Mercurial > repos > shellac > sam_consensus_v3

annotate env/lib/python3.9/site-packages/networkx/convert.py @ 0:4f3585e2f14b draft default tip

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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author shellac
date Mon, 22 Mar 2021 18:12:50 +0000
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0
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1 """Functions to convert NetworkX graphs to and from other formats.
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2
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3 The preferred way of converting data to a NetworkX graph is through the
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4 graph constructor. The constructor calls the to_networkx_graph() function
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5 which attempts to guess the input type and convert it automatically.
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6
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7 Examples
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8 --------
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9 Create a graph with a single edge from a dictionary of dictionaries
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10
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11 >>> d = {0: {1: 1}} # dict-of-dicts single edge (0,1)
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12 >>> G = nx.Graph(d)
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13
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14 See Also
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15 --------
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16 nx_agraph, nx_pydot
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17 """
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18 import warnings
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19 import networkx as nx
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20 from collections.abc import Collection, Generator, Iterator
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21
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22 __all__ = [
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23 "to_networkx_graph",
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24 "from_dict_of_dicts",
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25 "to_dict_of_dicts",
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26 "from_dict_of_lists",
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27 "to_dict_of_lists",
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28 "from_edgelist",
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29 "to_edgelist",
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30 ]
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31
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32
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33 def to_networkx_graph(data, create_using=None, multigraph_input=False):
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34 """Make a NetworkX graph from a known data structure.
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35
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36 The preferred way to call this is automatically
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37 from the class constructor
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38
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39 >>> d = {0: {1: {"weight": 1}}} # dict-of-dicts single edge (0,1)
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40 >>> G = nx.Graph(d)
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41
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42 instead of the equivalent
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43
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44 >>> G = nx.from_dict_of_dicts(d)
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45
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46 Parameters
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47 ----------
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48 data : object to be converted
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49
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50 Current known types are:
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51 any NetworkX graph
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52 dict-of-dicts
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53 dict-of-lists
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54 container (e.g. set, list, tuple) of edges
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55 iterator (e.g. itertools.chain) that produces edges
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56 generator of edges
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57 Pandas DataFrame (row per edge)
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58 numpy matrix
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59 numpy ndarray
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60 scipy sparse matrix
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61 pygraphviz agraph
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62
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63 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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64 Graph type to create. If graph instance, then cleared before populated.
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65
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66 multigraph_input : bool (default False)
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67 If True and data is a dict_of_dicts,
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68 try to create a multigraph assuming dict_of_dict_of_lists.
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69 If data and create_using are both multigraphs then create
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70 a multigraph from a multigraph.
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71
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72 """
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73 # NX graph
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74 if hasattr(data, "adj"):
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75 try:
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76 result = from_dict_of_dicts(
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77 data.adj,
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78 create_using=create_using,
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79 multigraph_input=data.is_multigraph(),
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80 )
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81 if hasattr(data, "graph"): # data.graph should be dict-like
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82 result.graph.update(data.graph)
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83 if hasattr(data, "nodes"): # data.nodes should be dict-like
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84 # result.add_node_from(data.nodes.items()) possible but
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85 # for custom node_attr_dict_factory which may be hashable
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86 # will be unexpected behavior
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87 for n, dd in data.nodes.items():
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88 result._node[n].update(dd)
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89 return result
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90 except Exception as e:
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91 raise nx.NetworkXError("Input is not a correct NetworkX graph.") from e
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92
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93 # pygraphviz agraph
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94 if hasattr(data, "is_strict"):
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95 try:
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96 return nx.nx_agraph.from_agraph(data, create_using=create_using)
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97 except Exception as e:
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98 raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from e
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99
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100 # dict of dicts/lists
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101 if isinstance(data, dict):
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102 try:
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103 return from_dict_of_dicts(
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104 data, create_using=create_using, multigraph_input=multigraph_input
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105 )
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106 except:
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107 try:
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108 return from_dict_of_lists(data, create_using=create_using)
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109 except Exception as e:
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110 raise TypeError("Input is not known type.") from e
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111
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112 # Pandas DataFrame
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113 try:
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114 import pandas as pd
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115
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116 if isinstance(data, pd.DataFrame):
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117 if data.shape[0] == data.shape[1]:
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118 try:
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119 return nx.from_pandas_adjacency(data, create_using=create_using)
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120 except Exception as e:
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121 msg = "Input is not a correct Pandas DataFrame adjacency matrix."
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122 raise nx.NetworkXError(msg) from e
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123 else:
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124 try:
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125 return nx.from_pandas_edgelist(
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126 data, edge_attr=True, create_using=create_using
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127 )
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128 except Exception as e:
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129 msg = "Input is not a correct Pandas DataFrame edge-list."
