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'_io.TextIOWrapper' object has no attribute 'split'

时间: 2023-08-11 15:08:22 浏览: 357
引用\[1\]:根据您提供的引用内容,问题中的错误信息"'_io.TextIOWrapper' object has no attribute 'split'"是在使用Python的_io.TextIOWrapper对象时出现的。这个错误提示表明在_io.TextIOWrapper对象上没有split属性。根据Python官网的文档,_io.TextIOWrapper是一个用于包装文本文件的类,它提供了对文件的读写操作。根据错误信息,可能是在对该对象进行split操作时出现了问题。 要解决这个问题,您可以检查您的代码中对_io.TextIOWrapper对象的使用。首先,确保您正确地创建了_io.TextIOWrapper对象,并且在使用split方法之前对其进行了正确的初始化。其次,确保您正在对一个字符串进行split操作,而不是对_io.TextIOWrapper对象进行操作。如果您确定代码中没有错误,那么可能是您使用的Python版本不支持split方法。根据引用\[1\]中提到的,softspace属性在Python3.0以上版本中可能已经被移除了,因此,如果您使用的是Python3.0以上的版本,您可以尝试切换到Python2.7版本来查看是否能够正常执行。您可以使用命令行中的"py -2"来切换到Python2.7版本。如果您的系统中没有安装Python2版本,您需要先安装Python2,并且可以同时安装Python2和Python3,然后使用"py -2"和"py -3"来实现版本切换。 总结回答您的问题,错误信息"'_io.TextIOWrapper' object has no attribute 'split'"表明在对_io.TextIOWrapper对象进行split操作时出现了问题。您可以检查代码中对_io.TextIOWrapper对象的使用,并确保正确初始化和操作该对象。如果问题仍然存在,您可以尝试切换到Python2.7版本来查看是否能够解决问题。 #### 引用[.reference_title] - *1* [AttributeError: ‘_io.TextIOWrapper‘ object has no attribute ‘softspace](https://blog.csdn.net/qq_44176343/article/details/118703692)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]
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import math import os import struct from argparse import ArgumentParser import av import numpy as np import open3d as o3d import rosbag import yaml from sensor_msgs import point_cloud2 import subprocess from protoc.octopudata_localizationinfo_pb2 import LocalizationInfoFrame, LocalizationInfo from protoc.octopudata_trackedobject_pb2 import TrackedObjectFrame, Object, TrackedObject from protoc.octopusdata_controlcommand_pb2 import CommandFrame, ControlCommand from protoc.octopusdata_gnss_pb2 import GnssPoint, GnssPoints from protoc.octopusdata_plantrajectory_pb2 import Trajectory, TrajectoryPoint, PlanTrajectory from protoc.octopusdata_predictionobstacles_pb2 import PerceptionObstacle, \ Obstacle, PredictionTrajectory, PathPoint, PredictionObstacles from protoc.octopusdata_routingpath_pb2 import RoutingPath, Path, Point, RoutingFrames from protoc.octopusdata_vehicleinfo_pb2 import VehicleFrame, VehicleInfo av.logging.set_level(av.logging.PANIC) codec_ctx = av.codec.Codec('hevc','r') h265_code = codec_ctx.create() class Pose: def __init__(self, q0, q1, q2, q3, x, y, z): self.q0 = q0 self.q1 = q1 self.q2 = q2 self.q3 = q3 self.x = x self.y = y self.z = z def get_ts(secs, nsecs): return int(secs * 1000 + nsecs / 1000000) def get_modulo(x, y, z): return math.sqrt(x * x + y * y + z * z) def yaw_from_quaternions(w, x, y, z): a = 2 * (w * z + x * y) b = 1 - 2 * (y * y + z * z) return math.atan2(a, b) def pose_has_nan(p): return math.isnan(p.x) or math.isnan(p.y) or math.isnan(p.z) or \ math.isnan(p.q0) or math.isnan(p.q1) or math.isnan(p.q2) or \ math.isnan(p.q3) def get_t(q0, q1, q2, q3, x, y, z): aa = 1 - 2 * (q2 * q2 + q3 * q3) ab = 2 * (q1 * q2 - q0 * q3) ac = 2 * (q1 * q3 + q0 * q2) ba = 2 * (q1 * q2 + q0 * q3) bb = 1 - 2 * (q1 * q1 + q3 * q3) bc = 2 * (q2 * q3 - q0 * q1) ca = 2 * (q1 * q3 - q0 * q2) cb = 2 * (q2 * q3 + q0 * q1) cc = 1 - 2 * (q1 * q1 + q2 * q2) t = np.mat([[aa, ab, ac, x], [ba, bb, bc, y], [ca, cb, cc, z], [0, 0, 0, 1]]) return t def get_label(perception_typ, subtype): if perception_typ == 3: return 'Pedestrian' elif perception_typ == 4: return 'Bi_Tricycle' elif perception_typ == 5: if subtype == 5: return 'Truck' elif subtype == 6: return 'Bus' else: return 'Car' else: return 'unknow' def main(args): for file in os.listdir(args.input): if file.endswith('.bag'): bag_path = os.path.join(args.input, file) bag = rosbag.Bag(bag_path, "r") output_dir = os.getenv('output_dir') if not os.path.exists(os.path.join(output_dir, 'innoPtClound_A4')): os.makedirs(os.path.join(output_dir, 'innoPtClound_A4')) if not os.path.exists(os.path.join(output_dir, 'innoPtClound_B2')): os.makedirs(os.path.join(output_dir, 'innoPtClound_B2')) if not os.path.exists(os.path.join(output_dir, 'radar_track_array_0')): os.makedirs(os.path.join(output_dir, 'radar_track_array_0')) if not os.path.exists(os.path.join(output_dir, 'radar_track_array_3')): os.makedirs(os.path.join(output_dir, 'radar_track_array_3')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_74')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_74')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_73')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_73')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_72')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_72')) if not os.path.exists(os.path.join(output_dir, 'mdc_camera_instance_71')): os.makedirs(os.path.join(output_dir, 'mdc_camera_instance_71')) routes = [] controls = [] plans = [] preds = [] gnss = [] vehs = [] locs = [] objs = [] ego_pose = None has_camera_71 = False has_camera_72 = False has_camera_73 = False has_camera_74 = False has_lidar_A4 = False has_lidar_B2 = False has_radar_0 = False has_radar_3 = False lidar_num = 0 image_num = 0 radar_num = 0 for topic, msg, t in bag.read_messages(): time_stamp = int(t.to_sec() * 1000) # 以 rosbag 时间戳(t)为基准,转换为 13 位时间戳 if topic == '/innoPtClound_A4': ### 图达通 时间辍应该是13位数字,图达通雷达8位,华为96线6位 # file_path = os.path.join(output_dir, 'innoPtClound_A4', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'innoPtClound_A4', '{}.pcd'.format(time_stamp)) # print(file_path) # 提取点云数据,包括 x, y, z points = list(point_cloud2.read_points(msg, field_names=["x", "y", "z", "intensity"], skip_nans=True)) if