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 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 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 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
