diff --git a/demo/traffic_signalB/mp0_p1.py b/demo/traffic_signalB/mp0_p1.py new file mode 100644 index 00000000..5800479e --- /dev/null +++ b/demo/traffic_signalB/mp0_p1.py @@ -0,0 +1,374 @@ +from typing import Tuple, List + +import numpy as np +from scipy.integrate import ode + +from verse import BaseAgent, Scenario +from verse.analysis.utils import wrap_to_pi +from verse.analysis.analysis_tree import TraceType, AnalysisTree +from verse.parser import ControllerIR +from vehicle_controller import VehicleMode, PedestrianMode +from verse.analysis import AnalysisTreeNode, AnalysisTree, AnalysisTreeNodeType + +import copy + +refine_profile = { + 'R1': [0], + 'R2': [0,0,0,3], + 'R3': [0,0,0,3] +} + +def tree_safe(tree: AnalysisTree): + for node in tree.nodes: + if node.assert_hits is not None: + return False + return True + +class PedestrianAgent(BaseAgent): + def __init__( + self, + id, + ): + self.decision_logic: ControllerIR = ControllerIR.empty() + self.id = id + + @staticmethod + def dynamic(t, state): + x, y, theta, v = state + x_dot = 0 + y_dot = v + theta_dot = 0 + v_dot = 0 + return [x_dot, y_dot, theta_dot, v_dot] + + def TC_simulate( + self, mode: List[str], init, time_bound, time_step, lane_map = None + ) -> TraceType: + time_bound = float(time_bound) + num_points = int(np.ceil(time_bound / time_step)) + trace = np.zeros((num_points + 1, 1 + len(init))) + trace[1:, 0] = [round(i * time_step, 10) for i in range(num_points)] + trace[0, 1:] = init + for i in range(num_points): + r = ode(self.dynamic) + r.set_initial_value(init) + res: np.ndarray = r.integrate(r.t + time_step) + init = res.flatten() + if init[3] < 0: + init[3] = 0 + trace[i + 1, 0] = time_step * (i + 1) + trace[i + 1, 1:] = init + return trace + +class VehicleAgent(BaseAgent): + def __init__( + self, + id, + code = None, + file_name = None, + accel_brake = 5, + accel_notbrake = 5, + accel_hardbrake = 20, + speed = 10 + ): + super().__init__( + id, code, file_name + ) + self.accel_brake = accel_brake + self.accel_notbrake = accel_notbrake + self.accel_hardbrake = accel_hardbrake + self.speed = speed + self.vmax = 20 + + @staticmethod + def dynamic(t, state, u): + x, y, theta, v = state + delta, a = u + x_dot = v * np.cos(theta + delta) + y_dot = v * np.sin(theta + delta) + theta_dot = v / 1.75 * np.sin(delta) + v_dot = a + return [x_dot, y_dot, theta_dot, v_dot] + + def action_handler(self, mode: List[str], state) -> Tuple[float, float]: + x, y, theta, v = state + vehicle_mode, = mode + vehicle_pos = np.array([x, y]) + a = 0 + lane_width = 3 + d = -y + if vehicle_mode == "Normal" or vehicle_mode == "Stop": + pass + elif vehicle_mode == "SwitchLeft": + d += lane_width + elif vehicle_mode == "SwitchRight": + d -= lane_width + elif vehicle_mode == "Brake": + a = max(-self.accel_brake, -v) + # a = -50 + elif vehicle_mode == "HardBrake": + a = max(-self.accel_hardbrake, -v) + # a = -50 + elif vehicle_mode == "Accel": + a = min(self.accel_notbrake, self.speed-v) + else: + raise ValueError(f"Invalid mode: {vehicle_mode}") + + heading = 0 + psi = wrap_to_pi(heading - theta) + steering = psi + np.arctan2(0.45 * d, v) + steering = np.clip(steering, -0.61, 0.61) + return steering, a + + def TC_simulate( + self, mode: List[str], init, time_bound, time_step, lane_map = None + ) -> TraceType: + time_bound = float(time_bound) + num_points = int(np.ceil(time_bound / time_step)) + trace = np.zeros((num_points + 1, 1 + len(init))) + trace[1:, 0] = [round(i * time_step, 10) for i in range(num_points)] + trace[0, 1:] = init + for i in range(num_points): + steering, a = self.action_handler(mode, init) + r = ode(self.dynamic) + r.set_initial_value(init).set_f_params([steering, a]) + res: np.ndarray = r.integrate(r.t + time_step) + init = res.flatten() + if init[3] < 0: + init[3] = 0 + trace[i + 1, 0] = time_step * (i + 1) + trace[i + 1, 1:] = init + return trace + +def dist(pnt1, pnt2): + return np.linalg.norm( + np.array(pnt1) - np.array(pnt2) + ) + +def get_extreme(rect1, rect2): + lb11 = rect1[0] + lb12 = rect1[1] + ub11 = rect1[2] + ub12 = rect1[3] + + lb21 = rect2[0] + lb22 = rect2[1] + ub21 = rect2[2] + ub22 = rect2[3] + + # Using rect 2 as reference + left = lb21 > ub11 + right = ub21 < lb11 + bottom = lb22 > ub12 + top = ub22 < lb12 + + if top and left: + dist_min = dist((ub11, lb12),(lb21, ub22)) + dist_max = dist((lb11, ub12),(ub21, lb22)) + elif bottom and left: + dist_min = dist((ub11, ub12),(lb21, lb22)) + dist_max = dist((lb11, lb12),(ub21, ub22)) + elif top and right: + dist_min = dist((lb11, lb12), (ub21, ub22)) + dist_max = dist((ub11, ub12), (lb21, lb22)) + elif bottom and right: + dist_min = dist((lb11, ub12),(ub21, lb22)) + dist_max = dist((ub11, lb12),(lb21, ub22)) + elif left: + dist_min = lb21 - ub11 + dist_max = np.sqrt((lb21 - ub11)**2 + max((ub22-lb12)**2, (ub12-lb22)**2)) + elif right: + dist_min = lb11 - ub21 + dist_max = np.sqrt((lb21 - ub11)**2 + max((ub22-lb12)**2, (ub12-lb22)**2)) + elif top: + dist_min = lb12 - ub22 + dist_max = np.sqrt((ub12 - lb22)**2 + max((ub21-lb11)**2, (ub11-lb21)**2)) + elif bottom: + dist_min = lb22 - ub12 + dist_max = np.sqrt((ub22 - lb12)**2 + max((ub21-lb11)**2, (ub11-lb21)**2)) + else: + dist_min = 0 + dist_max = max( + dist((lb11, lb12), (ub21, ub22)), + dist((lb11, ub12), (ub21, lb22)), + dist((ub11, lb12), (lb21, ub12)), + dist((ub11, ub12), (lb21, lb22)) + ) + return dist_min, dist_max + +class VehiclePedestrianSensor: + def __init__(self): + self.sensor_distance = 100 + + # The baseline sensor is omniscient. Each agent can get the state of all other agents + def sense(self, agent: BaseAgent, state_dict, lane_map, simulate): + len_dict = {} + cont = {} + disc = {} + len_dict = {"others": len(state_dict) - 1} + tmp = np.array(list(state_dict.values())[0][0]) + if simulate: + if agent.id == 'car': + len_dict['others'] = 1 + cont['ego.x'] = state_dict['car'][0][1] + cont['ego.y'] = state_dict['car'][0][2] + cont['ego.theta'] = state_dict['car'][0][3] + cont['ego.v'] = state_dict['car'][0][4] + disc['ego.agent_mode'] = state_dict['car'][1][0] + dist = np.sqrt( + (state_dict['car'][0][1]-state_dict['pedestrian'][0][1])**2+\ + (state_dict['car'][0][2]-state_dict['pedestrian'][0][2])**2 + ) + # cont['ego.dist'] = dist + if dist < self.sensor_distance: + cont['other.dist'] = dist + # cont['other.x'] = state_dict['pedestrian'][0][1] + # cont['other.y'] = state_dict['pedestrian'][0][2] + # cont['other.v'] = state_dict['pedestrian'][0][4] + else: + cont['other.dist'] = 1000 + # cont['other.x'] = 1000 + # cont['other.y'] = 1000 + # cont['other.v'] = 1000 + else: + if agent.id == 'car': + len_dict['others'] = 1 + dist_min, dist_max = get_extreme( + (state_dict['car'][0][0][1],state_dict['car'][0][0][2],state_dict['car'][0][1][1],state_dict['car'][0][1][2]), + (state_dict['pedestrian'][0][0][1],state_dict['pedestrian'][0][0][2],state_dict['pedestrian'][0][1][1],state_dict['pedestrian'][0][1][2]), + ) + cont['ego.x'] = [ + state_dict['car'][0][0][1], state_dict['car'][0][1][1] + ] + cont['ego.y'] = [ + state_dict['car'][0][0][2], state_dict['car'][0][1][2] + ] + cont['ego.theta'] = [ + state_dict['car'][0][0][3], state_dict['car'][0][1][3] + ] + cont['ego.v'] = [ + state_dict['car'][0][0][4], state_dict['car'][0][1][4] + ] + cont['other.dist'] = [ + dist_min, dist_max + ] + disc['ego.agent_mode'] = state_dict['car'][1][0] + if dist_min= 7): + print(bcolors.OKGREEN + f"Overall average velocity over {len(velo_list)} safe executions is {sum(velo_list)/len(velo_list)}. This is above the threshold of 7!😋" + bcolors.ENDC) + else: + print(bcolors.RED + f"Overall average velocity over {len(velo_list)} safe executions is {sum(velo_list)/len(velo_list)}. This is below the threshold of 7!