Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/generators/tests/test_internet_as_graphs.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 from networkx import is_connected, neighbors |
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2 from networkx.generators.internet_as_graphs import random_internet_as_graph |
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3 from networkx.testing import almost_equal |
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4 |
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5 |
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6 class TestInternetASTopology: |
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7 @classmethod |
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8 def setup_class(cls): |
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9 cls.n = 1000 |
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10 cls.seed = 42 |
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11 cls.G = random_internet_as_graph(cls.n, cls.seed) |
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12 cls.T = [] |
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13 cls.M = [] |
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14 cls.C = [] |
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15 cls.CP = [] |
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16 cls.customers = {} |
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17 cls.providers = {} |
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18 |
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19 for i in cls.G.nodes(): |
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20 if cls.G.nodes[i]["type"] == "T": |
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21 cls.T.append(i) |
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22 elif cls.G.nodes[i]["type"] == "M": |
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23 cls.M.append(i) |
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24 elif cls.G.nodes[i]["type"] == "C": |
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25 cls.C.append(i) |
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26 elif cls.G.nodes[i]["type"] == "CP": |
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27 cls.CP.append(i) |
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28 else: |
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29 raise ValueError( |
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30 "Inconsistent data in the graph\ |
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31 node attributes" |
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32 ) |
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33 cls.set_customers(i) |
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34 cls.set_providers(i) |
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35 |
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36 @classmethod |
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37 def set_customers(cls, i): |
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38 if i not in cls.customers: |
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39 cls.customers[i] = set() |
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40 for j in neighbors(cls.G, i): |
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41 e = cls.G.edges[(i, j)] |
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42 if e["type"] == "transit": |
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43 customer = int(e["customer"]) |
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44 if j == customer: |
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45 cls.set_customers(j) |
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46 cls.customers[i] = cls.customers[i].union(cls.customers[j]) |
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47 cls.customers[i].add(j) |
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48 elif i != customer: |
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49 raise ValueError( |
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50 "Inconsistent data in the graph\ |
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51 edge attributes" |
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52 ) |
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53 |
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54 @classmethod |
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55 def set_providers(cls, i): |
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56 if i not in cls.providers: |
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57 cls.providers[i] = set() |
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58 for j in neighbors(cls.G, i): |
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59 e = cls.G.edges[(i, j)] |
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60 if e["type"] == "transit": |
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61 customer = int(e["customer"]) |
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62 if i == customer: |
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63 cls.set_providers(j) |
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64 cls.providers[i] = cls.providers[i].union(cls.providers[j]) |
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65 cls.providers[i].add(j) |
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66 elif j != customer: |
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67 raise ValueError( |
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68 "Inconsistent data in the graph\ |
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69 edge attributes" |
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70 ) |
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71 |
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72 def test_wrong_input(self): |
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73 G = random_internet_as_graph(0) |
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74 assert len(G.nodes()) == 0 |
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75 |
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76 G = random_internet_as_graph(-1) |
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77 assert len(G.nodes()) == 0 |
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78 |
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79 G = random_internet_as_graph(1) |
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80 assert len(G.nodes()) == 1 |
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81 |
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82 def test_node_numbers(self): |
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83 assert len(self.G.nodes()) == self.n |
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84 assert len(self.T) < 7 |
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85 assert len(self.M) == int(round(self.n * 0.15)) |
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86 assert len(self.CP) == int(round(self.n * 0.05)) |
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87 numb = self.n - len(self.T) - len(self.M) - len(self.CP) |
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88 assert len(self.C) == numb |
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89 |
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90 def test_connectivity(self): |
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91 assert is_connected(self.G) |
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92 |
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93 def test_relationships(self): |
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94 # T nodes are not customers of anyone |
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95 for i in self.T: |
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96 assert len(self.providers[i]) == 0 |
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97 |
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98 # C nodes are not providers of anyone |
