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Module « scipy.ndimage »

Fonction labeled_comprehension - module scipy.ndimage

Signature de la fonction labeled_comprehension

def labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions=False) 

Description

help(scipy.ndimage.labeled_comprehension)

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
--------
>>> import numpy as np
>>> 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.,   0.])

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