python-peps/pep-0288.txt

PEP: 288
Title: Generators Attributes and Exceptions
Version: $Revision$
Last-Modified: $Date$
Author: python@rcn.com (Raymond D. Hettinger)
Status: Draft
Type: Standards Track
Created: 21-Mar-2002
Python-Version: 2.4
Post-History:


Abstract

    This PEP introduces ideas for enhancing the generators introduced
    in Python version 2.2 [1].  The goal is to increase the
    convenience, utility, and power of generators by providing a
    mechanism for passing data into a generator and for triggering
    exceptions inside a generator.

    These mechanisms were first proposed along with two other
    generator tools in PEP 279 [7].  They were split-off to this
    separate PEP to allow the ideas more time to mature and for
    alternatives to be considered.  Subsequently, the argument
    passing idea gave way to Detlef Lannert's idea of using attributes.


Rationale

    Python 2.2 introduced the concept of an iterable interface as
    proposed in PEP 234 [2].  The iter() factory function was provided
    as common calling convention and deep changes were made to use
    iterators as a unifying theme throughout Python.  The unification
    came in the form of establishing a common iterable interface for
    mappings, sequences, and file objects.

    Generators, as proposed in PEP 255 [1], were introduced as a means for
    making it easier to create iterators, especially ones with complex
    internal states.

    The next step in the evolution of generators is to allow generators to
    accept attribute assignments.  This allows data to be passed in a
    standard Python fashion.

    A related evolutionary step is to add a generator method to enable
    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.

    These suggestions are designed to take advantage of the existing
    implementation and require little additional effort to
    incorporate.  They are backwards compatible and require no new
    keywords.  They are being recommended for Python version 2.4.


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, generators need an automatic instance variable, __self__.

    Here is a minimal example:

        def mygen():
            while True:
                print __self__.data
                yield None

        g = mygen()
        g.data = 1
        g.next()                # prints 1
        g.data = 2
        g.next()                # prints 2

    The control flow of 'yield' and 'next' is unchanged by this
    proposal.  The only change is that data can be sent into the
    generator.  By analogy, consider the quality improvement from
    GOSUB (which had no argument passing mechanism) to modern
    procedure calls (which can pass in arguments and return values).
    Most of the underlying machinery is already in place, only the
    __self__ variable needs to be added.

    Yield is more than just a simple iterator creator.  It does
    something else truly wonderful -- it suspends execution and saves
    state.  It is good for a lot more than writing iterators.  This
    proposal further taps its capabilities by making it easier to
    share data with the generator.

    The attribute mechanism is especially useful for:
        1. Sending data to any generator
        2. Writing lazy consumers with complex execution states
        3. Writing co-routines (as demonstrated in Dr. Mertz's articles [3])

    The proposal is a clear improvement over the existing alternative
    of passing data via global variables.  It is also much simpler,
    more readable and easier to debug than an approach involving the
    threading module with its attendant mutexes, semaphores, and data
    queues.  A class-based approach competes well when there are no
    complex execution states or variable states.  However, when the
    complexity increases, generators with writable attributes are much
    simpler because they automatically save state (unlike classes
    which must explicitly save the variable and execution state in
    instance variables).


Examples

    Example of a Complex Consumer

    The encoder for arithmetic compression sends a series of
    fractional values to a complex, lazy consumer.  That consumer
    makes computations based on previous inputs and only writes out
    when certain conditions have been met.  After the last fraction is
    received, it has a procedure for flushing any unwritten data.


    Example of a Consumer Stream

        def filelike(packagename, appendOrOverwrite):
            data = []
            if appendOrOverwrite == 'w+':
                data.extend(packages[packagename])
            try:
                while True:
                    data.append(__self__.dat)
                    yield None
            except FlushStream:
                packages[packagename] = data

        ostream = filelike('mydest','w')
        ostream.dat = firstdat; ostream.next()
        ostream.dat = firstdat; ostream.next()
        ostream.throw(FlushStream)         # Throw is proposed below


    Example of a Complex Consumer

    Loop over the picture files in a directory, shrink them one at a
    time to thumbnail size using PIL [4], and send them to a lazy
    consumer.  That consumer is responsible for creating a large blank
    image, accepting thumbnails one at a time and placing them in a 5
    by 3 grid format onto the blank image.  Whenever the grid is full,
    it writes-out the large image as an index print.  A FlushStream
    exception indicates that no more thumbnails are available and that
    the partial index print should be written out if there are one or
    more thumbnails on it.


