Custom rolling mean and std window to avoid looping

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Python

I want to use custom window for computing mean and standard deviation using python. The window that I want to use has some values false and True. I just want only to consider the corresponding True value in the sliding window in my computation.
Here is how the sliding window look

[[ True  True  True  True  True  True  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True]
 [ True  True  True False False False False  True  True  True]
 [ True  True  True False False False False  True  True  True]
 [ True  True  True False False False False  True  True  True]
 [ True  True  True False False False False  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True]
 [ True  True  True  True  True  True  True  True  True  True]]

Sliding window will iterate over all rows and columns of images.
Is there a simple way to compute the mean and std using built-in rolling functions in xarray or pandas ?


It could be helpful this built-in

If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, and closed will be passed to get_window_bounds.

pandas.DataFrame.rolling — pandas 1.4.2 documentation[^]

What I have tried:

Using loop: it works but too time consuming when you have array 25k*16k

for rows in range(int(S/2),log_intensity.shape[0]-int(S/2)+1):
  for cols in range(int(S/2),log_intensity.shape[1]-int(S/2)+1):
    M= log_intensity[rows-int(S/2):rows+int(S/2),cols-int(S/2):cols+int(S/2)]
    T= log_intensity[rows,cols].values
    Mean= M.where(mask==True).mean().values
    Std= M.where(mask==True).std().values