Metadata-Version: 2.1
Name: bayfox
Version: 0.0.1a3
Summary: Experimental Bayesian planktic foraminifera calibration, for Python.
Home-page: https://github.com/brews/bayfox
Author: S. Brewster Malevich
Author-email: malevich@email.arizona.edu
License: GPLv3
Description: # bayfox
        
        [![Travis-CI Build Status](https://travis-ci.org/brews/bayfox.svg?branch=master)](https://travis-ci.org/brews/bayfox)
        
        Experimental Bayesian planktic foraminifera calibration, for Python.
        
        **Please note that this package is currently under development. It will eat your pet hamster.**
        
        ## Quick example
        
        First, load key packages and an example dataset:
        
            import numpy as np
            import bayfox as bfox
        
            example_file = bfox.get_example_data('VM21-30.csv')
            d = np.genfromtxt(example_file, delimiter=',', names=True, missing_values='NA')
        
        This data (from [Koutavas and Joanides 2012](https://doi.org/10.1029/2012PA002378))
        has three columns giving, down-core depth, sediment age (calendar years BP) and δ18O for *G. ruber* (white) (‰; VPDB). 
        The core site is in the Eastern Equatorial Pacific.
        
        We can make a prediction of sea-surface temperature (SST) with `predict_seatemp()`:
        
            prediction = bfox.predict_seatemp(d['d18O_ruber'], d18osw=0.239, prior_mean=24.9, prior_std=7.81)
        
        The values we're using for priors are roughly based on the range of SSTs we've seen for *G. ruber* sediment 
        cores in the modern period, though the prior standard deviation is twice the spread in the modern 
        record. Let's use δ18O of modern seawater (‰; VSMOW) near the site ([LeGrande and Schmidt 2006](https://doi.org/10.1029/2006GL026011)). We'll assume it's constant -- for simplicity. 
        We're also not correcting these proxies for changes in global ice volume, so these numbers will be off. Ideally we'd make 
        this correction to δ18Oc series before the prediction. See the 
        [`erebusfall` package](https://github.com/brews/erebusfall) for simple ice-volume correction in Python.
        
        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 `bfox.predictplot(prediction)`.
        
        This uses the pooled Bayesian calibration model, which is calibrated on annual SSTs. We can consider foram-specific 
        variability with:
        
            prediction = bfox.predict_seatemp(d['d18O_ruber'], d18osw=0.239, prior_mean=24.9, prior_std=7.81, 
                                              foram='G. ruber')
        
        which uses our hierarchical model calibrated on annual SSTs. We can also estimate foram-specific seasonal effects with:
        
            prediction = bfox.predict_seatemp(d['d18O_ruber'], d18osw=0.239, prior_mean=24.9, prior_std=7.81, 
                                              foram='G. ruber', seasonal_seatemp=True)
        
        This uses our hierarchical model calibrated on seasonal SSTs. Be sure to specify the foraminifera if you use this option.
        
        You can also predict δ18O for planktic calcite using similar options, using the `predict_d18oc()` function.
        
        ## Installation
        
        To install **bayfox** with *pip*, run:
        
            pip install bayfox
        
        
        To install **bayfox** with *conda*, run:
        
            conda install -c sbmalev bayfox
        
        **bayfox** 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/bayfox).
        
        
        ## License
        
        **bayfox** is available under the Open Source GPLv3 (https://www.gnu.org/licenses).
        
Keywords: marine paleoclimate paleoceanography d18o δ18o
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
