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