PySysML2 is a Python-based parser for the SysML 2.0 textual modeling language. Its main purpose is to parse a SysML 2.0 textual model into a Python object, and then transform that model into various data structures useful for data science and analysis.
PySysML2 has the following dependencies:
- anytree: provides the tree data structure, the basis for the Python model class
- graphviz: renders images of graphs
- numpy: numerical analysis package
- pandas: data analysis package, provides the DataFrame data structure
- openpyxl: allows Pandas to export to Excel
- antlr4: provides the language parsing workbench
Note that some of these packages (specifically Anytree, Graphviz, and Antlr4) are not available on Anaconda. Also, Pandas does not automatically install the required OpenPyxl module for exporting Excel, so that must be done separately.
See the Development section for installation instructions if you are a developer.
git clone [email protected]:TrekkieByDay/PySysML2.git
cd PySysML2/
pip install .After installation, the pysysml2 CLI tool should be available. The following demonstrates using the pysysml2 export command to export the SysML 2.0 textual model file to various output file formats.
❯ pysysml2 export examples/models/model_test_1.sysml2 --output-dir out/ --format json,txt,csv,xlsx,dot,png
Using output directory: /Users/delannmt/Workspace/Projects/react/sysml/PySysML2/tmp
Exporting to JSON...
Exporting to txt...
Exporting to csv...
Exporting to xlsx...
Exporting to dot...
Exporting to png...For more information about the pysysml2 export command, use the --help option:
❯ pysysml2 export --help
Usage: pysysml2 export [OPTIONS] MODEL_FILE
Export a SysML v2 model to various file formats.
Arguments:
MODEL_FILE The sysml2 model file. [required]
Options:
--format TEXT One or more comma-separated output file formats.
Supported formats: json,txt,csv,xlsx,dot,png
[default: json]
-o, --output-dir PATH The output directory for the generated file(s).
Defaults to current directory.
--help Show this message and exit.The examples/ directory contains an example Python script using pysysml2 to export sample SysML 2.0 textual models to various output file formats.
PySysML2 can be used through Jupyter notebooks. Check the PySysML2_notebook.ipynb notebook to test the parsing functionality using the provided SysML 2.0 models.
The pysysml2 directory contains all the code. It is divided into the grammar and modeling packages.
The grammar package contains all the Antlr4 parsing code. The primary artifact of interest is the SysML.g4 grammar source file, which defines the basic elements of SysML 2.0 that PySysML2 implements. This file is used by the stand-alone Antlr4 command-line application that generates the language parsing Python code. Everything in the distpy, .antlr, and distj directories is auto-generated, and only distpy is required for PySysML2. The sysml2_model_visitor.py module is an extension of the generated SysML2Visitor.py and is the interface between the language parse tree from the textual model and the PySysML2 toolset.
The modeling package contains the SysML 2.0 modeling implementation and export tools. The element module implements model elements, and the model module implements a SysML 2.0 model class built from element objects. All export functions are in model.py.
To use PySysML2 with Poetry, follow these steps:
- Install Poetry by following the instructions in the official documentation. TLDR: run the following command:
curl -sSL https://install.python-poetry.org | python3 -
- In the root directory of the project repository, run the following command to install all the required dependencies:
poetry install
This will install the main dependencies specified in the pyproject.toml file.
PySysML2 has a dev group in its pyproject.toml file that contains the dependencies required for development and testing. To install these dependencies, run the following command in the root directory of the cloned repository:
poetry install --group=dev
This will install the development dependencies, including packages such as pytest.
By default, Poetry creates a virtual environment for each project, so all the dependencies are installed locally to that environment. This ensures that different projects can have different dependencies and versions installed without interfering with each other.
To execute a command inside the virtual environment, use the poetry run command.
For instance, to run the tests for this project, run the following command:
poetry run pytest
To activate the virtual environment, run the following command:
poetry shell
To exit the virtual environment, use exit:
exit
The native poetry version command only updates the version in pyproject.toml. However, the
__version__ variable in pysysml2/__init__.py must also be updated. To ensure the updates
are done in sync, developers should use the poetry-bumpversion
plugin for Poetry.
poetry self add poetry-bumpversion
The poetry version command can then be used, and both versions will be updated
together. For example to bump the patch version:
❯ poetry version patch
Bumping version from 0.1.0 to 0.1.1
poetry-bumpversion: processed file: pysysml2/__init__.pyFor the convenience of users installing from source without Poetry, developers can
generate the setup.py file from the pyproject.toml using the poetry2setup tool
(requires Poetry dev group installation):
poetry run poetry2setup > setup.py
To support Anaconda distribution, developers can generate the conda environment file
from the pyproject.toml using the poetry2conda
(requires Poetry dev group installation):
poetry run poetry2conda pyproject.toml environment.yaml
Poetry is a Python packaging and dependency management tool that helps simplify the process of building, packaging, and distributing Python projects. It provides a simple and intuitive way to manage project dependencies, handle virtual environments, and create distributable packages.
Using Poetry has several benefits:
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- PEP standards compliance: Poetry is designed to comply with the Python Enhancement Proposal (PEP) standards, which helps ensure compatibility with other Python tools and libraries.
PEP standards are a set of guidelines and recommendations for how to structure, package, and distribute Python code. These standards help ensure that Python packages are well-designed, easy to use, and compatible with other Python tools and libraries.
Poetry is designed to follow the PEP standards, which makes it easier to create Python packages that are compliant with these guidelines. Here are some ways in which Poetry helps follow PEP standards:
- PEP 517/518 compliance: Poetry uses the PEP 517/518 standards for building and packaging Python projects, including build isolation to ensure that builds are reproducible and do not rely on the developer's environment. These standards help ensure compatibility with other Python tools such as pip and setuptools.
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