def generate(): states_ = sess.run(initial_state) gen = '' c = '[' while c != ']': gen += c x = np.zeros((batch_size, 1)) x[:, 0] = char2id[c] probs_, states_ = sess.run([probs, last_states], feed_dict={X: x, initial_state: states_}) probs_ = np.squeeze(probs_) pos = int(np.searchsorted(np.cumsum(probs_), np.random.rand() * np.sum(probs_))) c = id2char[pos] return gen[1:] def generate_with_head(head): states_ = sess.run(initial_state) gen = '' c = '[' i = 0 while c != ']': gen += c x = np.zeros((batch_size, 1)) x[:, 0] = char2id[c] probs_, states_ = sess.run([probs, last_states], feed_dict={X: x, initial_state: states_}) probs_ = np.squeeze(probs_) pos = int(np.searchsorted(np.cumsum(probs_), np.random.rand() * np.sum(probs_))) if (c == '[' or c == '。' or c == '，') and i < len(head): c = head[i] i += 1 else: c = id2char[pos] return gen[1:]

generate代码生成器

这个自动根据数据库表生成前端vue代码和后端java代码。并支持多种数据库(mysql、sqlserver、Oracle)等。只要少量的配置即可生成简单的web增删改查页面以及后端API接口。日常写代码，是一件非常需要耐心的事情，尤其是那些没有技术含量重复使用到的一些代码排列组合，比如前端的一些列表页面、搜索、页面和后端的增删改查，简单繁杂，这个时候就会使用到一些免费代码自动生成神器，让我们提高效率。这个自动生成代码工具是由java 和 velocity开发的。velocity是一个非常容易上手的语言。只要会java就可以很好的编辑velocity。

generate_inserts:从现有数据生成SQL插入语句

sp_generate_inserts 该工具最初来自。我试图重新接受错误修复的尝试没有引起任何回应，因此我在此处发布了更改后的版本。文献资料此过程使用给定表和视图中的现有数据生成INSERT语句。以后，您可以使用这些INSERT语句生成数据。当您必须运送或打包数据库应用程序时，这非常有用。当您必须将示例数据发送给供应商或技术支持提供商以进行故障排除时，此过程也很方便。好处来自表和视图的数据都可以编写脚本没有使用游标表名和带有空格的列名被处理处理所有数据类型，但图像，大文本和超过4个字节的二进制列除外空值得到妥善处理时间戳列被处理身份列被处理非常灵活且可配置非DBO拥有的表被处理计算列被处理您可以过滤要为其生成INSERT的行。例子例子1 要为表“ titles”生成INSERT语句： EXEC sp_generate_inserts ' t

def data_processing(data): # 日期缺失，补充 data.fillna(method='ffill', inplace=True) date_history = pd.DataFrame(data.iloc[:, 0]) data_history = pd.DataFrame(data.iloc[:, 1]) date_history = np.array(date_history) data_history = [x for item in np.array(data_history).tolist() for x in item] # 缺失值处理 history_time_list = [] for date in date_history: date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') #将字符串转为 datetime 对象 history_time_list.append(date_obj) start_time = history_time_list[0] # 起始时间 end_time = history_time_list[-1] # 结束时间 delta = datetime.timedelta(minutes=15) #时间间隔为15分钟 time_new_list = [] current_time = start_time while current_time <= end_time: time_new_list.append(current_time) current_time += delta # 缺失位置记录 code_list = [] for i in range(len(time_new_list)): code_list = code_list history_time_list = history_time_list while (time_new_list[i] - history_time_list[i]) != datetime.timedelta(minutes=0): history_time_list.insert(i, time_new_list[i]) code_list.append(i) for i in code_list: data_history.insert(i, data_history[i - 1]) # 输出补充好之后的数据 data = pd.DataFrame({'date': time_new_list, 'load': data_history}) return data 代码优化

def generate_time_list(date_history): """ 根据日期列表生成时间列表 """ history_time_list = [datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') for date in date_history] start_time = history_...

from flask import Flask, request app = Flask(name) @app.route('/', methods=['POST']) def generate_search_value(): search_key = request.form['searchKey'] search_value = generate_value_for_key(search_key) result = "Generated search value for {}: {}".format(search_key, search_value) return result def generate_value_for_key(key): # 在这里编写根据 searchKey 生成 searchValue 的代码 return "Generated value for {}".format(key) if name == 'main': app.run(host='0.0.0.0', port=3264)

