Feature Analysis

The Feature class comes with additional helper methods pre-built which provides insights over the usage of feature abstract class for creating features.

Lets say we have some features defined:

from featurelib import feature, validate, feature_info

class Feature1(feature):
    ...

class Feature2(feature):
    ...

# and so on.

Now, using the inbuilt class methods:

features class method.

Returns a comprehensive list of all available features in the entire project scope.
```
 features: dict[str, type] = feature.features()
```
dependency_graph class method

Returns a dependency graph of all features in the entire project scope.
```
 dependency_graph: dict[str, Set[str]] = feature.dependency_graph()
```
dependencies class method

Returns the set of dependencies for either the current feature or all dependencies of the current feature (direct or indirect).
```
 self_dependencies = Feature1.dependencies()
 all_dependencies = Feature1.dependencies(type='all')
```
composition_analysis class method.

Returns a CompositionAnalysisResult data class with attributes such as the feature name, it’s defined methods and it’s type (abstract or not).
```
 print(Feature1.composition_analysis())
```

The validate method

The validate method validates any given endpoint class for any potential errors such as illegal feature inheritance, MRO Conflicts, diamond inheritance, duplicated inheritance, and method rules and compatibility assessments. If the input is not an endpoint class will raise RuntimeError.

class Application(Feature1, Feature2, endpoint=True):
    ...

validate(Application)

This will generate warnings for any discrepancies found.

The feature_info method

This method returns either information on all registered features in the project scope or any particular feature.

all_info = feature_info()
specific_info = feature_info(Feature1)

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