Python-Dev July 2002

python-dev@python.org

80 participants
155 discussions

by barry＠zope.com

It has been a while since I posted a copy of PEP 1 to the mailing lists and newsgroups. I've recently done some updating of a few sections, so in the interest of gaining wider community participation in the Python development process, I'm posting the latest revision of PEP 1 here. A version of the PEP is always available on-line at http://www.python.org/peps/pep-0001.html Enjoy, -Barry -------------------- snip snip -------------------- PEP: 1 Title: PEP Purpose and Guidelines Version: $Revision: 1.36 $ Last-Modified: $Date: 2002/07/29 18:34:59 $ Author: Barry A. Warsaw, Jeremy Hylton Status: Active Type: Informational Created: 13-Jun-2000 Post-History: 21-Mar-2001, 29-Jul-2002 What is a PEP? PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature. We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. Because the PEPs are maintained as plain text files under CVS control, their revision history is the historical record of the feature proposal[1]. Kinds of PEPs There are two kinds of PEPs. A standards track PEP describes a new feature or implementation for Python. An informational PEP describes a Python design issue, or provides general guidelines or information to the Python community, but does not propose a new feature. Informational PEPs do not necessarily represent a Python community consensus or recommendation, so users and implementors are free to ignore informational PEPs or follow their advice. PEP Work Flow The PEP editor, Barry Warsaw <peps(a)python.org>, assigns numbers for each PEP and changes its status. The PEP process begins with a new idea for Python. It is highly recommended that a single PEP contain a single key proposal or new idea. The more focussed the PEP, the more successfully it tends to be. The PEP editor reserves the right to reject PEP proposals if they appear too unfocussed or too broad. If in doubt, split your PEP into several well-focussed ones. Each PEP must have a champion -- someone who writes the PEP using the style and format described below, shepherds the discussions in the appropriate forums, and attempts to build community consensus around the idea. The PEP champion (a.k.a. Author) should first attempt to ascertain whether the idea is PEP-able. Small enhancements or patches often don't need a PEP and can be injected into the Python development work flow with a patch submission to the SourceForge patch manager[2] or feature request tracker[3]. The PEP champion then emails the PEP editor <peps(a)python.org> with a proposed title and a rough, but fleshed out, draft of the PEP. This draft must be written in PEP style as described below. If the PEP editor approves, he will assign the PEP a number, label it as standards track or informational, give it status 'draft', and create and check-in the initial draft of the PEP. The PEP editor will not unreasonably deny a PEP. Reasons for denying PEP status include duplication of effort, being technically unsound, not providing proper motivation or addressing backwards compatibility, or not in keeping with the Python philosophy. The BDFL (Benevolent Dictator for Life, Guido van Rossum) can be consulted during the approval phase, and is the final arbitrator of the draft's PEP-ability. If a pre-PEP is rejected, the author may elect to take the pre-PEP to the comp.lang.python newsgroup (a.k.a. python-list(a)python.org mailing list) to help flesh it out, gain feedback and consensus from the community at large, and improve the PEP for re-submission. The author of the PEP is then responsible for posting the PEP to the community forums, and marshaling community support for it. As updates are necessary, the PEP author can check in new versions if they have CVS commit permissions, or can email new PEP versions to the PEP editor for committing. Standards track PEPs consists of two parts, a design document and a reference implementation. The PEP should be reviewed and accepted before a reference implementation is begun, unless a reference implementation will aid people in studying the PEP. Standards Track PEPs must include an implementation - in the form of code, patch, or URL to same - before it can be considered Final. PEP authors are responsible for collecting community feedback on a PEP before submitting it for review. A PEP that has not been discussed on python-list(a)python.org and/or python-dev(a)python.org will not be accepted. However, wherever possible, long open-ended discussions on public mailing lists should be avoided. Strategies to keep the discussions efficient include, setting up a separate SIG mailing list for the topic, having the PEP author accept private comments in the early design phases, etc. PEP authors should use their discretion here. Once the authors have completed a PEP, they must inform the PEP editor that it is ready for review. PEPs are reviewed by the BDFL and his chosen consultants, who may accept or reject a PEP or send it back to the author(s) for revision. Once a PEP has been accepted, the reference implementation must be completed. When the reference implementation is complete and accepted by the BDFL, the status will be changed to `Final.' A PEP can also be assigned status `Deferred.' The PEP author or editor can assign the PEP this status when no progress is being made on the PEP. Once a PEP is deferred, the PEP editor can re-assign it to draft status. A PEP can also be `Rejected'. Perhaps after all is said and done it was not a good idea. It is still important to have a record of this fact. PEPs can also be replaced by a different PEP, rendering the original obsolete. This is intended for Informational PEPs, where version 2 of an API can replace version 1. PEP work flow is as follows: Draft -> Accepted -> Final -> Replaced ^ +----> Rejected v Deferred Some informational PEPs may also have a status of `Active' if they are never meant to be completed. E.g. PEP 1. What belongs in a successful PEP? Each PEP should have the following parts: 1. Preamble -- RFC822 style headers containing meta-data about the PEP, including the PEP number, a short descriptive title (limited to a maximum of 44 characters), the names, and optionally the contact info for each author, etc. 2. Abstract -- a short (~200 word) description of the technical issue being addressed. 3. Copyright/public domain -- Each PEP must either be explicitly labelled as placed in the public domain (see this PEP as an example) or licensed under the Open Publication License[4]. 4. Specification -- The technical specification should describe the syntax and semantics of any new language feature. The specification should be detailed enough to allow competing, interoperable implementations for any of the current Python platforms (CPython, JPython, Python .NET). 5. Motivation -- The motivation is critical for PEPs that want to change the Python language. It should clearly explain why the existing language specification is inadequate to address the problem that the PEP solves. PEP submissions without sufficient motivation may be rejected outright. 6. Rationale -- The rationale fleshes out the specification by describing what motivated the design and why particular design decisions were made. It should describe alternate designs that were considered and related work, e.g. how the feature is supported in other languages. The rationale should provide evidence of consensus within the community and discuss important objections or concerns raised during discussion. 7. Backwards Compatibility -- All PEPs that introduce backwards incompatibilities must include a section describing these incompatibilities and their severity. The PEP must explain how the author proposes to deal with these incompatibilities. PEP submissions without a sufficient backwards compatibility treatise may be rejected outright. 