pyleoclim 0.7.3

A Python package for paleoclimate data analysis

Python Package for the Analysis of Paleoclimate Data

Paleoclimate data, whether from observations or model simulations, offer unique challenges to the analyst. Pyleoclim is a Python package primarily geared towards the analysis and visualization of paleoclimate data. Such data usually come in the form of timeseries with missing values and age uncertainties, so the package includes several low-level methods to deal with these issues to simplify the user's life, with intuitive, high-level analysis and plotting methods that support publication-quality scientific workflows.

There are many entry points to Pyleoclim, thanks to its underlying data structures. The package makes use of the Linked Paleo Data (LiPD) standard container and its associated utilities. The package is aware of age ensembles stored via LiPD and uses them for time-uncertain analyses, very much like GeoChronR.

LiPD, however, is not an obligatory entry point to Pyleoclim. Low-level modules are primarily based on NumPy arrays or Pandas dataframes, so Pyleoclim contains a lot of timeseries analysis code (e.g. spectral analysis, singular spectrum analysis, wavelet analysis, correlation analysis) that can apply to these more common types as well, including those generated by numerical models (via xarray). This makes the package suitable for rigorous model-data comparisons, like this one.

We've worked very hard to make Pyleoclim accessible to a wide variety of users, from establisher researchers to high-school students, and from seasoned Pythonistas to first-time programmers. A growing collection of workflows that use Pyleoclim are available as Jupyter notebooks on paleoBooks.

A series of training material is also available on paleoHackthon. You can run these training notebooks at any time in a myBinder environment. We also run training workshops several times a year. Follow us on our social media accounts for more information.

Versions

See our releases page for details on what's included in each version.

Documentation

Online documentation is available through readthedocs:

• Stable version (available through Pypi)
• Latest version (from the development branch)

Dependencies

pyleoclim only supports Python 3.8, 3.9

Installation

The latest stable release is available through Pypi. We recommend using Anaconda or Miniconda with a dedicated environment. Full installation instructions are available in the package documentation

Development

Pyleoclim development takes place on GitHub: https://github.com/LinkedEarth/Pyleoclim_util

Please submit any reproducible bugs you encounter to the issue tracker

License

The project is licensed under the GNU Public License. Please refer to the file call license. If you use the code in publications, please credit the work using this citation:

Deborah Khider, Feng Zhu, Julien Emile-Geay, Jun Hu, Alexander James, Pratheek Athreya, Myron Kwan, Daniel Garijo. (xxxx). Pyleoclim: vx.x.x Release. Zenodo. http://doi.org/10.5281/zenodo.1212692

Disclaimer

This material is based upon work supported by the National Science Foundation under Grant Number ICER-1541029. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the investigators and do not necessarily reflect the views of the National Science Foundation.

This research is funded in part by JP Morgan Chase & Co. Any views or opinions expressed herein are solely those of the authors listed, and may differ from the views and opinions expressed by JP Morgan Chase & Co. or its affilitates. This material is not a product of the Research Department of J.P. Morgan Securities LLC. This material should not be construed as an individual recommendation of for any particular client and is not intended as a recommendation of particular securities, financial instruments or strategies for a particular client. This material does not constitute a solicitation or offer in any jurisdiction.