# Copyright 2010-2017 Google
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# We are trying to group items in equal sized groups.
# Each item has a color and a value. We want the sum of values of each group to
# be as close to the average as possible.
# Furthermore, if one color is an a group, at least k items with this color must
# be in that group.

from __future__ import print_function
from __future__ import division

from ortools.sat.python import cp_model
import math

# Data

max_quantities = [["N_Total", 1944], ["P2O5", 1166.4], ["K2O", 1822.5],
                  ["CaO", 1458], ["MgO", 486], ["Fe", 9.7], ["B", 2.4]]

chemical_set = [["A", 0, 0, 510, 540, 0, 0, 0], ["B", 110, 0, 0, 0, 160, 0, 0],
                ["C", 61, 149, 384, 0, 30, 1, 0.2],
                ["D", 148, 70, 245, 0, 15, 1, 0.2],
                ["E", 160, 158, 161, 0, 10, 1, 0.2]]

num_products = len(max_quantities)
all_products = range(num_products)

num_sets = len(chemical_set)
all_sets = range(num_sets)

# Model

model = cp_model.CpModel()

# Scale quantities by 100.
max_set = [
    int(
        math.ceil(
            min(max_quantities[q][1] * 1000 / chemical_set[s][q + 1]
                for q in all_products
                if chemical_set[s][q + 1] != 0)))
    for s in all_sets
]

set_vars = [model.NewIntVar(0, max_set[s], "set_%i" % s) for s in all_sets]

epsilon = model.NewIntVar(0, 10000000, "epsilon")

for p in all_products:
  model.Add(
      sum(int(chemical_set[s][p + 1] * 10) * set_vars[s]
          for s in all_sets) <= int(max_quantities[p][1] * 10000))
  model.Add(
      sum(int(chemical_set[s][p + 1] * 10) * set_vars[s]
          for s in all_sets) >= int(max_quantities[p][1] * 10000) - epsilon)

model.Minimize(epsilon)

# Creates a solver and solves.
solver = cp_model.CpSolver()
status = solver.Solve(model)
print("Status = %s" % solver.StatusName(status))
# The objective value of the solution.
print("Optimal objective value = %f" % (solver.ObjectiveValue() / 10000.0))

for s in all_sets:
  print(
      "  %s = %f" % (chemical_set[s][0], solver.Value(set_vars[s]) / 1000.0),
      end=" ")
  print()
for p in all_products:
  name = max_quantities[p][0]
  max_quantity = max_quantities[p][1]
  quantity = sum(
      solver.Value(set_vars[s]) / 1000.0 * chemical_set[s][p + 1]
      for s in all_sets)
  print("%s: %f out of %f" % (name, quantity, max_quantity))