479 """ |
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480 N = len(creation_sequence) |
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481 two_size = sum(degree_sequence(creation_sequence)) |
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482 two_possible = N * (N - 1) |
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483 den = two_size / float(two_possible) |
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484 return den |
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485 |
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486 |
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487 def degree_correlation(creation_sequence): |
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488 """ |
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489 Return the degree-degree correlation over all edges. |
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490 """ |
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491 cs = creation_sequence |
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492 s1 = 0 # deg_i*deg_j |
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493 s2 = 0 # deg_i^2+deg_j^2 |
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494 s3 = 0 # deg_i+deg_j |
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495 m = 0 # number of edges |
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496 rd = cs.count("d") # number of d nodes to the right |
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497 rdi = [i for i, sym in enumerate(cs) if sym == "d"] # index of "d"s |
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498 ds = degree_sequence(cs) |
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499 for i, sym in enumerate(cs): |
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500 if sym == "d": |
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501 if i != rdi[0]: |
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502 print("Logic error in degree_correlation", i, rdi) |
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503 raise ValueError |
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504 rdi.pop(0) |
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505 degi = ds[i] |
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506 for dj in rdi: |
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507 degj = ds[dj] |
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508 s1 += degj * degi |
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509 s2 += degi**2 + degj**2 |
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510 s3 += degi + degj |
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511 m += 1 |
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512 denom = (2 * m * s2 - s3 * s3) |
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513 numer = (4 * m * s1 - s3 * s3) |
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514 if denom == 0: |
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515 if numer == 0: |
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516 return 1 |
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517 raise ValueError("Zero Denominator but Numerator is %s" % numer) |
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518 return numer / float(denom) |
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519 |
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520 |
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521 def shortest_path(creation_sequence, u, v): |
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522 """ |
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523 Find the shortest path between u and v in a |
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524 threshold graph G with the given creation_sequence. |
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525 |
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526 For an unlabeled creation_sequence, the vertices |
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527 u and v must be integers in (0,len(sequence)) referring |
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528 to the position of the desired vertices in the sequence. |
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529 |
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530 For a labeled creation_sequence, u and v are labels of veritices. |
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531 |
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532 Use cs=creation_sequence(degree_sequence,with_labels=True) |
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533 to convert a degree sequence to a creation sequence. |
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534 |
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535 Returns a list of vertices from u to v. |
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536 Example: if they are neighbors, it returns [u,v] |
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537 """ |
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538 # Turn input sequence into a labeled creation sequence |
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539 first = creation_sequence[0] |
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540 if isinstance(first, str): # creation sequence |
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541 cs = [(i, creation_sequence[i]) for i in range(len(creation_sequence))] |
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542 elif isinstance(first, tuple): # labeled creation sequence |
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543 cs = creation_sequence[:] |
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544 elif isinstance(first, int): # compact creation sequence |
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545 ci = uncompact(creation_sequence) |
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546 cs = [(i, ci[i]) for i in range(len(ci))] |
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547 else: |
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548 raise TypeError("Not a valid creation sequence type") |
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549 |
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550 verts = [s[0] for s in cs] |
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551 if v not in verts: |
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552 raise ValueError("Vertex %s not in graph from creation_sequence" % v) |
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553 if u not in verts: |
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554 raise ValueError("Vertex %s not in graph from creation_sequence" % u) |
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555 # Done checking |
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556 if u == v: |
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557 return [u] |
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558 |
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559 uindex = verts.index(u) |
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560 vindex = verts.index(v) |
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561 bigind = max(uindex, vindex) |
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562 if cs[bigind][1] == 'd': |
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563 return [u, v] |
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564 # must be that cs[bigind][1]=='i' |
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565 cs = cs[bigind:] |
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566 while cs: |
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567 vert = cs.pop() |
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568 if vert[1] == 'd': |
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569 return [u, vert[0], v] |
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570 # All after u are type 'i' so no connection |
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571 return -1 |
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572 |
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573 |
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574 def shortest_path_length(creation_sequence, i): |
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575 """ |
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576 Return the shortest path length from indicated node to |
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577 every other node for the threshold graph with the given |
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578 creation sequence. |
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579 Node is indicated by index i in creation_sequence unless |
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580 creation_sequence is labeled in which case, i is taken to |
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581 be the label of the node. |
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582 |
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583 Paths lengths in threshold graphs are at most 2. |
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584 Length to unreachable nodes is set to -1. |
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585 """ |
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586 # Turn input sequence into a labeled creation sequence |
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587 first = creation_sequence[0] |
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588 if isinstance(first, str): # creation sequence |