points: # 转换为 numpy 数组,添加 intensity, ring, timestamp 字段 np_points = np.array(points) # (N, 3), 包含 x, y, z # 转换为 Open3D 格式点云 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(np_points[:, :3]) # x, y, z pcd.colors = o3d.utility.Vector3dVector(np.tile(np_points[:, 3:4] / np_points[:, 3:4].max(), (1, 3))) # 用 intensity 作为灰度颜色 o3d.io.write_point_cloud(file_path, pcd) lidar_num += 1 has_lidar_A4 = True elif topic == '/innoPtClound_B2': ### 图达通 # file_path = os.path.join(output_dir, 'innoPtClound_B2', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'innoPtClound_B2', '{}.pcd'.format(time_stamp)) # print(file_path) # 提取点云数据,包括 x, y, z points = list(point_cloud2.read_points(msg, field_names=["x", "y", "z", "intensity"], skip_nans=True)) if points: # 转换为 numpy 数组,添加 intensity, ring, timestamp 字段 np_points = np.array(points) # (N, 3), 包含 x, y, z # 转换为 Open3D 格式点云 pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(np_points[:, :3]) # x, y, z pcd.colors = o3d.utility.Vector3dVector(np.tile(np_points[:, 3:4] / np_points[:, 3:4].max(), (1, 3))) # 用 intensity 作为灰度颜色 o3d.io.write_point_cloud(file_path, pcd) lidar_num += 1 has_lidar_B2 = True elif topic == 'mdc_camera_instance_74': ### 相机 时间辍应该是13位数字 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_74', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_74 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == 'mdc_camera_instance_73': ### 相机 时间辍应该是13位数字 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_73', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_73 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == 'mdc_camera_instance_72': ### 相机 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_72', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_72 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == 'mdc_camera_instance_71': ### 相机 # time_stamp = int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000) file_path = os.path.join(output_dir, 'mdc_camera_instance_71', '{}.jpg'.format(time_stamp)) packet = av.packet.Packet(msg.data) try: out = h265_code.decode(packet) for frame in out: if frame.format.name != 'rgb24': frame = frame.reformat(format='rgb24') img = frame.to_image() img.save(file_path) image_num += 1 has_camera_71 = True except Exception as e: print("{} frame can not trans to jpg".format(time_stamp), e) elif topic == '/radar_track_array_0': ### 大陆408 时间辍应该是13位数字 # file_path = os.path.join(output_dir, 'radar_track_array_0', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'radar_track_array_0', '{}.pcd'.format(time_stamp)) points = [] for track in msg.trackList: x, y, z = track.x, track.y, track.z vx, vy, ax, ay = track.vx, track.vy, track.ax, track.ay rcs, snr, yawRate = track.rcs, track.snr, track.yawRate obj_id, trackType, lifetime = track.id, track.trackType, track.lifetime # 过滤无效点 if np.isnan(x) or np.isnan(y) or np.isnan(z): continue points.append([x, y, z, vx, vy, ax, ay, rcs, snr, yawRate, obj_id, trackType, lifetime]) if not points: print("没有有效点云数据") continue points = np.array(points) # **写入 PCD 文件** with open(file_path, 'w') as f: # **写入 PCD 头部** f.write("# .PCD v0.7 - Point Cloud Data file format\n") f.write("VERSION 0.7\n") f.write(f"FIELDS x y z vx vy ax ay rcs snr yawRate id trackType lifetime\n") f.write("SIZE 4 4 4 4 4 4 4 4 4 4 4 4 4\n") f.write("TYPE F F F F F F F F F F I I I\n") # F = float, I = int f.write("COUNT 1 1 1 1 1 1 1 1 1 1 1 1 1\n") f.write(f"WIDTH {points.shape[0]}\n") f.write("HEIGHT 1\n") f.write("VIEWPOINT 0 0 0 1 0 0 0\n") f.write(f"POINTS {points.shape[0]}\n") f.write("DATA ascii\n") # **写入点云数据** np.savetxt(f, points, fmt="%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %d %d %d") radar_num += 1 has_radar_0 = True elif topic == '/radar_track_array_3': ### 大陆408 # file_path = os.path.join(output_dir, 'radar_track_array_3', '{}.pcd'.format(int(msg.header.stamp.secs * 1000 + # msg.header.stamp.nsecs / 1000000))) file_path = os.path.join(output_dir, 'radar_track_array_3', '{}.pcd'.format(time_stamp)) points = [] for track in msg.trackList: x, y, z = track.x, track.y, track.z vx, vy, ax, ay = track.vx, track.vy, track.ax, track.ay rcs, snr, yawRate = track.rcs, track.snr, track.yawRate obj_id, trackType, lifetime = track.id, track.trackType, track.lifetime # 过滤无效点 if np.isnan(x) or np.isnan(y) or np.isnan(z): continue points.append([x, y, z, vx, vy, ax, ay, rcs, snr, yawRate, obj_id, trackType, lifetime]) if not points: print("没有有效点云数据") continue points = np.array(points) # **写入 PCD 文件** with open(file_path, 'w') as f: # **写入 PCD 头部** f.write("# .PCD v0.7 - Point Cloud Data file format\n") f.write("VERSION 0.7\n") f.write(f"FIELDS x y z vx vy ax ay rcs snr yawRate id trackType lifetime\n") f.write("SIZE 4 4 4 4 4 4 4 4 4 4 4 4 4\n") f.write("TYPE F F F F F F F F F F I I I\n") # F = float, I = int f.write("COUNT 1 1 1 1 1 1 1 1 1 1 1 1 1\n") f.write(f"WIDTH {points.shape[0]}\n") f.write("HEIGHT 1\n") f.write("VIEWPOINT 0 0 0 1 0 0 0\n") f.write(f"POINTS {points.shape[0]}\n") f.write("DATA ascii\n") # **写入点云数据** np.savetxt(f, points, fmt="%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f %d %d %d") radar_num += 1 has_radar_3 = True elif topic == '/routing/routing_response_viz': rv = RoutingPath() rv.timestamp = int(t.secs * 1000 + t.nsecs / 1000000) rv.stamp_secs = t.secs rv.stamp_nsecs = t.nsecs mark_list = list() for mark in msg.markers: path_pb = Path() path_pb.id = mark.id point_list = [] for point in mark.points: point_pb = Point() point_pb.x = point.x point_pb.y = point.y point_pb.z = point.z point_list.append(point_pb) path_pb.path_point.extend(point_list) mark_list.append(path_pb) rv.routing_path_info.extend(mark_list) routes.append(rv) elif topic == '/holo/ControlCommand': cf = CommandFrame() cf.timestamp = int(t.secs * 1000 + t.nsecs / 1000000) cf.stamp_secs = t.secs cf.stamp_nsecs = t.nsecs cf.acceleration = msg.acceleration cf.front_wheel_angle = msg.front_wheel_angle cf.gear = msg.gear controls.append(cf) elif topic == '/planning/trajectory': tj = Trajectory() tj.timestamp = int(t.secs * 1000 + t.nsecs / 1000000) tj.stamp_secs = t.secs tj.stamp_nsecs = t.nsecs p_list = [] for point in msg.trajectory_points: p = TrajectoryPoint() p.x = point.path_point.point.x p.y = point.path_point.point.y p.z = point.path_point.point.z p.theta = point.path_point.theta p.kappa = point.path_point.kappa p.v = point.v p.a = point.a p.relative_time = point.relative_time p_list.append(p) tj.trajectory_points.extend(p_list) plans.append(tj) elif topic == '/prediction/prediction_obstacles': tr_pb = PerceptionObstacle() tr_pb.timestamp = int(msg.header.stamp.secs * 1000 + msg.header.stamp.nsecs / 1000000) tr_pb.stamp_secs = msg.header.stamp.secs tr_pb.stamp_nsecs = msg.header.stamp.nsecs obj_list = list() for obj in msg.prediction_obstacle: ob_pb = Obstacle() ob_pb.obstacle_timestamp = int(obj.timestamp * 1000) ob_pb.id = obj.perception_obstacle.id ob_pb.x = obj.perception_obstacle.position.x ob_pb.y = obj.perception_obstacle.position.y ob_pb.z = obj.perception_obstacle.position.z traj_pbs = [] for traj in obj.trajectory: traj_pb = PredictionTrajectory() points_pbs = [] for trajectory_point in traj.trajectory_points: point_pb = PathPoint() point_pb.x = trajectory_point.path_point.point.x point_pb.y = trajectory_point.path_point.point.y point_pb.z = trajectory_point.path_point.point.z point_pb.theta = trajectory_point.path_point.theta point_pb.kappa = trajectory_point.path_point.kappa point_pb.lane_id = trajectory_point.path_point.lane_id point_pb.v = trajectory_point.v point_pb.a = trajectory_point.a point_pb.relative_time = trajectory_point.relative_time points_pbs.append(point_pb) traj_pb.path_point.extend(points_pbs) traj_pbs.append(traj_pb) ob_pb.prediction_trajectory.extend(traj_pbs) obj_list.append(ob_pb) tr_pb.obstacle_info.extend(obj_list) preds.append(tr_pb) elif topic == '/inspvax': pb_loc_gnss = GnssPoint() pb_loc_gnss.stamp_secs = msg.header.stamp.secs # 1 pb_loc_gnss.stamp_nsecs = msg.header.stamp.nsecs # 2 pb_loc_gnss.timestamp = get_ts(msg.header.stamp.secs, msg.header.stamp.nsecs) pb_loc_gnss.latitude = msg.latitude # 3 pb_loc_gnss.longitude = msg.longitude # 4 pb_loc_gnss.elevation = msg.altitude gnss.append(pb_loc_gnss) elif topic == '/holo/VehicleInfoMagotan': veh_pb = VehicleFrame() veh_pb.stamp_secs = msg.timestamp.secs # 1 veh_pb.stamp_nsecs = msg.timestamp.nsecs # 2 veh_pb.timestamp = get_ts(veh_pb.stamp_secs, veh_pb.stamp_nsecs) veh_pb.gear_value = msg.gear # 4 veh_pb.vehicle_speed = msg.vehicle_speed * 3.6 # 5 veh_pb.steering_angle = msg.steering_angle # 6 veh_pb.longitude_acc = msg.longitude_acc veh_pb.lateral_acc = msg.lateral_acc veh_pb.turn_left_light = msg.turn_left_light veh_pb.turn_right_light = msg.turn_right_light veh_pb.brake = msg.brake_torque veh_pb.autonomy_status = 0 vehs.append(veh_pb) elif topic == '/localization/localization_info': lo_pb = LocalizationInfoFrame() lo_pb.timestamp = get_ts(msg.header.stamp.secs, msg.header.stamp.nsecs) lo_pb.stamp_secs = msg.header.stamp.secs lo_pb.stamp_nsecs = msg.header.stamp.nsecs lo_pb.pose_position_x = msg.pose.position.x lo_pb.pose_position_y = msg.pose.position.y lo_pb.pose_position_z = msg.pose.position.z lo_pb.pose_orientation_x = msg.pose.orientation.x lo_pb.pose_orientation_y = msg.pose.orientation.y lo_pb.pose_orientation_z = msg.pose.orientation.z lo_pb.pose_orientation_w = msg.pose.orientation.w lo_pb.pose_orientation_yaw = \ yaw_from_quaternions(msg.pose.orientation.w, msg.pose.orientation.x, msg.pose.orientation.y, msg.pose.orientation.z) lo_pb.velocity_linear = get_modulo(msg.pose.linear_velocity.x, msg.pose.linear_velocity.y, msg.pose.linear_velocity.z) lo_pb.velocity_angular = get_modulo(msg.pose.angular_velocity.x, msg.pose.angular_velocity.y, msg.pose.angular_velocity.z) lo_pb.acceleration_linear = get_modulo(msg.pose.linear_acceleration_vrf.x, msg.pose.linear_acceleration_vrf.y, msg.pose.linear_acceleration_vrf.z) lo_pb.acceleration_angular = get_modulo(msg.pose.angular_velocity_vrf.x, msg.pose.angular_velocity_vrf.y, msg.pose.angular_velocity_vrf.z) locs.append(lo_pb) ego_pose = Pose(msg.pose.orientation.w, msg.pose.orientation.x, msg.pose.orientation.y, msg.pose.orientation.z, msg.pose.position.x, msg.pose.position.y, msg.pose.position.z) elif topic == '/perception/perception_obstacles': if ego_pose is None or pose_has_nan(ego_pose): continue tr_pb = TrackedObjectFrame() tr_pb.timestamp = get_ts(msg.header.stamp.secs, msg.header.stamp.nsecs) tr_pb.stamp_secs = msg.header.stamp.secs tr_pb.stamp_nsecs = msg.header.stamp.nsecs obj_list = list() for object in msg.perception_obstacle: ob_pb = Object() ob_pb.id = object.id ob_pb.label = get_label(object.type, object.sub_type) ob_pb.pose_position_x = object.position.x ob_pb.pose_position_y = object.position.y ob_pb.pose_position_z = object.position.z ob_pb.pose_orientation_x = 0 ob_pb.pose_orientation_y = 0 