bamboost 0.14.0

BAMBOOST

Bamboost is a Python library built for datamanagement using the HDF5 file format. bamboost stands for a lightweight shelf which will boost your efficiency and which will totally break if you load it heavily. Just kidding, bamboo can fully carry pandas.
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[!important] Starting from version 0.10.0, bamboost breaks compatibility with previous versions. For previous versions, checkout the legacy branch.

About The Project

bamboost is a python data framework designed for managing scientific simulation data. It provides an organized model for storing, indexing, and retrieving simulation results, making it easier to work with large-scale computational studies. In its core, it is a filesystem storage model, providing directories for simulations, bundled in collections.

Principles

• Independence: Any dataset must be complete and understandable on it's own. You can copy or extract any of your data and distribute it without external dependencies.
• Path redundancy: Data must be referencable without knowledge of it's path. This serves several purposes: You can share your data easily ($e.g.$ supplementary material for papers), and renaming directories, moving files, switching computer, etc. will not break your data referencing.

This leads to the following requirements:

• Simulation parameters must be stored locally, inside the simulation directory. Crucially, not exclusively in a global database of any kind.
• Collections must have unique identifiers that are independent of its path.
• Simulations must have unique identifiers that are independent of its path.

Concept

We organize simulations in collections within structured directories. Let's consider the following directory:

test_data/
├── simulation_1/
│   ├── data.h5
│   ├── data.xdmf
│   ├── additional_file_1.txt
│   ├── additional_file_2.csv
├── simulation_2/
│   ├── data.h5
│   ├── additional_file_3.txt
└── .bamboost-collection-ABCD1234

This is a valid bamboost collection at the path ./test_data. It contains an identifier file giving this collection a unique identifier. In this case, it is ABCD1234. This file defines the unique ID of the collection.

It contains two entries; simulation_1 and simulation_2. As you can see, each simulation owns a directory inside a collection. The directory names are simultaneously used as their name as well as their ID. The unique identifier for a single simulation becomes the combination of the collection ID that it belongs to and the simulation ID. That means, the full identifier of simulation_1 is ABCD1234:simulation_1.

Each simulation contains a central HDF5 file named data.h5. This file is used to store the parameters, as well as generated data. The simulation API of bamboost provides extensive functionality to store and retrieve data from this file. However, users are not limited to this file, or using python in general. The reason why simulations are directories instead of just a single HDF file is that you can dump any file that belongs to this simulation into its path. This can be output from 3rd party software (think LAMMPS), additional input files such as images, and also scripts to reproduce the generated data.

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Getting Started

bamboost is available from the Python Package Index (PyPI) and can be installed using pip (or uv of course):

pip install bamboost

Prerequisites

To use bamboost with MPI, you need a working MPI installation. Additionally, you need

• mpi4py
• h5py with MPI support. For most distros you can get a version from it's package manager, or see building against Parallel HDF5

Installation

bamboost is available from the Python Package Index (PyPI) and can be installed using pip (or uv of course):

pip install bamboost

To install the latest version from this repository, you can use:

pip install git+https://github.com/smec-ethz/bamboost.git

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Usage

For a getting started guide, please see here: Getting started

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Roadmap

• Clear MPI handling

See the open issues for a full list of proposed features (and known issues).

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Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request on github.com/smec-ethz/tatva. Please use that repository to open issues or submit pull requests. If this project has been useful to you, consider giving the project a star!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT license. See LICENSE for more information.

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Contact

zrlf - florez@ethz.ch

Project Link: https://github.com/smec-ethz/bamboost

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Acknowledgments

• Best-README-Template for inspiration on how to structure this README.

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