Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/utils/misc.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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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_list_of_ints([1, 2, 3]) |
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9 True |
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10 >>> networkx.utils.is_list_of_ints([1, 2, "spam"]) |
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11 False |
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12 """ |
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13 |
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14 from collections import defaultdict |
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15 from collections import deque |
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16 import warnings |
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17 import sys |
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18 import uuid |
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19 from itertools import tee, chain |
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20 import networkx as nx |
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21 |
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22 |
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23 # some cookbook stuff |
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24 # used in deciding whether something is a bunch of nodes, edges, etc. |
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25 # see G.add_nodes and others in Graph Class in networkx/base.py |
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26 |
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27 |
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28 def is_string_like(obj): # from John Hunter, types-free version |
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29 """Check if obj is string.""" |
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30 msg = ( |
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31 "is_string_like is deprecated and will be removed in 3.0." |
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32 "Use isinstance(obj, str) instead." |
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33 ) |
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34 warnings.warn(msg, DeprecationWarning) |
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35 return isinstance(obj, str) |
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36 |
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37 |
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38 def iterable(obj): |
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39 """ Return True if obj is iterable with a well-defined len().""" |
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40 if hasattr(obj, "__iter__"): |
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41 return True |
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42 try: |
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43 len(obj) |
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44 except: |
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45 return False |
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46 return True |
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47 |
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48 |
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49 def empty_generator(): |
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50 """ Return a generator with no members """ |
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51 yield from () |
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52 |
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53 |
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54 def flatten(obj, result=None): |
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55 """ Return flattened version of (possibly nested) iterable object. """ |
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56 if not iterable(obj) or is_string_like(obj): |
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57 return obj |
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58 if result is None: |
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59 result = [] |
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60 for item in obj: |
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61 if not iterable(item) or is_string_like(item): |
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62 result.append(item) |
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63 else: |
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64 flatten(item, result) |
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65 return obj.__class__(result) |
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66 |
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67 |
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68 def make_list_of_ints(sequence): |
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69 """Return list of ints from sequence of integral numbers. |
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70 |
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71 All elements of the sequence must satisfy int(element) == element |
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72 or a ValueError is raised. Sequence is iterated through once. |
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73 |
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74 If sequence is a list, the non-int values are replaced with ints. |
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75 So, no new list is created |
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76 """ |
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77 if not isinstance(sequence, list): |
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78 result = [] |
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79 for i in sequence: |
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80 errmsg = f"sequence is not all integers: {i}" |
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81 try: |
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82 ii = int(i) |
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83 except ValueError: |
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84 raise nx.NetworkXError(errmsg) from None |
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85 if ii != i: |
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86 raise nx.NetworkXError(errmsg) |
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87 result.append(ii) |
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88 return result |
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89 # original sequence is a list... in-place conversion to ints |
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90 for indx, i in enumerate(sequence): |
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91 errmsg = f"sequence is not all integers: {i}" |
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92 if isinstance(i, int): |
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93 continue |
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94 try: |
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95 ii = int(i) |
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96 except ValueError: |
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97 raise nx.NetworkXError(errmsg) from None |
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98 if ii != i: |
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99 raise nx.NetworkXError(errmsg) |
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100 sequence[indx] = ii |
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101 return sequence |
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102 |
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103 |
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104 def is_list_of_ints(intlist): |
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105 """ Return True if list is a list of ints. """ |
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106 if not isinstance(intlist, list): |
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107 return False |
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108 for i in intlist: |
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109 if not isinstance(i, int): |
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110 return False |
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111 return True |
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112 |
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113 |
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114 def make_str(x): |
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115 """Returns the string representation of t.""" |
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116 msg = "make_str is deprecated and will be removed in 3.0. Use str instead." |
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117 warnings.warn(msg, DeprecationWarning) |
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118 return str(x) |
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119 |
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120 |
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121 def generate_unique_node(): |
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122 """ Generate a unique node label.""" |
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123 return str(uuid.uuid1()) |
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124 |
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125 |
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126 def default_opener(filename): |
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127 """Opens `filename` using system's default program. |
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128 |
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129 Parameters |
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130 ---------- |
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131 filename : str |
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132 The path of the file to be opened. |
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133 |
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134 """ |
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135 from subprocess import call |
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136 |
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137 cmds = { |
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138 "darwin": ["open"], |
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139 "linux": ["xdg-open"], |
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140 "linux2": ["xdg-open"], |
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141 "win32": ["cmd.exe", "/C", "start", ""], |
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142 } |
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143 cmd = cmds[sys.platform] + [filename] |
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144 call(cmd) |
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145 |
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146 |
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147 def dict_to_numpy_array(d, mapping=None): |
