Conditions | 12 |
Total Lines | 70 |
Lines | 0 |
Ratio | 0 % |
Tests | 0 |
CRAP Score | 156 |
Changes | 11 | ||
Bugs | 0 | Features | 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 _query_compiling() 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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85 | @listens_for(Query, "before_compile") |
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86 | def _query_compiling(query): |
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87 | # Get the session associated with the query: |
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88 | session = query.session |
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89 | |||
90 | # Skip if the session was in a trigger before |
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91 | # Prevents recursion in import code |
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92 | if hasattr(session, "_osmalchemy_in_trigger"): |
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93 | return |
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94 | |||
95 | # Prevent recursion by skipping already-seen queries |
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96 | if query in _visited_queries: |
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97 | return |
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98 | else: |
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99 | _visited_queries.add(query) |
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100 | |||
101 | # Check whether this query affects our model |
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102 | affected_models = set([c["type"] for c in query.column_descriptions]) |
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103 | our_models = set([osmalchemy.node, osmalchemy.way, osmalchemy.relation, |
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104 | osmalchemy.element]) |
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105 | if affected_models.isdisjoint(our_models): |
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106 | # None of our models is affected |
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107 | return |
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108 | |||
109 | # Check whether this query filters elements |
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110 | # Online update will only run on a specified set, not all data |
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111 | if query.whereclause is None: |
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112 | # No filters |
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113 | return |
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114 | |||
115 | # Analyse where clause looking for all looked-up fields |
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116 | trees = {} |
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117 | for target in our_models.intersection(affected_models): |
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118 | # Build expression trees first |
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119 | tree = _where_to_tree(query.whereclause, target) |
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120 | if not tree is None: |
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121 | trees[target.__name__] = tree |
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122 | |||
123 | # Bail out if no relevant trees were built |
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124 | if not trees: |
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125 | return |
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126 | |||
127 | # Compile to OverpassQL |
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128 | oql = _trees_to_overpassql(trees) |
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129 | |||
130 | # Look up query in cache |
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131 | hashed_oql = hash(_normalise_overpassql(oql)) |
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132 | cached_query = session.query(osmcachedquery).filter_by(oql_hash=hashed_oql).scalar() |
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133 | # Check age if cached query was found |
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134 | if cached_query: |
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135 | timediff = datetime.datetime.now() - cached_query.oql_queried |
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136 | if timediff.seconds < maxage: |
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137 | # Return and do nothing if query was run recently |
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138 | return |
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139 | |||
140 | # Run query online |
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141 | xml = _get_elements_by_query(osmalchemy._overpass, oql) |
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142 | |||
143 | # Import data |
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144 | session._osmalchemy_in_trigger = True |
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145 | _import_osm_xml(osmalchemy, session, xml) |
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146 | del session._osmalchemy_in_trigger |
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147 | |||
148 | # Store or update query time |
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149 | if not cached_query: |
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150 | cached_query = osmcachedquery() |
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151 | cached_query.oql_hash = hashed_oql |
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152 | cached_query.oql_queried = datetime.datetime.now() |
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153 | session.add(cached_query) |
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154 | session.commit() |
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155 |