DeadOrAlive project

Author : SANOU Abou

How launch the project

The project uses Python Language in its version 3.8. The required packages to install are listed in the file requirement.txt In order to install the needed package, you have to install pip. Once done, you have to run the following command.

pip install -r requirements.txt

Once the required package installed, let's run from the root folder of project the following command to launch the project.

python main.py

Run on Docker Container

You can also run the project inside a Docker Container. For this purpose the file Dockerfile was created.

 docker build -t deadoralive .
 docker run deadoralive

Explaining of the organization of project

.
├── data                   # static files of the project
├── core    
|   ├──models              # Package containing the definition of the models
|   |   ├── Cell           # File containing the definition of class Cell
|   |   ├── Grid           # File containing the definition of class Grid
|   |   ├── Position       # File containing the definition of class Position
|   |   └── State          # File containing the definition of class State  
|   └──io                  # Package for input and output logic
|       └── Input          # File Containing the Intializer of the Grid with initial and final petri state
├── .gitinore              # File specifies intentionally untracked files that Git should ignore
├── Dockerfie              # The declarative file for to build Docker container
├── main.py                # The entrypoint of the project
├── README.md              # Help file of the project
└── requirements.txt       # This file is used for specifying what python packages are required to run the project

Explaining of the algorithm:

The petri is modeled like a graph by using the adjacency matrix. The choice of the graph approach is explained by the fact that we can apply very simply a lot of algorithm o that such as BFS, DFS in order to find for instance the shortest path.

In order to avoid the conflict of state, we introduced a matrix where we stored the number of the alive neighbors of each cells . Once this matrix generated, we use this matrix to update the state of the cells in the grid.

On major axis, the algorithm can be summarised in the folllowing pseudo-code.

PROGRAM DeadOrAlive:
  Read grid
  Read number_generation;
  LOOP i .. 0 to number_generation
      neigbhoring_matrix <- get_matrix_number_neighbour_living()
      LOOP coordinate on each coordinates_of_grid
        grid[coordinate] <- update_grid(coordinate, neigbhoring_matrix) 
      ENDLOOP
  ENDLOOP;
END.

How to activate virtual environment created.

For Linux

source {path_where_store_virtual_env}/bin/activate

For Windows

.\env\Scripts\activate

Standard of entities'naming in the project

We have chosen to use the standard PEP 8 of python community in the entities naming in our project. Let's check out this link

Best pratices for develop

One of most useful best practices in Python is to use virtual environment. Let's check out the link to get more understanding about the virtual environment

How create a virtual environment.

python3 -m venv {path_where_store_virtual_env}

The next state is to activate the virtual env has been created Once activated, let's install the requirement file package by running the following command

pip install -r requirements.txt

Let's notice we have choosen to use logging system instead of the standard print of python in order to get more plexibility and more extensibility of the project.

Unit Tests part

The unit test are located in the folder test of project. To launch the test, you have to run the following command from your CLI.

pytest

CI/CD

I put a very basic CI/CD workfow in the git repo in order to launch the test trigged by the commit or merge on the master. For that, I used Github Action toolkit. The workflow is described in the file located as followed

.github/workflows/python-app.yml

AbouOpenSource / test_recrutement_ekinox