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 in1d(ar1, ar2, assume_unique=False):
"""
Test whether each element of an array is also present in a second
array.
See Also
--------
numpy.in1d : equivalent function for ndarrays
Notes
-----
.. versionadded:: 1.4.0
"""
if not assume_unique:
ar1, rev_idx = unique(ar1, return_inverse=True)
ar2 = unique(ar2)
ar = ma.concatenate( (ar1, ar2) )
# 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.
order = ar.argsort(kind='mergesort')
sar = ar[order]
equal_adj = (sar[1:] == sar[:-1])
flag = ma.concatenate( (equal_adj, [False] ) )
indx = order.argsort(kind='mergesort')[:len( ar1 )]
if assume_unique:
return flag[indx]
else:
return flag[indx][rev_idx]
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 intersect1d(ar1, ar2, assume_unique=False):
"""
Returns the unique elements common to both arrays.
Masked values are considered equal one to the other.
The output is always a masked array.
See Also
--------
numpy.intersect1d : equivalent function for ndarrays.
Examples
--------
>>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 3 --],
mask = [False False True],
fill_value = 999999)
"""
if assume_unique:
aux = ma.concatenate((ar1, ar2))
else:
# Might be faster than unique1d( intersect1d( ar1, ar2 ) )?
aux = ma.concatenate((unique(ar1), unique(ar2)))
aux.sort()
return aux[aux[1:] == aux[:-1]]
def in1d(ar1, ar2, assume_unique=False):
"""
Test whether each element of an array is also present in a second
array.
See Also
--------
numpy.in1d : equivalent function for ndarrays
Notes
-----
.. versionadded:: 1.4.0
"""
if not assume_unique:
ar1, rev_idx = unique(ar1, return_inverse=True)
ar2 = unique(ar2)
ar = ma.concatenate((ar1, ar2))
# 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.
order = ar.argsort(kind='mergesort')
sar = ar[order]
equal_adj = (sar[1:] == sar[:-1])
flag = ma.concatenate((equal_adj, [False]))
indx = order.argsort(kind='mergesort')[:len(ar1)]
if assume_unique:
return flag[indx]
else:
return flag[indx][rev_idx]
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 intersect1d(ar1, ar2, assume_unique=False):
"""
Returns the unique elements common to both arrays.
Masked values are considered equal one to the other.
The output is always a masked array.
See Also
--------
numpy.intersect1d : equivalent function for ndarrays.
Examples
--------
>>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 3 --],
mask = [False False True],
fill_value = 999999)
"""
if assume_unique:
aux = ma.concatenate((ar1, ar2))
else:
# Might be faster than unique1d( intersect1d( ar1, ar2 ) )?
aux = ma.concatenate((unique(ar1), unique(ar2)))
aux.sort()
return aux[aux[1:] == aux[:-1]]
def setxor1d(ar1, ar2):
"""
Set exclusive-or of 1D arrays with unique elements.
See Also
--------
numpy.setxor1d : equivalent function for ndarrays
"""
aux = ma.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
auxf = aux.filled()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = ma.concatenate(([True], (auxf[1:] != auxf[:-1]), [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = (flag[1:] == flag[:-1])
return aux[flag2]
def union1d(ar1, ar2):
"""
Union of two arrays.
See also
--------
numpy.union1d : equivalent function for ndarrays.
"""
return unique(ma.concatenate((ar1, ar2)))
def union1d(ar1, ar2):
"""
Union of two arrays.
See also
--------
numpy.union1d : equivalent function for ndarrays.
"""
return unique(ma.concatenate((ar1, ar2)))
def setxor1d(ar1, ar2):
"""
Set exclusive-or of 1D arrays with unique elements.
See Also
--------
numpy.setxor1d : equivalent function for ndarrays
"""
aux = ma.concatenate((ar1, ar2))
if aux.size == 0:
return aux
aux.sort()
auxf = aux.filled()
# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0
flag = ma.concatenate(([True], (auxf[1:] != auxf[:-1]), [True]))
# flag2 = ediff1d( flag ) == 0
flag2 = flag[1:] == flag[:-1]
return aux[flag2]
def union1d(ar1, ar2):
"""
Union of 1D arrays with unique elements.
See also
--------
numpy.union1d : equivalent function for ndarrays.
"""
return unique1d(ma.concatenate((ar1, ar2)))
def union1d(ar1, ar2):
"""
Union of 1D arrays with unique elements.
See also
--------
numpy.union1d : equivalent function for ndarrays.
