Mailman 3 October 2012 - Python-ideas

@classproperty, @abc.abstractclasspropery, etc.
by K. Richard Pixley Dec. 16, 2020

Dec. 16, 2020

There's a whole matrix of these and I'm wondering why the matrix is currently sparse rather than implementing them all. Or rather, why we can't stack them as: class foo(object): @classmethod @property def bar(cls, ...): ... Essentially the permutation are, I think: {'unadorned'|abc.abstract}{'normal'|static|class}{method|property|non-callable attribute}. concreteness implicit first arg type name comments {unadorned} {unadorned} method def foo(): exists now {unadorned} {unadorned} property @property exists now {unadorned} {unadorned} non-callable attribute x = 2 exists now {unadorned} static method @staticmethod exists now {unadorned} static property @staticproperty proposing {unadorned} static non-callable attribute {degenerate case - variables don't have arguments} unnecessary {unadorned} class method @classmethod exists now {unadorned} class property @classproperty or @classmethod;@property proposing {unadorned} class non-callable attribute {degenerate case - variables don't have arguments} unnecessary abc.abstract {unadorned} method @abc.abstractmethod exists now abc.abstract {unadorned} property @abc.abstractproperty exists now abc.abstract {unadorned} non-callable attribute @abc.abstractattribute or @abc.abstract;@attribute proposing abc.abstract static method @abc.abstractstaticmethod exists now abc.abstract static property @abc.staticproperty proposing abc.abstract static non-callable attribute {degenerate case - variables don't have arguments} unnecessary abc.abstract class method @abc.abstractclassmethod exists now abc.abstract class property @abc.abstractclassproperty proposing abc.abstract class non-callable attribute {degenerate case - variables don't have arguments} unnecessary I think the meanings of the new ones are pretty straightforward, but in case they are not... @staticproperty - like @property only without an implicit first argument. Allows the property to be called directly from the class without requiring a throw-away instance. @classproperty - like @property, only the implicit first argument to the method is the class. Allows the property to be called directly from the class without requiring a throw-away instance. @abc.abstractattribute - a simple, non-callable variable that must be overridden in subclasses @abc.abstractstaticproperty - like @abc.abstractproperty only for @staticproperty @abc.abstractclassproperty - like @abc.abstractproperty only for @classproperty --rich

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Specify number of items to allocate for array.array() constructor
by Sven Rahmann Feb. 22, 2020

Feb. 22, 2020

At the moment, the array module of the standard library allows to create arrays of different numeric types and to initialize them from an iterable (eg, another array). What's missing is the possiblity to specify the final size of the array (number of items), especially for large arrays. I'm thinking of suffix arrays (a text indexing data structure) for large texts, eg the human genome and its reverse complement (about 6 billion characters from the alphabet ACGT). The suffix array is a long int array of the same size (8 bytes per number, so it occupies about 48 GB memory). At the moment I am extending an array in chunks of several million items at a time at a time, which is slow and not elegant. The function below also initializes each item in the array to a given value (0 by default). Is there a reason why there the array.array constructor does not allow to simply specify the number of items that should be allocated? (I do not really care about the contents.) Would this be a worthwhile addition to / modification of the array module? My suggestions is to modify array generation in such a way that you could pass an iterator (as now) as second argument, but if you pass a single integer value, it should be treated as the number of items to allocate. Here is my current workaround (which is slow): def filled_array(typecode, n, value=0, bsize=(1<<22)): """returns a new array with given typecode (eg, "l" for long int, as in the array module) with n entries, initialized to the given value (default 0) """ a = array.array(typecode, [value]*bsize) x = array.array(typecode) r = n while r >= bsize: x.extend(a) r -= bsize x.extend([value]*r) return x

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format specifier for "not bytes"
by Daniel Holth July 5, 2013

July 5, 2013

While I was implementing JSON-JWS (JSON web signatures), a format which in Python 3 has to go from bytes > unicode > bytes > unicode several times in its construction, I notice I wrote a lot of bugs: "sha256=b'abcdef1234'" When I meant to say: "sha256=abcdef1234" Everything worked perfectly on Python 3 because the verifying code also generated the sha256=b'abcdef1234' as a comparison. I would have never noticed at all unless I had tried to verify the Python 3 output with Python 2. I know I'm a bad person for not having unit tests capable enough to catch this bug, a bug I wrote repeatedly in each layer of the bytes > unicode > bytes > unicode dance, and that there is no excuse for being confused at any time about the type of a variable, but I'm not willing to reform. Instead, I would like a new string formatting operator tentatively called 'notbytes': "sha256=%notbytes" % (b'abcdef1234'). It gives the same error as 'sha256='+b'abc1234' would: TypeError: Can't convert 'bytes' object to str implictly

