Mailman 3 May 2012 - Python-ideas

@classproperty, @abc.abstractclasspropery, etc.
by K. Richard Pixley 16 Dec '20

16 Dec '20

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 21 Feb '20

21 Feb '20

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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hexdump
by anatoly techtonik 29 Apr '13

29 Apr '13

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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Yielding through context managers
by Joshua Bartlett 08 Jan '13

08 Jan '13

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

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i18n and Python tracebacks
by Andre Roberge 28 Oct '12

28 Oct '12

I think it would be a good idea if Python tracebacks could be translated into languages other than English - and it would set a good example. For example, using French as my default local language, instead of >>> 1/0 Traceback (most recent call last): File "<stdin>", line 1, in <module> ZeroDivisionError: integer division or modulo by zero I might get something like >>> 1/0 Suivi d'erreur (appel le plus récent en dernier) : Fichier "<stdin>", à la ligne 1, dans <module> ZeroDivisionError: division entière ou modulo par zéro André

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Re: [Python-ideas] Cofunctions - Back to Basics
by Mark Shannon 20 Oct '12

20 Oct '12

Greg Ewing wrote: > Mark Shannon wrote: > >> Why not have proper co-routines, instead of hacked-up generators? > > What do you mean by a "proper coroutine"? > A parallel, non-concurrent, thread of execution. It should be able to transfer control from arbitrary places in execution, not within generators. Stackless provides coroutines. Greenlets are also coroutines (I think). Lua has them, and is implemented in ANSI C, so it can be done portably. See: http://www.jucs.org/jucs_10_7/coroutines_in_lua/de_moura_a_l.pdf (One of the examples in the paper uses coroutines to implement generators, which is obviously not required in Python :) ) Cheers, Mark.

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Cofunctions PEP - Revision 4
by Greg Ewing 13 Oct '12