"""
return unique1d(ma.concatenate((ar1, ar2)))
def concatenate_ds(datasets, axis=0):
""" concatenate two datasets along an existing dimension
Parameters:
-----------
datasets: sequence of datasets
axis: axis along which to concatenate
Returns:
--------
joint Dataset along axis
NOTE: will raise an error if variables are there which do not contain the required dimension
See Also:
---------
stack_ds
Examples:
---------
>>> a = da.zeros(axes=[list('abc')], dims=('x0',)) # 1-D DimArray
>>> b = da.zeros(axes=[list('abc'), [1,2]], dims=('x0','x1')) # 2-D DimArray
>>> ds = Dataset({'a':a,'b':b}) # dataset of 2 variables from an experiment
>>> a2 = da.ones(axes=[list('def')], dims=('x0',))
>>> b2 = da.ones(axes=[list('def'), [1,2]], dims=('x0','x1')) # 2-D DimArray
>>> ds2 = Dataset({'a':a2,'b':b2}) # dataset of 2 variables from a second experiment
>>> concatenate_ds([ds, ds2])
Dataset of 2 variables
dimensions: 'x0', 'x1'
0 / x0 (6): a to f
1 / x1 (2): 1 to 2
a: ('x0',)
b: ('x0', 'x1')
"""
# find the list of variables common to all datasets
variables = None
for ds in datasets:
# check that variables have the same variables
if variables is None:
variables = ds.keys()
else:
assert sorted(ds.keys()) == sorted(
variables), "variables differ across datasets"
# Compute concatenated dataset
dataset = Dataset()
for v in variables:
arrays = [ds[v] for ds in datasets]
array = concatenate(arrays, axis=axis)
dataset[v] = array
return dataset
def concatenate_ds(datasets, axis=0):
""" concatenate two datasets along an existing dimension
Parameters:
-----------
datasets: sequence of datasets
axis: axis along which to concatenate
Returns:
--------
joint Dataset along axis
NOTE: will raise an error if variables are there which do not contain the required dimension
See Also:
---------
stack_ds
Examples:
---------
>>> a = da.zeros(axes=[list('abc')], dims=('x0',)) # 1-D DimArray
>>> b = da.zeros(axes=[list('abc'), [1,2]], dims=('x0','x1')) # 2-D DimArray
>>> ds = Dataset({'a':a,'b':b}) # dataset of 2 variables from an experiment
>>> a2 = da.ones(axes=[list('def')], dims=('x0',))
>>> b2 = da.ones(axes=[list('def'), [1,2]], dims=('x0','x1')) # 2-D DimArray
>>> ds2 = Dataset({'a':a2,'b':b2}) # dataset of 2 variables from a second experiment
>>> concatenate_ds([ds, ds2])
Dataset of 2 variables
dimensions: 'x0', 'x1'
0 / x0 (6): a to f
1 / x1 (2): 1 to 2
a: ('x0',)
b: ('x0', 'x1')
"""
# find the list of variables common to all datasets
variables = None
for ds in datasets:
# check that variables have the same variables
if variables is None:
variables = ds.keys()
else:
assert sorted(ds.keys()) == sorted(variables), "variables differ across datasets"
# Compute concatenated dataset
dataset = Dataset()
for v in variables:
arrays = [ds[v] for ds in datasets]
array = concatenate(arrays, axis=axis)
dataset[v] = array
return dataset
def __getitem__(self,key):
if isinstance(key, str):
raise MAError, "Unavailable for masked array."
if type(key) is not tuple:
key = (key,)
objs = []
scalars = []
final_dtypedescr = None
for k in range(len(key)):
scalar = False
if type(key[k]) is slice:
step = key[k].step
start = key[k].start
stop = key[k].stop
if start is None:
start = 0
if step is None:
step = 1
if type(step) is type(1j):
size = int(abs(step))
newobj = np.linspace(start, stop, num=size)
else:
newobj = np.arange(start, stop, step)
elif type(key[k]) is str:
if (key[k] in 'rc'):
self.matrix = True
self.col = (key[k] == 'c')
continue
try:
self.axis = int(key[k])
continue
except (ValueError, TypeError):
raise ValueError, "Unknown special directive"
elif type(key[k]) in np.ScalarType:
newobj = asarray([key[k]])
scalars.append(k)
scalar = True
else:
newobj = key[k]
objs.append(newobj)
if isinstance(newobj, ndarray) and not scalar:
if final_dtypedescr is None:
final_dtypedescr = newobj.dtype
elif newobj.dtype > final_dtypedescr:
final_dtypedescr = newobj.dtype
if final_dtypedescr is not None:
for k in scalars:
objs[k] = objs[k].astype(final_dtypedescr)
res = concatenate(tuple(objs),axis=self.axis)
return self._retval(res)
def __getitem__(self, key):
if isinstance(key, str):
raise MAError, "Unavailable for masked array."
if type(key) is not tuple:
key = (key,)
objs = []
scalars = []
final_dtypedescr = None
for k in range(len(key)):
scalar = False
if type(key[k]) is slice:
step = key[k].step
start = key[k].start
stop = key[k].stop
if start is None:
start = 0
if step is None:
step = 1
if type(step) is type(1j):
size = int(abs(step))
newobj = np.linspace(start, stop, num=size)
else:
newobj = np.arange(start, stop, step)
elif type(key[k]) is str:
if key[k] in "rc":
self.matrix = True
self.col = key[k] == "c"
continue
try:
self.axis = int(key[k])
continue
except (ValueError, TypeError):
raise ValueError, "Unknown special directive"
elif type(key[k]) in np.ScalarType:
newobj = asarray([key[k]])
scalars.append(k)
scalar = True
else:
newobj = key[k]
objs.append(newobj)
if isinstance(newobj, ndarray) and not scalar:
if final_dtypedescr is None:
final_dtypedescr = newobj.dtype
elif newobj.dtype > final_dtypedescr:
final_dtypedescr = newobj.dtype
if final_dtypedescr is not None:
for k in scalars:
objs[k] = objs[k].astype(final_dtypedescr)
res = concatenate(tuple(objs), axis=self.axis)
return self._retval(res)
def _covhelper(x, y=None, rowvar=True, allow_masked=True):
"""
Private function for the computation of covariance and correlation
coefficients.