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148 """Convert a dictionary of dictionaries to a numpy array |
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149 with optional mapping.""" |
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150 try: |
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151 return dict_to_numpy_array2(d, mapping) |
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152 except (AttributeError, TypeError): |
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153 # AttributeError is when no mapping was provided and v.keys() fails. |
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154 # TypeError is when a mapping was provided and d[k1][k2] fails. |
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155 return dict_to_numpy_array1(d, mapping) |
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156 |
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157 |
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158 def dict_to_numpy_array2(d, mapping=None): |
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159 """Convert a dictionary of dictionaries to a 2d numpy array |
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160 with optional mapping. |
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161 |
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162 """ |
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163 import numpy |
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164 |
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165 if mapping is None: |
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166 s = set(d.keys()) |
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167 for k, v in d.items(): |
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168 s.update(v.keys()) |
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169 mapping = dict(zip(s, range(len(s)))) |
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170 n = len(mapping) |
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171 a = numpy.zeros((n, n)) |
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172 for k1, i in mapping.items(): |
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173 for k2, j in mapping.items(): |
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174 try: |
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175 a[i, j] = d[k1][k2] |
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176 except KeyError: |
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177 pass |
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178 return a |
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179 |
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180 |
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181 def dict_to_numpy_array1(d, mapping=None): |
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182 """Convert a dictionary of numbers to a 1d numpy array |
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183 with optional mapping. |
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184 |
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185 """ |
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186 import numpy |
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187 |
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188 if mapping is None: |
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189 s = set(d.keys()) |
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190 mapping = dict(zip(s, range(len(s)))) |
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191 n = len(mapping) |
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192 a = numpy.zeros(n) |
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193 for k1, i in mapping.items(): |
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194 i = mapping[k1] |
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195 a[i] = d[k1] |
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196 return a |
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197 |
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198 |
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199 def is_iterator(obj): |
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200 """Returns True if and only if the given object is an iterator |
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201 object. |
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202 |
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203 """ |
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204 has_next_attr = hasattr(obj, "__next__") or hasattr(obj, "next") |
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205 return iter(obj) is obj and has_next_attr |
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206 |
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207 |
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208 def arbitrary_element(iterable): |
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209 """Returns an arbitrary element of `iterable` without removing it. |
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210 |
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211 This is most useful for "peeking" at an arbitrary element of a set, |
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212 but can be used for any list, dictionary, etc., as well:: |
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213 |
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214 >>> arbitrary_element({3, 2, 1}) |
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215 1 |
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216 >>> arbitrary_element("hello") |
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217 'h' |
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218 |
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219 This function raises a :exc:`ValueError` if `iterable` is an |
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220 iterator (because the current implementation of this function would |
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221 consume an element from the iterator):: |
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222 |
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223 >>> iterator = iter([1, 2, 3]) |
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224 >>> arbitrary_element(iterator) |
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225 Traceback (most recent call last): |
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226 ... |
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227 ValueError: cannot return an arbitrary item from an iterator |
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228 |
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229 """ |
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230 if is_iterator(iterable): |
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231 raise ValueError("cannot return an arbitrary item from an iterator") |
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232 # Another possible implementation is ``for x in iterable: return x``. |
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233 return next(iter(iterable)) |
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234 |
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235 |
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236 # Recipe from the itertools documentation. |
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237 def consume(iterator): |
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238 "Consume the iterator entirely." |
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239 # Feed the entire iterator into a zero-length deque. |
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240 deque(iterator, maxlen=0) |
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241 |
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242 |
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243 # Recipe from the itertools documentation. |
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244 def pairwise(iterable, cyclic=False): |
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245 "s -> (s0, s1), (s1, s2), (s2, s3), ..." |
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246 a, b = tee(iterable) |
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247 first = next(b, None) |
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248 if cyclic is True: |
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249 return zip(a, chain(b, (first,))) |
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250 return zip(a, b) |
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251 |
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252 |
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253 def groups(many_to_one): |
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254 """Converts a many-to-one mapping into a one-to-many mapping. |
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255 |
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256 `many_to_one` must be a dictionary whose keys and values are all |
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257 :term:`hashable`. |
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258 |
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259 The return value is a dictionary mapping values from `many_to_one` |
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260 to sets of keys from `many_to_one` that have that value. |
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261 |
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262 For example:: |
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263 |
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264 >>> from networkx.utils import groups |
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265 >>> many_to_one = {"a": 1, "b": 1, "c": 2, "d": 3, "e": 3} |
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266 >>> groups(many_to_one) # doctest: +SKIP |
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267 {1: {'a', 'b'}, 2: {'c'}, 3: {'d', 'e'}} |
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268 |
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269 """ |
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270 one_to_many = defaultdict(set) |
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271 for v, k in many_to_one.items(): |
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272 one_to_many[k].add(v) |
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273 return dict(one_to_many) |
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274 |
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275 |
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276 def to_tuple(x): |
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277 """Converts lists to tuples. |
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278 |
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279 For example:: |
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280 |
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281 >>> from networkx.utils import to_tuple |
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282 >>> a_list = [1, 2, [1, 4]] |
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283 >>> to_tuple(a_list) |
