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All functions work best with integer numerical arrays on input
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(e.g. indices). For floating point arrays, innacurate results may appear due to
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usual round-off and floating point comparison issues.
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All functions work best with integer numerical arrays on input (e.g. indices).
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For floating point arrays, innacurate results may appear due to usual round-off
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and floating point comparison issues.
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Except unique1d, union1d and intersect1d_nu, all functions expect inputs with
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unique elements. Speed could be gained in some operations by an implementaion
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of sort(), that can provide directly the permutation vectors, avoiding thus
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unique elements. Speed could be gained in some operations by an implementaion of
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sort(), that can provide directly the permutation vectors, avoiding thus calls
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Run test_unique1d_speed() to compare performance of numpy.unique1d() and
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Run _test_unique1d_speed() to compare performance of numpy.unique1d() and
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numpy.unique() - it should be the same.
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To do: Optionally return indices analogously to unique1d for all functions.
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Author: Robert Cimrman
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created: 01.11.2005
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last revision: 12.10.2006
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last revision: 07.01.2007
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:Author: Robert Cimrman
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__all__ = ['ediff1d', 'unique1d', 'intersect1d', 'intersect1d_nu', 'setxor1d',
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'setmember1d', 'union1d', 'setdiff1d']
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def ediff1d(ary, to_end = None, to_begin = None):
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"""Array difference with prefixed and/or appended value.
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See also: unique1d, intersect1d, intersect1d_nu, setxor1d,
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setmember1d, union1d, setdiff1d
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def ediff1d(ary, to_end=None, to_begin=None):
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"""The differences between consecutive elements of an array, possibly with
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prefixed and/or appended values.
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This array will be flattened before the difference is taken.
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to_end : number, optional
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If provided, this number will be tacked onto the end of the returned
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to_begin : number, optional
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If provided, this number will be taked onto the beginning of the
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The differences. Loosely, this will be (ary[1:] - ary[:-1]).
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ary = nm.asarray(ary).flat
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ed = ary[1:] - ary[:-1]
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if to_begin is not None:
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if to_end is not None:
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ed = nm.r_[to_begin, ed, to_end]
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ed = nm.insert(ed, 0, to_begin)
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elif to_end is not None:
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ed = nm.append(ed, to_end)
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arrays.insert(0, to_begin)
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if to_end is not None:
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# We'll save ourselves a copy of a potentially large array in the common
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# case where neither to_begin or to_end was given.
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ed = nm.hstack(arrays)
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def unique1d(ar1, return_index=False):
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"""Unique elements of 1D array. When return_index is True, return
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also the indices indx such that ar1.flat[indx] is the resulting
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array of unique elements.
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See also: ediff1d, intersect1d, intersect1d_nu, setxor1d,
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setmember1d, union1d, setdiff1d
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"""Find the unique elements of 1D array.
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Most of the other array set operations operate on the unique arrays
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generated by this function.
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This array will be flattened if it is not already 1D.
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return_index : bool, optional
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If True, also return the indices against ar1 that result in the unique
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unique_indices : int array, optional
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The indices of the unique values. Only provided if return_index is True.
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numpy.lib.arraysetops : Module with a number of other functions
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for performing set operations on arrays.
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ar = nm.asarray(ar1).flatten()
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if return_index: return nm.empty(0, nm.bool), ar
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perm = ar.argsort()
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flag = nm.concatenate( ([True], aux[1:] != aux[:-1]) )
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return perm[flag], aux[flag]
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flag = nm.concatenate( ([True], ar[1:] != ar[:-1]) )
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def intersect1d( ar1, ar2 ):
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def intersect1d(ar1, ar2):
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"""Intersection of 1D arrays with unique elements.
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See also: ediff1d, unique1d, intersect1d_nu, setxor1d,
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setmember1d, union1d, setdiff1d
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Use unique1d() to generate arrays with only unique elements to use as inputs
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to this function. Alternatively, use intersect1d_nu() which will find the
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unique values for you.
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numpy.lib.arraysetops : Module with a number of other functions for
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performing set operations on arrays.
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aux = nm.concatenate((ar1,ar2))
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return aux[aux[1:] == aux[:-1]]
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def intersect1d_nu( ar1, ar2 ):
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def intersect1d_nu(ar1, ar2):
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"""Intersection of 1D arrays with any elements.
