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How to use scenario_wrapper method in pytest-bdd

Best Python code snippet using pytest-bdd_python

simulations.py

Source:simulations.py

1"""2SUMO simulation specific helper methods3"""4__author__ = "Peter Kocsis, Edmond Irani Liu"5__copyright__ = "TUM Cyber-Physical System Group"6__credits__ = []7__version__ = "0.1"8__maintainer__ = "Edmond Irani Liu"9__email__ = "edmond.irani@tum.de"10__status__ = "Integration"11import copy12import os13import pickle14from enum import unique, Enum15from math import sin, cos16from typing import Tuple, Dict, Optional17import numpy as np18from sumocr.sumo_config.default import DefaultConfig19from sumocr.interface.ego_vehicle import EgoVehicle20from sumocr.interface.sumo_simulation import SumoSimulation21from sumocr.maps.sumo_scenario import ScenarioWrapper22from sumocr.visualization.video import create_video23from sumocr.sumo_docker.interface.docker_interface import SumoInterface24from commonroad.scenario.scenario import Scenario25from commonroad.planning.planning_problem import PlanningProblemSet26from commonroad.common.solution import Solution27from commonroad.common.file_reader import CommonRoadFileReader28from main_interactive_CRplanner import InteractiveCRPlanner29import matplotlib.pyplot as plt30from commonroad.visualization.draw_dispatch_cr import draw_object31@unique32class SimulationOption(Enum):33    WITHOUT_EGO = "_without_ego"34    MOTION_PLANNER = "_planner"35    SOLUTION = "_solution"36def simulate_scenario(mode: SimulationOption,37                      conf: DefaultConfig,38                      scenario_wrapper: ScenarioWrapper,39                      scenario_path: str,40                      num_of_steps: int = None,41                      planning_problem_set: PlanningProblemSet = None,42                      solution: Solution = None,43                      use_sumo_manager: bool = False) -> Tuple[Scenario, Dict[int, EgoVehicle]]:44    """45    Simulates an interactive scenario with specified mode46    :param mode: 0 = without ego, 1 = with plugged in planner, 2 = with solution trajectory47    :param conf: config of the simulation48    :param scenario_wrapper: scenario wrapper used by the Simulator49    :param scenario_path: path to the interactive scenario folder50    :param num_of_steps: number of steps to simulate51    :param planning_problem_set: planning problem set of the scenario52    :param solution: solution to the planning problem53    :param use_sumo_manager: indicates whether to use the SUMO Manager54    :return: simulated scenario and dictionary with items {planning_problem_id: EgoVehicle}55    """56    if num_of_steps is None:57        num_of_steps = conf.simulation_steps58    sumo_interface = None59    if use_sumo_manager:60        sumo_interface = SumoInterface(use_docker=True)61        sumo_sim = sumo_interface.start_simulator()62        sumo_sim.send_sumo_scenario(conf.scenario_name,63                                    scenario_path)64    else:65        sumo_sim = SumoSimulation()66    # initialize simulation67    sumo_sim.initialize(conf, scenario_wrapper, None)68    if mode is SimulationOption.WITHOUT_EGO:69        # simulation without ego vehicle70        for step in range(num_of_steps):71            # set to dummy simulation72            sumo_sim.dummy_ego_simulation = True73            sumo_sim.simulate_step()74    elif mode is SimulationOption.MOTION_PLANNER:75        # simulation with plugged in planner76        def run_simulation():77            ego_vehicles = sumo_sim.ego_vehicles78            for step in range(num_of_steps):79                if use_sumo_manager:80                    ego_vehicles = sumo_sim.ego_vehicles81                # retrieve the CommonRoad scenario at the current time step, e.g. as an input for a predicition module82                current_scenario = sumo_sim.commonroad_scenario_at_time_step(sumo_sim.current_time_step)83                for idx, ego_vehicle in enumerate(ego_vehicles.values()):84                    # retrieve the current state of the ego vehicle85                    state_current_ego = ego_vehicle.current_state86                    # ====== plug in your motion planner here87                    # example motion planner which decelerates to full stop88                    planning_problem = list(planning_problem_set.planning_problem_dict.values())[0]89                    main_planner = InteractiveCRPlanner(current_scenario, ego_vehicle.current_state)90                    last_action = []91                    is_new_action_needed = True92                    next_state, last_action, is_new_action_needed = main_planner.planning(current_scenario,93                                                                                        planning_problem,94                                                                                        ego_vehicle,95                                                                                        sumo_sim.current_time_step,96                                                                                        last_action,97                                                                                        is_new_action_needed)98                    # plt.clf()99                    # draw_parameters = {100                    #     'time_begin': sumo_sim.current_time_step,101                    #     'scenario':102                    #         {'dynamic_obstacle': {'show_label': True, },103                    #         'lanelet_network': {'lanelet': {'show_label': False, }, },104                    #         },105                    # }106                    # draw_object(current_scenario, draw_params=draw_parameters)107                    # draw_object(planning_problem_set)108                    # plt.gca().set_aspect('equal')109                    # plt.pause(0.001)110                    # next_state = copy.deepcopy(state_current_ego)111                    # next_state.steering_angle = 0.0112                    # a = -5.0113                    # dt = 0.1114                    # if next_state.velocity > 0:115                    #     v = next_state.velocity116                    #     x, y = next_state.position117                    #     o = next_state.orientation118                    #     next_state.position = np.array([x + v * cos(o) * dt, y + v * sin(o) * dt])119                    #     next_state.velocity += a * dt120                    # ====== end of motion planner121                    # update the ego vehicle with new trajectory with only 1 state for the current step122                    next_state.time_step = 1123                    trajectory_ego = [next_state]124                    ego_vehicle.set_planned_trajectory(trajectory_ego)125                if use_sumo_manager:126                    # set the modified ego vehicles to synchronize in case of using sumo_docker127                    sumo_sim.ego_vehicles = ego_vehicles128                sumo_sim.simulate_step()129        run_simulation()130    elif mode is SimulationOption.SOLUTION:131        # simulation with given solution trajectory132        def run_simulation():133            ego_vehicles = sumo_sim.ego_vehicles134            for time_step in range(num_of_steps):135                if use_sumo_manager:136                    ego_vehicles = sumo_sim.ego_vehicles137                for idx_ego, ego_vehicle in enumerate(ego_vehicles.values()):138                    # update the ego vehicles with solution trajectories139                    trajectory_solution = solution.planning_problem_solutions[idx_ego].trajectory140                    next_state = copy.deepcopy(trajectory_solution.state_list[time_step])141                    next_state.time_step = 1142                    trajectory_ego = [next_state]143                    ego_vehicle.set_planned_trajectory(trajectory_ego)144                if use_sumo_manager:145                    # set the modified ego vehicles to synchronize in case of using SUMO Manager146                    sumo_sim.ego_vehicles = ego_vehicles147                sumo_sim.simulate_step()148        check_trajectories(solution, planning_problem_set, conf)149        run_simulation()150    # retrieve the simulated scenario in CR format151    simulated_scenario = sumo_sim.commonroad_scenarios_all_time_steps()152    # stop the simulation153    sumo_sim.stop()154    if use_sumo_manager:155        sumo_interface.stop_simulator()156    ego_vechicles = {list(planning_problem_set.planning_problem_dict.keys())[0]:157                         ego_v for _, ego_v in sumo_sim.ego_vehicles.items()}158    return simulated_scenario, ego_vechicles159def simulate_without_ego(interactive_scenario_path: str,160                         output_folder_path: str = None,161                         create_video: bool = False,162                         use_sumo_manager: bool = False) -> Tuple[Scenario, PlanningProblemSet]:163    """164    Simulates an interactive scenario without ego vehicle165    :param interactive_scenario_path: path to the interactive scenario folder166    :param output_folder_path: path