Mercurial > repos > shellac > guppy_basecaller

annotate env/lib/python3.7/site-packages/networkx/utils/misc.py @ 3:758bc20232e8 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload commit 2a0fe2cc28b09e101d37293e53e82f61762262ec"
author shellac
date Thu, 14 May 2020 16:20:52 -0400
parents 26e78fe6e8c4
children
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0
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1 """
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2 Miscellaneous Helpers for NetworkX.
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3
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4 These are not imported into the base networkx namespace but
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5 can be accessed, for example, as
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6
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7 >>> import networkx
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8 >>> networkx.utils.is_string_like('spam')
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9 True
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10 """
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11 # Authors: Aric Hagberg (hagberg@lanl.gov),
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12 # Dan Schult(dschult@colgate.edu),
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13 # Ben Edwards(bedwards@cs.unm.edu)
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14
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15 # Copyright (C) 2004-2019 by
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16 # Aric Hagberg <hagberg@lanl.gov>
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17 # Dan Schult <dschult@colgate.edu>
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18 # Pieter Swart <swart@lanl.gov>
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19 # All rights reserved.
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20 # BSD license.
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21 from collections import defaultdict
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22 from collections import deque
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23 import warnings
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24 import sys
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25 import uuid
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26 from itertools import tee, chain
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27 import networkx as nx
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28
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29 # itertools.accumulate is only available on Python 3.2 or later.
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30 #
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31 # Once support for Python versions less than 3.2 is dropped, this code should
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32 # be removed.
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33 try:
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34 from itertools import accumulate
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35 except ImportError:
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36 import operator
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37
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38 # The code for this function is from the Python 3.5 documentation,
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39 # distributed under the PSF license:
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40 # <https://docs.python.org/3.5/library/itertools.html#itertools.accumulate>
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41 def accumulate(iterable, func=operator.add):
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42 it = iter(iterable)
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43 try:
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44 total = next(it)
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45 except StopIteration:
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46 return
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47 yield total
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48 for element in it:
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49 total = func(total, element)
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50 yield total
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51
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52 # 2.x/3.x compatibility
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53 try:
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54 basestring
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55 except NameError:
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56 basestring = str
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57 unicode = str
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58
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59 # some cookbook stuff
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60 # used in deciding whether something is a bunch of nodes, edges, etc.
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61 # see G.add_nodes and others in Graph Class in networkx/base.py
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62
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63
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64 def is_string_like(obj): # from John Hunter, types-free version
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65 """Check if obj is string."""
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66 return isinstance(obj, basestring)
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67
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68
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69 def iterable(obj):
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70 """ Return True if obj is iterable with a well-defined len()."""
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71 if hasattr(obj, "__iter__"):
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72 return True
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73 try:
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74 len(obj)
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75 except:
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76 return False
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77 return True
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78
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79
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80 def flatten(obj, result=None):
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81 """ Return flattened version of (possibly nested) iterable object. """
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82 if not iterable(obj) or is_string_like(obj):
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83 return obj
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84 if result is None:
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85 result = []
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86 for item in obj:
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87 if not iterable(item) or is_string_like(item):
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88 result.append(item)
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89 else:
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90 flatten(item, result)
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91 return obj.__class__(result)
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92
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93
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94 def make_list_of_ints(sequence):
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95 """Return list of ints from sequence of integral numbers.
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96
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97 All elements of the sequence must satisfy int(element) == element
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98 or a ValueError is raised. Sequence is iterated through once.
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99
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100 If sequence is a list, the non-int values are replaced with ints.
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101 So, no new list is created
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102 """
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103 msg = 'sequence is not all integers: %s'
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104 if not isinstance(sequence, list):
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105 result = []
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106 for i in sequence:
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107 try:
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108 ii = int(i)
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109 except ValueError:
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110 raise nx.NetworkXError(msg % i) from None
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111 if ii != i:
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112 raise nx.NetworkXError(msg % i)
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113 result.append(ii)
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114 return result
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115 # original sequence is a list... in-place conversion to ints
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116 for indx, i in enumerate(sequence):
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117 if isinstance(i, int):
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118 continue
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119 try:
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120 ii = int(i)
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121 except ValueError:
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122 raise nx.NetworkXError(msg % i) from None
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123 if ii != i:
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124 raise nx.NetworkXError(msg % i)
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125 sequence[indx] = ii
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126 return sequence
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127
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128
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129 def is_list_of_ints(intlist):
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130 """ Return True if list is a list of ints. """
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131 if not isinstance(intlist, list):
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132 return False
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133 for i in intlist:
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134 if not isinstance(i, int):
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135 return False
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136 return True
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137
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138
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139 PY2 = sys.version_info[0] == 2
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140 if PY2:
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141 def make_str(x):
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142 """Returns the string representation of t."""
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143 if isinstance(x, unicode):
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144 return x
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145 else:
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146 # Note, this will not work unless x is ascii-encoded.
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147 # That is good, since we should be working with unicode anyway.
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148 # Essentially, unless we are reading a file, we demand that users
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149 # convert any encoded strings to unicode before using the library.
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150 #
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151 # Also, the str() is necessary to convert integers, etc.
