for i in range(len(self.lines) - 1, -1, -1):什么意思
时间: 2023-10-11 10:07:34 浏览: 70
这是一个 Python 的 for 循环语句,其中 range 函数用来生成一个整数范围序列。具体来说,该循环从列表 self.lines 的最后一个元素开始,一直迭代到第一个元素(不包含第一个元素),步长为 -1,也就是倒序遍历整个列表。例如,如果 self.lines 的长度为 5,那么该循环会从下标 4 开始,一直迭代到下标 0(不包括下标 0),步长为 -1。
相关问题
import pygame # 初始化Pygame pygame.init() # 设置窗口大小和标题 screen_width, screen_height = 600, 800 screen = pygame.display.set_mode((screen_width, screen_height)) pygame.display.set_caption("Tetris Game") class Block: def __init__(self, x, y, color): self.x = x self.y = y self.color = color self.shape = random.choice(shapes) self.rotation = 0 def move_down(self): self.y += 1 def move_left(self): self.x -= 1 def move_right(self): self.x += 1 def rotate(self): self.rotation = (self.rotation + 1) % len(self.shape) def main(): # 创建方块 block = Block(5, 0, random.choice(colors)) # 循环标志位 running = True # 游戏主循环 while running: # 事件处理 for event in pygame.event.get(): if event.type == pygame.QUIT: running = False # 绘制背景 screen.fill((255, 255, 255)) # 绘制方块 draw_block(block) # 更新屏幕 pygame.display.update() # 方块下落 block.move_down() # 检查方块是否到达底部 if block.y >= screen_height / block_size or check_collision(block): # 方块到达底部,创建新的方块 block = Block(5, 0, random.choice(colors)) # 检查是否有一行或多行方块被消除 remove_lines() # 延时 pygame.time.delay(100) def remove_lines(): global score lines = 0 for y in range(screen_height // block_size): if check_line(y): lines += 1 for x in range(screen_width // block_size): for i in range(len(blocks)): if blocks[i].x == x and blocks[i].y == y: del blocks[i] break if lines > 0: score += lines * 10 def draw_score(): font = pygame.font.Font(None, 36) score_text = font.render("Score: " + str(score), True, (0, 0, 0)) screen.blit(score_text, (10, 10))的系统概述
这段代码是一个使用Pygame库编写的俄罗斯方块游戏的主要部分,包括方块类的定义和移动方法,主循环的实现,方块下落和消除行的检查,以及得分的计算和显示。在主循环中,屏幕被填充为白色,方块被绘制在屏幕上,然后屏幕被更新,方块下落并检查是否到达底部或与其他方块碰撞,如果到达底部则创建新的方块。如果有一行或多行方块被消除,则得分增加。最后,得分被绘制在屏幕上。
优化代码 def cluster_format(self, start_time, end_time, save_on=True, data_clean=False, data_name=None): """ local format function is to format data from beihang. :param start_time: :param end_time: :return: """ # 户用簇级数据清洗 if data_clean: unused_index_col = [i for i in self.df.columns if 'Unnamed' in i] self.df.drop(columns=unused_index_col, inplace=True) self.df.drop_duplicates(inplace=True, ignore_index=True) self.df.reset_index(drop=True, inplace=True) dupli_header_lines = np.where(self.df['sendtime'] == 'sendtime')[0] self.df.drop(index=dupli_header_lines, inplace=True) self.df = self.df.apply(pd.to_numeric, errors='ignore') self.df['sendtime'] = pd.to_datetime(self.df['sendtime']) self.df.sort_values(by='sendtime', inplace=True, ignore_index=True) self.df.to_csv(data_name, index=False) # 调用基本格式化处理 self.df = super().format(start_time, end_time) module_number_register = np.unique(self.df['bat_module_num']) # if registered m_num is 0 and not changed, there is no module data if not np.any(module_number_register): logger.logger.warning("No module data!") sys.exit() if 'bat_module_voltage_00' in self.df.columns: volt_ref = 'bat_module_voltage_00' elif 'bat_module_voltage_01' in self.df.columns: volt_ref = 'bat_module_voltage_01' elif 'bat_module_voltage_02' in self.df.columns: volt_ref = 'bat_module_voltage_02' else: logger.logger.warning("No module data!") sys.exit() self.df.dropna(axis=0, subset=[volt_ref], inplace=True) self.df.reset_index(drop=True, inplace=True) self.headers = list(self.df.columns) # time duration of a cluster self.length = len(self.df) if self.length == 0: logger.logger.warning("After cluster data clean, no effective data!") raise ValueError("No effective data after cluster data clean.") self.cluster_stats(save_on) for m in range(self.mod_num): print(self.clusterid, self.mod_num) self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])
Here are some possible optimizations for the given code:
1. Instead of using a list comprehension to find columns with 'Unnamed' in their names, you can use the `filter()` function along with a lambda function to achieve the same result in a more concise way:
```
unused_index_col = list(filter(lambda x: 'Unnamed' in x, self.df.columns))
```
2. Instead of dropping duplicates and resetting the index separately, you can use the `drop_duplicates()` function with the `ignore_index` parameter set to `True` to achieve both in one step:
```
self.df.drop_duplicates(inplace=True, ignore_index=True)
```
3. Instead of using `sys.exit()` to terminate the program when there is no module data, you can raise a `ValueError` with an appropriate error message:
```
raise ValueError("No module data!")
```
4. Instead of using a series of `if` statements to find the voltage reference column, you can use the `loc` accessor with a boolean mask to select the first column that starts with 'bat_module_voltage':
```
volt_ref_col = self.df.columns[self.df.columns.str.startswith('bat_module_voltage')][0]
```
5. Instead of using a loop to append a single item to a list, you can use the `append()` method directly:
```
self.module_list.append(np.unique(self.df[f'bat_module_sn_{str(m).zfill(2)}'].dropna())[0])
```
By applying these optimizations, the code can become more concise and efficient.