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pyleoclim 0.2.2

A Python package for paleoclimate data analysis

Package Documentation

|PyPI| |PyPI| |PyPI| |license|


**Python Package for the Analysis of Paleoclimate Data**

**Table of contents**

- `What is it? <#what>`__
- `Installation <#install>`__
- `Version Information <#version>`__
- `Quickstart Guide <#quickstart>`__
- `Requirements <#req>`__
- `Further information <#further_info>`__
- `Contact <#contact>`__
- `License <#license>`__
- `Disclaimer <#disclaimer>`__

Current Version: 0.2.2

What is it?

Pyleoclim is a Python package primarily geared towards the analysis and
visualization of paleoclimate data. Such data often come in the form of
timeseries with missing values and age uncertainties, and the package
includes several low-level methods to deal with these issues, as well as
high-level methods that re-use those to perform scientific workflows.

The package assumes that data are stored in the Linked Paleo Data
(`LiPD <http:"" 12="" 1093="" 2016=""/>`__) format and makes
extensive use of the `LiPD
utilities <http:"" lipd-utilities=""/>`__. The package
is aware of age ensembles stored via LiPD and uses them for
time-uncertain analyses very much like
`GeoChronR <http:"" geochronr=""/>`__.

**Current capabilities**: - binning - interpolation - standardization -
plotting maps, timeseries, and basic age model information - paleo-aware
correlation analysis (isopersistent, isospectral and classical t-test)

**Future capabilities**: - paleo-aware singular spectrum analysis (AR(1)
null eigenvalue identification, missing data) - spectral analysis
(Multi-Taper Method, Lomb-Scargle) - weighted wavelet Z transform (WWZ)
- cross-wavelet analysis - index reconstruction - climate reconstruction
- ensemble methods for most of the above

If you have specific requests, please contact

Version Information

0.2.2: Change progressbar to tqdm and add standardization function
0.2.1: Update package requirements 0.2.0: Restructure the package so
that the main functions can be called without the use of a LiPD files
and associated timeseries objects. 0.1.4: Rename function using camel
case and consistency with LiPD utilities version 0.1.3:
Compatible with LiPD utilities version Function openLiPD()
renamed openLiPDs() 0.1.2: Compatible with LiPD utilities version Uses basemap instead of cartopy 0.1.1: Freezes the package
prior to version of LiPD utilities 0.1.0: First release


Python v3.4+ is required. Tested with Python v3.5

Pyleoclim is published through PyPi and easily installed via ``pip``


pip install pyleoclim

Quickstart guide

1. Open your command line application (Terminal or Command Prompt).

2. Install with command: ``pip install pyleoclim``

3. Wait for installation to complete, then:

3a. Import the package into your favorite Python environment (we
recommend the use of Spyder, which comes standard with the Anaconda

3b. Use Jupyter Notebook to go through the tutorial contained in the
``PyleoclimQuickstart.ipynb`` Notebook, which can be downloaded
`here <https:"" linkedearth="" pyleoclim_util="" tree="" master="" example="">`__.

4. Help with functionalities can be found in the Documentation folder on
our `GitHub
repository <https:"" linkedearth="" pyleoclim_util="" pyleoclim_documentation.pdf="">`__
and on `Pypi <https:"" pyleoclim=""/>`__.


- LiPD v0.2.0.2
- pandas v0.19+
- numpy v1.12+
- matplotlib v2.0+
- Basemap v1.0.7+
- scipy >=0.19.0
- statsmodel>=0.8.0
- seaborn>=0.7.0
- scikit-learn>=0.17.1
- tqdm>=4.14.0

The installer will automatically check for the needed updates

Further information



Python and Anaconda:

Jupyter Notebook:


Please report issues to


The project is licensed under the GNU Public License. Please refer to
the file call license.


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.

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