congress.datalog.compile Module¶congress.datalog.compile.Compiler¶Bases: object
Process Congress policy file.
print_parse_result()¶raise_errors()¶read_source(input, input_string=False, theories=None, use_modules=True)¶sigerr(error)¶sigwarn(error)¶congress.datalog.compile.DatalogSyntax(theories=None, use_modules=True)¶Bases: object
Read Datalog syntax and convert it to internal representation.
Lexer(char_stream, state=None)¶Bases: congress.datalog.Python2.CongressLexer.CongressLexer
displayRecognitionError(token_names, e)¶getErrorHeader(e)¶Parser(tokens, state=None)¶Bases: congress.datalog.Python2.CongressParser.CongressParser
displayRecognitionError(token_names, e)¶getErrorHeader(e)¶antlr_atom_str(antlr)¶atom_vars(antlr_atom)¶convert_to_congress(antlr)¶create(antlr)¶create_and_literals(antlr)¶create_atom_aux(antlr)¶create_atom_dual_arg_list(antlr)¶Get parameter list and named list
Return (i) a list of compile.Term representing the positionally specified parameters in the ANTLR atom and (ii) a dictionary mapping string/number to compile.Term representing the name/index-specified parameters. If there are errors self.errors is modified.
create_event(antlr)¶create_literal(antlr)¶create_modal_atom(antlr)¶create_rule(antlr)¶create_tablename(antlr)¶create_term(antlr)¶literal_and_vars(antlr_and)¶parse_file(input, input_string=False)¶rule_variables(antlr_rule)¶Get variables in the rule.
Returns a set of all variable names (as strings) that occur in the given rule ANTLR_RULE.
unused_variable_prefix(antlr_rule)¶Get unused variable prefix.
Returns variable prefix (string) that is used by no other variable in the rule ANTLR_RULE.
variable_name(antlr)¶congress.datalog.compile.Event(formula=None, insert=True, proofs=None, target=None)¶Bases: object
Represents a change to a formula.
formula¶insert¶is_insert()¶lstr()¶proofs¶tablename(default_theory=None)¶target¶congress.datalog.compile.Fact(table, values)¶Bases: tuple
Represent a Fact (a ground literal)
Use this class to represent a fact such as Foo(1,2,3). While one could use a Rule to represent the same fact, this Fact datastructure is more memory efficient than a Rule object since this Fact stores the information as a native tuple, containing native values like ints and strings. Notes that this subclasses from tuple.
SORT_RANK = 3¶congress.datalog.compile.Literal(table, arguments, location=None, negated=False, use_modules=True, id_=None, name=None, comment=None, original_str=None, named_arguments=None)¶Bases: object
Represents a possibly negated atomic statement, e.g. p(a, 17, b).
SORT_RANK = 5¶argument_names()¶Return names of all arguments. Ignores named_arguments.
arguments¶comment¶complement()¶Copies SELF and inverts is_negated.
create_from_iter(list)¶Create Literal from list.
LIST is a python list representing an atom, e.g. [‘p’, 17, “string”, 3.14]. Returns the corresponding Literal.
create_from_table_tuple(table, tuple)¶Create Literal from table and tuple.
TABLE is a string tablename. TUPLE is a python list representing a row, e.g. [17, “string”, 3.14]. Returns the corresponding Literal.
drop_theory()¶Destructively sets the theory to None.
drop_update()¶eliminate_column_references_and_pad_positional(theories, default_theory=None, index=0, prefix='')¶Expand column references to positional args and pad positional args.
Expand column references to traditional datalog positional args. Also pad positional args if too few are provided. Returns a new literal. If no column reference, unless no schema found for the table.
id¶invert_update()¶is_atom()¶is_builtin(check_arguments=True)¶is_ground()¶Return True if all args are non-vars. Ignores named_arguments.
is_negated()¶is_rule()¶is_update()¶location¶make_positive()¶Return handle to self or copy of self based on positive check.
