import csv
from pulp import LpProblem, LpVariable, LpMinimize, value

#Make new LP Problem
problem = LpProblem("Diet Problem", LpMinimize)

# Open both CSV files
data_reader = csv.reader(open("DietProblemData.csv",'rU'))
constraint_reader = csv.reader(open("Constraints.csv",'rU'))
data_reader.next()
constraint_reader.next()

variables,nutrients,costs,names = [],[],[],[]
for row in data_reader:
  variables.append( LpVariable("x_" + row[1], 0))
  nutrients.append( map( float , row[2 : -1]))
  names.append ( row[0] )
  costs.append( float(row[-1]) )

# add constraints for each nutrient
for row_num,row in enumerate(constraint_reader):
  min_constraint = 0 * variables[0] >= float(row[2])
  max_constraint = 0 * variables[0] <= float(row[3])
  for i in range(len(variables)):
    min_constraint += variables[i] * nutrients[i][row_num]
    max_constraint += variables[i] * nutrients[i][row_num]
  problem += min_constraint
  problem += max_constraint

# add objective function
obj_fn = 0 * variables[0]
for i in range(len(variables)):
  obj_fn += variables[i] * costs[i]
problem += obj_fn

problem.solve()

for i in range(len(variables)):
  if value(variables[i]) > 0.00001:
    print names[i].rjust(20) + " => " + str(value(variables[i]))
print "cost: " + str(value(obj_fn))