Conditions | 12 |
Total Lines | 62 |
Code Lines | 42 |
Lines | 0 |
Ratio | 0 % |
Tests | 22 |
CRAP Score | 19.3771 |
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 build.main.Main._validate_payload() 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 | """Main module of kytos/pathfinder Kytos Network Application.""" |
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84 | 1 | def _validate_payload(self, data): |
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85 | """Validate shortest_path v2/ POST endpoint.""" |
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86 | 1 | if data.get("desired_links"): |
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87 | 1 | if not isinstance(data["desired_links"], list): |
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88 | raise BadRequest( |
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89 | f"TypeError: desired_links is supposed to be a list." |
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90 | f" type: {type(data['desired_links'])}" |
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91 | ) |
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92 | |||
93 | 1 | if data.get("undesired_links"): |
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94 | if not isinstance(data["undesired_links"], list): |
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95 | raise BadRequest( |
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96 | f"TypeError: undesired_links is supposed to be a list." |
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97 | f" type: {type(data['undesired_links'])}" |
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98 | ) |
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99 | |||
100 | 1 | parameter = data.get("parameter") |
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101 | 1 | spf_attr = data.get("spf_attribute") |
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102 | 1 | if not spf_attr: |
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103 | 1 | spf_attr = parameter or "hop" |
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104 | 1 | data["spf_attribute"] = spf_attr |
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105 | |||
106 | 1 | if spf_attr not in self.graph.spf_edge_data_cbs: |
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107 | raise BadRequest( |
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108 | "Invalid 'spf_attribute'. Valid values: " |
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109 | f"{', '.join(self.graph.spf_edge_data_cbs.keys())}" |
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110 | ) |
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111 | |||
112 | 1 | try: |
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113 | 1 | data["spf_max_paths"] = max(int(data.get("spf_max_paths", 2)), 1) |
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114 | except (TypeError, ValueError): |
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115 | raise BadRequest( |
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116 | f"spf_max_paths {data.get('spf_max_pahts')} must be an int" |
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117 | ) |
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118 | |||
119 | 1 | spf_max_path_cost = data.get("spf_max_path_cost") |
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120 | 1 | if spf_max_path_cost: |
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121 | try: |
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122 | spf_max_path_cost = max(int(spf_max_path_cost), 1) |
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123 | data["spf_max_path_cost"] = spf_max_path_cost |
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124 | except (TypeError, ValueError): |
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125 | raise BadRequest( |
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126 | f"spf_max_path_cost {data.get('spf_max_path_cost')} must" |
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127 | " be an int" |
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128 | ) |
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129 | |||
130 | 1 | data["mandatory_metrics"] = data.get("mandatory_metrics", {}) |
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131 | 1 | data["flexible_metrics"] = data.get("flexible_metrics", {}) |
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132 | |||
133 | 1 | try: |
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134 | 1 | minimum_hits = data.get("minimum_flexible_hits") |
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135 | 1 | if minimum_hits: |
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136 | 1 | minimum_hits = min( |
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137 | len(data["flexible_metrics"]), max(0, int(minimum_hits)) |
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138 | ) |
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139 | 1 | data["minimum_flexible_hits"] = minimum_hits |
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140 | except (TypeError, ValueError): |
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141 | raise BadRequest( |
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142 | f"minimum_hits {data.get('minimum_flexible_hits')} must be an int" |
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143 | ) |
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144 | |||
145 | 1 | return data |
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146 | |||
220 |