diff --git a/src/helpers.py b/src/helpers.py
index be73614319b39dc36043437c64379342a96ce00e..d4180336ec63f4f5477d3f2a149b5cb146be5597 100644
--- a/src/helpers.py
+++ b/src/helpers.py
@@ -11,6 +11,8 @@ import math
 import os
 import socket
 import time
+import types
+
 
 import keras.backend as K
 import xarray as xr
@@ -53,6 +55,9 @@ class TimeTrackingWrapper:
         with TimeTracking(name=self.__wrapped__.__name__):
             return self.__wrapped__(*args, **kwargs)
 
+    def __get__(self, instance, cls):
+        return types.MethodType(self, instance)
+
 
 class TimeTracking(object):
     """
diff --git a/src/plotting/postprocessing_plotting.py b/src/plotting/postprocessing_plotting.py
index b8f67e3393c4443fed523902765278654e78a0a9..b61e832c80ac9ad83a5aa4a4b5310b17f6add098 100644
--- a/src/plotting/postprocessing_plotting.py
+++ b/src/plotting/postprocessing_plotting.py
@@ -347,6 +347,8 @@ class PlotConditionalQuantiles(AbstractPlotClass):
 
         :return:
         """
+        logging.info(f"start plotting {self.__class__.__name__}, scheduled number of plots: {(len(self.seasons) + 1) * 2}")
+
         if len(self.seasons) > 0:
             self._plot_seasons()
         self._plot_all()
@@ -384,6 +386,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
         :param season: List of seasons to use
         :return:
         """
+
         segmented_data, quantile_panel = self._prepare_plots(data, x_model, y_model)
         ylabel, xlabel = self._labels(x_model, self._opts["data_unit"])
         plot_name = f"{self.plot_name}{self.add_affix(season)}{self.add_affix(plot_name_affix)}_plot.pdf"
diff --git a/src/run_modules/experiment_setup.py b/src/run_modules/experiment_setup.py
index 150399cb2e4997a6b9adfb30dfa3ff89de73d4ac..09b9f143fc0442ee34ef5735366145be86b5fa07 100644
--- a/src/run_modules/experiment_setup.py
+++ b/src/run_modules/experiment_setup.py
@@ -21,7 +21,7 @@ DEFAULT_VAR_ALL_DICT = {'o3': 'dma8eu', 'relhum': 'average_values', 'temp': 'max
                         'pblheight': 'maximum'}
 DEFAULT_TRANSFORMATION = {"scope": "data", "method": "standardise", "mean": "estimate"}
 DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
-                     "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "plot_conditional_quantiles",
+                     "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
                      "PlotAvailability"]
 
 
diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index e8353048f2fa982a9d54900fab93142ed4f1ae30..dfeaf06533e8023cf872763e0f34d98c5dd27a01 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -195,7 +195,7 @@ class PostProcessing(RunEnvironment):
 
         if self.bootstrap_skill_scores is not None and "PlotBootstrapSkillScore" in plot_list:
             PlotBootstrapSkillScore(self.bootstrap_skill_scores, plot_folder=self.plot_path, model_setup="CNN")
-        if "plot_conditional_quantiles" in plot_list:
+        if "PlotConditionalQuantiles" in plot_list:
             PlotConditionalQuantiles(self.test_data.stations, data_pred_path=path, plot_folder=self.plot_path)
         if "PlotStationMap" in plot_list:
             PlotStationMap(generators={'b': self.test_data}, plot_folder=self.plot_path)