The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
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) Example 2
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) Example 3
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) Example 4
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) Example 5
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 Example 6
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) Example 7
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) Example 8
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 #
############################################################################### Example 9
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 Example 10
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 Example 11
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) Example 12
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) Example 13
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) Example 14
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) Example 15
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 #
############################################################################### Example 16
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 Example 17
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 Example 18
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) Example 19
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 Example 20
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 Example 21
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 Example 22
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 Example 23
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 