Metadata-Version: 2.1
Name: uvlparser
Version: 0.8.7
Summary: This module provides a get_tree function to obtain an ANTLR parse-tree from a UVL-defined feature model
Home-page: https://github.com/diverso-lab/uvl-diverso
Author: Pablo Pazo Jiménez
Author-email: pablopazojim@gmail.com
License: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown

# UVL parser

## Documentation

Documentation for UVL can be found in the project's [wiki](https://github.com/diverso-lab/uvl-diverso/wiki)

## Dev

This python UVL parser for diverso project is generated by [ANTLR](https://www.antlr.org/) from the UVL.g4 file, where the grammar is defined. In order to generate the UVL*.py files, first we need to install antlr python runtime:

```
pip install antlr4-python3-runtime
```

Then, this command should generate the files we need for the parser to work:


```
antlr4 -Dlanguage=Python3 -no-listener UVL.g4
```

With this, any grammar change or update done to the UVL.g4 file will be effective.

## Usage and integration

With the grammar defined and the parser generated, get_tree.py gives us a method that, returns a ParseTree object given an uvl file via absolute path. ParseTree is an ANTLR object which represents an AST-ish structure of the file we read with the defined grammar.

[This file](https://github.com/diverso-lab/fm_metamodel/blob/master/famapy/metamodels/fm_metamodel/transformations/uvl_transformation.py) performs a transformation from an ANTLR ParseTree to a fm_metamodel-structured FM. Pretty much every way to obtain data from this ParseTree can be seen there. However, we will give some insight into its structure for easier usage and integration.

First, these dependencies must be installed:

```
apt install antlr4
pip3 install antlr-denter
```

Now, after getting our ParseTree object from the parser:

### Features and relations

```
root_feature = parse_tree.features().child() #Returns a ChildContext object which contains the model root feature
```
For any ChildContext object:
```
child_context.feature_spec() # Returns the feature contained in the node
child_context.feature_spec().ref() # From the feature, return the node containing its name
child_context.feature_spec().ref().WORD()[0].getText() # From feature ref, obtain its actual name. We have to get the first element from WORD() as, in imports (yet to be supported), multiple WORDs are allowed on the same ref. getText() function returns a string with the text from the node called.

child_context.feature_spec().relation() # Returns the list of RelationContext objects children of the feature called
```

For any RelationContext object:
```
relation_context.relation_spec() # Return the node containing the relation
relation_context.relation_spec().RELATION_WORD().getText() # Return a string containing the relation's name
relation_context.relation_spec().child() # Return the list of ChildContext objects the relation is parent of, with each one being processed as described above
```

### Constraints
```
constraints_node = parse_tree.constraints() # Returns ConstraintsContext object
constraints_node.constraint() # Returns list of ConstraintContext objects, with each node containing a constraint
```

For any ConstraintContext object:
```
list(constraint_context.getChildren())[0] # Returns the constraint itself, it is a bit tricky due to its structure in the parser
constraint.WORD[0].getText() # Returns the left node of the constraint, [1] returns the right node
constraint.getChild(1).getText() # Returns constraint operator
```