8. Reference Implementation -- The reference implementation must be completed before any PEP is given status 'Final,' but it need not be completed before the PEP is accepted. It is better to finish the specification and rationale first and reach consensus on it before writing code. The final implementation must include test code and documentation appropriate for either the Python language reference or the standard library reference. PEP Template PEPs are written in plain ASCII text, and should adhere to a rigid style. There is a Python script that parses this style and converts the plain text PEP to HTML for viewing on the web[5]. PEP 9 contains a boilerplate[7] template you can use to get started writing your PEP. Each PEP must begin with an RFC822 style header preamble. The headers must appear in the following order. Headers marked with `*' are optional and are described below. All other headers are required. PEP: <pep number> Title: <pep title> Version: <cvs version string> Last-Modified: <cvs date string> Author: <list of authors' real names and optionally, email addrs> * Discussions-To: <email address> Status: <Draft | Active | Accepted | Deferred | Final | Replaced> Type: <Informational | Standards Track> * Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> * Python-Version: <version number> Post-History: <dates of postings to python-list and python-dev> * Replaces: <pep number> * Replaced-By: <pep number> The Author: header lists the names and optionally, the email addresses of all the authors/owners of the PEP. The format of the author entry should be address(a)dom.ain (Random J. User) if the email address is included, and just Random J. User if the address is not given. If there are multiple authors, each should be on a separate line following RFC 822 continuation line conventions. Note that personal email addresses in PEPs will be obscured as a defense against spam harvesters. Standards track PEPs must have a Python-Version: header which indicates the version of Python that the feature will be released with. Informational PEPs do not need a Python-Version: header. While a PEP is in private discussions (usually during the initial Draft phase), a Discussions-To: header will indicate the mailing list or URL where the PEP is being discussed. No Discussions-To: header is necessary if the PEP is being discussed privately with the author, or on the python-list or python-dev email mailing lists. Note that email addresses in the Discussions-To: header will not be obscured. Created: records the date that the PEP was assigned a number, while Post-History: is used to record the dates of when new versions of the PEP are posted to python-list and/or python-dev. Both headers should be in dd-mmm-yyyy format, e.g. 14-Aug-2001. PEPs may have a Requires: header, indicating the PEP numbers that this PEP depends on. PEPs may also have a Replaced-By: header indicating that a PEP has been rendered obsolete by a later document; the value is the number of the PEP that replaces the current document. The newer PEP must have a Replaces: header containing the number of the PEP that it rendered obsolete. PEP Formatting Requirements PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. The body of each section must be indented 4 spaces. Code samples inside body sections should be indented a further 4 spaces, and other indentation can be used as required to make the text readable. You must use two blank lines between the last line of a section's body and the next section heading. You must adhere to the Emacs convention of adding two spaces at the end of every sentence. You should fill your paragraphs to column 70, but under no circumstances should your lines extend past column 79. If your code samples spill over column 79, you should rewrite them. Tab characters must never appear in the document at all. A PEP should include the standard Emacs stanza included by example at the bottom of this PEP. A PEP must contain a Copyright section, and it is strongly recommended to put the PEP in the public domain. When referencing an external web page in the body of a PEP, you should include the title of the page in the text, with a footnote reference to the URL. Do not include the URL in the body text of the PEP. E.g. Refer to the Python Language web site [1] for more details. ... [1] http://www.python.org When referring to another PEP, include the PEP number in the body text, such as "PEP 1". The title may optionally appear. Add a footnote reference that includes the PEP's title and author. It may optionally include the explicit URL on a separate line, but only in the References section. Note that the pep2html.py script will calculate URLs automatically, e.g.: ... Refer to PEP 1 [7] for more information about PEP style ... References [7] PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton http://www.python.org/peps/pep-0001.html If you decide to provide an explicit URL for a PEP, please use this as the URL template: http://www.python.org/peps/pep-xxxx.html PEP numbers in URLs must be padded with zeros from the left, so as to be exactly 4 characters wide, however PEP numbers in text are never padded. Reporting PEP Bugs, or Submitting PEP Updates How you report a bug, or submit a PEP update depends on several factors, such as the maturity of the PEP, the preferences of the PEP author, and the nature of your comments. For the early draft stages of the PEP, it's probably best to send your comments and changes directly to the PEP author. For more mature, or finished PEPs you may want to submit corrections to the SourceForge bug manager[6] or better yet, the SourceForge patch manager[2] so that your changes don't get lost. If the PEP author is a SF developer, assign the bug/patch to him, otherwise assign it to the PEP editor. When in doubt about where to send your changes, please check first with the PEP author and/or PEP editor. PEP authors who are also SF committers, can update the PEPs themselves by using "cvs commit" to commit their changes. Remember to also push the formatted PEP text out to the web by doing the following: % python pep2html.py -i NUM where NUM is the number of the PEP you want to push out. See % python pep2html.py --help for details. Transferring PEP Ownership It occasionally becomes necessary to transfer ownership of PEPs to a new champion. In general, we'd like to retain the original author as a co-author of the transferred PEP, but that's really up to the original author. A good reason to transfer ownership is because the original author no longer has the time or interest in updating it or following through with the PEP process, or has fallen off the face of the 'net (i.e. is unreachable or not responding to email). A bad reason to transfer ownership is because you don't agree with the direction of the PEP. We try to build consensus around a PEP, but if that's not possible, you can always submit a competing PEP. If you are interested assuming ownership of a PEP, send a message asking to take over, addressed to both the original author and the PEP editor <peps(a)python.org>. If the original author doesn't respond to email in a timely manner, the PEP editor will make a unilateral decision (it's not like such decisions can be reversed. :). References and Footnotes [1] This historical record is available by the normal CVS commands for retrieving older revisions. For those without direct access to the CVS tree, you can browse the current and past PEP revisions via the SourceForge web site at http://cvs.sourceforge.net/cgi-bin/cvsweb.cgi/python/nondist/peps/?cvsroot=… [2] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [3] http://sourceforge.net/tracker/?atid=355470&group_id=5470&func=browse [4] http://www.opencontent.org/openpub/ [5] The script referred to here is pep2html.py, which lives in the same directory in the CVS tree as the PEPs themselves. Try "pep2html.py --help" for details. The URL for viewing PEPs on the web is http://www.python.org/peps/ [6] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [7] PEP 9, Sample PEP Template http://www.python.org/peps/pep-0009.html 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 End:

