baysparpy

An Open Source Python package for TEX86 calibration.

This package is based on the original BAYSPAR (BAYesian SPAtially-varying Regression) for MATLAB (https://github.com/jesstierney/BAYSPAR).

NOTE This repository and package is no longer actively maintained.

Quick example

First, load key packages and an example dataset:

import numpy as np
import bayspar as bsr

example_file = bsr.get_example_data('castaneda2010.csv')
d = np.genfromtxt(example_file, delimiter=',', names=True)

This dataset (from Castañeda et al. 2010) has two columns giving sediment age (calendar years BP) and TEX86.

We can make a "standard" prediction of sea-surface temperature (SST) with predict_seatemp():

prediction = bsr.predict_seatemp(d['tex86'], lon=34.0733, lat=31.6517,
                                 prior_std=6, temptype='sst')

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 bsr.predictplot() or bsr.densityplot().

For further details, examples, and additional prediction functions, see the online documentation (https://baysparpy.readthedocs.io).

Installation

To install baysparpy with pip, run:

$ pip install baysparpy

To install with conda, run:

$ conda install baysparpy -c sbmalev

Unfortunately, baysparpy is not compatible with Python 2.

Support and development

• Documentation is available online (https://baysparpy.readthedocs.io).
• Please feel free to report bugs and issues or view the source code on GitHub (https://github.com/brews/baysparpy).

License

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