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589 if isinstance(creation_sequence, list): |
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590 cs = creation_sequence[:] |
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591 else: |
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592 cs = list(creation_sequence) |
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593 elif isinstance(first, tuple): # labeled creation sequence |
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594 cs = [v[1] for v in creation_sequence] |
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595 i = [v[0] for v in creation_sequence].index(i) |
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596 elif isinstance(first, int): # compact creation sequence |
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597 cs = uncompact(creation_sequence) |
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598 else: |
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599 raise TypeError("Not a valid creation sequence type") |
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600 |
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601 # Compute |
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602 N = len(cs) |
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603 spl = [2] * N # length 2 to every node |
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604 spl[i] = 0 # except self which is 0 |
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605 # 1 for all d's to the right |
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606 for j in range(i + 1, N): |
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607 if cs[j] == "d": |
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608 spl[j] = 1 |
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609 if cs[i] == 'd': # 1 for all nodes to the left |
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610 for j in range(i): |
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611 spl[j] = 1 |
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612 # and -1 for any trailing i to indicate unreachable |
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613 for j in range(N - 1, 0, -1): |
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614 if cs[j] == "d": |
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615 break |
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616 spl[j] = -1 |
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617 return spl |
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618 |
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619 |
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620 def betweenness_sequence(creation_sequence, normalized=True): |
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621 """ |
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622 Return betweenness for the threshold graph with the given creation |
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623 sequence. The result is unscaled. To scale the values |
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624 to the iterval [0,1] divide by (n-1)*(n-2). |
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625 """ |
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626 cs = creation_sequence |
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627 seq = [] # betweenness |
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628 lastchar = 'd' # first node is always a 'd' |
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629 dr = float(cs.count("d")) # number of d's to the right of curren pos |
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630 irun = 0 # number of i's in the last run |
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631 drun = 0 # number of d's in the last run |
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632 dlast = 0.0 # betweenness of last d |
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633 for i, c in enumerate(cs): |
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634 if c == 'd': # cs[i]=="d": |
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635 # betweennees = amt shared with eariler d's and i's |
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636 # + new isolated nodes covered |
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637 # + new paths to all previous nodes |
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638 b = dlast + (irun - 1) * irun / dr + 2 * irun * (i - drun - irun) / dr |
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639 drun += 1 # update counter |
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640 else: # cs[i]="i": |
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641 if lastchar == 'd': # if this is a new run of i's |
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642 dlast = b # accumulate betweenness |
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643 dr -= drun # update number of d's to the right |
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644 drun = 0 # reset d counter |
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645 irun = 0 # reset i counter |
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646 b = 0 # isolated nodes have zero betweenness |
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647 irun += 1 # add another i to the run |
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648 seq.append(float(b)) |
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649 lastchar = c |
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650 |
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651 # normalize by the number of possible shortest paths |
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652 if normalized: |
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653 order = len(cs) |
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654 scale = 1.0 / ((order - 1) * (order - 2)) |
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655 seq = [s * scale for s in seq] |
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656 |
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657 return seq |
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658 |
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659 |
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660 def eigenvectors(creation_sequence): |
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661 """ |
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662 Return a 2-tuple of Laplacian eigenvalues and eigenvectors |
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663 for the threshold network with creation_sequence. |
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664 The first value is a list of eigenvalues. |
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665 The second value is a list of eigenvectors. |
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666 The lists are in the same order so corresponding eigenvectors |
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667 and eigenvalues are in the same position in the two lists. |
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668 |
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669 Notice that the order of the eigenvalues returned by eigenvalues(cs) |
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670 may not correspond to the order of these eigenvectors. |
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671 """ |
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672 ccs = make_compact(creation_sequence) |
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673 N = sum(ccs) |
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674 vec = [0] * N |
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675 val = vec[:] |
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676 # get number of type d nodes to the right (all for first node) |
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677 dr = sum(ccs[::2]) |
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678 |
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679 nn = ccs[0] |
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680 vec[0] = [1. / sqrt(N)] * N |
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681 val[0] = 0 |
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682 e = dr |
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683 dr -= nn |
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684 type_d = True |
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685 i = 1 |
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686 dd = 1 |
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687 while dd < nn: |
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688 scale = 1. / sqrt(dd * dd + i) |
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689 vec[i] = i * [-scale] + [dd * scale] + [0] * (N - i - 1) |
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690 val[i] = e |
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691 i += 1 |
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692 dd += 1 |
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693 if len(ccs) == 1: |
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694 return (val, vec) |
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695 for nn in ccs[1:]: |
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696 scale = 1. / sqrt(nn * i * (i + nn)) |
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697 vec[i] = i * [-nn * scale] + nn * [i * scale] + [0] * (N - i - nn) |
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698 # find eigenvalue |
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699 type_d = not type_d |