to the output folder167    :param create_video: indicates whether to create a mp4 of the simulated scenario168    :param use_sumo_manager: indicates whether to use the SUMO Manager169    :return: Tuple of the simulated scenario and the planning problem set170    """171    conf = load_sumo_configuration(interactive_scenario_path)172    scenario_file = os.path.join(interactive_scenario_path, f"{conf.scenario_name}.cr.xml")173    scenario, planning_problem_set = CommonRoadFileReader(scenario_file).open()174    scenario_wrapper = ScenarioWrapper()175    scenario_wrapper.sumo_cfg_file = os.path.join(interactive_scenario_path, f"{conf.scenario_name}.sumo.cfg")176    scenario_wrapper.initial_scenario = scenario177    # simulation without ego vehicle178    simulated_scenario_without_ego, _ = simulate_scenario(SimulationOption.WITHOUT_EGO, conf,179                                                          scenario_wrapper,180                                                          interactive_scenario_path,181                                                          num_of_steps=conf.simulation_steps,182                                                          planning_problem_set=planning_problem_set,183                                                          solution=None,184                                                          use_sumo_manager=use_sumo_manager)185    simulated_scenario_without_ego.scenario_id = scenario.scenario_id186    if create_video:187        create_video_for_simulation(simulated_scenario_without_ego, output_folder_path, planning_problem_set,188                                    {}, SimulationOption.WITHOUT_EGO.value)189    return simulated_scenario_without_ego, planning_problem_set190def simulate_with_solution(interactive_scenario_path: str,191                           output_folder_path: str = None,192                           solution: Solution = None,193                           create_video: bool = False,194                           use_sumo_manager: bool = False,195                           create_ego_obstacle: bool = False) -> Tuple[Scenario, PlanningProblemSet, Dict[int, EgoVehicle]]:196    """197    Simulates an interactive scenario with a given solution198    :param interactive_scenario_path: path to the interactive scenario folder199    :param output_folder_path: path to the output folder200    :param solution: solution to the planning problem201    :param create_video: indicates whether to create a mp4 of the simulated scenario202    :param use_sumo_manager: indicates whether to use the SUMO Manager203    :param create_ego_obstacle: indicates whether to create obstacles as the ego vehicles204    :return: Tuple of the simulated scenario and the planning problem set205    """206    if not isinstance(solution, Solution):207        raise Exception("Solution to the planning problem is not given.")208    conf = load_sumo_configuration(interactive_scenario_path)209    scenario_file = os.path.join(interactive_scenario_path, f"{conf.scenario_name}.cr.xml")210    scenario, planning_problem_set = CommonRoadFileReader(scenario_file).open()211    scenario_wrapper = ScenarioWrapper()212    scenario_wrapper.sumo_cfg_file = os.path.join(interactive_scenario_path, f"{conf.scenario_name}.sumo.cfg")213    scenario_wrapper.initial_scenario = scenario214    scenario_with_solution, ego_vehicles = simulate_scenario(SimulationOption.SOLUTION, conf,215                                                                       scenario_wrapper,216                                                                       interactive_scenario_path,217                                                                       num_of_steps=conf.simulation_steps,218                                                                       planning_problem_set=planning_problem_set,219                                                                       solution=solution,220                                                                       use_sumo_manager=use_sumo_manager)221    scenario_with_solution.scenario_id = scenario.scenario_id222    if create_video:223        create_video_for_simulation(scenario_with_solution, output_folder_path, planning_problem_set,224                                    ego_vehicles, SimulationOption.SOLUTION.value)225    if create_ego_obstacle:226        for pp_id, planning_problem in planning_problem_set.planning_problem_dict.items():227            obstacle_ego = ego_vehicles[pp_id].get_dynamic_obstacle()228            scenario_with_solution.add_objects(obstacle_ego)229    