这段代码创建了一个基于 Flask 的 Web 服务器，它监听来自客户端的 POST 请求，解析请求中的 searchKey 参数，并将其传递给 generate_value_for_key 函数进行处理，最后将处理结果返回给客户端。具体来说，这段...

import pandas as pdimport numpy as npimport talibimport tushare as ts# 先写出回测框架class Backtest(): def init(self, data, init_balance): self.data = data self.init_balance = init_balance self.position = 0 self.balance = init_balance self.equity = 0 def update_balance(self, price): self.equity = self.position * price self.balance = self.balance + self.equity def run(self, strategy): for i in range(1, len(self.data)): signal = strategy.generate_signal(self.data.iloc[:i, :]) price = self.data.iloc[i, 0] # 按照信号来调整持仓 if signal == 1: self.position = np.floor(self.balance / price) # 买入所有可用资金 elif signal == -1: self.position = 0 # 卖出所有股票 self.update_balance(price) print("日期:", self.data.index[i], "价格:", price, "信号:", signal, "账户价值:", self.balance) # 输出最后的回测结果 print("回测结果: 最开始的账户余额为", self.init_balance, "，最终的账户余额为", self.balance, "，因此您的盈亏为", self.balance-self.init_balance)# 再写出策略类class MACD_Strategy(): def init(self, fast_period, slow_period, signal_period): self.fast_period = fast_period self.slow_period = slow_period self.signal_period = signal_period def generate_signal(self, data): macd, signal, hist = talib.MACD(data["close"], fastperiod=self.fast_period, slowperiod=self.slow_period, signalperiod=self.signal_period) if hist[-1] > 0 and hist[-2] < 0: return 1 # 金叉，买入 elif hist[-1] < 0 and hist[-2] > 0: return -1 # 死叉，卖出 else: return 0 # 无操作# 最后的主程序if name == "main": # 下载数据 data = ts.get_hist_data("600000", start="2020-01-01", end="2021-01-01") data = data.sort_index() # 按日期排序 data = data.loc[:, ["open", "high", "close", "low", "volume"]] # 只保留这五列 data.index = pd.to_datetime(data.index) # 初始化回测 backtest = Backtest(data, init_balance=100000) # 初始化策略 strategy = MACD_Strategy(fast_period=12, slow_period=26, signal_period=9) # 运行回测 backtest.run(strategy)

这是一段Python代码，用于导入Pandas、Numpy、Talib以及Tushare这几个Python库。Pandas和Numpy是用于数据处理的库，Talib是用于技术指标计算的库，Tushare是用于获取股市数据的库。

def train_step(real_ecg, dim): noise = tf.random.normal(dim) for i in range(disc_steps): with tf.GradientTape() as disc_tape: generated_ecg = generator(noise, training=True) real_output = discriminator(real_ecg, training=True) fake_output = discriminator(generated_ecg, training=True) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) ### for tensorboard ### disc_losses.update_state(disc_loss) fake_disc_accuracy.update_state(tf.zeros_like(fake_output), fake_output) real_disc_accuracy.update_state(tf.ones_like(real_output), real_output) ####################### with tf.GradientTape() as gen_tape: generated_ecg = generator(noise, training=True) fake_output = discriminator(generated_ecg, training=True) gen_loss = generator_loss(fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) ### for tensorboard ### gen_losses.update_state(gen_loss) ####################### def train(dataset, epochs, dim): for epoch in tqdm(range(epochs)): for batch in dataset: train_step(batch, dim) disc_losses_list.append(disc_losses.result().numpy()) gen_losses_list.append(gen_losses.result().numpy()) fake_disc_accuracy_list.append(fake_disc_accuracy.result().numpy()) real_disc_accuracy_list.append(real_disc_accuracy.result().numpy()) ### for tensorboard ### # with disc_summary_writer.as_default(): # tf.summary.scalar('loss', disc_losses.result(), step=epoch) # tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch) # tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch) # with gen_summary_writer.as_default(): # tf.summary.scalar('loss', gen_losses.result(), step=epoch) disc_losses.reset_states() gen_losses.reset_states() fake_disc_accuracy.reset_states() real_disc_accuracy.reset_states() ####################### # Save the model every 5 epochs # if (epoch + 1) % 5 == 0: # generate_and_save_ecg(generator, epochs, seed, False) # checkpoint.save(file_prefix = checkpoint_prefix) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_ecg(generator, epochs, seed, False)

