types¶
Module with general purpose types.
- nutils.types.aspreprocessor(apply)¶
Convert
apply
into a preprocessor decorator. When applied to a function,wrapped
, the returned decorator preprocesses the arguments withapply
before callingwrapped
. Theapply
function should return a tuple ofargs
(tuple
orlist
) andkwargs
(dict
). The decorated functionwrapped
will be called withwrapped(*args, **kwargs)
. Theapply
function is allowed to change the signature of the decorated function.Examples
The following preprocessor swaps two arguments.
>>> @aspreprocessor ... def swapargs(a, b): ... return (b, a), {}
Decorating a function with
swapargs
will cause the arguments to be swapped before the wrapped function is called.>>> @swapargs ... def func(a, b): ... return a, b >>> func(1, 2) (2, 1)
- nutils.types.apply_annotations(wrapped)¶
Decorator that applies annotations to arguments. All annotations should be
callable
s taking one argument and returning a transformed argument. All annotations are strongly recommended to be idempotent.If a parameter of the decorated function has a default value
None
and the value of this parameter isNone
as well, the annotation is omitted.Examples
Consider the following function.
>>> @apply_annotations ... def f(a:tuple, b:int): ... return a + (b,)
When calling
f
with alist
andstr
as arguments, theapply_annotations()
decorator first appliestuple
andint
to the arguments before passing them to the decorated function.>>> f([1, 2], '3') (1, 2, 3)
The following example illustrates the behavior of parameters with default value
None
.>>> addone = lambda x: x+1 >>> @apply_annotations ... def g(a:addone=None): ... return a
When calling
g
without arguments or with argumentNone
, the annotationaddone
is not applied. Note thatNone + 1
would raise an exception.>>> g() is None True >>> g(None) is None True
When passing a different value, the annotation is applied:
>>> g(1) 2
- nutils.types.argument_canonicalizer(signature)¶
Returns a function that converts arguments matching
signature
to canonical positional and keyword arguments. If possible, an argument is added to the list of positional arguments, otherwise to the keyword arguments dictionary. The returned arguments include default values.- Parameters:
signature (
inspect.Signature
) – The signature of a function to generate canonical arguments for.- Returns:
A function that returns a
tuple
of atuple
of positional arguments and adict
of keyword arguments.- Return type:
Examples
Consider the following function.
>>> def f(a, b=4, *, c): pass
The
argument_canonicalizer
forf
is generated as follows:>>> canon = argument_canonicalizer(inspect.signature(f))
Calling
canon
with parameterb
passed as keyword returns arguments with parameterb
as positional argument:>>> canon(1, c=3, b=2) ((1, 2), {'c': 3})
When calling
canon
without parameterb
the default value is added to the positional arguments:>>> canon(1, c=3) ((1, 4), {'c': 3})
- nutils.types.nutils_hash(data)¶
Compute a stable hash of immutable object
data
. The hash is not affected by Python’s hash randomization (seeobject.__hash__()
).
- class nutils.types.CacheMeta(name, bases, namespace, **kwargs)¶
Bases:
ABCMeta
Metaclass that adds caching functionality to properties and methods listed in the special attribute
__cache__
. If an attribute is of typeproperty
, the value of the property will be computed at the first attribute access and served from cache subsequently. If an attribute is a method, the arguments and return value are cached and the cached value will be used if a subsequent call is made with the same arguments; if not, the cache will be overwritten. The cache lives in private attributes in the instance. The metaclass supports the use of__slots__
. If a subclass redefines a cached property or method (in the sense of this metaclass) of a base class, the property or method of the subclass is not automatically cached;__cache__
should be used in the subclass explicitly.Examples
An example of a class with a cached property:
>>> class T(metaclass=CacheMeta): ... __cache__ = 'x', ... @property ... def x(self): ... print('uncached') ... return 1
The print statement is added to illustrate when method
x
(as defined above) is called:>>> t = T() >>> t.x uncached 1 >>> t.x 1
An example of a class with a cached method:
>>> class U(metaclass=CacheMeta): ... __cache__ = 'y', ... def y(self, a): ... print('uncached') ... return a
Again, the print statement is added to illustrate when the method
y
(as defined above) is called:>>> u = U() >>> u.y(1) uncached 1 >>> u.y(1) 1 >>> u.y(2) uncached 2 >>> u.y(2) 2
- class nutils.types.Immutable(*args, **kwargs)¶
Bases:
object
Base class for immutable types. This class adds equality tests, traditional hashing (
hash()
), nutils hashing (nutils_hash()
) and pickling, all based solely on the (positional) intialization arguments,args
for future reference. Keyword-only arguments are not supported. All arguments should be hashable bynutils_hash()
.Positional and keyword initialization arguments are canonicalized automatically (by
argument_canonicalizer()
). If the__init__
method of a subclass is decorated with preprocessors (seeaspreprocessor()
), the preprocessors are applied to the initialization arguments andargs
becomes the preprocessed positional part.Examples
Consider the following class.
