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scipy.ndimage.

# center_of_mass

scipy.ndimage.center_of_mass(input, labels=None, index=None)

  Calculate the center of mass of the values of an array at labels.

  Parameters:
    inputndarray

      Data from which to calculate center-of-mass. The masses can either be positive or negative.

    labelsndarray, optional

      Labels for objects in input, as generated by ndimage.label. Only used with index. Dimensions must be the same as input.

    indexint or sequence of ints, optional

      Labels for which to calculate centers-of-mass. If not specified, the combined center of mass of all labels greater than zero will be calculated. Only used with labels.

  Returns:
    center_of_masstuple, or list of tuples

      Coordinates of centers-of-mass.

  Examples

      >>> import numpy as np
      >>> a = np.array(([0,0,0,0],
      ...               [0,1,1,0],
      ...               [0,1,1,0],
      ...               [0,1,1,0]))
      >>> from scipy import ndimage
      >>> ndimage.center_of_mass(a)
      (2.0, 1.5)

  Calculation of multiple objects in an image

      >>> b = np.array(([0,1,1,0],
      ...               [0,1,0,0],
      ...               [0,0,0,0],
      ...               [0,0,1,1],
      ...               [0,0,1,1]))
      >>> lbl = ndimage.label(b)[0]
      >>> ndimage.center_of_mass(b, lbl, [1,2])
      [(0.33333333333333331, 1.3333333333333333), (3.5, 2.5)]

  Negative masses are also accepted, which can occur for example when bias is removed from measured data due to random noise.

      >>> c = np.array(([-1,0,0,0],
      ...               [0,-1,-1,0],
      ...               [0,1,-1,0],
      ...               [0,1,1,0]))
      >>> ndimage.center_of_mass(c)
      (-4.0, 1.0)

  If there are division by zero issues, the function does not raise an error but rather issues a RuntimeWarning before returning inf and/or NaN.

      >>> d = np.array([-1, 1])
      >>> ndimage.center_of_mass(d)
      (inf,)