ob_pb.pose_orientation_z = math.sin(object.theta / 2) ob_pb.pose_orientation_w = math.cos(object.theta / 2) ob_pb.pose_orientation_yaw = object.theta ob_pb.dimensions_x = object.length ob_pb.dimensions_y = object.width ob_pb.dimensions_z = object.height ob_pb.speed_vector_linear_x = object.velocity.x ob_pb.speed_vector_linear_y = object.velocity.y ob_pb.speed_vector_linear_z = object.velocity.z world_obj = np.transpose(np.array([[object.position.x, object.position.y, object.position.z, 1]])) world_ego_t = get_t(ego_pose.q0, ego_pose.q1, ego_pose.q2, ego_pose.q3, ego_pose.x, ego_pose.y, ego_pose.z) try: world_ego_invt = np.linalg.pinv(world_ego_t) except Exception as err: print('pinv failed:', world_ego_t) raise err vehicle_obj = world_ego_invt * world_obj ob_pb.relative_position_x = vehicle_obj[0] ob_pb.relative_position_y = vehicle_obj[1] ob_pb.relative_position_z = vehicle_obj[2] obj_list.append(ob_pb) tr_pb.objects.extend(obj_list) objs.append(tr_pb) print(f"lidar_num : {lidar_num}") print(f"image_num : {image_num}") print(f"radar_num : {radar_num}") folders = [] if len(routes) > 0: os.makedirs(os.path.join(output_dir, 'routing_routing_response_viz')) folders.append({'folder': 'routing_routing_response_viz', 'sensor_type': 'routing_path'}) route_out = RoutingFrames() route_out.routing_frame.extend(routes) with open(os.path.join(output_dir, 'routing_routing_response_viz', 'route.pb'), "wb") as c: c.write(route_out.SerializeToString()) if len(controls) > 0: os.makedirs(os.path.join(output_dir, 'holo_ControlCommand')) folders.append({'folder': 'holo_ControlCommand', 'sensor_type': 'control'}) ctl_cmd_pb_out = ControlCommand() ctl_cmd_pb_out.command_frame.extend(controls) with open(os.path.join(output_dir, 'holo_ControlCommand', 'control.pb'), "wb") as c: c.write(ctl_cmd_pb_out.SerializeToString()) if len(plans) > 0: os.makedirs(os.path.join(output_dir, 'planning_trajectory')) folders.append({'folder': 'planning_trajectory', 'sensor_type': 'planning_trajectory'}) plan_traj_pb_out = PlanTrajectory() plan_traj_pb_out.trajectory_info.extend(plans) with open(os.path.join(output_dir, 'planning_trajectory', 'planning.pb'), "wb") as p: p.write(plan_traj_pb_out.SerializeToString()) if len(preds) > 0: os.makedirs(os.path.join(output_dir, 'prediction_prediction_obstacles')) folders.append({'folder': 'prediction_prediction_obstacles', 'sensor_type': 'predicted_objects'}) pred_obstacles_pb_out = PredictionObstacles() pred_obstacles_pb_out.perception_obstacle.extend(preds) with open(os.path.join(output_dir, 'prediction_prediction_obstacles', 'predicted.pb'), "wb") as p: p.write(pred_obstacles_pb_out.SerializeToString()) if len(gnss) > 0: os.makedirs(os.path.join(output_dir, 'inspvax')) folders.append({'folder': 'inspvax', 'sensor_type': 'gnss'}) gn_pb_out = GnssPoints() gn_pb_out.gnss_points.extend(gnss) with open(os.path.join(output_dir, 'inspvax', 'gnss.pb'), "wb") as g: g.write(gn_pb_out.SerializeToString()) if len(vehs) > 0: os.makedirs(os.path.join(output_dir, 'holo_VehicleInfoMagotan')) folders.append({'folder': 'holo_VehicleInfoMagotan', 'sensor_type': 'vehicle'}) veh_pb_out = VehicleInfo() veh_pb_out.vehicle_info.extend(vehs) with open(os.path.join(output_dir, 'holo_VehicleInfoMagotan', 'vehicle.pb'), "wb") as v: v.write(veh_pb_out.SerializeToString()) if len(locs) > 0: os.makedirs(os.path.join(output_dir, 'localization_localization_info')) folders.append({'folder': 'localization_localization_info', 'sensor_type': 'ego_tf'}) lo_pb_out = LocalizationInfo() lo_pb_out.localization_info.extend(locs) with open(os.path.join(output_dir, 'localization_localization_info', 'ego_tf.pb'), "wb") as lo: lo.write(lo_pb_out.SerializeToString()) if len(objs) > 0: os.makedirs(os.path.join(output_dir, 'perception_perception_obstacles')) folders.append({'folder': 'perception_perception_obstacles', 'sensor_type': 'object_array_vision'}) tr_pb_out = TrackedObject() tr_pb_out.tracked_object.extend(objs) with open(os.path.join(output_dir, 'perception_perception_obstacles', 'object_array_vision.pb'), "wb") as tr: tr.write(tr_pb_out.SerializeToString()) if has_camera_74: folders.append({'folder': 'mdc_camera_instance_74', 'sensor_type': 'camera'}) if has_camera_73: folders.append({'folder': 'mdc_camera_instance_73', 'sensor_type': 'camera'}) if has_camera_72: folders.append({'folder': 'mdc_camera_instance_72', 'sensor_type': 'camera'}) if has_camera_71: folders.append({'folder': 'mdc_camera_instance_71', 'sensor_type': 'camera'}) if has_lidar_A4: if args.calibration_id: folders.append({'folder': 'innoPtClound_A4', 'sensor_type': 'lidar', 'calibration_item_id': args.calibration_id}) else: folders.append({'folder': 'innoPtClound_A4', 'sensor_type': 'lidar'}) if has_lidar_B2: if args.calibration_id: folders.append({'folder': 'innoPtClound_B2', 'sensor_type': 'lidar', 'calibration_item_id': args.calibration_id}) else: folders.append({'folder': 'innoPtClound_B2', 'sensor_type': 'lidar'}) if has_radar_0: folders.append({'folder': 'radar_track_array_0', 'sensor_type': 'radar'}) if has_radar_3: folders.append({'folder': 'radar_track_array_3', 'sensor_type': 'radar'}) collect_yaml = {'folders': folders} with open(os.path.join(output_dir, "opendata_to_platform.yaml"), 'w', encoding='utf-8') as collect_file: yaml.safe_dump(collect_yaml, collect_file) with open(os.path.join(os.getenv('output_dir'), '_SUCCESS'), 'w') as f: f.write("") os.system('chmod -R a+r ${output_dir}/*') if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('-i', '--input', help="input bag path", default=os.getenv('rosbag_path')) parser.add_argument('-o', '--output', default=os.getenv('output_dir'), help="result output directory, default to ./bags/") parser.add_argument('-ci', '--calibration_id', type=int) params = parser.parse_args() main(params)

