山特维克4315和4325区别

应用不同、材质不同。

1、山特维克4315以及4325一种用于对钢件和钢铸件进行精车到粗车的CVD涂层硬质材质。4315在大多数不同的工况下都能确保同样可靠的性能，是进行钢件车削的首选材质。也可用于CoroCut1-2，用于管件切断和切槽应用而4325只能用于高硬度钢件切除。

2、其次4325材质均含有大量可循环利用的硬质合金。相较于使用初级原材料制造刀具，使用回收而来的整体硬质合金制造新刀具能节省能耗达70%之多，同时这也意味着二氧化碳排放量将减少40%，4315材质采用的是硬质合金，硬度方面相较于4325较弱。

This article explains the new features in Python 3.5, compared to 3.4. Python 3.5 was released on September 13, 2015. See the changelog for a full list of changes.

See also

PEP 478 - Python 3.5 Release Schedule

Summary – Release highlights

New syntax features:

PEP 492, coroutines with async and await syntax.

PEP 465, a new matrix multiplication operator: a @ b.

PEP 448, additional unpacking generalizations.

New library modules:

typing: PEP 484 – Type Hints.

zipapp: PEP 441 Improving Python ZIP Application Support.

New built-in features:

bytes % args, bytearray % args: PEP 461 – Adding % formatting to bytes and bytearray.

New bytes.hex(), bytearray.hex() and memoryview.hex() methods. (Contributed by Arnon Yaari in bpo-9951.)

memoryview now supports tuple indexing (including multi-dimensional). (Contributed by Antoine Pitrou in bpo-23632.)

Generators have a new gi_yieldfrom attribute, which returns the object being iterated by yield from expressions. (Contributed by Benno Leslie and Yury Selivanov in bpo-24450.)

A new RecursionError exception is now raised when maximum recursion depth is reached. (Contributed by Georg Brandl in bpo-19235.)

CPython implementation improvements:

When the LC_TYPE locale is the POSIX locale (C locale), sys.stdin and sys.stdout now use the surrogateescape error handler, instead of the strict error handler. (Contributed by Victor Stinner in bpo-19977.)

.pyo files are no longer used and have been replaced by a more flexible scheme that includes the optimization level explicitly in .pyc name. (See PEP 488 overview.)

Builtin and extension modules are now initialized in a multi-phase process, which is similar to how Python modules are loaded. (See PEP 489 overview.)

Significant improvements in the standard library:

collections.OrderedDict is now implemented in C, which makes it 4 to 100 times faster.

The ssl module gained support for Memory BIO, which decouples SSL protocol handling from network IO.

The new os.scandir() function provides a better and significantly faster way of directory traversal.

functools.lru_cache() has been mostly reimplemented in C, yielding much better performance.

The new subprocess.run() function provides a streamlined way to run subprocesses.

The traceback module has been significantly enhanced for improved performance and developer convenience.

Security improvements:

SSLv3 is now disabled throughout the standard library. It can still be enabled by instantiating a ssl.SSLContext manually. (See bpo-22638 for more details; this change was backported to CPython 3.4 and 2.7.)

HTTP cookie parsing is now stricter, in order to protect against potential injection attacks. (Contributed by Antoine Pitrou in bpo-22796.)

Windows improvements:

A new installer for Windows has replaced the old MSI. See Using Python on Windows for more information.

Windows builds now use Microsoft Visual C++ 14.0, and extension modules should use the same.

Please read on for a comprehensive list of user-facing changes, including many other smaller improvements, CPython optimizations, deprecations, and potential porting issues.

New Features

PEP 492 - Coroutines with async and await syntax

PEP 492 greatly improves support for asynchronous programming in Python by adding awaitable objects, coroutine functions, asynchronous iteration, and asynchronous context managers.

Coroutine functions are declared using the new async def syntax:

>>>

>>> async def coro():

... return 'spam'

Inside a coroutine function, the new await expression can be used to suspend coroutine execution until the result is available. Any object can be awaited, as long as it implements the awaitable protocol by defining the __await__() method.

PEP 492 also adds async for statement for convenient iteration over asynchronous iterables.

An example of a rudimentary HTTP client written using the new syntax:

import asyncio

async def http_get(domain):

reader, writer = await asyncio.open_connection(domain, 80)

writer.write(b'\r\n'.join([

b'GET / HTTP/1.1',

b'Host: %b' % domain.encode('latin-1'),

b'Connection: close',

b'', b''

]))

async for line in reader:

print('>>>', line)

writer.close()

loop = asyncio.get_event_loop()

try:

loop.run_until_complete(http_get('example.com'))

finally:

loop.close()

Similarly to asynchronous iteration, there is a new syntax for asynchronous context managers. The following script:

import asyncio

async def coro(name, lock):

print('coro {}: waiting for lock'.format(name))

async with lock:

print('coro {}: holding the lock'.format(name))

await asyncio.sleep(1)

print('coro {}: releasing the lock'.format(name))

loop = asyncio.get_event_loop()

lock = asyncio.Lock()

coros = asyncio.gather(coro(1, lock), coro(2, lock))

try:

loop.run_until_complete(coros)

finally:

loop.close()

will output:

coro 2: waiting for lock

coro 2: holding the lock

coro 1: waiting for lock

coro 2: releasing the lock

coro 1: holding the lock

coro 1: releasing the lock

Note that both async for and async with can only be used inside a coroutine function declared with async def.