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hexdump
by anatoly techtonik April 29, 2013

April 29, 2013

Just an idea of usability fix for Python 3. hexdump module (function or bytes method is better) as simple, easy and intuitive way for dumping binary data when writing programs in Python. hexdump(bytes) - produce human readable dump of binary data, byte-by-byte representation, separated by space, 16-byte rows Rationale: 1. Debug. Generic binary data can't be output to console. A separate helper is needed to print, log or store its value in human readable format in database. This takes time. 2. Usability. binascii is ugly: name is not intuitive any more, there are a lot of functions, and it is not clear how it relates to unicode. 3. Serialization. It is convenient to have format that can be displayed in a text editor. Simple tools encourage people to use them. Practical example: >>> print(b) � � � �� >>> b '\xe6\xb0\x08\x04\xe7\x9e\x08\x04\xe7\xbc\x08\x04\xe7\xd5\x08\x04\xe7\xe4\x08\x04\xe6\xb0\x08\x04\xe7\xf0\x08\x04\xe7\xff\x08\x04\xe8\x0b\x08\x04\xe8\x1a\x08\x04\xe6\xb0\x08\x04\xe6\xb0\x08\x04' >>> print(binascii.hexlify(data)) e6b00804e79e0804e7bc0804e7d50804e7e40804e6b00804e7f00804e7ff0804e80b0804e81a0804e6b00804e6b00804 >>> >>> data = hexdump(b) >>> print(data) E6 B0 08 04 E7 9E 08 04 E7 BC 08 04 E7 D5 08 04 E7 E4 08 04 E6 B0 08 04 E7 F0 08 04 E7 FF 08 04 E8 0B 08 04 E8 1A 08 04 E6 B0 08 04 E6 B0 08 04 >>> >>> # achieving the same output with binascii is overcomplicated >>> data_lines = [binascii.hexlify(b)[i:min(i+32, len(binascii.hexlify(b)))] for i in xrange(0, len(binascii.hexlify(b)), 32)] >>> data_lines = [' '.join(l[i:min(i+2, len(l))] for i in xrange(0, len(l), 2)).upper() for l in data_lines] >>> print('\n'.join(data_lines)) E6 B0 08 04 E7 9E 08 04 E7 BC 08 04 E7 D5 08 04 E7 E4 08 04 E6 B0 08 04 E7 F0 08 04 E7 FF 08 04 E8 0B 08 04 E8 1A 08 04 E6 B0 08 04 E6 B0 08 04 On the other side, getting rather useless binascii output from hexdump() is quite trivial: >>> data.replace(' ','').replace('\n','').lower() 'e6b00804e79e0804e7bc0804e7d50804e7e40804e6b00804e7f00804e7ff0804e80b0804e81a0804e6b00804e6b00804' But more practical, for example, would be counting offset from hexdump: >>> print( ''.join( '%05x: %s\n' % (i*16,l) for i,l in enumerate(hexdump(b).split('\n')))) Etc. Conclusion: By providing better building blocks on basic level Python will become a better tool for more useful tasks. References: [1] http://stackoverflow.com/questions/2340319/python-3-1-1-string-to-hex [2] http://en.wikipedia.org/wiki/Hex_dump -- anatoly t.