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284 (1, 2, (1, 4)) |
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285 |
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286 """ |
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287 if not isinstance(x, (tuple, list)): |
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288 return x |
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289 return tuple(map(to_tuple, x)) |
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290 |
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291 |
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292 def create_random_state(random_state=None): |
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293 """Returns a numpy.random.RandomState instance depending on input. |
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294 |
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295 Parameters |
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296 ---------- |
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297 random_state : int or RandomState instance or None optional (default=None) |
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298 If int, return a numpy.random.RandomState instance set with seed=int. |
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299 if numpy.random.RandomState instance, return it. |
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300 if None or numpy.random, return the global random number generator used |
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301 by numpy.random. |
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302 """ |
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303 import numpy as np |
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304 |
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305 if random_state is None or random_state is np.random: |
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306 return np.random.mtrand._rand |
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307 if isinstance(random_state, np.random.RandomState): |
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308 return random_state |
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309 if isinstance(random_state, int): |
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310 return np.random.RandomState(random_state) |
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311 msg = ( |
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312 f"{random_state} cannot be used to generate a numpy.random.RandomState instance" |
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313 ) |
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314 raise ValueError(msg) |
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315 |
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316 |
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317 class PythonRandomInterface: |
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318 try: |
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319 |
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320 def __init__(self, rng=None): |
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321 import numpy |
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322 |
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323 if rng is None: |
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324 self._rng = numpy.random.mtrand._rand |
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325 self._rng = rng |
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326 |
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327 except ImportError: |
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328 msg = "numpy not found, only random.random available." |
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329 warnings.warn(msg, ImportWarning) |
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330 |
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331 def random(self): |
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332 return self._rng.random_sample() |
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333 |
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334 def uniform(self, a, b): |
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335 return a + (b - a) * self._rng.random_sample() |
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336 |
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337 def randrange(self, a, b=None): |
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338 return self._rng.randint(a, b) |
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339 |
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340 def choice(self, seq): |
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341 return seq[self._rng.randint(0, len(seq))] |
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342 |
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343 def gauss(self, mu, sigma): |
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344 return self._rng.normal(mu, sigma) |
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345 |
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346 def shuffle(self, seq): |
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347 return self._rng.shuffle(seq) |
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348 |
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349 # Some methods don't match API for numpy RandomState. |
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350 # Commented out versions are not used by NetworkX |
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351 |
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352 def sample(self, seq, k): |
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353 return self._rng.choice(list(seq), size=(k,), replace=False) |
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354 |
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355 def randint(self, a, b): |
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356 return self._rng.randint(a, b + 1) |
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357 |
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358 # exponential as expovariate with 1/argument, |
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359 def expovariate(self, scale): |
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360 return self._rng.exponential(1 / scale) |
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361 |
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362 # pareto as paretovariate with 1/argument, |
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363 def paretovariate(self, shape): |
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364 return self._rng.pareto(shape) |
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365 |
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366 |
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367 # weibull as weibullvariate multiplied by beta, |
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368 # def weibullvariate(self, alpha, beta): |
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369 # return self._rng.weibull(alpha) * beta |
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370 # |
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371 # def triangular(self, low, high, mode): |
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372 # return self._rng.triangular(low, mode, high) |
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373 # |
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374 # def choices(self, seq, weights=None, cum_weights=None, k=1): |
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375 # return self._rng.choice(seq |
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376 |
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377 |
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378 def create_py_random_state(random_state=None): |
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379 """Returns a random.Random instance depending on input. |
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380 |
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381 Parameters |
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382 ---------- |
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383 random_state : int or random number generator or None (default=None) |
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384 If int, return a random.Random instance set with seed=int. |
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385 if random.Random instance, return it. |
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386 if None or the `random` package, return the global random number |
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387 generator used by `random`. |
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388 if np.random package, return the global numpy random number |
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389 generator wrapped in a PythonRandomInterface class. |
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390 if np.random.RandomState instance, return it wrapped in |
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391 PythonRandomInterface |
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392 if a PythonRandomInterface instance, return it |
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393 """ |
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394 import random |
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395 |
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396 try: |
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397 import numpy as np |
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398 |
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399 if random_state is np.random: |
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400 return PythonRandomInterface(np.random.mtrand._rand) |
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401 if isinstance(random_state, np.random.RandomState): |
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402 return PythonRandomInterface(random_state) |
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403 if isinstance(random_state, PythonRandomInterface): |
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404 return random_state |
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405 except ImportError: |
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406 pass |
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407 |
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408 if random_state is None or random_state is random: |
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409 return random._inst |
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410 if isinstance(random_state, random.Random): |
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411 return random_state |
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412 if isinstance(random_state, int): |
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413 return random.Random(random_state) |
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414 msg = f"{random_state} cannot be used to generate a random.Random instance" |
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415 raise ValueError(msg) |