remove deprecated README

tutorials/1_search_algorithms/README_TREESEARCH.md

-Tutorials on Tree Search Algorithms
-============
-This is a short guide on how to use the search algorithms in `GSMP/motion_planner/search_algorithms` for lectures and tutorials.
-
-Set up
-============
-For using the code, you have to install the tools as described in the README in the root folder. For using the code in
-Pycharm, open a new project in the repository's root directory `graph-search-planner` and set the 
-`commonroad-search/GSMP` folder as *Source Root*, to avoid path issues.
-
-# Documentation
-For running the search algorithms, you can currently specify three different configurations which mainly influence the
-visualization of the search and are defined in `GSMP/motion_planner/PlotConfig.py`:
-
-### Default: 
-
-* This configuration should only be used in the jupyter notebook tutorial
-(`step_1_tutorial_notebooks/step_1_1_cr_uninformed_search_tutorial.ipynb` and
-`step_1_tutorial_notebooks/step_1_2_cr_informed_search_tutorial.ipynb`). 
-It first runs the algorithm and stores the state of each motion primitive, 
-i.e., explored/frontier/currently exploring etc., for each step. 
-Then it plots these steps using
-*ipywidgets*, so the student can step through the search in the jupyter notebook. 
-
-* The color code for plotting is taken from the AIMA tutorial, but can be changed 
- in `GSMP/motion_planner/PlotConfig.py`. 
-
-* By setting PLOT_LEGEND to True, a legend with all states of
-motion primitives (see `MotionPrimitiveStatus` in `GSMP/motion_planner/PlotHelper.py`) is plotted. 
-
-* To reduce the number of steps, you can turn off that motion primitives with collisions are plotted one
- after the other by setting PLOT_COLLISION_STEPS to False.
-
-### StudentScript: 
-
-This configuration uses the same parameters as the Default configuration, but it can be run in Pycharm,
-e.g. when executing `main.py`. It plots the different steps while executing the search algorithm. It should help
-students to easily go through the code.
-
-# How to run
-
-It is advised to add the directory `1_basics-tree_search_algorithms` to your python interpreter as *Source Root*.
-It fixes the Unresolved References error messages in PyCharm.
-
-It can be run in two different ways:
-
-###Jupyter Notebook
-
-Start a Jupyter Server, then navigate to the directory `step_1_tutorial_notebooks`, open up the notebooks and follow
-instructions.
-
-###Python script
-
-Simply run the `main.py` from the directory `1_basics-tree_search_algorithms`. In PyCharm it can be achieved with a
-right click on the file `main.py` and then *Run*.
\ No newline at end of file

tutorials/1_search_algorithms/example.py

@@ -18,51 +18,57 @@ from maneuver_automaton.maneuver_automaton import ManeuverAutomaton
 from motion_planner.motion_planner import MotionPlanner
 from motion_planner.plot_config import StudentScriptPlotConfig
 
-# configurations
-path_scenario = '../../scenarios/tutorial/ZAM_Tutorial_Urban-3_2.xml'
-id_type_vehicle = 2
-file_motion_primitives = 'V_9.0_9.0_Vstep_0_SA_-0.2_0.2_SAstep_0.4_T_0.5_Model_BMW320i.xml'
-config_plot = StudentScriptPlotConfig(DO_PLOT=True)
-
-# load scenario and planning problem set
-scenario, planning_problem_set = CommonRoadFileReader(path_scenario).open()
-# retrieve the first planning problem
-planning_problem = list(planning_problem_set.planning_problem_dict.values())[0]
-
-# plot scenario
-plt.figure(figsize=(8, 8))
-draw_object(scenario)
-draw_object(planning_problem_set)
-plt.gca().set_aspect('equal')
-plt.margins(0, 0)
-plt.show()
-# close the figure to continue!
-
-# create maneuver automaton and planning problem
-automaton = ManeuverAutomaton.generate_automaton(file_motion_primitives)
-
-# comment out the planners which you don't want to execute
-dict_motion_planners = {
-    0: (MotionPlanner.BreadthFirstSearch, "Breadth First Search"),
-    1: (MotionPlanner.DepthFirstSearch, "Depth First Search"),
-    2: (MotionPlanner.DepthLimitedSearch, "Depth Limited Search"),
-    3: (MotionPlanner.UniformCostSearch, "Uniform Cost Search"),
-    4: (MotionPlanner.GreedyBestFirstSearch, "Greedy Best First Search"),
-    5: (MotionPlanner.AStarSearch, "A* Search")
-
-    # 6: (MotionPlanner.StudentMotionPlanner, "Student Planner"),
-    # 7: (MotionPlanner.StudentMotionPlannerExample, "Student Planner Example")
-}
-
-for (class_planner, name_planner) in dict_motion_planners.values():
-    planner = class_planner(scenario=scenario, planning_problem=planning_problem,
-                            automaton=automaton, plot_config=config_plot)
-
-    # start search
-    print(name_planner + " started..")
-    planner.execute_search()
-    plt.title(name_planner)
-    # close the figure to continue
+
+def main():
+    # configurations
+    path_scenario = '../../scenarios/tutorial/ZAM_Tutorial_Urban-3_2.xml'
+    id_type_vehicle = 2
+    file_motion_primitives = 'V_9.0_9.0_Vstep_0_SA_-0.2_0.2_SAstep_0.4_T_0.5_Model_BMW320i.xml'
+    config_plot = StudentScriptPlotConfig(DO_PLOT=True)
+
+    # load scenario and planning problem set
+    scenario, planning_problem_set = CommonRoadFileReader(path_scenario).open()
+    # retrieve the first planning problem
+    planning_problem = list(planning_problem_set.planning_problem_dict.values())[0]
+
+    # plot scenario
+    plt.figure(figsize=(8, 8))
+    draw_object(scenario)
+    draw_object(planning_problem_set)
+    plt.gca().set_aspect('equal')
+    plt.margins(0, 0)
     plt.show()
+    # close the figure to continue!
+
+    # create maneuver automaton and planning problem
+    automaton = ManeuverAutomaton.generate_automaton(file_motion_primitives)
+
+    # comment out the planners which you don't want to execute
+    dict_motion_planners = {
+        0: (MotionPlanner.BreadthFirstSearch, "Breadth First Search"),
+        # 1: (MotionPlanner.DepthFirstSearch, "Depth First Search"),
+        # 2: (MotionPlanner.DepthLimitedSearch, "Depth Limited Search"),
+        # 3: (MotionPlanner.UniformCostSearch, "Uniform Cost Search"),
+        # 4: (MotionPlanner.GreedyBestFirstSearch, "Greedy Best First Search"),
+        5: (MotionPlanner.AStarSearch, "A* Search")
+
+        # 6: (MotionPlanner.StudentMotionPlanner, "Student Planner"),
+        # 7: (MotionPlanner.StudentMotionPlannerExample, "Student Planner Example")
+    }
+
+    for (class_planner, name_planner) in dict_motion_planners.values():
+        planner = class_planner(scenario=scenario, planning_problem=planning_problem,
+                                automaton=automaton, plot_config=config_plot)
+
+        # start search
+        print(name_planner + " started..")
+        planner.execute_search()
+        plt.title(name_planner)
+        # close the figure to continue
+        plt.show()
+
 
 print('Done')
+
+if __name__ == '__main__':
+    main()