Mercurial > repos > shellac > guppy_basecaller

annotate env/lib/python3.7/site-packages/networkx/convert.py @ 4:79f47841a781 draft

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"planemo upload commit 2a0fe2cc28b09e101d37293e53e82f61762262ec"
author shellac
date Thu, 14 May 2020 16:47:39 -0400
parents 26e78fe6e8c4
children
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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 # Copyright (C) 2006-2013 by
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19 # Aric Hagberg <hagberg@lanl.gov>
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20 # Dan Schult <dschult@colgate.edu>
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21 # Pieter Swart <swart@lanl.gov>
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22 # All rights reserved.
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23 # BSD license.
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24 import warnings
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25 import networkx as nx
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26 __author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>',
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27 'Pieter Swart (swart@lanl.gov)',
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28 'Dan Schult(dschult@colgate.edu)'])
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29 __all__ = ['to_networkx_graph',
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30 'from_dict_of_dicts', 'to_dict_of_dicts',
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31 'from_dict_of_lists', 'to_dict_of_lists',
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32 'from_edgelist', 'to_edgelist']
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33
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34
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35 def to_networkx_graph(data, create_using=None, multigraph_input=False):
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36 """Make a NetworkX graph from a known data structure.
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37
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38 The preferred way to call this is automatically
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39 from the class constructor
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40
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41 >>> d = {0: {1: {'weight':1}}} # dict-of-dicts single edge (0,1)
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42 >>> G = nx.Graph(d)
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43
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44 instead of the equivalent
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45
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46 >>> G = nx.from_dict_of_dicts(d)
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47
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48 Parameters
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49 ----------
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50 data : object to be converted
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51
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52 Current known types are:
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53 any NetworkX graph
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54 dict-of-dicts
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55 dict-of-lists
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56 list 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(data.adj,
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77 create_using=create_using,
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78 multigraph_input=data.is_multigraph())
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79 if hasattr(data, 'graph'): # data.graph should be dict-like
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80 result.graph.update(data.graph)
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81 if hasattr(data, 'nodes'): # data.nodes should be dict-like
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82 # result.add_node_from(data.nodes.items()) possible but
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83 # for custom node_attr_dict_factory which may be hashable
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84 # will be unexpected behavior
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85 for n, dd in data.nodes.items():
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86 result._node[n].update(dd)
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87 return result
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88 except:
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89 raise nx.NetworkXError("Input is not a correct NetworkX graph.")
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90
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91 # pygraphviz agraph
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92 if hasattr(data, "is_strict"):
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93 try:
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94 return nx.nx_agraph.from_agraph(data, create_using=create_using)
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95 except:
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96 raise nx.NetworkXError("Input is not a correct pygraphviz graph.")
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97
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98 # dict of dicts/lists
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99 if isinstance(data, dict):
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100 try:
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101 return from_dict_of_dicts(data, create_using=create_using,
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102 multigraph_input=multigraph_input)
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103 except:
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104 try:
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105 return from_dict_of_lists(data, create_using=create_using)
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106 except:
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107 raise TypeError("Input is not known type.")
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108
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109 # list or generator of edges
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110
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111 if (isinstance(data, (list, tuple)) or
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112 any(hasattr(data, attr) for attr in ['_adjdict', 'next', '__next__'])):
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113 try:
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114 return from_edgelist(data, create_using=create_using)
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115 except:
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116 raise nx.NetworkXError("Input is not a valid edge list")
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117
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118 # Pandas DataFrame
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119 try:
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120 import pandas as pd
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121 if isinstance(data, pd.DataFrame):
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122 if data.shape[0] == data.shape[1]:
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123 try:
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124 return nx.from_pandas_adjacency(data, create_using=create_using)
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125 except:
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126 msg = "Input is not a correct Pandas DataFrame adjacency matrix."
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127 raise nx.NetworkXError(msg)
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128 else:
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129 try:
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130 return nx.from_pandas_edgelist(data, edge_attr=True, create_using=create_using)
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131 except:
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132 msg = "Input is not a correct Pandas DataFrame edge-list."
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133 raise nx.NetworkXError(msg)
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134 except ImportError:
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135 msg = 'pandas not found, skipping conversion test.'
