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 average(a, axis=None, weights=None, returned=False):
"""
Average the array over the given axis.
Parameters
----------
axis : {None,int}, optional
Axis along which to perform the operation.
If None, applies to a flattened version of the array.
weights : {None, sequence}, optional
Sequence of weights.
The weights must have the shape of a, or be 1D with length
the size of a along the given axis.
If no weights are given, weights are assumed to be 1.
returned : {False, True}, optional
Flag indicating whether a tuple (result, sum of weights/counts)
should be returned as output (True), or just the result (False).
"""
a = asarray(a)
mask = a.mask
ash = a.shape
if ash == ():
ash = (1,)
if axis is None:
if mask is nomask:
if weights is None:
n = a.sum(axis=None)
d = float(a.size)
else:
w = filled(weights, 0.0).ravel()
n = umath.add.reduce(a._data.ravel() * w)
d = umath.add.reduce(w)
del w
else:
if weights is None:
n = a.filled(0).sum(axis=None)
d = umath.add.reduce((-mask).ravel().astype(int))
else:
w = array(filled(weights, 0.0), float, mask=mask).ravel()
n = add.reduce(a.ravel() * w)
d = add.reduce(w)
del w
else:
if mask is nomask:
if weights is None:
d = ash[axis] * 1.0
n = add.reduce(a._data, axis, dtype=float)
else:
w = filled(weights, 0.0)
wsh = w.shape
if wsh == ():
wsh = (1,)
if wsh == ash:
w = np.array(w, float, copy=0)
n = add.reduce(a * w, axis)
d = add.reduce(w, axis)
del w
elif wsh == (ash[axis],):
ni = ash[axis]
r = [None] * len(ash)
r[axis] = slice(None, None, 1)
w = eval("w[" + repr(tuple(r)) + "] * ones(ash, float)")
n = add.reduce(a * w, axis, dtype=float)
d = add.reduce(w, axis, dtype=float)
del w, r
else:
raise ValueError, "average: weights wrong shape."
else:
if weights is None:
n = add.reduce(a, axis, dtype=float)
d = umath.add.reduce((-mask), axis=axis, dtype=float)
else:
w = filled(weights, 0.0)
wsh = w.shape
if wsh == ():
wsh = (1,)
if wsh == ash:
w = array(w, dtype=float, mask=mask, copy=0)
n = add.reduce(a * w, axis, dtype=float)
d = add.reduce(w, axis, dtype=float)
elif wsh == (ash[axis],):
ni = ash[axis]
r = [None] * len(ash)
r[axis] = slice(None, None, 1)
w = eval("w[" + repr(tuple(r)) + "] * masked_array(ones(ash, float), mask)")
n = add.reduce(a * w, axis, dtype=float)
d = add.reduce(w, axis, dtype=float)
else:
raise ValueError, "average: weights wrong shape."
del w
if n is masked or d is masked:
return masked
result = n / d
del n
if isinstance(result, MaskedArray):
if ((axis is None) or (axis == 0 and a.ndim == 1)) and (result.mask is nomask):
result = result._data
if returned:
if not isinstance(d, MaskedArray):
d = masked_array(d)
if isinstance(d, ndarray) and (not d.shape == result.shape):
d = ones(result.shape, dtype=float) * d
if returned:
return result, d
else:
return result
def average(a, axis=None, weights=None, returned=False):
"""Average the array over the given axis.
Parameters
----------
axis : {None,int}, optional
Axis along which to perform the operation.
If None, applies to a flattened version of the array.
weights : {None, sequence}, optional
Sequence of weights.
The weights must have the shape of a, or be 1D with length
the size of a along the given axis.
If no weights are given, weights are assumed to be 1.
returned : {False, True}, optional
Flag indicating whether a tuple (result, sum of weights/counts)
should be returned as output (True), or just the result (False).
"""
a = asarray(a)
mask = a.mask
ash = a.shape
if ash == ():
ash = (1,)
if axis is None:
if mask is nomask:
if weights is None:
n = a.sum(axis=None)
d = float(a.size)
else:
w = filled(weights, 0.0).ravel()
n = umath.add.reduce(a._data.ravel() * w)
d = umath.add.reduce(w)
del w
else:
if weights is None:
n = a.filled(0).sum(axis=None)
d = umath.add.reduce((-mask).ravel().astype(int))
else:
w = array(filled(weights, 0.0), float, mask=mask).ravel()
n = add.reduce(a.ravel() * w)
d = add.reduce(w)
del w
else:
if mask is nomask:
if weights is None:
d = ash[axis] * 1.0
n = add.reduce(a._data, axis, dtype=float)
else:
w = filled(weights, 0.0)
wsh = w.shape
if wsh == ():
wsh = (1,)
if wsh == ash:
w = np.array(w, float, copy=0)
n = add.reduce(a*w, axis)
d = add.reduce(w, axis)
del w
elif wsh == (ash[axis],):
ni = ash[axis]
r = [None]*len(ash)
r[axis] = slice(None, None, 1)
w = eval ("w["+ repr(tuple(r)) + "] * ones(ash, float)")
n = add.reduce(a*w, axis, dtype=float)
d = add.reduce(w, axis, dtype=float)
del w, r
else:
raise ValueError, 'average: weights wrong shape.'
else:
if weights is None:
n = add.reduce(a, axis, dtype=float)
d = umath.add.reduce((-mask), axis=axis, dtype=float)
else:
w = filled(weights, 0.0)
wsh = w.shape
if wsh == ():
wsh = (1,)
if wsh == ash:
w = array(w, dtype=float, mask=mask, copy=0)
n = add.reduce(a*w, axis, dtype=float)
d = add.reduce(w, axis, dtype=float)
elif wsh == (ash[axis],):
ni = ash[axis]
r = [None]*len(ash)
r[axis] = slice(None, None, 1)
w = eval ("w["+ repr(tuple(r)) + \
"] * masked_array(ones(ash, float), mask)")
n = add.reduce(a*w, axis, dtype=float)
d = add.reduce(w, axis, dtype=float)
else:
raise ValueError, 'average: weights wrong shape.'
del w
if n is masked or d is masked:
return masked
result = n/d
del n
if isinstance(result, MaskedArray):
if ((axis is None) or (axis==0 and a.ndim == 1)) and \
(result.mask is nomask):
result = result._data
if returned:
if not isinstance(d, MaskedArray):
d = masked_array(d)
if isinstance(d, ndarray) and (not d.shape == result.shape):
d = ones(result.shape, dtype=float) * d
if returned:
return result, d
else:
return result