3 weeks, 5 days

RE: [Python-Dev] Those import related syntax errors again...

by Samuele Pedroni

Hi. [Mark Hammond] > The point isn't about my suffering as such. The point is more that > python-dev owns a tiny amount of the code out there, and I don't believe we > should put Python's users through this. > > Sure - I would be happy to "upgrade" all the win32all code, no problem. I > am also happy to live in the bleeding edge and take some pain that will > cause. > > The issue is simply the user base, and giving Python a reputation of not > being able to painlessly upgrade even dot revisions. I agree with all this. [As I imagined explicit syntax did not catch up and would require lot of discussions.] [GvR] > > Another way is to use special rules > > (similar to those for class defs), e.g. having > > > > <frag> > > y=3 > > def f(): > > exec "y=2" > > def g(): > > return y > > return g() > > > > print f() > > </frag> > > > > # print 3. > > > > Is that confusing for users? maybe they will more naturally expect 2 > > as outcome (given nested scopes). > > This seems the best compromise to me. It will lead to the least > broken code, because this is the behavior that we had before nested > scopes! It is also quite easy to implement given the current > implementation, I believe. > > Maybe we could introduce a warning rather than an error for this > situation though, because even if this behavior is clearly documented, > it will still be confusing to some, so it is better if we outlaw it in > some future version. > Yes this can be easy to implement but more confusing situations can arise: <frag> y=3 def f(): y=9 exec "y=2" def g(): return y return y,g() print f() </frag> What should this print? the situation leads not to a canonical solution as class def scopes. or <frag> def f(): from foo import * def g(): return y return g() print f() </frag> [Mark Hammond] > > This probably won't be a very popular suggestion, but how about pulling > > nested scopes (I assume they are at the root of the problem) > > until this can be solved cleanly? > > Agreed. While I think nested scopes are kinda cool, I have lived without > them, and really without missing them, for years. At the moment the cure > appears worse then the symptoms in at least a few cases. If nothing else, > it compromises the elegant simplicity of Python that drew me here in the > first place! > > Assuming that people really _do_ want this feature, IMO the bar should be > raised so there are _zero_ backward compatibility issues. I don't say anything about pulling nested scopes (I don't think my opinion can change things in this respect) but I should insist that without explicit syntax IMO raising the bar has a too high impl cost (both performance and complexity) or creates confusion. [Andrew Kuchling] > >Assuming that people really _do_ want this feature, IMO the bar should be > >raised so there are _zero_ backward compatibility issues. > > Even at the cost of additional implementation complexity? At the cost > of having to learn "scopes are nested, unless you do these two things > in which case they're not"? > > Let's not waffle. If nested scopes are worth doing, they're worth > breaking code. Either leave exec and from..import illegal, or back > out nested scopes, or think of some better solution, but let's not > introduce complicated backward compatibility hacks. IMO breaking code would be ok if we issue warnings today and implement nested scopes issuing errors tomorrow. But this is simply a statement about principles and raised impression. IMO import * in an inner scope should end up being an error, not sure about 'exec's. We will need a final BDFL statement. regards, Samuele Pedroni.

