rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/skimage/util/unique.py

@@ -1,50 +0,0 @@
-import numpy as np
-
-
-def unique_rows(ar):
-    """Remove repeated rows from a 2D array.
-
-    In particular, if given an array of coordinates of shape
-    (Npoints, Ndim), it will remove repeated points.
-
-    Parameters
-    ----------
-    ar : 2-D ndarray
-        The input array.
-
-    Returns
-    -------
-    ar_out : 2-D ndarray
-        A copy of the input array with repeated rows removed.
-
-    Raises
-    ------
-    ValueError : if `ar` is not two-dimensional.
-
-    Notes
-    -----
-    The function will generate a copy of `ar` if it is not
-    C-contiguous, which will negatively affect performance for large
-    input arrays.
-
-    Examples
-    --------
-    >>> ar = np.array([[1, 0, 1],
-    ...                [0, 1, 0],
-    ...                [1, 0, 1]], np.uint8)
-    >>> unique_rows(ar)
-    array([[0, 1, 0],
-           [1, 0, 1]], dtype=uint8)
-    """
-    if ar.ndim != 2:
-        raise ValueError("unique_rows() only makes sense for 2D arrays, "
-                         f"got {ar.ndim}")
-    # the view in the next line only works if the array is C-contiguous
-    ar = np.ascontiguousarray(ar)
-    # np.unique() finds identical items in a raveled array. To make it
-    # see each row as a single item, we create a view of each row as a
-    # byte string of length itemsize times number of columns in `ar`
-    ar_row_view = ar.view(f"|S{ar.itemsize * ar.shape[1]}")
-    _, unique_row_indices = np.unique(ar_row_view, return_index=True)
-    ar_out = ar[unique_row_indices]
-    return ar_out