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152 # unicode(3) works, but unicode(3, 'unicode-escape') wants a buffer
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153 #
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154 return unicode(str(x), 'unicode-escape')
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155 else:
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156 def make_str(x):
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157 """Returns the string representation of t."""
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158 return str(x)
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159
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160
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161 def generate_unique_node():
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162 """ Generate a unique node label."""
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163 return str(uuid.uuid1())
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164
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165
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166 def default_opener(filename):
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167 """Opens `filename` using system's default program.
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168
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169 Parameters
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170 ----------
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171 filename : str
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172 The path of the file to be opened.
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173
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174 """
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175 from subprocess import call
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176
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177 cmds = {'darwin': ['open'],
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178 'linux': ['xdg-open'],
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179 'linux2': ['xdg-open'],
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180 'win32': ['cmd.exe', '/C', 'start', '']}
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181 cmd = cmds[sys.platform] + [filename]
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182 call(cmd)
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183
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184
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185 def dict_to_numpy_array(d, mapping=None):
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186 """Convert a dictionary of dictionaries to a numpy array
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187 with optional mapping."""
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188 try:
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189 return dict_to_numpy_array2(d, mapping)
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190 except (AttributeError, TypeError):
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191 # AttributeError is when no mapping was provided and v.keys() fails.
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192 # TypeError is when a mapping was provided and d[k1][k2] fails.
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193 return dict_to_numpy_array1(d, mapping)
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194
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195
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196 def dict_to_numpy_array2(d, mapping=None):
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197 """Convert a dictionary of dictionaries to a 2d numpy array
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198 with optional mapping.
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199
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200 """
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201 import numpy
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202 if mapping is None:
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203 s = set(d.keys())
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204 for k, v in d.items():
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205 s.update(v.keys())
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206 mapping = dict(zip(s, range(len(s))))
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207 n = len(mapping)
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208 a = numpy.zeros((n, n))
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209 for k1, i in mapping.items():
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210 for k2, j in mapping.items():
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211 try:
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212 a[i, j] = d[k1][k2]
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213 except KeyError:
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214 pass
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215 return a
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216
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217
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218 def dict_to_numpy_array1(d, mapping=None):
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219 """Convert a dictionary of numbers to a 1d numpy array
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220 with optional mapping.
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221
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222 """
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223 import numpy
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224 if mapping is None:
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225 s = set(d.keys())
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226 mapping = dict(zip(s, range(len(s))))
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227 n = len(mapping)
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228 a = numpy.zeros(n)
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229 for k1, i in mapping.items():
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230 i = mapping[k1]
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231 a[i] = d[k1]
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232 return a
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233
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234
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235 def is_iterator(obj):
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236 """Returns True if and only if the given object is an iterator
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237 object.
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238
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239 """
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240 has_next_attr = hasattr(obj, '__next__') or hasattr(obj, 'next')
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241 return iter(obj) is obj and has_next_attr
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242
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243
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244 def arbitrary_element(iterable):
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245 """Returns an arbitrary element of `iterable` without removing it.
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246
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247 This is most useful for "peeking" at an arbitrary element of a set,
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248 but can be used for any list, dictionary, etc., as well::
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249
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250 >>> arbitrary_element({3, 2, 1})
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251 1
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252 >>> arbitrary_element('hello')
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253 'h'
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254
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255 This function raises a :exc:`ValueError` if `iterable` is an
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256 iterator (because the current implementation of this function would
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257 consume an element from the iterator)::
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258
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259 >>> iterator = iter([1, 2, 3])
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260 >>> arbitrary_element(iterator)
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261 Traceback (most recent call last):
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262 ...
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263 ValueError: cannot return an arbitrary item from an iterator
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264
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265 """
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266 if is_iterator(iterable):
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267 raise ValueError('cannot return an arbitrary item from an iterator')
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268 # Another possible implementation is ``for x in iterable: return x``.
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269 return next(iter(iterable))
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270
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271
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272 # Recipe from the itertools documentation.
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273 def consume(iterator):
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274 "Consume the iterator entirely."
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275 # Feed the entire iterator into a zero-length deque.
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276 deque(iterator, maxlen=0)
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277
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278
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279 # Recipe from the itertools documentation.
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280 def pairwise(iterable, cyclic=False):
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281 "s -> (s0, s1), (s1, s2), (s2, s3), ..."
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282 a, b = tee(iterable)
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283 first = next(b, None)
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284 if cyclic is True:
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285 return zip(a, chain(b, (first,)))
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286 return zip(a, b)
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287
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288
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289 def groups(many_to_one):
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290 """Converts a many-to-one mapping into a one-to-many mapping.
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291
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292 `many_to_one` must be a dictionary whose keys and values are all
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293 :term:`hashable`.
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294
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295 The return value is a dictionary mapping values from `many_to_one`
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296 to sets of keys from `many_to_one` that have that value.
