Merge branch 'main' into pfs_filename_comma

Author: ecomodeller

docs/index.qmd

@@ -1,5 +1,6 @@
 ---
 toc: false
+format-links: false
 ---
 
 ![](MIKE-IO-Logo-Pos-RGB.svg)

docs/user-guide/dataarray.qmd

@@ -175,6 +175,7 @@ Other methods that also return a DataArray:
 * [`interp_like`](`mikeio.DataArray.interp_like`) - Spatio (temporal) interpolation (see example [Dfsu interpolation](../examples/dfsu/spatial_interpolation.qmd)
 * [`interp_time()`](`mikeio.DataArray.interp_time`) - Temporal interpolation (see example [Time interpolation](../examples/Time-interpolation.qmd))
 * [`dropna()`](`mikeio.DataArray.dropna`) - Remove time steps where all items are NaN
+* [`fillna()`](`mikeio.DataArray.fillna`) - Fill missing values with a constant value
 * [`squeeze()`](`mikeio.DataArray.squeeze`) - Remove axes of length 1
 
 ### Conversion:

docs/user-guide/dataset.qmd

@@ -177,6 +177,7 @@ Other methods that also return a Dataset:
 * [`interp_like`](`mikeio.Dataset.interp_like`) - Spatio (temporal) interpolation (see [Dfsu interpolation notebook](../examples/dfsu/spatial_interpolation.qmd)
 * [`interp_time()`](`mikeio.Dataset.interp_time`) - Temporal interpolation (see [Time interpolation notebook](../examples/Time-interpolation.qmd))
 * [`dropna()`](`mikeio.Dataset.dropna`) - Remove time steps where all items are NaN
+* [`fillna()`](`mikeio.Dataset.fillna`) - Fill missing values with a constant value
 * [`squeeze()`](`mikeio.Dataset.squeeze`) - Remove axes of length 1
 
 ### Conversion:

mikeio/dataset/_dataarray.py

@@ -484,6 +484,34 @@ def to_numpy(self) -> np.ndarray:
     def _has_time_axis(self) -> bool:
         return self.dims[0][0] == "t"
 
+    def fillna(self, value: float = 0.0) -> "DataArray":
+        """Fill NA/NaN value.
+
+        Parameters
+        ----------
+        value: float, optional
+            Value used to fill missing values. Default is 0.0.
+
+        Examples
+        --------
+        ```{python}
+        import numpy as np
+        import mikeio
+
+        da = mikeio.DataArray([np.nan, 1.0])
+        da
+        ```
+
+        ```{python}
+        da.fillna(0.0)
+        ```
+
+        """
+        da = self.copy()
+        x = da.values
+        x[np.isnan(x)] = value
+        return da
+
     def dropna(self) -> "DataArray":
         """Remove time steps where values are NaN."""
         if not self._has_time_axis:

mikeio/dataset/_dataset.py

@@ -202,7 +202,7 @@ def _parse_items(
     ) -> list[ItemInfo]:
         if items is None:
             # default Undefined items
-            item_infos = [ItemInfo(f"Item_{j+1}") for j in range(n_items_data)]
+            item_infos = [ItemInfo(f"Item_{j + 1}") for j in range(n_items_data)]
         else:
             if len(items) != n_items_data:
                 raise ValueError(
@@ -416,6 +416,19 @@ def copy(self) -> "Dataset":
         """Returns a copy of this dataset."""
         return deepcopy(self)
 
+    def fillna(self, value: float = 0.0) -> "Dataset":
+        """Fill NA/NaN value.
+
+        Parameters
+        ----------
+        value: float, optional
+            Value used to fill missing values. Default is 0.0.
+
+        """
+        res = {name: da.fillna(value=value) for name, da in self._data_vars.items()}
+
+        return Dataset(data=res, validate=False)
+
     def dropna(self) -> "Dataset":
         """Remove time steps where all items are NaN."""
         if not self[0]._has_time_axis:  # type: ignore

tests/test_dataset.py

@@ -1533,7 +1533,7 @@ def test_create_dataset_with_many_items():
 
     for i in range(n_items):
         x = np.random.random(nt)
-        da = mikeio.DataArray(data=x, time=time, item=mikeio.ItemInfo(f"Item {i+1}"))
+        da = mikeio.DataArray(data=x, time=time, item=mikeio.ItemInfo(f"Item {i + 1}"))
         das.append(da)
 
     ds = mikeio.Dataset(das)
@@ -1636,3 +1636,23 @@ def test_read_write_single_timestep_preserves_dt(tmp_path):
 
     dfs2 = mikeio.open(outfn)
     assert dfs2.timestep == pytest.approx(10800.0)
+
+
+def test_fillna() -> None:
+    ds = mikeio.Dataset(
+        {
+            "foo": mikeio.DataArray(np.array([np.nan, 1.0])),
+            "bar": mikeio.DataArray(np.array([2.0, np.nan])),
+            "baz": mikeio.DataArray(np.array([2.0, 3.0])),
+        }
+    )
+    assert np.isnan(ds["foo"].to_numpy()[0])
+    assert np.isnan(ds["bar"].to_numpy()[-1])
+
+    ds_filled = ds.fillna()
+
+    assert ds_filled["foo"].to_numpy()[0] == pytest.approx(0.0)
+    assert ds_filled["bar"].to_numpy()[-1] == pytest.approx(0.0)
+
+    # original dataset is not modified
+    assert np.isnan(ds["foo"].to_numpy()[0])