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99 for i in self.C: |
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100 assert len(self.customers[i]) == 0 |
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101 |
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102 # CP nodes are not providers of anyone |
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103 for i in self.CP: |
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104 assert len(self.customers[i]) == 0 |
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105 |
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106 # test whether there is a customer-provider loop |
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107 for i in self.G.nodes(): |
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108 assert len(self.customers[i].intersection(self.providers[i])) == 0 |
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109 |
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110 # test whether there is a peering with a customer or provider |
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111 for i, j in self.G.edges(): |
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112 if self.G.edges[(i, j)]["type"] == "peer": |
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113 assert j not in self.customers[i] |
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114 assert i not in self.customers[j] |
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115 assert j not in self.providers[i] |
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116 assert i not in self.providers[j] |
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117 |
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118 def test_degree_values(self): |
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119 d_m = 0 # multihoming degree for M nodes |
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120 d_cp = 0 # multihoming degree for CP nodes |
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121 d_c = 0 # multihoming degree for C nodes |
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122 p_m_m = 0 # avg number of peering edges between M and M |
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123 p_cp_m = 0 # avg number of peering edges between CP and M |
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124 p_cp_cp = 0 # avg number of peering edges between CP and CP |
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125 t_m = 0 # probability M's provider is T |
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126 t_cp = 0 # probability CP's provider is T |
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127 t_c = 0 # probability C's provider is T |
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128 |
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129 for i, j in self.G.edges(): |
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130 e = self.G.edges[(i, j)] |
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131 if e["type"] == "transit": |
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132 cust = int(e["customer"]) |
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133 if i == cust: |
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134 prov = j |
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135 elif j == cust: |
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136 prov = i |
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137 else: |
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138 raise ValueError( |
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139 "Inconsistent data in the graph edge\ |
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140 attributes" |
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141 ) |
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142 if cust in self.M: |
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143 d_m += 1 |
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144 if self.G.nodes[prov]["type"] == "T": |
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145 t_m += 1 |
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146 elif cust in self.C: |
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147 d_c += 1 |
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148 if self.G.nodes[prov]["type"] == "T": |
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149 t_c += 1 |
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150 elif cust in self.CP: |
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151 d_cp += 1 |
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152 if self.G.nodes[prov]["type"] == "T": |
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153 t_cp += 1 |
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154 else: |
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155 raise ValueError( |
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156 "Inconsistent data in the graph edge\ |
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157 attributes" |
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158 ) |
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159 elif e["type"] == "peer": |
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160 if self.G.nodes[i]["type"] == "M" and self.G.nodes[j]["type"] == "M": |
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161 p_m_m += 1 |
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162 if self.G.nodes[i]["type"] == "CP" and self.G.nodes[j]["type"] == "CP": |
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163 p_cp_cp += 1 |
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164 if ( |
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165 self.G.nodes[i]["type"] == "M" |
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166 and self.G.nodes[j]["type"] == "CP" |
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167 or self.G.nodes[i]["type"] == "CP" |
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168 and self.G.nodes[j]["type"] == "M" |
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169 ): |
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170 p_cp_m += 1 |
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171 else: |
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172 raise ValueError( |
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173 "Unexpected data in the graph edge\ |
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174 attributes" |
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175 ) |
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176 |
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177 assert almost_equal(d_m / len(self.M), 2 + (2.5 * self.n) / 10000, places=0) |
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178 assert almost_equal(d_cp / len(self.CP), 2 + (1.5 * self.n) / 10000, places=0) |
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179 assert almost_equal(d_c / len(self.C), 1 + (5 * self.n) / 100000, places=0) |
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180 |
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181 assert almost_equal(p_m_m / len(self.M), 1 + (2 * self.n) / 10000, places=0) |
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182 assert almost_equal(p_cp_m / len(self.CP), 0.2 + (2 * self.n) / 10000, places=0) |
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183 assert almost_equal( |
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184 p_cp_cp / len(self.CP), 0.05 + (2 * self.n) / 100000, places=0 |
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185 ) |
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186 |
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187 assert almost_equal(t_m / d_m, 0.375, places=1) |
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188 assert almost_equal(t_cp / d_cp, 0.375, places=1) |
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189 assert almost_equal(t_c / d_c, 0.125, places=1) |