An Open Source Python package for TEX86 calibration
Project description
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).
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 git+git://github.com/brews/baysparpy.git@stable
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).
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