persistent, pythonic trees for heterogeneous data
In many fields of science, especially those analyzing experimental or simulation data, there is often an existing ecosystem of specialized tools and file formats which new tools must work around, for better or worse. Furthermore, centralized database solutions may be suboptimal for data storage for a number of reasons, including insufficient hardware infrastructure, variety and heterogeneity of raw data, the need for data portability, etc. This is particularly the case for fields centered around simulation: simulation systems can vary widely in size, composition, rules, paramaters, and starting conditions. And with increases in computational power, it is often necessary to store intermediate results obtained from large amounts of simulation data so it can be accessed and explored interactively.
These problems make data management difficult, and serve as a barrier to answering scientific questions. To make things easier, datreant is a Python package that addresses the tedious and time-consuming logistics of intermediate data storage and retrieval. It solves a boring problem, so we can focus on interesting ones.
For more information on what datreant is and what it does, check out the official documentation.
See the installation instructions for installation details. The package itself is pure Python.
If you want to work on the code, either for yourself or to contribute back to the project, clone the repository to your local machine with:
git clone https://github.com/datreant/datreant.core.git
This project is still under heavy development, and there are certainly rough edges and bugs. Issues and pull requests welcome!
Check out our contributor’s guide to learn how to get started with contributing back.
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