16 years, 3 months

PEP: Support for System Upgrades

by M.-A. Lemburg

PEP: 0??? Title: Support for System Upgrades Version: $Revision: 0.0 $ Author: mal(a)lemburg.com (Marc-Andr? Lemburg) Status: Draft Type: Standards Track Python-Version: 2.3 Created: 19-Jul-2001 Post-History: Abstract This PEP proposes strategies to allow the Python standard library to be upgraded in parts without having to reinstall the complete distribution or having to wait for a new patch level release. Problem Python currently does not allow overriding modules or packages in the standard library per default. Even though this is possible by defining a PYTHONPATH environment variable (the paths defined in this variable are prepended to the Python standard library path), there is no standard way of achieving this without changing the configuration. Since Python's standard library is starting to host packages which are also available separately, e.g. the distutils, email and PyXML packages, which can also be installed independently of the Python distribution, it is desireable to have an option to upgrade these packages without having to wait for a new patch level release of the Python interpreter to bring along the changes. Proposed Solutions This PEP proposes two different but not necessarily conflicting solutions: 1. Adding a new standard search path to sys.path: $stdlibpath/system-packages just before the $stdlibpath entry. This complements the already existing entry for site add-ons $stdlibpath/site-packages which is appended to the sys.path at interpreter startup time. To make use of this new standard location, distutils will need to grow support for installing certain packages in $stdlibpath/system-packages rather than the standard location for third-party packages $stdlibpath/site-packages. 2. Tweaking distutils to install directly into $stdlibpath for the system upgrades rather than into $stdlibpath/site-packages. The first solution has a few advantages over the second: * upgrades can be easily identified (just look in $stdlibpath/system-packages) * upgrades can be deinstalled without affecting the rest of the interpreter installation * modules can be virtually removed from packages; this is due to the way Python imports packages: once it finds the top-level package directory it stay in this directory for all subsequent package submodule imports * the approach has an overall much cleaner design than the hackish install on top of an existing installation approach The only advantages of the second approach are that the Python interpreter does not have to changed and that it works with older Python versions. Both solutions require changes to distutils. These changes can also be implemented by package authors, but it would be better to define a standard way of switching on the proposed behaviour. Scope Solution 1: Python 2.3 and up Solution 2: all Python versions supported by distutils Credits None References None Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil End: -- Marc-Andre Lemburg CEO eGenix.com Software GmbH _______________________________________________________________________ eGenix.com -- Makers of the Python mx Extensions: mxDateTime,mxODBC,... Python Consulting: http://www.egenix.com/ Python Software: http://www.egenix.com/files/python/

18 years, 5 months

PEP 282 Implementation

by Vinay Sajip

I've uploaded my logging module, the proposed implementation for PEP 282, for committer review, to the SourceForge patch manager: http://sourceforge.net/tracker/index.php?func=detail&aid=578494&group_id=547 0&atid=305470 I've assigned it to Mark Hammond as (a) he had posted some comments to Trent Mick's original PEP posting, and (b) Barry Warsaw advised not assigning to PythonLabs people on account of their current workload. The file logging.py is (apart from some test scripts) all that's supposed to go into Python 2.3. The file logging-0.4.6.tar.gz contains the module, an updated version of the PEP (which I mailed to Barry Warsaw on 26th June), numerous test/example scripts, TeX documentation etc. You can also refer to http://www.red-dove.com/python_logging.html Here's hoping for a speedy review :-) Regards, Vinay Sajip

18 years, 8 months

HAVE_CONFIG_H

by Guido van Rossum

I see no references to HAVE_CONFIG_H in the source code (except one #undef in readline.c), yet we #define it on the command line. Is that still necessary? --Guido van Rossum (home page: http://www.python.org/~guido/)

