apis/examples_python/g2np__example1_8py_source.html

from gams import *

import numpy as np

import gams2numpy

import sys


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 ; '''


if __name__ == "__main__":

    if len(sys.argv) > 1:

        ws = GamsWorkspace(system_directory = sys.argv[1])

    else:

        ws = GamsWorkspace()


    plants   = np.array([["Seattle", ""], ["San-Diego", ""]])

    markets  = np.array([["New-York", ""], ["Chicago", ""], ["Topeka", ""]])

    capacity = np.array([["Seattle", 350.0], ["San-Diego", 600.0]], dtype=object)

    demand   = np.array([["New-York", 325.0], ["Chicago", 300.0], ["Topeka", 275.0]], dtype=object)

    distance = np.array([["Seattle",   "New-York", 2.5],

                         ["Seattle",   "Chicago",  1.7],

                         ["Seattle",   "Topeka",   1.8],

                         ["San-Diego", "New-York", 2.5],

                         ["San-Diego", "Chicago",  1.8],

                         ["San-Diego", "Topeka",   1.4]], dtype=object)


    db = ws.add_database()

    g2np = gams2numpy.Gams2Numpy(ws.system_directory)


    i = db.add_set("i", 1, "canning plants")

    g2np.gmdFillSymbolStr(db, i, plants)


    j = db.add_set("j", 1, "markets")

    g2np.gmdFillSymbolStr(db, j, markets)


    a = db.add_parameter_dc("a", [i], "capacity of plant i in cases")

    g2np.gmdFillSymbolStr(db, a, capacity)


    b = db.add_parameter_dc("b", [j], "demand at market j in cases")

    g2np.gmdFillSymbolStr(db, b, demand)


    d = db.add_parameter_dc("d", [i,j], "distance in thousands of miles")

    g2np.gmdFillSymbolStr(db, d, distance)


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

    g2np.gmdFillSymbolStr(db, f, np.array([[90]]))


    job = ws.add_job_from_string(get_model_text())

    opt = ws.add_options()


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

    opt.all_model_types = "xpress"


    job.run(opt, databases = db)

    for rec in g2np.gmdReadSymbolStr(job.out_db, "x"):

        print("x({},{}): level={}, marginal={}".format(*rec[0:-3]))