😱" + bcolors.ENDC) + + return {sum(velo_list)/len(velo_list)}, float(len(unsafe_init))/float(len(tree_list)), unsafe_init + +def combine_tree(tree_list: List[AnalysisTree]): + combined_trace={} + for tree in tree_list: + for node in tree.nodes: + for agent_id in node.agent: + traces = node.trace + trace = np.array(traces[agent_id]) + if agent_id not in combined_trace: + combined_trace[agent_id]={} + for i in range (0, len(trace), 2): + step = round(trace[i][0], 3) + if step not in combined_trace[agent_id]: + combined_trace[agent_id][step]=[trace[i], trace[i+1]] + else: + lower = np.min([combined_trace[agent_id][step][0],trace[i]], 0) + upper = np.max([combined_trace[agent_id][step][1],trace[i+1]], 0) + combined_trace[agent_id][step]=[lower, upper] + + final_trace = {agent_id:np.array(list(combined_trace[agent_id].values())).flatten().reshape((-1, trace[i].size)).tolist() for agent_id in combined_trace} + root = AnalysisTreeNode(final_trace,None,tree_list[0].root.mode,None,None, node.agent, None,None,[],0,10,AnalysisTreeNodeType.REACH_TUBE,0) + return AnalysisTree(root) diff --git a/demo/traffic_signalB/mp0_p2.py b/demo/traffic_signalB/mp0_p2.py new file mode 100644 index 00000000..bcd8c5cf --- /dev/null +++ b/demo/traffic_signalB/mp0_p2.py @@ -0,0 +1,438 @@ +from typing import Tuple, List + +import numpy as np +from scipy.integrate import ode + +from verse import BaseAgent, Scenario +from verse.analysis.utils import wrap_to_pi +from verse.analysis.analysis_tree import TraceType, AnalysisTree +from verse.parser import ControllerIR +from vehicle_controller import VehicleMode +from verse.analysis import AnalysisTreeNode, AnalysisTree, AnalysisTreeNodeType +from traffic_controller import TLMode +import copy + +refine_profile = { + 'R1': [0], + 'R2': [0], + 'R3': [0,0,0,3] +} + +def tree_safe(tree: AnalysisTree): + for node in tree.nodes: + if node.assert_hits is not None: + return False + return True + +def verify_refine(scenario: Scenario, time_horizon, time_step): + + + init_car = scenario.init_dict['car'] + init_tl = scenario.init_dict['tl'] + partition_depth = 0 + if init_car[1][3] - init_car[0][3]>0.1: + exp = 'R3' + elif init_car[1][0] - init_car[0][0] >= 75: + exp = 'R2' + else: + exp = 'R1' + res_list = [] + init_queue = [] + if init_car[1][3]-init_car[0][3] > 0.1: + car_v_init_range = np.linspace(init_car[0][3], init_car[1][3], 5) + else: + car_v_init_range = [init_car[0][3], init_car[1][3]] + if init_car[1][0]-init_car[0][0] > 1: + car_x_init_range = np.linspace(init_car[0][0], init_car[1][0], 5) + else: + car_x_init_range = [init_car[0][0], init_car[1][0]] + for i in range(len(car_x_init_range)-1): + for j in range(len(car_v_init_range)-1): + tmp = copy.deepcopy(init_car) + tmp[0][0] = car_x_init_range[i] + tmp[1][0] = car_x_init_range[i+1] + tmp[0][3] = car_v_init_range[j] + tmp[1][3] = car_v_init_range[j+1] + init_queue.append((tmp, init_tl, partition_depth)) + #init_queue = [([[70, -5 ,0, 3],[75 , 5, 0, 7]], init_tl, 0)] + start = len(init_queue) + # init_queue = [(init_car, init_ped, partition_depth)] + from alive_progress import alive_bar + progress = 0 + with alive_bar(100, max_cols = 140, manual =True) as safe_bar: + safe_bar.title( bcolors.OKGREEN + '% Verified Safe (May be at 0% for some time)'+ bcolors.ENDC) + + while init_queue!=[]: + car_init, tl_init, partition_depth = init_queue.pop(0) + if(partition_depth >=5): + print(bcolors.OKBLUE +"If the % Safe bar hasn't gone up past 0 by now, we recommend exiting" + bcolors.ENDC) + print(bcolors.BOLD + f"######## Current Partition Depth: {partition_depth}, car x: [{car_init[0][0]}, {car_init[1][0]}] car v: [{car_init[0][3]}, {car_init[1][3]}]" + bcolors.ENDC) + scenario.set_init_single('car', car_init, (VehicleMode.Normal,)) + scenario.set_init_single('tl', tl_init, (TLMode.GREEN,)) + traces = scenario.verify(time_horizon, time_step) + scenario.verifier.loop_cache = [] + + if not tree_safe(traces): + # Partition car and pedestrian initial state + idx = refine_profile[exp][partition_depth%len(refine_profile[exp])] + if car_init[1][idx] - car_init[0][idx] < 0.01: + print(bcolors.RED + f"Can't partition initial set dimension {idx} anymore. Scenario is likely unsafe😭" + bcolors.ENDC) + res_list.append(traces) + return res_list + car_v_init = (car_init[0][idx] + car_init[1][idx])/2 + car_init1 = copy.deepcopy(car_init) + car_init1[1][idx] = car_v_init + init_queue.append((car_init1, tl_init, partition_depth+1)) + car_init2 = copy.deepcopy(car_init) + car_init2[0][idx] = car_v_init + init_queue.append((car_init2, tl_init, partition_depth+1)) + + else: + progress +=(1/(start*(2**partition_depth))) + safe_bar(progress) + + res_list.append(traces) + print("Verify Tasks left to run: " + str(len(init_queue))) + # com_traces = combine_tree(res_list) + print( bcolors.OKGREEN + "Verify Refine: Scenario is SAFE😃" + bcolors.ENDC) + return res_list + +class TrafficSignalAgent(BaseAgent): + def __init__( + self, + id, + file_name + ): + super().__init__(id, code = None, file_name = file_name) + + def TC_simulate( + self, mode: List[str], init, time_bound, time_step, lane_map = None + ) -> TraceType: + time_bound = float(time_bound) + num_points = int(np.ceil(time_bound / time_step)) + trace = np.zeros((num_points + 1, 1 + len(init))) + trace[1:, 0] = [round((i+1) * time_step, 10) for i in range(num_points)] + trace[:,-1] = trace[:,0]+init[4] + trace[:, 1:-1] = init[:-1] + return trace +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKCYAN = '\033[96m' + OKGREEN = '\033[92m' + RED = '\033[31m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + +class VehicleAgent(BaseAgent): + def __init__( + self, + id, + code = None, + file_name = None, + accel_brake = 1, + accel_notbrake = 1, + accel_hardbrake = 5, + speed = 10 + ): + super().__init__( + id, code, file_name + ) + self.accel_brake = accel_brake + self.accel_notbrake = accel_notbrake + self.accel_hardbrake = accel_hardbrake + self.speed = speed + self.vmax = 20 + + @staticmethod + def dynamic(t, state, u): + x, y, theta, v = state + delta, a = u + x_dot = v * np.cos(theta + delta) + y_dot = v * np.sin(theta + delta) + theta_dot = v / 1.75 * np.sin(delta) + v_dot = a + return [x_dot, y_dot, theta_dot, v_dot] + + def action_handler(self, mode: List[str], state) -> Tuple[float, float]: + x, y, theta, v = state + vehicle_mode, = mode + vehicle_pos = np.array([x, y]) + a = 0 + lane_width = 3 + d = -y + if vehicle_mode == "Normal" or vehicle_mode == "Stop": + pass + elif vehicle_mode == "SwitchLeft": + d += lane_width + elif vehicle_mode == "SwitchRight": + d -= lane_width + elif vehicle_mode == "Brake": + a = max(-self.accel_brake, -v) + # a = -50 + elif vehicle_mode == "HardBrake": + a = max(-self.accel_hardbrake, -v) + # a = -50 + elif vehicle_mode == "Accel": + a = min(self.accel_notbrake, self.speed-v) + else: + raise ValueError(f"Invalid mode: {vehicle_mode}") + + heading = 0 + psi = wrap_to_pi(heading - theta) + steering = psi + np.arctan2(0.45 * d, v) + steering = np.clip(steering, -0.61, 0.61) + return steering, a + + def TC_simulate( + self, mode: List[str], init, time_bound, time_step, lane_map = None + ) -> TraceType: + time_bound = float(time_bound) + num_points = int(np.ceil(time_bound / time_step)) + trace = np.zeros((num_points + 1, 1 + len(init))) + trace[1:, 0] = [round(i * time_step, 10) for i in range(num_points)] + trace[0, 1:] = init + for i in range(num_points): + steering, a = self.action_handler(mode, init) + r = ode(self.dynamic) + r.set_initial_value(init).set_f_params([steering, a]) + res: np.ndarray = r.integrate(r.t + time_step) + init = res.flatten() + if init[3] < 0: + init[3] = 0 + trace[i + 1, 0] = time_step * (i + 1) + trace[i + 1, 1:] = init + return trace + +def dist(pnt1, pnt2): + return np.linalg.norm( + np.array(pnt1) - np.array(pnt2) + ) + +def get_extreme(rect1, rect2): + lb11 = rect1[0] + lb12 = rect1[1] + ub11 = rect1[2] + ub12 = rect1[3] + + lb21 = rect2[0] + lb22 = rect2[1] + ub21 = rect2[2] + ub22 = rect2[3] + + # Using rect 2 as reference + left = lb21 > ub11 + right = ub21 < lb11 + bottom = lb22 > ub12 + top = ub22 < lb12 + + if top and left: + dist_min = dist((ub11, lb12),(lb21, ub22)) + dist_max = dist((lb11, ub12),(ub21, lb22)) + elif bottom and left: + dist_min = dist((ub11, ub12),(lb21, lb22)) + dist_max = dist((lb11, lb12),(ub21, ub22)) + elif top and right: + dist_min = dist((lb11, lb12), (ub21, ub22)) + dist_max = dist((ub11, ub12), (lb21, lb22)) + elif bottom and right: + dist_min = dist((lb11, ub12),(ub21, lb22)) + dist_max = dist((ub11, lb12),(lb21, ub22)) + elif left: + dist_min = lb21 - ub11 + dist_max = np.sqrt((lb21 - ub11)**2 + max((ub22-lb12)**2, (ub12-lb22)**2)) + elif right: + dist_min = lb11 - ub21 + dist_max = np.sqrt((lb21 - ub11)**2 + max((ub22-lb12)**2, (ub12-lb22)**2)) + elif top: + dist_min = lb12 - ub22 + dist_max = np.sqrt((ub12 - lb22)**2 + max((ub21-lb11)**2, (ub11-lb21)**2)) + elif bottom: + dist_min = lb22 - ub12 + dist_max = np.sqrt((ub22 - lb12)**2 + max((ub21-lb11)**2, (ub11-lb21)**2)) + else: + dist_min = 0 + dist_max = max( + dist((lb11, lb12), (ub21, ub22)), + dist((lb11, ub12), (ub21, lb22)), + dist((ub11, lb12), (lb21, ub12)), + dist((ub11, ub12), (lb21, lb22)) + ) + return dist_min, dist_max + +class TrafficSensor: + def __init__(self): + self.sensor_distance = 60 + + # The baseline sensor is omniscient. Each agent can get the state of all other agents + def sense(self, agent: BaseAgent, state_dict, lane_map, simulate): + len_dict = {} + cont = {} + disc = {} + len_dict = {"others": len(state_dict) - 1} + #tmp = np.array(list(state_dict.values())[0][0]) + if simulate: + if agent.id == 'car': + len_dict['others'] = 1 + cont['ego.x'] = state_dict['car'][0][1] + cont['ego.y'] = state_dict['car'][0][2] + cont['ego.theta'] = state_dict['car'][0][3] + cont['ego.v'] = state_dict['car'][0][4] + disc['ego.agent_mode'] = state_dict['car'][1][0] + disc['other.signal_mode'] = state_dict['tl'][1][0] + # dist = np.sqrt( + # (state_dict['car'][0][1]-state_dict['tl'][0][1])**2+\ + # (state_dict['car'][0][2]-state_dict['tl'][0][2])**2 + # ) + # dist = state_dict['tl'][0][1] - state_dict['car'][0][1] + # if dist < self.sensor_distance: + # cont['other.dist'] = dist + # else: + # cont['other.dist'] = 1000 + cont['other.x'] = state_dict['tl'][0][1] + elif agent.id == 'tl': + cont['ego.x'] = state_dict['tl'][0][1] + cont['ego.y'] = state_dict['tl'][0][2] + cont['ego.theta'] = state_dict['tl'][0][3] + cont['ego.v'] = state_dict['tl'][0][4] + cont['ego.timer'] = state_dict['tl'][0][5] + disc['ego.signal_mode'] = state_dict['tl'][1][0] + else: + if agent.id == 'car': + len_dict['others'] = 1 + # dist_min, dist_max = get_extreme( + # (state_dict['car'][0][0][1],state_dict['car'][0][0][2],state_dict['car'][0][1][1],state_dict['car'][0][1][2]), + # (state_dict['tl'][0][0][1],state_dict['tl'][0][0][2],state_dict['tl'][0][1][1],state_dict['tl'][0][1][2]), + # ) + # dist_min = min(abs(state_dict['car'][0][0][1]-state_dict['tl'][0][0][1]), abs(state_dict['car'][0][1][1]-state_dict['tl'][0][0][1])) + # dist_max = max(abs(state_dict['car'][0][0][1]-state_dict['tl'][0][0][1]), abs(state_dict['car'][0][1][1]-state_dict['tl'][0][0][1])) + cont['ego.x'] = [ + state_dict['car'][0][0][1], state_dict['car'][0][1][1] + ] + cont['ego.y'] = [ + state_dict['car'][0][0][2], state_dict['car'][0][1][2] + ] + cont['ego.theta'] = [ + state_dict['car'][0][0][3], state_dict['car'][0][1][3] + ] + cont['ego.v'] = [ + state_dict['car'][0][0][4], state_dict['car'][0][1][4] + ] + cont['other.x'] = [ + state_dict['tl'][0][0][1], state_dict['tl'][0][1][1] + ] + # cont['other.dist'] = [ + # dist_min, dist_max + # ] + disc['ego.agent_mode'] = state_dict['car'][1][0] + disc['other.signal_mode'] = state_dict['tl'][1][0] + # if dist_min= 6.5): + print(bcolors.OKGREEN + f"Overall average velocity over {len(velo_list)} safe executions is {sum(velo_list)/len(velo_list)}. This is above the threshold of 6.5!😋" + bcolors.ENDC) + else: + print(bcolors.RED + f"Overall average velocity over {len(velo_list)} safe executions is {sum(velo_list)/len(velo_list)}. This is below the threshold of 6.5!😱" + bcolors.ENDC) + + return {sum(velo_list)/len(velo_list)}, float(len(unsafe_init))/float(len(tree_list)), unsafe_init + +def combine_tree(tree_list: List[AnalysisTree]): + combined_trace={} + for tree in tree_list: + for node in tree.nodes: + for agent_id in node.agent: + traces = node.trace + trace = np.array(traces[agent_id]) + if agent_id not in combined_trace: + combined_trace[agent_id]={} + for i in range (0, len(trace), 2): + step = round(trace[i][0], 3) + if step not in combined_trace[agent_id]: + combined_trace[agent_id][step]=[trace[i], trace[i+1]] + else: + lower = np.min([combined_trace[agent_id][step][0],trace[i]], 0) + upper = np.max([combined_trace[agent_id][step][1],trace[i+1]], 0) + combined_trace[agent_id][step]=[lower, upper] + + final_trace = {agent_id:np.array(list(combined_trace[agent_id].values())).flatten().reshape((-1, np.array(list(combined_trace[agent_id].values())).shape[-1])).tolist() for agent_id in combined_trace} + root = AnalysisTreeNode(final_trace,None,tree_list[0].root.mode,None,None, node.agent, None,None,[],0,10,AnalysisTreeNodeType.REACH_TUBE,0) + return AnalysisTree(root) diff --git a/demo/traffic_signalB/plotter_test.py b/demo/traffic_signalB/plotter_test.py new file mode 100644 index 00000000..a64a80f8 --- /dev/null +++ b/demo/traffic_signalB/plotter_test.py @@ -0,0 +1,65 @@ +from mp0_p1 import VehicleAgent, PedestrianAgent, VehiclePedestrianSensor +from verse import Scenario, ScenarioConfig +from vehicle_controller import VehicleMode, PedestrianMode + +from verse.plotter.plotter3D import * +from verse.plotter.plotter3D_new import * +import pyvista as pv +import plotly.graph_objects as go +import copy + +if __name__ == "__main__": + import os + script_dir = os.path.realpath(os.path.dirname(__file__)) + input_code_name = os.path.join(script_dir, "vehicle_controller.py") + vehicle = VehicleAgent('car', file_name=input_code_name) + pedestrian = PedestrianAgent('pedestrian') + + scenario = Scenario(ScenarioConfig(init_seg_length=1, parallel=False)) + + scenario.add_agent(vehicle) + scenario.add_agent(pedestrian) + scenario.set_sensor(VehiclePedestrianSensor()) + + + # init_car = [[4,-5,0,8],[5,5,0,8]] + # init_pedestrian = [[170,-55,0,3],[175,-52,0,5]] + + init_car = [[-5, -5 ,0,8],[5,5,0,8]] + init_pedestrian = [[170, -55, 0,3],[175, -53,0,5]] + + scenario.set_init_single( + 'car', init_car,(VehicleMode.Normal,) + ) + scenario.set_init_single( + 'pedestrian', init_pedestrian, (PedestrianMode.Normal,) + ) + + # traces = [] + # fig = go.Figure() + # n=3 + # for i in range(n): + # trace = scenario.simulate(50, 0.1) + # traces.append(trace) + # fig = simulation_tree_3d(trace, fig,\ + # 0,'time', 1,'x',2,'y') + # avg_vel, unsafe_frac, unsafe_init = eval_velocity(traces) + # fig.show() + #fig = pv.Plotter() + #fig.show(interactive_update=True) + + # # ----------- verify no refine: Uncomment this block to perform verification without refinement ---------- + + fig = pv.Plotter() + fig.show(interactive_update=True) + traces = scenario.verify(80, 0.1,fig ) + + fig = plot3dReachtube(traces,'car',0,1,2,'b',fig) + fig = plot3dReachtube(traces,'pedestrian',0,1,2,'r',fig) + fig.show() + + # fig = go.Figure() + # fig = reachtube_tree_3d(traces, fig,\ + # 0,'time', 1,'x',2,'y') + # fig.show() + # # ----------------------------------------- diff --git a/demo/traffic_signalB/run_app.py b/demo/traffic_signalB/run_app.py new file mode 100644 index 00000000..7330f07e --- /dev/null +++ b/demo/traffic_signalB/run_app.py @@ -0,0 +1,403 @@ +from wsgiref.validate import PartialIteratorWrapper +from mp0_p1 import VehicleAgent, PedestrianAgent, VehiclePedestrianSensor, eval_velocity, sample_init +from verse import Scenario, ScenarioConfig +from vehicle_controller import VehicleMode, PedestrianMode + +from verse.plotter.plotter2D import * +from verse.plotter.plotter3D import * + +from verse.plotter.plotter3D_new import * +import plotly.graph_objects as go + + +import sys +import numpy as np +import pyvista as pv +import pyvistaqt as pvqt +from PyQt6.