textX 0.2

Meta-language in Arpeggio inspired by Xtext

textX is a meta-language for building Domain-Specific Languages (DSLs) inspired by Xtext.

textX follows the syntax and semantics of Xtext but differs in some places and is implemented 100% in Python using Arpeggio parser. It is fully dynamic - no code generation at all!

Installation

pip install textX

Quick start

There is no docs at the moment but here is a quick introduction what can be done. For more see examples.

1. Write a language description in textX (file hello.tx):

HelloWorldModel:
  'hello' to_greet+=Who[',']
;

Who:
  name = /[^,]*/
;

Description consists of a set of parsing rules which at the same time describe Python classes that will be used to instantiate object of your model.

2. Create meta-model from textX language description:

from textx.metamodel import metamodel_from_file
hello_meta = metamodel_from_file('hello.tx')

3. Optionally export meta-model to dot (visualize your language abstract syntax):

from textx.export import metamodel_export
metamodel_export(hello_meta, 'hello_meta.dot')

You can see that for each rule from language description an appropriate Python class has been created. A BASETYPE hierarchy is builtin. Each meta-model has it.

4. Create some content (i.e. model) in your new language (example.hello):

hello World, Solar System, Universe

Your language syntax is also described by language rules from step 1.

5. Use meta-model to create models from textual description:

example_hello_model = hello_meta.model_from_file('example.hello')

Textual model ‘example.hello’ will be parsed and transformed to a plain Python object graph. Object classes are those defined by the meta-model.

6. Optionally export model to dot to visualize it:

from textx.export import model_export
model_export(example_hello_model, 'example.dot')

This is an object graph automatically constructed from ‘example.hello’ file.

7. Use your model: interpret it, generate code … It is a plain Python graph of objects with plain attributes!