Python-ideas April 2011

python-ideas@python.org

63 participants
37 discussions

by Andre Roberge

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

8 years, 1 month

Cofunctions PEP - Revision 4

by Greg Ewing

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:

8 years, 1 month

PEP-3151 pattern-matching

by Devin Jeanpierre

Hello, PEP-3151 < http://www.python.org/dev/peps/pep-3151/ > mentions a really weird syntax for pattern-matching. I was hoping I could suggest an alternative that's both more concise, and possible to implement without doing something drastic like changing existing syntax or semantics. The PEP offers the pattern-matching syntax: > except IOError as e if e.errno == errno.ENOENT: ... I'd instead suggest something along the lines of > except io_error(errno.ENOENT): ... Implementing this is fairly straightforward, I've included it in the bottom of this email. Raising an exception becomes `raise io_error(errno.ENOENT)(msg)`. Some notational sugar can be implemented (for example, `raise io_error(errno.ENOENT, msg)` could also be made to work). I'm not fussed about the details. I personally prefer keeping the errnos as a big part of handling exceptions, they're common across OSes and involve less research / memorization for those that are already aware of errno. I guess what I'd like to see is the same exception hierarchy proposed by the PEP, but with the addition of allowing errnos to be specified by pattern-matching, so that errors not covered by the hierarchy, or more specific than the hierarchy, can be concisely caught. However, I'm not really well-versed in the pros and cons for all of this. Above all, I'd like for the pattern matching alternative to be a bit more reasonable. It doesn't have to be verbose and it doesn't have to involve new syntax. Apologies for any mistakes in the code, they are my own. Here's the code: # evil global state or something error_classes = {} def io_error(errno_, msg=None): # or something, you get the idea try: cls = error_classes[errno_] except LookupError: class IOErrorWithErrno(IOError): errno = errno_ cls = error_classes[errno_] = IOErrorWithErrno return error_classes[errno_] # example of usage import errno try: raise io_error(errno.ENOENT)("<Error message>") except io_error(errno.ENOENT): print("success") Thanks for your time! Devin Jeanpierre

9 years, 6 months

a few decorator recipes

by Mathias Panzenböck

A few decorator recipes that might be worthwhile to add to functools: A way to assign annotations to functions/classes: def annotations(**annots): def deco(obj): if hasattr(obj,'__annotations__'): obj.__annotations__.update(annots) else: obj.__annotations__ = annots return obj return deco _NONE = object() def getannot(obj, key, default=_NONE): if hasattr(obj, '__annotations__'): if default is _NONE: return obj.__annotations__[key] else: return obj.__annotations__.get(key, default) elif default is _NONE: raise KeyError(key) else: return default def setannot(obj, key, value): if hasattr(obj, '__annotations__'): obj.__annotations__[key] = value else: obj.__annotations__ = {key: value} Usage: >>> @annotations(foo='bar',egg='spam') >>> def foo(): >>> pass >>> >>> getannot(foo, 'egg') 'spam' A way to assign values to classes/functions (not of much use for classes, of course): def assign(**values): def deco(obj): for key in values: setattr(obj, key, values[key]) return obj return deco Usage: >>> @assign(bla='bleh',x=12) >>> def foo(): >>> pass >>> >>> foo.x 12

9 years, 6 months

Disallow orderring comparison to NaN

by Alexander Belopolsky

Another spin-off from the "[Python-Dev] PyObject_RichCompareBool identity shortcut" thread: > I would like to discuss another peculiarity of NaNs: > >>>> float('nan') < 0 > False >>>> float('nan') > 0 > False > > This property in my experience causes much more trouble than nan == > nan being false. The problem is that common sorting or binary search > algorithms may degenerate into infinite loops in the presence of nans. > This may even happen when searching for a finite value in a large > array that contains a single nan. Errors like this do happen in the > wild and and after chasing a bug like this programmers tend to avoid > nans at all costs. Oftentimes this leads to using "magic" > placeholders such as 1e300 for missing data. > > Since py3k has already made None < 0 an error, it may be reasonable > for float('nan') < 0 to raise an error as well (probably ValueError > rather than TypeError). This will not make lists with nans sortable > or searchable using binary search, but will make associated bugs > easier to find. >

