Python numpy 模块，_NoValue() 实例源码
我们从Python开源项目中，提取了以下23个代码示例，用于说明如何使用numpy._NoValue()。
def sometrue(a, axis=None, out=None, keepdims=np._NoValue):
"""
Check whether some values are true.
Refer to `any` for full documentation.
See Also
--------
any : equivalent function
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.any(axis=axis, out=out, **kwargs)
def test_numpy_reloading():
# gh-7844. Also check that relevant globals retain their identity.
import numpy as np
import numpy._globals
_NoValue = np._NoValue
VisibleDeprecationWarning = np.VisibleDeprecationWarning
ModuleDeprecationWarning = np.ModuleDeprecationWarning
reload(np)
assert_(_NoValue is np._NoValue)
assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
assert_raises(RuntimeError, reload, numpy._globals)
reload(np)
assert_(_NoValue is np._NoValue)
assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
def sometrue(a, axis=None, out=None, keepdims=np._NoValue):
"""
Check whether some values are true.
Refer to `any` for full documentation.
See Also
--------
any : equivalent function
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.any(axis=axis, out=out, **kwargs)
def test_numpy_reloading():
# gh-7844. Also check that relevant globals retain their identity.
import numpy as np
import numpy._globals
_NoValue = np._NoValue
VisibleDeprecationWarning = np.VisibleDeprecationWarning
ModuleDeprecationWarning = np.ModuleDeprecationWarning
reload(np)
assert_(_NoValue is np._NoValue)
assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
assert_raises(RuntimeError, reload, numpy._globals)
reload(np)
assert_(_NoValue is np._NoValue)
assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
def std(self, axis=None, dtype=None, out=None, ddof=0,
keepdims=np._NoValue):
"""
Returns the standard deviation of the array elements along given axis.
Masked entries are ignored.
Refer to `numpy.std` for full documentation.
See Also
--------
ndarray.std : corresponding function for ndarrays
numpy.std : Equivalent function
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
dvar = self.var(axis, dtype, out, ddof, **kwargs)
if dvar is not masked:
if out is not None:
np.power(out, 0.5, out=out, casting='unsafe')
return out
dvar = sqrt(dvar)
return dvar
def product(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the product of array elements over a given axis.
See Also
--------
prod : equivalent function; see for details.
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return um.multiply.reduce(a, axis=axis, dtype=dtype, out=out, **kwargs)
def alltrue(a, axis=None, out=None, keepdims=np._NoValue):
"""
Check if all elements of input array are true.
See Also
--------
numpy.all : Equivalent function; see for details.
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.all(axis=axis, out=out, **kwargs)
def _check_mask_axis(mask, axis, keepdims=np._NoValue):
"Check whether there are masked values along the given axis"
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
if mask is not nomask:
return mask.all(axis=axis, **kwargs)
return nomask
###############################################################################
# Masking functions #
###############################################################################
def any(self, axis=None, out=None, keepdims=np._NoValue):
"""
Returns True if any of the elements of `a` evaluate to True.
Masked values are considered as False during computation.
Refer to `numpy.any` for full documentation.
See Also
--------
ndarray.any : corresponding function for ndarrays
numpy.any : equivalent function
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
mask = _check_mask_axis(self._mask, axis, **kwargs)
if out is None:
d = self.filled(False).any(axis=axis, **kwargs).view(type(self))
if d.ndim:
d.__setmask__(mask)
elif mask:
d = masked
return d
self.filled(False).any(axis=axis, out=out, **kwargs)
if isinstance(out, MaskedArray):
if out.ndim or mask:
out.__setmask__(mask)
return out
def prod(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the product of the array elements over the given axis.
Masked elements are set to 1 internally for computation.
Refer to `numpy.prod` for full documentation.
Notes
-----
Arithmetic is modular when using integer types, and no error is raised
on overflow.
See Also
--------
ndarray.prod : corresponding function for ndarrays
numpy.prod : equivalent function
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
_mask = self._mask
newmask = _check_mask_axis(_mask, axis, **kwargs)
# No explicit output
if out is None:
result = self.filled(1).prod(axis, dtype=dtype, **kwargs)
rndim = getattr(result, 'ndim', 0)
if rndim:
result = result.view(type(self))
result.__setmask__(newmask)
elif newmask:
result = masked
return result
# Explicit output
result = self.filled(1).prod(axis, dtype=dtype, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getattr(out, '_mask', nomask)
if (outmask is nomask):
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
return out
def min(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
try:
return obj.min(axis=axis, fill_value=fill_value, out=out, **kwargs)
except (AttributeError, TypeError):
# If obj doesn't have a min method, or if the method doesn't accept a
# fill_value argument
return asanyarray(obj).min(axis=axis, fill_value=fill_value,
out=out, **kwargs)
def max(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
try:
return obj.max(axis=axis, fill_value=fill_value, out=out, **kwargs)
except (AttributeError, TypeError):
# If obj doesn't have a max method, or if the method doesn't accept a
# fill_value argument
return asanyarray(obj).max(axis=axis, fill_value=fill_value,
out=out, **kwargs)
def product(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the product of array elements over a given axis.