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget, QPushButton +from PyQt6.QtWebEngineWidgets import QWebEngineView +import threading +import time + +class MainWindow(QMainWindow): + def __init__(self): + super().__init__() + + self.setWindowTitle("PyVista in Qt Web App") + self.setGeometry(100, 100, 1200, 800) + self.plane1_position = 0 + self.plane2_position = 0 + self.thread = None + + # Main Widget and Layout + main_widget = QWidget() + layout = QVBoxLayout() + + # PyVistaQt QtInteractor (for 3D visualization) + self.plotter = pvqt.QtInteractor(main_widget) + #self.plotter.setMinimumHeight(400) # Fixed to half of 800px + + layout.addWidget(self.plotter.interactor) + + # QWebEngineView (for web UI) + self.web_view = QWebEngineView() + layout.addWidget(self.plotter.interactor) + #self.plotter.setMinimumHeight(400) # Fixed to half of 800px + + # QWebEngineView (for web UI) + self.web_view = QWebEngineView() + self.web_view.setHtml(""" + + + Embedded PyVista + + + + +
+ + +
+
+ + +
0
+
50
+
100
+
150
+
200
+ + +
+
+
+
+
+ + +
200
+
150
+
100
+
50
+
0
+ + +
+
+
+
+
+
✈ 1
+
✈ 2
+
+ + + """) + layout.addWidget(self.web_view) + + # Run button to trigger saving the positions + self.run_button = QPushButton("Run") + self.run_button.clicked.connect(self.run_button_clicked) + layout.addWidget(self.run_button) + + main_widget.setLayout(layout) + self.setCentralWidget(main_widget) + + def run_verse(self, x1,y1, r1 , x2,y2, r2 ): + import os + script_dir = os.path.realpath(os.path.dirname(__file__)) + input_code_name = os.path.join(script_dir, "vehicle_controller.py") + vehicle = VehicleAgent('car', file_name=input_code_name) + pedestrian = PedestrianAgent('pedestrian') + + scenario = Scenario(ScenarioConfig(init_seg_length=1, parallel=False)) + + scenario.add_agent(vehicle) + scenario.add_agent(pedestrian) + scenario.set_sensor(VehiclePedestrianSensor()) + + + # init_car = [[4,-5,0,8],[5,5,0,8]] + # init_pedestrian = [[170,-55,0,3],[175,-52,0,5]] + + init_car = [[x1 - r1,y1 -r1 ,0,8],[x1+r1,y1 +r1,0,8]] + init_pedestrian = [[x2 -r2, y2- r2,0,3],[x2 +r2,y2+r2,0,5]] + + scenario.set_init_single( + 'car', init_car,(VehicleMode.Normal,) + ) + scenario.set_init_single( + 'pedestrian', init_pedestrian, (PedestrianMode.Normal,) + ) + + # traces = [] + # fig = go.Figure() + # n=3 + # for i in range(n): + # trace = scenario.simulate(50, 0.1) + # traces.append(trace) + # fig = simulation_tree_3d(trace, fig,\ + # 0,'time', 1,'x',2,'y') + # avg_vel, unsafe_frac, unsafe_init = eval_velocity(traces) + # fig.show() + #fig = pv.Plotter() + #fig.show(interactive_update=True) + _ = self.plotter.add_axes_at_origin( xlabel='', + ylabel='', + zlabel='') + traces = scenario.verify(80, 0.1, self.plotter) + + # Add an interactive 3D sphere + def run_button_clicked(self): + self.web_view.page().runJavaScript("getPlanePositions();", self.handle_positions) + def start_animation(self, x1,y1,r1, x2,y2,r2): + """Starts a background thread to move the sphere.""" + if(self.thread): + self.thread.join() + self.plotter.clear() + + self.thread = threading.Thread(target=self.run_verse,args=[x1,y1,r1, x2,y2,r2], daemon=True) + self.thread.start() + + + def handle_positions(self, positions): + def position_to_int(pos): + x = int(pos['x'][:-2]) + y = int(pos['y'][:-2]) + return (x/2,y/2) + + + # positions is a JavaScript object containing the current positions of the planes + print("Positions of planes:", positions) + + # Process the positions and use them in the 3D visualization or other tasks + self.plane1_position = positions['plane1'] + self.plane2_position = positions['plane2'] + + x1, y1 = position_to_int(self.plane1_position) + x2, y2 = position_to_int(self.plane2_position) + + self.start_animation(x1, y1,1,x2,y2,1) + + #print(self.plane1_position, self.plane2_position) + + # You can further process these positions in your PyVista 3D + def _set_python_bridge(self, result): + """Sets the python bridge object in JS""" + self.web_view.page().runJavaScript("window.pyQtApp = pyQtApp;", self._set_python_bridge) + + # Start background thread for animation + + + +# Run the Qt Application +if __name__ == "__main__": + app = QApplication(sys.argv) + main_window = MainWindow() + main_window.show() + sys.exit(app.exec()) + + +# if __name__ == "__main__": +# import os +# script_dir = os.path.realpath(os.path.dirname(__file__)) +# input_code_name = os.path.join(script_dir, "vehicle_controller.py") +# vehicle = VehicleAgent('car', file_name=input_code_name) +# pedestrian = PedestrianAgent('pedestrian') + +# scenario = Scenario(ScenarioConfig(init_seg_length=1, parallel=False)) + +# scenario.add_agent(vehicle) +# scenario.add_agent(pedestrian) +# scenario.set_sensor(VehiclePedestrianSensor()) + +# # # ----------- Different initial ranges ------------- +# # # Uncomment this block to use R1 +# init_car = [[4,-5,0,8],[5,5,0,8]] +# init_pedestrian = [[170,-55,0,3],[175,-52,0,5]] +# # # ----------------------------------------- + +# # # Uncomment this block to use R2 +# # init_car = [[-5,-5,0,7.5],[5,5,0,8.5]] +# # init_pedestrian = [[175,-55,0,3],[175,-55,0,3]] +# # # ----------------------------------------- + +# # # Uncomment this block to use R3 +# # init_car = [[-5,-5,0,7.5],[5,5,0,8.5]] +# # init_pedestrian = [[173,-55,0,3],[176,-53,0,3]] +# # # ----------------------------------------- + +# scenario.set_init_single( +# 'car', init_car,(VehicleMode.Normal,) +# ) +# scenario.set_init_single( +# 'pedestrian', init_pedestrian, (PedestrianMode.Normal,) +# ) + +# # traces = [] +# # fig = go.Figure() +# # n=3 +# # for i in range(n): +# # trace = scenario.simulate(50, 0.1) +# # traces.append(trace) +# # fig = simulation_tree_3d(trace, fig,\ +# # 0,'time', 1,'x',2,'y') +# # avg_vel, unsafe_frac, unsafe_init = eval_velocity(traces) +# # fig.show() +# fig = pv.Plotter() +# fig.show(interactive_update=True) +# traces = scenario.verify(80, 0.1, fig) + + +# # fig = pv.Plotter() +# # fig = plot3dReachtube(traces,'car',0,1,2,'b',fig) +# # fig = plot3dReachtube(traces,'pedestrian',0,1,2,'r',fig) + +# # fig = reachtube_tree_3d(traces, fig,\ +# # 0,'time', 1,'x',2,'y') +# # fig.show() + + + + + + diff --git a/demo/traffic_signalB/traffic_controller.py b/demo/traffic_signalB/traffic_controller.py new file mode 100644 index 00000000..23404b08 --- /dev/null +++ b/demo/traffic_signalB/traffic_controller.py @@ -0,0 +1,34 @@ +from enum import Enum, auto +import copy +from typing import List + +class TLMode(Enum): + GREEN=auto() + YELLOW=auto() + RED=auto() + +class State: + x: float + y: float + theta: float + v: float + timer: float + signal_mode: TLMode + + def __init__(self, x, y, theta, v, timer, signal_mode: TLMode): + pass + +def