forked from 170010011/fr
51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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import numpy as np
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def unique_rows(ar):
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"""Remove repeated rows from a 2D array.
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In particular, if given an array of coordinates of shape
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(Npoints, Ndim), it will remove repeated points.
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Parameters
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----------
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ar : 2-D ndarray
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The input array.
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Returns
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-------
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ar_out : 2-D ndarray
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A copy of the input array with repeated rows removed.
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Raises
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------
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ValueError : if `ar` is not two-dimensional.
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Notes
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-----
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The function will generate a copy of `ar` if it is not
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C-contiguous, which will negatively affect performance for large
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input arrays.
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Examples
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--------
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>>> ar = np.array([[1, 0, 1],
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... [0, 1, 0],
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... [1, 0, 1]], np.uint8)
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>>> unique_rows(ar)
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array([[0, 1, 0],
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[1, 0, 1]], dtype=uint8)
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"""
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if ar.ndim != 2:
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raise ValueError("unique_rows() only makes sense for 2D arrays, "
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"got %dd" % ar.ndim)
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# the view in the next line only works if the array is C-contiguous
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ar = np.ascontiguousarray(ar)
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# np.unique() finds identical items in a raveled array. To make it
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# see each row as a single item, we create a view of each row as a
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# byte string of length itemsize times number of columns in `ar`
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ar_row_view = ar.view('|S%d' % (ar.itemsize * ar.shape[1]))
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_, unique_row_indices = np.unique(ar_row_view, return_index=True)
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ar_out = ar[unique_row_indices]
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return ar_out
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