old_contents: from pyscipopt import Model, LP
  
  def test_lp():
      # create solver instance
      s = Model()
  
      # add some variables
      x = s.addVar("x", vtype = 'C', obj = 1.0)
      y = s.addVar("y", vtype = 'C', obj = 2.0)
  
      assert x.getObj() == 1.0
      assert y.getObj() == 2.0
  
      s.setObjective(4.0 * y, clear = False)
      assert x.getObj() == 1.0
      assert y.getObj() == 4.0
  
      # add some constraint
      c = s.addCons(x + 2 * y >= 1.0)
      s.chgLhs(c, 5.0)
      s.chgRhs(c, 5.0)
  
      # solve problem
      s.optimize()
  
      solution = s.getBestSol()
  
      # print solution
      assert (s.getVal(x) == s.getSolVal(solution, x))
      assert (s.getVal(y) == s.getSolVal(solution, y))
      assert round(s.getVal(x)) == 5.0
      assert round(s.getVal(y)) == 0.0
  
  
  def test_lpi():
      # create LP instance, minimizing by default
      myLP = LP()
  
      # create cols w/o coefficients, 0 objective coefficient and 0,\infty bounds
      myLP.addCols(2 * [[]])
  
      # create rows
      myLP.addRow(entries = [(0,1),(1,2)] ,lhs = 5)
      lhs, rhs = myLP.getSides()
      assert lhs[0] == 5.0
      assert rhs[0] == myLP.infinity()
  
      assert(myLP.ncols() == 2)
      myLP.chgObj(0, 1.0)
      myLP.chgObj(1, 4.0)
  
      solval = myLP.solve()
  
      assert round(5.0 == solval)
  
  if __name__ == "__main__":
      test_lp()
      test_lpi()
  
new_contents: from pyscipopt import Model, LP
  
  def test_lp():
      # create solver instance
      s = Model()
  
      # add some variables
      x = s.addVar("x", vtype = 'C', obj = 1.0)
      y = s.addVar("y", vtype = 'C', obj = 2.0)
  
      assert x.getObj() == 1.0
      assert y.getObj() == 2.0
  
      s.setObjective(4.0 * y, clear = False)
      assert x.getObj() == 1.0
      assert y.getObj() == 4.0
  
      # add some constraint
      c = s.addCons(x + 2 * y >= 1.0)
      s.chgLhs(c, 5.0)
      s.chgRhs(c, 5.0)
  
      # solve problem
      s.optimize()
  
      solution = s.getBestSol()
  
      # print solution
      assert (s.getVal(x) == s.getSolVal(solution, x))
      assert (s.getVal(y) == s.getSolVal(solution, y))
      assert round(s.getVal(x)) == 5.0
      assert round(s.getVal(y)) == 0.0
  
      s.freeProb()
      s = Model()
      x = s.addVar("x", vtype = 'C', obj = 1.0)
      y = s.addVar("y", vtype = 'C', obj = 2.0)
      c = s.addCons(x + 2 * y >= 1.0)
      s.setMaximize()
  
      s.delCons(c)
  
      s.optimize()
  
      assert s.getStatus() == 'unbounded'
  
  
  def test_lpi():
      # create LP instance, minimizing by default
      myLP = LP()
  
      # create cols w/o coefficients, 0 objective coefficient and 0,\infty bounds
      myLP.addCols(2 * [[]])
  
      # create rows
      myLP.addRow(entries = [(0,1),(1,2)] ,lhs = 5)
      lhs, rhs = myLP.getSides()
      assert lhs[0] == 5.0
      assert rhs[0] == myLP.infinity()
  
      assert(myLP.ncols() == 2)
      myLP.chgObj(0, 1.0)
      myLP.chgObj(1, 4.0)
  
      solval = myLP.solve()
  
      assert round(5.0 == solval)
  
  if __name__ == "__main__":
      test_lp()
      test_lpi()
  
subject: add unittest for delCons()