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MINT Data Transformations

A framework to construct a transformation pipeline based on some specification from users.

Spatial Transformations (from GDAL): cropping, regirdding, resampling, etc. Model-specific Transformations: Topoflow, PIHM, Cycles, Econ, etc.

A list of available transformations can be found in the wiki

Table of Contents

Installation

The easiest way to install and use the software is using docker:

  1. Clone the repository
git clone https://github.com/mintproject/MINT-Transformation.git
cd MINT-Transformation
  1. Build docker image
docker build -t mint_dt .

However, you can directly install the software without docker by replacing the second step with:

conda env create -f environment.yml

Post installation steps: (will be removed in the future)

mkdir /tmp
chmod 1777 /tmp

Usage

You can use the software through the command line application or through the web application.

Command line application

With conda environment

  1. Activate the environment first
conda activate mintdt
  1. Run the pipeline:
dotenv -f [env_path] run python -m dtran.main exec_pipeline --config [config_path]

Arguments:

With docker

docker run --rm -v $(pwd):/ws -v /tmp:/tmp  mintproject/mint_dt [config_path]

Web application

With conda environment

  1. Start the server by running the following command from the root folder:
PYTHONPATH=$(pwd)/webapp:$(pwd) dotenv run python webapp/api/app.py

Open URL http://0.0.0.0:10010 on your browser

With docker

Run image with local mount and port 5000 exposed

docker run --rm -p 5000:5000 -v $(pwd):/ws -it --entrypoint=/bin/bash mint_dt

Public server

We have a deployed transformation service running here. Demo video on how to use the service can be found here