In NumPy, a sliding window is a way of selecting a subset of consecutive elements from an array, where the window “slides” over the array at a certain stride. This is often useful for computing moving averages, smoothing noisy data, or performing convolution operations. To compute averages using a sliding window over an array in NumPy, you can use the following functions:
1. Using “Convolution with a Uniform Window”:
One way to compute sliding window averages is to convolve the array with a uniform window, where each element of the window has the same weight. This function creates a uniform window of size window_size using np.ones(), normalizes it to sum to 1 using division by window_size, and then applies it to the array using np.convolve() with mode='valid' to ensure that the output has the same length as the input.
2. Using “Convolution with a Gaussian Window”:
Another approach is to use a Gaussian window instead of a uniform window. A Gaussian window can be useful if you want to give more weight to elements near the center of the window. This function creates a Gaussian window of size window_size with a standard deviation of sigma using np.exp() and np.linspace(), normalizes it to sum to 1 using division by window.sum(), and then applies it to the array using np.convolve() with mode='valid'.
3. Using “Rolling Window with Mean”:
A third approach is to use NumPy’s rolling function to apply a moving window to the array and then take the mean of each window. This function uses np.lib.stride_tricks.sliding_window_view() to create a view of the input array with windows of size window_size, and then takes the mean of each window along axis 1 using np.mean().
Example:
Depending on your specific use case, you may want to experiment with different window shapes or smoothing functions to achieve the desired result.