return scenario_with_solution, planning_problem_set, ego_vehicles230def simulate_with_planner(interactive_scenario_path: str,231                          output_folder_path: str = None,232                          create_video: bool = False,233                          use_sumo_manager: bool = False,234                          create_ego_obstacle: bool = False) \235        -> Tuple[Scenario, PlanningProblemSet, Dict[int, EgoVehicle]]:236    """237    Simulates an interactive scenario with a plugged in motion planner238    :param interactive_scenario_path: path to the interactive scenario folder239    :param output_folder_path: path to the output folder240    :param create_video: indicates whether to create a mp4 of the simulated scenario241    :param use_sumo_manager: indicates whether to use the SUMO Manager242    :param create_ego_obstacle: indicates whether to create obstacles from the planned trajectories as the ego vehicles243    :return: Tuple of the simulated scenario, planning problem set, and list of ego vehicles244    """245    conf = load_sumo_configuration(interactive_scenario_path)246    scenario_file = os.path.join(interactive_scenario_path, f"{conf.scenario_name}.cr.xml")247    scenario, planning_problem_set = CommonRoadFileReader(scenario_file).open()248    scenario_wrapper = ScenarioWrapper()249    scenario_wrapper.sumo_cfg_file = os.path.join(interactive_scenario_path, f"{conf.scenario_name}.sumo.cfg")250    scenario_wrapper.initial_scenario = scenario251    scenario_with_planner, ego_vehicles = simulate_scenario(SimulationOption.MOTION_PLANNER, conf,252                                                                      scenario_wrapper,253                                                                      interactive_scenario_path,254                                                                      num_of_steps=conf.simulation_steps,255                                                                      planning_problem_set=planning_problem_set,256                                                                      use_sumo_manager=use_sumo_manager)257    scenario_with_planner.scenario_id = scenario.scenario_id258    if create_video:259        create_video_for_simulation(scenario_with_planner, output_folder_path, planning_problem_set,260                                    ego_vehicles, SimulationOption.MOTION_PLANNER.value)261    if create_ego_obstacle:262        for pp_id, planning_problem in planning_problem_set.planning_problem_dict.items():263            obstacle_ego = ego_vehicles[pp_id].get_dynamic_obstacle()264            scenario_with_planner.add_objects(obstacle_ego)265    return scenario_with_planner, planning_problem_set, ego_vehicles266def load_sumo_configuration(interactive_scenario_path: str) -> DefaultConfig:267    with open(os.path.join(interactive_scenario_path, "simulation_config.p"), "rb") as input_file:268        conf = pickle.load(input_file)269    return conf270def check_trajectories(solution: Solution, pps: PlanningProblemSet, config: DefaultConfig):271    assert len(set(solution.planning_problem_ids) - set(pps.planning_problem_dict.keys())) == 0, \272        f"Provided solution trajectories with IDs {solution.planning_problem_ids} don't match " \273        f"planning problem IDs{list(pps.planning_problem_dict.keys())}"274    for s in solution.planning_problem_solutions:275        if s.trajectory.final_state.time_step < config.simulation_steps:276            raise ValueError(f"The simulation requires {config.simulation_steps}"277                             f"states, but the solution only provides"278                             f"{s.trajectory.final_state.time_step} time steps!")279def create_video_for_simulation(scenario_with_planner: Scenario, output_folder_path: str,280                                planning_problem_set: PlanningProblemSet,281                                ego_vehicles: Optional[Dict[int, EgoVehicle]],282                                suffix: str, follow_ego: bool = True):283    """Creates the mp4 animation for the simulation result."""284    if not output_folder_path:285        print("Output folder not specified, skipping mp4 generation.")286        return287    # create list of planning problems and trajectories288    list_planning_problems = []289    # create mp4 animation290    create_video(scenario_with_planner,291                 output_folder_path,292                 planning_problem_set=planning_problem_set,293                 trajectory_pred=ego_vehicles,294                 follow_ego=follow_ego,...

