numbers
— 数值抽象基类
¶
源代码: Lib/numbers.py
numbers
模块 (
PEP 3141
) defines a hierarchy of numeric
抽象基类
which progressively define more operations. None of the types defined in this module can be instantiated.
numbers.
Number
¶
The root of the numeric hierarchy. If you just want to check if an argument
x
is a number, without caring what kind, use
isinstance(x,
Number)
.
numbers.
Complex
¶
Subclasses of this type describe complex numbers and include the operations that work on the built-in
complex
type. These are: conversions to
complex
and
bool
,
real
,
imag
,
+
,
-
,
*
,
/
,
abs()
,
conjugate()
,
==
,和
!=
. All except
-
and
!=
are abstract.
real
¶
Abstract. Retrieves the real component of this number.
imag
¶
Abstract. Retrieves the imaginary component of this number.
conjugate
(
)
¶
Abstract. Returns the complex conjugate. For example,
(1+3j).conjugate()
==
(1-3j)
.
numbers.
Real
¶
To
Complex
,
Real
adds the operations that work on real numbers.
In short, those are: a conversion to
float
,
math.trunc()
,
round()
,
math.floor()
,
math.ceil()
,
divmod()
,
//
,
%
,
<
,
<=
,
>
,和
>=
.
Real also provides defaults for
complex()
,
real
,
imag
,和
conjugate()
.
Implementors should be careful to make equal numbers equal and hash them to the same values. This may be subtle if there are two different extensions of the real numbers. For example,
fractions.Fraction
implements
hash()
as follows:
def __hash__(self):
if self.denominator == 1:
# Get integers right.
return hash(self.numerator)
# Expensive check, but definitely correct.
if self == float(self):
return hash(float(self))
else:
# Use tuple's hash to avoid a high collision rate on
# simple fractions.
return hash((self.numerator, self.denominator))
There are, of course, more possible ABCs for numbers, and this would be a poor hierarchy if it precluded the possibility of adding those. You can add
MyFoo
between
Complex
and
Real
with:
class MyFoo(Complex): ...
MyFoo.register(Real)
We want to implement the arithmetic operations so that mixed-mode operations either call an implementation whose author knew about the types of both arguments, or convert both to the nearest built in type and do the operation there. For subtypes of
Integral
, this means that
__add__()
and
__radd__()
should be defined as:
class MyIntegral(Integral):
def __add__(self, other):
if isinstance(other, MyIntegral):
return do_my_adding_stuff(self, other)
elif isinstance(other, OtherTypeIKnowAbout):
return do_my_other_adding_stuff(self, other)
else:
return NotImplemented
def __radd__(self, other):
if isinstance(other, MyIntegral):
return do_my_adding_stuff(other, self)
elif isinstance(other, OtherTypeIKnowAbout):
return do_my_other_adding_stuff(other, self)
elif isinstance(other, Integral):
return int(other) + int(self)
elif isinstance(other, Real):
return float(other) + float(self)
elif isinstance(other, Complex):
return complex(other) + complex(self)
else:
return NotImplemented
There are 5 different cases for a mixed-type operation on subclasses of
Complex
. I’ll refer to all of the above code that doesn’t refer to
MyIntegral
and
OtherTypeIKnowAbout
as “boilerplate”.
a
will be an instance of
A
, which is a subtype of
Complex
(
a
:
A
<:
Complex
), and
b
:
B
<:
Complex
. I’ll consider
a
+
b
:
- 若
Adefines an__add__()which acceptsb, all is well.- 若
Afalls back to the boilerplate code, and it were to return a value from__add__(), we’d miss the possibility thatBdefines a more intelligent__radd__(), so the boilerplate should returnNotImplementedfrom__add__(). (OrAmay not implement__add__()at all.)- Then
B’s__radd__()gets a chance. If it acceptsa, all is well.- If it falls back to the boilerplate, there are no more possible methods to try, so this is where the default implementation should live.
- 若
B <: A, Python triesB.__radd__beforeA.__add__. This is ok, because it was implemented with knowledge ofA, so it can handle those instances before delegating toComplex.
若
A
<:
Complex
and
B
<:
Real
without sharing any other knowledge, then the appropriate shared operation is the one involving the built in
complex
, and both
__radd__()
s land there, so
a+b
==
b+a
.
Because most of the operations on any given type will be very similar, it can be useful to define a helper function which generates the forward and reverse instances of any given operator. For example,
fractions.Fraction
使用:
def _operator_fallbacks(monomorphic_operator, fallback_operator):
def forward(a, b):
if isinstance(b, (int, Fraction)):
return monomorphic_operator(a, b)
elif isinstance(b, float):
return fallback_operator(float(a), b)
elif isinstance(b, complex):
return fallback_operator(complex(a), b)
else:
return NotImplemented
forward.__name__ = '__' + fallback_operator.__name__ + '__'
forward.__doc__ = monomorphic_operator.__doc__
def reverse(b, a):
if isinstance(a, Rational):
# Includes ints.
return monomorphic_operator(a, b)
elif isinstance(a, numbers.Real):
return fallback_operator(float(a), float(b))
elif isinstance(a, numbers.Complex):
return fallback_operator(complex(a), complex(b))
else:
return NotImplemented
reverse.__name__ = '__r' + fallback_operator.__name__ + '__'
reverse.__doc__ = monomorphic_operator.__doc__
return forward, reverse
def _add(a, b):
"""a + b"""
return Fraction(a.numerator * b.denominator +
b.numerator * a.denominator,
a.denominator * b.denominator)
__add__, __radd__ = _operator_fallbacks(_add, operator.add)
# ...