| Conditions | 13 | 
| Total Lines | 59 | 
| Lines | 0 | 
| Ratio | 0 % | 
| Tests | 41 | 
| CRAP Score | 13 | 
| Changes | 5 | ||
| Bugs | 0 | Features | 1 | 
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 Dictionary.update_value() 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 | """Converter classes for builtin container types.""" | ||
| 32 | 1 | def update_value(self, data, *, auto_track=True): | |
| 33 | 1 | cls = self.__class__ | |
| 34 | 1 | value = cls.create_default() | |
| 35 | |||
| 36 | # Convert object attributes to a dictionary | ||
| 37 | 1 | attrs = common.attrs[cls].copy() | |
| 38 | 1 | if isinstance(data, cls): | |
| 39 | 1 |             dictionary = {} | |
| 40 | 1 | for k, v in data.items(): | |
| 41 | 1 | if k in attrs: | |
| 42 | 1 | dictionary[k] = v | |
| 43 | 1 | for k, v in data.__dict__.items(): | |
| 44 | 1 | if k in attrs: | |
| 45 | 1 | dictionary[k] = v | |
| 46 | else: | ||
| 47 | 1 | dictionary = to_dict(data) | |
| 48 | |||
| 49 | # Map object attributes to types | ||
| 50 | 1 | for name, data2 in dictionary.items(): | |
| 51 | |||
| 52 | 1 | try: | |
| 53 | 1 | converter = attrs.pop(name) | |
| 54 | 1 | except KeyError: | |
| 55 | 1 | if auto_track: | |
| 56 | 1 | converter = standard.match(name, data2, nested=True) | |
| 57 | 1 | common.attrs[cls][name] = converter | |
| 58 | else: | ||
| 59 | 1 | msg = "Ignored unknown nested file attribute: %s = %r" | |
| 60 | 1 | log.warning(msg, name, data2) | |
| 61 | 1 | continue | |
| 62 | |||
| 63 | 1 | try: | |
| 64 | 1 | attr = self[name] | |
| 65 | 1 | except KeyError: | |
| 66 | 1 | attr = converter.create_default() | |
| 67 | |||
| 68 | 1 | if all((isinstance(attr, converter), | |
| 69 | issubclass(converter, Container))): | ||
| 70 | 1 | attr.update_value(data2, auto_track=auto_track) | |
| 71 | else: | ||
| 72 | 1 | attr = converter.to_value(data2) | |
| 73 | |||
| 74 | 1 | value[name] = attr | |
| 75 | |||
| 76 | # Create default values for unmapped types | ||
| 77 | 1 | for name, converter in attrs.items(): | |
| 78 | 1 | value[name] = converter.create_default() | |
| 79 | 1 | msg = "Default value for missing nested object attribute: %s = %r" | |
| 80 | 1 | log.info(msg, name, value[name]) | |
| 81 | |||
| 82 | # Execute custom __init__ validations | ||
| 83 | 1 | try: | |
| 84 | 1 | cls(**value) | |
|  | |||
| 85 | 1 | except TypeError as exception: | |
| 86 | 1 |             log.warning("%s: %s", cls.__name__, exception) | |
| 87 | |||
| 88 | # Apply the new value | ||
| 89 | 1 | self.clear() | |
| 90 | 1 | self.update(value) | |
| 91 | |||
| 215 | 
Generally, there is nothing wrong with usage of
*or**arguments. For readability of the code base, we suggest to not over-use these language constructs though.For more information, we can recommend this blog post from Ned Batchelder including its comments which also touches this aspect.