gambatools 0.20

A library for formal language education. It contains support for DFAs, NFAs, PDAs, Turing machines, context free grammars and regular expressions.

GAMBA tools

The gambatools package is a Python 3 package that aims to support education in theoretical computer science at Eindhoven University of Technology. It contains a library for DFAs, NFAs, PDAs, Turing machines, context free grammars and regular expressions. Moreover, the package provides Jupyter notebooks with exercises. The library has been developed by Wieger Wesselink, and it was designed together with Erik de Vink.

GAMBA

The code was developed as part of the GAMBA project, which stands for: Grammars and Automata Made Boffo and Assessible. The goals of the project are to support

• practising and assessing formal language techniques
• self-paced learning outside contact hours
• immediate feedback while practising
• automated grading for assessment

(boffo: extremely successful, sensational)

License

The code is distributed under the GPL-3.0-or-later license.

Installation

The package can be installed using

pip install gambatools

The required python packages can be found in requirements.txt.

Rendering images

For visualization the graphviz python package is used. To render the generated DOT source code, you also need to install Graphviz. See the Graphviz website for further instructions. Make sure that the directory containing the dot executable is on your systems’ path.

Documentation

The file https://wiegerw.github.io/gambatools/pdf/main.pdf contains formal specifications of the algorithms in the library. Note that the code maps almost one-to-one to the specifications, so in order to understand the code please consult the pseudocode specifications.

Notebooks

The directory notebooks contains a number of Jupyter notebooks with exercises. In notebooks/with-answers the correct answers are already given, while in notebooks/without-answers they have been left out. The answers to the exercises are checked automatically. Whenever the user makes a mistake, appropriate feedback is given.

For specifying a DFA, NFA, etc. a line based textual input format is used, see the example below. The documentation contains a section that describes the syntax, while in the examples directory a number of examples can be found.

input_symbols 0 1
states qA qB qC qD

initial qA
final qC

qA qB 0
qA qD 1
qB qB 0
qB qC 1
qC qB 0
qC qC 1
qD qD 0
qD qD 1

Notebook generation

For convenience there is a mechanism to automatically generate notebooks from templates. The notebooks in notebooks/with-answers and notebooks/without-answers have been generated using the commands

make_notebook.py -o without-answers notebooks.batch
make_notebook.py --with-answers -o with-answers notebooks.batch

The templates contain tags of the form <<tag>> that are substituted by the make_notebook.py script. This generation is still experimental, and there is currently no documentation available for this.

Configuration

• GambaTools.pda_epsilon_closure_max_iterations: this parameter is a limit on the number of iterations in the function pda_epsilon_closure. This value is introduced to avoid infinite computations on PDAs that contain epsilon-loops. Note that this limit may cause the function pda_words_up_to_n to miss words in exceptional cases.

Contact

If you are interested in using the package for education or have questions or feedback, the authors can be reached by email: [e-mail] or [e-mail].