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The async API of the future
by Guido van Rossum Jan. 16, 2013

Jan. 16, 2013

Work priorities don't allow me to spend another day replying in detail to the various emails on this topic, but I am still keeping up reading! I have read Greg's response to my comparison between Future+yield-based coroutines and his yield-from-based, Future-free coroutines, and after having written a small prototype, I am now pretty much convinced that Greg's way is superior. This doesn't mean you can't use generators or yield-from for other purposes! It's just that *if* you are writing a coroutine for use with a certain schedule, you must use yield and yield-from in accordance to the scheduler's rules. However, code you call can still use yield and yield-from for iteration, and you can still use for-loops. In particular, if f is a coroutine, it can still write "for x in g(): ..." where g is a generator meant to be an iterator. However if g were instead a coroutine, f should call it using "yield from g()", and f and g should agree on the interface of their scheduler. As to other topics, my current feeling is that we should try to separately develop requirements and prototype implementations of the I/O loop of the future, and to figure the loosest possible coupling between that and a coroutine scheduler (or any other type of scheduler). In particular, I think the I/O loop should not assume the event handlers are implemented using coroutines -- but if someone wants to write an awesome coroutine scheduler, they should be able to delegate all their I/O waiting needs to the I/O loop with very little trouble. To me, this means that the I/O loop probably should use "plain" callback functions (i.e., not Futures, Deferreds or coroutines). We should also standardize the interface to the I/O loop so that 3rd parties can plug in their own I/O loop -- I don't see an end to the debate whether the best C library for event handling is libevent, libev or libuv. While the focus of the I/O loop should be on single-threaded event handling, some standard interface should exist so that you can run certain code in a separate thread and wait for its completion -- I've found this handy when calling socket.getaddrinfo(), which may block. (Apparently async DNS lookups are really hard -- I read some complaints about libevent's DNS lookups, and IIUC many Firefox lockups are due to this.) But there may be other uses for this too. An issue in the design of the I/O loop is the strain between a ready-based and completion-based design. The typical Unix design (whether based on select or any of the poll variants) is usually ready-based; but on Windows, the only way to get high performance is to base it on IOCP, which is completion-based (i.e. you start a specific async operation, like writing N bytes, and the I/O loop tells you when it is done). I would like people to be able to write fast event handling programs on Windows too, and ideally the only change would be the implementation of the I/O loop. But I don't know how tenable that is given the dramatically different style used by IOCP and the need to use native Windows API for all async I/O -- it sounds like we could only do this if the library providing the I/O loop implementation also wrapped all I/O operations, andthat may be a bit much. Finally, there should also be some minimal interface so that multiple I/O loops can interact -- at least in the case where one I/O loop belongs to a GUI library. It seems this is a solved problem (as well solved as you can hope for) to Twisted, so we should just adopt their approach. -- --Guido van Rossum (python.org/~guido)

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Yielding through context managers
by Joshua Bartlett Jan. 9, 2013

Jan. 9, 2013

I'd like to propose adding the ability for context managers to catch and handle control passing into and out of them via yield and generator.send() / generator.next(). For instance, class cd(object): def __init__(self, path): self.inner_path = path def __enter__(self): self.outer_path = os.getcwd() os.chdir(self.inner_path) def __exit__(self, exc_type, exc_val, exc_tb): os.chdir(self.outer_path) def __yield__(self): self.inner_path = os.getcwd() os.chdir(self.outer_path) def __send__(self): self.outer_path = os.getcwd() os.chdir(self.inner_path) Here __yield__() would be called when control is yielded through the with block and __send__() would be called when control is returned via .send() or .next(). To maintain compatibility, it would not be an error to leave either __yield__ or __send__ undefined. The rationale for this is that it's sometimes useful for a context manager to set global or thread-global state as in the example above, but when the code is used in a generator, the author of the generator needs to make assumptions about what the calling code is doing. e.g. def my_generator(path): with cd(path): yield do_something() do_something_else() Even if the author of this generator knows what effect do_something() and do_something_else() have on the current working directory, the author needs to assume that the caller of the generator isn't touching the working directory. For instance, if someone were to create two my_generator() generators with different paths and advance them alternately, the resulting behaviour could be most unexpected. With the proposed change, the context manager would be able to handle this so that the author of the generator doesn't need to make these assumptions. Naturally, nested with blocks would be handled by calling __yield__ from innermost to outermost and __send__ from outermost to innermost. I rather suspect that if this change were included, someone could come up with a variant of the contextlib.contextmanager decorator to simplify writing generators for this sort of situation. Cheers, J. D. Bartlett

