https://github.com/brews/baysplinepy
The BAYSPLINE alkenone UK'37 calibration, in Python.
https://github.com/brews/baysplinepy
alkenone bayesian-inference calibration marine paleoceanography paleoclimate python uk37
Last synced: about 1 year ago
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The BAYSPLINE alkenone UK'37 calibration, in Python.
- Host: GitHub
- URL: https://github.com/brews/baysplinepy
- Owner: brews
- License: gpl-3.0
- Created: 2017-12-29T18:42:22.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2020-02-04T17:06:51.000Z (over 6 years ago)
- Last Synced: 2025-04-17T01:27:36.066Z (about 1 year ago)
- Topics: alkenone, bayesian-inference, calibration, marine, paleoceanography, paleoclimate, python, uk37
- Language: Python
- Homepage:
- Size: 197 KB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
baysplinepy
===========
.. 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 `_ 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).