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- 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 : ndarray, shape (M, N)
- The input array.
- Returns
- -------
- ar_out : ndarray, shape (P, N)
- 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
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