decisionLogic(ego: State): + output = copy.deepcopy(ego) + if ego.signal_mode == TLMode.GREEN and ego.timer > 20: + output.signal_mode = TLMode.YELLOW + output.timer = 0 + if ego.signal_mode == TLMode.YELLOW and ego.timer > 5: + output.signal_mode = TLMode.RED + output.timer = 0 + if ego.signal_mode == TLMode.RED and ego.timer > 20: + output.signal_mode = TLMode.GREEN + output.timer = 0 + + # assert True + return output \ No newline at end of file diff --git a/demo/traffic_signalB/traffic_signal_scenario.py b/demo/traffic_signalB/traffic_signal_scenario.py deleted file mode 100644 index de38b271..00000000 --- a/demo/traffic_signalB/traffic_signal_scenario.py +++ /dev/null @@ -1,65 +0,0 @@ -from mp4_p2 import VehicleAgent, TrafficSignalAgent, TrafficSensor, eval_velocity, sample_init -from verse import Scenario, ScenarioConfig -from vehicle_controller import VehicleMode, TLMode - -from verse.plotter.plotter2D import * -from verse.plotter.plotter3D_new import * -import plotly.graph_objects as go -import copy - -if __name__ == "__main__": - import os - script_dir = os.path.realpath(os.path.dirname(__file__)) - input_code_name = os.path.join(script_dir, "vehicle_controller.py") - vehicle = VehicleAgent('car', file_name=input_code_name) - input_code_name = os.path.join(script_dir, "traffic_controller.py") - tl = TrafficSignalAgent('tl', file_name=input_code_name) - - scenario = Scenario(ScenarioConfig(init_seg_length=1, parallel=False)) - - scenario.add_agent(vehicle) - scenario.add_agent(tl) - scenario.set_sensor(TrafficSensor()) - - # # ----------- Different initial ranges ------------- - # # Uncomment this block to use R1 - init_car = [[0,-5,0,5],[50,5,0,5]] - init_trfficlight = [[300,0,0,0,0],[300,0,0,0,0]] - # # ----------------------------------------- - - # # Uncomment this block to use R2 - # init_car = [[0,-5,0,5],[100,5,0,5]] - # init_trfficlight = [[300,0,0,0,0],[300,0,0,0,0]] - # # ----------------------------------------- - - # # Uncomment this block to use R3 - # init_car = [[0,-5,0,0],[100,5,0,10]] - # init_trfficlight = [[300,0,0,0,0],[300,0,0,0,0]] - # # ----------------------------------------- - - scenario.set_init_single( - 'car', init_car,(VehicleMode.Normal,) - ) - scenario.set_init_single( - 'tl', init_trfficlight, (TLMode.GREEN,) - ) - - # ----------- Simulate simple: Uncomment this block to perform single simple simulation ------------- - #trace = scenario.simulate_simple(80, 0.1) - #avg_vel, unsafe_frac, unsafe_init = eval_velocity([trace]) - #fig = go.Figure() - #fig = simulation_tree_3d(trace, fig,\ - # 0,'time', 1,'x',2,'y') - #fig.show() - # ----------------------------------------- - - - # ----------- Simulate single: Uncomment this block to perform single simulation ------------- - trace = scenario.simulate(80, 0.1) - avg_vel, unsafe_frac, unsafe_init = eval_velocity([trace]) - fig = go.Figure() - fig = simulation_tree_3d(trace, fig,\ - 0,'time', 1,'x',2,'y') - fig.write_html('simulate.html', auto_open=True) - # ----------------------------------------- - diff --git a/demo/traffic_signalB/vehicle_controller.py b/demo/traffic_signalB/vehicle_controller.py new file mode 100644 index 00000000..3f64de40 --- /dev/null +++ b/demo/traffic_signalB/vehicle_controller.py @@ -0,0 +1,49 @@ +from enum import Enum, auto +import copy +from typing import List + +class PedestrianMode(Enum): + Normal=auto() + +class VehicleMode(Enum): + Normal = auto() + Brake = auto() + Accel = auto() + HardBrake = auto() + +class State: + x: float + y: float + theta: float + v: float + agent_mode: VehicleMode + + def __init__(self, x, y, theta, v, agent_mode: VehicleMode): + pass + +def decisionLogic(ego: State, other: State): + output = copy.deepcopy(ego) + + if ego.agent_mode != VehicleMode.Accel and other.dist > 100: + output.agent_mode = VehicleMode.Accel + + # TODO: Edit this part of decision logic + if ego.agent_mode == VehicleMode.Accel and other.dist < 30: + output.agent_mode = VehicleMode.Brake + + # if ego.agent_mode != VehicleMode.Accel and other.dist < 25: + # output.agent_mode = VehicleMode.Accel + + # if ego.agent_mode != VehicleMode.Brake and other.dist < 10: + # output.agent_mode = VehicleMode.Brake + + + + + + ########################################### + + # DO NOT CHANGE THIS + #assert other.dist > 1.0, "Too Close!" + + return output \ No newline at end of file diff --git a/nodes.jsonl b/nodes.jsonl new file mode 100644 index 00000000..5ac3f25c --- /dev/null +++ b/nodes.jsonl @@ -0,0 +1,2 @@ +{"id": 2, "trace": {"pedestrian": [[13.5, 170.0000000001521, -14.473656477011712, 0.0, 3.0000000000574705], [13.600000000000001, 174.9999999998479, 15.973656477001017, 0.0, 4.9999999999425295], [13.600000000000001, 170.0000000001521, -14.173656477000575, 0.0, 3.0000000000574705], [13.700000000000001, 174.9999999998479, 16.473656476989877, 0.0, 4.9999999999425295], [13.700000000000001, 170.0000000001521, -13.873656476989435, 0.0, 3.0000000000574705], [13.8, 174.9999999998479, 16.97365647697874, 0.0, 4.9999999999425295], [13.8, 170.0000000001521, -13.573656476978295, 0.0, 3.0000000000574705], [13.9, 174.9999999998479, 17.4736564769676, 0.0, 4.9999999999425295], [13.9, 170.0000000001521, -13.273656476967158, 0.0, 3.0000000000574705], [14.0, 174.9999999998479, 17.97365647695646, 0.0, 4.9999999999425295], [14.0, 170.0000000001521, -12.973656476956018, 0.0, 3.0000000000574705], [14.100000000000001, 174.9999999998479, 18.473656476945326, 0.0, 4.9999999999425295], [14.100000000000001, 170.0000000001521, -12.673656476944881, 0.0, 3.0000000000574705], [14.200000000000001, 174.9999999998479, 18.973656476934185, 0.0, 4.9999999999425295], [14.200000000000001, 170.0000000001521, -12.373656476933743, 0.0, 3.0000000000574705], [14.3, 174.9999999998479, 19.473656476923047, 0.0, 4.9999999999425295], [14.3, 170.0000000001521, -12.0736564769226, 0.0, 3.0000000000574705], [14.4, 174.9999999998479, 19.97365647691191, 0.0, 4.9999999999425295], [14.4, 170.0000000001521, -11.773656476911466, 0.0, 3.0000000000574705], [14.5, 174.9999999998479, 20.473656476900775, 0.0, 4.9999999999425295], [14.5, 170.0000000001521, -11.473656476900327, 0.0, 3.0000000000574705], [14.600000000000001, 174.9999999998479, 20.973656476889634, 0.0, 4.9999999999425295], [14.600000000000001, 170.0000000001521, -11.173656476889189, 0.0, 3.0000000000574705], 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8.509283110680954e-09, 10.000000000000014], [79.5, 415.8584897472816, -1.6689348996536118e-07, -8.135535002173582e-09, 9.999999999997502], [79.60000000000001, 647.1795629497567, 1.6689348996536118e-07, 8.135535002173582e-09, 10.000000000000014], [79.60000000000001, 416.85848974728117, -1.595632663590101e-07, -7.778204676596287e-09, 9.99999999999776], [79.7, 648.1795629497566, 1.595632663590101e-07, 7.778204676596287e-09, 10.000000000000014], [79.7, 417.8584897472809, -1.5255505192258324e-07, -7.436571498153523e-09, 9.999999999997994], [79.8, 649.1795629497565, 1.5255505192258324e-07, 7.436571498153523e-09, 10.000000000000014], [79.8, 418.8584897472805, -1.4585469991940352e-07, -7.1099464417271756e-09, 9.999999999998193], [79.9, 650.1795629497564, 1.4585469991940352e-07, 7.1099464417271756e-09, 10.000000000000014], [79.9, 419.8584897472804, -1.3944868740964151e-07, -6.797669470422667e-09, 9.999999999998373], [80.0, 651.1795629497562, 1.3944868740964151e-07, 6.797669470422667e-09, 10.000000000000014]]} diff --git a/requirements.txt b/requirements.txt index f54b6281..d05b9ce5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -ray~=2.4.0 +ray>=0 astunparse~=1.6.3 beautifulsoup4~=4.11.1 intervaltree~=3.1.0 @@ -12,7 +12,7 @@ pyvista~=0.35.2 scipy~=1.9 six~=1.14.0 sympy~=1.6.2 -torch~=1.13.1 +torch>=1.13.1 tqdm~=4.64.1 z3-solver treelib~=1.6.1 diff --git a/verse/analysis/nodes.jsonl b/verse/analysis/nodes.jsonl new file mode 100644 index 00000000..e69de29b diff --git a/verse/analysis/pyvista_test.py b/verse/analysis/pyvista_test.py new file mode 100644 index 00000000..2ba6e46e --- /dev/null +++ b/verse/analysis/pyvista_test.py @@ -0,0 +1,79 @@ +import sys +import numpy as np +import pyvista as pv +import pyvistaqt as pvqt +from PyQt6.QtWidgets import QApplication, QMainWindow, QVBoxLayout, QWidget +from PyQt6.QtWebEngineWidgets import QWebEngineView +import threading +import time + +class MainWindow(QMainWindow): + def __init__(self): + super().