这段代码看起来像是一个用TensorFlow实现的GAN（生成对抗网络）的训练过程。其中train_step函数定义了每一步的训练过程，包括了生成器和判别器的训练。接着train函数定义了整个训练过程，包括了epochs个epoch的训练...

def generate_music(fp,seq_len,generate_length =128,gate_split = 0.2): inputs,notes_list = convert_midi(fp,seq_len) if inputs is None: return last_step = Fraction(notes_list[-1].offset) for i in range(generate_length): output = model.predict(inputs[np.newaxis,:,:])[0] offsets = np.argmax(output[-len(offsets_vocab):]) offsets = Fraction(offsets_vocab[offsets]) n_notes = np.reshape(output[:len(offsets_vocab)*len(durations_vocab)],(len(offsets_vocab),len(durations_vocab))) n_notes = np.argmax(n_notes,axis=-1) for idx,note_durations in enumerate(n_notes): duration = Fraction(durations_vocab[note_durations]) cur_step = offsets + last_step if duration != 0: note = NoteClass.Note(notes_vocab[idx]) note.offset = cur_step note.duration = Duration(quarterLength=duration) #print(idx, note.offset,note.duration) notes_list.append(note) last_step = cur_step stream = Stream(notes_list) stream.write("midi","output_midi01.mid")

这段代码是用来生成乐曲的，其中的fp参数是midi文件路径，seq_len是序列长度，generate_length是生成的音符数，gate_split是一个门限用于控制生成的音符是否被添加到乐曲中。它首先调用了convert_midi函数...

请问pandas中有什么可以代替这个函数：def generate_time_series(start,end,t1,t2): current = start end_item = end time_delta = datetime.timedelta(days=t1,hours=t2) time_series = [current] while current < end_item: next1 = current + time_delta time_series.append(next1) current = next1 return time_series

你可以使用pandas中的date_range函数代替该函数。它可以生成一个时间序列，可以指定开始时间、结束时间以及时间间隔。例如，可以使用以下代码生成与您的函数相同的时间序列： import pandas as pd ...

class Generations(): def init(self): self.generations = [] def first_generation(self): out = [] for i in range(population): nn = NeuroNetwork() nn.init_neuro_network(network[0], network[1], network[2]) out.append(nn.get_weights()) self.generations.append(Generation()) return out def next_generation(self): if len(self.generations) == 0: return False gen = self.generations[-1].generate_next_generation() self.generations.append(Generation()) return gen def add_genome(self, genome): if len(self.generations) == 0: return False return self.generations[-1].add_genome(genome)

这段代码是一个遗传算法的实现，用于生成和演化神经网络的权重，以优化神经网络的性能。具体来说，这个遗传算法包含了一个 Generation 类和一个 Generations 类。Generation 类表示一个代，包含了多个 Genome...

用python完成实践（中文词频统计）：对中文词频统计项目（import jieba import wordcloud class ChineseWordCounter: def init(self): self.content = '' self.words = [] self.word_frequencies = {} def readfile_demo_with(self, filename): with open(filename, encoding='utf-8') as fp: self.content = fp.read() def seperate_words(self): ignore_word = ('的', '与', '个', '和') for word in jieba.cut(self.content, cut_all=False): if (len(word) > 1) and (word not in ignore_word): self.words.append(word) def count_words(self): for word in self.words: if word in self.word_frequencies.keys(): self.word_frequencies[word] += 1 else: self.word_frequencies[word] = 1 def draw_wordcloud(self, img_file): font = 'c:/windows/font/方正粗黑宋简体.ttf' cloudengine = wordcloud.WordCloud(font_path=font) cloudengine.generate(" ".join(self.words)) cloudengine.to_file(img_file)）进行修改，把统计出的词频保存到指定的csv文件中。

font = 'c:/windows/font/方正粗黑宋简体.ttf' cloudengine = wordcloud.WordCloud(font_path=font) cloudengine.generate(" ".join(self.words)) cloudengine.to_file(img_file) 你可以调用 save_word_...

from flask import Flask, request app = Flask(name) @app.route('/', methods=['POST']) def generate_b(): a = request.form['a'] # 根据 a 生成对应的 b b = "Generated result for a: {}".format(a) return b if name == 'main': app.run(host='0.0.0.0', port=3264)

这是一个使用 Flask 框架的 Python 代码，它创建了一个 Web 应用程序，提供了一个 POST 请求的接口 '/generate_b'。当接收到请求时，它会从请求中获取参数 'a' 的值，根据 'a' 的值生成对应的 'b'，并将 'b' 作为...

