scipy.ndimage.measurements.labeled_comprehension

scipy.ndimage.measurements.labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions=False)

Roughly equivalent to [func(input[labels == i]) for i in index].

Sequentially applies an arbitrary function (that works on array_like input) to subsets of an n-D image array specified by labels and index. The option exists to provide the function with positional parameters as the second argument.

Parameters:

input : array_like

Data from which to select labels to process.

labels : array_like or None

Labels to objects in input. If not None, array must be same shape as input. If None, func is applied to raveled input.

index : int, sequence of ints or None

Subset of labels to which to apply func. If a scalar, a single value is returned. If None, func is applied to all non-zero values of labels.

func : callable

Python function to apply to labels from input.

out_dtype : dtype

Dtype to use for result.

default : int, float or None

Default return value when a element of index does not exist in labels.

pass_positions : bool, optional

If True, pass linear indices to func as a second argument. Default is False.

Returns:

result : ndarray

Result of applying func to each of labels to input in index.

Examples

>>> a = np.array([[1, 2, 0, 0],
                  [5, 3, 0, 4],
                  [0, 0, 0, 7],
                  [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> lbl, nlbl = ndimage.label(a)
>>> lbls = np.arange(1, nlbl+1)
>>> ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, 0)
array([ 2.75,  5.5 ,  6.  ])

Falling back to default:

>>> lbls = np.arange(1, nlbl+2)
>>> ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, -1)
array([ 2.75,  5.5 ,  6.  , -1.  ])

Passing positions:

>>> def fn(val, pos):
...     print("fn says: %s : %s" % (val, pos))
...     return (val.sum()) if (pos.sum() % 2 == 0) else (-val.sum())
...
>>> ndimage.labeled_comprehension(a, lbl, lbls, fn, float, 0, True)
fn says: [1 2 5 3] : [0 1 4 5]
fn says: [4 7] : [7 11]
fn says: [9 3] : [12 13]
array([ 11.,  11., -12.])