>>> class Plain(Immutable): ... def __init__(self, a, b): ... pass
Calling
Plain
with equivalent positional or keyword arguments produces equal instances:>>> Plain(1, 2) == Plain(a=1, b=2) True
Passing unhashable values to
Plain
will fail:>>> Plain([1, 2], [3, 4]) Traceback (most recent call last): ... TypeError: unhashable type: 'list'
This can be solved by adding and applying annotations to
__init__
. The following class converts its initialization arguments totuple
automaticaly:>>> class Annotated(Immutable): ... @apply_annotations ... def __init__(self, a:tuple, b:tuple): ... pass
Calling
Annotated
with eitherlist
s of1, 2
and3, 4
ortuple
s gives equal instances:>>> Annotated([1, 2], [3, 4]) == Annotated((1, 2), (3, 4)) True
- class nutils.types.Singleton(*args, **kwargs)¶
Bases:
Immutable
Subclass of
Immutable
that creates a single instance per unique set of initialization arguments.Examples
Consider the following class.
>>> class Plain(Singleton): ... def __init__(self, a, b): ... pass
Calling
Plain
with equivalent positional or keyword arguments produces one instance:>>> Plain(1, 2) is Plain(a=1, b=2) True
Consider the folling class with annotations.
>>> class Annotated(Singleton): ... @apply_annotations ... def __init__(self, a:tuple, b:tuple): ... pass
Calling
Annotated
with eitherlist
s of1, 2
and3, 4
ortuple
s gives a single instance:>>> Annotated([1, 2], [3, 4]) is Annotated((1, 2), (3, 4)) True
- __eq__(self, value, /)¶
Return self==value.
- class nutils.types.arraydata(dtype, shape, bytes)¶
Bases:
Singleton
hashable array container.
The container can be used for fast equality checks and for dictionary keys. Data is copied at construction and canonicalized by casting it to the platform’s primary data representation (e.g. int64 i/o int32). It can be retrieved via
numpy.asarray()
. Additionally thearraydata
object provides direct access to the array’s shape, dtype and bytes.Example
>>> a = numpy.array([1,2,3]) >>> w = arraydata(a) >>> w == arraydata([1,2,4]) # NOTE: equality only if entire array matches False >>> numpy.asarray(w) array([1, 2, 3])
- nutils.types.strictint(value)¶
Converts any type that is a subclass of
numbers.Integral
(e.g.int
andnumpy.int64
) toint
, and fails otherwise. Notable differences with the behavior ofint
:strictint()
does not convert astr
to anint
.strictint()
does not truncatefloat
to anint
.
Examples
>>> strictint(1), type(strictint(1)) (1, <class 'int'>) >>> strictint(numpy.int64(1)), type(strictint(numpy.int64(1))) (1, <class 'int'>) >>> strictint(1.0) Traceback (most recent call last): ... ValueError: not an integer: 1.0 >>> strictint('1') Traceback (most recent call last): ... ValueError: not an integer: '1'
- nutils.types.strictfloat(value)¶
Converts any type that is a subclass of
numbers.Real
(e.g.float
andnumpy.float64
) tofloat
, and fails otherwise. Notable difference with the behavior offloat
:strictfloat()
does not convert astr
to anfloat
.