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136 warnings.warn(msg, ImportWarning)
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137
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138 # numpy matrix or ndarray
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139 try:
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140 import numpy
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141 if isinstance(data, (numpy.matrix, numpy.ndarray)):
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142 try:
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143 return nx.from_numpy_matrix(data, create_using=create_using)
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144 except:
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145 raise nx.NetworkXError(
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146 "Input is not a correct numpy matrix or array.")
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147 except ImportError:
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148 warnings.warn('numpy not found, skipping conversion test.',
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149 ImportWarning)
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150
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151 # scipy sparse matrix - any format
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152 try:
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153 import scipy
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154 if hasattr(data, "format"):
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155 try:
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156 return nx.from_scipy_sparse_matrix(data, create_using=create_using)
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157 except:
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158 raise nx.NetworkXError(
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159 "Input is not a correct scipy sparse matrix type.")
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160 except ImportError:
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161 warnings.warn('scipy not found, skipping conversion test.',
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162 ImportWarning)
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163
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164 raise nx.NetworkXError(
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165 "Input is not a known data type for conversion.")
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166
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167
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168 def to_dict_of_lists(G, nodelist=None):
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169 """Returns adjacency representation of graph as a dictionary of lists.
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170
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171 Parameters
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172 ----------
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173 G : graph
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174 A NetworkX graph
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175
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176 nodelist : list
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177 Use only nodes specified in nodelist
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178
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179 Notes
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180 -----
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181 Completely ignores edge data for MultiGraph and MultiDiGraph.
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182
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183 """
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184 if nodelist is None:
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185 nodelist = G
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186
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187 d = {}
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188 for n in nodelist:
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189 d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
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190 return d
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191
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192
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193 def from_dict_of_lists(d, create_using=None):
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194 """Returns a graph from a dictionary of lists.
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195
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196 Parameters
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197 ----------
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198 d : dictionary of lists
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199 A dictionary of lists adjacency representation.
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200
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201 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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202 Graph type to create. If graph instance, then cleared before populated.
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203
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204 Examples
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205 --------
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206 >>> dol = {0: [1]} # single edge (0,1)
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207 >>> G = nx.from_dict_of_lists(dol)
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208
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209 or
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210
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211 >>> G = nx.Graph(dol) # use Graph constructor
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212
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213 """
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214 G = nx.empty_graph(0, create_using)
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215 G.add_nodes_from(d)
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216 if G.is_multigraph() and not G.is_directed():
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217 # a dict_of_lists can't show multiedges. BUT for undirected graphs,
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218 # each edge shows up twice in the dict_of_lists.
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219 # So we need to treat this case separately.
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220 seen = {}
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221 for node, nbrlist in d.items():
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222 for nbr in nbrlist:
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223 if nbr not in seen:
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224 G.add_edge(node, nbr)
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225 seen[node] = 1 # don't allow reverse edge to show up
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226 else:
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227 G.add_edges_from(((node, nbr) for node, nbrlist in d.items()
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228 for nbr in nbrlist))
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229 return G
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230
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231
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232 def to_dict_of_dicts(G, nodelist=None, edge_data=None):
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233 """Returns adjacency representation of graph as a dictionary of dictionaries.
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234
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235 Parameters
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236 ----------
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237 G : graph
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238 A NetworkX graph
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239
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240 nodelist : list
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241 Use only nodes specified in nodelist
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242
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243 edge_data : list, optional
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244 If provided, the value of the dictionary will be
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245 set to edge_data for all edges. This is useful to make
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246 an adjacency matrix type representation with 1 as the edge data.
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247 If edgedata is None, the edgedata in G is used to fill the values.
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248 If G is a multigraph, the edgedata is a dict for each pair (u,v).
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249 """
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250 dod = {}
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251 if nodelist is None:
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252 if edge_data is None:
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253 for u, nbrdict in G.adjacency():
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254 dod[u] = nbrdict.copy()
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255 else: # edge_data is not None
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256 for u, nbrdict in G.adjacency():
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257 dod[u] = dod.fromkeys(nbrdict, edge_data)
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258 else: # nodelist is not None
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259 if edge_data is None:
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260 for u in nodelist:
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261 dod[u] = {}
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262 for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
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263 dod[u][v] = data
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264 else: # nodelist and edge_data are not None
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265 for u in nodelist:
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266 dod[u] = {}
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267 for v in (v for v in G[u] if v in nodelist):
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268 dod[u][v] = edge_data
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269 return dod
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270
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271
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272 def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
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273 """Returns a graph from a dictionary of dictionaries.
