`scipy.ndimage._measurements:labeled_comprehension`

source: /scipy/ndimage/_measurements.py :426

Signature

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

Summary

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

Extended Summary

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 an 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.

Notes

Array API Standard Support

labeled_comprehension has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ⚠️ no JIT             ⛔                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

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)

✗

Falling back to `default`:

lbls = np.arange(1, nlbl+2)
ndimage.labeled_comprehension(a, lbl, lbls, np.mean, float, -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)

✓

Aliases

scipy.ndimage.labeled_comprehension