How to efficiently compute the rolling mean on a multi-dimensional NumPy array?
I'm writing unit tests and I recently switched to I'm dealing with I'm working with a 3D NumPy array that represents time series data for multiple variables across different sensors..... The shape of my array is `(num_time_points, num_sensors, num_variables)`, and I need to calculate the rolling mean over the time dimension. However, I'm concerned about the performance of my current approach, especially as the size of the array grows. I first tried using a loop to calculate the rolling mean like this: ```python import numpy as np # Example data num_time_points = 1000 num_sensors = 10 num_variables = 5 data = np.random.rand(num_time_points, num_sensors, num_variables) # Rolling mean function window = 5 rolling_means = np.zeros_like(data) for i in range(num_time_points): if i < window: rolling_means[i] = np.mean(data[:i+1], axis=0) else: rolling_means[i] = np.mean(data[i-window+1:i+1], axis=0) ``` While this works correctly, I noticed that it is quite slow for larger datasets, and I am getting performance degradation. I also checked the output manually, and it seems correct, but I am not sure if there are any edge cases I might be missing. I then considered using `np.convolve` for a more efficient calculation, but I am not sure how to apply it across the different dimensions. Here's what I attempted: ```python # Attempting to use convolve rolling_means_convolve = np.empty_like(data) for sensor in range(num_sensors): for variable in range(num_variables): rolling_means_convolve[:, sensor, variable] = np.convolve(data[:, sensor, variable], np.ones(window)/window, mode='same') ``` However, I received a warning about the boundaries not being handled correctly, and the results were not as expected—especially at the edges where the window falls out of bounds. What is the best way to compute the rolling mean efficiently while handling edge cases properly in a multi-dimensional NumPy array? Are there built-in functions that could simplify my approach, or do I need to stick with custom implementations? I'm working on a application that needs to handle this. I'd be grateful for any help. For reference, this is a production REST API. What's the best practice here? What's the best practice here?