feat(model): allow {0,1} bounds for binaries in add_variables

doc/release_notes.rst

@@ -6,6 +6,10 @@ Upcoming Version
 
 * ``Model.to_netcdf`` now records the writing linopy version in the ``_linopy_version`` dataset attribute. Files written by older versions (without the attribute) continue to read unchanged.
 
+**Features**
+
+* ``add_variables(binary=True, ...)`` now accepts ``lower``/``upper`` bounds, as long as they are 0 or 1. Previously binary bounds could only be set via the ``.lower``/``.upper`` setters after creation. (https://github.com/PyPSA/linopy/issues/776)
+
 **Bug fixes**
 
 * LP file export now honors bounds tightened below ``[0, 1]`` on a binary variable via the ``.lower``/``.upper`` setters after creation (e.g. ``upper = 0``). Previously such bounds were written only by ``io_api="direct"`` and dropped by ``io_api="lp"``. (https://github.com/PyPSA/linopy/issues/776)

linopy/model.py

@@ -620,11 +620,11 @@ def add_variables(
         Parameters
         ----------
         lower : float/array_like, optional
-            Lower bound of the variable(s). Ignored if `binary` is True.
-            The default is -inf.
+            Lower bound of the variable(s). For binary variables it
+            defaults to 0 and, if given, must be 0 or 1. The default is -inf.
         upper : TYPE, optional
-            Upper bound of the variable(s). Ignored if `binary` is True.
-            The default is inf.
+            Upper bound of the variable(s). For binary variables it
+            defaults to 1 and, if given, must be 0 or 1. The default is inf.
         coords : list/dict/xarray.Coordinates, optional
             The coords of the variable array. When provided with **named
             dimensions** (a ``Mapping``, ``xarray.Coordinates``, a
@@ -773,10 +773,14 @@ def add_variables(
             )
 
         if binary:
-            if (lower != -inf) or (upper != inf):
-                raise ValueError("Binary variables cannot have lower or upper bounds.")
-            else:
-                lower, upper = 0, 1
+            if np.isscalar(lower) and lower == -inf:
+                lower = 0
+            elif not (np.isin(lower, (0, 1)) | pd.isna(lower)).all():
+                raise ValueError("Binary variable lower bounds must be 0 or 1.")
+            if np.isscalar(upper) and upper == inf:
+                upper = 1
+            elif not (np.isin(upper, (0, 1)) | pd.isna(upper)).all():
+                raise ValueError("Binary variable upper bounds must be 0 or 1.")
 
         if semi_continuous:
             if not np.isscalar(lower) or float(lower) <= 0:  # type: ignore[arg-type]

test/test_variable_assignment.py

@@ -3,6 +3,8 @@
 This module aims at testing the correct assignment of variable to the model.
 """
 
+from typing import Any
+
 import dask
 import numpy as np
 import pandas as pd
@@ -248,6 +250,63 @@ def test_variable_assignment_binary_with_error() -> None:
         m.add_variables(lower=-2, coords=coords, binary=True)
 
 
+def test_variable_assignment_binary_force_on() -> None:
+    """A scalar bound defaults the other end: lower=1 forces the binary on."""
+    forced_on = Model().add_variables(
+        binary=True, lower=1, coords=[pd.RangeIndex(4, name="t")]
+    )
+    assert (forced_on.lower.values == 1).all()
+    assert (forced_on.upper.values == 1).all()
+
+
+@pytest.mark.parametrize(
+    "upper",
+    [
+        pytest.param([1, 1, 0, 0], id="list"),
+        pytest.param(np.array([1.0, 1.0, 0.0, 0.0]), id="ndarray"),
+        pytest.param(pd.Series([1, 1, 0, 0]), id="series"),
+        pytest.param(
+            xr.DataArray([1, np.nan, 0, 1], dims="t", coords={"t": range(4)}),
+            id="dataarray-nan",
+        ),
+    ],
+)
+def test_variable_assignment_binary_array_bounds_ok(upper: Any) -> None:
+    """0/1 bounds accepted, NaN tolerated (for masking), across containers."""
+    Model().add_variables(binary=True, upper=upper, coords=[pd.RangeIndex(4, name="t")])
+
+
+@pytest.mark.parametrize(
+    "upper",
+    [
+        pytest.param([1, 1, 2, 0], id="list"),
+        pytest.param(np.array([0.5, 1.0, 0.0, 1.0]), id="fractional"),
+        pytest.param(pd.Series([2, 1, 0, 1]), id="series"),
+        pytest.param(
+            xr.DataArray([1, np.nan, 2, 0], dims="t", coords={"t": range(4)}),
+            id="dataarray-nan",
+        ),
+    ],
+)
+def test_variable_assignment_binary_array_bounds_error(upper: Any) -> None:
+    """A non-0/1 value is rejected, even when NaN is also present."""
+    with pytest.raises(ValueError, match="must be 0 or 1"):
+        Model().add_variables(
+            binary=True, upper=upper, coords=[pd.RangeIndex(4, name="t")]
+        )
+
+
+@pytest.mark.parametrize("bound", [0, 1, 0.0, 1.0])
+def test_variable_assignment_binary_scalar_bound_ok(bound: float) -> None:
+    Model().add_variables(binary=True, upper=bound, coords=[pd.RangeIndex(2)])
+
+
+@pytest.mark.parametrize("bound", [0.5, 2, -1])
+def test_variable_assignment_binary_scalar_bound_error(bound: float) -> None:
+    with pytest.raises(ValueError, match="must be 0 or 1"):
+        Model().add_variables(binary=True, upper=bound, coords=[pd.RangeIndex(2)])
+
+
 def test_variable_assignment_integer() -> None:
     m = Model()