def test_constraint_matrix():
m = Model()
x = m.add_variables(coords=[range(10)])
y = m.add_variables()
m.add_constraints(x, "=", 0)
A = m.constraints.to_matrix()
assert A.shape == (10, 11)
def model():
m = Model()
lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
m.add_variables(lower, upper, name="x")
m.add_variables(lower, upper, name="y")
return m
def test_str_arrays():
m = Model()
x = m.add_variables(4, pd.Series([8, 10]))
y = m.add_variables(0, pd.DataFrame([[1, 2], [3, 4], [5, 6]]).T)
da = int_to_str(x.values)
assert da.dtype == object
def test_str_arrays_chunked():
m = Model(chunk="auto")
x = m.add_variables(4, pd.Series([8, 10]))
y = m.add_variables(0, pd.DataFrame([[1, 2], [3, 4], [5, 6]]).T)
da = int_to_str(y.compute().values)
assert da.dtype == object
def test_nvars():
m = Model()
m.add_variables(coords=[range(10)])
assert m.variables.nvars == 10
mask = pd.Series([True] * 5 + [False] * 5)
m.add_variables(coords=[range(10)], mask=mask)
assert m.variables.nvars == 15
def test_str_arrays_with_nans():
m = Model()
x = m.add_variables(4, pd.Series([8, 10]), name="x")
# now expand the second dimension, expended values of x will be nan
y = m.add_variables(0, pd.DataFrame([[1, 2], [3, 4], [5, 6]]), name="y")
assert m["x"][-1].item() == -1
da = int_to_str(m["x"].values)
assert da.dtype == object
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 test_nan_in_variable_upper():
m = Model()
x = m.add_variables(upper=np.nan, 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)
with pytest.raises(ValueError):
m.solve()
def test_get_name_by_label():
m = Model()
m.add_variables(coords=[range(10)], name="x")
m.add_variables(coords=[range(10)], name="asd")
assert m.variables.get_name_by_label(4) == "x"
assert m.variables.get_name_by_label(14) == "asd"
with pytest.raises(ValueError):
m.variables.get_name_by_label(30)
with pytest.raises(ValueError):
m.variables.get_name_by_label("asd")
def model_with_inf():
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_constraints(1 * x, "<=", np.inf)
m.objective = 2 * x + y
return m
def test_constraints_accessor():
m = Model()
lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
x = m.add_variables(lower, upper)
y = m.add_variables()
m.add_constraints(1 * x + 10 * y, "=", 0)
assert m.constraints["con0"].shape == (10, 10)
assert isinstance(m.constraints[["con0"]], linopy.constraints.Constraints)
assert isinstance(m.constraints.inequalities,
linopy.constraints.Constraints)
assert isinstance(m.constraints.equalities, linopy.constraints.Constraints)
def modified_model():
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")
c = m.add_constraints(x + y, ">=", 10)
y.lower = 9
c.lhs = 2 * x + y
m.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_variable_repr():
m = Model()
m.variables.__repr__()
x = m.add_variables()
x.__repr__()
x._repr_html_()
m.variables.__repr__()
y = m.add_variables(coords=[pd.Index([1, 2, 3], name="time")], name="y")
y.__repr__()
y._repr_html_()
m.variables.__repr__()
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_constraint_matrix_masked_constraints_and_variables():
"""
Test constraint matrix with missing constraints.
"""
# now with missing variables
m = Model()
mask = pd.Series([False] * 5 + [True] * 5)
x = m.add_variables(coords=[range(10)], mask=mask)
m.add_variables()
m.add_constraints(x, "=", 0, mask=mask)
A = m.constraints.to_matrix(filter_missings=True)
assert A.shape == (5, 6)
assert A.shape == (m.ncons, m.nvars)
A = m.constraints.to_matrix(filter_missings=False)
assert A.shape == (m._cCounter, m._xCounter)
def test_constraint_repr():
m = Model()
x = m.add_variables()
c = m.add_constraints(x, ">=", 0)
c.__repr__()
c._repr_html_()
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))
def test_variable_sanitize():
m = Model()
x = m.add_variables(coords=[range(10)])
# convert intentionally to float with nans
x = x.where([True] * 4 + [False] * 6, np.nan)
x = x.sanitize()
assert isinstance(x, linopy.variables.Variable)
assert x.loc[9].item() == -1
def test_variable_modification():
m = Model()
x = m.add_variables(0, 10)
x.upper = 20
assert x.upper.item() == 20
x.lower = 8
assert x.lower == 8
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 test_variable_type_preservation():
m = Model()
x = m.add_variables(coords=[range(10)])
assert isinstance(x.bfill("dim_0"), linopy.variables.Variable)
assert isinstance(x.broadcast_like(x.to_array()),
linopy.variables.Variable)
assert isinstance(x.clip(max=20), linopy.variables.Variable)
assert isinstance(x.ffill("dim_0"), linopy.variables.Variable)
assert isinstance(x.fillna(-1), linopy.variables.Variable)
def test_constraint_matrix_masked_variables():
"""
Test constraint matrix with missing variables.
In this case the variables that are used in the constraints are
missing. The matrix shoud not be built for constraints which have
variables which are missing.
"""
# now with missing variables
m = Model()
mask = pd.Series([False] * 5 + [True] * 5)
x = m.add_variables(coords=[range(10)], mask=mask)
m.add_variables()
m.add_constraints(x, "=", 0)
A = m.constraints.to_matrix(filter_missings=True)
assert A.shape == (5, 6)
assert A.shape == (m.ncons, m.nvars)
A = m.constraints.to_matrix(filter_missings=False)
assert A.shape == (m._cCounter, m._xCounter)
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 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(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 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_variable_modification_M():
m = Model()
lower = pd.Series(0, index=range(10))
upper = pd.Series(range(10, 20), index=range(10))
x = m.add_variables(lower, upper)
new_upper = pd.Series(range(25, 35), index=range(10))
x.upper = new_upper
assert isinstance(x.upper, xr.DataArray)
assert (x.upper == new_upper).all()
new_lower = pd.Series(range(15, 25), index=range(10))
x.lower = new_lower
assert isinstance(x.lower, xr.DataArray)
assert (x.lower == new_lower).all()
def test_get_name_by_label():
m = Model()
x = m.add_variables(coords=[range(10)])
y = m.add_variables(coords=[range(10)])
m.add_constraints(x + y <= 10, name="first")
m.add_constraints(x - y >= 5, name="second")
assert m.constraints.get_name_by_label(4) == "first"
assert m.constraints.get_name_by_label(14) == "second"
with pytest.raises(ValueError):
m.constraints.get_name_by_label(30)
with pytest.raises(ValueError):
m.constraints.get_name_by_label("first")