Fix pandas 3.0 compatibility

.github/workflows/full_test.yml

@@ -7,20 +7,28 @@ on:
     branches: [main]
 
 jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v2
+        with:
+          version: 0.6.2
+          src: src
+
   build:
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: ["3.10", "3.14"]
+        pandas-version: ["pandas2", "pandas3"]
+        exclude:
+          - python-version: "3.10"
+            pandas-version: "pandas3"
 
     steps:
       - uses: actions/checkout@v4
 
-      - uses: astral-sh/ruff-action@v2
-        with:
-          version: 0.6.2
-          src: src
-
       - name: Set up uv
         uses: astral-sh/setup-uv@v6
         with:
@@ -30,6 +38,17 @@ jobs:
       - name: Install dependencies
         run: uv sync --group test --no-dev
 
+      - name: Install pandas 2.x
+        if: matrix.pandas-version == 'pandas2'
+        run: uv run pip install "pandas>=2.0,<3.0"
+
+      - name: Install pandas 3.x
+        if: matrix.pandas-version == 'pandas3'
+        run: uv run pip install "pandas>=3.0,<4.0"
+
+      - name: Show pandas version
+        run: uv run python -c "import pandas; print(f'pandas {pandas.__version__}')"
+
       - name: Type check
         run: make typecheck
 

src/modelskill/comparison/_comparison.py

@@ -55,6 +55,18 @@ def _parse_dataset(data: xr.Dataset) -> xr.Dataset:
         # matched_data = self._matched_data_to_xarray(matched_data)
     assert "Observation" in data.data_vars
 
+    # Normalize datetime precision to avoid xarray interp issues with pandas 3.0
+    # Different data sources may have different precisions (datetime64[s], datetime64[us], etc.)
+    # Use nanoseconds (ns) for backward compatibility with pandas 2.x
+    # Note: The dtype.kind == "M" check is required because some datasets use
+    # non-datetime indexes (e.g., RangeIndex in tests). Only DatetimeIndex has
+    # the .as_unit() method, so we must skip normalization for other index types.
+    if data.time.dtype.kind == "M":  # M = datetime64
+        time_pd = data.time.to_index()  # Preserves freq attribute
+        if time_pd.dtype != "datetime64[ns]":
+            time_index = time_pd.as_unit("ns")
+            data = data.assign_coords(time=time_index)
+
     # no missing values allowed in Observation
     if data["Observation"].isnull().any():
         raise ValueError("Observation data must not contain missing values.")
@@ -331,12 +343,12 @@ def _matched_data_to_xarray(
             )
 
     # check that items.obs and items.model are numeric
-    if not np.issubdtype(df[items.obs].dtype, np.number):
+    if not pd.api.types.is_numeric_dtype(df[items.obs].dtype):
         raise ValueError(
             "Observation data is of type {df[items.obs].dtype}, it must be numeric"
         )
     for m in items.model:
-        if not np.issubdtype(df[m].dtype, np.number):
+        if not pd.api.types.is_numeric_dtype(df[m].dtype):
             raise ValueError(
                 f"Model data: {m} is of type {df[m].dtype}, it must be numeric"
             )

src/modelskill/comparison/_utils.py

@@ -46,9 +46,9 @@ def _add_spatial_grid_to_df(
         bins_y = np.arange(y_start, y_end + binsize / 2, binsize)
     # cut and get bin centre
     df["xBin"] = pd.cut(df.x, bins=bins_x)
-    df["xBin"] = df["xBin"].apply(lambda x: x.mid)
+    df["xBin"] = df["xBin"].apply(lambda x: x.mid if pd.notna(x) else x)
     df["yBin"] = pd.cut(df.y, bins=bins_y)
-    df["yBin"] = df["yBin"].apply(lambda x: x.mid)
+    df["yBin"] = df["yBin"].apply(lambda x: x.mid if pd.notna(x) else x)
 
     return df
 

src/modelskill/metrics.py

@@ -588,9 +588,11 @@ def peak_ratio(
     time = obs.index
 
     # Calculate number of years
-    dt_int = (time[1:].values - time[0:-1].values).view("int64")
-    dt_int_mode = float(stats.mode(dt_int, keepdims=False)[0]) / 1e9  # in seconds
-    N_years = dt_int_mode / 24 / 3600 / 365.25 * len(time)
+    # Use total_seconds() to handle any datetime precision (ns, us, ms, s)
+    dt = time[1:] - time[:-1]
+    dt_seconds = dt.total_seconds().values
+    dt_mode_seconds = float(stats.mode(dt_seconds, keepdims=False)[0])
+    N_years = dt_mode_seconds / 24 / 3600 / 365.25 * len(time)
     peak_index, AAP_ = _partial_duration_series(
         time,
         obs,

