baysplinepy 0.0.2

A Python implementation of BAYSPLINE

An open source Python package for alkenone UK’37 calibration.

baysplinepy is based on the original BAYSPLINE software for MATLAB (https://github.com/jesstierney/BAYSPLINE). BAYSPLINE is a Bayesian calibration for the alkenone paleothermometer, as published in Tierney & Tingley (2018).

NOTE that this package is under active development. Code and documentation may not be complete and may change in the near future.

Example

First, load packages and an example dataset:

import numpy as np
import bayspline as bsl

example_file = bsl.get_example_data('tierney2016-p178-15p.csv')
d = np.genfromtxt(example_file, delimiter=',', names=True)

This dataset (from Tierney et al. 2015) has three columns giving core depth (cm), sediment age (calendar years BP), and UK’37.

We can predict sea-surface temperatures (SST) from UK’37 with bsl.predict_sst():

prediction = bsl.predict_sst(d['uk37'], prior_std=10)

To see actual numbers from the prediction, directly parse prediction.ensemble or use prediction.percentile() to get the 5%, 50% and 95% percentiles.

You can also plot your prediction with bsl.predictplot() or bsl.densityplot().

Alternatively, we can make inferences about UK’37 from SST with bsl.predict_uk():

sst = np.arange(1, 25)
prediction = bsl.predict_uk(sst)

Installation

Install baysplinepy in conda with:

$ conda install baysplinepy -c sbmalev

To install with pip, run:

$ pip install baysplinepy

Unfortunately, baysplinepy is not compatible with Python 2.

Support and development

• Please feel free to report bugs and issues or view the source code on GitHub (https://github.com/brews/baysplinepy).

License

baysplinepy is available under the Open Source GPLv3 (https://www.gnu.org/licenses).