本文整理汇总了Python中numpy.core.numeric.double方法的典型用法代码示例。如果您正苦于以下问题：Python numeric.double方法的具体用法？Python numeric.double怎么用？Python numeric.double使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy.core.numeric的用法示例。
在下文中一共展示了numeric.double方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: typename
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import double [as 别名]
def typename(char):
"""
Return a description for the given data type code.
Parameters
----------
char : str
Data type code.
Returns
-------
out : str
Description of the input data type code.
See Also
--------
dtype, typecodes
Examples
--------
>>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
... 'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
>>> for typechar in typechars:
... print(typechar, ' : ', np.typename(typechar))
...
S1 : character
? : bool
B : unsigned char
D : complex double precision
G : complex long double precision
F : complex single precision
I : unsigned integer
H : unsigned short
L : unsigned long integer
O : object
Q : unsigned long long integer
S : string
U : unicode
V : void
b : signed char
d : double precision
g : long precision
f : single precision
i : integer
h : short
l : long integer
q : long long integer
"""
return _namefromtype[char]
#-----------------------------------------------------------------------------
#determine the "minimum common type" for a group of arrays. 示例2: common_type
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import double [as 别名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays except int64 and uint64 can be safely cast to the
returned dtype without loss of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision] 示例3: common_type
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import double [as 别名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays can be safely cast to the returned dtype without loss
of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision] 示例4: common_type
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import double [as 别名]
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays except int64 and uint64 can be safely cast to the
returned dtype without loss of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<type 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<type 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<type 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision] 示例5: typename
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import double [as 别名]
def typename(char):
"""
Return a description for the given data type code.
Parameters
----------
char : str
Data type code.
Returns
-------
out : str
Description of the input data type code.
See Also
--------
dtype, typecodes
Examples
--------
>>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
... 'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
>>> for typechar in typechars:
... print typechar, ' : ', np.typename(typechar)
...
S1 : character
? : bool
B : unsigned char
D : complex double precision
G : complex long double precision
F : complex single precision
I : unsigned integer
H : unsigned short
L : unsigned long integer
O : object
Q : unsigned long long integer
S : string
U : unicode
V : void
b : signed char
d : double precision
g : long precision
f : single precision
i : integer
h : short
l : long integer
q : long long integer
"""
return _namefromtype[char]
#-----------------------------------------------------------------------------
#determine the "minimum common type" for a group of arrays.