[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

tests/test_calibration.py

@@ -21,9 +21,9 @@ def test_spotpy_calibration():
         "drainage_area": np.array([10]),
         "model_name": "Dummy",
     }
-    
+
     mask = np.array([0, 0, 0, 0, 1, 1])
-    
+
     best_parameters, best_simulation, best_objfun = perform_calibration(
         model_config,
         "mae",
@@ -38,16 +38,16 @@ def test_spotpy_calibration():
     assert len(best_parameters) == len(bounds_high)
 
     # Test that the objective function is calculated correctly
-    objfun = get_objective_function( 
-                                    model_config["Qobs"], 
-                                    best_simulation, 
-                                    obj_func="mae",
-                                    mask=mask,
-                                    )
-    
+    objfun = get_objective_function(
+        model_config["Qobs"],
+        best_simulation,
+        obj_func="mae",
+        mask=mask,
+    )
+
     assert objfun == best_objfun
 
     # Test dummy model response
     model_config["parameters"] = [5, 5, 5]
     Qsim = dummy_model(model_config)
-    assert Qsim[3] == 3500.00
+    assert Qsim[3] == 3500.00

xhydro/modelling/calibration.py

@@ -65,26 +65,27 @@
 from spotpy.parameter import Uniform
 
 from xhydro.modelling.hydrological_modelling import hydrological_model_selector
-from xhydro.modelling.obj_funcs import (get_objective_function, 
-                                        get_objfun_minimize_or_maximize,
-                                        get_optimizer_minimize_or_maximize,
-                                        )
+from xhydro.modelling.obj_funcs import (
+    get_objective_function,
+    get_objfun_minimize_or_maximize,
+    get_optimizer_minimize_or_maximize,
+)
 
 
 class spot_setup:
     """Create the spotpy calibration system that is used for hydrological model calibration."""
 
-    def __init__(self, 
-                 model_config, 
-                 bounds_high, 
-                 bounds_low, 
-                 obj_func=None,
-                 take_negative=False,
-                 mask=None,
-                 transform=None,
-                 epsilon=None,
-                 ):
-        
+    def __init__(
+        self,
+        model_config,
+        bounds_high,
+        bounds_low,
+        obj_func=None,
+        take_negative=False,
+        mask=None,
+        transform=None,
+        epsilon=None,
+    ):
         """Initialize the spot_setup object.
 
         The initialization of the spot_setup object includes a generic
@@ -115,7 +116,7 @@ def __init__(self,
             - "rsr" : Ratio of RMSE to standard deviation.
             - "volume_error": Total volume error over the period.
         """
-        
+
         # Gather the model_config dictionary and obj_func string, and other
         # optional arguments.
         self.model_config = model_config
@@ -176,16 +177,17 @@ def objectivefunction(
             - simulation, observation : vectors of streamflow used to compute
               the objective function
         """
-
-        obj_fun_val = get_objective_function(evaluation,
-                                             simulation,
-                                             obj_func=self.obj_func,
-                                             take_negative=self.take_negative,
-                                             mask=self.mask,
-                                             transform=self.transform,
-                                             epsilon=self.epsilon,
-                                             )
-
+
+        obj_fun_val = get_objective_function(
+            evaluation,
+            simulation,
+            obj_func=self.obj_func,
+            take_negative=self.take_negative,
+            mask=self.mask,
+            transform=self.transform,
+            epsilon=self.epsilon,
+        )
+
         return obj_fun_val
 
 
@@ -215,7 +217,7 @@ def perform_calibration(
         It will be up to the user to provide the data that the model requires.
     obj_func : str
         The objective function used for calibrating. Can be any one of these:
-        
+
             - "abs_bias" : Absolute value of the "bias" metric
             - "abs_pbias": Absolute value of the "pbias" metric
             - "abs_volume_error" : Absolute value of the volume_error metric
@@ -231,7 +233,7 @@ def perform_calibration(
             - "rmse" : Root Mean Square Error
             - "rrmse" : Relative Root Mean Square Error (RMSE-to-mean ratio)
             - "rsr" : Ratio of RMSE to standard deviation.
-            
+
     bounds_high : np.array
         High bounds for the model parameters to be calibrated. Spotpy will sample parameter sets from
         within these bounds. The size must be equal to the number of parameters to calibrate.
@@ -259,13 +261,13 @@ def perform_calibration(
     # for the objective function and the working direction of the algorithm.
     of_maximize = get_objfun_minimize_or_maximize(obj_func)
     algo_maximize = get_optimizer_minimize_or_maximize(algorithm)
-    
+
     # They are not working in the same direction. Take the negative of the OF.
     if of_maximize != algo_maximize:
         take_negative = True
     else:
         take_negative = False
-        
+
     # Set up the spotpy object to prepare the calibration
     spotpy_setup = spot_setup(
         model_config,
@@ -277,7 +279,7 @@ def perform_calibration(
         transform=transform,
         epsilon=epsilon,
     )
-    
+
     # Select an optimization algorithm and parameterize it, then run the
     # optimization process.
     if algorithm == "DDS":
@@ -311,12 +313,12 @@ def perform_calibration(
     # Reconvert objective function if required.
     if take_negative:
         bestobjf = bestobjf * -1
-        
+
     # Update the parameter set to put the best parameters in model_config...
     model_config.update({"parameters": best_parameters})
 
     # ... which can be used to run the hydrological model and get the best Qsim.
     Qsim = hydrological_model_selector(model_config)
 
     # Return the best parameters, Qsim and best objective function value.
-    return best_parameters, Qsim, bestobjf
+    return best_parameters, Qsim, bestobjf