Merge branch 'master' into oetc-doc

Author: FabianHofmann

.github/workflows/claude.yml

@@ -31,7 +31,7 @@ jobs:
       id-token: write
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
         with:
           fetch-depth: 1
 

.github/workflows/codeql.yml

@@ -55,7 +55,7 @@ jobs:
         # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL

.github/workflows/release.yml

@@ -11,7 +11,7 @@ jobs:
     name: Build and verify package
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - uses: hynek/build-and-inspect-python-package@v2
 
   release:
@@ -20,7 +20,7 @@ jobs:
     needs: [build]
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - uses: softprops/action-gh-release@v2
         with:
           generate_release_notes: true
@@ -36,7 +36,7 @@ jobs:
     permissions:
       id-token: write
     steps:
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v5
         with:
           name: Packages
           path: dist

.github/workflows/test-models.yml

@@ -31,7 +31,7 @@ jobs:
         shell: bash -l {0}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           repository: PyPSA/pypsa-eur
           ref: master

.github/workflows/test.yml

@@ -18,7 +18,7 @@ jobs:
     name: Build and verify package
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
         with:
           fetch-depth: 0 # Needed for setuptools_scm
       - uses: hynek/build-and-inspect-python-package@v2
@@ -42,12 +42,12 @@ jobs:
         - windows-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
       with:
         fetch-depth: 0 # Needed for setuptools_scm
 
     - name: Set up Python ${{ matrix.python-version }} on ${{ matrix.os }}
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
 
@@ -74,7 +74,7 @@ jobs:
         choco install glpk
 
     - name: Download package
-      uses: actions/download-artifact@v4
+      uses: actions/download-artifact@v5
       with:
         name: Packages
         path: dist
@@ -102,17 +102,17 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
       with:
         fetch-depth: 0 # Needed for setuptools_scm
 
     - name: Set up Python 3.12
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: 3.12
 
     - name: Download package
-      uses: actions/download-artifact@v4
+      uses: actions/download-artifact@v5
       with:
         name: Packages
         path: dist

doc/release_notes.rst

@@ -2,7 +2,6 @@ Release Notes
 =============
 
 Upcoming Version
------------------
 
 * Replace pandas-based LP file writing with polars implementation for significantly improved performance on large models
 * Consolidate "lp" and "lp-polars" io_api options - both now use the optimized polars backend
@@ -22,7 +21,6 @@ Version 0.5.6
 **Breaking Changes**
 
 * With this release, the package support for Python 3.9 was dropped and support for Python 3.10 was officially added.
-
 * The selection of a single item in `__getitem__` now returns a `Variable` instead of a `ScalarVariable`.
 
 

linopy/common.py

@@ -254,7 +254,9 @@ def as_dataarray(
         arr = pandas_to_dataarray(arr, coords=coords, dims=dims, **kwargs)
     elif isinstance(arr, np.ndarray):
         arr = numpy_to_dataarray(arr, coords=coords, dims=dims, **kwargs)
-    elif isinstance(arr, np.number | int | float | str | bool | list):
+    elif isinstance(arr, np.number):
+        arr = DataArray(float(arr), coords=coords, dims=dims, **kwargs)
+    elif isinstance(arr, int | float | str | bool | list):
         arr = DataArray(arr, coords=coords, dims=dims, **kwargs)
 
     elif not isinstance(arr, DataArray):
@@ -361,22 +363,35 @@ def to_polars(ds: Dataset, **kwargs: Any) -> pl.DataFrame:
 
 def check_has_nulls_polars(df: pl.DataFrame, name: str = "") -> None:
     """
-    Checks if the given DataFrame contains any null values and raises a ValueError if it does.
+    Checks if the given DataFrame contains any null or NaN values and raises a ValueError if it does.
 
     Args:
     ----
-        df (pl.DataFrame): The DataFrame to check for null values.
+        df (pl.DataFrame): The DataFrame to check for null or NaN values.
         name (str): The name of the data container being checked.
 
     Raises:
     ------
-        ValueError: If the DataFrame contains null values,
-        a ValueError is raised with a message indicating the name of the constraint and the fields containing null values.
+        ValueError: If the DataFrame contains null or NaN values,
+        a ValueError is raised with a message indicating the name of the constraint and the fields containing null/NaN values.
     """
+    # Check for null values in all columns
     has_nulls = df.select(pl.col("*").is_null().any())
     null_columns = [col for col in has_nulls.columns if has_nulls[col][0]]
-    if null_columns:
-        raise ValueError(f"{name} contains nan's in field(s) {null_columns}")
+
+    # Check for NaN values only in numeric columns (avoid enum/categorical columns)
+    numeric_cols = [
+        col for col, dtype in zip(df.columns, df.dtypes) if dtype.is_numeric()
+    ]
+
+    nan_columns = []
+    if numeric_cols:
+        has_nans = df.select(pl.col(numeric_cols).is_nan().any())
+        nan_columns = [col for col in has_nans.columns if has_nans[col][0]]
+
+    invalid_columns = list(set(null_columns + nan_columns))
+    if invalid_columns:
+        raise ValueError(f"{name} contains nan's in field(s) {invalid_columns}")
 
 
 def filter_nulls_polars(df: pl.DataFrame) -> pl.DataFrame: