A python code for automatic construction of Fischer plots using proxy data
Abstract Fischer plots are widely used in paleoenvironmental research as graphic representations of sea- and lake-level changes through mapping linearly corrected variation of accumulative cycle thickness over cycle number or stratum depth. Some kinds of paleoenvironmental proxy data (especially sub...
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Nature Portfolio
2021
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oai:doaj.org-article:b7bb376b08c4448c827a3faa1a7223792021-12-02T15:52:47ZA python code for automatic construction of Fischer plots using proxy data10.1038/s41598-021-90017-92045-2322https://doaj.org/article/b7bb376b08c4448c827a3faa1a7223792021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90017-9https://doaj.org/toc/2045-2322Abstract Fischer plots are widely used in paleoenvironmental research as graphic representations of sea- and lake-level changes through mapping linearly corrected variation of accumulative cycle thickness over cycle number or stratum depth. Some kinds of paleoenvironmental proxy data (especially subsurface data, such as natural gamma-ray logging data), which preserve continuous cyclic signals and have been largely collected, are potential materials for constructing Fischer Plots. However, it is laborious to count the cycles preserved in these proxy data manually and map Fischer plots with these cycles. In this paper, we introduce an original open-source Python code “PyFISCHERPLOT” for constructing Fischer Plots in batches utilizing paleoenvironmental proxy data series. The principle of constructing Fischer plots based on proxy data, the data processing and usage of the PyFISCHERPLOT code and the application cases of the code are presented. The code is compared with existing methods for constructing Fischer plots.Daming YangYongjian HuangZongyang ChenQinghua HuangYanguang RenChengshan WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
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Medicine R Science Q Daming Yang Yongjian Huang Zongyang Chen Qinghua Huang Yanguang Ren Chengshan Wang A python code for automatic construction of Fischer plots using proxy data |
description |
Abstract Fischer plots are widely used in paleoenvironmental research as graphic representations of sea- and lake-level changes through mapping linearly corrected variation of accumulative cycle thickness over cycle number or stratum depth. Some kinds of paleoenvironmental proxy data (especially subsurface data, such as natural gamma-ray logging data), which preserve continuous cyclic signals and have been largely collected, are potential materials for constructing Fischer Plots. However, it is laborious to count the cycles preserved in these proxy data manually and map Fischer plots with these cycles. In this paper, we introduce an original open-source Python code “PyFISCHERPLOT” for constructing Fischer Plots in batches utilizing paleoenvironmental proxy data series. The principle of constructing Fischer plots based on proxy data, the data processing and usage of the PyFISCHERPLOT code and the application cases of the code are presented. The code is compared with existing methods for constructing Fischer plots. |
format |
article |
author |
Daming Yang Yongjian Huang Zongyang Chen Qinghua Huang Yanguang Ren Chengshan Wang |
author_facet |
Daming Yang Yongjian Huang Zongyang Chen Qinghua Huang Yanguang Ren Chengshan Wang |
author_sort |
Daming Yang |
title |
A python code for automatic construction of Fischer plots using proxy data |
title_short |
A python code for automatic construction of Fischer plots using proxy data |
title_full |
A python code for automatic construction of Fischer plots using proxy data |
title_fullStr |
A python code for automatic construction of Fischer plots using proxy data |
title_full_unstemmed |
A python code for automatic construction of Fischer plots using proxy data |
title_sort |
python code for automatic construction of fischer plots using proxy data |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/b7bb376b08c4448c827a3faa1a722379 |
work_keys_str_mv |
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_version_ |
1718385594121322496 |