linopy - pypi Package Compare versions

Comparing version 0.6.2 to 0.6.3

doc/conf.py

@@ -16,3 +16,3 @@ # Configuration file for the Sphinx documentation builder.
 # sys.path.insert(0, os.path.abspath('.'))
-import pkg_resources  # part of setuptools
+import linopy
 
@@ -26,8 +26,5 @@ # -- Project information -----------------------------------------------------
 # The full version, including alpha/beta/rc tags
-version = pkg_resources.get_distribution("linopy").version
+version = linopy.__version__
 release = "master" if "dev" in version else version
 
-# For some reason is this needed, otherwise autosummary does fail on RTD but not locally
-import linopy  # noqa
-
 # -- General configuration ---------------------------------------------------
@@ -34,0 +31,0 @@

doc/release_notes.rst

@@ -7,2 +7,10 @@ Release Notes
 
+Version 0.6.3
+--------------
+
+**Fix Regression**
+
+*  Reinsert broadcasting logic of mask object to be fully compatible with performance improvements in version 0.6.2 using `np.where` instead of `xr.where`.
+
+
 Version 0.6.2
@@ -9,0 +17,0 @@ --------------

linopy.egg-info/PKG-INFO

 Metadata-Version: 2.4
 Name: linopy
-Version: 0.6.2
+Version: 0.6.3
 Summary: Linear optimization with N-D labeled arrays in Python
@@ -30,3 +30,3 @@ Author-email: Fabian Hofmann <[e-mail]>
 Requires-Dist: dask>=0.18.0
-Requires-Dist: polars>=1.31
+Requires-Dist: polars>=1.31.1
 Requires-Dist: tqdm
@@ -33,0 +33,0 @@ Requires-Dist: deprecation

linopy.egg-info/requires.txt

@@ -7,3 +7,3 @@ scipy
 dask>=0.18.0
-polars>=1.31
+polars>=1.31.1
 tqdm
@@ -10,0 +10,0 @@ deprecation

linopy/common.py

@@ -289,2 +289,28 @@ #!/usr/bin/env python3
 
+def broadcast_mask(mask: DataArray, labels: DataArray) -> DataArray:
+    """
+    Broadcast a boolean mask to match the shape of labels.
+
+    Ensures that mask dimensions are a subset of labels dimensions, broadcasts
+    the mask accordingly, and fills any NaN values (from missing coordinates)
+    with False while emitting a FutureWarning.
+    """
+    assert set(mask.dims).issubset(labels.dims), (
+        "Dimensions of mask not a subset of resulting labels dimensions."
+    )
+    mask = mask.broadcast_like(labels)
+    if mask.isnull().any():
+        warn(
+            "Mask contains coordinates not covered by the data dimensions. "
+            "Missing values will be filled with False (masked out). "
+            "In a future version, this will raise an error. "
+            "Use mask.reindex() or `linopy.align()` to explicitly handle missing "
+            "coordinates.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        mask = mask.fillna(False).astype(bool)
+    return mask
+
+
 # TODO: rename to to_pandas_dataframe
@@ -291,0 +317,0 @@ def to_dataframe(

linopy/model.py

@@ -32,2 +32,3 @@ """
     best_int,
+    broadcast_mask,
     maybe_replace_signs,
@@ -555,2 +556,3 @@ replace_by_map,
             mask = as_dataarray(mask, coords=data.coords, dims=data.dims).astype(bool)
+            mask = broadcast_mask(mask, data.labels)
 
@@ -576,3 +578,2 @@ # Auto-mask based on NaN in bounds (use numpy for speed)
         if mask is not None:
-            # Use numpy where for speed (38x faster than xarray where)
             data.labels.values = np.where(mask.values, data.labels.values, -1)
@@ -751,10 +752,4 @@
         if mask is not None:
-            mask = as_dataarray(mask).astype(bool)
-            # TODO: simplify
-            assert set(mask.dims).issubset(data.dims), (
-                "Dimensions of mask not a subset of resulting labels dimensions."
-            )
-            # Broadcast mask to match data shape for correct numpy where behavior
-            if mask.shape != data.labels.shape:
-                mask, _ = xr.broadcast(mask, data.labels)
+            mask = as_dataarray(mask, coords=data.coords, dims=data.dims).astype(bool)
+            mask = broadcast_mask(mask, data.labels)
 
@@ -770,7 +765,5 @@ # Auto-mask based on null expressions or NaN RHS (use numpy for speed)
                 coords, dims, rhs_notnull = original_rhs_mask
-                # Broadcast RHS mask to match data shape if needed
                 if rhs_notnull.shape != auto_mask_values.shape:
                     rhs_da = DataArray(rhs_notnull, coords=coords, dims=dims)
-                    rhs_da, _ = xr.broadcast(rhs_da, data.labels)
-                    rhs_notnull = rhs_da.values
+                    rhs_notnull = rhs_da.broadcast_like(data.labels).values
                 auto_mask_values = auto_mask_values & rhs_notnull
@@ -793,3 +786,2 @@ auto_mask_arr = DataArray(
         if mask is not None:
-            # Use numpy where for speed (38x faster than xarray where)
             data.labels.values = np.where(mask.values, data.labels.values, -1)
@@ -796,0 +788,0 @@

linopy/version.py

@@ -31,5 +31,5 @@ # file generated by setuptools-scm
 
-__version__ = version = '0.6.2'
-__version_tuple__ = version_tuple = (0, 6, 2)
+__version__ = version = '0.6.3'
+__version_tuple__ = version_tuple = (0, 6, 3)
 
