Универсальное построение условий для scipy.minimize

import numpy as np
from scipy.optimize import minimize, Bounds
 
chemix = {'N1': [0.108, 0.145, 11.610, 2.772, 6.930, 1.655, 0, 0.900, 5.6],
          'N2': [0.099, 0.148, 12.150, 2.901, 7.650, 1.827, 0, 0.900, 10],
          'N3': [0.098, 0.150, 11.704, 2.795, 7.656, 1.828, 0, 0.880, 9],
          'N4': [0.405, 0.270, 13.064, 3.120, 8.372, 1.999, 0, 0.920, 32.65],
          'N5': [0, 0, 0, 0, 0, 0, 2.000, 0.950, 61.20]
          }
 
 
temp_val = []
for val in chemix.values():
    temp_val.append(val)
 
x0 = np.zeros((1, len(chemix)))
arr = np.array(temp_val)
 
# arr2 = np.zeros((arr.shape[0], arr.shape[1] - 2))
# for i in range(len(arr)):
#    arr2[i] = arr[i][:-2]
 
 
def func(x):
    result = sum(sum(arr.transpose() * x))
    return result
 
 
ineq_cons = {'type': 'ineq',
             'fun': lambda x: np.array([-3.000 + 0.108 * x[0] + 0.099 * x[1] + 0.098 * x[2] + 0.405 * x[3] + 0 * x[4],
                                        0.145 * x[0] + 0.148 * x[1] + 0.150 * x[2] + 0.270 * x[3] + 0 * x[4],
                                        11.610 * x[0] + 12.150 * x[1] + 11.704 * x[2] + 13.064 * x[3] + 0 * x[4],
                                        2.772 * x[0] + 2.901 * x[1] + 2.795 * x[2] + 3.120 * x[3] + 0 * x[4],
                                        -180 + 6.930 * x[0] + 7.650 * x[1] + 7.656 * x[2] + 8.372 * x[3] + 0 * x[4],
                                        1.655 * x[0] + 1.827 * x[1] + 1.828 * x[2] + 1.999 * x[3] + 0 * x[4],
                                        -0.150 + 0 * x[0] + 0 * x[1] + 0 * x[2] + 0 * x[3] + 2.000 * x[4]])
             }
eq_cons = {'type': 'eq',
           'fun': lambda x: np.array([-20 + 0.900 * x[0] + 0.900 * x[1] + 0.880 * x[2] + 0.920 * x[3] + 0.950 * x[4]])
           }
 
bnds = Bounds([0, 0, 0, 0, 0], [np.inf, np.inf, np.inf, np.inf, np.inf])
res = minimize(func, x0, constraints=[eq_cons, ineq_cons], bounds=bnds, options={'disp': True})