6 14

Support data: URLs in urllib
by Mathias Panzenböck Nov. 16, 2012

Nov. 16, 2012

Sometimes it would be handy to read data:-urls just like any other url. While it is pretty easy to parse a data: url yourself I think it would be nice if urllib could do this for you. Example data url parser: >>> import base64 >>> import urllib.parse >>> >>> def read_data_url(url): >>> scheme, data = url.split(":") >>> assert scheme == "data", "unsupported scheme: "+scheme >>> mimetype, data = data.split(",") >>> if mimetype.endswith(";base64"): >>> return mimetype[:-7] or None, base64.b64decode(data.encode("UTF-8")) >>> else: >>> return mimetype or None, urllib.parse.unquote(data).encode("UTF-8") See also: http://tools.ietf.org/html/rfc2397 -panzi

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Windows temporary file association for Python files
by anatoly techtonik Nov. 7, 2012

Nov. 7, 2012

I wonder if it will make the life easier if Python was installed with .py association to "%PYTHON_HOME%\python.exe" "%1" %* It will remove the need to run .py scripts in virtualenv with explicit 'python' prefix. Example how it doesn't work right now E:\virtenv32\Scripts>echo import sys; print(sys.version) > test.py E:\virtenv32\Scripts>test.py 3.3.0 (v3.3.0:bd8afb90ebf2, Sep 29 2012, 10:55:48) [MSC v.1600 32 bit (Intel)] E:\virtenv32\Scripts>python test.py 3.2.3 (default, Apr 11 2012, 07:15:24) [MSC v.1500 32 bit (Intel)] If Python file association was specified with "%PYTHON_HOME%\python.exe" "%1" %* then virtualenv could override this variable when setting the environment to set correct executable for .py files. -- anatoly t.

