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The main purpose of this library is to faciliate the reusability of data and data processing pipelines.For this, damast introduces a means to associate metadata with data frames and enables consistency checking.

To ensure semantic consistency, transformation steps in a pipeline can be annotated withallowed data ranges for inputs and outputs, as well as units.

class LatLonTransformer(PipelineElement):    """    The LatLonTransformer will consume a lat(itude) and a lon(gitude) column and perform    cyclic normalization. It will add four columns to a dataframe, namely lat_x, lat_y, lon_x, lon_y.    """    @damast.core.describe("Lat/Lon cyclic transformation")    @damast.core.input({        "lat": {"unit": "deg"},        "lon": {"unit": "deg"}    })    @damast.core.output({        "lat_x": {"value_range": MinMax(-1.0, 1.0)},        "lat_y": {"value_range": MinMax(-1.0, 1.0)},        "lon_x": {"value_range": MinMax(-1.0, 1.0)},        "lon_y": {"value_range": MinMax(-1.0, 1.0)}    })    def transform(self, df: AnnotatedDataFrame) -> AnnotatedDataFrame:        lat_cyclic_transformer = CycleTransformer(features=["lat"], n=180.0)        lon_cyclic_transformer = CycleTransformer(features=["lon"], n=360.0)        _df = lat_cyclic_transformer.fit_transform(df=df)        _df = lon_cyclic_transformer.fit_transform(df=_df)        df._dataframe = _df        return df

For detailed examples, check the documentation at:https://simula.github.io/damast

Firstly, you will want to create you an isolated development environment for Python, that being conda or venv-based.The following will go through a venv based setup.

Let us assume you operate with a 'workspace' directory for this project:

    cd workspace

Here, you will create a virtual environment.Get an overview over venv (command):

    python -m venv --help

Create your venv and activate it:

    python -m venv damast-venv    source damast-venv/bin/activate

Clone the repo and install:

    git clone https://github.com/simula/damast    cd damast    pip install -e ".[test,dev]"

or alternatively:

    pip install damast[test,dev]

If you prefer to work or start with a docker container you can build it using the providedDockerfile

    docker build -t damast:latest -f Dockerfile .

To enter the container:

    docker run -it --rm damast:latest /bin/bash

To get the usage documentation it is easiest to check the published documentationhere.

Otherwise, you can also locally generate the latest documentation once you installed the package:

    tox -e build_docs

Then open the documentation with a browser:

    <yourbrowser> _build/html/index.html

Install the project and use the predefined default test environment:

tox -e py

This project is open to contributions. For details on how to contribute please check theContribution Guidelines

This project is licensed under theBSD-3-Clause License.

Copyright (c) 2023-2025Simula Research Laboratory, Oslo, Norway

This work has been derived from work that is part of theT-SAR projectSome derived work is mainly part of the specific data processing for the 'maritime' domain.

The development of this library is part of the EU-projectAI4COPSEC which receives fundingfrom the Horizon Europe framework programme under Grant Agreement N. 101190021.