Either returns SELF if is_negated() is false or returns copy of SELF where is_negated() is set to false.
make_update(is_insert=True)¶name¶named_arguments¶negated¶original_str¶plug(binding, caller=None)¶Assumes domain of BINDING is Terms. Ignores named_arguments.
pretty_str()¶set_comment(comment)¶set_id(id)¶set_name(name)¶set_original_str(original_str)¶table¶tablename(default_service=None)¶theory_name()¶variable_names()¶Return variable names in arguments. Ignores named_arguments.
variables()¶Return variables in arguments. Ignores named_arguments.
congress.datalog.compile.ObjectConstant(name, type, location=None)¶Bases: congress.datalog.compile.Term
Represents a term with a fixed value.
FLOAT = 'FLOAT'¶INTEGER = 'INTEGER'¶SORT_RANK = 2¶STRING = 'STRING'¶is_object()¶is_variable()¶location¶name¶type¶congress.datalog.compile.Rule(head, body, location=None, id=None, name=None, comment=None, original_str=None)¶Bases: object
Represents a rule, e.g. p(x) :- q(x).
SORT_RANK = 6¶body¶comment¶drop_theory()¶Destructively sets the theory to None in all heads.
drop_update()¶eliminate_column_references_and_pad_positional(theories, default_theory=None)¶Return version of SELF /w col refs removed and pos args padded.
All column references removed. Positional args padded up to required length. Throws exception if RULE is inconsistent with schemas.
head¶heads¶id¶invert_update()¶is_atom()¶is_rule()¶is_update()¶location¶make_update(is_insert=True)¶name¶original_str¶plug(binding, caller=None)¶plug_body(binding, caller=None)¶plug_heads(binding, caller=None)¶pretty_str()¶set_comment(comment)¶set_id(id)¶set_name(name)¶set_original_str(original_str)¶tablename(theory=None)¶tablenames(theory=None, body_only=False, include_builtin=False, include_modal=True)¶Return all the tablenames occurring in this rule.
theory_name()¶variable_names()¶variables()¶congress.datalog.compile.RuleDependencyGraph(formulas=None, theory=None, include_atoms=True, select_head=None, select_body=None, head_to_body=True)¶Bases: congress.datalog.utility.BagGraph
A Graph representing the table dependencies of rules.
Creates a Graph that includes one node for each table and an edge <u,v> if there is some rule with u in the head and v in the body. THEORY is the name of the theory to be used for any literal whose theory is None. INCLUDE_ATOMS is a boolean controlling whether atoms should contribute to nodes. SELECT_HEAD is a function that returns True for those head literals that should be included in the graph. SELECT_BODY is a function that returns True for those body literals that should be included in the graph. HEAD_TO_BODY controls whether edges are oriented from the tables in the head toward the tables in the body, or vice versa.
find_definitions(tables)¶find_dependencies(tables)¶formula_delete(formula, theory=None, include_atoms=True, select_head=None, select_body=None)¶Delete rows/edges for the given FORMULA.
formula_insert(formula, theory=None, include_atoms=True, select_head=None, select_body=None)¶Insert rows/edges for the given FORMULA.
formula_nodes_edges(formula, theory=None, include_atoms=True, select_head=None, select_body=None)¶Compute dependency graph nodes and edges for FORMULA.
Returns (NODES, EDGES, MODALS), where NODES/EDGES are sets and MODALS is a ModalIndex. Each EDGE is a tuple of the form (source, destination, label).
formula_update(events, include_atoms=True, select_head=None, select_body=None)¶Modify graph with inserts/deletes in EVENTS.
Returns list of changes.
table_delete(table)¶tables()¶tables_with_modal(modal)¶undo_changes(changes)¶Reverse the given changes.
Each change is either (‘node’, <node>, <is-insert>) or (‘edge’, <src_node>, <dst_node>, <label>, <is_insert>) or (‘modal’, <modal-index>, <is-insert>).
congress.datalog.compile.Schema(dictionary=None, complete=False)¶Bases: object
Meta-data about a collection of tables.
arity(tablename)¶Returns the number of columns for the given TABLENAME.
Return None if TABLENAME is unknown.
col(cols)¶column_name(tablename, column)¶Returns name for given COLUMN or None if it is unknown.
column_number(tablename, column)¶Returns the 0-indexed position of the given COLUMN for TABLENAME.