18 years, 9 months

PEP 293, Codec Error Handling Callbacks

by Walter Dörwald

Here's another new PEP. Bye, Walter Dörwald ---------------------------------------------------------------------- PEP: 293 Title: Codec Error Handling Callbacks Version: $Revision: 1.1 $ Last-Modified: $Date: 2002/06/19 03:22:11 $ Author: Walter Dörwald Status: Draft Type: Standards Track Created: 18-Jun-2002 Python-Version: 2.3 Post-History: Abstract This PEP aims at extending Python's fixed codec error handling schemes with a more flexible callback based approach. Python currently uses a fixed error handling for codec error handlers. This PEP describes a mechanism which allows Python to use function callbacks as error handlers. With these more flexible error handlers it is possible to add new functionality to existing codecs by e.g. providing fallback solutions or different encodings for cases where the standard codec mapping does not apply. Specification Currently the set of codec error handling algorithms is fixed to either "strict", "replace" or "ignore" and the semantics of these algorithms is implemented separately for each codec. The proposed patch will make the set of error handling algorithms extensible through a codec error handler registry which maps handler names to handler functions. This registry consists of the following two C functions: int PyCodec_RegisterError(const char *name, PyObject *error) PyObject *PyCodec_LookupError(const char *name) and their Python counterparts codecs.register_error(name, error) codecs.lookup_error(name) PyCodec_LookupError raises a LookupError if no callback function has been registered under this name. Similar to the encoding name registry there is no way of unregistering callback functions or iterating through the available functions. The callback functions will be used in the following way by the codecs: when the codec encounters an encoding/decoding error, the callback function is looked up by name, the information about the error is stored in an exception object and the callback is called with this object. The callback returns information about how to proceed (or raises an exception). For encoding, the exception object will look like this: class UnicodeEncodeError(UnicodeError): def __init__(self, encoding, object, start, end, reason): UnicodeError.__init__(self, "encoding '%s' can't encode characters " + "in positions %d-%d: %s" % (encoding, start, end-1, reason)) self.encoding = encoding self.object = object self.start = start self.end = end self.reason = reason This type will be implemented in C with the appropriate setter and getter methods for the attributes, which have the following meaning: * encoding: The name of the encoding; * object: The original unicode object for which encode() has been called; * start: The position of the first unencodable character; * end: (The position of the last unencodable character)+1 (or the length of object, if all characters from start to the end of object are unencodable); * reason: The reason why object[start:end] couldn't be encoded. If object has consecutive unencodable characters, the encoder should collect those characters for one call to the callback if those characters can't be encoded for the same reason. The encoder is not required to implement this behaviour but may call the callback for every single character, but it is strongly suggested that the collecting method is implemented. The callback must not modify the exception object. If the callback does not raise an exception (either the one passed in, or a different one), it must return a tuple: (replacement, newpos) replacement is a unicode object that the encoder will encode and emit instead of the unencodable object[start:end] part, newpos specifies a new position within object, where (after encoding the replacement) the encoder will continue encoding. If the replacement string itself contains an unencodable character the encoder raises the exception object (but may set a different reason string before raising). Should further encoding errors occur, the encoder is allowed to reuse the exception object for the next call to the callback. Furthermore the encoder is allowed to cache the result of codecs.lookup_error. If the callback does not know how to handle the exception, it must raise a TypeError. Decoding works similar to encoding with the following differences: The exception class is named UnicodeDecodeError and the attribute object is the original 8bit string that the decoder is currently decoding. The decoder will call the callback with those bytes that constitute one undecodable sequence, even if there is more than one undecodable sequence that is undecodable for the same reason directly after the first one. E.g. for the "unicode-escape" encoding, when decoding the illegal string "\\u00\\u01x", the callback will be called twice (once for "\\u00" and once for "\\u01"). This is done to be able to generate the correct number of replacement characters. The replacement returned from the callback is a unicode object that will be emitted by the decoder as-is without further processing instead of the undecodable object[start:end] part. There is a third API that uses the old strict/ignore/replace error handling scheme: PyUnicode_TranslateCharmap/unicode.translate The proposed patch will enhance PyUnicode_TranslateCharmap, so that it also supports the callback registry. This has the additional side effect that PyUnicode_TranslateCharmap will support multi-character replacement strings (see SF feature request #403100 [1]). For PyUnicode_TranslateCharmap the exception class will be named UnicodeTranslateError. PyUnicode_TranslateCharmap will collect all consecutive untranslatable characters (i.e. those that map to None) and call the callback with them. The replacement returned from the callback is a unicode object that will be put in the translated result as-is, without further processing. All encoders and decoders are allowed to implement the callback functionality themselves, if they recognize the callback name (i.e. if it is a system callback like "strict", "replace" and "ignore"). The proposed patch will add two additional system callback names: "backslashreplace" and "xmlcharrefreplace", which can be used for encoding and translating and which will also be implemented in-place for all encoders and PyUnicode_TranslateCharmap. The Python equivalent of these five callbacks will look like this: def strict(exc): raise exc def ignore(exc): if isinstance(exc, UnicodeError): return (u"", exc.end) else: raise TypeError("can't handle %s" % exc.