Hello World example

This is an example of very simple Hello World like language.


A .tx file extension is used for textX grammar. See textX grammar on what you can do inside a grammar file, including comments!

These are the steps to build a very basic Hello World - like language.

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

      'hello' to_greet+=Who[',']
      name = /[^,]*/

    Description consists of a set of parsing rules which at the same time describe Python classes that will be dynamically created and used to instantiate objects of your model. This small example consists of two rules: HelloWorldModel and Who. HelloWorldModel starts with the keyword hello after which a one or more Who object must be written separated by commas. Who objects will be parsed, instantiated and stored in a to_greet list on a HelloWorldModel object. Who objects consists only of its names which must be matched the regular expression rule /[^,]*/ (match non-comma zero or more times). Please see textX grammar section for more information on writing grammar rules.

  2. At this point you can check and visualise meta-model using following command from the command line:

    $ textx generate hello.tx --target dot
    Generating dot target from models:
    -> /home/igor/repos/textX/textX/examples/hello_world/hello.dot
      To convert to png run "dot -Tpng -O hello.dot"

    hello meta-model

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

  3. 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.

    If we break down the text of the example model it looks like this:

    hello model parts

    We see that the whole line is a HelloWorldModel and the parts World, Solar System, and Universe are Who objects. Red coloured text is syntactic noise that is needed by the parser (and programmers) to recognize the boundaries of the objects in the text.

  4. To use your models from Python first create meta-model from textX language description (file hello.py):

    from textx import metamodel_from_file
    hello_meta = metamodel_from_file('hello.tx')
  5. Than use meta-model to create models from textual description:

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

    Textual model example.hello will be parsed and transformed to plain Python objects. Python classes of the created objects are those defined by the meta-model. Returned object hello_model will be a reference to the root of the model, i.e. the object of class HelloWorldModel. You can use the model as any other Python object. For example:

    print("Greeting", ", ".join([to_greet.name
                                for to_greet in hello_model.to_greet]))
  6. You can optionally export model to dot file to visualize it. Run following from the command line:

    $ textx generate example.hello --grammar hello.tx --target dot
    Generating dot target from models:
    -> /home/igor/repos/textX/textX/examples/hello_world/example.dot
      To convert to png run "dot -Tpng -O example.dot"

    Example hello model

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

    We see that each Who object is contained in the python attribute to_greet of list type which is defined by the grammar.

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


Try out a complete tutorial for building a simple robot language.