def objf(a, b, c):
gcpsf = lambda ps: a*np.exp(-(ps/c)**b)
try:
res = simf(gcpsf, r0=r0, r1=r1, t0=t0, t1=t1)
except ValueError:
res = 0
return res
def f(r0=0.3, r1=0, t0=100, t1=1):
# objective function
def objf(a, b, c):
gcpsf = lambda ps: a*np.exp(-(ps/c)**b)
try:
res = simf(gcpsf, r0=r0, r1=r1, t0=t0, t1=t1)
except ValueError:
res = 0
return res
# parameter space
pbounds = {'a': (0, 1), 'b': (0, 10), 'c': (-1, -0.01)}
# bayesian optimization
optimizer = BayesianOptimization(
f=objf,
pbounds=pbounds,
verbose=1,
random_state=1
)
# # prior
# optimizer.probe(
# params={'a': 0.4, 'b': 1, 'c': -1},
# lazy=True
# )
# reiterate
optimizer.set_bounds(new_bounds={'a': (0, 1)})
optimizer.maximize(
init_points=10,
n_iter=100,
)
return optimizer.max['target'], simf(eller, r0=r0, r1=r1, t0=t0, t1=t1)
from Functions import gcmaxf, net_gainf, simf
from scipy import optimize
import numpy as np
import matplotlib.pyplot as plt
def eller(ps):
gcmax = gcmaxf(ps)
f1 = lambda gc: -net_gainf(gc, ps)
gc = optimize.minimize_scalar(f1, bounds=(0, gcmax), method='bounded')
return gc.x
if __name__ == "__main__":
total_net_gain = simf(eller)
print(total_net_gain)
x = np.linspace(-2, 0, 100)
y = [eller(i) for i in x]
y2 = [0.4 * np.exp(i) for i in x]
plt.plot(x, y)
plt.plot(x, y2, color='red')
from Functions import simf
import matplotlib.pyplot as plt
s0, t = 1, 100
a, D, l, n, Z = 1.6, 0.005, 1.8 * 10**-5, 0.43, 0.8
PLC, s = simf(s0, t, a, D, l, n, Z)
# figure
fig, ax = plt.subplots()
ax.plot(PLC[0::24], color='black')
ax.set_xlabel('Days', fontsize=14)
ax.set_ylabel('Permanent PLC', fontsize=14)
ax.set_ylim(0, 1)
ax2 = ax.twinx()
ax2.plot(s[:-1][0::24], color='blue')
ax2.set_ylabel('Relative soil water availability', color='blue', fontsize=14)
ax2.set_ylim(0, 1)