5 6

Async API: some code to review
by Guido van Rossum Nov. 7, 2012

Nov. 7, 2012

I am finally ready to show the code I worked on for the past two weeks. This is definitely not ready for anything except as a quick demo, but I learned enough while writing it to feel comfortable with the PEP 380 paradigm. I've set up a Hg repo on code.google.com, and I picked a codename: tulip. View the code here: http://code.google.com/p/tulip/source/browse/ It runs on Linux and OSX; I have no easy access to Windows but I'd be happy to take contributions. Key files in the directory: - main.py: the main program for testing, and a rough HTTP client - sockets.py: transports for sockets and SSL, and a buffering layer - scheduling.py: a Task class and related stuff; this is where the PEP 380 scheduler is implemented - polling.py: an event loop and basic polling implementations for: select(), poll(), epoll(), kqueue() Other junk: .hgignore, Makefile, README, p3time.py (benchmark yield from vs. plain functions), longlines.py (stupid style checker) More detailed discussions per file follows; please read the code along with my description (separately they may not make much sense): polling.py: http://code.google.com/p/tulip/source/browse/polling.py I found it remarkably easy to come up with polling implementations using all those different system calls. I ended up mixing in the pollster class with the event loop class, although I'm not sure that's the best design -- perhaps it's better if the event loop just references the pollster as a separate object. The pollster has a very simple API: add_reader(fd, callback, *args), add_writer(<ditto>), remove_reader(fd), remove_writer(fd), and poll(timeout) -> list of events. (fd means file descriptor.) There's also pollable() which just checks if there are any fds registered. My implementation requires fd to be an int, but that could easily be extended to support other types of event sources. I'm not super happy that I have parallel reader/writer APIs, but passing a separate read/write flag didn't come out any more elegant, and I don't foresee other operation types (though I may be wrong). The event list started out as a tuple of (fd, flag, callback, args), where flag is 'r' or 'w' (easily extensible); in practice neither the fd nor the flag are used, and one of the last things I did was to wrap callback and args into a simple object that allows cancelling the callback; the add_*() methods return this object. (This could probably use a little more abstraction.) Note that poll() doesn't call the callbacks -- that's up to the event loop. The event loop has two basic ways to register callbacks: call_soon(callback, *args) causes callback(*args) to be called the next time the event loop runs; call_later(delay, callback, *args) schedules a callback at some time (relative or absolute) in the future. It also inherits add_reader() and add_writer() from the pollster. Then there is run(), which runs the event loop until there's nothing left to do (no readers, no writers, no soon or later callbacks), and run_once(), which goes through the entire list of event sources once. (I think the order in which I do this isn't quite right but it works for now.) Finally, there's a helper class (ThreadRunner) here which lets you run something in a separate thread using the features of concurrent.futures. It uses the "self-pipe trick" (Google it :-) to ensure that the poll() call wakes up -- this is needed by call_in_thread() at the next layer (scheduling.py). (There may be a race condition here, but I think it can be fixed.) Note that there are no yields (or yield froms) here; that's for the next layer: scheduling.py: http://code.google.com/p/tulip/source/browse/scheduling.py This is the scheduler for PEP-380 style coroutines. I started with a Scheduler class and operations along the lines of Greg Ewing's design, with a Scheduler instance as a global variable, but ended up ripping it out in favor of a Task object that represents a single stack of generators chained via yield-from. There is a Context object holding the event loop and the current task in thread-local storage, so that multiple threads can (and must) have independent event loops. Most user (and much library) code in this system should be written as generators invoking other generators directly using yield from. However to run something as an independent task, you wrap the generator call in a Task() constructor, possibly giving it a timeout, and then calling its start() method. A Task also acts a little like a future -- you can wait() for it, add done-callbacks, and it preserves the return value of the generator call. This can be used to introduce concurrency or to give something a separate timeout. (There are also primitives to wait for the first N completed of a bunch of Tasks.) To invoke a primitive I/O operation, you call the current task's block() method and then immediately yield (similar to Greg Ewing's approach). There are helpers block_r() and block_w() that arrange for a task to block until a file descriptor is ready for reading/writing. Examples of their use are in sockets.py. There is also call_in_thread() which integrates with polling.ThreadRunner to run a function in a separate thread and wait for it. Also used in sockets.py. In the docstrings I use the prefix "COROUTINE:" to indicate public APIs that should be invoked using yield from. sockets.py: http://code.google.com/p/tulip/source/browse/sockets.py This implements some internet primitives using the APIs in scheduling.py (including block_r() and block_w()). I call them transports but they are different from transports Twisted; they are closer to idealized sockets. SocketTransport wraps a plain socket, offering recv() and send() methods that must be invoked using yield from. SslTransport wraps an ssl socket (luckily in Python 2.6 and up, stdlib ssl sockets have good async support!). Then there is a BufferedReader class that implements more traditional read() and readline() coroutines (i.e., to be invoked using yield from), the latter handy for line-oriented transports. Finally there are some functions for connecting sockets, the highest-level one create_transport(). These use call_in_thread() to run socket.getaddrinfo() in a thread (this provides IPv6 support). I don't particularly care about the exact abstractions in this module; they are convenient and I was surprised how easy it was to add SSL, but still these mostly serve as somewhat realistic examples of how to use scheduling.py. (Afterthought: I think the SocketTransport's recv() and send() methods could be made more similar to SslTransport.) More examples in the final file: main.py: http://code.google.com/p/tulip/source/browse/main.py There is a simplistic HTTP client here built on top of the sockets.*Transport abstractions. And the main code exercises this by spawning four tasks fetching a variety of URLs (more when you uncomment a block of code) and waiting for their results. The code is a bit of a mess because I used it as a place to try out various APIs. I'm most interested in feedback on the design of polling.py and scheduling.py, and to a lesser extent on the design of sockets.py; main.py is just an example of how this style works out in practice. Sorry for the brain-dump style; I would like to write it all up better, but at the same time waiting longer doesn't necessarily make it better, so here it is, for all to see. (I also have a list of problems I had to debug during the development and what I learned from that; but that's too raw to post right now.) -- --Guido van Rossum (python.org/~guido)

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with-statement syntactic quirk
by Barry Warsaw Nov. 5, 2012

Nov. 5, 2012

with-statements have a syntactic quirk, which I think would be useful to fix. This is true in Python 2.7 through 3.3, but it's likely not fixable until 3.4, unless of course it's a bug <wink>. Legal: >>> with open('/etc/passwd') as p1, open('/etc/passwd') as p2: pass Not legal: >>> with (open('/etc/passwd') as p1, open('/etc/passwd') as p2): pass Why is this useful? If you need to wrap this onto multiple lines, say to fit it within line length limits. IWBNI you could write it like this: with (open('/etc/passwd') as p1, open('/etc/passwd') as p2): pass This seems analogous to using parens to wrap long if-statements, but maybe there's some subtle corner of the grammar that makes this problematic (like 'with' treating the whole thing as a single context manager). Of course, you can wrap with backslashes, but ick! Cheers, -Barry

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