def log_modify(student, old_data): now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) with open("log.txt", "a") as f: f.write(f"{now}\n") f.write(f"修改前：{old_data}\n") f.write(f"修改后：{student}\n") f.write("\n")

这段代码的作用是将学生信息的修改记录写入日志文件中。其中，`student` 是修改后的学生信息对象，`old_data` 是修改前的学生信息。代码首先获取当前时间，然后在日志文件中写入一条记录，包括修改前后的学生信息和时间戳，并在记录之间加上空行。日志文件的路径为当前目录下的 `log.txt` 文件。如果该文件不存在，则会自动创建。

相关问题

没有GPU，优化程序class point_cloud_generator(): def init(self, rgb_file, depth_file, save_ply, camera_intrinsics=[312.486, 243.928, 382.363, 382.363]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1)[:self.width * self.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1) def write_ply(self): start = time.time() float_formatter = lambda x: "%.4f" % x points = [] for i in self.data_ply

It that the code is generating a point cloud from an RGB-D image pair. Since you mentioned that you do not have a GPU, one possible optimization could be to use the `numba` library to speed up the computation. Here is how you can modify the code to use `numba`:

1. Import the `numba` library by adding the following line at the top of your code:

   import numba

2. Add the `@numba.jit(nopython=True)` decorator to the `compute` method to enable `numba` JIT compilation:

   @numba.jit(nopython=True)
   def compute(self):
       # rest of the code

3. Modify the `for` loops in the `compute` method to use `numba`'s `prange` function to parallelize the computation across multiple CPU cores:

   for i in numba.prange(self.width):
       # rest of the code

   for i in numba.prange(self.height):
       # rest of the code

4. Add the `@numba.jit(nopython=True)` decorator to the `write_ply` method as well, since it is also computationally intensive:

   @numba.jit(nopython=True)
   def write_ply(self):
       # rest of the code

With these modifications, the `compute` and `write_ply` methods should run faster. However, keep in mind that `numba`'s JIT compilation can take some time during the first function call, so subsequent calls should be faster. Also, note that `numba`'s `prange` function only works with integer indices, so you may need to convert floating-point indices to integers before using them in the loops.

import pyinotify class EventHandler(pyinotify.ProcessEvent): def __init__(self, path): self.path = path def process_IN_MODIFY(self, event): print("File modified:", event.pathname) wm = pyinotify.WatchManager() notifier = pyinotify.Notifier(wm, EventHandler()) paths = ['/opt/tomcat/logs', '/home/temp'] for path in paths: wm.add_watch(path, pyinotify.IN_MODIFY, rec=True, auto_add=True) notifier.loop()报错： notifier = pyinotify.Notifier(wm, EventHandler()) TypeError: __init__() takes exactly 2 arguments (1 given)

这个错误提示说明 `EventHandler` 类的 `__init__` 方法需要传入 `path` 参数，但是在 `pyinotify.Notifier` 实例化时没有传入该参数，因此需要在实例化 `EventHandler` 时传入 `path` 参数，例如：

import pyinotify 

class EventHandler(pyinotify.ProcessEvent):
    def __init__(self, path):
        self.path = path

    def process_IN_MODIFY(self, event):
        print("File modified:", event.pathname)

wm = pyinotify.WatchManager()
notifier = pyinotify.Notifier(wm, EventHandler('/opt/tomcat/logs'))
wm.add_watch('/opt/tomcat/logs', pyinotify.IN_MODIFY, rec=True, auto_add=True)
notifier.loop()

注意，`wm.add_watch` 也需要改为仅传入一个路径参数。如果你想监控多个路径，可以在循环中分别创建 `EventHandler` 实例，并为每个实例传入相应的路径参数。