Conditions | 37 |
Total Lines | 185 |
Code Lines | 114 |
Lines | 0 |
Ratio | 0 % |
Tests | 70 |
CRAP Score | 37 |
Changes | 0 |
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like abydos.distance._jaro.sim_strcmp95() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
1 | # -*- coding: utf-8 -*- |
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46 | 1 | def sim_strcmp95(src, tar, long_strings=False): |
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47 | """Return the strcmp95 similarity of two strings. |
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48 | |||
49 | This is a Python translation of the C code for strcmp95: |
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50 | http://web.archive.org/web/20110629121242/http://www.census.gov/geo/msb/stand/strcmp.c |
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51 | :cite:`Winkler:1994`. |
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52 | The above file is a US Government publication and, accordingly, |
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53 | in the public domain. |
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54 | |||
55 | This is based on the Jaro-Winkler distance, but also attempts to correct |
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56 | for some common typos and frequently confused characters. It is also |
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57 | limited to uppercase ASCII characters, so it is appropriate to American |
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58 | names, but not much else. |
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59 | |||
60 | :param str src: source string for comparison |
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61 | :param str tar: target string for comparison |
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62 | :param bool long_strings: set to True to "Increase the probability of a |
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63 | match when the number of matched characters is large. This option |
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64 | allows for a little more tolerance when the strings are large. It is |
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65 | not an appropriate test when comparing fixed length fields such as |
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66 | phone and social security numbers." |
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67 | :returns: strcmp95 similarity |
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68 | :rtype: float |
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69 | |||
70 | >>> sim_strcmp95('cat', 'hat') |
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71 | 0.7777777777777777 |
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72 | >>> sim_strcmp95('Niall', 'Neil') |
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73 | 0.8454999999999999 |
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74 | >>> sim_strcmp95('aluminum', 'Catalan') |
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75 | 0.6547619047619048 |
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76 | >>> sim_strcmp95('ATCG', 'TAGC') |
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77 | 0.8333333333333334 |
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78 | """ |
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79 | |||
80 | 1 | def _in_range(char): |
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81 | """Return True if char is in the range (0, 91).""" |
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82 | 1 | return 91 > ord(char) > 0 |
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83 | |||
84 | 1 | ying = src.strip().upper() |
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85 | 1 | yang = tar.strip().upper() |
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86 | |||
87 | 1 | if ying == yang: |
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88 | 1 | return 1.0 |
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89 | # If either string is blank - return - added in Version 2 |
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90 | 1 | if not ying or not yang: |
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91 | 1 | return 0.0 |
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92 | |||
93 | 1 | adjwt = defaultdict(int) |
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94 | 1 | sp_mx = ( |
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95 | ('A', 'E'), |
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96 | ('A', 'I'), |
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97 | ('A', 'O'), |
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98 | ('A', 'U'), |
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99 | ('B', 'V'), |
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100 | ('E', 'I'), |
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101 | ('E', 'O'), |
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102 | ('E', 'U'), |
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103 | ('I', 'O'), |
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104 | ('I', 'U'), |
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105 | ('O', 'U'), |
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106 | ('I', 'Y'), |
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107 | ('E', 'Y'), |
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108 | ('C', 'G'), |
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109 | ('E', 'F'), |
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110 | ('W', 'U'), |
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111 | ('W', 'V'), |
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112 | ('X', 'K'), |
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113 | ('S', 'Z'), |
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114 | ('X', 'S'), |
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115 | ('Q', 'C'), |
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116 | ('U', 'V'), |
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117 | ('M', 'N'), |
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118 | ('L', 'I'), |
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119 | ('Q', 'O'), |
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120 | ('P', 'R'), |
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121 | ('I', 'J'), |
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122 | ('2', 'Z'), |
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123 | ('5', 'S'), |
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124 | ('8', 'B'), |
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125 | ('1', 'I'), |
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126 | ('1', 'L'), |
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127 | ('0', 'O'), |
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128 | ('0', 'Q'), |
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129 | ('C', 'K'), |
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130 | ('G', 'J'), |
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131 | ) |
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132 | |||
133 | # Initialize the adjwt array on the first call to the function only. |
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134 | # The adjwt array is used to give partial credit for characters that |
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135 | # may be errors due to known phonetic or character recognition errors. |
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136 | # A typical example is to match the letter "O" with the number "0" |
