Pyleoclim: Paleoclimate Timeseries Analysis and Visualization With Python - Khider, Deborah; Emile‐Geay, Julien; Zhu, Feng; James, Alexander; Landers, Jordan; Ratnakar, Varun; Gil, Yolanda (Paleoceanography and paleoclimatology, 2022)
We present a Python package geared toward the intuitive analysis and visualization of paleoclimate timeseries, Pyleoclim. The code is open‐source, object‐oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code's philosophy, structure, and base functionalities and apply it to three paleoclimate problems: (a) orbital‐scale climate variability in a deep‐sea core, illustrating spectral, wavelet, and coherency analysis in the presence of age uncertainties; (b) correlating a high‐resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (c) model‐data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting Findable, Accessible, Interoperable, and Reusable software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud‐executable Jupyter notebooks, to encourage adoption by new users. Plain Language Summary This article describes a software application called Pyleoclim meant to help scientists analyze datasets of ordered observations, particularly applicable to the study of past climates, environments, and ecology. Pyleoclim is meant to be used by domain scientists as well as students interested in learning more about Earth's climate through examples provided in the documentation and online tutorials. Pyleoclim is intended to help scientists save time with their analyses, documenting the steps for better transparency, and as such, allows other scientists to reproduce results from previous studies. Key Points Pyleoclim makes timeseries analysis tools accessible to practicing scientists, via a user‐friendly Python package Three Jupyter Notebooks illustrate how Pyleoclim facilitates common and advanced tasks Pyleoclim can enhance reproducibility and rigor of paleogeoscientific workflows involving timeseries