__name__) def replace(exc): if isinstance(exc, UnicodeEncodeError): return ((exc.end-exc.start)*u"?", exc.end) elif isinstance(exc, UnicodeDecodeError): return (u"\\ufffd", exc.end) elif isinstance(exc, UnicodeTranslateError): return ((exc.end-exc.start)*u"\\ufffd", exc.end) else: raise TypeError("can't handle %s" % exc.__name__) def backslashreplace(exc): if isinstance(exc, (UnicodeEncodeError, UnicodeTranslateError)): s = u"" for c in exc.object[exc.start:exc.end]: if ord(c)<=0xff: s += u"\\x%02x" % ord(c) elif ord(c)<=0xffff: s += u"\\u%04x" % ord(c) else: s += u"\\U%08x" % ord(c) return (s, exc.end) else: raise TypeError("can't handle %s" % exc.__name__) def xmlcharrefreplace(exc): if isinstance(exc, (UnicodeEncodeError, UnicodeTranslateError)): s = u"" for c in exc.object[exc.start:exc.end]: s += u"&#%d;" % ord(c) return (s, exc.end) else: raise TypeError("can't handle %s" % exc.__name__) These five callback handlers will also be accessible to Python as codecs.strict_error, codecs.ignore_error, codecs.replace_error, codecs.backslashreplace_error and codecs.xmlcharrefreplace_error. Rationale Most legacy encoding do not support the full range of Unicode characters. For these cases many high level protocols support a way of escaping a Unicode character (e.g. Python itself supports the \x, \u and \U convention, XML supports character references via &#xxx; etc.). When implementing such an encoding algorithm, a problem with the current implementation of the encode method of Unicode objects becomes apparent: For determining which characters are unencodable by a certain encoding, every single character has to be tried, because encode does not provide any information about the location of the error(s), so # (1) us = u"xxx" s = us.encode(encoding) has to be replaced by # (2) us = u"xxx" v = [] for c in us: try: v.append(c.encode(encoding)) except UnicodeError: v.append("&#%d;" % ord(c)) s = "".join(v) This slows down encoding dramatically as now the loop through the string is done in Python code and no longer in C code. Furthermore this solution poses problems with stateful encodings. For example UTF-16 uses a Byte Order Mark at the start of the encoded byte string to specify the byte order. Using (2) with UTF-16, results in an 8 bit string with a BOM between every character. To work around this problem, a stream writer - which keeps state between calls to the encoding function - has to be used: # (3) us = u"xxx" import codecs, cStringIO as StringIO writer = codecs.getwriter(encoding) v = StringIO.StringIO() uv = writer(v) for c in us: try: uv.write(c) except UnicodeError: uv.write(u"&#%d;" % ord(c)) s = v.getvalue() To compare the speed of (1) and (3) the following test script has been used: # (4) import time us = u"äa"*1000000 encoding = "ascii" import codecs, cStringIO as StringIO t1 = time.time() s1 = us.encode(encoding, "replace") t2 = time.time() writer = codecs.getwriter(encoding) v = StringIO.StringIO() uv = writer(v) for c in us: try: uv.write(c) except UnicodeError: uv.write(u"?") s2 = v.getvalue() t3 = time.time() assert(s1==s2) print "1:", t2-t1 print "2:", t3-t2 print "factor:", (t3-t2)/(t2-t1) On Linux this gives the following output (with Python 2.3a0): 1: 0.274321913719 2: 51.1284689903 factor: 186.381278466 i.e. (3) is 180 times slower than (1). Codecs must be stateless, because as soon as a callback is registered it is available globally and can be called by multiple encode() calls. To be able to use stateful callbacks, the errors parameter for encode/decode/translate would have to be changed from char * to PyObject *, so that the callback could be used directly, without the need to register the callback globally. As this requires changes to lots of C prototypes, this approach was rejected. Currently all encoding/decoding functions have arguments const Py_UNICODE *p, int size or const char *p, int size to specify the unicode characters/8bit characters to be encoded/decoded. So in case of an error the codec has to create a new unicode or str object from these parameters and store it in the exception object. The callers of these encoding/decoding functions extract these parameters from str/unicode objects themselves most of the time, so it could speed up error handling if these object were passed directly. As this again requires changes to many C functions, this approach has been rejected. Implementation Notes A sample implementation is available as SourceForge patch #432401 [2]. The current version of this patch differs from the specification in the following way: * The error information is passed from the codec to the callback not as an exception object, but as a tuple, which has an additional entry state, which can be used for additional information the codec might want to pass to the callback. * There are two separate registries (one for encoding/translating and one for decoding) The class codecs.StreamReaderWriter uses the errors parameter for both reading and writing. To be more flexible this should probably be changed to two separate parameters for reading and writing. The errors parameter of PyUnicode_TranslateCharmap is not availably to Python, which makes testing of the new functionality of PyUnicode_TranslateCharmap impossible with Python scripts. The patch should add an optional argument errors to unicode.translate to expose the functionality and make testing possible. Codecs that do something different than encoding/decoding from/to unicode and want to use the new machinery can define their own exception classes and the strict handlers will automatically work with it. The other predefined error handlers are unicode specific and expect to get a Unicode(Encode|Decode|Translate)Error exception object so they won't work. Backwards Compatibility The semantics of unicode.encode with errors="replace" has changed: The old version always stored a ? character in the output string even if no character was mapped to ? in the mapping. With the proposed patch, the replacement string from the callback callback will again be looked up in the mapping dictionary. But as all supported encodings are ASCII based, and thus map ? to ?, this should not be a problem in practice. References [1] SF feature request #403100 "Multicharacter replacements in PyUnicode_TranslateCharmap" http://www.python.org/sf/403100 [2] SF patch #432401 "unicode encoding error callbacks" http://www.python.org/sf/432401 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 End:

