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https://github.com/arhcoder/pytomatas
🧿 My own Python Library (available on PyPi) to implementate and simulate Automatons like DFA, NFA, PDA and Turing Machine for real projects, with documentation, examples and a project example :3
https://github.com/arhcoder/pytomatas
automata automatas library pypi python simulator states-machine turing-machine
Last synced: about 2 months ago
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🧿 My own Python Library (available on PyPi) to implementate and simulate Automatons like DFA, NFA, PDA and Turing Machine for real projects, with documentation, examples and a project example :3
- Host: GitHub
- URL: https://github.com/arhcoder/pytomatas
- Owner: arhcoder
- License: other
- Created: 2023-06-04T07:04:10.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-06-05T02:36:50.000Z (over 1 year ago)
- Last Synced: 2024-03-30T01:02:35.764Z (9 months ago)
- Topics: automata, automatas, library, pypi, python, simulator, states-machine, turing-machine
- Language: Python
- Homepage: https://pypi.org/project/pytomatas
- Size: 69.3 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🤖 Pytomatas
**📌 Version 1.1.4**
Pytomatas allows to simulate Acceptor Automata in the console with Python, implementing its characteristics using different definitions (mathematics included), with the following types:
* **[DFA](https://github.com/arhcoder/Pytomatas/blob/master/docs/dfa.md "DFA") (Deterministic Finite Automaton)**;
* **[NFA](https://github.com/arhcoder/Pytomatas/blob/master/docs/nfa.md "NFA") (Non-deterministic Finite Automaton)**;
* **[PDA](https://github.com/arhcoder/Pytomatas/blob/master/docs/pda.md "PDA") (Push-Down Automaton)**;
* **[TM](https://github.com/arhcoder/Pytomatas/blob/master/docs/tm.md "TM") (Turing Machine)**;
What can do?
- Create and manage various types of automaton: DFA, NFA, PDA, TM.
- Visualize automata information in console.
- Simulate automata acceptors based on strings.
- Observe the processes of steps and transitions when introducing a string to the automaton.
## 📍 Index
- **🛠 [Installation](#-installation)**;
- **💻 [Usage](#-usage)**;
- **🧿 [First implementation](#-first-implementation)**;
- **🧿 [Second implementation](#-second-implementation)**;
- **📓 [Documentation](#-documentation)**;
- **📚 [Examples](#-examples)**;
- **🤖 [DFA](https://github.com/arhcoder/Pytomatas/blob/master/docs/dfa.md "DFA")**;
- **🤖 [NFA](https://github.com/arhcoder/Pytomatas/blob/master/docs/nfa.md "NFA")**;
- **🤖 [PDA](https://github.com/arhcoder/Pytomatas/blob/master/docs/pda.md "PDA")**;
- **🤖 [TM](https://github.com/arhcoder/Pytomatas/blob/master/docs/tm.md "TM")**;
- **🔐 [Safebox](https://github.com/arhcoder/Pytomatas/blob/master/docs/xsafebox.md)**;
- **📁 [Repository](#-repository)**;
- **✍ [Contributing](#-contributing)**;
- **📜 [License](#-license)**;
## 🛠 Installation
You can install Pytomatas using pip:
```bash
pip install Pytomatas
```
## 💻 Usage
There are two ways in which automata can be implemented:
1. Creating the empty automaton and then adding the properties.
2. Creating the automaton by passing its characteristics as in the mathematical definition.
Example of a DFA implementation...
