# Here is a simple LP, set up entirely in Python or the Gurobi shell
# This is modified from Gurobi's example mip1.py on the webpage:
# http://www.gurobi.com/documentation/8.0/examples/mip1_py.html#subsubsection:mip1.py

from gurobipy import *

# Create a model, giving it a name
#
coffeeLP = Model("MyCoffeeStory")

# Create variables
x = coffeeLP.addVar(vtype=GRB.CONTINUOUS, name="coffee")
y = coffeeLP.addVar(vtype=GRB.CONTINUOUS, name="sugar")

# Set objective
coffeeLP.setObjective(3 * x , GRB.MAXIMIZE)

# Add constraint: 4 x <= 12
coffeeLP.addConstr(4 * x <= 12, "coffee_limit")


# Now we optimize:
#
coffeeLP.optimize()

####################################################
# Here are a whole bunch of things to try:
####################################################

coffeeLP.printAttr("X")

our_vars = coffeeLP.getVars()
our_vars
x
y

our_vars[0]         # this is the first variable
our_vars[1]         # this is the second variable

our_vars[0].varName       # this is the varName (variable name) of our_vars[0]
our_vars[0].x

y.varName      # the variable name of the variable y above
our_vars[1].varName    # the same thing

# a few equivalent ways to examine all the variables
#
for v in coffeeLP.getVars():
    print(v.varName)

for v in coffeeLP.getVars():
    print('%s ' % (v.varName))

for v in coffeeLP.getVars():
    print('%s %g' % (v.varName, v.x))

for v in coffeeLP.getVars():
    print('%s has the value: %g' % (v.varName, v.x))

for i in range(0,len(our_vars))
    print( our_vars[i].varName )