18 years, 10 months

Priority queue (binary heap) python code

by Kevin O'Connor

I often find myself needing priority queues in python, and I've finally broken down and written a simple implementation. Previously I've used sorted lists (via bisect) to get the job done, but the heap code significantly improves performance. There are C based implementations, but the effort of compiling in an extension often isn't worth the effort. I'm including the code here for everyone's amusement. Any chance something like this could make it into the standard python library? It would save a lot of time for lazy people like myself. :-) Cheers, -Kevin def heappush(heap, item): pos = len(heap) heap.append(None) while pos: parentpos = (pos - 1) / 2 parent = heap[parentpos] if item <= parent: break heap[pos] = parent pos = parentpos heap[pos] = item def heappop(heap): endpos = len(heap) - 1 if endpos <= 0: return heap.pop() returnitem = heap[0] item = heap.pop() pos = 0 while 1: child2pos = (pos + 1) * 2 child1pos = child2pos - 1 if child2pos < endpos: child1 = heap[child1pos] child2 = heap[child2pos] if item >= child1 and item >= child2: break if child1 > child2: heap[pos] = child1 pos = child1pos continue heap[pos] = child2 pos = child2pos continue if child1pos < endpos: child1 = heap[child1pos] if child1 > item: heap[pos] = child1 pos = child1pos break heap[pos] = item return returnitem -- ------------------------------------------------------------------------ | Kevin O'Connor "BTW, IMHO we need a FAQ for | | kevin(a)koconnor.net 'IMHO', 'FAQ', 'BTW', etc. !" | ------------------------------------------------------------------------

18 years, 10 months

RE: companies data for sorting comparisons

by Tim Peters

Kevin Altis kindly forwarded a 1.5MB XML database with about 6600 company records. A record looks like this after running his script to turn them into Python dicts: {'Address': '395 Page Mill Road\nPalo Alto, CA 94306', 'Company': 'Agilent Technologies Inc.', 'Exchange': 'NYSE', 'NumberOfEmployees': '41,000', 'Phone': '(650) 752-5000', 'Profile': 'http://biz.yahoo.com/p/a/a.html', 'Symbol': 'A', 'Web': 'http://www.agilent.com'} It appears to me that the XML file is maintained by hand, in order of ticker symbol. But people make mistakes when alphabetizing by hand, and there are 37 indices i such that data[i]['Symbol'] > data[i+1]['Symbol'] So it's "almost sorted" by that measure, with a few dozen glitches -- and msort should be able to exploit this! I think this is an important case of real-life behavior. The proper order of Yahoo profile URLs is also strongly correlated with ticker symbol, while both the company name and web address look weakly correlated, so there's hope that msort can get some benefit on those too. Here are runs sorting on all field names, building a DSU tuple list to sort via values = [(x.get(fieldname), x) for x in data] Each field sort was run 5 times under sort, and under msort. So 5 times are reported for each sort, reported in milliseconds, and listed from quickest to slowest: Sorting on field 'Address' -- 6589 records via sort: 43.03 43.35 43.37 43.54 44.14 via msort: 45.15 45.16 45.25 45.26 45.30 Sorting on field 'Company' -- 6635 records via sort: 40.41 40.55 40.61 42.36 42.63 via msort: 30.68 30.80 30.87 30.99 31.10 Sorting on field 'Exchange' -- 6579 records via sort: 565.28 565.49 566.70 567.12 567.45 via msort: 573.29 573.61 574.55 575.34 576.46 Sorting on field 'NumberOfEmployees' -- 6531 records via sort: 120.15 120.24 120.26 120.31 122.58 via msort: 134.25 134.29 134.50 134.74 135.09 Sorting on field 'Phone' -- 6589 records via sort: 53.76 53.80 53.81 53.82 56.03 via msort: 56.05 56.10 56.19 56.21 56.86 Sorting on field 'Profile' -- 6635 records via sort: 58.66 58.71 58.84 59.02 59.50 via msort: 8.74 8.81 8.98 8.99 8.99 Sorting on field 'Symbol' -- 6635 records via sort: 39.92 40.11 40.19 40.38 40.62 via msort: 6.49 6.52 6.53 6.72 6.73 Sorting on field 'Web' -- 6632 records via sort: 47.23 47.29 47.36 47.45 47.45 via msort: 37.12 37.27 37.33 37.42 37.89 So the hopes are realized: msort gets huge benefit from the nearly-sorted Symbol field, also huge benefit from the correlated Profile field, and highly significant benefit from the weakly correlated Company and Web fields. K00L! The Exchange field sort is so bloody slow because there are few distinct Exchange values, and whenever there's a tie on those the tuple comparison routine tries to break it by comparing the dicts. Note that I warned about this kind of thing a week or two ago, in the context of trying to implement priority queues by storing and comparing (priority, object) tuples -- it can be a timing disaster if priorities are ever equal. The other fields (Phone, etc) are in essentially random order, and msort is systematically a bit slower on all of those. Note that these are all string comparisons. I don't think it's a coincidence that msort went from a major speedup on the Python-Dev task, to a significant slowdown, when I removed all duplicate lines and shuffled the corpus first. Only part of this can be accounted for by # of comparisons. On a given random input, msort() may do fewer or more comparisons than sort(), but doing many trials suggests that sort() has a small edge in # of compares on random data, on the order of 1 or 2% This is easy to believe, since msort does a few things it *knows* won't repay the cost if the order happens to be random. These are well worth it, since they're what allow msort to get huge wins when the data isn't random. But that's not enough to account for things like the >10% higher runtime in the NumberOfEmployees sort. I can't reproduce this magnitude of systematic slowdown when doing random sorts on floats or ints, so I conclude it has something to do with string compares. Unlike int and float compares, a string compare takes variable time, depending on how long the common prefix is. I'm not aware of specific research on this topic, but it's plausible to me that partitioning may be more effective than merging at reducing the number of comparisons specifically involving "nearly equal" elements. Indeed, the fastest string-sorting methods I know of move heaven and earth to avoid redundant prefix comparisons, and do so by partitioning. Turns out <wink> that doesn't account for it, though. Here are the total number of comparisons (first number on each line) done for each sort, and the sum across all string compares of the number of common prefix characters (second number on each line): Sorting on field Address' -- 6589 records via sort: 76188 132328 via msort: 76736 131081 Sorting on field 'Company' -- 6635 records via sort: 76288 113270 via msort: 56013 113270 Sorting on field 'Exchange' -- 6579 records via sort: 34851 207185 via msort: 37457 168402 Sorting on field 'NumberOfEmployees' -- 6531 records via sort: 76167 116322 via msort: 76554 112436 Sorting on field 'Phone' -- 6589 records via sort: 75972 278188 via msort: 76741 276012 Sorting on field 'Profile' -- 6635 records via sort: 76049 1922016 via msort: 8750 233452 Sorting on field 'Symbol' -- 6635 records via sort: 76073 73243 via msort: 8724 16424 Sorting on field 'Web' -- 6632 records via sort: 76207 863837 via msort: 58811 666852 Contrary to prediction, msort always got the smaller "# of equal prefix characters" total, even in the Exchange case, where it did nearly 10% more total comparisons. Python's string compare goes fastest if the first two characters aren't the same, so maybe sort() gets a systematic advantage there? Nope. Turns out msort() gets out early 17577 times on that basis when doing NumberOfEmployees, but sort() only gets out early 15984 times. I conclude that msort is at worst only a tiny bit slower when doing NumberOfEmployees, and possibly a little faster. The only measure that doesn't agree with that conclusion is time.clock() -- but time is such a subjective thing I won't let that bother me <wink>.