9 years, 7 months

Proposal for new-style decorators

by Christophe Schlick

Hello everybody, My name is Christophe Schlick, from the University of Bordeaux, France. I've been using Python for years, but it happens that this is my first post on a Python-related mailing list. For the first time, I feel that I may have some topic interesting enough for the Python community, so I would be very happy to get any kind of feeback (positive or negative) on it. Thanks in advance... The goal of this post is to propose a new syntax for defining decorators in Python. I would like to fully explain the rationale and the benefits of this proposal, as I guess that the discussion might be more fruitful if all details are written down once for all. However this has led me to a very long post (a kind of informal PEP) that was blocked by the moderators. So, following their recommendation, I've divided the text in three successive parts: (1) the rationale, (2) the proposal, (3) the proposed implementation and additional questions/remarks. For better clarity of the description, I will call the proposed syntax as "new-style decorators" (NSD, for short) and rename the classic syntax as "old-style decorators" (OSD), following the terms used some years ago with the (re)definition of classes. By the way, the introducing process for NSD shares many aspects with the process used for introducing new-style classes, including the following features: * No existing syntax is broken: the only thing required to create a new-style decorator is to decorate itself by a newly-introduced decorator called... "decorator" (well, this sentence is less recursive than it might appear at the first reading). * Every thing that can be done with OSD is possible with NSD, but NSD offer additional more user-friendly features. * NSD can peacefully live together with OSD in the same code. An NSD may even decorate an OSD (and vice-versa), however some properties of the NSD are lost with such a combination. -------------------------------------------------- 1 - Why bother with a new syntax? To explain what I don't like with the current syntax of decorators, let me take the example of a basic decorator (called 'old_style_repeat_fix') that simply repeats 3 times its undecorated function, and adds some tracing to the standard output. Here is the code: #--- def old_style_repeat_fix(func): """docstring for decorating function""" # @wraps(func) def dummy_func_name_never_used(*args, **keys): """docstring for decorated function""" print "apply 'old_style_repeat_fix' on %r" % func.__name__ for loop in range(3): func(*args, **keys) return dummy_func_name_never_used #--- Even if such code snippets have become quite usual since the introduction of decorators in Python 2.2, many people have argued (and I am obviously one of them) that the decorator syntax is a bit cumbersome. First, it imposes the use of nested functions, which often reduces readability by moving the function signature and docstring too far from the corresponding code. Second, as anonymous lambdas expressions can usually not be employed for decorating functions, the programmer has no other choice than to create a dummy function name (only used for one single 'return' statement), which is never a good coding principle, whatever the programming language. Once you have tried to teach decorators to a bunch of students, you really understand how much this syntax leverages the difficulty to grab the idea. The situation is even worse when the decorator needs some arguments: let's create an extended decorator (called 'old_style_repeat_var) that includes an integer 'n' to control the number of iterations, and a boolean 'trace' to control the tracing behavior. Here is the code: #--- def old_style_repeat_var(n=3, trace=True): """docstring for decorating function""" def dummy_deco_name_never_used(func): """docstring never used""" # @wraps(func) def dummy_func_name_never_used(*args, **keys): """docstring for decorated function""" if trace: print "apply 'old_style_repeat_var' on %r" % func.__name__ for loop in range(n): func(*args, **keys) return dummy_func_name_never_used return dummy_deco_name_never_used #--- This time a two-level function nesting is required and the code needs two dummy names for these two nested functions. Note that the docstring of the middle nested function is even totally invisible for introspection tools. So whether you like nested functions or not, there is some evidence here that the current syntax is somehow suboptimal. Another drawback of OSD is that they do not gently collaborate with introspection and documentation tools. For instance, let's apply our decorator on a silly 'test' function: #--- @old_style_repeat_var(n=5) # 'trace' keeps its default value def test(first=0, last=0): """docstring for undecorated function""" print "test: first=%s last=%s" % (first, last) #--- Now, if we try 'help' on it, we get the following answer: #--- >>> help(test) dummy_func_name_never_used(*args, **keys) docstring for decorated function #--- which means that neither the name, nor the docstring, nor the signature of the 'test' function are correct. Things are a little better when using the 'wraps' function from the standard 'functools' module (simply uncomment the line '@wraps(func)' in the code of 'old_style_repeat_var'): #--- >>> help(test) test(*args, **keys) """docstring for undecorated function""" #--- '@wraps(func)' copies the name and the docstring from the undecorated function to the decorated one, in order to get some useful piece of information when using 'help'. However, the signature of the function still comes from the decorated function, not the genuine one. The reason is that signature copying is not an easy process. The only solution is to inspect the undecorated function and then use 'exec' to generate a wrapper with a correct signature. This is basically what is done in the 'decorator' package (available at PyPI) written by Michele Simionato. There has been a lengthy discussion in python-dev (in 2009 I guess, but I can't find the archive right now) whether to include or not this package in the standard library. As far as I know, there is currently no clear consensus whether this is a good idea or not, because there has always been a mixed feeling from the community about transparent copy from the undecorated to the decorated function (even about the 'wraps' function): on one hand, transparent copy is cool for immediate help, for automatic documentation and for introspection tools, but on the other hand, it totally hides the decorating process which is not always what is wanted... or needed. The syntax for NSD presented in this proposal tries to improve this situation by offering two desirable features, according to the Zen of Python: * "flat is better than nested": no nested functions with dummy names are required, even when parameters are passed to the decorator; only one single decorating function has to be written by the programmer, whatever the kind of decorator. * "explicit is better than implicit": introspection of a decorated function explicitely reveals the decoration process, and allows one to get the name/signature/docstring not only for the corresponding undecorated function, but also for any number of chained decorators that have been applied on it. ------ to be continued in Part 2... CS

