final lint and docstring fixes, tests all pass

xhydro/optimal_interpolation/compare_result.py

@@ -1,12 +1,12 @@
 """Compare results between simulations and observations."""
 
+import datetime as dt
 import pathlib
 import sys
 from typing import Union
 
 import numpy as np
 
-import datetime as dt
 import xhydro.optimal_interpolation.functions.utilities as util
 from xhydro.modelling.obj_funcs import get_objective_function
 

xhydro/optimal_interpolation/functions/ECF_climate_correction.py

@@ -1,4 +1,5 @@
 """Empirical Covariance Function climate correction package."""
+
 from functools import partial
 
 import numpy as np
@@ -7,6 +8,7 @@
 from .mathematical_algorithms import calculate_average_distance, eval_covariance_bin
 from .utilities import general_ecf, initialize_nan_arrays
 
+
 def correction(
     flow_obs: np.ndarray,
     flow_sim: np.ndarray,

xhydro/optimal_interpolation/functions/mathematical_algorithms.py

@@ -1,4 +1,5 @@
 """Set of mathematical algorithms required for the optimal interpolation."""
+
 import numpy as np
 
 

xhydro/optimal_interpolation/functions/optimal_interpolation.py

@@ -383,6 +383,7 @@ def initialize_data_arrays(time_range: int, station_count: int) -> tuple[ndarray
         drained_area,
     )
 
+
 def retreive_data(args: dict) -> dict[str, Any]:
     """Retrieve data from files to populate the Optimal Interpolation (OI) algorithm.
 
@@ -524,20 +525,6 @@ def parallelize_operation(args: dict, parallelize: bool = True) -> np.array:
        4. Run pool.map, which maps inputs (iterators) to the function.
        5. Collect the results and unzip the tuple returning from pool.map.
        6. Close the pool and return the parsed results.
-
-    Parameters
-    ----------
-    args : dict
-        A dictionary containing the necessary information to retrieve and preprocess data. Keys include 'flow_obs',
-        'flow_sim', 'start_date', 'end_date', 'time_range', 'stations_info', 'stations_mapping', 'stations_id',
-        'percentiles' and 'station_count'.
-    parallelize : bool
-        Flag to make the code run in parallel. True to use parallelization.
-
-    Returns
-    -------
-    array-like
-        Flow quantiles associated to the desired percentiles after optimal interpolation.
     """
     station_count = args["station_count"]
     percentiles = args["percentiles"]