"""
The Full Whiskas Model Python Formulation for the PuLP Modeller

Authors: Antony Phillips, Dr Stuart Mitchell  2007
"""

# Import PuLP modeler functions
from pulp import *

# Creates a list of the Ingredients
Ingredients = ["CHICKEN", "BEEF", "MUTTON", "RICE", "WHEAT", "GEL"]

# A dictionary of the costs of each of the Ingredients is created
costs = {
    "CHICKEN": 0.013,
    "BEEF": 0.008,
    "MUTTON": 0.010,
    "RICE": 0.002,
    "WHEAT": 0.005,
    "GEL": 0.001,
}

# A dictionary of the protein percent in each of the Ingredients is created
proteinPercent = {
    "CHICKEN": 0.100,
    "BEEF": 0.200,
    "MUTTON": 0.150,
    "RICE": 0.000,
    "WHEAT": 0.040,
    "GEL": 0.000,
}

# A dictionary of the fat percent in each of the Ingredients is created
fatPercent = {
    "CHICKEN": 0.080,
    "BEEF": 0.100,
    "MUTTON": 0.110,
    "RICE": 0.010,
    "WHEAT": 0.010,
    "GEL": 0.000,
}

# A dictionary of the fibre percent in each of the Ingredients is created
fibrePercent = {
    "CHICKEN": 0.001,
    "BEEF": 0.005,
    "MUTTON": 0.003,
    "RICE": 0.100,
    "WHEAT": 0.150,
    "GEL": 0.000,
}

# A dictionary of the salt percent in each of the Ingredients is created
saltPercent = {
    "CHICKEN": 0.002,
    "BEEF": 0.005,
    "MUTTON": 0.007,
    "RICE": 0.002,
    "WHEAT": 0.008,
    "GEL": 0.000,
}

# Create the 'prob' variable to contain the problem data
prob = LpProblem("The Whiskas Problem", LpMinimize)

# A dictionary called 'ingredient_vars' is created to contain the referenced Variables
ingredient_vars = LpVariable.dicts("Ingr", Ingredients, 0)

# The objective function is added to 'prob' first
prob += (
    lpSum([costs[i] * ingredient_vars[i] for i in Ingredients]),
    "Total Cost of Ingredients per can",
)

# The five constraints are added to 'prob'
prob += lpSum([ingredient_vars[i] for i in Ingredients]) == 100, "PercentagesSum"
prob += (
    lpSum([proteinPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 8.0,
    "ProteinRequirement",
)
prob += (
    lpSum([fatPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 6.0,
    "FatRequirement",
)
prob += (
    lpSum([fibrePercent[i] * ingredient_vars[i] for i in Ingredients]) <= 2.0,
    "FibreRequirement",
)
prob += (
    lpSum([saltPercent[i] * ingredient_vars[i] for i in Ingredients]) <= 0.4,
    "SaltRequirement",
)

# The problem data is written to an .lp file
prob.writeLP("WhiskasModel2.lp")

# The problem is solved using PuLP's choice of Solver
prob.solve()

# The status of the solution is printed to the screen
print("Status:", LpStatus[prob.status])

# Each of the variables is printed with it's resolved optimum value
for v in prob.variables():
    print(v.name, "=", v.varValue)

# The optimised objective function value is printed to the screen
print("Total Cost of Ingredients per can = ", value(prob.objective))
