def _process(t):
"""Encode tree *t* and return the string."""
# tree transformations
if normalize_options['make_variables']:
t.reset_variables(normalize_options['make_variables'])
if normalize_options['canonicalize_roles']:
t = transform.canonicalize_roles(t, model)
if normalize_options['rearrange'] == 'canonical':
layout.rearrange(t, key=model.canonical_order)
elif normalize_options['rearrange'] == 'random':
layout.rearrange(t, key=model.random_order)
g = layout.interpret(t, model)
# graph transformations
if normalize_options['reify_edges']:
g = transform.reify_edges(g, model)
if normalize_options['dereify_edges']:
g = transform.dereify_edges(g, model)
if normalize_options['reify_attributes']:
g = transform.reify_attributes(g)
if normalize_options['indicate_branches']:
g = transform.indicate_branches(g, model)
if triples:
return codec.format_triples(g.triples,
indent=bool(
format_options.get('indent',
True)))
else:
return codec.encode(g, **format_options)
def test_canonicalize_roles_default_codec():
parse = def_codec.parse
norm = lambda t: canonicalize_roles(t, def_model)
format = lambda t: def_codec.format(t, indent=None)
t = norm(parse('(a / alpha :ARG1 (b / beta))'))
assert format(t) == '(a / alpha :ARG1 (b / beta))'
t = norm(parse('(a / alpha :ARG1-of-of (b / beta))'))
assert format(t) == '(a / alpha :ARG1 (b / beta))'
t = norm(parse('(a / alpha :mod-of (b / beta))'))
assert format(t) == '(a / alpha :mod-of (b / beta))'
def test_canonicalize_roles_amr_codec():
parse = amr_codec.parse
norm = lambda t: canonicalize_roles(t, amr_model)
format = lambda t: amr_codec.format(t, indent=None)
t = norm(parse('(a / alpha :ARG1 (b / beta))'))
assert format(t) == '(a / alpha :ARG1 (b / beta))'
t = norm(parse('(a / alpha :ARG1-of-of (b / beta))'))
assert format(t) == '(a / alpha :ARG1 (b / beta))'
t = norm(parse('(a / alpha :mod-of (b / beta))'))
assert format(t) == '(a / alpha :domain (b / beta))'
t = norm(parse('(a / alpha :mod-of~1 (b / beta))'))
assert format(t) == '(a / alpha :domain~1 (b / beta))'
def _process_in(t, model, normalize_options):
"""Encode tree *t* and return the string."""
# tree transformations
if normalize_options['canonicalize_roles']:
t = transform.canonicalize_roles(t, model)
g = layout.interpret(t, model)
# graph transformations
if normalize_options['reify_edges']:
g = transform.reify_edges(g, model)
if normalize_options['dereify_edges']:
g = transform.dereify_edges(g, model)
if normalize_options['reify_attributes']:
g = transform.reify_attributes(g)
if normalize_options['indicate_branches']:
g = transform.indicate_branches(g, model)
return g