moved everything into /trunk/ to create the standard svn layout, for use with tools that require this (eg. git-svn). IMPORTANT: do NOT do an `svn up`. instead, re-use your working copy's existing files with `svn switch` (http://svnbook.red-bean.com/en/1.6/svn.ref.svn.c.switch.html).
Replaced repr() with strings.urepr() (or equivalent) everywhere needed, to avoid future UnicodeEncodeErrors
Removed trailing whitespace on non-empty lines
parallelproc.py: MultiProducerPool: Removed warning if not using parallel processing because this also gets generated when it's explicitly turned off, which is currently the case and clutters up stderr when testing
Renamed parallel.py to parallelproc.py to avoid conflict with new system parallel module on vegbiendev
parallel.py: MultiProducerPool: Added code to create a shared Namespace object, commented out. Updated share() doc comment to reflect that it will writably share the values as well.
dicts.py: Turned id_dict() factory function into IdDict class. parallel.py: MultiProducerPool: Added share_vars(). main_loop(): Only consider the program to be done if the queue is empty and there are no running tasks.
parallel.py: prepickle(): Pickle all objects in vars_id_dict_ by ID, not just unpicklable ones. This ensures that a DB connection created in the main process will be shared with subprocesses by reference (id()) instead of by value, so that each process can take advantage of e.g. shared caches in the connection object. Note that this may require some synchronization.
parallel.py: MultiProducerPool.main_loop(): Got rid of no longer correct doc comment
parallel.py: MultiProducerPool: Pickle objects by ID if they're accessible to the main_loop process. This should allow e.g. DB connections and pools to be pickled, if they were defined in the main process.
parallel.py: MultiProducerPool.apply_async(): Prepickle all function args. Try pickling the args before the queue pickles them, to get better debugging output.
parallel.py: Fixed bugs: Added self param to instance methods and inner classes where needed
parallel.py: Changed to use multi-producer pool, which requires calling pool.main_loop()
parallel.py: Pool: Added doc comment
parallel.py: Pool: apply_async(): Return a result object like multiprocessing.Pool.apply_async()
Added parallel.py for parallel processing