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See also: ediff1d, unique1d, intersect1d, setxor1d,
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setmember1d, union1d, setdiff1d
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The input arrays do not have unique elements like intersect1d() requires.
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numpy.lib.arraysetops : Module with a number of other functions for
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performing set operations on arrays.
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# Might be faster then unique1d( intersect1d( ar1, ar2 ) )?
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# Might be faster than unique1d( intersect1d( ar1, ar2 ) )?
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aux = nm.concatenate((unique1d(ar1), unique1d(ar2)))
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return aux[aux[1:] == aux[:-1]]
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def setxor1d( ar1, ar2 ):
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def setxor1d(ar1, ar2):
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"""Set exclusive-or of 1D arrays with unique elements.
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See also: ediff1d, unique1d, intersect1d, intersect1d_nu,
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setmember1d, union1d, setdiff1d
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Use unique1d() to generate arrays with only unique elements to use as inputs
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The values that are only in one, but not both, of the input arrays.
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numpy.lib.arraysetops : Module with a number of other functions for
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performing set operations on arrays.
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aux = nm.concatenate((ar1, ar2))
109
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if aux.size == 0:
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# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
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flag = nm.concatenate( ([True], aux[1:] != aux[:-1], [True] ) )
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flag2 = flag[1:] == flag[:-1]
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return aux[flag2]
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def setmember1d( ar1, ar2 ):
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"""Return an array of shape of ar1 containing 1 where the elements of
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ar1 are in ar2 and 0 otherwise.
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See also: ediff1d, unique1d, intersect1d, intersect1d_nu, setxor1d,
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def setmember1d(ar1, ar2):
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"""Return a boolean array of shape of ar1 containing True where the elements
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of ar1 are in ar2 and False otherwise.
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Use unique1d() to generate arrays with only unique elements to use as inputs
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The values ar1[mask] are in ar2.
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numpy.lib.arraysetops : Module with a number of other functions for
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performing set operations on arrays.
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zlike = nm.zeros_like
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ar1 = nm.asarray( ar1 )
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ar2 = nm.asarray( ar2 )
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ar = nm.concatenate( (ar1, ar2 ) )
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tt = nm.concatenate( (zlike( ar1 ), zlike( ar2 ) + 1) )
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b1 = nm.zeros( ar1.shape, dtype = nm.int8 )
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b2 = nm.ones( ar2.shape, dtype = nm.int8 )
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tt = nm.concatenate( (b1, b2) )
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# We need this to be a stable sort, so always use 'mergesort' here. The
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# values from the first array should always come before the values from the
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perm = ar.argsort(kind='mergesort')
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# flag = ediff1d( aux, 1 ) == 0
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flag = nm.concatenate( (aux[1:] == aux[:-1], [False] ) )
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ii = nm.where( flag * aux2 )[0]
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perm[ii+1] = perm[ii]
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indx = perm.argsort()[:len( ar1 )]
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indx = perm.argsort(kind='mergesort')[:len( ar1 )]
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return flag[indx]
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def union1d( ar1, ar2 ):
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"""Union of 1D arrays with unique elements.
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See also: ediff1d, unique1d, intersect1d, intersect1d_nu, setxor1d,
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setmember1d, setdiff1d
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def union1d(ar1, ar2):
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Union of 1D arrays with unique elements.
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Use unique1d() to generate arrays with only unique elements to use as inputs
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numpy.lib.arraysetops : Module with a number of other functions for
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performing set operations on arrays.
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return unique1d( nm.concatenate( (ar1, ar2) ) )
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def setdiff1d( ar1, ar2 ):
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def setdiff1d(ar1, ar2):
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"""Set difference of 1D arrays with unique elements.
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See also: ediff1d, unique1d, intersect1d, intersect1d_nu, setxor1d,
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Use unique1d() to generate arrays with only unique elements to use as inputs
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The values in ar1 that are not in ar2.
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numpy.lib.arraysetops : Module with a number of other functions for
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performing set operations on arrays.
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aux = setmember1d(ar1,ar2)
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if aux.size == 0:
added
removed
numpy/lib/arraysetops.py
