https://github.com/kelvich/tlaplus_jupyter
Jupyter kernel for TLA⁺
https://github.com/kelvich/tlaplus_jupyter
jupyter-kernel model-checking tla
Last synced: about 1 month ago
JSON representation
Jupyter kernel for TLA⁺
- Host: GitHub
- URL: https://github.com/kelvich/tlaplus_jupyter
- Owner: kelvich
- License: bsd-3-clause
- Created: 2019-07-11T13:09:09.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-09-12T13:10:53.000Z (over 2 years ago)
- Last Synced: 2025-05-07T04:44:17.635Z (about 1 month ago)
- Topics: jupyter-kernel, model-checking, tla
- Language: Python
- Size: 78.1 KB
- Stars: 119
- Watchers: 7
- Forks: 8
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://ci.appveyor.com/project/kelvich/tlaplus-jupyter/branch/master)
[](https://travis-ci.org/kelvich/tlaplus_jupyter)
[](https://mybinder.org/v2/gh/kelvich/tlaplus_jupyter/master?filepath=intro.ipynb)
# tlaplus_jupyter
Jupyter kernel for TLA⁺ and Pluscal specification languages.
* Syntax highlight based on official lexer.
* REPL functionality for expressions.
* Can be executed online with Binder. [Try it now!](https://mybinder.org/v2/gh/kelvich/tlaplus_jupyter/master?filepath=intro.ipynb)
* No need to install TLA Toolbox: Java and Python will be enough.
## Installation
`tlaplus_jupyter` is a python package installable with `pip`. Python 2 and 3 are supported. To install run:
```
pip install tlaplus_jupyter
python -m tlaplus_jupyter.install
```
The last step will register `tlaplus_jupyter` as a Jupyter kernel in your system and will download `tla2tools.jar`. After that Jupyter can be started as usual:
```
jupyter notebook
```
To create a new TLA⁺ notebook click on the `New` button and select TLA⁺ in a dropdown menu. It is also handy to enable line numbering inside cells (View > Toggle Line Numbers) since syntax checker refers to problems by their line numbers.
Note that by default `python -m tlaplus_jupyter.install` enables TLC execution [statistics collection](https://github.com/tlaplus/tlaplus/blob/master/tlatools/src/util/ExecutionStatisticsCollector.md). Pass `--help` to see how to opt out.
## Usage
Basic usage is explained in an [intro notebook](https://mybinder.org/v2/gh/kelvich/tlaplus_jupyter/master?filepath=intro.ipynb).
`tlaplus_jupyter` supports several types of cells with different behavior on execution:
1. Cells with `full module definition`. Upon execution kernel will perform syntax check (with tla2sany.SANY) and report errors if any. If the module contains Pluscal program kernel will also translate it to TLA.
2. Cell starting with `%tlc:ModuleName` where `ModuleName` is the name of one of the modules previously executed. In this case, the cell is treated as a config file for the TLC model checker. For example to check spec `Spec` and invariant `TypeOk` of model `DieHardTLA` execute following:
```
%tlc:DieHardTLA
SPECIFICATION Spec
INVARIANT TypeOK
```
Init and next state formula can be set after keywords `INIT` and `NEXT` correspondingly. Constant definitions should follow `CONSTANTS` keyword separated by newline or commas. Description of possible config statements and syntax is given in chapter 14 of [Specifying systems](https://www.microsoft.com/en-us/research/publication/specifying-systems-the-tla-language-and-tools-for-hardware-and-software-engineers/) book.
Custom TLC flags may be specified after the module name:
```
%tlc:DieHardTLA -deadlock
SPECIFICATION Spec
```
TLC evaluation happens in the context of all defined modules. So if model refers to another model that other model should be at some cell too.
3. Cells containing neither `%`-magic nor module definition are treated as a constant expression and will print its results on execution. As with `!tlc` evaluation happens in the context of all defined modules, so the expression can refer to anything defined in evaluated modules.
4. Command `%log` / `%log on` / `%log off` correspondingly shows kernel log / enables logging / disables logging for currently open notebook.
## Sharing executable models with Binder
TLA⁺ models shared on Github can be easily made runnable by coping [Dockerfile](Dockerfile) to the repository root. After that, URL to such repo can be used at [Binder](https://mybinder.org) to start a dynamic TLA⁺ environment.
## Related Projects
[vscode-tlaplus](https://github.com/alygin/vscode-tlaplus) Cool plugin for VSCode editor with syntax highlight and custom widgets for displaying traces.
## License
[BSD](LICENSE)