Code 分享 Notebook 保存成功 Python 3 (ipykernel) import os import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tensorflow.keras import layers,losses,optimizers, Sequential from tensorflow.keras.layers import InputLayer, Dense, LSTM, Dropout from tensorflow.keras.models import load_model from tensorflow.keras import Model 0秒 + Code + Markdown stock_data = pd.read_csv('IBM_stock_data.csv') 0秒 + Code + Markdown stock_data.head() 0秒 date Open High Low Close Volume Price Change % 0 1999/11/1 98.50 98.81 96.37 96.75 9551800 0.000000 1 1999/11/2 96.75 96.81 93.69 94.81 11105400 -2.005168 2 1999/11/3 95.87 95.94 93.50 94.37 10369100 -0.464086 3 1999/11/4 94.44 94.44 90.00 91.56 16697600 -2.977641 4 1999/11/5 92.75 92.94 90.19 90.25 13737600 -1.430756 + Code + Markdown scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(stock_data['Close'].values.reshape(-1, 1)) 0秒 + Code + Markdown scaled_data[0] 0秒 array([0.23131139]) + Code + Markdown len(scaled_data) 0秒 6293 + Code + Markdown training_data_len = int(np.ceil(len(scaled_data) * 0.8)) #向上取整 0秒 + Code + Markdown train_data = scaled_data[0:training_data_len] X_train, y_train = [], [] time_step = 10 # 时间窗口,模型基于前10个时间步长进行预测。可以尝试不同长度(如20、30)并观察效果变化。 0秒 + Code + Markdown for i in range(len(train_data) - time_step - 1): X_train.append(train_data[i:(i + time_step), 0]) y_train.append(train_data[i + time_step, 0]) 0秒 + Code + Markdown X_train, y_train = np.array(X_train), np.array(y_train) X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1) 0秒 + Code + Markdown X_train.shape 0秒 (5024, 10, 1) + Code + Markdown X_test, y_test = [], [] test_data = scaled_data[training_data_len - time_step:] ​ for i in range(len(test_data) - time_step): X_test.append(test_data[i:(i + time_step), 0]) y_test.append(test_data[i + time_step, 0]) ​ X_test, y_test = np.array(X_test), np.array(y_test) X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1) 0秒 + Code + Markdown model = Sequential() model.add(InputLayer(input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=64, return_sequences=True)) # 调整Dropout: 当前设置为0.3,可以尝试在不同的层上使用不同的Dropout值,例如0.2~0.5之间。Dropout的作用是防止过拟合。 model.add(Dropout(0.3)) model.add(LSTM(units=64, return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(units=32)) model.add(Dropout(0.2)) # 增加回归层: 如果希望更高的拟合精度,可以添加多个Dense层,例如在输出前再增加一层Dense。 model.add(Dense(units=1)) 3秒 2025-06-21 16:21:14.205566: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory 2025-06-21 16:21:14.205665: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303) 2025-06-21 16:21:14.205686: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (dsw-1161427-779497c87d-j6j54): /proc/driver/nvidia/version does not exist 2025-06-21 16:21:14.206190: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. + Code + Markdown def MyRNN(): model = Sequential([ layers.InputLayer(input_shape=(X_train.shape[1],1)), layers.SimpleRNN(units=64, dropout=0.5, return_sequences=True, unroll=True), layers.SimpleRNN(units=64, dropout=0.5, unroll=True), layers.Dense(1)] ) return model 0秒 + Code + Markdown model = MyRNN() model.compile(optimizer='adam', loss='mean_squared_error') 0秒 + Code + Markdown history = model.fit( X_train, y_train, epochs=2, # 批大小(batch_size): 尝试不同的batch_size,例如16、32、64,以找到训练稳定性和准确性之间的平衡 batch_size=32, #callbacks=[early_stopping, lr_scheduler] ) 12秒 Epoch 1/2 WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f02c8692040> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module 'gast' has no attribute 'Constant' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x7f02c8692040> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module 'gast' has no attribute 'Constant' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert 157/157 [==============================] - 9s 20ms/step - loss: 0.0396 Epoch 2/2 157/157 [==============================] - 3s 21ms/step - loss: 0.0266 + Code + Markdown model.save('my_model.keras') 0秒 + Code + Markdown train_loss = model.evaluate(X_train, y_train, verbose=0) test_loss = model.evaluate(X_test, y_test, verbose=0) 5秒 WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7f02cc2b5dc0> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module 'gast' has no attribute 'Constant' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x7f02cc2b5dc0> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module 'gast' has no attribute 'Constant' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert + Code + Markdown # 训练集上计算的损失值,数值越小表示模型在训练数据上的拟合效果越好 print(f"Training Loss: {train_loss:.4f}") # 测试集上计算的损失值,反映了模型在未见过的数据上的表现。测试损失略高于训练损失,但差距不大,说明模型在新数据上的表现依然良好。 print(f"Testing Loss: {test_loss:.4f}") 0秒 Training Loss: 0.0412 Testing Loss: 0.0480 + Code + Markdown model = load_model('my_model.keras') predictions = model.predict(X_test) predictions = scaler.inverse_transform(predictions) # 反归一化预测值 2秒 WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x7f0276f14040> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module 'gast' has no attribute 'Constant' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x7f0276f14040> and will run it as-is. Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module 'gast' has no attribute 'Constant' To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert + Code + Markdown import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体为黑体 plt.rcParams['axes.unicode_minus'] = False # 正确显示负号 0秒 + Code + Markdown %matplotlib inline 0秒 + Code + Markdown Code train = stock_data[:training_data_len] valid = stock_data[training_data_len:] valid.loc[:, 'Predictions'] = predictions ​ ​ # 绘制图像 plt.figure(figsize=(14, 5)) plt.title('股票价格预测', fontsize=20) # 图表标题改为中文 plt.xlabel('日期', fontsize=14) # X 轴标签改为中文 plt.ylabel('收盘价', fontsize=14) # Y 轴标签改为中文 plt.plot(train['date'], train['Close'], label='训练数据', color='blue') # 训练数据标签改为中文 plt.plot(valid['date'], valid['Close'], label='真实价格', color='green') # 真实价格标签改为中文 plt.plot(valid['date'], valid['Predictions'], label='预测价格', color='red') # 预测价格标签改为中文 plt.legend() # 添加图例 # 添加保存图像的代码 plt.savefig('stock_price_predictions.png') # 保存图像 plt.show() ​ # 计算和输出评估指标 rmse = np.sqrt(np.mean(np.square(predictions - y_test))) mae = np.mean(np.abs(predictions - y_test)) print(f'均方根误差 (RMSE): {rmse}, 平均绝对误差 (MAE): {mae}') # 输出信息改为中文 4分钟58秒 /opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1667: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy self.obj[key] = value findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans. findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 32929 (\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 31080 (\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 20215 (\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 26684 (\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 39044 (\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 27979 (\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans. findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans. findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 26085 (\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 26399 (\N{CJK UNIFIED IDEOGRAPH-671F}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 25910 (\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 30424 (\N{CJK UNIFIED IDEOGRAPH-76D8}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 35757 (\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 32451 (\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 25454 (\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 30495 (\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /tmp/ipykernel_18835/4137420046.py:16: UserWarning: Glyph 23454 (\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font. plt.savefig('stock_price_predictions.png') # 保存图像 /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26085 (\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26399 (\N{CJK UNIFIED IDEOGRAPH-671F}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25910 (\N{CJK UNIFIED IDEOGRAPH-6536}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30424 (\N{CJK UNIFIED IDEOGRAPH-76D8}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20215 (\N{CJK UNIFIED IDEOGRAPH-4EF7}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32929 (\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 31080 (\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26684 (\N{CJK UNIFIED IDEOGRAPH-683C}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39044 (\N{CJK UNIFIED IDEOGRAPH-9884}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 27979 (\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25454 (\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30495 (\N{CJK UNIFIED IDEOGRAPH-771F}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) /opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 23454 (\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from current font. fig.canvas.print_figure(bytes_io, **kw) 均方根误差 (RMSE): 105.03158114646604, 平均绝对误差 (MAE): 104.21013102460681误差好大,还有中文显示不成功

运行代码: import pandas as pd import numpy as np import os import sys import io # ------------------------- 编码兼容设置 ------------------------- # 强制标准输出/错误流使用UTF-8编码 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8') # 设置Python环境变量,避免joblib并行计算时的编码问题 os.environ["PYTHONIOENCODING"] = "utf-8" # --------------------------------------------------------------- from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression, Ridge, Lasso from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error import matplotlib.pyplot as plt import seaborn as sns # 1. 加载数据(明确指定文件路径编码) file_path = r"C:\Users\刘涵\Desktop\数模标准\模拟题一\C题\卷烟吸阻数据.xlsx" # 原始路径(含中文) # 读取原始数据(Sheet1)或标准化数据(Sheet2) try: df = pd.read_excel(file_path, sheet_name='Sheet1') # 原始数据 # df = pd.read_excel(file_path, sheet_name='Sheet2') # 标准化数据(可选) except UnicodeDecodeError: # 如果仍报错,尝试用二进制模式读取并指定编码(仅适用于xlsx文件) df = pd.read_excel(file_path, engine='openpyxl', sheet_name='Sheet1') # 显示数据基本信息 print("数据基本信息:") print(df.info()) print("\n前5行数据:") print(df.head()) print("\n描述性统计:") print(df.describe()) # 2. 数据预处理 X = df.drop(columns=['吸阻(Pa)']) # 特征 y = df['吸阻(Pa)'] # 目标变量 # 数据标准化(仅对原始数据需要,若使用Sheet2的标准化数据则跳过) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 3. 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split( X_scaled, y, test_size=0.2, random_state=42 ) # 4. 