pyPhenology

Plant phenology models in python with a scikit-learn inspired API

Full documentation

http://pyphenology.readthedocs.io/en/master/

Installation

Requires: scipy, pandas, joblib, and numpy

Install via pip

pip install pyPhenology

Or install the latest version from Github

pip install git+git://github.com/sdtaylor/pyPhenology

Usage

A Thermal Time growing degree day model:

from pyPhenology import models, utils
observations, predictors = utils.load_test_data(name='vaccinium')
model = models.ThermalTime()
model.fit(observations, predictors)
model.get_params()
{'t1': 85.704951490688927, 'T': 7.0814430573372666, 'F': 185.36866570243012}

Any of the parameters in a model can be set to a fixed value. For example the thermal time model with the threshold T set to 0 degrees C

model = models.ThermalTime(parameters={'T':0})
model.fit(observations, predictors)
model.get_params()
{'t1': 26.369813953905265, 'F': 333.76534368004388, 'T': 0}

Citation

If you use this software in your research please cite it as:

Taylor, S. D. (2018). pyPhenology: A python framework for plant phenology modelling. Journal of Open Source Software, 3(28), 827. https://doi.org/10.21105/joss.00827

Bibtex:

@article{Taylor2018,
author = {Taylor, Shawn David},
doi = {10.21105/joss.00827},
journal = {Journal of Open Source Software},
mendeley-groups = {Software/Data},
month = {aug},
number = {28},
pages = {827},
title = {{pyPhenology: A python framework for plant phenology modelling}},
url = {http://joss.theoj.org/papers/10.21105/joss.00827},
volume = {3},
year = {2018}
}

Acknowledgments

Development of this software was funded by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through Grant GBMF4563 to Ethan P. White.