| """ |
| The arraycrop module contains functions to crop values from the edges of an |
| n-dimensional array. |
| """ |
|
|
| import numpy as np |
| from numbers import Integral |
|
|
| __all__ = ['crop'] |
|
|
|
|
| def crop(ar, crop_width, copy=False, order='K'): |
| """Crop array `ar` by `crop_width` along each dimension. |
| |
| Parameters |
| ---------- |
| ar : array-like of rank N |
| Input array. |
| crop_width : {sequence, int} |
| Number of values to remove from the edges of each axis. |
| ``((before_1, after_1),`` ... ``(before_N, after_N))`` specifies |
| unique crop widths at the start and end of each axis. |
| ``((before, after),) or (before, after)`` specifies |
| a fixed start and end crop for every axis. |
| ``(n,)`` or ``n`` for integer ``n`` is a shortcut for |
| before = after = ``n`` for all axes. |
| copy : bool, optional |
| If `True`, ensure the returned array is a contiguous copy. Normally, |
| a crop operation will return a discontiguous view of the underlying |
| input array. |
| order : {'C', 'F', 'A', 'K'}, optional |
| If ``copy==True``, control the memory layout of the copy. See |
| ``np.copy``. |
| |
| Returns |
| ------- |
| cropped : array |
| The cropped array. If ``copy=False`` (default), this is a sliced |
| view of the input array. |
| """ |
| ar = np.array(ar, copy=False) |
|
|
| if isinstance(crop_width, Integral): |
| crops = [[crop_width, crop_width]] * ar.ndim |
| elif isinstance(crop_width[0], Integral): |
| if len(crop_width) == 1: |
| crops = [[crop_width[0], crop_width[0]]] * ar.ndim |
| elif len(crop_width) == 2: |
| crops = [crop_width] * ar.ndim |
| else: |
| raise ValueError( |
| f'crop_width has an invalid length: {len(crop_width)}\n' |
| f'crop_width should be a sequence of N pairs, ' |
| f'a single pair, or a single integer' |
| ) |
| elif len(crop_width) == 1: |
| crops = [crop_width[0]] * ar.ndim |
| elif len(crop_width) == ar.ndim: |
| crops = crop_width |
| else: |
| raise ValueError( |
| f'crop_width has an invalid length: {len(crop_width)}\n' |
| f'crop_width should be a sequence of N pairs, ' |
| f'a single pair, or a single integer' |
| ) |
|
|
| slices = tuple(slice(a, ar.shape[i] - b) for i, (a, b) in enumerate(crops)) |
| if copy: |
| cropped = np.array(ar[slices], order=order, copy=True) |
| else: |
| cropped = ar[slices] |
| return cropped |
|
|
