Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/util/arraycrop.py

@@ -0,0 +1,63 @@
+"""
+The arraycrop module contains functions to crop values from the edges of an
+n-dimensional array.
+"""
+
+import numpy as np
+
+__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),)`` 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.
+    """
+    # Since arraycrop is in the critical import path, we lazy import distutils
+    # to check the version of numpy
+    # After numpy 1.15, a new backward compatible function have been
+    # implemented.
+    # See https://github.com/numpy/numpy/pull/11966
+    from distutils.version import LooseVersion as Version
+    old_numpy = Version(np.__version__) < Version('1.16')
+    if old_numpy:
+        from numpy.lib.arraypad import _validate_lengths
+    else:
+        from numpy.lib.arraypad import _as_pairs
+
+    ar = np.array(ar, copy=False)
+    if old_numpy:
+        crops = _validate_lengths(ar, crop_width)
+    else:
+        crops = _as_pairs(crop_width, ar.ndim, as_index=True)
+    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