Home of the Geeppetto Python API. The API allows to create a Geppetto Model from Python.
Until pygeppetto is still in development, it is highly recommended to use a virtualenv in order to deploy it. Once you have a dedicated virtualenv, you can simply install pygeppetto:
$ python setup.py install
Pygeppetto API Basic Usage
First, import the pygeppetto API:
import model as pygeppetto
This will load the pygeppetto API and name it
pygeppetto. Then, you can create
instances and handle them:
# We create a new lib flib = pygeppetto.GeppettoLibrary(name='mylib') # We create a GeppettoModel instance and we set a name a assign a lib root = pygeppetto.GeppettoModel(name='MyGeppettoModel', libraries=[flib])
The pygeppetto API also allows you to set all attributes in a "classical" fashion:
root = pygeppetto.GeppettoModel() # We create a GeppettoModel instance root.name = 'MyGeppettoModel' # We set a name flib = pygeppetto.GeppettoLibrary() # We create a new lib flib.name = 'mylib' root.libraries.append(flib) # We add the new lib to the created root
If you wan to open an existing XMI, you need to use a
required, but prefered).
# We import the class that will be used to read the XMI from PyEcore from pyecore.resources import ResourceSet, URI # We create a new resource set (not required, but better) rset = ResourceSet()
ResourceSet, we are able to read the Geppetto XMI:
model_url = URI('tests/xmi-data/MediumNet.net.nml.xmi') # The model URI resource = rset.get_resource(model_url) # We load the model geppettomodel = resource.contents # We get the root
At the end of this script,
geppettomodel contains the model root.
In order to serialize a new version of the modified model, there is two options. The first one is to serialize onto the existing resource (i.e: in the same file), or to serialize in a new one:
# Using the first option resource.save() # Using the second option resource.save(output=URI('my_new_file.xmi'))
- Python 2.7 or Python >= 3.4
pyecore-py2for Python 2.7,
pyecorefor Python 3
geppettoModel.ecore evolves, the static metamodel must be regenerated.
The process of adding a new version is the following:
- Copy the of the new
ecore/(in order to keep a version from which the static metamodel is generated).
- Generate the new version of the static metamodel.
- Manually merge modifications between the current and the new version (if there is manual modifications in the current version).
- Run the tests
How to Generate a New Version
The pygeppetto API is generated from the
using the PyEcore Acceleo generator
.ecore is a copy of the
development branch). The script can be directly used in Eclipse as a simple
Acceleo generator. The generated code had been directly placed inside the
repository without manual modification.
If manual modifications have been introduced in the version of the static Geppetto metamodel (e.g: implementation of some methods or technical method additions), this version must be manually merged with the new generated one (e.g: using meld or other tool).
Run the Tests
Tests are written using
pytest and are run using
tox. To launch all the
tests the following command is enough:
Or, if you want to avoid using
tox, you can just:
$ python -m pytest tests/
Currently, the tests are only related to the ability to read/write more or less huge tests models. The test matrix used by tox considers Python 2 and Python 3.