neither 'pass' (v 1.2) nor 'break' (v 1.3) but 'continue'.

Whitespace normalization.

loop-invariant, save a little time by multiplying idf by 1024. outside
the loop.

the number of words in the index (at least to return a number
comparable to the number displayed under "# objects" by TextIndex).

Index management screen.  Ditto for clear().  So group them together
and adjust the comment.  (So is manage_main, but since it's a DTML
method, it can stay in its separate UI group.)

still only supports a trailing *, so the pipeline should honor that;
added a comment to the Splitter class referring to globToWordIds().

well check it in.  This yields an overall 133% speedup on a "hot" search
for 'python' in my python-dev archive (a word that appears in all but
2 documents).  For those who read the email, turned out it was a
significant speedup to iterate over an IIBTree's items rather than to
materialize the items into an explicit list first.

This is now within 20% of simply doing "IIBucket(the_IIBTree)" (i.e.,
no arithmetic at all), so there's no significant possibility remaining
for speeding the inner score loop.

creating it anonymously and then pulling it out of the zc_index
object.

once we have more than one on the menu.)

in percentages; strip the percent sign to avoid a traceback calling
int() when these variables are used.

I'm unclear whether this is really the right thing, but at least this
prevents crashes when nothing is entered in the search box.

_fieldname; simply return 0 in this case.

is *disabled*.

Fix typo in docstring.

- Rephrased the description of the grammar, pointing out that the
  lexicon decides on globbing syntax.

- Refactored term and atom parsing (moving atom parsing into a
  separate method).  The previously checked-in version accidentally
  accepted some invalid forms like ``foo AND -bar''; this is fixed.

tests/testQueryParser.py:

- Each test is now in a separate method; this produces more output
  (alas) but makes pinpointing the errors much simpler.

- Added some tests catching ``foo AND -bar'' and similar.

- Added an explicit test class for the handling of stopwords.  The
  "and/" test no longer has to check self.__class__.

- Some refactoring of the TestQueryParser class; the utility methods
  are now in a base class TestQueryParserBase, in a different order;
  compareParseTrees() now shows the parse tree it got when raising an
  exception.  The parser is now self.parser instead of self.p (see
  below).

tests/testZCTextIndex.py:

- setUp() no longer needs to assign to self.p; the parser is
  consistently called self.parser now.

:-)

ILexicon.py:

  - Added parseTerms() and isGlob().

  - Added get_word(), get_wid() (get_word() is old; get_wid() for symmetry).

  - Reflowed some text.

IQueryParser.py:

  - Expanded docs for parseQuery().

  - Added getIgnored() and parseQueryEx().

IPipelineElement.py:

  - Added processGlob().

Lexicon.py:

  - Added parseTerms() and isGlob().

  - Added get_wid().

  - Some pipeline elements now support processGlob().

ParseTree.py:

  - Clarified the error message for calling executeQuery() on a
    NotNode.

QueryParser.py (lots of changes):

  - Change private names __tokens etc. into protected _tokens etc.

  - Add getIgnored() and parseQueryEx() methods.

  - The atom parser now uses the lexicon's parseTerms() and isGlob()
    methods.

  - Query parts that consist only of stopwords (as determined by the
    lexicon), or of stopwords and negated terms, yield None instead of
    a parse tree node; the ignored term is added to self._ignored.
    None is ignored when combining terms for AND/OR/NOT operators, and
    when an operator has no non-None operands, the operator itself
    returns None.  When this None percolates all the way to the top,
    the parser raises a ParseError exception.

tests/testQueryParser.py:

  - Changed test expressions of the form "a AND b AND c" to "aa AND bb
    AND cc" so that the terms won't be considered stopwords.

  - The test for "and/" can only work for the base class.

tests/testZCTextIndex.py:

  - Added copyright notice.

  - Refactor testStopWords() to have two helpers, one for success, one
    for failures.

  - Change testStopWords() to require parser failure for those queries
    that have only stopwords or stopwords plus negated terms.

  - Improve compareSet() to sort the sets of keys, and use a more
    direct way of extracting the keys.  This wasn't strictly needed
    (nothing fails without this), but the old approach of copying the
    keys into a dict in a loop depends on the dict hashing to always
    return keys in the same order.

guido@.

when/if merge day comes for the installer this will make for less
confusion :-)

display the search time in milliseconds too.

for start and end of run.  Show elapsed wall-clock time in minutes.

msgs to display).  Changed the module docstring to separate the index-
generation args from the query args.

original doc text gets restored.

went wrong if they fail.

me to call it braindead <wink>).

PACK_INTERVAL.

uncomment the test cases that were failing in these contexts.

Read it and weep <wink>:  In an AND context, None is treated like the
universal set, which jibes with the convenient fiction that stop words
appear in every doc.  However, in AND NOT and OR contexts, None is
treated like the empty set, which doesn't jibe with anything except that
we want

    real_word AND NOT stop_word

and

    real_word OR stop_word

to act like

    real_word

If we treated None as if it were the universal set, these results would
be (respectively) the empty set and the universal set instead.

At a higher level, we *are* consistent with the notion that a query with
a stop word acts the same as if the clause with the stop word weren't
present.  That's what really drives this schizophrenic (context-dependent)
treatment of None.

empty.

tests that currently fail are currently commented out.

Key question:  If someone does a search on a stopword, and nothing else is
in the query, what do we want to do?  Return all docs in a random order?
Return no docs?  Raise an exception?

Second question:  What if someone does a query on

    rare_word AND NOT stop_word

?

do the same query and work as ZCTextIndex would do.

Produce a result set, pump it into NBest, and extract the 10 best.