__init__() + + self.setWindowTitle("PyVista in Qt Web App") + self.setGeometry(100, 100, 1200, 800) + + # Main Widget and Layout + main_widget = QWidget() + layout = QVBoxLayout() + + # PyVistaQt QtInteractor (for 3D visualization) + self.plotter = pvqt.QtInteractor(main_widget) + layout.addWidget(self.plotter.interactor) + + # QWebEngineView (for web UI) + self.web_view = QWebEngineView() + self.web_view.setHtml(""" + + Embedded PyVista + +

PyVista Qt Web App

+

3D Visualization is Embedded Below.

+ + + """) + layout.addWidget(self.web_view) + + main_widget.setLayout(layout) + self.setCentralWidget(main_widget) + + # Add an interactive 3D sphere + self.add_sphere() + + # Start background thread for animation + self.start_animation() + + def add_sphere(self): + """Add an interactive sphere to the PyVista scene.""" + sphere = pv.Sphere(radius=0.3, center=(0, 0, 0)) + self.actor = self.plotter.add_mesh(sphere, color="red") + + def start_animation(self): + """Starts a background thread to move the sphere.""" + thread = threading.Thread(target=self.animate_sphere, daemon=True) + thread.start() + + def animate_sphere(self): + """Move the sphere in a circular motion.""" + t = 0 + while True: + new_x = np.sin(t) * 2 + new_y = np.cos(t) * 2 + new_z = np.sin(t / 2) * 1 + + # Update the actor's position + self.actor.SetPosition(new_x, new_y, new_z) + + # Refresh the plotter + self.plotter.render() + + t += 0.1 # Adjust motion speed + time.sleep(0.05) # Control update frequency + +# Run the Qt Application +if __name__ == "__main__": + app = QApplication(sys.argv) + main_window = MainWindow() + main_window.show() + sys.exit(app.exec()) diff --git a/verse/analysis/simulator.py b/verse/analysis/simulator.py index a0383842..112250b1 100644 --- a/verse/analysis/simulator.py +++ b/verse/analysis/simulator.py @@ -700,7 +700,7 @@ def get_transition_simulate( aid: (node.trace[aid][0], node.mode[aid], node.static[aid]) for aid in node.agent } cont_var_dict_template, discrete_variable_dict, len_dict = sensor.sense( - agent, state_dict, track_map + agent, state_dict, track_map, True ) agent_guard_dict[agent_id].append((path, discrete_variable_dict)) @@ -720,7 +720,7 @@ def get_transition_simulate( agent_state, agent_mode, agent_static = state_dict[agent_id] agent_state = agent_state[1:] continuous_variable_dict, orig_disc_vars, _ = sensor.sense( - agent, state_dict, track_map + agent, state_dict, track_map,True ) unchecked_cache_guards = [ g[:2] for g in cached_guards[agent_id] if g[2] < idx diff --git a/verse/analysis/utils.py b/verse/analysis/utils.py new file mode 100644 index 00000000..c7ad0133 --- /dev/null +++ b/verse/analysis/utils.py @@ -0,0 +1,437 @@ +import copy +import importlib +import itertools +from typing import Any, Tuple, Dict, Callable, List, Optional, Type, TypeVar, Union, Sequence + +import numpy as np + +# Useful types +Vector = Union[np.ndarray, Sequence[float]] +Matrix = Union[np.ndarray, Sequence[Sequence[float]]] +Interval = Union[ + np.ndarray, + Tuple[Vector, Vector], + Tuple[Matrix, Matrix], + Tuple[float, float], + List[Vector], + List[Matrix], + List[float], +] + + +def to_serializable(arg: Union[np.ndarray, List]) -> List: + if isinstance(arg, np.ndarray): + return arg.tolist() + return arg + + +def do_every(duration: float, timer: float) -> bool: + return duration < timer + + +def lmap(v: float, x: Interval, y: Interval) -> float: + """Linear map of value v with range x to desired range y.""" + return y[0] + (v - x[0]) * (y[1] - y[0]) / (x[1] - x[0]) + + +def get_class_path(cls: Callable) -> str: + return cls.__module__ + "." + cls.__qualname__ + + +def class_from_path(path: str) -> Callable: + module_name, class_name = path.rsplit(".", 1) + class_object = getattr(importlib.import_module(module_name), class_name) + return class_object + + +def constrain(x: float, a: float, b: float) -> np.ndarray: + return np.clip(x, a, b) + + +def not_zero(x: float, eps: float = 1e-2) -> float: + if abs(x) > eps: + return x + elif x >= 0: + return eps + else: + return -eps + + +def wrap_to_pi(x: float) -> float: + return ((x + np.pi) % (2 * np.pi)) - np.pi + + +def point_in_rectangle(point: Vector, rect_min: Vector, rect_max: Vector) -> bool: + """ + Check if a point is inside a rectangle + + :param point: a point (x, y) + :param rect_min: x_min, y_min + :param rect_max: x_max, y_max + """ + return rect_min[0] <= point[0] <= rect_max[0] and rect_min[1] <= point[1] <= rect_max[1] + + +def point_in_rotated_rectangle( + point: np.ndarray, center: np.ndarray, length: float, width: float, angle: float +) -> bool: + """ + Check if a point is inside a rotated rectangle + + :param point: a point + :param center: rectangle center + :param length: rectangle length + :param width: rectangle width + :param angle: rectangle angle [rad] + :return: is the point inside the rectangle + """ + c, s = np.cos(angle), np.sin(angle) + r = np.array([[c, -s], [s, c]]) + ru = r.dot(point - center) + return point_in_rectangle(ru, (-length / 2, -width / 2), (length / 2, width / 2)) + + +def point_in_ellipse( + point: Vector, center: Vector, angle: float, length: float, width: float +) -> bool: + """ + Check if a point is inside an ellipse + + :param point: a point + :param center: ellipse center + :param angle: ellipse main axis angle + :param length: ellipse big axis + :param width: ellipse small axis + :return: is the point inside the ellipse + """ + c, s = np.cos(angle), np.sin(angle) + r = np.matrix([[c, -s], [s, c]]) + ru = r.dot(point - center) + return np.sum(np.square(ru / np.array([length, width]))) < 1 + + +def rotated_rectangles_intersect( + rect1: Tuple[Vector, float, float, float], rect2: Tuple[Vector, float, float, float] +) -> bool: + """ + Do two rotated rectangles intersect? + + :param rect1: (center, length, width, angle) + :param rect2: (center, length, width, angle) + :return: do they? + """ + return has_corner_inside(rect1, rect2) or has_corner_inside(rect2, rect1) + + +def rect_corners( + center: np.ndarray, + length: float, + width: float, + angle: float, + include_midpoints: bool = False, + include_center: bool = False, +) -> List[np.ndarray]: + """ + Returns the positions of the corners of a rectangle. + :param center: the rectangle center + :param length: the rectangle length + :param width: the rectangle width + :param angle: the rectangle angle + :param include_midpoints: include middle of edges + :param include_center: include the center of the rect + :return: a list of positions + """ + center = np.array(center) + half_l = np.array([length / 2, 0]) + half_w = np.array([0, width / 2]) + corners = [-half_l - half_w, -half_l + half_w, +half_l + half_w, +half_l - half_w] + if include_center: + corners += [[0, 0]] + if include_midpoints: + corners += [-half_l, half_l, -half_w, half_w] + + c, s = np.cos(angle), np.sin(angle) + rotation = np.array([[c, -s], [s, c]]) + return (rotation @ np.array(corners).T).T + np.tile(center, (len(corners), 1)) + + +def has_corner_inside( + rect1: Tuple[Vector, float, float, float], rect2: Tuple[Vector, float, float, float] +) -> bool: + """ + Check if rect1 has a corner inside rect2 + + :param rect1: (center, length, width, angle) + :param rect2: (center, length, width, angle) + """ + return any( + [ + point_in_rotated_rectangle(p1, *rect2) + for p1 in rect_corners(*rect1, include_midpoints=True, include_center=True) + ] + ) + + +def project_polygon(polygon: Vector, axis: Vector) -> Tuple[float, float]: + min_p, max_p = None, None + for p in polygon: + projected = p.dot(axis) + if min_p is None or projected < min_p: + min_p = projected + if max_p is None or projected > max_p: + max_p = projected + return min_p, max_p + + +def interval_distance(min_a: float, max_a: float, min_b: float, max_b: float): + """ + Calculate the distance between [minA, maxA] and [minB, maxB] + The distance will be negative if the intervals overlap + """ + return min_b - max_a if min_a < min_b else min_a - max_b + + +def are_polygons_intersecting( + a: Vector, b: Vector, displacement_a: Vector, displacement_b: Vector +) -> Tuple[bool, bool, Optional[np.ndarray]]: + """ + Checks if the two polygons are intersecting. + + See https://www.codeproject.com/Articles/15573/2D-Polygon-Collision-Detection + + :param a: polygon A, as a list of [x, y] points + :param b: polygon B, as a list of [x, y] points + :param displacement_a: velocity of the polygon A + :param displacement_b: velocity of the polygon B + :return: are intersecting, will intersect, translation vector + """ + intersecting = will_intersect = True + min_distance = np.inf + translation, translation_axis = None, None + for polygon in [a, b]: + for p1, p2 in zip(polygon, polygon[1:]): + normal = np.array([-p2[1] + p1[1], p2[0] - p1[0]]) + normal /= np.linalg.norm(normal) + min_a, max_a = project_polygon(a, normal) + min_b, max_b = project_polygon(b, normal) + + if interval_distance(min_a, max_a, min_b, max_b) > 0: + intersecting = False + + velocity_projection = normal.dot(displacement_a - displacement_b) + if velocity_projection < 0: + min_a += velocity_projection + else: + max_a += velocity_projection + + distance = interval_distance(min_a, max_a, min_b, max_b) + if distance > 0: + will_intersect = False + if not intersecting and not will_intersect: + break + if abs(distance) < min_distance: + min_distance = abs(distance) + d = a[:-1].mean(axis=0) - b[:-1].mean(axis=0) # center difference + translation_axis = normal if d.dot(normal) > 0 else -normal + + if will_intersect: + translation = min_distance * translation_axis + return intersecting, will_intersect, translation + + +def confidence_ellipsoid( + data: Dict[str, np.ndarray], + lambda_: float = 1e-5, + delta: float = 0.1, + sigma: float = 0.1, + param_bound: float = 1.0, +) -> Tuple[np.ndarray, np.ndarray, float]: + """ + Compute a confidence ellipsoid over the parameter theta, where y = theta^T phi + + :param data: a dictionary {"features": [phi_0,...,phi_N], "outputs": [y_0,...,y_N]} + :param lambda_: l2 regularization parameter + :param delta: confidence level + :param sigma: noise covariance + :param param_bound: an upper-bound on the parameter norm + :return: estimated theta, Gramian matrix G_N_lambda, radius beta_N + """ + phi = np.array(data["features"]) + y = np.array(data["outputs"]) + g_n_lambda = 1 / sigma * np.transpose(phi) @ phi + lambda_ * np.identity(phi.shape[-1]) + theta_n_lambda = np.linalg.inv(g_n_lambda) @ np.transpose(phi) @ y / sigma + d = theta_n_lambda.shape[0] + beta_n = ( + np.sqrt(2 * np.log(np.sqrt(np.linalg.det(g_n_lambda) / lambda_**d) / delta)) + + np.sqrt(lambda_ * d) * param_bound + ) + return theta_n_lambda, g_n_lambda, beta_n + + +def confidence_polytope( + data: dict, parameter_box: np.ndarray +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, float]: + """ + Compute a confidence polytope over the parameter theta, where y = theta^T phi + + :param data: a dictionary {"features": [phi_0,...,phi_N], "outputs": [y_0,...,y_N]} + :param parameter_box: a box [theta_min, theta_max] containing the parameter theta + :return: estimated theta, polytope vertices, Gramian matrix G_N_lambda, radius beta_N + """ + param_bound = np.amax(np.abs(parameter_box)) + theta_n_lambda, g_n_lambda, beta_n = confidence_ellipsoid(data, param_bound=param_bound) + + values, pp = np.linalg.eig(g_n_lambda) + radius_matrix = np.sqrt(beta_n) * np.linalg.inv(pp) @ np.diag(np.sqrt(1 / values)) + h = np.array(list(itertools.product([-1, 1], repeat=theta_n_lambda.shape[0]))) + d_theta = np.array([radius_matrix @ h_k for h_k in h]) + + # Clip the parameter and confidence region within the prior parameter box. + theta_n_lambda = np.clip(theta_n_lambda, parameter_box[0], parameter_box[1]) + for k, _ in enumerate(d_theta): + d_theta[k] = np.clip( + d_theta[k], parameter_box[0] - theta_n_lambda, parameter_box[1] - theta_n_lambda + ) + return theta_n_lambda, d_theta, g_n_lambda, beta_n + + +def is_valid_observation( + y: np.ndarray, + phi: np.ndarray, + theta: np.ndarray, + gramian: np.ndarray, + beta: float, + sigma: float = 0.1, +) -> bool: + """ + Check if a new observation (phi, y) is valid according to a confidence ellipsoid on theta. + + :param y: observation + :param phi: feature + :param theta: estimated parameter + :param gramian: Gramian matrix + :param beta: ellipsoid radius + :param sigma: noise covariance + :return: validity of the observation + """ + y_hat = np.tensordot(theta, phi, axes=[0, 0]) + error = np.linalg.norm(y - y_hat) + eig_phi, _ = np.linalg.eig(phi.transpose() @ phi) + eig_g, _ = np.linalg.eig(gramian) + error_bound = np.sqrt(np.amax(eig_phi) / np.amin(eig_g)) * beta + sigma + return error < error_bound + + +def is_consistent_dataset(data: dict, parameter_box: np.ndarray = None) -> bool: + """ + Check whether a dataset {phi_n, y_n} is consistent + + The last observation should be in the confidence ellipsoid obtained by the N-1 first observations. + + :param data: a dictionary {"features": [phi_0,...,phi_N], "outputs": [y_0,...,y_N]} + :param parameter_box: a box [theta_min, theta_max] containing the parameter theta + :return: consistency of the dataset + """ + train_set = copy.deepcopy(data) + y, phi = train_set["outputs"].pop(-1), train_set["features"].pop(-1) + y, phi = np.array(y)[..., np.newaxis], np.array(phi)[..., np.newaxis] + if train_set["outputs"] and train_set["features"]: + theta, _, gramian, beta = confidence_polytope(train_set, parameter_box=parameter_box) + return is_valid_observation(y, phi, theta, gramian, beta) + else: + return True + + +def near_split(x, num_bins=None, size_bins=None): + """ + Split a number into several bins with near-even distribution. + + You can either set the number of bins, or their size. + The sum of bins always equals the total. + :param x: number to split + :param num_bins: number of bins + :param size_bins: size of bins + :return: list of bin sizes + """ + if num_bins: + quotient, remainder = divmod(x, num_bins) + return [quotient + 1] * remainder + [quotient] * (num_bins - remainder) + elif size_bins: + return near_split(x, num_bins=int(np.ceil(x / size_bins))) + + +def distance_to_circle(center, radius, direction): + scaling = radius * np.ones((2, 1)) + a = np.linalg.norm(direction / scaling) ** 2 + b = -2 * np.dot(np.transpose(center), direction / np.square(scaling)) + c = np.linalg.norm(center / scaling) ** 2 - 1 + root_inf, root_sup = solve_trinom(a, b, c) + if root_inf and root_inf > 0: + distance = root_inf + elif root_sup and root_sup > 0: + distance = 0 + else: + distance = np.infty + return distance + + +def distance_to_rect(line: