Mercurial > repos > shellac > guppy_basecaller
annotate env/lib/python3.7/site-packages/networkx/utils/misc.py @ 0:26e78fe6e8c4 draft
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
author | shellac |
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date | Sat, 02 May 2020 07:14:21 -0400 |
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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') |