Coroutine functions are intended to be run inside a compatible event loop, such as the asyncio loop.

Note

Changed in version 3.5.2: Starting with CPython 3.5.2, __aiter__ can directly return asynchronous iterators. Returning an awaitable object will result in a PendingDeprecationWarning.

See more details in the Asynchronous Iterators documentation section.

See also

PEP 492 – Coroutines with async and await syntax

PEP written and implemented by Yury Selivanov.

PEP 465 - A dedicated infix operator for matrix multiplication

PEP 465 adds the @ infix operator for matrix multiplication. Currently, no builtin Python types implement the new operator, however, it can be implemented by defining __matmul__(), __rmatmul__(), and __imatmul__() for regular, reflected, and in-place matrix multiplication. The semantics of these methods is similar to that of methods defining other infix arithmetic operators.

Matrix multiplication is a notably common operation in many fields of mathematics, science, engineering, and the addition of @ allows writing cleaner code:

S = (H @ beta - r).T @ inv(H @ V @ H.T) @ (H @ beta - r)

instead of:

S = dot((dot(H, beta) - r).T,

dot(inv(dot(dot(H, V), H.T)), dot(H, beta) - r))

NumPy 1.10 has support for the new operator:

>>>

>>> import numpy

>>> x = numpy.ones(3)

>>> x

array([ 1., 1., 1.])

>>> m = numpy.eye(3)

>>> m

array([[ 1., 0., 0.],

[ 0., 1., 0.],

[ 0., 0., 1.]])

>>> x @ m

array([ 1., 1., 1.])

See also

PEP 465 – A dedicated infix operator for matrix multiplication

PEP written by Nathaniel J. Smith; implemented by Benjamin Peterson.

PEP 448 - Additional Unpacking Generalizations

PEP 448 extends the allowed uses of the * iterable unpacking operator and ** dictionary unpacking operator. It is now possible to use an arbitrary number of unpackings in function calls:

>>>

>>> print(*[1], *[2], 3, *[4, 5])

1 2 3 4 5

>>> def fn(a, b, c, d):

... print(a, b, c, d)

...

>>> fn(**{'a': 1, 'c': 3}, **{'b': 2, 'd': 4})

1 2 3 4

Similarly, tuple, list, set, and dictionary displays allow multiple unpackings (see Expression lists and Dictionary displays):

>>>

>>> *range(4), 4

(0, 1, 2, 3, 4)

>>> [*range(4), 4]

[0, 1, 2, 3, 4]

>>> {*range(4), 4, *(5, 6, 7)}

{0, 1, 2, 3, 4, 5, 6, 7}

>>> {'x': 1, **{'y': 2}}

{'x': 1, 'y': 2}

See also

PEP 448 – Additional Unpacking Generalizations

PEP written by Joshua Landau; implemented by Neil Girdhar, Thomas Wouters, and Joshua Landau.

PEP 461 - percent formatting support for bytes and bytearray

PEP 461 adds support for the % interpolation operator to bytes and bytearray.

While interpolation is usually thought of as a string operation, there are cases where interpolation on bytes or bytearrays makes sense, and the work needed to make up for this missing functionality detracts from the overall readability of the code. This issue is particularly important when dealing with wire format protocols, which are often a mixture of binary and ASCII compatible text.

Examples:

>>>

>>> b'Hello %b!' % b'World'

b'Hello World!'

>>> b'x=%i y=%f' % (1, 2.5)

b'x=1 y=2.500000'

Unicode is not allowed for %b, but it is accepted by %a (equivalent of repr(obj).encode('ascii', 'backslashreplace')):

>>>

>>> b'Hello %b!' % 'World'

Traceback (most recent call last):

File "", line 1, in

TypeError: %b requires bytes, or an object that implements __bytes__, not 'str'

>>> b'price: %a' % '10?'

b"price: '10\\u20ac'"

Note that %s and %r conversion types, although supported, should only be used in codebases that need compatibility with Python 2.

See also

PEP 461 – Adding % formatting to bytes and bytearray

PEP written by Ethan Furman; implemented by Neil Schemenauer and Ethan Furman.

PEP 484 - Type Hints

Function annotation syntax has been a Python feature since version 3.0 (PEP 3107), however the semantics of annotations has been left undefined.

Experience has shown that the majority of function annotation uses were to provide type hints to function parameters and return values. It became evident that it would be beneficial for Python users, if the standard library included the base definitions and tools for type annotations.

PEP 484 introduces a provisional module to provide these standard definitions and tools, along with some conventions for situations where annotations are not available.

For example, here is a simple function whose argument and return type are declared in the annotations:

def greeting(name: str) -> str:

return 'Hello ' + name

While these annotations are available at runtime through the usual __annotations__ attribute, no automatic type checking happens at runtime.

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