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A Python package for paleoclimate data analysis

Project description

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**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: 0.4.6

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 <>`__) format and makes
extensive use of the `LiPD
utilities <>`__. The package
is aware of age ensembles stored via LiPD and uses them for
time-uncertain analyses very much like
`GeoChronR <>`__.

**Current capabilities**: - binning - interpolation - standardization -
plotting maps, timeseries, and basic age model information - paleo-aware
correlation analysis (isopersistent, isospectral and classical t-test) -
weighted wavelet Z transform (WWZ) - age modelling through Bchron

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

If you have specific requests, please contact

Version Information

| 0.4.6: Fix an issue when copying the .so files
| 0.4.5: Update to to include proper .so file according to
| 0.4.4: New fix for .so issue
| 0.4.3: New fix for .so issue
| 0.4.2: Fix issue concerning download of .so files
| 0.4.1: Fix issues with tarball
| 0.4.0: New functionalities: map nearest records by archive type, plot
ensemble time series, age modelling through Bchron
| 0.3.1: New functionalities: segment a timeseries using a gap detection
criteria, update to summary plot to perform spectral analysis
| 0.3.0: Compatibility with LiPD 1.3 and Spectral module added
| 0.2.5: Fix error on loading (Looking for Spectral Module)
| 0.2.4: Fix load error from init
| 0.2.3: Freeze LiPD version to 1.2 to avoid conflicts with 1.3
| 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
| 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

Will not run on a Windows system

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 <>`__.

4. Help with functionalities can be found in the Documentation folder on
`here <>`__.


- LiPD 0.2.5+
- pandas v0.22+
- numpy v1.14+
- matplotlib v2.0+
- Basemap v1.0.7+
- scipy v0.19.0+
- statsmodel v0.8.0+
- seaborn 0.7.0+
- scikit-learn 0.17.1+
- tqdm 4.14.0+
- pathos 0.2.0+
- tqdm 4.14+
- rpy2 2.8.4+

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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