Source code: Lib/numbers.py
The numbers
module (PEP 3141) defines a hierarchy of numeric abstract base classes which progressively define more operations. None of the types defined in this module can be instantiated.
class 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)
.
class 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()
, ==
, and !=
. All except -
and !=
are abstract.
real
Abstract. Retrieves the real component of this number.
imag
Abstract. Retrieves the imaginary component of this number.
abstractmethod conjugate()
Abstract. Returns the complex conjugate. For example, (1+3j).conjugate()
== (1-3j)
.
class 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()
, //
, %
, <
, <=
, >
, and >=
.
Real also provides defaults for complex()
, real
, imag
, and conjugate()
.
class numbers.Rational
Subtypes Real
and adds numerator
and denominator
properties, which should be in lowest terms. With these, it provides a default for float()
.
numerator
Abstract.
denominator
Abstract.
class numbers.Integral
Subtypes Rational
and adds a conversion to int
. Provides defaults for float()
, numerator
, and denominator
. Adds abstract methods for **
and bit-string operations: <<
, >>
, &
, ^
, |
, ~
.
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
:
A
defines an __add__()
which accepts b
, all is well.A
falls back to the boilerplate code, and it were to return a value from __add__()
, we’d miss the possibility that B
defines a more intelligent __radd__()
, so the boilerplate should return NotImplemented
from __add__()
. (Or A
may not implement __add__()
at all.)B
’s __radd__()
gets a chance. If it accepts a
, all is well.B <: A
, Python tries B.__radd__
before A.__add__
. This is ok, because it was implemented with knowledge of A
, so it can handle those instances before delegating to Complex
.If 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
uses:
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) # ...
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Licensed under the PSF License.
https://docs.python.org/3.8/library/numbers.html