Examples
>>> strictfloat(1), type(strictfloat(1)) (1.0, <class 'float'>) >>> strictfloat(numpy.float64(1.2)), type(strictfloat(numpy.float64(1.2))) (1.2, <class 'float'>) >>> strictfloat(1.2+3.4j) Traceback (most recent call last): ... ValueError: not a real number: (1.2+3.4j) >>> strictfloat('1.2') Traceback (most recent call last): ... ValueError: not a real number: '1.2'
- nutils.types.strictstr(value)¶
Returns
value
unmodified if it is astr
, and fails otherwise. Notable difference with the behavior ofstr
:strictstr()
does not call__str__
methods of objects to automatically convert objects tostr
s.
Examples
Passing a
str
tostrictstr()
works:>>> strictstr('spam') 'spam'
Passing an
int
will fail:>>> strictstr(1) Traceback (most recent call last): ... ValueError: not a 'str': 1
- class nutils.types.strict(*args, **kwargs)¶
Bases:
object
Type checker. The function
strict[cls](value)
returnsvalue
unmodified ifvalue
is an instance ofcls
, otherwise aValueError
is raised.Examples
The
strict[int]
function passes integers unmodified:>>> strict[int](1) 1
Other types fail:
>>> strict[int]('1') Traceback (most recent call last): ... ValueError: expected an object of type 'int' but got '1' with type 'str'
- __weakref__¶
list of weak references to the object (if defined)
- class nutils.types.tuple(*args, **kwargs)¶
Bases:
tuple
Wrapper of
tuple
that supports a user-defined item constructor via the notationtuple[I]
, withI
the item constructor. This is shorthand forlambda items: tuple(map(I, items))
. The item constructor should be any callable that takes one argument.Examples
A tuple with items processed with
strictint()
:>>> tuple[strictint]((False, 1, 2, numpy.int64(3))) (0, 1, 2, 3)
If the item constructor raises an exception, the construction of the
tuple
failes accordingly:>>> tuple[strictint]((1, 2, 3.4)) Traceback (most recent call last): ... ValueError: not an integer: 3.4
- class nutils.types.frozendict(base)¶
Bases:
Mapping
An immutable version of
dict
. Thefrozendict
is hashable and both the keys and values should be hashable as well.Custom key and value constructors can be supplied via the
frozendict[K,V]
notation, withK
the key constructor andV
the value constructor, which is roughly equivalent tolambda *args, **kwargs: {K(k): V(v) for k, v in dict(*args, **kwargs).items()}
.Examples
A
frozendict
withstrictstr()
as key constructor andstrictfloat()
as value constructor:>>> frozendict[strictstr,strictfloat]({'spam': False}) frozendict({'spam': 0.0})
Similar but with non-strict constructors:
>>> frozendict[str,float]({None: '1.2'}) frozendict({'None': 1.2})
Applying the strict constructors to invalid data raises an exception:
>>> frozendict[strictstr,strictfloat]({None: '1.2'}) Traceback (most recent call last): ... ValueError: not a 'str': None
- class nutils.types.frozenmultiset(items)¶
Bases:
Container
An immutable multiset. A multiset is a generalization of a set: items may occur more than once. Two mutlisets are equal if they have the same set of items and the same item multiplicities.
A custom item constructor can be supplied via the notation
frozenmultiset[I]
, withI
the item constructor. This is shorthand forlambda items: frozenmultiset(map(I, items))
. The item constructor should be any callable that takes one argument.Examples
>>> a = frozenmultiset(['spam', 'bacon', 'spam']) >>> b = frozenmultiset(['sausage', 'spam'])
The
frozenmultiset
objects support+
,-
and&
operators:>>> a + b frozenmultiset(['spam', 'bacon', 'spam', 'sausage', 'spam']) >>> a - b frozenmultiset(['bacon', 'spam']) >>> a & b frozenmultiset(['spam'])
The order of the items is irrelevant:
>>> frozenmultiset(['spam', 'spam', 'eggs']) == frozenmultiset(['spam', 'eggs', 'spam']) True
The multiplicities, however, are not:
>>> frozenmultiset(['spam', 'spam', 'eggs']) == frozenmultiset(['spam', 'eggs']) False
- __and__(self, other)¶
Return a
frozenmultiset
with elements from the left and right hand sides with strict positive multiplicity, where the multiplicity is the minimum of multiplicitie of the left and right hand side.
