from . import utils
from .base import BaseModel
[docs]class Sequential(BaseModel):
"""The sequential model
This uses a triangular response for chilling, and growing degree days for forcing.
Parameters:
t0 : int
The doy which chilling accumulation beings
c_t_min: int | float
Triangular response parameter. The minimum temperature where
chilling accumulates.
c_t_opt: int | float
Triangular response parameter. The optimum temperature where
chilling accumulates.
c_t_max: int | float
Triangular response parameter. The maximum temperature where
chilling accumulates.
C : int | float
Total chilling units required.
f_t : int | float
The threshold above which warming forcing accumulates
F : int, > 0
The total forcing units required
"""
[docs] def __init__(self, parameters={}):
BaseModel.__init__(self)
self.all_required_parameters = {'t0': (-67, 298), 'c_t_min': (-25, 10), 'c_t_opt': (-10, 10),
'c_t_max': (0, 10), 'C': (0, 100), 'f_t': (0, 20), 'F': (0, 1000)}
self._organize_parameters(parameters)
self._required_data = {'predictor_columns': ['site_id', 'year', 'doy', 'temperature'],
'predictors': ['temperature', 'doy_series']}
def _apply_model(self, temperature, doy_series, t0, c_t_min, c_t_opt, c_t_max,
C, f_t, F):
# Enforce realistic values for the triangle response parameters
bad_values = True if c_t_min >= c_t_opt else False
bad_values = True if c_t_min >= c_t_max else False
bad_values = True if c_t_opt >= c_t_max else False
if bad_values:
to_return = temperature[0].copy()
to_return[:] = 9999
return to_return
chill_days = utils.transforms.triangle_response(temperature.copy(), t_min=c_t_min,
t_opt=c_t_opt, t_max=c_t_max)
chill_days[doy_series < t0] = 0
chill_days = utils.transforms.forcing_accumulator(chill_days)
# Where adequate chill has not yet accumulated
temperature[chill_days < C] = 0
# Warming threshold temperature
temperature[temperature < f_t] = 0
accumulated_gdd = utils.transforms.forcing_accumulator(temperature)
return utils.transforms.doy_estimator(forcing=accumulated_gdd,
doy_series=doy_series,
threshold=F)