pyPhenology.models.Uniforc

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

Uniforc model

Single phase forcing model using a sigmoid function for forcing units.

Event happens on \(DOY\) when the following is met:

\[\sum_{t=t_{1}}^{DOY}R_{f}(T_{i})\geq F^{*} \]

where:

\[R_{f}(T_{i}) = \frac{1}{1 + e^{b(T_{i}-c)}}\]

Parameters:

t1 : int

\(t_{1}\) - The DOY which forcing accumulating beings

default : (-67,298)

F : int, > 0

\(F^{*}\) - The total forcing units required

default : (0,200)

b : int, < 0

\(b\) - Sigmoid function parameter

default : (-20,0)

c : int

\(c\) - Sigmoid function parameter

default (-50,50)

Notes:

Chuine, I. (2000). A Unified Model for Budburst of Trees. Journal of Theoretical Biology, 207(3), 337–347. http://doi.org/10.1006/jtbi.2000.2178

__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