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130 raise nx.NetworkXError(msg) from e
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131 except ImportError:
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132 msg = "pandas not found, skipping conversion test."
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133 warnings.warn(msg, ImportWarning)
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134
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135 # numpy matrix or ndarray
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136 try:
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137 import numpy
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138
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139 if isinstance(data, (numpy.matrix, numpy.ndarray)):
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140 try:
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141 return nx.from_numpy_matrix(data, create_using=create_using)
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142 except Exception as e:
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143 raise nx.NetworkXError(
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144 "Input is not a correct numpy matrix or array."
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145 ) from e
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146 except ImportError:
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147 warnings.warn("numpy not found, skipping conversion test.", ImportWarning)
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148
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149 # scipy sparse matrix - any format
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150 try:
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151 import scipy
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152
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153 if hasattr(data, "format"):
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154 try:
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155 return nx.from_scipy_sparse_matrix(data, create_using=create_using)
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156 except Exception as e:
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157 raise nx.NetworkXError(
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158 "Input is not a correct scipy sparse matrix type."
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159 ) from e
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160 except ImportError:
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161 warnings.warn("scipy not found, skipping conversion test.", ImportWarning)
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162
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163 # Note: most general check - should remain last in order of execution
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164 # Includes containers (e.g. list, set, dict, etc.), generators, and
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165 # iterators (e.g. itertools.chain) of edges
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166
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167 if isinstance(data, (Collection, Generator, Iterator)):
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168 try:
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169 return from_edgelist(data, create_using=create_using)
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170 except Exception as e:
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171 raise nx.NetworkXError("Input is not a valid edge list") from e
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172
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173 raise nx.NetworkXError("Input is not a known data type for conversion.")
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174
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175
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176 def to_dict_of_lists(G, nodelist=None):
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177 """Returns adjacency representation of graph as a dictionary of lists.
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178
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179 Parameters
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180 ----------
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181 G : graph
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182 A NetworkX graph
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183
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184 nodelist : list
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185 Use only nodes specified in nodelist
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186
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187 Notes
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188 -----
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189 Completely ignores edge data for MultiGraph and MultiDiGraph.
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190
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191 """
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192 if nodelist is None:
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193 nodelist = G
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194
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195 d = {}
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196 for n in nodelist:
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197 d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
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198 return d
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199
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200
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201 def from_dict_of_lists(d, create_using=None):
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202 """Returns a graph from a dictionary of lists.
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203
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204 Parameters
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205 ----------
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206 d : dictionary of lists
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207 A dictionary of lists adjacency representation.
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208
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209 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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210 Graph type to create. If graph instance, then cleared before populated.
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211
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212 Examples
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213 --------
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214 >>> dol = {0: [1]} # single edge (0,1)
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215 >>> G = nx.from_dict_of_lists(dol)
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216
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217 or
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218
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219 >>> G = nx.Graph(dol) # use Graph constructor
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220
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221 """
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222 G = nx.empty_graph(0, create_using)
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223 G.add_nodes_from(d)
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224 if G.is_multigraph() and not G.is_directed():
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225 # a dict_of_lists can't show multiedges. BUT for undirected graphs,
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226 # each edge shows up twice in the dict_of_lists.
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227 # So we need to treat this case separately.
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228 seen = {}
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229 for node, nbrlist in d.items():
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230 for nbr in nbrlist:
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231 if nbr not in seen:
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232 G.add_edge(node, nbr)
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233 seen[node] = 1 # don't allow reverse edge to show up
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234 else:
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235 G.add_edges_from(
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236 ((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
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237 )
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238 return G
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239
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240
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241 def to_dict_of_dicts(G, nodelist=None, edge_data=None):
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242 """Returns adjacency representation of graph as a dictionary of dictionaries.
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243
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244 Parameters
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245 ----------
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246 G : graph
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247 A NetworkX graph
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248
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249 nodelist : list
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250 Use only nodes specified in nodelist
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251
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252 edge_data : list, optional
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253 If provided, the value of the dictionary will be
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254 set to edge_data for all edges. This is useful to make
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255 an adjacency matrix type representation with 1 as the edge data.
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256 If edgedata is None, the edgedata in G is used to fill the values.
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257 If G is a multigraph, the edgedata is a dict for each pair (u,v).
