You would like to discover a simple, powerful and user-friendly software to visualize and process 2-D datasets in a few clicks? PyAnalySeries allows for efficient visualization and processing of 2-D datasets, in particular time-series, without any programming skills. Its simplicity and user-friendly visualization interface make it an extremely valuable software both for research applications and teaching activities.
PyAnalySeries is the new multi-platform version of the former and now obsolete time-series processing program called “AnalySeries” (Paillard et al., 1996). Written in Python, PyAnalySeries is easily portable across platforms (e.g. Linux, MacOS and Windows). Importing 2-D datasets is simple by copy-pasting from an open worksheet. A user-friendly graphical interface and efficient shortcuts rapidly create various types of 2-D data graphs (e.g. plots on the same or different X or Y axes), which can be interactively modified and exported as final figures. PyAnalySeries also gives access to a full set of astronomical series (e.g. precession, obliquity, eccentricity) and insolation series (for a given date or an integrated interval) from several references. The software provides the original possibilities of resampling and smoothing 2-D data, as well as that of interpolation-based correlation with different records of two archives simultaneously, which is classically used to derive age models in paleoclimate studies. PyAnalySeries is available with Open Access on our GitHub repository (https://github.com/PaleoIPSL/PyAnalySeries) and Zenodo (https://zenodo.org/records/15238083). Users are strongly encouraged to post questions and share suggestions of improvement on the GitHub space of discussion.
Beyond its original use in processing paleoclimatic data, PyAnalySeries is useful for any kind of 2-D datasets, such as elemental concentrations on river waters over time, the processing of electromagnetic radiation, any meteorological and climatic time series, biostatistics, sensors, economics. It is also a didactic platform useful in hands-on teaching activities. This makes it a valuable tool for training the next generation of Earth scientists.
During the short course, we will explain to users how to download and install the software, and show the main functionalities of PyAnalySeries using typical 2-D datasets. We also invite participants to bring their own 2-D data and try this simple time-series processing program by themselves.