def __init__(self, growth_type='orig'):
Kernel.__init__(self)
self.L = 0.0
self.U = 10.0
self.T = range(5)
self.name = 'EED'
self.time_independent = True
self.survival_params = [ 1.34, 0.92 ]
self.growth_params = [ 0.37, 0.73, 0.127, 0.23 ]
self.fecundity_params = [ 0.034, 0.038 ]
if growth_type == 'slow':
self.growth_params = [ 0.0, 1.01, 0.005, 0.001 ]
self.name = 'EEDSlow'
self.discontinuities = [ 0.15, 0.25 ]
def __init__(self, **kwargs):
Kernel.__init__(self)
self.L = 1.0
self.U = 150.0
self.T = range(5)
self.name = 'Zuidema'
self.time_dependent = False
# parashorea chinensis
self.survival_params = [ 0.98 ]
self.growth_params = [ 42.1, 144.0, 2.258, 0.1054 ] # a, b, c, sd
self.k_st_params = [ -3.232, 0.146, 0.829 ] # b, mu, sdl
self.k_ts_params = [ 0.118, 1.078, 0.369 ] # a54, mu, sd
self.k_ss = asarray([[ 0.700, 0.0, 0.0, 0.0 ],
[ 0.101, 0.704, 0.0, 0.0 ],
[ 0.0, 0.136, 0.793, 0.0 ],
[ 0.0, 0.0, 0.096, 0.819 ]])
def __init__(self):
Kernel.__init__(self)
self.time_dependent = True
# read climate data and measurements (cm^2)
climate = read_csv('kernels/artr/climate.csv')
measurements = read_csv('kernels/artr/survivalDataARTR.csv')
years = sorted(list(set([ int(x.year) for x in measurements ])))
fecundity_measurements = read_csv('kernels/artr/fecdat.csv')
log_areas = {}
for year in years:
log_areas[year] = [ float(x.logArea) for x in measurements
if int(x.year) == year ]
# set attributes
self.n0 = log_areas[years[0]]
self.log_areas = log_areas
self.years = years
self.climate = climate
self.fec_meas = fecundity_measurements
self.mortality_type = 'noexp'
self.fecundity_type = 'uniform_exp'
self._tweak_covariat = None
self._tweak_factor = 1.0
# set parameters
self.params = {
'growth': {
'mean': np.array(
[ -6.134753578, 0.765515413, 0.006574618, 0.362328659 ]),
'covariance': np.array(
[[ 2.3003264050, -3.551420e-04, -9.540608e-04, -1.202835e-01 ],
[ -0.0003551420, 3.699543e-04, 1.274024e-06, -6.374569e-05 ],
[ -0.0009540608, 1.274024e-06, 3.545569e-06, 3.898729e-05 ],
[ -0.1202834507, -6.374569e-05, 3.898729e-05, 6.350332e-03 ]])
},
'survival': {
'mean': np.array(
[ -1.482555395, 0.753372235, 0.003454076, -0.001100492 ]),
'covariance':
np.array(
[[ 1.149244e-01, -1.422819e-02, -4.075111e-04, 5.042504e-05 ],
[ -1.422819e-02, 1.058307e-02, 5.006554e-05, -3.693585e-05 ],
[ -4.075111e-04, 5.006554e-05, 1.520893e-06, -1.896803e-07 ],
[ 5.042504e-05, -3.693585e-05, -1.896803e-07, 1.351324e-07 ]])
},
'fecundity': {
'mean': np.array(
[ 3.16420466, 0.06773083, 0.02146190 ]),
'covariance':
np.array(
[[ 2.900329e-02, 3.790527e-04, 2.113102e-05 ],
[ 3.790527e-04, 1.101385e-04, 8.486999e-06 ],
[ 2.113102e-05, 8.486999e-06, 1.079253e-05 ]])
}
}
# default parameters
self.growth_params = self.params['growth']['mean']
self.survival_params = self.params['survival']['mean']
self.fecundity_params = self.params['fecundity']['mean']