Current File : //opt/alt/python27/lib64/python2.7/site-packages/sqlalchemy/sql/util.pyc
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i����( t deque( t chaini ( t operators( t visitors( t _deep_annotate( t _deep_deannotate( t _shallow_annotate( t
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return the first index and element from the list of
clauses which can be joined against the selectable. returns
None, None if no match is found.
e.g.::
clause1 = table1.join(table2)
clause2 = table4.join(table5)
join_to = table2.join(table3)
find_join_source([clause1, clause2], join_to) == clause1
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c C s� t t | � � } g } xK t | � D]= \ } } x. | D]& } | j | � r8 | j | � Pq8 q8 Wq% Wt | � d k r� g } x[ | D]S } | | } x@ | D]8 } t t | � � j t | � � r� | j | � Pq� q� Wq� W| r� | Sn | S( s� Given a list of FROM clauses and a selectable,
return the indexes from the list of
clauses which is derived from the selectable.
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k rE| ^ qE} n | r�| d k r�t t | � � S| Sd S( s Given a list of FROM clauses, a selectable,
and optional ON clause, return a list of integer indexes from the
clauses list indicating the clauses that can be joined from.
The presence of an "onclause" indicates that at least one clause can
definitely be joined from; if the list of clauses is of length one
and the onclause is given, returns that index. If the list of clauses
is more than length one, and the onclause is given, attempts to locate
which clauses contain the same columns.
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+,c s/ g � � � � f d � � t � | � � d S( s� Produce a traversal of the given expression, delivering
column comparisons to the given function.
The function is of the form::
def my_fn(binary, left, right)
For each binary expression located which has a
comparison operator, the product of "left" and
"right" will be delivered to that function,
in terms of that binary.
Hence an expression like::
and_(
(a + b) == q + func.sum(e + f),
j == r
)
would have the traversal::
a <eq> q
a <eq> e
a <eq> f
b <eq> q
b <eq> e
b <eq> f
j <eq> r
That is, every combination of "left" and
"right" that doesn't further contain
a binary comparison is passed as pairs.
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| � | j | � q� q$ x! | j � D] } | j | � q� Wq$ W| S( sq Break up an 'order by' expression into individual column-expressions,
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� } g t g | D] } t | � ^ qD � D] } | | k r] | ^ q] S( s� Given the columns clause and ORDER BY of a selectable,
return a list of column expressions that can be added to the collist
corresponding to the ORDER BY, without repeating those already
in the collist.
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if the target is plainly present in the search without any
subqueries or aliases involved.
Basically descends through Joins.
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be addressable in the WHERE clause of a SELECT if this element were
in the columns clause.N( R R R Rl RI RA R ( Rc t include_scalar_selectst filter_RP RM t sub( ( sF /opt/alt/python27/lib64/python2.7/site-packages/sqlalchemy/sql/util.pyt surface_column_elementso s c C s% t t t | � � j t | � � � S( s: Return True if left/right have some overlapping selectable( t boolR* R+ R, ( RF RG ( ( sF /opt/alt/python27/lib64/python2.7/site-packages/sqlalchemy/sql/util.pyt selectables_overlap� s c s3 g � � f d � } t j | i i | d 6� � S( s� Return an ordered list of "bound" values in the given clause.
E.g.::
>>> expr = and_(
... table.c.foo==5, table.c.foo==7
... )
>>> bind_values(expr)
[5, 7]
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as well as long values to a given number of characters.
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to IS NULL.
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equivalents.
the set is reduced to the smallest list of columns which have no natural
equivalent present in the list. A "natural equivalent" means that two
columns will ultimately represent the same value because they are related
by a foreign key.
\*clauses is an optional list of join clauses which will be traversed
to further identify columns that are "equivalent".
\**kw may specify 'ignore_nonexistent_tables' to ignore foreign keys
whose tables are not yet configured, or columns that aren't yet present.
This function is primarily used to determine the most minimal "primary
key" from a selectable, by reducing the set of primary key columns present
in the selectable to just those that are not repeated.
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E.g.::
table1 = Table('sometable', metadata,
Column('col1', Integer),
Column('col2', Integer)
)
table2 = Table('someothertable', metadata,
Column('col1', Integer),
Column('col2', Integer)
)
condition = table1.c.col1 == table2.c.col1
make an alias of table1::
s = table1.alias('foo')
calling ``ClauseAdapter(s).traverse(condition)`` converts
condition to read::
s.c.col1 == table2.c.col1
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s� Extends ClauseAdapter with extra utility functions.
Key aspects of ColumnAdapter include:
* Expressions that are adapted are stored in a persistent
.columns collection; so that an expression E adapted into
an expression E1, will return the same object E1 when adapted
a second time. This is important in particular for things like
Label objects that are anonymized, so that the ColumnAdapter can
be used to present a consistent "adapted" view of things.
* Exclusion of items from the persistent collection based on
include/exclude rules, but also independent of hash identity.
This because "annotated" items all have the same hash identity as their
parent.
* "wrapping" capability is added, so that the replacement of an expression
E can proceed through a series of adapters. This differs from the
visitor's "chaining" feature in that the resulting object is passed
through all replacing functions unconditionally, rather than stopping
at the first one that returns non-None.
* An adapt_required option, used by eager loading to indicate that
We don't trust a result row column that is not translated.
This is to prevent a column from being interpreted as that
of the child row in a self-referential scenario, see
inheritance/test_basic.py->EagerTargetingTest.test_adapt_stringency
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