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297
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298 For example::
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299
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300 >>> from networkx.utils import groups
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301 >>> many_to_one = {'a': 1, 'b': 1, 'c': 2, 'd': 3, 'e': 3}
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302 >>> groups(many_to_one) # doctest: +SKIP
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303 {1: {'a', 'b'}, 2: {'c'}, 3: {'d', 'e'}}
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304
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305 """
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306 one_to_many = defaultdict(set)
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307 for v, k in many_to_one.items():
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308 one_to_many[k].add(v)
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309 return dict(one_to_many)
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310
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311
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312 def to_tuple(x):
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313 """Converts lists to tuples.
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314
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315 For example::
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316
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317 >>> from networkx.utils import to_tuple
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318 >>> a_list = [1, 2, [1, 4]]
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319 >>> to_tuple(a_list)
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320 (1, 2, (1, 4))
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321
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322 """
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323 if not isinstance(x, (tuple, list)):
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324 return x
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325 return tuple(map(to_tuple, x))
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326
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327
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328 def create_random_state(random_state=None):
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329 """Returns a numpy.random.RandomState instance depending on input.
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330
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331 Parameters
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332 ----------
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333 random_state : int or RandomState instance or None optional (default=None)
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334 If int, return a numpy.random.RandomState instance set with seed=int.
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335 if numpy.random.RandomState instance, return it.
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336 if None or numpy.random, return the global random number generator used
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337 by numpy.random.
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338 """
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339 import numpy as np
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340
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341 if random_state is None or random_state is np.random:
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342 return np.random.mtrand._rand
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343 if isinstance(random_state, np.random.RandomState):
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344 return random_state
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345 if isinstance(random_state, int):
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346 return np.random.RandomState(random_state)
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347 msg = '%r cannot be used to generate a numpy.random.RandomState instance'
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348 raise ValueError(msg % random_state)
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349
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350
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351 class PythonRandomInterface(object):
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352 try:
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353 def __init__(self, rng=None):
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354 import numpy
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355 if rng is None:
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356 self._rng = numpy.random.mtrand._rand
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357 self._rng = rng
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358 except ImportError:
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359 msg = 'numpy not found, only random.random available.'
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360 warnings.warn(msg, ImportWarning)
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361
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362 def random(self):
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363 return self._rng.random_sample()
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364
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365 def uniform(self, a, b):
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366 return a + (b - a) * self._rng.random_sample()
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367
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368 def randrange(self, a, b=None):
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369 return self._rng.randint(a, b)
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370
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371 def choice(self, seq):
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372 return seq[self._rng.randint(0, len(seq))]
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373
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374 def gauss(self, mu, sigma):
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375 return self._rng.normal(mu, sigma)
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376
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377 def shuffle(self, seq):
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378 return self._rng.shuffle(seq)
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379
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380 # Some methods don't match API for numpy RandomState.
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381 # Commented out versions are not used by NetworkX
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382
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383 def sample(self, seq, k):
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384 return self._rng.choice(list(seq), size=(k,), replace=False)
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385
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386 def randint(self, a, b):
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387 return self._rng.randint(a, b + 1)
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388
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389 # exponential as expovariate with 1/argument,
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390 def expovariate(self, scale):
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391 return self._rng.exponential(1/scale)
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392
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393 # pareto as paretovariate with 1/argument,
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394 def paretovariate(self, shape):
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395 return self._rng.pareto(shape)
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396
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397 # weibull as weibullvariate multiplied by beta,
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398 # def weibullvariate(self, alpha, beta):
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399 # return self._rng.weibull(alpha) * beta
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400 #
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401 # def triangular(self, low, high, mode):
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402 # return self._rng.triangular(low, mode, high)
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403 #
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404 # def choices(self, seq, weights=None, cum_weights=None, k=1):
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405 # return self._rng.choice(seq
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406
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407
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408 def create_py_random_state(random_state=None):
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409 """Returns a random.Random instance depending on input.
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410
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411 Parameters
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412 ----------
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413 random_state : int or random number generator or None (default=None)
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414 If int, return a random.Random instance set with seed=int.
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415 if random.Random instance, return it.
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416 if None or the `random` package, return the global random number
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417 generator used by `random`.
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418 if np.random package, return the global numpy random number
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419 generator wrapped in a PythonRandomInterface class.
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420 if np.random.RandomState instance, return it wrapped in
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421 PythonRandomInterface
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422 if a PythonRandomInterface instance, return it
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423 """
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424 import random
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425 try:
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426 import numpy as np
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427 if random_state is np.random:
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428 return PythonRandomInterface(np.random.mtrand._rand)
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429 if isinstance(random_state, np.random.RandomState):
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430 return PythonRandomInterface(random_state)
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431 if isinstance(random_state, PythonRandomInterface):
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432 return random_state
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433 has_numpy = True
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434 except ImportError:
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435 has_numpy = False
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436
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437 if random_state is None or random_state is random:
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438 return random._inst
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439 if isinstance(random_state, random.Random):
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440 return random_state
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441 if isinstance(random_state, int):
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442 return random.Random(random_state)
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443 msg = '%r cannot be used to generate a random.Random instance'
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444 raise ValueError(msg % random_state)
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445
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446
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447 # fixture for pytest
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448 def setup_module(module):
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449 import pytest
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450 numpy = pytest.importorskip('numpy')