"""
x = ma.array(x, ndmin=2, copy=True, dtype=float)
xmask = ma.getmaskarray(x)
# Quick exit if we can't process masked data
if not allow_masked and xmask.any():
raise ValueError("Cannot process masked data...")
#
if x.shape[0] == 1:
rowvar = True
# Make sure that rowvar is either 0 or 1
rowvar = int(bool(rowvar))
axis = 1 - rowvar
if rowvar:
tup = (slice(None), None)
else:
tup = (None, slice(None))
#
if y is None:
xnotmask = np.logical_not(xmask).astype(int)
else:
y = array(y, copy=False, ndmin=2, dtype=float)
ymask = ma.getmaskarray(y)
if not allow_masked and ymask.any():
raise ValueError("Cannot process masked data...")
if xmask.any() or ymask.any():
if y.shape == x.shape:
# Define some common mask
common_mask = np.logical_or(xmask, ymask)
if common_mask is not nomask:
x.unshare_mask()
y.unshare_mask()
xmask = x._mask = y._mask = ymask = common_mask
x = ma.concatenate((x, y), axis)
xnotmask = np.logical_not(np.concatenate((xmask, ymask),
axis)).astype(int)
x -= x.mean(axis=rowvar)[tup]
return (x, xnotmask, rowvar)
def _covhelper(x, y=None, rowvar=True, allow_masked=True):
"""
Private function for the computation of covariance and correlation
coefficients.
"""
x = ma.array(x, ndmin=2, copy=True, dtype=float)
xmask = ma.getmaskarray(x)
# Quick exit if we can't process masked data
if not allow_masked and xmask.any():
raise ValueError("Cannot process masked data...")
#
if x.shape[0] == 1:
rowvar = True
# Make sure that rowvar is either 0 or 1
rowvar = int(bool(rowvar))
axis = 1 - rowvar
if rowvar:
tup = (slice(None), None)
else:
tup = (None, slice(None))
#
if y is None:
xnotmask = np.logical_not(xmask).astype(int)
else:
y = array(y, copy=False, ndmin=2, dtype=float)
ymask = ma.getmaskarray(y)
if not allow_masked and ymask.any():
raise ValueError("Cannot process masked data...")
if xmask.any() or ymask.any():
if y.shape == x.shape:
# Define some common mask
common_mask = np.logical_or(xmask, ymask)
if common_mask is not nomask:
x.unshare_mask()
y.unshare_mask()
xmask = x._mask = y._mask = ymask = common_mask
x = ma.concatenate((x, y), axis)
xnotmask = np.logical_not(np.concatenate((xmask, ymask), axis)).astype(int)
x -= x.mean(axis=rowvar)[tup]
return (x, xnotmask, rowvar)
def intersect1d(ar1, ar2):
"""
Returns the repeated or unique elements belonging to the two arrays.
Masked values are assumed equals one to the other.
The output is always a masked array
See Also
--------
numpy.intersect1d : equivalent function for ndarrays.
Examples
--------
>>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 1 3 3 --],
mask = [False False False False True],
fill_value = 999999)
"""
aux = ma.concatenate((ar1,ar2))
aux.sort()
return aux[aux[1:] == aux[:-1]]
def intersect1d(ar1, ar2):
"""
Returns the repeated or unique elements belonging to the two arrays.
Masked values are assumed equals one to the other.
The output is always a masked array
See Also
--------
numpy.intersect1d : equivalent function for ndarrays.
Examples
--------
>>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 1 3 3 --],
mask = [False False False False True],
fill_value = 999999)
"""
aux = ma.concatenate((ar1, ar2))
aux.sort()
return aux[aux[1:] == aux[:-1]]
def intersect1d_nu(ar1, ar2):
""" This function is deprecated. Use ma.intersect1d() instead."""
# Might be faster than unique1d( intersect1d( ar1, ar2 ) )?
aux = ma.concatenate((unique1d(ar1), unique1d(ar2)))
aux.sort()
return aux[aux[1:] == aux[:-1]]
def intersect1d_nu(ar1, ar2):
""" This function is deprecated. Use ma.intersect1d() instead."""
# Might be faster than unique1d( intersect1d( ar1, ar2 ) )?
aux = ma.concatenate((unique1d(ar1), unique1d(ar2)))
aux.sort()
return aux[aux[1:] == aux[:-1]]
