README.md

AISAT

Setting up local development environment

1. Clone the repository

>>> git clone https://github.com/friyaz/AISAT.git

2. Set origin

>>> cd AISAT
>>> git set remote origin https://github.com/friyaz/AISAT.git

3. Activate the virtual environment

>>> pip install virtualenv
>>> virtualenv aisat_dev
>>> source aisat_dev/bin/activate

4. Install the dependencies

>>> pip install -r /path/to/requirements.txt

5. Deactivate the virtual environment To decativate the virtual environment and use your original environment, simply type deactivate.

>>> deactivate

Project Organization

├── LICENSE
├── README.md                               <- The top-level README for developers using this project.
├── data
│   ├── processed                           <- The final, canonical data sets for modeling.
│   └── raw                                 <- The original, immutable data dump.
│
├── docs                                    <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                                  <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks                               <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                              the creator's initials, and a short `-` delimited description, e.g.
│                                              `1.0-jqp-initial-data-exploration`.
│
├── references                              <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│
├── requirements.txt                        <- The requirements file for reproducing the analysis environment, e.g.
│                                              generated with `pip freeze > requirements.txt`
│
├── AISAT                                   <- Source code for use in this project.
│   │
│   ├── data                                <- Scripts to download or generate data
│   │
│   ├── utils                                <- Scripts utilities used during data generation or training
│   │
│   ├── training                            <- Scripts to train models
│   │
│   ├── validate                            <- Scripts to validate models
│   │
│   └── visualization                       <- Scripts to create exploratory and results oriented visualizations