在下面的代码如何处理import random import datetime def generate_test_paper(n): """ 生成n道加减法，结果在0到100以内的测试卷 """ test_paper = [] for i in range(n): x = random.randint(0, 100) y = random.randint(0, 100) op = random.choice(['+', '-']) if op == '+': result = x + y else: result = x - y test_paper.append((x, op, y, result)) return test_paper def save_test_result(test_result): """ 记录测试结果，包括测试日期和每道题是否正确 """ date = datetime.date.today() with open('test_result.txt', 'a') as f: f.write(str(date) + '\n') for result in test_result: if result: f.write('正确\n') else: f.write('错误\n') f.write('\n') def take_test(): """ 进行测试 """ n = 50 test_paper = generate_test_paper(n) test_result = [] for x, op, y, result in test_paper: print('{} {} {} = '.format(x, op, y), end='') answer = int(input()) if answer == result: print('回答正确') test_result.append(True) else: print('回答错误') test_result.append(False) save_test_result(test_result) if name == 'main': take_test()

在这个代码中，我们可以在 take_test() 函数中加入中断测试的功能，当用户在测试过程中按下 Ctrl + C 时，程序会捕获 KeyboardInterrupt 异常，并输出当前测试进度以及已经回答的题目数量和正确率等信息。...

import random import math def is_prime(number): if number < 2: return False for i in range(2, int(number 0.5) + 1): if number % i == 0: return False return Truedef generate_key(length): while True: p = random.randint(2 (length//2 - 1), 2 (length//2)) if is_prime(p): break while True: q = random.randint(2 (length//2 - 1), 2 ** (length//2)) if is_prime(q) and q != p: break n = p * q phi_n = (p - 1) * (q - 1) while True: e = random.randint(2, phi_n - 1) if math.gcd(e, phi_n) == 1: break d = pow(e, -1, phi_n) public_key = (n, e) private_key = (n, d) return public_key, private_keydef encrypt(message, public_key): n, e = public_key m = int.from_bytes(message.encode(), 'big') c = pow(m, e, n) return c.to_bytes((c.bit_length() + 7) // 8, 'big') def decrypt(ciphertext, private_key): n, d = private_key c = int.from_bytes(ciphertext, 'big') m = pow(c, d, n) return m.to_bytes((m.bit_length() + 7) // 8, 'big') def main(): message = "Hello, this is a test message!" print("Original message:", message) public_key, private_key = generate_key(512) print("Public key:", public_key) print("Private key:", private_key) encrypted_message = encrypt(message, public_key) print("Encrypted message:", encrypted_message) decrypted_message = decrypt(encrypted_message, private_key) print("Decrypted message:", decrypted_message.decode()) if name == "main": main()

首先定义了一个判断是否为质数的函数is_prime()，然后定义了一个生成公私密钥对的函数generate_key()，其中使用了随机数生成大素数，然后计算出公私密钥对。接着定义了加密函数encrypt()和解密函数decrypt()，分别...

def generate(**kwargs): """ 随机生成动漫头像，并根据netd的分数选择较好的 """ for k_, v_ in kwargs.items(): setattr(opt, k_, v_) device=t.device('cuda') if opt.gpu else t.device('cpu') netg, netd = NetG(opt).eval(), NetD(opt).eval() noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std) noises = noises.to(device) map_location = lambda storage, loc: storage netd.load_state_dict(t.load(opt.netd_path, map_location=map_location)) netg.load_state_dict(t.load(opt.netg_path, map_location=map_location)) netd.to(device) netg.to(device) # 生成图片，并计算图片在判别器的分数 fake_img = netg(noises) scores = netd(fake_img).detach() # 挑选最好的某几张 indexs = scores.topk(opt.gen_num)[1] result = [] for ii in indexs: result.append(fake_img.data[ii]) # 保存图片 tv.utils.save_image(t.stack(result), opt.gen_img, normalize=True, value_range=(-1, 1))解释一下

noises是一个大小为(opt.gen_search_num, opt.nz, 1, 1)的正态分布噪声，并通过设置opt.gen_mean和opt.gen_std来控制其均值和标准差。noises被移动到选定的设备上。然后，生成器和判别器都被移动到选定的...

