minimize.py

from numpy import array, sum as npsum
from scipy.optimize import minimize

cons = (
    {'type': 'ineq', 'fun': lambda x:  array([25 - 0.2 * x[0] - 0.4 * x[1] - 0.33 * x[2]])},
    {'type': 'ineq', 'fun': lambda x:  array([130 - 5 * x[0] - 8.33 * x[2]])},
    {'type': 'ineq', 'fun': lambda x:  array([16 - 0.6 * x[1] - 0.33 * x[2]])},
    {'type': 'ineq', 'fun': lambda x:  array([7 - 0.2 * x[0] - 0.1 * x[1] - 0.33 * x[2]])},
    {'type': 'ineq', 'fun': lambda x:  array([14 - 0.5 * x[1]])},
    {'type': 'ineq', 'fun': lambda x:  array([x[0]])},
    {'type': 'ineq', 'fun': lambda x:  array([x[1]])},
    {'type': 'ineq', 'fun': lambda x:  array([x[2]])},
    {'type': 'eq',   'fun': lambda x:  array([1 - npsum(x)])}
)


f = lambda x: -1 * (x[0] + x[1] + x[2])

res = minimize(f, [0, 0, 0], method='SLSQP', constraints=cons, options={'disp': True})

print(res)