RMS of a cube

rms.py

import numpy as np
from scipy.ndimage import generic_filter


def rms_1d(trace_segment):
    """
    This function runs on every piece of `size` defined in the
    generic_filter, below. It returns a single value.
    """
    return np.sqrt(np.sum(trace_segment**2)/trace_segment.size)

arr = np.random.random((10, 10, 20)) - 0.5
generic_filter(arr,
               rms_1d,
               size=(1, 1, 3),  # Size gives shape of input to function.
               mode='nearest'   # How to handle edges.
              )