fix paths in tutorials

Author: MarleneBusch

tutorials/02_heliostat_raytracing_distributed_tutorial.py

@@ -39,20 +39,21 @@
             scenario_file=scenario_file,
             device=device,
         )
-        # Set a ray extinction factor responsible for global shading of rays (0.0 -> no global shading, 1.0 -> full global shading).
-        ray_extinction_factor = 0.0
-
-        # Use a heliostat target light source mapping to specify which heliostat in your scenario should be activated,
-        # which heliostat will receive which incident ray direction for alignment and on which target it will be raytraced.
-        # If no mapping is provided, all heliostats are selected, and they will all receive the default incident ray direction
-        # from a sun positioned directly in the south and they will all be raytraced on the first target found in your scenario.
-        heliostat_target_light_source_mapping = None
-        # If you want to customize the mapping, choose the following style: list[tuple[str, str, torch.Tensor]]
-        # heliostat_target_light_source_mapping = [
-        #     ("heliostat_1", "target_name_2", incident_ray_direction_tensor_1),
-        #     ("heliostat_2", "target_name_2", incident_ray_direction_tensor_2),
-        #     (...)
-        # ]
+
+    # Set a ray extinction factor responsible for global shading of rays (0.0 -> no global shading, 1.0 -> full global shading).
+    ray_extinction_factor = 0.0
+
+    # Use a heliostat target light source mapping to specify which heliostat in your scenario should be activated,
+    # which heliostat will receive which incident ray direction for alignment and on which target it will be raytraced.
+    # If no mapping is provided, all heliostats are selected, and they will all receive the default incident ray direction
+    # from a sun positioned directly in the south and they will all be raytraced on the first target found in your scenario.
+    heliostat_target_light_source_mapping = None
+    # If you want to customize the mapping, choose the following style: list[tuple[str, str, torch.Tensor]]
+    # heliostat_target_light_source_mapping = [
+    #     ("heliostat_1", "target_name_2", incident_ray_direction_tensor_1),
+    #     ("heliostat_2", "target_name_2", incident_ray_direction_tensor_2),
+    #     (...)
+    # ]
 
     bitmap_resolution = torch.tensor([256, 256])
 

tutorials/04_kinematic_reconstruction_tutorial.py

@@ -184,13 +184,12 @@ def create_plots(
             # ....
         ],
     ),
-    # ...
 ]
 
 # Or if you have a directory with downloaded data use this code to create a mapping.
 # heliostat_data_mapping = paint_scenario_parser.build_heliostat_data_mapping(
-#     base_path="base/path/data",
-#     heliostat_names=["heliostat_1"],
+#     base_path="/workVERLEIHNIX/share/PAINT",
+#     heliostat_names=["AA39", "AA31"],
 #     number_of_measurements=5,
 #     image_variant="flux",
 #     randomize=True,
@@ -204,8 +203,8 @@ def create_plots(
     config_dictionary.batch_size: 50,
     config_dictionary.log_step: 3,
     config_dictionary.early_stopping_delta: 1e-4,
-    config_dictionary.early_stopping_patience: 300,
-    config_dictionary.early_stopping_window: 300,
+    config_dictionary.early_stopping_patience: 10,
+    config_dictionary.early_stopping_window: 20,
 }
 # Configure the learning rate scheduler.
 scheduler_dict = {

tutorials/05_motor_positions_optimizer_tutorial.py

@@ -16,7 +16,7 @@
 
 #############################################################################################################
 # Define helper functions for the plots.
-# Skip to line 110 for the tutorial code.
+# Skip to line 111 for the tutorial code.
 #############################################################################################################