定义模型及超参数网格 models = { '线性回归': LinearRegression(), '岭回归': Ridge(), 'Lasso回归': Lasso(), '随机森林': RandomForestRegressor(random_state=42), '梯度提升': GradientBoostingRegressor(random_state=42), '支持向量机': SVR() } param_grids = { '岭回归': {'alpha': [0.1, 1, 10]}, 'Lasso回归': {'alpha': [0.001, 0.01, 0.1]}, '随机森林': { 'n_estimators': [100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5] }, '梯度提升': { 'n_estimators': [100, 200], 'learning_rate': [0.05, 0.1, 0.2], 'max_depth': [3, 5] }, '支持向量机': { 'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['scale', 'auto'] } } # 5. 模型训练与调优(增加异常捕获) results = {} best_models = {} for name, model in models.items(): print(f"\n=== 训练模型: {name} ===") try: if name in param_grids: # 使用网格搜索优化超参数 grid_search = GridSearchCV( estimator=model, param_grid=param_grids[name], cv=5, scoring='neg_mean_squared_error', n_jobs=-1 # 若仍报错,可改为n_jobs=1(关闭并行) ) grid_search.fit(X_train, y_train) best_model = grid_search.best_estimator_ best_params = grid_search.best_params_ print(f"最佳参数: {best_params}") else: # 直接训练基础模型 best_model = model.fit(X_train, y_train) best_params = "无超参数可调" # 预测与评估 y_pred = best_model.predict(X_test) mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) mae = mean_absolute_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) results[name] = { 'MSE': mse, 'RMSE': rmse, 'MAE': mae, 'R²': r2, '最佳参数': best_params } best_models[name] = best_model print(f"测试集评估结果:\nMSE={mse:.2f}\nRMSE={rmse:.2f}\nMAE={mae:.2f}\nR²={r2:.4f}") except Exception as e: print(f"训练模型 {name} 时出错: {str(e)}") continue # 6. 模型性能对比 if results: results_df = pd.DataFrame(results).T print("\n=== 模型性能对比 ===") print(results_df.sort_values('R²', ascending=False)) else: print("所有模型训练失败,请检查数据和环境配置。") # 7. 可视化分析(仅当有模型成功时执行) if '随机森林' in best_models: try: # 特征重要性 feature_importances = best_models['随机森林'].feature_importances_ feature_importance_df = pd.DataFrame({ '特征': X.columns, '重要性': feature_importances }).sort_values('重要性', ascending=False) plt.figure(figsize=(12, 8)) sns.barplot(x='重要性', y='特征', data=feature_importance_df.head(10)) plt.title('Top 10 重要特征') plt.show() except Exception as e: print(f"绘制特征重要性图时出错: {str(e)}") # 8. 模型保存(可选) # from joblib import dump # if '随机森林' in best_models: # dump(best_models['随机森林'], 'best_cigarette_resistance_model.pkl') # dump(scaler, 'scaler.pkl') 出现: AttributeError Traceback (most recent call last) Cell In[1], line 9 5 import io 7 # ------------------------- 编码兼容设置 ------------------------- 8 # 强制标准输出/错误流使用UTF-8编码 ----> 9 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') 10 sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8') 11 # 设置Python环境变量,避免joblib并行计算时的编码问题 AttributeError: 'OutStream' object has no attribute 'buffer'。输出完整的解决问题修改后的代码

inputs: {'query': '你好'} /home/lk/projects/langchain/25-3-26-prompt/predict.py:34: LangChainDeprecationWarning: The method Chain.run was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:~invoke instead. generated_cypher = self.cypher_generation_chain.run( ⚠️ 发生错误: 'CustomGraphCypherQAChain' object has no attribute 'qa_llm' 这个报错 import json from langchain_community.graphs import Neo4jGraph from langchain.chains import GraphCypherQAChain from langchain.prompts import PromptTemplate from langchain_openai import ChatOpenAI from langchain_ollama import ChatOllama # 连接 Neo4j 数据库 graph = Neo4jGraph( url="neo4j+s://bf0befc9.databases.neo4j.io", # 替换为你的 URL username="neo4j", password="Hl7FXJhH1azmgC34YhaweYyigcKwQK3wiRfNVNeWru8", database="neo4j" ) # graph = Neo4jGraph( # url='neo4j+s://a36ad2ff.databases.neo4j.io', # username="neo4j", # password="X_K2K49tuQt9VU0athTxpoNUMTs51h76H1kXUrLgoWs", # database="neo4j" # ) # cypher_llm = ChatOpenAI( # base_url="https://api.chatanywhere.tech/v1", # api_key='sk-hZEb9uq0085t1EFFIm4RoXAvH4ktKshUFgWwDv3BneS0SIZp', # model="gpt-4o-mini" # ) # 生成 Cypher 语句的提示模板 class CustomGraphCypherQAChain(GraphCypherQAChain): def _call(self, inputs): try: # 原始查询逻辑 # schema = Schema() print(f"inputs: {inputs}") # 打印输入内容,调试用 generated_cypher = self.cypher_generation_chain.run( inputs["query"], callbacks=self.callbacks ) # 执行Cypher查询 context = self.graph.query(generated_cypher) # 空结果处理 if not context: # 构造兜底提示 empty_prompt = "当前知识库未找到相关数据,请根据你的通用知识回答:{question}" return self.qa_llm.invoke(empty_prompt.format(question=inputs["query"])) # 正常结果处理 return self.qa_chain.invoke( {"question": inputs["query"], "context": context}, callbacks=self.callbacks, ) except Exception as e: # 异常兜底 error_prompt = f"查询执行失败(错误:{str(e)}),请自行回答问题:{inputs['query']}" return self.qa_llm.invoke(error_prompt) qa_llm = ChatOllama( base_url="http://223.109.239.9:20006", model="deepseek-r1:70b", # system_prompt = cypher_prompt ) cypher_llm = ChatOpenAI( base_url="https://api.chatanywhere.tech/v1", api_key='sk-hZEb9uq0085t1EFFIm4RoXAvH4ktKshUFgWwDv3BneS0SIZp', model="gpt-4o-mini" ) # 构建 Cypher 生成和查询链 cypher_chain = CustomGraphCypherQAChain.from_llm( graph=graph, cypher_llm=qa_llm, qa_llm=qa_llm, validate_cypher=True, verbose=True, allow_dangerous_requests=True ) # 循环对话 def chat(): print("🔹 进入 Neo4j 智能助手,输入 'exit' 退出。") while True: user_query = input("\n💬 你问:") if user_query.lower() == "exit": print("👋 再见!") break try: clean_result = cypher_chain.invoke(user_query) # 只返回结果部分 # response = cypher_chain.invoke(user_query) # # 提取最终的回答,去除多余部分 # result = response.get('result', '没有找到相关答案') # clean_result = result.split('<think>')[0] # 去掉think部分 # clean_result = clean_result.split('Generated Cypher:')[0] # 去掉Cypher部分 print("\n🤖 机器人:", clean_result) # response = cypher_chain.invoke(user_query) # print("\n🤖 机器人:", response) except Exception as e: print("\n⚠️ 发生错误:", str(e)) # 启动对话 if __name__ == "__main__": chat()