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274
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275 Parameters
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276 ----------
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277 d : dictionary of dictionaries
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278 A dictionary of dictionaries adjacency representation.
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279
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280 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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281 Graph type to create. If graph instance, then cleared before populated.
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282
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283 multigraph_input : bool (default False)
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284 When True, the values of the inner dict are assumed
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285 to be containers of edge data for multiple edges.
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286 Otherwise this routine assumes the edge data are singletons.
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287
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288 Examples
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289 --------
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290 >>> dod = {0: {1: {'weight': 1}}} # single edge (0,1)
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291 >>> G = nx.from_dict_of_dicts(dod)
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292
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293 or
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294
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295 >>> G = nx.Graph(dod) # use Graph constructor
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296
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297 """
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298 G = nx.empty_graph(0, create_using)
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299 G.add_nodes_from(d)
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300 # is dict a MultiGraph or MultiDiGraph?
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301 if multigraph_input:
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302 # make a copy of the list of edge data (but not the edge data)
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303 if G.is_directed():
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304 if G.is_multigraph():
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305 G.add_edges_from((u, v, key, data)
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306 for u, nbrs in d.items()
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307 for v, datadict in nbrs.items()
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308 for key, data in datadict.items())
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309 else:
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310 G.add_edges_from((u, v, data)
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311 for u, nbrs in d.items()
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312 for v, datadict in nbrs.items()
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313 for key, data in datadict.items())
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314 else: # Undirected
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315 if G.is_multigraph():
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316 seen = set() # don't add both directions of undirected graph
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317 for u, nbrs in d.items():
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318 for v, datadict in nbrs.items():
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319 if (u, v) not in seen:
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320 G.add_edges_from((u, v, key, data)
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321 for key, data in datadict.items())
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322 seen.add((v, u))
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323 else:
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324 seen = set() # don't add both directions of undirected graph
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325 for u, nbrs in d.items():
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326 for v, datadict in nbrs.items():
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327 if (u, v) not in seen:
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328 G.add_edges_from((u, v, data)
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329 for key, data in datadict.items())
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330 seen.add((v, u))
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331
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332 else: # not a multigraph to multigraph transfer
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333 if G.is_multigraph() and not G.is_directed():
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334 # d can have both representations u-v, v-u in dict. Only add one.
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335 # We don't need this check for digraphs since we add both directions,
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336 # or for Graph() since it is done implicitly (parallel edges not allowed)
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337 seen = set()
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338 for u, nbrs in d.items():
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339 for v, data in nbrs.items():
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340 if (u, v) not in seen:
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341 G.add_edge(u, v, key=0)
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342 G[u][v][0].update(data)
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343 seen.add((v, u))
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344 else:
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345 G.add_edges_from(((u, v, data)
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346 for u, nbrs in d.items()
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347 for v, data in nbrs.items()))
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348 return G
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349
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350
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351 def to_edgelist(G, nodelist=None):
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352 """Returns a list of edges in the graph.
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353
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354 Parameters
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355 ----------
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356 G : graph
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357 A NetworkX graph
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358
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359 nodelist : list
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360 Use only nodes specified in nodelist
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361
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362 """
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363 if nodelist is None:
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364 return G.edges(data=True)
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365 return G.edges(nodelist, data=True)
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366
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367
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368 def from_edgelist(edgelist, create_using=None):
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369 """Returns a graph from a list of edges.
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370
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371 Parameters
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372 ----------
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373 edgelist : list or iterator
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374 Edge tuples
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375
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376 create_using : NetworkX graph constructor, optional (default=nx.Graph)
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377 Graph type to create. If graph instance, then cleared before populated.
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378
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379 Examples
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380 --------
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381 >>> edgelist = [(0, 1)] # single edge (0,1)
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382 >>> G = nx.from_edgelist(edgelist)
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383
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384 or
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385
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386 >>> G = nx.Graph(edgelist) # use Graph constructor
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387
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388 """
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389 G = nx.empty_graph(0, create_using)
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390 G.add_edges_from(edgelist)
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391 return G