cleaned calibration

xhydro/calibration.py

@@ -12,6 +12,7 @@
 from spotpy.parameter import Uniform
 import spotpy
 import hydroeval as he
+from xhydro.hydrological_modelling import  hydrological_model_selector
 
 class spot_setup(object):
 
@@ -80,32 +81,7 @@ def perform_calibration(model_config, evaluation_metric, maximize, bounds_high,
     #objfun = get_objective_function_value(model_config['Qobs'], Qsim, best_parameters, obj_func=evaluation_metric)
 
     return best_parameters, Qsim, bestobjf
-
-
-def dummy_model(model_config):    
-
-    """
-    Dummy model to show the implementation we should be aiming for. Each model 
-    will have its own required data that users can pass. We could envision a setup
-    where a class generates the model_config object for all model formats making it 
-    possible to replace models on the fly
-    """
-
-    precip = model_config['precip']
-    temperature = model_config['temperature']
-    drainage_area = model_config['drainage_area']
-    parameters = model_config['parameters']
-
-    Q = np.empty(len(precip))
-
-    for t in range(0,len(precip)):
-
-        Q[t]= (precip[t] * parameters[0] + abs(temperature[t])*parameters[1])* parameters[2] * drainage_area
-
-
-    return Q
-
-
+
 
 
 def transform_flows(Qobs, Qsim, transform=None, epsilon=None):
@@ -139,17 +115,6 @@ def transform_flows(Qobs, Qsim, transform=None, epsilon=None):
     return Qobs, Qsim
 
 
-
-def hydrological_model_selector(model_config):
-
-    if model_config['model_name'] == 'Dummy':
-        Qsim = dummy_model(model_config)
-
-        # ADD OTHER MODELS HERE
-
-    return Qsim
-
-
 def get_objective_function_value(Qobs, Qsim, params, transform=None, epsilon=None, obj_func=None):
 
     # Transform flows if needed