import pandas as pd import numpy as np import os import sys import io # ------------------------- 编码兼容设置 ------------------------- # 强制标准输出/错误流使用UTF-8编码 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8') # 设置Python环境变量,避免joblib并行计算时的编码问题 os.environ["PYTHONIOENCODING"] = "utf-8" # --------------------------------------------------------------- from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression, Ridge, Lasso from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error import matplotlib.pyplot as plt import seaborn as sns # 1. 加载数据(明确指定文件路径编码) file_path = r"C:\Users\刘涵\Desktop\数模标准\模拟题一\C题\卷烟吸阻数据.xlsx" # 原始路径(含中文) # 读取原始数据(Sheet1)或标准化数据(Sheet2) try: df = pd.read_excel(file_path, sheet_name='Sheet1') # 原始数据 # df = pd.read_excel(file_path, sheet_name='Sheet2') # 标准化数据(可选) except UnicodeDecodeError: # 如果仍报错,尝试用二进制模式读取并指定编码(仅适用于xlsx文件) df = pd.read_excel(file_path, engine='openpyxl', sheet_name='Sheet1') # 显示数据基本信息 print("数据基本信息:") print(df.info()) print("\n前5行数据:") print(df.head()) print("\n描述性统计:") print(df.describe()) # 2. 数据预处理 X = df.drop(columns=['吸阻(Pa)']) # 特征 y = df['吸阻(Pa)'] # 目标变量 # 数据标准化(仅对原始数据需要,若使用Sheet2的标准化数据则跳过) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 3. 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split( X_scaled, y, test_size=0.2, random_state=42 ) # 4. 定义模型及超参数网格 models = { '线性回归': LinearRegression(), '岭回归': Ridge(), 'Lasso回归': Lasso(), '随机森林': RandomForestRegressor(random_state=42), '梯度提升': GradientBoostingRegressor(random_state=42), '支持向量机': SVR() } param_grids = { '岭回归': {'alpha': [0.1, 1, 10]}, 'Lasso回归': {'alpha': [0.001, 0.01, 0.1]}, '随机森林': { 'n_estimators': [100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5] }, '梯度提升': { 'n_estimators': [100, 200], 'learning_rate': [0.05, 0.1, 0.2], 'max_depth': [3, 5] }, '支持向量机': { 'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['scale', 'auto'] } } # 5. 模型训练与调优(增加异常捕获) results = {} best_models = {} for name, model in models.items(): print(f"\n=== 训练模型: {name} ===") try: if name in param_grids: # 使用网格搜索优化超参数 grid_search = GridSearchCV( estimator=model, param_grid=param_grids[name], cv=5, scoring='neg_mean_squared_error', n_jobs=-1 # 若仍报错,可改为n_jobs=1(关闭并行) ) grid_search.fit(X_train, y_train) best_model = grid_search.best_estimator_ best_params = grid_search.best_params_ print(f"最佳参数: {best_params}") else: # 直接训练基础模型 best_model = model.fit(X_train, y_train) best_params = "无超参数可调" # 预测与评估 y_pred = best_model.predict(X_test) mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) mae = mean_absolute_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) results[name] = { 'MSE': mse, 'RMSE': rmse, 'MAE': mae, 'R²': r2, '最佳参数': best_params } best_models[name] = best_model print(f"测试集评估结果:\nMSE={mse:.2f}\nRMSE={rmse:.2f}\nMAE={mae:.2f}\nR²={r2:.4f}") except Exception as e: print(f"训练模型 {name} 时出错: {str(e)}") continue # 6. 模型性能对比 if results: results_df = pd.DataFrame(results).T print("\n=== 模型性能对比 ===") print(results_df.sort_values('R²', ascending=False)) else: print("所有模型训练失败,请检查数据和环境配置。") # 7. 可视化分析(仅当有模型成功时执行) if '随机森林' in best_models: try: # 特征重要性 feature_importances = best_models['随机森林'].feature_importances_ feature_importance_df = pd.DataFrame({ '特征': X.columns, '重要性': feature_importances }).sort_values('重要性', ascending=False) plt.figure(figsize=(12, 8)) sns.barplot(x='重要性', y='特征', data=feature_importance_df.head(10)) plt.title('Top 10 重要特征') plt.show() except Exception as e: print(f"绘制特征重要性图时出错: {str(e)}") # 8. 模型保存(可选) # from joblib import dump # if '随机森林' in best_models: # dump(best_models['随机森林'], 'best_cigarette_resistance_model.pkl') # dump(scaler, 'scaler.pkl') 运行后出现 --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[2], line 9 5 import io 7 # ------------------------- 编码兼容设置 ------------------------- 8 # 强制标准输出/错误流使用UTF-8编码 ----> 9 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') 10 sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8') 11 # 设置Python环境变量,避免joblib并行计算时的编码问题 AttributeError: 'OutStream' object has no attribute 'buffer'

(defense_env) [liubm@localhost New Folder]$ while IFS= read -r file; do defense-finder run "$file"; done < filelist.txt 2025-06-10 20:40:58 | INFO | Received file ESC_AB1928AA_AS.result.fasta.gz 2025-06-10 20:40:58 | WARNING | Out directory /data4/liubm/BaiQi/20250604/New Folder already exists. Existing DefenseFinder output will be overwritten gzip: stdout: Broken pipe Traceback (most recent call last): File "/data4/liubm/anaconda3/envs/defense_env/bin/defense-finder", line 10, in <module> sys.exit(cli()) File "/data4/liubm/anaconda3/envs/defense_env/lib/python3.8/site-packages/click/core.py", line 1130, in __call__ return self.main(*args, **kwargs) File "/data4/liubm/anaconda3/envs/defense_env/lib/python3.8/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/data4/liubm/anaconda3/envs/defense_env/lib/python3.8/site-packages/click/core.py", line 1657, in invoke return _process_result(sub_ctx.command.invoke(sub_ctx)) File "/data4/liubm/anaconda3/envs/defense_env/lib/python3.8/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/data4/liubm/anaconda3/envs/defense_env/lib/python3.8/site-packages/click/core.py", line 760, in invoke return __callback(*args, **kwargs) File "/data4/liubm/anaconda3/envs/defense_env/lib/python3.8/site-packages/defense_finder_cli/main.py", line 127, in run if sf.guess_alphabet() == Alphabet.dna(): AttributeError: 'pyhmmer.easel.SequenceFile' object has no attribute 'guess_alphabet' 这个怎么办啊

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