def setmember1d(ar1, ar2):
"""
Return a boolean array set True where first element is in second array.
See Also
--------
numpy.setmember1d : equivalent function for ndarrays.
"""
ar1 = ma.asanyarray(ar1)
ar2 = ma.asanyarray(ar2)
ar = ma.concatenate((ar1, ar2))
b1 = ma.zeros(ar1.shape, dtype=np.int8)
b2 = ma.ones(ar2.shape, dtype=np.int8)
tt = ma.concatenate((b1, b2))
# We need this to be a stable sort, so always use 'mergesort' here. The
# values from the first array should always come before the values from the
# second array.
perm = ar.argsort(kind="mergesort")
aux = ar[perm]
aux2 = tt[perm]
# flag = ediff1d( aux, 1 ) == 0
flag = ma.concatenate((aux[1:] == aux[:-1], [False]))
ii = ma.where(flag * aux2)[0]
aux = perm[ii + 1]
perm[ii + 1] = perm[ii]
perm[ii] = aux
#
indx = perm.argsort(kind="mergesort")[: len(ar1)]
#
return flag[indx]
def setmember1d(ar1, ar2):
"""
Return a boolean array set True where first element is in second array.
See Also
--------
numpy.setmember1d : equivalent function for ndarrays.
"""
ar1 = ma.asanyarray(ar1)
ar2 = ma.asanyarray( ar2 )
ar = ma.concatenate((ar1, ar2 ))
b1 = ma.zeros(ar1.shape, dtype = np.int8)
b2 = ma.ones(ar2.shape, dtype = np.int8)
tt = ma.concatenate((b1, b2))
# We need this to be a stable sort, so always use 'mergesort' here. The
# values from the first array should always come before the values from the
# second array.
perm = ar.argsort(kind='mergesort')
aux = ar[perm]
aux2 = tt[perm]
# flag = ediff1d( aux, 1 ) == 0
flag = ma.concatenate((aux[1:] == aux[:-1], [False]))
ii = ma.where( flag * aux2 )[0]
aux = perm[ii+1]
perm[ii+1] = perm[ii]
perm[ii] = aux
#
indx = perm.argsort(kind='mergesort')[:len( ar1 )]
#
return flag[indx]
def setmember1d(ar1, ar2):
""" This function is deprecated. Use ma.in1d() instead."""
ar1 = ma.asanyarray(ar1)
ar2 = ma.asanyarray( ar2 )
ar = ma.concatenate((ar1, ar2 ))
b1 = ma.zeros(ar1.shape, dtype = np.int8)
b2 = ma.ones(ar2.shape, dtype = np.int8)
tt = ma.concatenate((b1, b2))
# We need this to be a stable sort, so always use 'mergesort' here. The
# values from the first array should always come before the values from the
# second array.
perm = ar.argsort(kind='mergesort')
aux = ar[perm]
aux2 = tt[perm]
# flag = ediff1d( aux, 1 ) == 0
flag = ma.concatenate((aux[1:] == aux[:-1], [False]))
ii = ma.where( flag * aux2 )[0]
aux = perm[ii+1]
perm[ii+1] = perm[ii]
perm[ii] = aux
#
indx = perm.argsort(kind='mergesort')[:len( ar1 )]
#
return flag[indx]
def setmember1d(ar1, ar2):
""" This function is deprecated. Use ma.in1d() instead."""
ar1 = ma.asanyarray(ar1)
ar2 = ma.asanyarray(ar2)
ar = ma.concatenate((ar1, ar2))
b1 = ma.zeros(ar1.shape, dtype=np.int8)
b2 = ma.ones(ar2.shape, dtype=np.int8)
tt = ma.concatenate((b1, b2))
# We need this to be a stable sort, so always use 'mergesort' here. The
# values from the first array should always come before the values from the
# second array.
perm = ar.argsort(kind='mergesort')
aux = ar[perm]
aux2 = tt[perm]
# flag = ediff1d( aux, 1 ) == 0
flag = ma.concatenate((aux[1:] == aux[:-1], [False]))
ii = ma.where(flag * aux2)[0]
aux = perm[ii + 1]
perm[ii + 1] = perm[ii]
perm[ii] = aux
#
indx = perm.argsort(kind='mergesort')[:len(ar1)]
#
return flag[indx]
def ediff1d(arr, to_end=None, to_begin=None):
"""
Computes the differences between consecutive elements of an array.
This function is the equivalent of `numpy.ediff1d` that takes masked
values into account.
See Also
--------
numpy.eddif1d : equivalent function for ndarrays.
Returns
-------
output : MaskedArray
"""
arr = ma.asanyarray(arr).flat
ed = arr[1:] - arr[:-1]
arrays = [ed]
#
if to_begin is not None:
arrays.insert(0, to_begin)
if to_end is not None:
arrays.append(to_end)
#
if len(arrays) != 1:
# We'll save ourselves a copy of a potentially large array in the common
# case where neither to_begin or to_end was given.
ed = hstack(arrays)
#
return ed
def ediff1d(arr, to_end=None, to_begin=None):
"""
Computes the differences between consecutive elements of an array.
This function is the equivalent of `numpy.ediff1d` that takes masked
values into account.
See Also
--------
numpy.eddif1d : equivalent function for ndarrays.
Returns
-------
output : MaskedArray
"""
arr = ma.asanyarray(arr).flat
ed = arr[1:] - arr[:-1]
arrays = [ed]
#
if to_begin is not None:
arrays.insert(0, to_begin)
if to_end is not None:
arrays.append(to_end)
#
if len(arrays) != 1:
# We'll save ourselves a copy of a potentially large array in the common
# case where neither to_begin or to_end was given.
ed = hstack(arrays)
#
return ed