- __add__(self, other)¶
Return a
frozenmultiset
with elements from the left and right hand sides with a multiplicity equal to the sum of the left and right hand sides.
- __sub__(self, other)¶
Return a
frozenmultiset
with elements from the left hand sides with a multiplicity equal to the difference of the multiplicity of the left and right hand sides, truncated to zero. Elements with multiplicity zero are omitted.
- nutils.types.frozenarray(arg, *, copy=True, dtype=None)¶
Create read-only Numpy array.
- Parameters:
arg (
numpy.ndarray
or array_like) – Input data.copy (
bool
) – If True (the default), do not modify the argument in place. No copy is ever forced if the argument is already immutable.dtype (
numpy.dtype
or dtype_like, optional) – The desired data-type for the array.
- Return type:
- class nutils.types.c_array(*args, **kwargs)¶
Bases:
object
Converts an array-like object to a ctypes array with a specific dtype. The function
c_array[dtype](array)
returnsarray
unmodified ifarray
is already a ctypes array. Ifarray
is anumpy.ndarray
, the array is converted if thedtype
is correct and the array is contiguous; otherwiseValueError
is raised. Otherwise,array
is first converted to a contiguousnumpy.ndarray
and then converted to ctypes array. In the first two cases changes made to the ctypes array are reflected by thearray
argument: both are essentially views of the same data. In the third case, changes to eitherarray
or the returned ctypes array are not reflected by the other.- __weakref__¶
list of weak references to the object (if defined)
- nutils.types.lru_cache(func)¶
Buffer-aware cache.
Returns values from a cache for previously seen arguments. Arguments must be hasheable objects or immutable Numpy arrays, the latter identified by the underlying buffer. Destruction of the buffer triggers a callback that removes the corresponding cache entry.
At present, any writeable array will silently disable caching. This bevaviour is transitional, with future versions requiring that all arrays be immutable.
Caution
When a decorated function returns an object that references its argument (for instance, by returning the argument itself), the cached value keeps an argument’s reference count from falling to zero, causing the object to remain in cache indefinitely. For this reason, care must be taken that the decorator is only applied to functions that return objects with no references to its arguments.
- class nutils.types.attributes(**args)¶
Bases:
object
Dictionary-like container with attributes instead of keys, instantiated using keyword arguments:
>>> A = attributes(foo=10, bar=True) >>> A attributes(bar=True, foo=10) >>> A.foo 10
- __weakref__¶
list of weak references to the object (if defined)
- nutils.types.hashable_function(identifier)¶
Decorator that wraps the decorated function and adds a Nutils hash.
Return a decorator that wraps the decorated function and adds a Nutils hash based solely on the given
identifier
. The identifier can be anything that has a Nutils hash. The identifier should represent the behavior of the function and should be changed when the behavior of the function changes.If used on methods, this decorator behaves like
staticmethod()
.Examples
Make some function
func
hashable:>>> @hashable_function('func v1') ... def func(a, b): ... return a + b ...
The Nutils hash can be obtained by calling
nutils_hash
onfunc
:>>> nutils_hash(func).hex() 'b7fed72647f6a88dd3ce3808b2710eede7d7b5a5'
Note that the hash is based solely on the identifier passed to the decorator. If we create another function
other
with the same identifier asfunc
, then both have the same hash, despite returning different values.>>> @hashable_function('func v1') ... def other(a, b): ... return a * b ... >>> nutils_hash(other) == nutils_hash(func) True >>> func(1, 2) == other(1, 2) False
The decorator can also be applied on methods:
>>> class Spam: ... @hashable_function('Spam.eggs v1') ... def eggs(a, b): ... # NOTE: `self` is absent because `hashable_function` behaves like `staticmethod`. ... return a + b ...
The hash of
eggs
accessed via the class or an instance is the same:>>> spam = Spam() >>> nutils_hash(Spam.eggs).hex() 'dfdbb0ce20b617b17c3b854c23b2b9f7deb94cc6' >>> nutils_hash(spam.eggs).hex() 'dfdbb0ce20b617b17c3b854c23b2b9f7deb94cc6'