diff --git a/README.md b/README.md
index dc797a6fb2a4daecbc4e8ee8ae01868de8d1bf98..7692df4a049b8c8e6713fe6bc0cf1d25650e6446 100644
--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@ We assume that you have downloaded or cloned the project from GitLab or
 * Make sure that CUDA 10.0 is installed if you want to use Nvidia GPUs (compatible with TensorFlow 1.13.1).
 
 Depending on your system (GPU available or not) you can create a virtual environment by executing
-`python3 -m venv venv`. Make sure that the venv is activated (`source venv/bin/activate`). Afterwards
+`python3.6 -m venv venv`. Make sure that the venv is activated (`source venv/bin/activate`). Afterwards
 you can install the requirements into the venv: 
 * CPU version: `pip install -r requirements.txt`
 * GPU version: `pip install -r requirements_gpu.txt`
diff --git a/run.py b/run.py
index ebde68affbec45448fab452cffc962ca53b406ce..ec30d5d41a39932cb7c9b6eb4f94457d6518d4f9 100644
--- a/run.py
+++ b/run.py
@@ -17,7 +17,7 @@ from src.run_modules.training import Training
 
 def main(parser_args):
     
-    station_filename = "German_background_stations.json"  # "German_stations.json"
+    station_filename = "German_background_stations.json"
     with open(station_filename) as jfile:
         stations = json.load(jfile)
 
@@ -27,11 +27,11 @@ def main(parser_args):
                         data_path=f"{os.getcwd()}/raw_input_IntelliO3-ts/",
                         # hpc_hosts=["yo"], #
                         stations=stations,
-                        # stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
+                        evaluate_bootstraps=False,
                         station_type='background', window_lead_time=4, window_history_size=6,
                         trainable=False, create_new_model=False, permute_data_on_training=True,
                         extreme_values=3., train_min_length=365, val_min_length=365, test_min_length=365,
-                        create_new_bootstraps=True,
+                        create_new_bootstraps=False,
                         plot_list=["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore",
                                    "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
                                    "PlotAvailability"],