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 apply_along_axis(func1d, axis, arr, *args, **kwargs):
"""
(This docstring should be overwritten)
"""
arr = array(arr, copy=False, subok=True)
nd = arr.ndim
if axis < 0:
axis += nd
if axis >= nd:
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d." % (axis, nd))
ind = [0] * (nd - 1)
i = np.zeros(nd, "O")
indlist = range(nd)
indlist.remove(axis)
i[axis] = slice(None, None)
outshape = np.asarray(arr.shape).take(indlist)
i.put(indlist, ind)
j = i.copy()
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
# if res is a number, then we have a smaller output array
asscalar = np.isscalar(res)
if not asscalar:
try:
len(res)
except TypeError:
asscalar = True
# Note: we shouldn't set the dtype of the output from the first result...
# ...so we force the type to object, and build a list of dtypes
# ...we'll just take the largest, to avoid some downcasting
dtypes = []
if asscalar:
dtypes.append(np.asarray(res).dtype)
outarr = zeros(outshape, object)
outarr[tuple(ind)] = res
Ntot = np.product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
outarr[tuple(ind)] = res
dtypes.append(asarray(res).dtype)
k += 1
else:
res = array(res, copy=False, subok=True)
j = i.copy()
j[axis] = [slice(None, None)] * res.ndim
j.put(indlist, ind)
Ntot = np.product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = res.shape
dtypes.append(asarray(res).dtype)
outshape = flatten_inplace(outshape)
outarr = zeros(outshape, object)
outarr[tuple(flatten_inplace(j.tolist()))] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1 - nd)):
ind[n - 1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
j.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
outarr[tuple(flatten_inplace(j.tolist()))] = res
dtypes.append(asarray(res).dtype)
k += 1
max_dtypes = np.dtype(np.asarray(dtypes).max())
if not hasattr(arr, "_mask"):
result = np.asarray(outarr, dtype=max_dtypes)
else:
result = asarray(outarr, dtype=max_dtypes)
result.fill_value = ma.default_fill_value(result)
return result
def apply_along_axis(func1d, axis, arr, *args, **kwargs):
"""Execute func1d(arr[i],*args) where func1d takes 1-D arrays and
arr is an N-d array. i varies so as to apply the function along
the given axis for each 1-d subarray in arr.
"""
arr = core.array(arr, copy=False, subok=True)
nd = arr.ndim
if axis < 0:
axis += nd
if (axis >= nd):
raise ValueError("axis must be less than arr.ndim; axis=%d, rank=%d."
% (axis,nd))
ind = [0]*(nd-1)
i = np.zeros(nd,'O')
indlist = range(nd)
indlist.remove(axis)
i[axis] = slice(None,None)
outshape = np.asarray(arr.shape).take(indlist)
i.put(indlist, ind)
j = i.copy()
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
# if res is a number, then we have a smaller output array
asscalar = np.isscalar(res)
if not asscalar:
try:
len(res)
except TypeError:
asscalar = True
# Note: we shouldn't set the dtype of the output from the first result...
#...so we force the type to object, and build a list of dtypes
#...we'll just take the largest, to avoid some downcasting
dtypes = []
if asscalar:
dtypes.append(np.asarray(res).dtype)
outarr = zeros(outshape, object)
outarr[tuple(ind)] = res
Ntot = np.product(outshape)
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= outshape[n]) and (n > (1-nd)):
ind[n-1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
outarr[tuple(ind)] = res
dtypes.append(asarray(res).dtype)
k += 1
else:
res = core.array(res, copy=False, subok=True)
j = i.copy()
j[axis] = ([slice(None, None)] * res.ndim)
j.put(indlist, ind)
Ntot = np.product(outshape)
holdshape = outshape
outshape = list(arr.shape)
outshape[axis] = res.shape
dtypes.append(asarray(res).dtype)
outshape = flatten_inplace(outshape)
outarr = zeros(outshape, object)
outarr[tuple(flatten_inplace(j.tolist()))] = res
k = 1
while k < Ntot:
# increment the index
ind[-1] += 1
n = -1
while (ind[n] >= holdshape[n]) and (n > (1-nd)):
ind[n-1] += 1
ind[n] = 0
n -= 1
i.put(indlist, ind)
j.put(indlist, ind)
res = func1d(arr[tuple(i.tolist())], *args, **kwargs)
outarr[tuple(flatten_inplace(j.tolist()))] = res
dtypes.append(asarray(res).dtype)
k += 1
max_dtypes = np.dtype(np.asarray(dtypes).max())
if not hasattr(arr, '_mask'):
result = np.asarray(outarr, dtype=max_dtypes)
else:
result = core.asarray(outarr, dtype=max_dtypes)
result.fill_value = core.default_fill_value(result)
return result