9 years, 7 months

[[Python-Dev] PyObject_RichCompareBool identity shortcut] About specializing comparing objects in collections

by ZS

Returning to the original topic post on the comparison of objects in containers ... Could be asked to enter a special method __equals__ of objects for comparison in containers (by default, if it is not defined - use usual method for compatibility), just as is done in C# (it's special object's method Equals is used to compare items in the collection). ----- Zaur

9 years, 7 months

[[Python-Dev] PyObject_RichCompareBool identity shortcut] About specializing comparing objects in collections

by ZS

It was necessary to clarify the reason for the possible introduction of a special method __equals__. The fact that the objects in python are often interpreted as values. Method __eq__, IMHO is used more for comparing objects as values. Introduction of method __equals__ can explicitly indicate the method of comparison of the objects as objects and __eq__ objects as values. If method __equals__ is not defined then __eq__ will be used. -- Zaur

9 years, 7 months

Make all keywords legal as an identifier

by haael

Hello, guys. I did post this idea a few months ago. Now the revised version. Goal: Let _all_ alphanumeric keywords be legal as names for variables, functions and classes, even the ones that are reserved words now. Rationale: 1. Python took most good English words as reserved tokens. Situation goes worse from version to version. I often have hard time searching for acceptable synonyms. 2. Because of that, old Python programs cease to work, even if they do not use any abandoned features. Their only sin is using certain words that further versions of Python have stolen away. 3. Sometimes one needs to import keywords from some other language, XML be an example, or "translate" another programming language into Python in one way or another. Keyword reservation is a big problem then; it does not allow to use the natural Python syntax. Solution: Let the parser treat all keywords that come after a dot (".") as regular identifiers. For attributes, nothing changes: > boo.for = 7 For names that are not attributes, only one syntax change is needed: let a dot precede any identifier. > .with = 3 Of course, if a keyword is not preceded by a dot, it would be treated as a reserved word, just like now. > with = 3 # syntax error There is only one case where a dot is used as a prefix of an identifier and that is a relative module import. > from .boo import foo My change is consistent with this case. One benefit would be that converting current programs to work with future versions would be a matter of simple grep. Python is a great language. In my opinion, this change is the one last step to make it every geeky teenager's wet dream: the language where one can redefine almost anything. When I work with some problem, I always try to translate it to Python, solve and translate back. Prohibited identifier names are the main obstacle. So, let's set the identifiers free and swallow all the world, making Python the least common denominator of every computer problem on this planet. Regards, Bartosz Tarnowski ------------------------------------------------- Jedz te potrawy, aby uchronic sie przed rakiem! Sprawdz >> http://linkint.pl/f2946

9 years, 7 months

Re: [Python-ideas] [Python-Dev] the role of assert in the standard library ?

by Tarek Ziadé

This is a thread from python-dev I am moving to python-ideas, because I want to debate the "assert" keyword :) On Thu, Apr 28, 2011 at 12:27 PM, Michael Foord <fuzzyman(a)voidspace.org.uk> wrote: > On 28/04/2011 09:34, Terry Reedy wrote: >> >> On 4/28/2011 3:54 AM, Tarek Ziadé wrote: >>> >>> Hello >>> >>> I removed some assert calls in distutils some time ago because the >>> package was not behaving correctly when people were using Python with >>> the --optimize flag. In other words, assert became a full part of the >>> code logic and removing them via -O was changing the behavior. >>> >>> In my opinion assert should be avoided completely anywhere else than >>> in the tests. If this is a wrong statement, please let me know why :) >> >> My understanding is that assert can be used in production code but only to >> catch logic errors by testing supposed invariants or postconditions. It >> should not be used to test usage errors, including preconditions. In other >> words, assert presence or absence should not affect behavior unless the code >> has a bug. But it does affect the behaviour at the end: when the code has a bug, then the way the code works is affected and differs depending if -O is used. Let me take an example: bucket = [] def add(stuff): bucket.append(stuff) def purge_and_do_something(): bucket.clear() assert len(bucket) == 0 ... do something by being sure the bucket is empty ... here, we could say assert is legitimate, as it just checks a post-condition. But this code is not thread-safe and if it's run via several threads, some code could add something in the bucket while purge() check for the assertion. So, if I run this code using -O it will not behave the same: it will seem to work. I am not arguing against the fact that this code should be changed and set up a lock. My point is that I do not understand why there are assert calls to check post-conditions. Those seem to be relics from the developer that marked something she needed to take care of in her code, but did not yet. e.g. like a TODO or a XXX or a NotImplementedError Moreover, why -O and -OO are removing assertions in that case ? even if the assert is "supposed not to happen" it's present in the code and can happen (or well, remove it). So "optimize" potentially changes the behaviour of the code. >From what I understood so far, a line with an assert should be considered as a dead code if we want the code to behave always the same way. I remove dead code in my code... Cheers Tarek -- Tarek Ziadé | http://ziade.org

9 years, 7 months

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Python-ideas April 2011