return Water_Draws, Mfct_goods, Energy_Ds
def partial_objective(Mfct_goods):
total = 0
for county in range(N):
total += Mfct_goods[county] * p_mn
return total
def sole_objective(params):
"""Partial objective, plus the cost of fossil fuels."""
lambda_Cs = params[0:N]
sigma_Cs = params[N:2 * N]
Water_Draws, Mfct_goods, Energy_Ds = simulate_all(lambda_Cs, sigma_Cs)
return partial_objective(Mfct_goods) - sum(Energy_Ds) * lib.p_E
bounds = [(0, W_surf[county] / ((1 - alpha[county]) * Area[county]))
for county in range(N)] + [(0, None)] * N
if __name__ == '__main__':
result = lib.maximize(sole_objective, generate, bounds)
print result
print bounds
return -overdraws
def constraint_energy(params):
Crop_yields, Food_Ss, Food_Ds, Biofuel_Stocks, Biofuel_Es, \
Water_Draws, Energy_Ss, Energy_Ds, Mfct_goods = simulate_all(params)
return sum(Energy_Ss) - sum(Energy_Ds)
bounds = biofuel.bounds + fossil.bounds + agriculture.bounds + industry.bounds
constraints = [{'type': 'ineq', 'fun': constraint_water}, {'type': 'ineq', 'fun': constraint_energy}]
def safe_generate():
"""Generates a starting point that satisfies both constraints."""
# Initial conditions
invalid = True
attempts = 0
while invalid:
params = generate()
ineq1 = constraint_water(params)
ineq2 = constraint_energy(params)
invalid = ineq1 < 0 or ineq2 < 0
attempts += 1
return params
if __name__ == '__main__':
result = lib.maximize(objective, safe_generate, bounds, iterations=100, constraints=constraints)
print result
'type': 'ineq',
'fun': constraint_water
}, {
'type': 'ineq',
'fun': constraint_energy
}]
def safe_generate():
"""Generates a starting point that satisfies both constraints."""
# Initial conditions
invalid = True
attempts = 0
while invalid:
params = generate()
ineq1 = constraint_water(params)
ineq2 = constraint_energy(params)
invalid = ineq1 < 0 or ineq2 < 0
attempts += 1
return params
if __name__ == '__main__':
result = lib.maximize(objective,
safe_generate,
bounds,
iterations=100,
constraints=constraints)
print result
return 0
# Assumes that energy supply comes from nowhere else
def sole_objective(betas, Crop_yields, Energy_Ds):
Biofuel_Stocks, Biofuel_Es, Water_Draws = simulate_all(betas, Crop_yields)
total = 0
for county in range(N):
if Biofuel_Es[county] > Energy_Ds[county]:
total += (Biofuel_Es[county] -
Energy_Ds[county]) * lib.p_E / lib.markup
else:
total += (Biofuel_Es[county] - Energy_Ds[county]) * lib.p_E
return total
bounds = [(0, 1.0)] * N
if __name__ == '__main__':
# External parameters
Crop_yields = [500000] * 5
Energy_Ds = [100] * 5
print lib.maximize(sole_objective,
generate,
bounds,
args=(Crop_yields, Energy_Ds))
import csv
import numpy as np
import merged, agriculture, lib
with open('prices.csv', 'w') as fp:
writer = csv.writer(fp)
writer.writerow(['p_E', 'p_F', 'objective'] +
['beta' + str(ii) for ii in range(1, 6)] + \
['phi' + str(ii) for ii in range(1, 6)] + \
['lambda_A' + str(ii) for ii in range(1, 6)] + \
['sigma_A' + str(ii) for ii in range(1, 6)] + \
['lambda_C' + str(ii) for ii in range(1, 6)] + \
['sigma_C' + str(ii) for ii in range(1, 6)])
for p_E in np.exp(np.linspace(np.log(.1), np.log(10),
60)): #[.1, .2, .5, 1.0, 2.0, 5.0, 10.0]:
for p_F in np.exp(
np.linspace(np.log(.1), np.log(10),
60)): #[.1, .2, .5, 1.0, 2.0, 5.0, 10.0]:
print p_E, p_F
lib.p_E = p_E
agriculture.p_F = p_F
result = lib.maximize(merged.objective,
merged.safe_generate,
merged.bounds,
iterations=20,
constraints=merged.constraints)
writer.writerow([p_E, p_F] + [result['fun']] + result['x'])
else:
total += (Food_Ss[county] - Food_Ds[county]) * p_F
return total
def sole_objective(params, betas):
"""Partial objective, plus cost of energy."""
