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_reify_attributes():
decode = def_codec.decode
norm = lambda g: reify_attributes(g)
encode = lambda g: def_codec.encode(g, indent=None)
g = norm(decode('(a / alpha :mod 5)'))
assert encode(g) == '(a / alpha :mod (_ / 5))'
g = norm(decode('(a / alpha :mod~1 5~2)'))
assert encode(g) == '(a / alpha :mod~1 (_ / 5~2))'
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
def test_issue_35():
# https://github.com/goodmami/penman/issues/35
# don't re-encode; these (presumably) bad graphs probably won't
# round-trip without changes. Changes may be predictable, but I
# don't want to test and guarantee some particular output
g = amr_codec.decode('(a / alpha :mod b :mod (b / beta))')
g = reify_edges(g, amr_model)
assert g.triples == [('a', ':instance', 'alpha'), ('_', ':ARG1', 'a'),
('_', ':instance', 'have-mod-91'),
('_', ':ARG2', 'b'), ('_2', ':ARG1', 'a'),
('_2', ':instance', 'have-mod-91'),
('_2', ':ARG2', 'b'), ('b', ':instance', 'beta')]
g = amr_codec.decode('(a / alpha :mod 7 :mod 7))')
g = reify_attributes(g)
assert g.triples == [('a', ':instance', 'alpha'), ('a', ':mod', '_'),
('_', ':instance', '7'), ('a', ':mod', '_2'),
('_2', ':instance', '7')]