13 Oct '12

Here's an updated version of the PEP reflecting my recent suggestions on how to eliminate 'codef'. PEP: XXX Title: Cofunctions Version: $Revision$ Last-Modified: $Date$ Author: Gregory Ewing <greg.ewing(a)canterbury.ac.nz> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 13-Feb-2009 Python-Version: 3.x Post-History: Abstract ======== A syntax is proposed for defining and calling a special type of generator called a 'cofunction'. It is designed to provide a streamlined way of writing generator-based coroutines, and allow the early detection of certain kinds of error that are easily made when writing such code, which otherwise tend to cause hard-to-diagnose symptoms. This proposal builds on the 'yield from' mechanism described in PEP 380, and describes some of the semantics of cofunctions in terms of it. However, it would be possible to define and implement cofunctions independently of PEP 380 if so desired. Specification ============= Cofunction definitions ---------------------- A cofunction is a special kind of generator, distinguished by the presence of the keyword ``cocall`` (defined below) at least once in its body. It may also contain ``yield`` and/or ``yield from`` expressions, which behave as they do in other generators. From the outside, the distinguishing feature of a cofunction is that it cannot be called the same way as an ordinary function. An exception is raised if an ordinary call to a cofunction is attempted. Cocalls ------- Calls from one cofunction to another are made by marking the call with a new keyword ``cocall``. The expression :: cocall f(*args, **kwds) is evaluated by first checking whether the object ``f`` implements a ``__cocall__`` method. If it does, the cocall expression is equivalent to :: yield from f.__cocall__(*args, **kwds) except that the object returned by __cocall__ is expected to be an iterator, so the step of calling iter() on it is skipped. If ``f`` does not have a ``__cocall__`` method, or the ``__cocall__`` method returns ``NotImplemented``, then the cocall expression is treated as an ordinary call, and the ``__call__`` method of ``f`` is invoked. Objects which implement __cocall__ are expected to return an object obeying the iterator protocol. Cofunctions respond to __cocall__ the same way as ordinary generator functions respond to __call__, i.e. by returning a generator-iterator. Certain objects that wrap other callable objects, notably bound methods, will be given __cocall__ implementations that delegate to the underlying object. Grammar ------- The full syntax of a cocall expression is described by the following grammar lines: :: atom: cocall | <existing alternatives for atom> cocall: 'cocall' atom cotrailer* '(' [arglist] ')' cotrailer: '[' subscriptlist ']' | '.' NAME Note that this syntax allows cocalls to methods and elements of sequences or mappings to be expressed naturally. For example, the following are valid: :: y = cocall self.foo(x) y = cocall funcdict[key](x) y = cocall a.b.c[i].d(x) Also note that the final calling parentheses are mandatory, so that for example the following is invalid syntax: :: y = cocall f # INVALID New builtins, attributes and C API functions -------------------------------------------- To facilitate interfacing cofunctions with non-coroutine code, there will be a built-in function ``costart`` whose definition is equivalent to :: def costart(obj, *args, **kwds): try: m = obj.__cocall__ except AttributeError: result = NotImplemented else: result = m(*args, **kwds) if result is NotImplemented: raise TypeError("Object does not support cocall") return result There will also be a corresponding C API function :: PyObject *PyObject_CoCall(PyObject *obj, PyObject *args, PyObject *kwds) It is left unspecified for now whether a cofunction is a distinct type of object or, like a generator function, is simply a specially-marked function instance. If the latter, a read-only boolean attribute ``__iscofunction__`` should be provided to allow testing whether a given function object is a cofunction. Motivation and Rationale ======================== The ``yield from`` syntax is reasonably self-explanatory when used for the purpose of delegating part of the work of a generator to another function. It can also be used to good effect in the implementation of generator-based coroutines, but it reads somewhat awkwardly when used for that purpose, and tends to obscure the true intent of the code. Furthermore, using generators as coroutines is somewhat error-prone. If one forgets to use ``yield from`` when it should have been used, or uses it when it shouldn't have, the symptoms that result can be extremely obscure and confusing. Finally, sometimes there is a need for a function to be a coroutine even though it does not yield anything, and in these cases it is necessary to resort to kludges such as ``if 0: yield`` to force it to be a generator. The ``cocall`` construct address the first issue by making the syntax directly reflect the intent, that is, that the function being called forms part of a coroutine. The second issue is addressed by making it impossible to mix coroutine and non-coroutine code in ways that don't make sense. If the rules are violated, an exception is raised that points out exactly what and where the problem is. Lastly, the need for dummy yields is eliminated by making it possible for a cofunction to call both cofunctions and ordinary functions with the same syntax, so that an ordinary function can be used in place of a cofunction that yields zero times. Record of Discussion ==================== An earlier version of this proposal required a special keyword ``codef`` to be used in place of ``def`` when defining a cofunction, and disallowed calling an ordinary function using ``cocall``. However, it became evident that these features were not necessary, and the ``codef`` keyword was dropped in the interests of minimising the number of new keywords required. The use of a decorator instead of ``codef`` was also suggested, but the current proposal makes this unnecessary as well. It has been questioned whether some combination of decorators and functions could be used instead of a dedicated ``cocall`` syntax. While this might be possible, to achieve equivalent error-detecting power it would be necessary to write cofunction calls as something like :: yield from cocall(f)(args) making them even more verbose and inelegant than an unadorned ``yield from``. It is also not clear whether it is possible to achieve all of the benefits of the cocall syntax using this kind of approach. Prototype Implementation ======================== An implementation of an earlier version of this proposal in the form of patches to Python 3.1.2 can be found here: http://www.cosc.canterbury.ac.nz/greg.ewing/python/generators/cofunctions.h… If this version of the proposal is received favourably, the implementation will be updated to match. Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

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stdlib crowdsourcing
by anatoly techtonik 13 Jun '12