lambda_As = params[0:N]
sigma_As = params[N:2 * N]
Crop_yields, Food_Ss, Food_Ds, Water_Draws, Energy_Ds = simulate_all(
lambda_As, sigma_As, betas)
return partial_objective(Food_Ss, Food_Ds) - sum(Energy_Ds) * lib.p_E
# Cannot use more water than available, but may use any amount of nergy
bounds = [(0, W_surf[county] / (alpha[county] * Area[county])) for county in range(N)] + \
[(0, None)] * N
if __name__ == '__main__':
betas = [0.5] * 5
result = lib.maximize(sole_objective, generate, bounds, args=(betas, ))
print result
Crop_yields, Food_Ss, Food_Ds, Water_Draws, Energy_Ds = simulate_all(
result['x'][0:N], result['x'][N:2 * N], betas)
print np.mean(Crop_yields)
return Biofuel_Stocks, Biofuel_Es, Water_Draws
# Just sells of any remaining
def partial_objective(Biofuel_Es, Energy_Ds):
if sum(Biofuel_Es) > sum(Energy_Ds):
return sum(Biofuel_Es) - sum(Energy_Ds)*lib.p_E / lib.markup
return 0
# Assumes that energy supply comes from nowhere else
def sole_objective(betas, Crop_yields, Energy_Ds):
Biofuel_Stocks, Biofuel_Es, Water_Draws = simulate_all(betas, Crop_yields)
total = 0
for county in range(N):
if Biofuel_Es[county] > Energy_Ds[county]:
total += (Biofuel_Es[county] - Energy_Ds[county])*lib.p_E / lib.markup
else:
total += (Biofuel_Es[county] - Energy_Ds[county])*lib.p_E
return total
bounds = [(0, 1.0)] * N
if __name__ == '__main__':
# External parameters
Crop_yields = [500000] * 5
Energy_Ds = [100] * 5
print lib.maximize(sole_objective, generate, bounds, args=(Crop_yields, Energy_Ds))
Water_Draw = lambda_f * Fossil_E
return Fossil_E, Water_Draw
def simulate_all(phis):
Fossil_Es = []
Water_Draws = []
for county in range(N):
Fossil_E, Water_Draw = simulate_county(phis[county])
Fossil_Es.append(Fossil_E)
Water_Draws.append(Water_Draw)
return Fossil_Es, Water_Draws
def objective(phis):
total = 0
for county in range(N):
total -= phis[county] * lib.p_E # this all costs us
return total
bounds = [(0, 1e9)] * N
if __name__ == '__main__':
result = lib.maximize(objective, generate, bounds)
print result
print bounds
if Food_Ss[county] > Food_Ds[county]:
total += (Food_Ss[county] - Food_Ds[county])*p_F / lib.markup
else:
total += (Food_Ss[county] - Food_Ds[county])*p_F
return total
def sole_objective(params, betas):
"""Partial objective, plus cost of energy."""
lambda_As = params[0:N]
sigma_As = params[N:2*N]
Crop_yields, Food_Ss, Food_Ds, Water_Draws, Energy_Ds = simulate_all(lambda_As, sigma_As, betas)
return partial_objective(Food_Ss, Food_Ds) - sum(Energy_Ds)*lib.p_E
# Cannot use more water than available, but may use any amount of nergy
bounds = [(0, W_surf[county] / (alpha[county] * Area[county])) for county in range(N)] + \
[(0, None)] * N
if __name__ == '__main__':
betas = [0.5] * 5
result = lib.maximize(sole_objective, generate, bounds, args=(betas,))
print result
Crop_yields, Food_Ss, Food_Ds, Water_Draws, Energy_Ds = simulate_all(result['x'][0:N], result['x'][N:2*N], betas)
print np.mean(Crop_yields)