'''
@file
This is the 4th model in a series of tutorial examples. Here we show:
  - How to define data using Python data structures
  - How to prepare a GamsDatabase from Python data structures
'''

from gams import *
import sys
import os


def get_model_text():
    return '''
  Sets
       i   canning plants
       j   markets

  Parameters
       a(i)   capacity of plant i in cases
       b(j)   demand at market j in cases
       d(i,j) distance in thousands of miles
  Scalar f  freight in dollars per case per thousand miles;

$if not set gdxincname $abort 'no include file name for data file provided'
$gdxin %gdxincname%
$load i j a b d f
$gdxin

  Parameter c(i,j)  transport cost in thousands of dollars per case ;

            c(i,j) = f * d(i,j) / 1000 ;

  Variables
       x(i,j)  shipment quantities in cases
       z       total transportation costs in thousands of dollars ;

  Positive Variable x ;

  Equations
       cost        define objective function
       supply(i)   observe supply limit at plant i
       demand(j)   satisfy demand at market j ;

  cost ..        z  =e=  sum((i,j), c(i,j)*x(i,j)) ;

  supply(i) ..   sum(j, x(i,j))  =l=  a(i) ;

  demand(j) ..   sum(i, x(i,j))  =g=  b(j) ;

  Model transport /all/ ;

  Solve transport using lp minimizing z ;

  Display x.l, x.m ; '''


# Note that the user must create a path /tmp inside the path where the .py is located, before running this file!

if __name__ == "__main__":
    if len(sys.argv) > 1:
        ws = GamsWorkspace(os.path.join(".", "tmp"), sys.argv[1])
         
    else:
        ws = GamsWorkspace(os.path.join(".", "tmp"))

    plants = ["São Paulo", "San-Diego"]
    markets = ["New-York", "Chicago", "Topeka"]
    capacity = {"São Paulo": 350.0, "San-Diego": 600.0}
    demand = {"New-York": 325.0, "Chicago": 300.0, "Topeka": 275.0}
    distance = {("São Paulo",   "New-York"): 2.5,
                ("São Paulo",   "Chicago"): 1.7,
                ("São Paulo",   "Topeka"): 1.8,
                ("San-Diego", "New-York"): 2.5,
                ("San-Diego", "Chicago"): 1.8,
                ("San-Diego", "Topeka"): 1.4
                }

    print(plants)
    db = ws.add_database()

    i = db.add_set("i", 1, "canning plants")
    for p in plants:
        i.add_record(p)

    j = db.add_set("j", 1, "markets")
    for m in markets:
        j.add_record(m)

    a = db.add_parameter_dc("a", [i], "capacity of plant i in cases")
    for p in plants:
        a.add_record(p).value = capacity[p]

    b = db.add_parameter_dc("b", [j], "demand at market j in cases")
    for m in markets:
        b.add_record(m).value = demand[m]

    d = db.add_parameter_dc("d", [i, j], "distance in thousands of miles")
    for k, v in iter(distance.items()):
        d.add_record(k).value = v

    f = db.add_parameter(
        "f", 0, "freight in dollars per case per thousand miles")
    f.add_record().value = 90

    t4 = ws.add_job_from_string(get_model_text())
    opt = ws.add_options()

    opt.defines["gdxincname"] = db.name
    opt.all_model_types = "xpress"

    t4.run(opt, databases=db)
    for rec in t4.out_db["x"]:
        print("x(" + rec.key(0) + "," + rec.key(1) + "): level=" +
              str(rec.level) + " marginal=" + str(rec.marginal))