13 Jun '12

The problem with stdlib - it is all damn subjective. There is no process to add functions and modules if you're not well-behaved and skilled in public debates and don't have really a lot of time to be a champion of your module/function. In other words - it is hard (if not impossible for 80% of Python Earth population). So, many people and projects decide to opt-out. Take a look at Twisted - a lot of useful stuff, but not in Python stdlib. So.. Provide a way for people to opt-out from core stuff, but still allow to share the changes and update code if necessary. This will require: - a local stdlib Python path convention - snippet normalization function and AST hash dumper - web site with stats - source code crawler How it works: 1. Every project maintains its own stdlib directory with functions that they feel are good to have in standard library 2. Functions are placed so that they are imported as if from standard library, but this time with stdlib prefix 3. The license for this directory is public domain to remove all legal barriers (credits are welcome, but optional) 4. Crawler (probably PyPI) scans this stdlib dir, finds functions, normalizes them, calculates hash and submits to web site 4.1 Normalization is required to find the shared function copy/pasted across different projects with different indentation level, docstrings, parameters/variable names etc. 4.2 Hash is calculated upon AST. There are at least three hashes for each entry: 4.2.1 Full hash - all docstrings and variable names are preserved, whitespace normalized 4.2.2 Stripped hash - docstrings are stripped, variable names are normalized 4.2.3 Signature hash - a mark placed in a comment above function name, either calculated from function signature or generated randomly, used for manual tracking of copy/paste e.g. pd:ac546df6b8340a92 5. Web site maintains usage and popularity staff, accepts votes on inclusion of snippets User stories: 1. "I want to find if there is a better/updated version of my function available" 1.1 I enter hash into web site search form 1.2 Site gives me a link to my snippet 1.3 I can see what people proposed to replace this function with 1.4 I can choose the function with most votes 1.5 I can flag the functions I may find irrelevant or 1.5 I can tag the functions that divert in different direction than I need to filter them 2. "I want to reuse code snippets without additional dependencies on 3rd party projects" 1.1 Just place them into my own stdlib directory 3. "I want to update code snippets when there is an update for them" 1.1 I run scanner, it extracts signature hashes, stripped hashes and looks if web-site version of signature matches normalized hash 4. "I want to see what people want to include in the next Python version" 1.1 A call for proposals is made 1.2 People place wannabe's into their stdlib dirs 1.3 Crawl generates new functions on a web site 1.4 Functions are categorized 1.5 Optionally included / declined with a short one-liner reason - why 1.6 Optionally provided with more detailed info why --- feature creep cut --- 5. "I want to see what functions are popular in other languages" 1.1 A separate crawler for Ruby, PHP etc. stdlib converts their AST into compatible format where possible 1.2 Submit to site stats 6. "I want to download the function in Ruby format" 1.1 AST converter tries to do the job automatically where possible 1.2 If it fails - you are encouraged to fix the converter rules or write the replacement for this signature manually Just an idea. -- anatoly t.

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shutil.run (Was: shutil.runret and shutil.runout)
by anatoly techtonik 05 Jun '12

05 Jun '12

Hello again, I've finally found some time to partially process the replies and came up with a better solution than subprocess.* and shutil.runret/runout Disclaimer: I don't say that suprocess suxx - it is powerful and very awesome under the hood. What I want to say that its final user interface is awful - for such complex thing as this it should have been passed through several iteration cycles before settling down. Therefore, inspired by Fabric API, I've finally found the solution - shutil.run() function: https://bitbucket.org/techtonik/shutil-run/src run(command, combine_stderr=True): Run command through a system shell, return output string with additional properties: output.succeeded - result of the operation True/False output.return_code - specific return code output.stderr - stderr contents if combine_stderr=False `combine_stderr` if set, makes stderr merged into output string, otherwise it will be available as `output.stderr` attribute. Example: from shellrun import run output = run('ls -la') if output.succeeded: print(output) else: print("Error %s" % output.return_code) That's the most intuitive way I found so far. Objective advantages: 1. Better than subprocess.call(cmd, shell=true) subprocess.check_call(cmd, shell=true) subprocess.check_output(cmd, shell=True) because it is just shutil.run(cmd) i.e. short, simple and _easy to remember_ 2. With shutil.run() you don't need to rewrite your check_call() or check_output() with Popen() if you need to get return_code in addition to stderr contents on error 3. shutil.run() is predictable and consistent - its arguments are not dependent on each other, their combination doesn't change the function behavior over and over requiring you iterate over the documentation and warnings again and again 4. shutil.run() is the correct next level API over subprocess base level. subprocess executes external process - that is its role, but automatic ability to execute external process inside another external process (shell) looks like a hack to me. Practical, but still a hack. 5. No required exception catching, which doesn't work for shell=True anyway 6. No need to learn subprocess.PIPE routing magic (not an argument for hackers, I know) Subjective advantages: 1. More beautiful 2. More simple 3. More readable 4. Practical 5. Obvious 6. It easy to explain Hopefully, it can find its way in stdlib instead of http://shell-command.readthedocs.org/ -- anatoly t.

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FormatRepr in reprlib for declaring simple repr functions easily
by Ronny Pfannschmidt 31 May '12

31 May '12

Hi, i consider my utility class FormatRepr finished, its currently availiable in ( http://pypi.python.org/pypi/reprtools/0.1 ) it supplies a descriptor that allows to simply declare __repr__ methods based on object attributes. i think it greatly enhances readability for those things, as its DRY and focuses on the parts *i* consider important (e.E. what accessible attribute gets formatted how) there is no need ot repeat attribute names or care if something is a property,class-attribute or object attribute (one of the reasons why a simple .format(**vars(self)) will not always work) oversimplified example: .. code-block:: python from reprtools import FormatRepr class User(object): __repr__ = FormatRepr("<User {name}>") def __init__(self, name): self.name = name >>> User('test') <User test> -- Ronny

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