docs: def predict(self, lon, lat, **kwargs):
          """
          distance, abs_mag, r_physical
          """
          assert self.classifier is not None, 'ERROR'
          
          pred = np.zeros(len(lon))
          cut_geometry, flags_geometry = self.applyGeometry(lon, lat)
          
          x_test = []
          for key, operation in self.config['operation']['params_intrinsic']:
              assert operation.lower() in ['linear', 'log'], 'ERROR'
              if operation.lower() == 'linear':
                  x_test.append(kwargs[key])
              else:
                  x_test.append(np.log10(kwargs[key]))
  
          x_test = np.vstack(x_test).T
          
          #import pdb; pdb.set_trace()
          pred[cut_geometry] = self.classifier.predict_proba(x_test[cut_geometry])[:,1]
  
          self.validatePredict(pred, flags_geometry, lon, lat, kwargs['r_physical'], kwargs['abs_mag'], kwargs['distance'])
  
          return pred, flags_geometry