Tuple[np.ndarray, np.ndarray], rect: List[np.ndarray]): + """ + Compute the intersection between a line segment and a rectangle. + + See https://math.stackexchange.com/a/2788041. + :param line: a line segment [R, Q] + :param rect: a rectangle [A, B, C, D] + :return: the distance between R and the intersection of the segment RQ with the rectangle ABCD + """ + r, q = line + a, b, c, d = rect + u = b - a + v = d - a + u, v = u / np.linalg.norm(u), v / np.linalg.norm(v) + rqu = (q - r) @ u + rqv = (q - r) @ v + interval_1 = [(a - r) @ u / rqu, (b - r) @ u / rqu] + interval_2 = [(a - r) @ v / rqv, (d - r) @ v / rqv] + interval_1 = interval_1 if rqu >= 0 else list(reversed(interval_1)) + interval_2 = interval_2 if rqv >= 0 else list(reversed(interval_2)) + if ( + interval_distance(*interval_1, *interval_2) <= 0 + and interval_distance(0, 1, *interval_1) <= 0 + and interval_distance(0, 1, *interval_2) <= 0 + ): + return max(interval_1[0], interval_2[0]) * np.linalg.norm(q - r) + else: + return np.inf + + +def solve_trinom(a, b, c): + delta = b**2 - 4 * a * c + if delta >= 0: + return (-b - np.sqrt(delta)) / (2 * a), (-b + np.sqrt(delta)) / (2 * a) + else: + return None, None + + +def sample_rect(rect: List[List[float]], seed=None) -> List[float]: + # point = [] + # for i in range(len(rect[0])): + if seed is not None: + np.random.seed(seed) + res = np.random.uniform(rect[0], rect[1]).tolist() + return res + + +T = TypeVar("T") + + +def dedup(l: List[T], f: Callable[[T], Any] = (lambda a: a)) -> List[T]: + o = [] + dl = [(i, f(i)) for i in l] + for i, k in dl: + for _, k_ in o: + if k == k_: + break + else: + o.append((i, k)) + return [i for i, _ in o] diff --git a/verse/analysis/verifier.py b/verse/analysis/verifier.py index e056d0f4..52591fda 100644 --- a/verse/analysis/verifier.py +++ b/verse/analysis/verifier.py @@ -8,6 +8,7 @@ import ast import ray, time from verse.parser import unparse +import json from verse.analysis.analysis_tree import AnalysisTreeNode, AnalysisTree, TraceType from verse.analysis.dryvr import calc_bloated_tube, SIMTRACENUM @@ -24,7 +25,10 @@ from verse.parser.parser import find, ModePath, unparse from verse.agents.base_agent import BaseAgent from verse.automaton import GuardExpressionAst, ResetExpression - +import json +import os +import pyvista as pv +from verse.plotter.plotter3D import * pp = functools.partial(pprint.pprint, compact=True, width=130) PathDiffs = List[Tuple[BaseAgent, ModePath]] @@ -52,6 +56,29 @@ class ReachConsts: agent_dict: Dict +def save_node(node): + filename = "nodes.json" + + # Load existing data if the file exists + if os.path.exists(filename): + with open(filename, "r") as file: + try: + data = json.load(file) + except json.JSONDecodeError: + data = [] # Start fresh if file is corrupted + else: + data = [] + + # Append new node data + data.append({ "id": node.id, "trace": node.trace }) + + # Write back the updated data + with open(filename, "w") as file: + print("dumping") + json.dump(data, file, indent=4) + + + class Verifier: def __init__(self, config): self.reachtube_tree = None @@ -384,6 +411,8 @@ def compute_full_reachtube_step( remain_time: float, consts: ReachConsts, max_height: int, + ax: pv.Plotter, + params={}, ) -> Tuple[int, int, List[AnalysisTreeNode], Dict[str, TraceType], list]: # t = timeit.default_timer() @@ -550,6 +579,20 @@ def compute_full_reachtube_step( else: for agent in node.agent: node.trace[agent] = node.trace[agent][: (idx + 1)] + + print("writing data") + data = [] + ids = [] + for agent_id in node.trace: + ids.append(agent_id) + + l = len(node.trace[ids[0]]) + for i in range(0, l, 2): + for agent_id in ids: + trace = node.trace[agent_id] + data.append([trace[i], trace[i + 1]]) + plot3dReachtubeSingle(data, 0, 1, 2, ax, 'b', edge=True) + #ax.render() return ( node.id, later, @@ -653,7 +696,44 @@ def compute_full_reachtube_step( else: if all_possible_transitions: for agent_idx in node.agent: - node.trace[agent_idx] = node.trace[agent_idx][: (max_end_idx + 1)] + node.trace[agent_idx] = node.trace[agent_idx][: (max_end_idx + 1)] + + + # with open("nodes.jsonl", "a") as file: + # print("dumping") + # try: + # file.write(json.dumps({ "id": node.id, "trace": node.trace }) + "\n") + # except json.JSONDecodeError: + # print("Error") + # if(node.start_time == 29.7): + # print(len(node.trace['car'])) + # print(len(node.trace['pedestrian'])) + # with open("nodes.jsonl", "a") as file: + # print("dumping") + # try: + # file.write(json.dumps({ "id": node.id, "trace": node.trace['car'] }) + "\n") + # except json.JSONDecodeError: + # print("Error") + + data = [] + ids = [] + for agent_id in node.trace: + ids.append(agent_id) + + l = len(node.trace[ids[0]]) + for i in range(0, l, 2): + for agent_id in ids: + trace = node.trace[agent_id] + data.append([trace[i], trace[i + 1]]) + plot3dReachtubeSingle(data, 0, 1, 2, ax, 'b', edge=True) + ax.render() + + #ax.show() + + + + + return ( node.id, later, @@ -739,6 +819,7 @@ def compute_full_reachtube( reachability_method, run_num, past_runs, + ax, params={}, ): if max_height == None: @@ -840,6 +921,7 @@ def compute_full_reachtube( remain_time, consts, max_height, + ax, params, ), max_height, diff --git a/verse/plotter/plotter3D.py b/verse/plotter/plotter3D.py index 220c6742..22f100b7 100644 --- a/verse/plotter/plotter3D.py +++ b/verse/plotter/plotter3D.py @@ -12,10 +12,13 @@ def plot3dReachtubeSingle(tube, x_dim, y_dim, z_dim, ax, color, edge=True): - for lb, ub in tube: - box = [[lb[x_dim], lb[y_dim], lb[z_dim]], [ub[x_dim], ub[y_dim], ub[z_dim]]] - poly = pc.box2poly(np.array(box).T) - ax = plot_polytope_3d(poly.A, poly.b, ax=ax, color=color, edge=edge) + + for i, (lb, ub) in enumerate(tube): + if(i%11 ==0 ): + box = [[lb[x_dim], lb[y_dim], lb[z_dim]], [ub[x_dim], ub[y_dim], ub[z_dim]]] + poly = pc.box2poly(np.array(box).T) + ax = plot_polytope_3d(poly.A, poly.b, ax=ax, color=color, edge=edge) + return ax @@ -47,6 +50,7 @@ def plot3dReachtube(root, agent_id, x_dim, y_dim, z_dim, color="b", ax=None, edg queue = [root] idx = 0 + while queue != []: node = queue.pop(0) traces = node.trace @@ -59,6 +63,7 @@ def plot3dReachtube(root, agent_id, x_dim, y_dim, z_dim, color="b", ax=None, edg # for key in sorted(node.upper_bound): # upper_bound.append(node.upper_bound[key]) ax = plot3dReachtubeSingle(data, x_dim, y_dim, z_dim, ax, color, edge=edge) + queue += node.child idx += 1 @@ -82,11 +87,15 @@ def plot_polytope_3d(A, b, ax=None, color="red", trans=0.2, edge=True): volume = cloud.delaunay_3d() shell = volume.extract_geometry() if len(shell.points) <= 0: + ax.reset_camera() + return ax ax.add_mesh(shell, opacity=trans, color=color) if edge: edges = shell.extract_feature_edges(20) ax.add_mesh(edges, color="k", line_width=0.1, opacity=0.5) + ax.reset_camera() + return ax diff --git a/verse/scenario/scenario.py b/verse/scenario/scenario.py index 51ca899e..53177ee5 100644 --- a/verse/scenario/scenario.py +++ b/verse/scenario/scenario.py @@ -337,7 +337,7 @@ def simulate_simple(self, time_horizon, time_step, max_height=None, seed=None) - self.past_runs.append(tree) return tree - def verify(self, time_horizon, time_step, max_height=None, params={}) -> AnalysisTree: + def verify(self, time_horizon, time_step, ax, max_height=None, params={}) -> AnalysisTree: '''Compute the set of reachable states, starting from a set of initial states states.''' _check_ray_init(self.config.parallel) self._check_init() @@ -369,6 +369,7 @@ def verify(self, time_horizon, time_step, max_height=None, params={}) -> Analysi self.config.reachability_method, len(self.past_runs), self.past_runs, + ax, params, ) self.past_runs.append(tree)