Home of the Geeppetto Python API. The API allows to create a Geppetto Model from Python.
## Installation
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
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:
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 register first all the
EPackages
from the pygeppetto API:
# 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()
# Register all the EPackages of pygeppetto inside the ResourceSet
rset.metamodel_registry[pygeppetto.nsURI] = pygeppetto
for subpack in pygeppetto.eSubpackages:
rset.metamodel_registry[subpack.nsURI] = subpack
Then, we are able to read 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[0] # 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 >= 3.3
pyecore
>= 0.1.2
If the geppettoModel.ecore
evolves, the static metamodel must be regenerated.
The process of adding a new version is the following:
- Copy the of the new
geppettoModel.ecore
insideecore/
(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
geppettoModel.ecore
using the PyEcore Acceleo generator
(ecore2pyecore.mtl
).
The .ecore
is a copy of the geppettoModel.ecore
from
org.geppetto.model
(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).
Tests are written using pytest
and are run using tox
. To launch all the
tests the following command is enough:
$ tox
Currently, the tests are only related to the ability to read/write tests models.