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258 """
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259 dod = {}
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260 if nodelist is None:
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261 if edge_data is None:
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262 for u, nbrdict in G.adjacency():
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263 dod[u] = nbrdict.copy()
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264 else: # edge_data is not None
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265 for u, nbrdict in G.adjacency():
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266 dod[u] = dod.fromkeys(nbrdict, edge_data)
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267 else: # nodelist is not None
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268 if edge_data is None:
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269 for u in nodelist:
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270 dod[u] = {}
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271 for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
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272 dod[u][v] = data
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273 else: # nodelist and edge_data are not None
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274 for u in nodelist:
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275 dod[u] = {}
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276 for v in (v for v in G[u] if v in nodelist):
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277 dod[u][v] = edge_data
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278 return dod
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279
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280
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281 def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
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282 """Returns a graph from a dictionary of dictionaries.
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283
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284 Parameters
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285 ----------
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286 d : dictionary of dictionaries
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287 A dictionary of dictionaries adjacency representation.
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288
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289 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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290 Graph type to create. If graph instance, then cleared before populated.
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291
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292 multigraph_input : bool (default False)
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293 When True, the values of the inner dict are assumed
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294 to be containers of edge data for multiple edges.
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295 Otherwise this routine assumes the edge data are singletons.
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296
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297 Examples
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298 --------
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299 >>> dod = {0: {1: {"weight": 1}}} # single edge (0,1)
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300 >>> G = nx.from_dict_of_dicts(dod)
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301
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302 or
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303
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304 >>> G = nx.Graph(dod) # use Graph constructor
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305
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306 """
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307 G = nx.empty_graph(0, create_using)
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308 G.add_nodes_from(d)
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309 # is dict a MultiGraph or MultiDiGraph?
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310 if multigraph_input:
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311 # make a copy of the list of edge data (but not the edge data)
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312 if G.is_directed():
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313 if G.is_multigraph():
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314 G.add_edges_from(
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315 (u, v, key, data)
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316 for u, nbrs in d.items()
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317 for v, datadict in nbrs.items()
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318 for key, data in datadict.items()
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319 )
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320 else:
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321 G.add_edges_from(
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322 (u, v, data)
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323 for u, nbrs in d.items()
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324 for v, datadict in nbrs.items()
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325 for key, data in datadict.items()
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326 )
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327 else: # Undirected
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328 if G.is_multigraph():
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329 seen = set() # don't add both directions of undirected graph
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330 for u, nbrs in d.items():
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331 for v, datadict in nbrs.items():
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332 if (u, v) not in seen:
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333 G.add_edges_from(
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334 (u, v, key, data) for key, data in datadict.items()
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335 )
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336 seen.add((v, u))
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337 else:
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338 seen = set() # don't add both directions of undirected graph
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339 for u, nbrs in d.items():
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340 for v, datadict in nbrs.items():
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341 if (u, v) not in seen:
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342 G.add_edges_from(
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343 (u, v, data) for key, data in datadict.items()
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344 )
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345 seen.add((v, u))
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346
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347 else: # not a multigraph to multigraph transfer
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348 if G.is_multigraph() and not G.is_directed():
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349 # d can have both representations u-v, v-u in dict. Only add one.
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350 # We don't need this check for digraphs since we add both directions,
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351 # or for Graph() since it is done implicitly (parallel edges not allowed)
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352 seen = set()
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353 for u, nbrs in d.items():
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354 for v, data in nbrs.items():
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355 if (u, v) not in seen:
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356 G.add_edge(u, v, key=0)
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357 G[u][v][0].update(data)
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358 seen.add((v, u))
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359 else:
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360 G.add_edges_from(
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361 ((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
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362 )
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363 return G
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364
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365
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366 def to_edgelist(G, nodelist=None):
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367 """Returns a list of edges in the graph.
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368
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369 Parameters
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370 ----------
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371 G : graph
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372 A NetworkX graph
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373
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374 nodelist : list
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375 Use only nodes specified in nodelist
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376
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377 """
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378 if nodelist is None:
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379 return G.edges(data=True)
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380 return G.edges(nodelist, data=True)
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381
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382
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383 def from_edgelist(edgelist, create_using=None):
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384 """Returns a graph from a list of edges.
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385
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386 Parameters
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387 ----------
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388 edgelist : list or iterator
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389 Edge tuples
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390
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391 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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392 Graph type to create. If graph instance, then cleared before populated.
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393
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394 Examples
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395 --------
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396 >>> edgelist = [(0, 1)] # single edge (0,1)
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397 >>> G = nx.from_edgelist(edgelist)
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398
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399 or
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400
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401 >>> G = nx.Graph(edgelist) # use Graph constructor
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402
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403 """
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404 G = nx.empty_graph(0, create_using)
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405 G.add_edges_from(edgelist)
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406 return G