📦 Welcome to TStore

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🚀 Quick start

tstore is a library for flexible storage and processing of time series data.

✨ Features

• TStore: hierarchically-structured specification to efficiently store geospatial time series data based on Apache Parquet and GeoParquet.
• TSDF: tabular Python object to store univariate and multivariate time series data, with support for pandas, dask and polars.

See the key concepts page of the documentation for more details of the background and tstore features.

📖 Explore the TStore documentation

To discover all TStore download, manipulation, analysis and plotting utilities, or how to contribute your custom retrieval to TStore:

• please read the software documentation available at tstore.readthedocs.io/en/latest/.
• dive into the Jupyter Notebooks Tutorials.

🛠️ Installation

conda

TStore can be installed via conda on Linux, Mac, and Windows. Install the package by typing the following command in the terminal:

bash conda install ts-store

In case conda-forge is not set up for your system yet, see the easy to follow instructions on conda-forge.

pip

TStore can be installed also via pip on Linux, Mac, and Windows. On Windows you can install WinPython to get Python and pip running.

Install the TStore package by typing the following command in the terminal:

bash pip install ts-store

To install the latest development version via pip, see the documentation.

💭 Feedback and Contributing Guidelines

If you aim to contribute your data or discuss the future development of TStore, we highly suggest to join the TStore Slack Workspace

Feel free to also open a GitHub Issue or a GitHub Discussion specific to your questions or ideas.

Citation

If you are using TStore in your publication please cite our Zenodo repository:

Ghiggi Gionata. ltelab/tstore. Zenodo.

If you want to cite a specific software version, have a look at the Zenodo site.

License

The content of this repository is released under the terms of the MIT license.

OUTDATED HERE BELOW

Requirements

• mamba, which can be installed using conda or mambaforge (see the official installation instructions)
• snakemake, which can be installed using conda or mamba

Instructions

1. Create a conda environment:

bash snakemake -c1 create_environment

1. Activate it (if using conda, replace mamba for conda):

bash mamba activate tstore

1. Register the IPython kernel for Jupyter:

bash snakemake -c1 register_ipykernel

1. Activate pre-commit for the git repository:

bash pre-commit install pre-commit install --hook-type commit-msg

Acknowledgments

• Based on the cookiecutter-data-snake :snake: template for reproducible data science.