Conditions | 21 |
Total Lines | 68 |
Code Lines | 43 |
Lines | 0 |
Ratio | 0 % |
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 glances.plugins.glances_quicklook.Plugin.msg_curse() 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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91 | def msg_curse(self, args=None, max_width=10): |
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92 | """Return the list to display in the UI.""" |
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93 | # Init the return message |
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94 | ret = [] |
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95 | |||
96 | # Only process if stats exist... |
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97 | if not self.stats or self.is_disabled(): |
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98 | return ret |
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99 | |||
100 | # Define the data: Bar (default behavior) or Sparkline |
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101 | sparkline_tag = False |
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102 | if self.args.sparkline and self.history_enable() and not self.args.client: |
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103 | data = Sparkline(max_width) |
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104 | sparkline_tag = data.available |
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105 | if not sparkline_tag: |
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106 | # Fallback to bar if Sparkline module is not installed |
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107 | data = Bar(max_width, percentage_char=self.get_conf_value('percentage_char', default=['|'])[0]) |
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108 | |||
109 | # Build the string message |
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110 | if 'cpu_name' in self.stats and 'cpu_hz_current' in self.stats and 'cpu_hz' in self.stats: |
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111 | msg_name = self.stats['cpu_name'] |
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112 | if self.stats['cpu_hz_current'] and self.stats['cpu_hz']: |
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113 | msg_freq = ' - {:.2f}/{:.2f}GHz'.format( |
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114 | self._hz_to_ghz(self.stats['cpu_hz_current']), self._hz_to_ghz(self.stats['cpu_hz']) |
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115 | ) |
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116 | else: |
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117 | msg_freq = '' |
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118 | if len(msg_name + msg_freq) - 6 <= max_width: |
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119 | ret.append(self.curse_add_line(msg_name)) |
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120 | ret.append(self.curse_add_line(msg_freq)) |
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121 | ret.append(self.curse_new_line()) |
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122 | for key in ['cpu', 'mem', 'swap']: |
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123 | if key == 'cpu' and args.percpu: |
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124 | if sparkline_tag: |
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125 | raw_cpu = self.get_raw_history(item='percpu', nb=data.size) |
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126 | for cpu_index, cpu in enumerate(self.stats['percpu']): |
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127 | if sparkline_tag: |
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128 | # Sparkline display an history |
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129 | data.percents = [i[1][cpu_index]['total'] for i in raw_cpu] |
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130 | # A simple padding in order to align metrics to the right |
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131 | data.percents += [None] * (data.size - len(data.percents)) |
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132 | else: |
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133 | # Bar only the last value |
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134 | data.percent = cpu['total'] |
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135 | if cpu[cpu['key']] < 10: |
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136 | msg = '{:3}{} '.format(key.upper(), cpu['cpu_number']) |
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137 | else: |
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138 | msg = '{:4} '.format(cpu['cpu_number']) |
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139 | ret.extend(self._msg_create_line(msg, data, key)) |
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140 | ret.append(self.curse_new_line()) |
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141 | else: |
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142 | if sparkline_tag: |
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143 | # Sparkline display an history |
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144 | data.percents = [i[1] for i in self.get_raw_history(item=key, nb=data.size)] |
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145 | # A simple padding in order to align metrics to the right |
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146 | data.percents += [None] * (data.size - len(data.percents)) |
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147 | else: |
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148 | # Bar only the last value |
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149 | data.percent = self.stats[key] |
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150 | msg = '{:4} '.format(key.upper()) |
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151 | ret.extend(self._msg_create_line(msg, data, key)) |
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152 | ret.append(self.curse_new_line()) |
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153 | |||
154 | # Remove the last new line |
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155 | ret.pop() |
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156 | |||
157 | # Return the message with decoration |
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158 | return ret |
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159 | |||
177 |