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General Scripting Library

Project description

GSL is inspired by, but completely independent of, iMatix’ GSL (Generator scripting language). It is a Python-based tool for model-oriented programming, which you can read about here, again borrowing from iMatix.

In contrast to iMatix’ GSL, this tool does not use its own scripting language but Python; with Python 3.6’s f-strings, it is very appropriate for code generation tasks. GSL’s focus lies on reading the source models and making them available in a convenient form, and providing utilities that are useful during code generation, especially for output handling and string manipulation.

Installation

Installing a release

GSL is available on pypi. Depending on whether you want to use YAML or ANTLR models, install the extras:

pip install gsl[antlr,yaml]

Installing for development

First, clone the project and install dependencies and extras:

pip install -e git+https://github.com/SillyFreak/gsl#egg=gsl[dev,antlr,yaml]
# or, alternatively
pip install invoke antlr4-python3-runtime ruamel.yaml
git clone https://github.com/SillyFreak/gsl

We recommend using pip, because that will also make the g4v shell command available.

Then, if you plan on using the g4v tool (which requires ANTLR), generate its parser and visitor:

cd env/src/gsl/
# or, alternatively
cd gsl

invoke g4v-antlr g4v-g4v

Note

The g4v-g4v task overwrites the G4VVisitor.py file, which is part of the g4v tool itself. If you break that file, you will have to reset it from git before g4v-g4v works again.

The same applies to incompatible changes to g4v: if you extend the tool in an incompatible way, you will have to bootstrap G4VVisitor.py yourself.

Minimal usage example

Model-oriented programming’s benefits become most visible for complex use cases; still, here is a small example that shows core features of GSL:

from gsl import pseudo_tuple, lines, print_to
from gsl.yaml import YAML

Class = pseudo_tuple('Class', ('name', 'members',))
Field = pseudo_tuple('Field', ('name',))
Method = pseudo_tuple('Method', ('name',))

def load_model():
    yaml = YAML(typ='safe')
    yaml.register_class(Class)
    yaml.register_class(Field)
    yaml.register_class(Method)
    return yaml.load("""\
    - !Class
      name: HelloWorld
      members:
      - !Field
        name: foo
      - !Method
        name: bar
    """)

def generate_code(model):
    for class_model in model:
        generate_class_code(class_model)

def generate_class_code(class_model):
    def field_code(field):
        yield from lines(f"""\
    private int {field.name};""")

    def method_code(method):
        yield from lines(f"""\
    public void {method.name}() {{
        // TODO
    }}""")

    @print_to(f"{class_model.name}.java")
    def code():
        yield from lines(f"""\
public class {class_model.name} {{""")
        for member in class_model.members:
            yield from lines(f"""\

""")
            if isinstance(member, Field):
                yield from field_code(member)
            elif isinstance(member, Method):
                yield from method_code(member)
        yield from lines(f"""\
}}""")

generate_code(load_model())

Output:

public class HelloWorld {

    private int foo;

    public void bar() {
        // TODO
    }
}

Some of the things seen here are:

  • the use of pseudo_tuple together with YAML type tags to produce a high-level model. namedtuple wouldn’t work here, because it is immutable (the YAML library separates construction and initialization of nodes to support cycles). Pseudo tuples are modifiable and allow auxiliary fields, giving you the option to augment the model with inferred information.
  • yield and yield from to create the actual code piece by piece, without pushing side effects like print into the guts of the code generator. By yielding code line by line instead of writing outright to a file, it is easy to post-process code before writing it out.
  • f-strings and a code style convention using multiline strings that aligns output code with the beginning of lines.
  • separation of concerns by using different functions, and a naming convention that helps understanding these concerns:
    • generate_code generates all code for this module. In this case there is only one class to generate, but there could be multiple classes or different kinds of sources to print with no problem.
    • generate_..._code functions generate a single kind of source code.
    • ..._code generator functions create the actual code by yielding it line by line. We use nested functions here, as fields and methods are only used for class code, but code reuse is of course easily possible.
    • The code generator function is a particular case. The @print_to(filename) decorator calls it immediately and writes all lines to the given file. In that sense, the whole function works like a with block, where the block body is a generator function.

If you’re thinking most of this is plain Python and some coding conventions, nothing gsl specific: you’re right! Python 3.6 is already a great tool with great development environments, and it would be a shame to take that power away from you. GSL just provides some useful tools that, combined with Python and some conventions, allow you to do model oriented programming at high velocity.

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