See Also
--------
prod : equivalent function; see for details.
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return um.multiply.reduce(a, axis=axis, dtype=dtype, out=out, **kwargs)
def alltrue(a, axis=None, out=None, keepdims=np._NoValue):
"""
Check if all elements of input array are true.
See Also
--------
numpy.all : Equivalent function; see for details.
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.all(axis=axis, out=out, **kwargs)
def _check_mask_axis(mask, axis, keepdims=np._NoValue):
"Check whether there are masked values along the given axis"
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
if mask is not nomask:
return mask.all(axis=axis, **kwargs)
return nomask
###############################################################################
# Masking functions #
###############################################################################
def any(self, axis=None, out=None, keepdims=np._NoValue):
"""
Returns True if any of the elements of `a` evaluate to True.
Masked values are considered as False during computation.
Refer to `numpy.any` for full documentation.
See Also
--------
ndarray.any : corresponding function for ndarrays
numpy.any : equivalent function
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
mask = _check_mask_axis(self._mask, axis, **kwargs)
if out is None:
d = self.filled(False).any(axis=axis, **kwargs).view(type(self))
if d.ndim:
d.__setmask__(mask)
elif mask:
d = masked
return d
self.filled(False).any(axis=axis, out=out, **kwargs)
if isinstance(out, MaskedArray):
if out.ndim or mask:
out.__setmask__(mask)
return out
def prod(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the product of the array elements over the given axis.
Masked elements are set to 1 internally for computation.
Refer to `numpy.prod` for full documentation.
Notes
-----
Arithmetic is modular when using integer types, and no error is raised
on overflow.
See Also
--------
ndarray.prod : corresponding function for ndarrays
numpy.prod : equivalent function
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
_mask = self._mask
newmask = _check_mask_axis(_mask, axis, **kwargs)
# No explicit output
if out is None:
result = self.filled(1).prod(axis, dtype=dtype, **kwargs)
rndim = getattr(result, 'ndim', 0)
if rndim:
result = result.view(type(self))
result.__setmask__(newmask)
elif newmask:
result = masked
return result
# Explicit output
result = self.filled(1).prod(axis, dtype=dtype, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getattr(out, '_mask', nomask)
if (outmask is nomask):
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
return out
def max(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue):
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
try:
return obj.max(axis=axis, fill_value=fill_value, out=out, **kwargs)
except (AttributeError, TypeError):
# If obj doesn't have a max method, or if the method doesn't accept a
# fill_value argument
return asanyarray(obj).max(axis=axis, fill_value=fill_value,
out=out, **kwargs)
def sum(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the sum of the array elements over the given axis.
Masked elements are set to 0 internally.
Refer to `numpy.sum` for full documentation.
See Also
--------
ndarray.sum : corresponding function for ndarrays
numpy.sum : equivalent function
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> print(x)
[[1 -- 3]
[-- 5 --]
[7 -- 9]]
>>> print(x.sum())
25
>>> print(x.sum(axis=1))
[4 5 16]
>>> print(x.sum(axis=0))
[8 5 12]
>>> print(type(x.sum(axis=0, dtype=np.int64)[0]))
<type 'numpy.int64'>
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
_mask = self._mask
newmask = _check_mask_axis(_mask, axis, **kwargs)
# No explicit output
if out is None:
result = self.filled(0).sum(axis, dtype=dtype, **kwargs)
rndim = getattr(result, 'ndim', 0)
if rndim:
result = result.view(type(self))
result.__setmask__(newmask)
elif newmask:
result = masked
return result
# Explicit output
result = self.filled(0).sum(axis, dtype=dtype, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getattr(out, '_mask', nomask)
if (outmask is nomask):
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
return out
def mean(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Returns the average of the array elements along given axis.
Masked entries are ignored, and result elements which are not
finite will be masked.
Refer to `numpy.mean` for full documentation.
See Also
--------
ndarray.mean : corresponding function for ndarrays
numpy.mean : Equivalent function
numpy.ma.average: Weighted average.