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700 if type_d: |
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701 e = i + dr |
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702 dr -= nn |
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703 else: |
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704 e = dr |
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705 val[i] = e |
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706 st = i |
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707 i += 1 |
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708 dd = 1 |
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709 while dd < nn: |
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710 scale = 1. / sqrt(i - st + dd * dd) |
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711 vec[i] = [0] * st + (i - st) * [-scale] + [dd * scale] + [0] * (N - i - 1) |
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712 val[i] = e |
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713 i += 1 |
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714 dd += 1 |
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715 return (val, vec) |
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716 |
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717 |
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718 def spectral_projection(u, eigenpairs): |
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719 """ |
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720 Returns the coefficients of each eigenvector |
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721 in a projection of the vector u onto the normalized |
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722 eigenvectors which are contained in eigenpairs. |
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723 |
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724 eigenpairs should be a list of two objects. The |
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725 first is a list of eigenvalues and the second a list |
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726 of eigenvectors. The eigenvectors should be lists. |
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727 |
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728 There's not a lot of error checking on lengths of |
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729 arrays, etc. so be careful. |
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730 """ |
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731 coeff = [] |
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732 evect = eigenpairs[1] |
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733 for ev in evect: |
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734 c = sum([evv * uv for (evv, uv) in zip(ev, u)]) |
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735 coeff.append(c) |
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736 return coeff |
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737 |
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738 |
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739 def eigenvalues(creation_sequence): |
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740 """ |
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741 Return sequence of eigenvalues of the Laplacian of the threshold |
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742 graph for the given creation_sequence. |
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743 |
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744 Based on the Ferrer's diagram method. The spectrum is integral |
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745 and is the conjugate of the degree sequence. |
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746 |
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747 See:: |
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748 |
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749 @Article{degree-merris-1994, |
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750 author = {Russel Merris}, |
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751 title = {Degree maximal graphs are Laplacian integral}, |
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752 journal = {Linear Algebra Appl.}, |
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753 year = {1994}, |
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754 volume = {199}, |
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755 pages = {381--389}, |
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756 } |
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757 |
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758 """ |
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759 degseq = degree_sequence(creation_sequence) |
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760 degseq.sort() |
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761 eiglist = [] # zero is always one eigenvalue |
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762 eig = 0 |
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763 row = len(degseq) |
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764 bigdeg = degseq.pop() |
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765 while row: |
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766 if bigdeg < row: |
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767 eiglist.append(eig) |
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768 row -= 1 |
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769 else: |
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770 eig += 1 |
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771 if degseq: |
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772 bigdeg = degseq.pop() |
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773 else: |
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774 bigdeg = 0 |
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775 return eiglist |
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776 |
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777 |
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778 # Threshold graph creation routines |
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779 |
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780 @py_random_state(2) |
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781 def random_threshold_sequence(n, p, seed=None): |
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782 """ |
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783 Create a random threshold sequence of size n. |
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784 A creation sequence is built by randomly choosing d's with |
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785 probabiliy p and i's with probability 1-p. |
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786 |
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787 s=nx.random_threshold_sequence(10,0.5) |
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788 |
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789 returns a threshold sequence of length 10 with equal |
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790 probably of an i or a d at each position. |
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791 |
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792 A "random" threshold graph can be built with |
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793 |
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794 G=nx.threshold_graph(s) |
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795 |
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796 seed : integer, random_state, or None (default) |
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797 Indicator of random number generation state. |
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798 See :ref:`Randomness<randomness>`. |
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799 """ |
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800 if not (0 <= p <= 1): |
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801 raise ValueError("p must be in [0,1]") |
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802 |
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803 cs = ['d'] # threshold sequences always start with a d |
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804 for i in range(1, n): |
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805 if seed.random() < p: |
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806 cs.append('d') |
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807 else: |
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808 cs.append('i') |
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809 return cs |
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810 |
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811 |
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812 # maybe *_d_threshold_sequence routines should |
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813 # be (or be called from) a single routine with a more descriptive name |
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814 # and a keyword parameter? |
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815 def right_d_threshold_sequence(n, m): |
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816 """ |
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817 Create a skewed threshold graph with a given number |
