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Rolling window for 1D arrays in Numpy?

Is there a way to efficiently implement a rolling window for 1D arrays in Numpy?

For example, I have this pure Python code snippet to calculate the rolling standard deviations for a 1D list, where observations is the 1D list of values, and n is the window length for the standard deviation:

stdev = []
for i, data in enumerate(observations[n-1:]):
    strip = observations[i:i+n]
    mean = sum(strip) / n
    stdev.append(sqrt(250*sum([(s-mean)**2 for s in strip])/(n-1)))

Is there a way to do this completely within Numpy, i.e., without any Python loops? The standard deviation is trivial with numpy.std, but the rolling window part completely stumps me.

I found this blog post regarding a rolling window in Numpy, but it doesn't seem to be for 1D arrays.

Solution

  • Just use the blog code, but apply your function to the result.

    i.e.

    numpy.std(rolling_window(observations, n), 1)

    where you have (from the blog):

    def rolling_window(a, window):
    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
    strides = a.strides + (a.strides[-1],)
    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)