Returns None if TABLENAME or COLUMNNAME are unknown. Returns COLUMN if it is a number.
columns(tablename)¶Returns the list of column names for the given TABLENAME.
Return None if the tablename’s columns are unknown.
revert(change)¶Revert change made by update.
Return None
update(item, is_insert)¶Returns the schema change of this update.
Return schema change.
congress.datalog.compile.Tablename(table=None, service=None, modal=None)¶Bases: object
SORT_RANK = 4¶build_service_table(service, table)¶Return string service:table.
create_from_tablename(tablename, service=None, use_modules=True)¶drop_service()¶drop_update()¶Drop the update.
If end of table name is + or -, return a copy without the sign. If table name does not end in + or -, make no copy.
global_tablename(prefix=None)¶invert_update()¶Invert the update.
If end of table name is + or -, return a copy after switching the copy’s sign. Does not make a copy if table name does not end in + or -.
is_update()¶make_update(is_insert=True)¶Turn the tablename into a +/- update.
matches(service, table, modal)¶modal¶name(default_service=None)¶Compute string name with default service.
parse_service_table(tablename)¶Given tablename returns (service, name).
same(other, default_service)¶Equality but where default_service is used for None service.
service¶table¶congress.datalog.compile.Term¶Bases: object
Represents the union of Variable and ObjectConstant.
Should only be instantiated via factory method.
create_from_python(value, force_var=False)¶Create Variable or ObjectConstants.
To create variable, FORCE_VAR needs to be true. There is currently no way to avoid this since variables are strings.
congress.datalog.compile.Variable(name, location=None)¶Bases: congress.datalog.compile.Term
Represents a term without a fixed value.
SORT_RANK = 1¶is_object()¶is_variable()¶location¶name¶congress.datalog.compile.check_schema_consistency(item, theories, theory=None)¶congress.datalog.compile.fact_errors(atom, theories=None, theory=None)¶Checks if ATOM has any errors.
THEORIES is a dictionary mapping a theory name to a theory object.
congress.datalog.compile.fact_has_no_theory(atom)¶Checks that ATOM has an empty theory. Returns exceptions.
congress.datalog.compile.find_subpolicy(rules, required_tables, prohibited_tables, output_tables)¶Return a subset of rules pertinent to the parameters.
| Param: | rules is the collection of Datalog rules to analyze | 
|---|---|
| Param: | required_tables is the set of tablenames that a rule must depend on | 
| Param: | prohibited_tables is the set of tablenames that a rule must NOT depend on. | 
| Param: | output_tables is the set of tablenames that all rules must support. | 
Table R depends on table T if T occurs in the body of a rule with R in the head, or T occurs in the body of a rule where R depends on the table in the head of that rule.
The subset of RULES chosen has several properties:
congress.datalog.compile.formulas_to_string(formulas)¶Convert formulas to string.
Takes an iterable of compiler sentence objects and returns a string representing that iterable, which the compiler will parse into the original iterable.
congress.datalog.compile.get_compiler(args, theories=None, use_modules=True)¶Run compiler as per ARGS and return the compiler object.
congress.datalog.compile.is_atom(x)¶Returns True if object X is an atomic Datalog formula.
congress.datalog.compile.is_atom_like(x)¶congress.datalog.compile.is_atom_rule(x)¶congress.datalog.compile.is_datalog(x)¶Returns True if X is an atom or a rule with one head.
congress.datalog.compile.is_extended_datalog(x)¶Returns True if X is a valid datalog sentence.
Allows X to be a multi_rule in addition to IS_DATALOG().
congress.datalog.compile.is_literal(x)¶Check if x is Literal.
Returns True if X is a possibly negated atomic Datalog formula and one that if replaced by an ATOM syntactically be replaced by an ATOM.
congress.datalog.compile.is_literal_like(x)¶congress.datalog.compile.is_literal_rule(x)¶congress.datalog.compile.is_multi_rule(x)¶Returns True if X is a rule with multiple heads.
congress.datalog.compile.is_recursive(x)¶Check for recursive.