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137 | 1 | for i in sp_mx: |
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138 | 1 | adjwt[(i[0], i[1])] = 3 |
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139 | 1 | adjwt[(i[1], i[0])] = 3 |
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140 | |||
141 | 1 | if len(ying) > len(yang): |
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142 | 1 | search_range = len(ying) |
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143 | 1 | minv = len(yang) |
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144 | else: |
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145 | 1 | search_range = len(yang) |
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146 | 1 | minv = len(ying) |
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147 | |||
148 | # Blank out the flags |
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149 | 1 | ying_flag = [0] * search_range |
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150 | 1 | yang_flag = [0] * search_range |
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151 | 1 | search_range = max(0, search_range // 2 - 1) |
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152 | |||
153 | # Looking only within the search range, count and flag the matched pairs. |
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154 | 1 | num_com = 0 |
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155 | 1 | yl1 = len(yang) - 1 |
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156 | 1 | for i in range(len(ying)): |
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157 | 1 | low_lim = (i - search_range) if (i >= search_range) else 0 |
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158 | 1 | hi_lim = (i + search_range) if ((i + search_range) <= yl1) else yl1 |
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159 | 1 | for j in range(low_lim, hi_lim + 1): |
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160 | 1 | if (yang_flag[j] == 0) and (yang[j] == ying[i]): |
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161 | 1 | yang_flag[j] = 1 |
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162 | 1 | ying_flag[i] = 1 |
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163 | 1 | num_com += 1 |
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164 | 1 | break |
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165 | |||
166 | # If no characters in common - return |
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167 | 1 | if num_com == 0: |
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168 | 1 | return 0.0 |
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169 | |||
170 | # Count the number of transpositions |
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171 | 1 | k = n_trans = 0 |
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172 | 1 | for i in range(len(ying)): |
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173 | 1 | if ying_flag[i] != 0: |
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174 | 1 | j = 0 |
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175 | 1 | for j in range(k, len(yang)): # pragma: no branch |
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176 | 1 | if yang_flag[j] != 0: |
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177 | 1 | k = j + 1 |
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178 | 1 | break |
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179 | 1 | if ying[i] != yang[j]: |
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180 | 1 | n_trans += 1 |
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181 | 1 | n_trans //= 2 |
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182 | |||
183 | # Adjust for similarities in unmatched characters |
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184 | 1 | n_simi = 0 |
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185 | 1 | if minv > num_com: |
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186 | 1 | for i in range(len(ying)): |
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187 | 1 | if ying_flag[i] == 0 and _in_range(ying[i]): |
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188 | 1 | for j in range(len(yang)): |
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189 | 1 | if yang_flag[j] == 0 and _in_range(yang[j]): |
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190 | 1 | if (ying[i], yang[j]) in adjwt: |
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191 | 1 | n_simi += adjwt[(ying[i], yang[j])] |
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192 | 1 | yang_flag[j] = 2 |
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193 | 1 | break |
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194 | 1 | num_sim = n_simi / 10.0 + num_com |
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195 | |||
196 | # Main weight computation |
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197 | 1 | weight = ( |
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198 | num_sim / len(ying) |
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199 | + num_sim / len(yang) |
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200 | + (num_com - n_trans) / num_com |
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201 | ) |
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202 | 1 | weight /= 3.0 |
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203 | |||
204 | # Continue to boost the weight if the strings are similar |
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205 | 1 | if weight > 0.7: |
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206 | |||
207 | # Adjust for having up to the first 4 characters in common |
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208 | 1 | j = 4 if (minv >= 4) else minv |
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209 | 1 | i = 0 |
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210 | 1 | while (i < j) and (ying[i] == yang[i]) and (not ying[i].isdigit()): |
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211 | 1 | i += 1 |
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212 | 1 | if i: |
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213 | 1 | weight += i * 0.1 * (1.0 - weight) |
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214 | |||
215 | # Optionally adjust for long strings. |
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216 | |||
217 | # After agreeing beginning chars, at least two more must agree and |
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218 | # the agreeing characters must be > .5 of remaining characters. |
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219 | 1 | if ( |
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220 | long_strings |
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221 | and (minv > 4) |
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222 | and (num_com > i + 1) |
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223 | and (2 * num_com >= minv + i) |
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224 | ): |
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225 | 1 | if not ying[0].isdigit(): |
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226 | 1 | weight += (1.0 - weight) * ( |
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227 | (num_com - i - 1) / (len(ying) + len(yang) - i * 2 + 2) |
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228 | ) |
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229 | |||
230 | 1 | return weight |
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231 | |||
490 |