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818 of vertices (n) and a given number of edges (m). |
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819 |
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820 The routine returns an unlabeled creation sequence |
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821 for the threshold graph. |
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822 |
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823 FIXME: describe algorithm |
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824 |
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825 """ |
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826 cs = ['d'] + ['i'] * (n - 1) # create sequence with n insolated nodes |
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827 |
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828 # m <n : not enough edges, make disconnected |
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829 if m < n: |
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830 cs[m] = 'd' |
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831 return cs |
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832 |
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833 # too many edges |
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834 if m > n * (n - 1) / 2: |
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835 raise ValueError("Too many edges for this many nodes.") |
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836 |
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837 # connected case m >n-1 |
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838 ind = n - 1 |
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839 sum = n - 1 |
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840 while sum < m: |
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841 cs[ind] = 'd' |
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842 ind -= 1 |
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843 sum += ind |
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844 ind = m - (sum - ind) |
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845 cs[ind] = 'd' |
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846 return cs |
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847 |
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848 |
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849 def left_d_threshold_sequence(n, m): |
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850 """ |
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851 Create a skewed threshold graph with a given number |
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852 of vertices (n) and a given number of edges (m). |
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853 |
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854 The routine returns an unlabeled creation sequence |
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855 for the threshold graph. |
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856 |
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857 FIXME: describe algorithm |
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858 |
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859 """ |
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860 cs = ['d'] + ['i'] * (n - 1) # create sequence with n insolated nodes |
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861 |
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862 # m <n : not enough edges, make disconnected |
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863 if m < n: |
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864 cs[m] = 'd' |
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865 return cs |
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866 |
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867 # too many edges |
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868 if m > n * (n - 1) / 2: |
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869 raise ValueError("Too many edges for this many nodes.") |
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870 |
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871 # Connected case when M>N-1 |
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872 cs[n - 1] = 'd' |
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873 sum = n - 1 |
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874 ind = 1 |
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875 while sum < m: |
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876 cs[ind] = 'd' |
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877 sum += ind |
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878 ind += 1 |
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879 if sum > m: # be sure not to change the first vertex |
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880 cs[sum - m] = 'i' |
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881 return cs |
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882 |
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883 |
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884 @py_random_state(3) |
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885 def swap_d(cs, p_split=1.0, p_combine=1.0, seed=None): |
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886 """ |
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887 Perform a "swap" operation on a threshold sequence. |
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888 |
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889 The swap preserves the number of nodes and edges |
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890 in the graph for the given sequence. |
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891 The resulting sequence is still a threshold sequence. |
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892 |
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893 Perform one split and one combine operation on the |
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894 'd's of a creation sequence for a threshold graph. |
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895 This operation maintains the number of nodes and edges |
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896 in the graph, but shifts the edges from node to node |
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897 maintaining the threshold quality of the graph. |
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898 |
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899 seed : integer, random_state, or None (default) |
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900 Indicator of random number generation state. |
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901 See :ref:`Randomness<randomness>`. |
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902 """ |
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903 # preprocess the creation sequence |
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904 dlist = [i for (i, node_type) in enumerate(cs[1:-1]) if node_type == 'd'] |
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905 # split |
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906 if seed.random() < p_split: |
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907 choice = seed.choice(dlist) |
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908 split_to = seed.choice(range(choice)) |
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909 flip_side = choice - split_to |
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910 if split_to != flip_side and cs[split_to] == 'i' and cs[flip_side] == 'i': |
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911 cs[choice] = 'i' |
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912 cs[split_to] = 'd' |
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913 cs[flip_side] = 'd' |
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914 dlist.remove(choice) |
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915 # don't add or combine may reverse this action |
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916 # dlist.extend([split_to,flip_side]) |
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917 # print >>sys.stderr,"split at %s to %s and %s"%(choice,split_to,flip_side) |
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918 # combine |
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919 if seed.random() < p_combine and dlist: |
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920 first_choice = seed.choice(dlist) |
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921 second_choice = seed.choice(dlist) |
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922 target = first_choice + second_choice |
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923 if target >= len(cs) or cs[target] == 'd' or first_choice == second_choice: |
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924 return cs |
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925 # OK to combine |
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926 cs[first_choice] = 'i' |
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927 cs[second_choice] = 'i' |
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928 cs[target] = 'd' |
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929 # print >>sys.stderr,"combine %s and %s to make %s."%(first_choice,second_choice,target) |
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930 |
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931 return cs |