Data

Here lives all exploration and processing pipes developed for Mangrove Atlas platform.

Project Organization

├── LICENSE                 <- The LICENSE using this project.
├── README.md               <- The top-level README for developers using this project.
├── CHANGELOG.md            <- The top-level CHANGELOG for developers using this project.
├── env.default     <- Environment vars definition
├── Makefile             <- Makefile with commands
├── .editorconfig   <- Helps maintain consistent coding styles
├── .pre-commit-config  <- Helps setup github basic precommit hooks
├── docker-compose.yml   <- Docker configs environment definition
├── .gitignore     <- files don't want to copy in githubs
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├── data
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
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└── notebooks           <- Naming convention is a number (for ordering),
    │                       the creator's initials, and a short `-` delimited e.g.
    │                       `1.0-jqp-initial-data-exploration`.
    │
    ├── .env
    ├── .dockerignore
    ├── requirements.txt           <- Notebooks requirements
    ├── Dockerfile                 <- Sets up Jupyter notebooks environment
    ├── jupyter_notebook_config.py / jupyter_server_config.py <- Configure Jupyter notebooks
    │
    ├── template_notebooks        <- where the notebooks template will live.
    └── Lab                 <- Testing and development
        │
        ├── data_exploration
        ├── data_processing
            ├── GEE
            ├── layers
            └── widgets-v2

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How to use

The notebooks are organized in two main folders: data_exploration and data_processing. The first one is used to explore the data and the second one is used to store the notebook pipes used to process the data.

Data is

Steps for use

Docker

• With docker and docker-compose in your system, you can develop inside containers:

make up

And if you want to get into the main container:

make inside

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Local

• Install requirements on your machine:

make requirements

• Set up a new environment in your machine

make create_environment && make requirements

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Project based on the cookiecutter data science project template. #cookiecutterdatascience