more clean up

.github/workflows/python-package.yml

@@ -24,7 +24,7 @@ jobs:
       with:
         micromamba-version: "latest"
         generate-run-shell: true
-        environment-file: environment.yml
+        environment-file: environment-dev.yml
         cache-environment: true
         post-cleanup: 'all'
     - name: Run tests

analysis/analyze_scalar.py

@@ -17,10 +17,9 @@
 # Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
 
 # pylint: disable=unused-import,too-many-positional-arguments
+
 """
 Analyze RAGIS ensemble.
-
-
 """
 
 import time
@@ -104,30 +103,33 @@ def filter_config(ds: xr.Dataset, params: List[str]) -> xr.DataArray:
 
 @timeit
 def prepare_simulations(
-    filenames: List[Path | str],
+    filenames: List[Union[Path, str]],
     config: Dict,
     reference_date: str,
     parallel: bool = True,
-    engine: str = "netcdf4",
+    engine: str = "h5netcdf",
 ) -> xr.Dataset:
     """
     Prepare simulations by loading and processing ensemble datasets.
 
     This function loads ensemble datasets from the specified filenames, processes them
     according to the provided configuration, and returns the processed dataset. The
-    processing steps include sorting, dropping NaNs, standardizing variable names,
-    calculating cumulative variables, and normalizing cumulative variables.
+    processing steps include sorting, converting byte strings to strings, dropping NaNs,
+    standardizing variable names, calculating cumulative variables, and normalizing
+    cumulative variables.
 
     Parameters
     ----------
     filenames : List[Union[Path, str]]
         A list of file paths to the ensemble datasets.
     config : Dict
         A dictionary containing configuration settings for processing the datasets.
+    reference_date : str
+        The reference date for normalizing cumulative variables.
     parallel : bool, optional
         Whether to load the datasets in parallel, by default True.
     engine : str, optional
-        The engine to use for loading the datasets, by default "netcdf4".
+        The engine to use for loading the datasets, by default "h5netcdf".
 
     Returns
     -------
@@ -148,7 +150,8 @@ def prepare_simulations(
     ...         "smb_flux": "smb_flux"
     ...     }
     ... }
-    >>> ds = prepare_simulations(filenames, config)
+    >>> reference_date = "2000-01-01"
+    >>> ds = prepare_simulations(filenames, config, reference_date)
     """
     ds = prp.load_ensemble(filenames, parallel=parallel, engine=engine).sortby("basin")
     ds = xr.apply_ufunc(np.vectorize(convert_bstrings_to_str), ds, dask="parallelized")
@@ -192,6 +195,8 @@ def prepare_observations(
         A dictionary containing configuration settings for processing the datasets.
     reference_date : str
         The reference date for normalizing cumulative variables.
+    engine : str, optional
+        The engine to use for loading the datasets, by default "h5netcdf".
 
     Returns
     -------
@@ -237,22 +242,49 @@ def prepare_observations(
 
 
 @timeit
-def config_to_dataframe(config: xr.DataArray, ensemble: str | None = None):
+def config_to_dataframe(
+    config: xr.DataArray, ensemble: Union[str, None] = None
+) -> pd.DataFrame:
     """
     Convert an xarray DataArray configuration to a pandas DataFrame.
 
+    This function converts the input DataArray containing configuration data into a
+    pandas DataFrame. The dimensions of the DataArray (excluding 'pism_config_axis')
+    are used as the index, and the 'pism_config_axis' values are used as columns.
+
     Parameters
     ----------
     config : xr.DataArray
         The input DataArray containing the configuration data.
+    ensemble : Union[str, None], optional
+        An optional string to add as a column named 'Ensemble' in the DataFrame, by default None.
 
     Returns
     -------
     pd.DataFrame
         A DataFrame where the dimensions of the DataArray (excluding 'pism_config_axis')
         are used as the index, and the 'pism_config_axis' values are used as columns.
+
+    Examples
+    --------
+    >>> config = xr.DataArray(
+    ...     data=[[1, 2, 3], [4, 5, 6]],
+    ...     dims=["time", "pism_config_axis"],
+    ...     coords={"time": [0, 1], "pism_config_axis": ["param1", "param2", "param3"]}
+    ... )
+    >>> df = config_to_dataframe(config)
+    >>> print(df)
+    pism_config_axis  time  param1  param2  param3
+    0                   0       1       2       3
+    1                   1       4       5       6
+
+    >>> df = config_to_dataframe(config, ensemble="Ensemble1")
+    >>> print(df)
+    pism_config_axis  time  param1  param2  param3   Ensemble
+    0                   0       1       2       3  Ensemble1
+    1                   1       4       5       6  Ensemble1
     """
-    dims = [dim for dim in config.dims if not dim in ["pism_config_axis"]]
+    dims = [dim for dim in config.dims if dim != "pism_config_axis"]
     df = config.to_dataframe().reset_index()
     df = df.pivot(index=dims, columns="pism_config_axis", values="pism_config")
     df.reset_index(inplace=True)
@@ -282,10 +314,41 @@ def convert_bstrings_to_str(element: Any) -> Any:
 
 
 def plot_outliers(
-    filtered_da: xr.DataArray, outliers_da: xr.DataArray, filename: Path | str
+    filtered_da: xr.DataArray, outliers_da: xr.DataArray, filename: Union[Path, str]
 ):
     """
-    Plot outliers.
+    Plot outliers in the given DataArrays and save the plot to a file.
+
+    This function creates a plot with the filtered data and outliers, and saves the plot
+    to the specified filename. The filtered data is plotted in black, and the outliers
+    are plotted in red.
+
+    Parameters
+    ----------
+    filtered_da : xr.DataArray
+        The DataArray containing the filtered data.
+    outliers_da : xr.DataArray
+        The DataArray containing the outliers.
+    filename : Union[Path, str]
+        The path or filename where the plot will be saved.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> filtered_da = xr.DataArray(
+    ...     data=[[1, 2, 3], [4, 5, 6]],
+    ...     dims=["time", "exp_id"],
+    ...     coords={"time": [0, 1], "exp_id": [0, 1, 2]}
+    ... )
+    >>> outliers_da = xr.DataArray(
+    ...     data=[[7, 8, 9], [10, 11, 12]],
+    ...     dims=["time", "exp_id"],
+    ...     coords={"time": [0, 1], "exp_id": [0, 1, 2]}
+    ... )
+    >>> plot_outliers(filtered_da, outliers_da, "outliers_plot.png")
     """
     fig, ax = plt.subplots(1, 1)
     if filtered_da.size > 0:
@@ -1295,7 +1358,7 @@ def plot_obs_sims_3(
                         hue="sensitivity_indices_group", ax=ax, lw=0.75, label=g.values
                     )
                 ax.legend()
-                ax.set_title(f"S1 for {basin} filtered by {filter_var}")
+                ax.set_title(f"{indices_var} for {basin} filtered by {filter_var}")
                 fn = (
                     result_dir
                     / Path("figures")