将下面代码修改成正确格式import random import math def is_prime(number): if number < 2: return False for i in range(2, int(number 0.5) + 1): if number % i == 0: return False return Truedef generate_key(length): while True: p = random.randint(2 (length//2 - 1), 2 (length//2)) if is_prime(p): break while True: q = random.randint(2 (length//2 - 1), 2 ** (length//2)) if is_prime(q) and q != p: break n = p * q phi_n = (p - 1) * (q - 1) while True: e = random.randint(2, phi_n - 1) if math.gcd(e, phi_n) == 1: break d = pow(e, -1, phi_n) public_key = (n, e) private_key = (n, d) return public_key, private_keydef encrypt(message, public_key): n, e = public_key m = int.from_bytes(message.encode(), 'big') c = pow(m, e, n) return c.to_bytes((c.bit_length() + 7) // 8, 'big') def decrypt(ciphertext, private_key): n, d = private_key c = int.from_bytes(ciphertext, 'big') m = pow(c, d, n) return m.to_bytes((m.bit_length() + 7) // 8, 'big') def main(): message = "Hello, this is a test message!" print("Original message:", message) public_key, private_key = generate_key(512) print("Public key:", public_key) print("Private key:", private_key) encrypted_message = encrypt(message, public_key) print("Encrypted message:", encrypted_message) decrypted_message = decrypt(encrypted_message, private_key) print("Decrypted message:", decrypted_message.decode()) if name == "main": main()

def generate_key(length): while True: p = random.randint(2 ** (length//2 - 1), 2 ** (length//2)) if is_prime(p): break while True: q = random.randint(2 ** (length//2 - 1), 2 ** (length//2)...

@app.route('/', methods=['GET', 'POST']) def index(): if request.method == 'POST': job = request.form.get('job') results = Job.query.filter(Job.job_name.ilike(f'%{job}%')).all() if 'word_cloud' in request.form: job_tags = [tag.strip() for result in results for tag in result.job_tags.split(',')] generate_word_cloud(job_tags) return render_template('word_cloud_chart.html') elif 'salary_pie' in request.form: salary_data = {} for result in results: salary = result.salary if salary in salary_data: salary_data[salary] += 1 else: salary_data[salary] = 1 generate_salary_pie(salary_data) return render_template('salary_pie_chart.html') elif 'geo_chart' in request.form: generate_geo_chart(results) return render_template('geo_chart.html') return render_template('index.html') if name == 'main': app.run(debug=True)

这段代码是一个 Flask 应用程序，用于展示职位相关的数据可视化图表。当用户在网页上输入职位名称并提交时，会查询数据库中匹配的职位数据，并根据用户选择的不同图表类型生成相应的图表，包括词云图、饼图和地理图...

import random from collections import deque # 定义状态类 class State: def init(self, location, direction, grid): self.location = location # 吸尘器位置坐标 self.direction = direction # 吸尘器方向 self.grid = grid # 环境状态矩阵 # 定义操作符 actions = ['UP', 'DOWN', 'LEFT', 'RIGHT'] movements = { 'UP': (-1, 0), 'DOWN': (1, 0), 'LEFT': (0, -1), 'RIGHT': (0, 1) } def move(state, action): # 根据操作进行移动 row, col = state.location dr, dc = movements[action] new_location = (row + dr, col + dc) new_direction = action new_grid = state.grid.copy() new_grid[row][col] = 0 return State(new_location, new_direction, new_grid) # 实现广度优先搜索算法 def bfs(initial_state): queue = deque([initial_state]) while queue: state = queue.popleft() if is_goal_state(state): return state for action in actions: new_state = move(state, action) queue.append(new_state) return None # 判断是否为目标状态 def is_goal_state(state): for row in state.grid: for cell in row: if cell != 0: return False return True # 构造初始状态 def generate_initial_state(): location = (random.randint(0, 2), random.randint(0, 2)) direction = random.choice(actions) grid = [[1 if random.random() < 0.2 else 0 for _ in range(3)] for _ in range(3)] return State(location, direction, grid) # 运行搜索算法 initial_state = generate_initial_state() goal_state = bfs(initial_state) # 评价性能 def calculate_path_cost(state): path_cost = 0 for row in state.grid: for cell in row: if cell != 0: path_cost += 1 return path_cost def calculate_search_cost(): search_cost = 0 queue = deque([initial_state]) while queue: state = queue.popleft() search_cost += 1 if is_goal_state(state): return search_cost for action in actions: new_state = move(state, action) queue.append(new_state) return search_cost path_cost = calculate_path_cost(goal_state) search_cost = calculate_search_cost() print("目标状态路径代价：", path_cost) print("搜索开销：", search_cost) 错误为:list index out of range 请改正

