pyPhenology.models.Alternating

class pyPhenology.models.Alternating(parameters={})

Alternating model.

Originally defined in Cannell & Smith 1983.

Phenological event happens the first day that forcing is greater than an exponential curve of number of chill days.

\[\sum_{t=t1}^{DOY}R_{f}(T_{i})\geq a + be^{cNCD(t)}\]

where:

\[R_{f}(T_{i}) = max(T_{i}-threshold, 0)\]

Parameters:

a : int | float

\(a\) - Intercept of chill day curve

default : (-1000,1000)

b : int | float, > 0

\(b\) - Slope of chill day curve

default : (0,5000)

c : int | float, < 0

\(c\) - scale parameter of chill day curve

default : (-5,0)

threshold : int | float

\(threshold\) - Degree threshold above which forcing accumulates, and below which chilling accumulates.

default : 5

t1 : int

:math:`` - DOY which forcing and chilling accumulation starts.

default : 1 (Jan 1)

Notes:

Cannell, M. G. R., & Smith, R. I. (1983). Thermal Time, Chill Days and Prediction of Budburst in Picea sitchensis. The Journal of Applied Ecology, 20(3), 951. https://doi.org/10.2307/2403139

__init__(parameters={})

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([parameters])	Initialize self.
fit(observations, predictors[, …])	Estimate the parameters of a model
get_params()	Get the fitted parameters
predict([to_predict, predictors])	Make predictions
save_params(filename[, overwrite])	Save the parameters for a model
score([metric, doy_observed, to_predict, …])	Evaluate a prediction given observed doy values