#### 🧿 First implementation
**1. Creating empty automata and then give the data:**
```python
from Pytomatas.dfa import DFA
# Creates a DFA called "my_dfa":
my_dfa = DFA()
# Define to "my_dfa" a set of states names:
my_dfa.setStates( {"q0", "q1", "qfinal"} )
# Define to "my_dfa" a set of states characters of the alphabet:
my_dfa.setAlphabet( {"a", "b"} )
# Set the "Initial state" name of "my_dfa":
my_dfa.setInitial( "q0" )
# Define to "my_dfa" the set of "Final states" names:
my_dfa.setFinals( {"qfinal", "qf2"} )
# Add transitions to the DFA:
my_dfa.addTransition( ("q0", "a", "q1") )
my_dfa.addTransition( ("q0", "b", "qfinal") )
my_dfa.addTransition( ("q1", "a", "q1") )
my_dfa.addTransition( ("q1", "b", "q1") )
my_dfa.addTransition( ("qfinal", "a", "qfinal") )
my_dfa.addTransition( ("qfinal", "b", "qfinal") )
# Add more data to the existing one already in the automata:
my_dfa.addSymbol("c")
my_dfa.addState("qx")
my_dfa.addState("finalState2")
my_dfa.addFinal("finalState2")
# Prints the DFA information:
my_dfa.show()
# Check if a string is accepted on the defined automata "my_dfa":
# It returns True or False if the string is accepted or not:
word = "aaabb"
my_dfa.accepts(word)
# Checks if the string is accepted, but prints all the process and steps on transitions;
# Shows the flow of states while reading the string;
my_dfa.accepts(word, stepByStep=True)
```
#### 🧿 Second implementation
**2. Creating the automata passing the data:**
```python
from Pytomatas.dfa import DFA
# Declare the States:
Q = {"q0", "qa", "q1", "qb", "q2", "qf", "qx"}
# Declare the Alphabet:
A = {"a", "b"}
# Declare the Initial (start) state:
S = "q0"
# Declare the Finals states:
F = {"q2", "q3"}
# Declare the Transitions:
T = [
("q0", "a", "qa"),
("q0", "b", "q1"),
("qa", "a", "qa"),
("qa", "b", "qb"),
("qf", "b", "qf"),
("qx", "a", "qx"),
("qx", "b", "qx")
]
# Declare the Automata:
my_dfa = DFA(Q, A, T, S, F)
# Show the automata information:
my_dfa.show()
# Check if a string is accepted on the defined automata "my_dfa":
# It returns True or False if the string is accepted or not:
word = "aaabb"
my_dfa.accepts(word)
# Checks if the string is accepted, but prints all the process and steps on transitions;
# Shows the flow of states while reading the string;
# It returns True or False if the string is accepted or not:
my_dfa.accepts(word, stepByStep=True)
```
#### 🛑 NOTE: These are only implementation examples, not actual implementations, so they are not complete real automata definitions 👆
* For more detailed information about the attributes and methods of the class, refer to **[Documentation](#-Documentation "Documentation")**.
* For more detailed usage instructions and examples, please refer to **[Examples](#-Examples "Examples")**.
## 📓 Documentation
Go to **[THIS LINK](http://github.com/arhcoder/Pytomatas/blob/master/docs/automatas.md "THIS LINK")** to see the documentation on all the features of the different types of automata, the functions they have, and examples of their implementation.
## 📚 Examples
1. **[DFA (Deterministic Finite Automaton)](https://github.com/arhcoder/Pytomatas/blob/master/docs/dfa.md "DFA").**
2. **[NFA (Non-deterministic Finite Automaton)](https://github.com/arhcoder/Pytomatas/blob/master/docs/nfa.md "NFA").**
3. **[PDA (Push-Down Automaton)](https://github.com/arhcoder/Pytomatas/blob/master/docs/pda.md "PDA").**
4. **[TM (Turing Machine)](https://github.com/arhcoder/Pytomatas/blob/master/docs/tm.md "TM").**
5. **[Safebox Automata Implementation Project](https://github.com/arhcoder/Pytomatas/blob/master/docs/xsafebox.md).**
## 📁 Repository
Go to **[THIS LINK](https://github.com/arhcoder/Pytomatas)** to check out the source code.
## ✍ Contributing
Contributions are welcome! If you encounter any issues, have suggestions, or would like to contribute to the project, please feel free to open an issue or submit a pull request on **[this repository](https://github.com/arhcoder/Pytomatas)**.
## 📜 License
This project is licensed under the MIT License - see the **[LICENSE](https://github.com/arhcoder/Pytomatas/blob/master/LICENSE)** file for details.
**Made with 💜 by @arhcoder**;