The BAYSPLINE alkenone UK'37 calibration, in Python.
GPL-3.0 License
.. image:: https://travis-ci.org/brews/baysplinepy.svg?branch=master :target: https://travis-ci.org/brews/baysplinepy
An open source Python package for alkenone UK'37 <https://en.wikipedia.org/wiki/Alkenone>
_ 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) <http://doi.org/10.1002/2017PA003201>
_.
NOTE that this package is under active development. Code and documentation may not be complete and may change in the near future.
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 <https://doi.org/10.1038/ngeo2603>
_)
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)
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.
baysplinepy is available under the Open Source GPLv3 (https://www.gnu.org/licenses).