18 years, 10 months

Single- vs. Multi-pass iterability

by David Abrahams

I keep running into the problem that there is no reliable way to introspect about whether a type supports multi-pass iterability (in the sense that an input stream might support only a single pass, but a list supports multiple passes). I suppose you could check for __getitem__, but that wouldn't cover linked lists, for example. Can anyone channel Guido's intent for me? Is this an oversight or a deliberate design decision? Is there an interface for checking multi-pass-ability that I've missed? TIA, Dave

18 years, 10 months

split('') revisited

by Andrew Koenig

Back in February, there was a thread in comp.lang.python (and, I think, also on Python-Dev) that asked whether the following behavior: >>> 'abcde'.split('') Traceback (most recent call last): File "<stdin>", line 1, in ? ValueError: empty separator was a bug or a feature. The prevailing opinion at the time seemed to be that there was not a sensible, unique way of defining this operation, so rejecting it was a feature. That answer didn't bother me particularly at the time, but since then I have learned a new fact (or perhaps an old fact that I didn't notice at the time) that has changed my mind: Section 4.2.4 of the library reference says that the 'split' method of a regular expression object is defined as Identical to the split() function, using the compiled pattern. This claim does not appear to be correct: >>> import re >>> re.compile('').split('abcde') ['abcde'] This result differs from the result of using the string split method. In other words, the documentation doesn't match the actual behavior, so the status quo is broken. It seems to me that there are four reasonable courses of action: 1) Do nothing -- the problem is too trivial to worry about. 2) Change string split (and its documentation) to match regexp split. 3) Change regexp split (and its documentation) to match string split. 4) Change both string split and regexp split to do something else :-) My first impulse was to argue that (4) is right, and that the behavior should be as follows >>> 'abcde'.split('') ['a', 'b', 'c', 'd', 'e'] >>> import re >>> re.compile('').split('abcde') ['a', 'b', 'c', 'd', 'e'] When this discussion came up last time, I think there was an objection that s.split('') was ambiguous: What argument is there in favor of 'abcde'.split('') being ['a', 'b', 'c', 'd', 'e'] instead of, say, ['', 'a', 'b', 'c', 'd', 'e', ''] or, for that matter, ['', 'a', '', 'b', '', 'c', '', 'd', '', 'e', '']? I made the counterargument that one could disambiguate by adding the rule that no element of the result could be equal to the delimiter. Therefore, if s is a string, s.split('') cannot contain any empty strings. However, looking at the behavior of regular expression splitting more closely, I become more confused. Can someone explain the following behavior to me? >>> re.compile('a|(x?)').split('abracadabra') ['', None, 'br', None, 'c', None, 'd', None, 'br', None, '']

18 years, 10 months

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Python-Dev July 2002