scenario.py

Source:scenario.py

...140        for arg in scenario.get_example_params():141            if arg not in function_args:142                function_args.append(arg)143        @pytest.mark.usefixtures(*function_args)144        def scenario_wrapper(request):145            _execute_scenario(feature, scenario, request, encoding)146            return fn(*[request.getfixturevalue(arg) for arg in args])147        for param_set in scenario.get_params():148            if param_set:149                scenario_wrapper = pytest.mark.parametrize(*param_set)(scenario_wrapper)150        for tag in scenario.tags.union(feature.tags):151            config = CONFIG_STACK[-1]152            config.hook.pytest_bdd_apply_tag(tag=tag, function=scenario_wrapper)153        scenario_wrapper.__doc__ = u"{feature_name}: {scenario_name}".format(154            feature_name=feature_name, scenario_name=scenario_name155        )156        scenario_wrapper.__scenario__ = scenario157        scenario_wrapper.__pytest_bdd_counter__ = counter158        scenario.test_function = scenario_wrapper...

test_multiset.py

Source:test_multiset.py

1from copy import deepcopy2from itertools import product3import pytest4from tiny_algos.minmultiset import MaxMultiSet, MinMultiSet5from tiny_algos.multiset import MultiSet6PINF = 9223372036854775807  # 2 ** 63 - 17NINF = -PINF8MIN_DEFAULT = PINF9MAX_DEFAULT = NINF10class ScenarioWrapper:11    def __init__(self, base, action, description):12        self.base = base13        self.action = action14        self.description = description15    def __repr__(self):16        return f'"{self.description}"'17scenarios = [18    ScenarioWrapper(19        base=[],20        action=[21            ("add", 1),22            ("add", 2),23            ("add", -1),24        ],25        description="construct with empty list",26    ),27    ScenarioWrapper(28        base=[1, -1],29        action=[30            ("remove", 1),31            ("remove", -1),32            ("add", 1),33            ("add", -1),34        ],35        description="Remove all and add",36    ),37    ScenarioWrapper(38        base=[0, 1, 1, 2],39        action=[40            ("add", 3),41            ("add", 2),42            ("add", -1),43            ("add", -2),44        ],45        description="add",46    ),47    ScenarioWrapper(48        base=[0, 1, 1, 2],49        action=[50            ("remove", 1),51            ("remove", 2),52            ("remove", 0),53        ],54        description="remove",55    ),56    ScenarioWrapper(57        base=[2, 4, 0, 2],58        action=[59            ("add", 3),60            ("remove", 4),61            ("add", 1),62            ("remove", 0),63            ("add", 1),64            ("add", 3),65            ("remove", 1),66            ("remove", 3),67            ("remove", 1),68            ("remove", 2),69        ],70        description="add and remove",71    ),72]73class MultiSetWrapper:74    def __init__(self, ms_cls, extremes):75        self.ms_cls = ms_cls76        self.extremes = extremes77    def __repr__(self):78        return self.ms_cls.__name__79multisets = [80    MultiSetWrapper(MultiSet, extremes=("min", "max")),81    MultiSetWrapper(MinMultiSet, extremes=("min")),82    MultiSetWrapper(MaxMultiSet, extremes=("max")),83]84@pytest.mark.parametrize(85    ["scenario_wrapper", "multiset_wrapper"],86    product(scenarios, multisets),87)88def test_multiset(scenario_wrapper, multiset_wrapper):89    ms_cls = multiset_wrapper.ms_cls90    extremes = multiset_wrapper.extremes91    base = deepcopy(scenario_wrapper.base)92    action = deepcopy(scenario_wrapper.action)93    test_list = list(base)94    test_ms = ms_cls(list(base))95    def check_extremes():96        if "min" in extremes:97            assert test_ms.min == min(test_list, default=MIN_DEFAULT)98        if "max" in extremes:99            assert test_ms.max == max(test_list, default=MAX_DEFAULT)100    check_extremes()101    for action, value in action:102        if action == "add":103            test_list.append(value)104            test_ms.add(value)105        elif action == "remove":106            test_list.remove(value)107            test_ms.remove(value)...

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