-__commit_id__ = commit_id = 'gc9f83bbd3'
+__commit_id__ = commit_id = 'g16d6f3264'

PKG-INFO

 Metadata-Version: 2.4
 Name: linopy
-Version: 0.6.2
+Version: 0.6.3
 Summary: Linear optimization with N-D labeled arrays in Python
@@ -30,3 +30,3 @@ Author-email: Fabian Hofmann <[e-mail]>
 Requires-Dist: dask>=0.18.0
-Requires-Dist: polars>=1.31
+Requires-Dist: polars>=1.31.1
 Requires-Dist: tqdm
@@ -33,0 +33,0 @@ Requires-Dist: deprecation

pyproject.toml

@@ -36,3 +36,3 @@ [build-system]
     "dask>=0.18.0",
-    "polars>=1.31",
+    "polars>=1.31.1",
     "tqdm",
@@ -39,0 +39,0 @@ "deprecation",

test/test_constraints.py

@@ -165,2 +165,37 @@ #!/usr/bin/env python3
 
+def test_masked_constraints_broadcast() -> None:
+    m: Model = 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()
+
+    mask = pd.Series([True] * 5 + [False] * 5)
+    m.add_constraints(1 * x + 10 * y, EQUAL, 0, name="bc1", mask=mask)
+    assert (m.constraints.labels.bc1[0:5, :] != -1).all()
+    assert (m.constraints.labels.bc1[5:10, :] == -1).all()
+
+    mask2 = xr.DataArray([True] * 5 + [False] * 5, dims=["dim_1"])
+    m.add_constraints(1 * x + 10 * y, EQUAL, 0, name="bc2", mask=mask2)
+    assert (m.constraints.labels.bc2[:, 0:5] != -1).all()
+    assert (m.constraints.labels.bc2[:, 5:10] == -1).all()
+
+    mask3 = xr.DataArray(
+        [True, True, False, False, False],
+        dims=["dim_0"],
+        coords={"dim_0": range(5)},
+    )
+    with pytest.warns(FutureWarning, match="Missing values will be filled"):
+        m.add_constraints(1 * x + 10 * y, EQUAL, 0, name="bc3", mask=mask3)
+    assert (m.constraints.labels.bc3[0:2, :] != -1).all()
+    assert (m.constraints.labels.bc3[2:5, :] == -1).all()
+    assert (m.constraints.labels.bc3[5:10, :] == -1).all()
+
+    # Mask with extra dimension not in data should raise
+    mask4 = xr.DataArray([True, False], dims=["extra_dim"])
+    with pytest.raises(AssertionError, match="not a subset"):
+        m.add_constraints(1 * x + 10 * y, EQUAL, 0, name="bc4", mask=mask4)
+
+
 def test_non_aligned_constraints() -> None:
@@ -167,0 +202,0 @@ m: Model = Model()

test/test_variables.py

@@ -110,2 +110,35 @@ #!/usr/bin/env python3
 
+def test_variables_mask_broadcast() -> None:
+    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)])
+
+    mask = pd.Series([True] * 5 + [False] * 5)
+    x = m.add_variables(lower, upper, name="x", mask=mask)
+    assert (x.labels[0:5, :] != -1).all()
+    assert (x.labels[5:10, :] == -1).all()
+
+    mask2 = xr.DataArray([True] * 5 + [False] * 5, dims=["dim_1"])
+    y = m.add_variables(lower, upper, name="y", mask=mask2)
+    assert (y.labels[:, 0:5] != -1).all()
+    assert (y.labels[:, 5:10] == -1).all()
+
+    mask3 = xr.DataArray(
+        [True, True, False, False, False],
+        dims=["dim_0"],
+        coords={"dim_0": range(5)},
+    )
+    with pytest.warns(FutureWarning, match="Missing values will be filled"):
+        z = m.add_variables(lower, upper, name="z", mask=mask3)
+    assert (z.labels[0:2, :] != -1).all()
+    assert (z.labels[2:5, :] == -1).all()
+    assert (z.labels[5:10, :] == -1).all()
+
+    # Mask with extra dimension not in data should raise
+    mask4 = xr.DataArray([True, False], dims=["extra_dim"])
+    with pytest.raises(AssertionError, match="not a subset"):
+        m.add_variables(lower, upper, name="w", mask=mask4)
+
+
 def test_variables_get_name_by_label(m: Model) -> None:
@@ -112,0 +145,0 @@ assert m.variables.get_name_by_label(4) == "x"

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