pyPhenology.models.Uniforc

class pyPhenology.models.Uniforc(parameters={'c': (-50, 50), 't1': (-67, 298), 'F': (0, 200), 'b': (-20, 0)})[source]

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:

\[R_{f}(T_{i}) = \frac{1}{1 + e^{b(T_{i}-c)}}\]
t1 : int
\(t_{1}\) - The DOY which forcing accumulating beings
F : int, > 0
\(F^{*}\) - The total forcing units required
b : int
\(b\) - Sigmoid function parameter
c : int
\(c\) - Sigmoid function parameter

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={'c': (-50, 50), 't1': (-67, 298), 'F': (0, 200), 'b': (-20, 0)})[source]

Methods

__init__([parameters])
fit(observations, temperature[, method, …]) Estimate the parameters of a model.
get_doy_fitting_estimates(**params)
get_error(**kargs)
get_initial_bounds()
get_params()
predict([to_predict, temperature, doy_series]) Predict the DOY given temperature data and associated site/year info All model parameters must be set either in the initial model call or by running fit().
save_params(filename) Save the parameters for a model
score([metric])