src/modelskill/model/dummy.py

@@ -28,7 +28,7 @@ class DummyModelResult:
     --------
     >>> import pandas as pd
     >>> import modelskill as ms
-    >>> df = pd.DataFrame([0.0, 1.0], index=pd.date_range("2000", freq="H", periods=2))
+    >>> df = pd.DataFrame([0.0, 1.0], index=pd.date_range("2000", freq="h", periods=2))
     >>> obs = ms.PointObservation(df, name="foo")
     >>> mr = ms.DummyModelResult(strategy='mean')
     >>> pmr = mr.extract(obs)

src/modelskill/timeseries/_point.py

@@ -122,6 +122,17 @@ def _convert_to_dataset(
             data = data.rename({time_dim_name: "time"})
         ds = data
 
+    # Normalize datetime precision to avoid xarray interp issues with pandas 3.0
+    # Different data sources (dfs0 files, DataFrames) may have different precisions
+    # (datetime64[s], datetime64[us], datetime64[ns], etc.), and xarray.interp()
+    # fails when interpolating between datasets with mismatched precisions.
+    # Use nanoseconds (ns) for backward compatibility with pandas 2.x
+    if ds.time.dtype.kind == "M":  # M = datetime
+        time_pd = ds.time.to_index()  # Preserves freq attribute
+        if time_pd.dtype != "datetime64[ns]":
+            time_index = time_pd.as_unit("ns")
+            ds = ds.assign_coords(time=time_index)
+
     name = _validate_data_var_name(varname)
 
     n_unique_times = len(ds.time.to_index().unique())

tests/test_comparercollection.py

@@ -570,12 +570,31 @@ def test_plot_accepts_figsize(cc_plot_function):
     assert a, b == figsize
 
 
-def test_peak_ratio(cc):
-    """Non existent peak ratio"""
-    cc = cc.sel(model="m1")
-    sk = cc.skill(metrics=["peak_ratio"])
-
-    assert sk.loc["fake point obs", "peak_ratio"] == pytest.approx(1.119999999)
+def test_peak_ratio():
+    """Test peak_ratio with synthetic data containing clear, verifiable peaks"""
+    # Create data with 2 clear peaks:
+    # Peak 1: obs=5.0, model=5.5 → ratio=1.1
+    # Peak 2: obs=6.0, model=6.6 → ratio=1.1
+    # Expected peak_ratio = mean([1.1, 1.1]) = 1.1
+    times = pd.date_range("2020-01-01", periods=100, freq="h")
+    obs_vals = np.zeros(100)
+    mod_vals = np.zeros(100)
+
+    # Create peak 1 around index 10
+    obs_vals[8:13] = [0, 1, 5, 1, 0]
+    mod_vals[8:13] = [0, 1.1, 5.5, 1.1, 0]
+
+    # Create peak 2 around index 50
+    obs_vals[48:53] = [0, 1, 6, 1, 0]
+    mod_vals[48:53] = [0, 1.1, 6.6, 1.1, 0]
+
+    df = pd.DataFrame({"Observation": obs_vals, "model": mod_vals}, index=times)
+
+    cmp = ms.from_matched(df, obs_item=0, name="synthetic_peaks")
+    sk = cmp.skill(metrics=["peak_ratio"])
+
+    # Model peaks are 1.1x observation peaks
+    assert sk.to_dataframe()["peak_ratio"].values == pytest.approx(1.1, abs=0.01)
 
 
 def test_peak_ratio_2(cc_pr):

tests/test_simple_compare.py

@@ -78,12 +78,12 @@ def test_matching_pointobservation_with_trackmodelresult_is_not_possible():
     # ignore the data
     tdf = pd.DataFrame(
         {"x": [1, 2], "y": [1, 2], "m1": [0, 0]},
-        index=pd.date_range("2017-10-27 13:00:01", periods=2, freq="4S"),
+        index=pd.date_range("2017-10-27 13:00:01", periods=2, freq="4s"),
     )
     mr = ms.TrackModelResult(tdf, item="m1", x_item="x", y_item="y")
     pdf = pd.DataFrame(
         data={"level": [0.0, 0.0]},
-        index=pd.date_range("2017-10-27 13:00:01", periods=2, freq="4S"),
+        index=pd.date_range("2017-10-27 13:00:01", periods=2, freq="4s"),
     )
     obs = ms.PointObservation(pdf, item="level")
     with pytest.raises(TypeError, match="TrackModelResult"):