|Title:||Generators Attributes and Exceptions|
|Author:||Raymond Hettinger <python at rcn.com>|
This PEP proposes to enhance generators by providing mechanisms for raising exceptions and sharing data with running generators.
This PEP is withdrawn. The exception raising mechanism was extended and subsumed into PEP 343. The attribute passing capability never built a following, did not have a clear implementation, and did not have a clean way for the running generator to access its own namespace.
Currently, only class based iterators can provide attributes and exception handling. However, class based iterators are harder to write, less compact, less readable, and slower. A better solution is to enable these capabilities for generators. Enabling attribute assignments allows data to be passed to and from running generators. The approach of sharing data using attributes pervades Python. Other approaches exist but are somewhat hackish in comparison. Another evolutionary step is to add a generator method to allow exceptions to be passed to a generator. Currently, there is no clean method for triggering exceptions from outside the generator. Also, generator exception passing helps mitigate the try/finally prohibition for generators. The need is especially acute for generators needing to flush buffers or close resources upon termination. The two proposals are backwards compatible and require no new keywords. They are being recommended for Python version 2.5.
Specification for Generator Attributes
Essentially, the proposal is to emulate attribute writing for classes. The only wrinkle is that generators lack a way to refer to instances of themselves. So, the proposal is to provide a function for discovering the reference. For example: def mygen(filename): self = sys.get_generator() myfile = open(filename) for line in myfile: if len(line) < 10: continue self.pos = myfile.tell() yield line.upper() g = mygen('sample.txt') line1 = g.next() print 'Position', g.pos Uses for generator attributes include: 1. Providing generator clients with extra information (as shown above). 2. Externally setting control flags governing generator operation (possibly telling a generator when to step in or step over data groups). 3. Writing lazy consumers with complex execution states (an arithmetic encoder output stream for example). 4. Writing co-routines (as demonstrated in Dr. Mertz's articles ). The control flow of 'yield' and 'next' is unchanged by this proposal. The only change is that data can passed to and from the generator. Most of the underlying machinery is already in place, only the access function needs to be added.
Specification for Generator Exception Passing:
Add a .throw(exception) method to the generator interface: def logger(): start = time.time() log =  try: while True: log.append(time.time() - start) yield log[-1] except WriteLog: writelog(log) g = logger() for i in [10,20,40,80,160]: testsuite(i) g.next() g.throw(WriteLog) There is no existing work-around for triggering an exception inside a generator. It is the only case in Python where active code cannot be excepted to or through. Generator exception passing also helps address an intrinsic limitation on generators, the prohibition against their using try/finally to trigger clean-up code . Note A: The name of the throw method was selected for several reasons. Raise is a keyword and so cannot be used as a method name. Unlike raise which immediately raises an exception from the current execution point, throw will first return to the generator and then raise the exception. The word throw is suggestive of putting the exception in another location. The word throw is already associated with exceptions in other languages. Alternative method names were considered: resolve(), signal(), genraise(), raiseinto(), and flush(). None of these fit as well as throw(). Note B: To keep the throw() syntax simple only the instance version of the raise syntax would be supported (no variants for "raise string" or "raise class, instance"). Calling "g.throw(instance)" would correspond to writing "raise instance" immediately after the most recent yield.
 Dr. David Mertz's draft columns for Charming Python: http://gnosis.cx/publish/programming/charming_python_b5.txt http://gnosis.cx/publish/programming/charming_python_b7.txt  PEP 255 Simple Generators: http://www.python.org/dev/peps/pep-0255/  Proof-of-concept recipe: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/164044
This document has been placed in the public domain.