For the hackathon days, please use the link to the JupyterHub provided to the participants.
If you wish to play with our Notebooks without installing Pyleoclim locally, please use the Binder link below:
The notebook directory contains the following tutorials:
- Introduction to Jupyter
- Introduction to Pyleoclim (structures & objects)
- Exploratory Data Analysis of a LiPD file
- Forcing and response
- Spectral & Wavelet Analysis
- Model-data confrontations in the frequency domain
- Model-data confrontations in the time domain
The first two notebooks are designed to get your feet wet with Python and Pyleoclim and if you're new to either, we strongly recommend you start there. If you're familiar with Python, skip the first notebook. Notebook 3-8 walk through scientific workflows using Pyleoclim and can be completed in any order.
Data to run the notebooks are provided in the data folder unless explictily called within the notebooks. Please leave the data folder at the same level as this GitHub repository if downloading on your local machine.
Please report any bugs or feature request in our issues.
Pyleoclim is a Python package geared towards timeseries analysis of time-uncertain data.
The package can (but do not necessarily have to) directly work with data in the Linked Paleo Data (LiPD) format. The advantage of working with that format is that the code contains automated data transformation, making working with paleoclimate data easier and faster.
All notebooks herein are provided under an Apache 2.0 license.
We needn't tell you that making research tools accessible requires time and effort. If you find any of these resources useful and use them in your own research, please do us the kindness of one or more citations. Notebooks in this collection is registered on Zenodo, and associated with a digital object identifier (DOI). A ready-to-use citation is provided on this GitHub repository in APA and BibTex (in the About section on the right panel, click on "Cite this repository"). If you use any of the standards (LiPD) or the software (Pyleoclim), please cite them as well. It will make us (and NSF!) very happy to hear that these investments spawned more research.
If you use any of the data on this repository, please cite the original authors of the study.