Examples
--------
>>> a = np.ma.array([1,2,3], mask=[False, False, True])
>>> a
masked_array(data = [1 2 --],
mask = [False False True],
fill_value = 999999)
>>> a.mean()
1.5
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
if self._mask is nomask:
result = super(MaskedArray, self).mean(axis=axis,
dtype=dtype, **kwargs)
else:
dsum = self.sum(axis=axis, dtype=dtype, **kwargs)
cnt = self.count(axis=axis, **kwargs)
if cnt.shape == () and (cnt == 0):
result = masked
else:
result = dsum * 1. / cnt
if out is not None:
out.flat = result
if isinstance(out, MaskedArray):
outmask = getattr(out, '_mask', nomask)
if (outmask is nomask):
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = getattr(result, '_mask', nomask)
return out
return result
def var(self, axis=None, dtype=None, out=None, ddof=0,
keepdims=np._NoValue):
"""
Returns the variance of the array elements along given axis.
Masked entries are ignored, and result elements which are not
finite will be masked.
Refer to `numpy.var` for full documentation.
See Also
--------
ndarray.var : corresponding function for ndarrays
numpy.var : Equivalent function
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
# Easy case: nomask, business as usual
if self._mask is nomask:
return self._data.var(axis=axis, dtype=dtype, out=out,
ddof=ddof, **kwargs)
# Some data are masked, yay!
cnt = self.count(axis=axis, **kwargs) - ddof
danom = self - self.mean(axis, dtype, keepdims=True)
if iscomplexobj(self):
danom = umath.absolute(danom) ** 2
else:
danom *= danom
dvar = divide(danom.sum(axis, **kwargs), cnt).view(type(self))
# Apply the mask if it's not a scalar
if dvar.ndim:
dvar._mask = mask_or(self._mask.all(axis, **kwargs), (cnt <= 0))
dvar._update_from(self)
elif getattr(dvar, '_mask', False):
# Make sure that masked is returned when the scalar is masked.
dvar = masked
if out is not None:
if isinstance(out, MaskedArray):
out.flat = 0
out.__setmask__(True)
elif out.dtype.kind in 'biu':
errmsg = "Masked data information would be lost in one or "\
"more location."
raise MaskError(errmsg)
else:
out.flat = np.nan
return out
# In case with have an explicit output
if out is not None:
# Set the data
out.flat = dvar
# Set the mask if needed
if isinstance(out, MaskedArray):
out.__setmask__(dvar.mask)
return out
return dvar
def sum(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the sum of the array elements over the given axis.
Masked elements are set to 0 internally.
Refer to `numpy.sum` for full documentation.
See Also
--------
ndarray.sum : corresponding function for ndarrays
numpy.sum : equivalent function
Examples
--------
>>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> print(x)
[[1 -- 3]
[-- 5 --]
[7 -- 9]]
>>> print(x.sum())
25
>>> print(x.sum(axis=1))
[4 5 16]
>>> print(x.sum(axis=0))
[8 5 12]
>>> print(type(x.sum(axis=0, dtype=np.int64)[0]))
<type 'numpy.int64'>
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
_mask = self._mask
newmask = _check_mask_axis(_mask, axis, **kwargs)
# No explicit output
if out is None:
result = self.filled(0).sum(axis, dtype=dtype, **kwargs)
rndim = getattr(result, 'ndim', 0)
if rndim:
result = result.view(type(self))
result.__setmask__(newmask)
elif newmask:
result = masked
return result
# Explicit output
result = self.filled(0).sum(axis, dtype=dtype, out=out, **kwargs)
if isinstance(out, MaskedArray):
outmask = getattr(out, '_mask', nomask)
if (outmask is nomask):
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = newmask
return out
def mean(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Returns the average of the array elements along given axis.
Masked entries are ignored, and result elements which are not
finite will be masked.
Refer to `numpy.mean` for full documentation.
See Also
--------
ndarray.mean : corresponding function for ndarrays
numpy.mean : Equivalent function
numpy.ma.average: Weighted average.
Examples
--------
>>> a = np.ma.array([1,2,3], mask=[False, False, True])
>>> a
masked_array(data = [1 2 --],
mask = [False False True],
fill_value = 999999)
>>> a.mean()
1.5
"""
kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims}
if self._mask is nomask:
result = super(MaskedArray, self).mean(axis=axis,
dtype=dtype, **kwargs)[()]
else:
dsum = self.sum(axis=axis, dtype=dtype, **kwargs)
cnt = self.count(axis=axis, **kwargs)
if cnt.shape == () and (cnt == 0):
result = masked
else:
result = dsum * 1. / cnt
if out is not None:
out.flat = result
if isinstance(out, MaskedArray):
outmask = getattr(out, '_mask', nomask)
if (outmask is nomask):
outmask = out._mask = make_mask_none(out.shape)
outmask.flat = getattr(result, '_mask', nomask)
return out
return result