1 在构造初始状态 generate_initial_state() 函数中，你使用了 random.randint(0, 2) 来生成 location 的随机坐标。但是，由于列表索引是从0开始的，所以应该使用 random.randint(0, 1) 来生成随机坐标。 2...

class NormalLoss(nn.Module): def init(self,ignore_lb=255, *args, **kwargs): super( NormalLoss, self).init() self.criteria = nn.CrossEntropyLoss(ignore_index=ignore_lb, reduction='none') def forward(self, logits, labels): N, C, H, W = logits.size() loss = self.criteria(logits, labels) return torch.mean(loss) class Fusionloss(nn.Module): def init(self): super(Fusionloss, self).init() self.sobelconv=Sobelxy() def forward(self,image_vis,image_ir,labels,generate_img,i): image_y=image_vis[:,:1,:,:] x_in_max=torch.max(image_y,image_ir) loss_in=F.l1_loss(x_in_max,generate_img) y_grad=self.sobelconv(image_y) ir_grad=self.sobelconv(image_ir) generate_img_grad=self.sobelconv(generate_img) x_grad_joint=torch.max(y_grad,ir_grad) loss_grad=F.l1_loss(x_grad_joint,generate_img_grad) loss_total=loss_in+10*loss_grad return loss_total,loss_in,loss_grad

首先从image_vis中提取灰度通道image_y，然后计算image_y和image_ir的最大值x_in_max，并使用F.l1_loss计算其与generate_img之间的L1损失loss_in。接下来，分别计算image_y、image_ir和generate_img的梯度，并取最大...

具体代码为startpoint =carla.Location(x= 44.42400879,y= 7.18429443,z= 0.27530716) endpoint = carla.Location(x= 209.9933594, y= 9.80837036, z= 0.27530716) # 生成NPC车辆 def generate_npc_vehicle(): global blueprint global transform blueprint = world.get_blueprint_library().find("vehicle.tesla.model3") color = random.choice(blueprint.get_attribute('color').recommended_values) blueprint.set_attribute('color', color) blueprint.set_attribute('role_name', 'autopilot') transform = carla.Transform(startpoint) NPC = world.spawn_actor(blueprint, transform) # 已生成车辆 NPC.set_autopilot(True) NPC.apply_control(carla.VehicleControl(throttle=1.0, steer=0.0, brake=0.0, hand_brake=False, reverse=False, manual_gear_shift=False, gear=0)) return NPC def destroy_npc_vehicle(a): a.destroy() # 触发器事件 def on_trigger_begin_overlap(other_actor): global NPC if isinstance(other_actor, carla.Vehicle) and other_actor == NPC: destroy_npc_vehicle(NPC) NPC = generate_npc_vehicle() # 生成触发器 def generate_trigger(): trigger_bp =world.get_blueprint_library().find("sensor.other.obstacle") trigger_transform = carla.Transform(endpoint) trigger = world.spawn_actor(trigger_bp, trigger_transform) trigger.box_extent = carla.Vector3D(1.0,0.1, 0) trigger.listen(lambda event: on_trigger_begin_overlap(event.other_actor)) return trigger # prepare the light state of the cars to spawn light_state = vls.NONE if args.car_lights_on: light_state = vls.Position | vls.LowBeam | vls.LowBeam NPC = generate_npc_vehicle() trigger = generate_trigger()

首先定义了起点和终点的坐标，然后定义了生成NPC车辆的函数generate_npc_vehicle()，函数内部选择了蓝图为"vehicle.tesla.model3"的车辆，设置了车辆的颜色和角色名称等属性，然后使用spawn_actor()函数生成车辆对象...

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