    Example of a Producer and Consumer Used Together in a Pipe-like Fashion

        'Analogy to Linux style pipes:  source | upper | sink'
        sink = sinkgen()
        for word in source():
            sink.data = word.upper()
            sink.next()


Initialization Mechanism

    If the attribute passing idea is accepted, Detlef Lannert further
    proposed that generator instances have attributes initialized to
    values in the generator's func_dict.  This makes it easy to set
    default values.  For example:

        def mygen():
            while True:
                print __self__.data
                yield None
        mygen.data = 0

        g = mygen()             # g initialized with .data set to 0
        g.next()                # prints 0
        g.data = 1
        g.next()                # prints 1


Rejected Alternative

    One idea for passing data into a generator was to pass an argument
    through next() and make a assignment using the yield keyword:

        datain = yield dataout
          . . .
        dataout = gen.next(datain)

    The intractable problem is that the argument to the first next() call
    has to be thrown away, because it doesn't correspond to a yield keyword.


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 [1].  Without .throw(), the
    current work-around forces the resolution or clean-up code to be
    moved outside the generator.

    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 seem to fit
    as well as throw().

    Note B: The throw syntax should exactly match raise's syntax:

        throw([expression, [expression, [expression]]])

    Accordingly, it should be implemented to handle all of the following:

        raise string                    g.throw(string)
        raise string, data              g.throw(string,data)
        raise class, instance           g.throw(class,instance)
        raise instance                  g.throw(instance)
        raise                           g.throw()


    Comments from GvR:  I'm not convinced that the cleanup problem that
        this is trying to solve exists in practice. I've never felt
        the need to put yield inside a try/except. I think the PEP
        doesn't make enough of a case that this is useful.

        This one gets a big fat -1 until there's a good motivational
        section.

    Comments from Ka-Ping Yee:  I agree that the exception issue needs to
        be resolved and [that] you have suggested a fine solution.

    Comments from Neil Schemenauer:  The exception passing idea is one I
        hadn't thought of before and looks interesting.  If we enable
        the passing of values back, then we should add this feature
        too.

    Comments for Magnus Lie Hetland:  Even though I cannot speak for the
        ease of implementation, I vote +1 for the exception passing
        mechanism.

    Comments from the Community:  The response has been mostly favorable.  One
        negative comment from GvR is shown above.  The other was from
        Martin von Loewis who was concerned that it could be difficult
        to implement and is withholding his support until a working
        patch is available.  To probe Martin's comment, I checked with
        the implementers of the original generator PEP for an opinion
        on the ease of implementation.  They felt that implementation
        would be straight-forward and could be grafted onto the
        existing implementation without disturbing its internals.

    Author response:  When the sole use of generators is to simplify writing
        iterators for lazy producers, then the odds of needing
        generator exception passing are slim.  If, on the other hand,
        generators are used to write lazy consumers, create
        coroutines, generate output streams, or simply for their
        marvelous capability for restarting a previously frozen state,
        THEN the need to raise exceptions will come up frequently.


References

    [1] PEP 255 Simple Generators
        http://www.python.org/peps/pep-0255.html

    [2] PEP 234 Iterators
        http://www.python.org/peps/pep-0234.html

    [3] Dr. David Mertz's draft column for Charming Python.
        http://gnosis.cx/publish/programming/charming_python_b5.txt
        http://gnosis.cx/publish/programming/charming_python_b7.txt

    [4] PIL, the Python Imaging Library can be found at:
        http://www.pythonware.com/products/pil/


Copyright

    This document has been placed in the public domain.



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