X can be either a Graph or a list of rules. Returns T iff the list of rules RULES has a table defined in Terms of itself.
congress.datalog.compile.is_regular_rule(x)¶Returns True if X is a rule with a single head.
congress.datalog.compile.is_result(x)¶Check if x is result representation.
Returns T iff x is a formula or tablename representing the result of an action invocation.
congress.datalog.compile.is_rule(x)¶Returns True if x is a rule.
congress.datalog.compile.is_stratified(rules)¶Check if rules are stratified.
Returns T iff the list of rules RULES has no table defined in terms of its negated self.
congress.datalog.compile.is_update(x)¶Returns T iff x is a formula or tablename representing an update.
congress.datalog.compile.keywords_safety(lit)¶congress.datalog.compile.literal_schema(literal, theories, default_theory=None, theory_assertion=None)¶Return the schema that applies to LITERAL or None.
| Param: | LITERAL is a Literal for which we want the schema | 
|---|---|
| Param: | THEORIES is a dictionary mapping the name of the theory to the theory object | 
| Param: | DEFAULT_THEORY is the theory to use if no theory is recorded as part of LITERAL | 
| Returns: | the schema that applies to LITERAL or None | 
congress.datalog.compile.literal_schema_consistency(literal, theories, theory=None)¶Returns list of errors, but does no checking if column references.
congress.datalog.compile.literal_theory(literal, theories, default_theory=None)¶Return the theory that applies to LITERAL or None.
| Param: | LITERAL is a Literal for which we want the schema | 
|---|---|
| Param: | THEORIES is a dictionary mapping the name of the theory to the theory object | 
| Param: | DEFAULT_THEORY is the theory to use if no theory is recorded as part of LITERAL | 
| Returns: | the theory that applies to LITERAL or None | 
congress.datalog.compile.parse(policy_string, theories=None, use_modules=True)¶Run compiler on policy string and return the parsed formulas.
congress.datalog.compile.parse1(policy_string, theories=None, use_modules=True)¶Run compiler on policy string and return 1st parsed formula.
congress.datalog.compile.parse_file(filename, theories=None)¶Compile the file.
Run compiler on policy stored in FILENAME and return the parsed formulas.
congress.datalog.compile.print_antlr(tree)¶Print an antlr Tree.
congress.datalog.compile.print_tree(tree, text, kids, ind=0)¶Helper function for printing.
Print out TREE using function TEXT to extract node description and function KIDS to compute the children of a given node. IND is a number representing the indentation level.
congress.datalog.compile.reorder_for_safety(rule)¶Reorder the rule.
Moves builtins/negative literals so that when left-to-right evaluation is performed all of a builtin’s inputs are bound by the time that builtin is evaluated. Reordering is stable, meaning that if the rule is properly ordered, no changes are made.
congress.datalog.compile.rule_body_safety(rule)¶Check rule body for safety.
Checks if every variable in a negative literal also appears in a positive literal in the body. Checks if every variable in a builtin input appears in the body. Returns list of exceptions.
congress.datalog.compile.rule_errors(rule, theories=None, theory=None)¶Returns list of errors for RULE.
congress.datalog.compile.rule_head_has_no_theory(rule, permit_head=None)¶Checks if head of rule has None for theory. Returns exceptions.
PERMIT_HEAD is a function that takes a literal as argument and returns True if the literal is allowed to have a theory in the head.
congress.datalog.compile.rule_head_safety(rule)¶Checks if every variable in the head of RULE is also in the body.
Returns list of exceptions.
congress.datalog.compile.rule_modal_safety(rule)¶Check if the rule obeys the restrictions on modals.
congress.datalog.compile.rule_schema_consistency(rule, theories, theory=None)¶Returns list of problems with rule’s schema.
congress.datalog.compile.schema_consistency(thing, theories, theory=None)¶congress.datalog.compile.stratification(rules)¶Stratify the rules.
Returns a dictionary from table names to an integer representing the strata to which the table is assigned or None if the rules are not stratified.
congress.datalog.compile.string_is_servicename(name)¶Returns True if @name can be a servicename in the policy language.
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