def create_model(N):
m = Model()
coords = [arange(N), arange(N)]
x = m.add_variables(coords=coords)
y = m.add_variables(coords=coords)
m.add_constraints(x - y >= arange(N))
m.add_constraints(x + y >= 0)
m.add_objective((2 * x).sum() + y.sum())
return m
def milp_model_r():
m = Model()
lower = pd.Series(0, range(10))
x = m.add_variables(coords=[lower.index], name="x", binary=True)
y = m.add_variables(lower, name="y")
m.add_constraints(x + y, ">=", 10)
m.add_objective(2 * x + y)
return m
def model_chunked():
m = Model(chunk="auto")
x = m.add_variables(name="x")
y = m.add_variables(name="y")
m.add_constraints(2 * x + 6 * y, ">=", 10)
m.add_constraints(4 * x + 2 * y, ">=", 3)
m.add_objective(2 * y + x)
return m
def model_anonymous_constraint():
m = Model(chunk=None)
x = m.add_variables(name="x")
y = m.add_variables(name="y")
m.add_constraints(2 * x + 6 * y >= 10)
m.add_constraints(4 * x + 2 * y >= 3)
m.add_objective(2 * y + x)
return m
def test_nan_in_objective():
m = Model()
x = m.add_variables(name="x")
y = m.add_variables(name="y")
m.add_constraints(2 * x + 6 * y, ">=", np.nan)
m.add_constraints(4 * x + 2 * y, ">=", 3)
m.add_objective(np.nan * y + x)
with pytest.raises(ValueError):
m.solve()
def model(n, solver, integerlabels):
m = Model()
if integerlabels:
N, M = [arange(n), arange(n)]
else:
N, M = [arange(n).astype(float), arange(n).astype(str)]
x = m.add_variables(coords=[N, M])
y = m.add_variables(coords=[N, M])
m.add_constraints(x - y >= N)
m.add_constraints(x + y >= 0)
m.add_objective((2 * x).sum() + y.sum())
m.solve(solver)
return
def masked_constraint_model():
m = Model()
lower = pd.Series(0, range(10))
x = m.add_variables(lower, name="x")
y = m.add_variables(lower, name="y")
mask = pd.Series([True] * 8 + [False, False])
m.add_constraints(x + y, ">=", 10, mask=mask)
# for the last two entries only the following constraint will be active
m.add_constraints(x + y, ">=", 5)
m.add_objective(2 * x + y)
return m
def masked_variable_model():
m = Model()
lower = pd.Series(0, range(10))
x = m.add_variables(lower, name="x")
mask = pd.Series([True] * 8 + [False, False])
y = m.add_variables(lower, name="y", mask=mask)
m.add_constraints(x + y, ">=", 10)
m.add_constraints(y, ">=", 0)
m.add_objective(2 * x + y)
return m
def test_to_blocks(tmp_path):
m = Model()
lower = pd.Series(range(20))
upper = pd.Series(range(30, 50))
x = m.add_variables(lower, upper)
y = m.add_variables(lower, upper)
m.add_constraints(x + y, "<=", 10)
m.add_objective(2 * x + 3 * y)
m.blocks = xr.DataArray([1] * 10 + [2] * 10)
m.to_block_files(tmp_path)
def test_to_file(tmp_path):
import gurobipy
m = Model()
x = m.add_variables(4, pd.Series([8, 10]))
y = m.add_variables(0, pd.DataFrame([[1, 2], [3, 4], [5, 6]]))
m.add_constraints(x + y, "<=", 10)
m.add_objective(2 * x + 3 * y)
fn = tmp_path / "test.lp"
m.to_file(fn)
gurobipy.read(str(fn))
def test_to_netcdf(tmp_path):
m = Model()
x = m.add_variables(4, pd.Series([8, 10]))
y = m.add_variables(0, pd.DataFrame([[1, 2], [3, 4], [5, 6]]))
m.add_constraints(x + y, "<=", 10)
m.add_objective(2 * x + 3 * y)
fn = tmp_path / "test.nc"
m.to_netcdf(fn)
p = read_netcdf(fn)
for k in m.scalar_attrs:
if k != "objective_value":
assert getattr(m, k) == getattr(p, k)
for k in m.dataset_attrs:
assert_equal(getattr(m, k), getattr(p, k))
