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spaCy/spacy/tokens/token.pyx
Matthew Honnibal 8aa7882762
Make NORM a token attribute (#3029) 
See #3028. The solution in this patch is pretty debateable.

What we do is give the TokenC struct a .norm field, by repurposing the previously idle .sense attribute. It's nice to repurpose a previous field because it means the TokenC doesn't change size, so even if someone's using the internals very deeply, nothing will break.

The weird thing here is that the TokenC and the LexemeC both have an attribute named NORM. This arguably assists in backwards compatibility. On the other hand, maybe it's really bad! We're changing the semantics of the attribute subtly, so maybe it's better if someone calling lex.norm gets a breakage, and instead is told to write lex.default_norm?

Overall I believe this patch makes the NORM feature work the way we sort of expected it to work. Certainly it's much more like how the docs describe it, and more in line with how we've been directing people to use the norm attribute. We'll also be able to use token.norm to do stuff like spelling correction, which is pretty cool.
2018-12-08 10:49:10 +01:00

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# cython: infer_types=True
# coding: utf8
from __future__ import unicode_literals
from libc.string cimport memcpy
from cpython.mem cimport PyMem_Malloc, PyMem_Free
# Compiler crashes on memory view coercion without this. Should report bug.
from cython.view cimport array as cvarray
cimport numpy as np
np.import_array()
import numpy
from ..typedefs cimport hash_t
from ..lexeme cimport Lexeme
from .. import parts_of_speech
from ..attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
from ..attrs cimport IS_BRACKET, IS_QUOTE, IS_LEFT_PUNCT, IS_RIGHT_PUNCT
from ..attrs cimport IS_OOV, IS_TITLE, IS_UPPER, IS_CURRENCY, LIKE_URL, LIKE_NUM, LIKE_EMAIL
from ..attrs cimport IS_STOP, ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX
from ..attrs cimport LENGTH, CLUSTER, LEMMA, POS, TAG, DEP
from ..compat import is_config
from ..errors import Errors, Warnings, user_warning, models_warning
from .. import util
from .underscore import Underscore, get_ext_args
cdef class Token:
"""An individual token – i.e. a word, punctuation symbol, whitespace,
etc."""
@classmethod
def set_extension(cls, name, **kwargs):
if cls.has_extension(name) and not kwargs.get('force', False):
raise ValueError(Errors.E090.format(name=name, obj='Token'))
Underscore.token_extensions[name] = get_ext_args(**kwargs)
@classmethod
def get_extension(cls, name):
return Underscore.token_extensions.get(name)
@classmethod
def has_extension(cls, name):
return name in Underscore.token_extensions
@classmethod
def remove_extension(cls, name):
if not cls.has_extension(name):
raise ValueError(Errors.E046.format(name=name))
return Underscore.token_extensions.pop(name)
def __cinit__(self, Vocab vocab, Doc doc, int offset):
"""Construct a `Token` object.
vocab (Vocab): A storage container for lexical types.
doc (Doc): The parent document.
offset (int): The index of the token within the document.
"""
self.vocab = vocab
self.doc = doc
self.c = &self.doc.c[offset]
self.i = offset
def __hash__(self):
return hash((self.doc, self.i))
def __len__(self):
"""The number of unicode characters in the token, i.e. `token.text`.
RETURNS (int): The number of unicode characters in the token.
"""
return self.c.lex.length
def __unicode__(self):
return self.text
def __bytes__(self):
return self.text.encode('utf8')
def __str__(self):
if is_config(python3=True):
return self.__unicode__()
return self.__bytes__()
def __repr__(self):
return self.__str__()
def __richcmp__(self, Token other, int op):
# http://cython.readthedocs.io/en/latest/src/userguide/special_methods.html
if other is None:
if op in (0, 1, 2):
return False
else:
return True
cdef Doc my_doc = self.doc
cdef Doc other_doc = other.doc
my = self.idx
their = other.idx
if op == 0:
return my < their
elif op == 2:
if my_doc is other_doc:
return my == their
else:
return False
elif op == 4:
return my > their
elif op == 1:
return my <= their
elif op == 3:
if my_doc is other_doc:
return my != their
else:
return True
elif op == 5:
return my >= their
else:
raise ValueError(Errors.E041.format(op=op))
@property
def _(self):
return Underscore(Underscore.token_extensions, self,
start=self.idx, end=None)
cpdef bint check_flag(self, attr_id_t flag_id) except -1:
"""Check the value of a boolean flag.
flag_id (int): The ID of the flag attribute.
RETURNS (bool): Whether the flag is set.
EXAMPLE:
>>> from spacy.attrs import IS_TITLE
>>> doc = nlp(u'Give it back! He pleaded.')
>>> token = doc[0]
>>> token.check_flag(IS_TITLE)
True
"""
return Lexeme.c_check_flag(self.c.lex, flag_id)
def nbor(self, int i=1):
"""Get a neighboring token.
i (int): The relative position of the token to get. Defaults to 1.
RETURNS (Token): The token at position `self.doc[self.i+i]`.
"""
if self.i+i < 0 or (self.i+i >= len(self.doc)):
raise IndexError(Errors.E042.format(i=self.i, j=i, length=len(self.doc)))
return self.doc[self.i+i]
def similarity(self, other):
"""Make a semantic similarity estimate. The default estimate is cosine
similarity using an average of word vectors.
other (object): The object to compare with. By default, accepts `Doc`,
`Span`, `Token` and `Lexeme` objects.
RETURNS (float): A scalar similarity score. Higher is more similar.
"""
if 'similarity' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['similarity'](self)
if hasattr(other, '__len__') and len(other) == 1 and hasattr(other, "__getitem__"):
if self.c.lex.orth == getattr(other[0], 'orth', None):
return 1.0
elif hasattr(other, 'orth'):
if self.c.lex.orth == other.orth:
return 1.0
if self.vocab.vectors.n_keys == 0:
models_warning(Warnings.W007.format(obj='Token'))
if self.vector_norm == 0 or other.vector_norm == 0:
user_warning(Warnings.W008.format(obj='Token'))
return 0.0
return (numpy.dot(self.vector, other.vector) /
(self.vector_norm * other.vector_norm))
property lex_id:
"""RETURNS (int): Sequential ID of the token's lexical type."""
def __get__(self):
return self.c.lex.id
property rank:
"""RETURNS (int): Sequential ID of the token's lexical type, used to
index into tables, e.g. for word vectors."""
def __get__(self):
return self.c.lex.id
property string:
"""Deprecated: Use Token.text_with_ws instead."""
def __get__(self):
return self.text_with_ws
property text:
"""RETURNS (unicode): The original verbatim text of the token."""
def __get__(self):
return self.orth_
property text_with_ws:
"""RETURNS (unicode): The text content of the span (with trailing
whitespace).
"""
def __get__(self):
cdef unicode orth = self.vocab.strings[self.c.lex.orth]
if self.c.spacy:
return orth + u' '
else:
return orth
property prob:
"""RETURNS (float): Smoothed log probability estimate of token type."""
def __get__(self):
return self.c.lex.prob
property sentiment:
"""RETURNS (float): A scalar value indicating the positivity or
negativity of the token."""
def __get__(self):
if 'sentiment' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['sentiment'](self)
return self.c.lex.sentiment
property lang:
"""RETURNS (uint64): ID of the language of the parent document's
vocabulary.
"""
def __get__(self):
return self.c.lex.lang
property idx:
"""RETURNS (int): The character offset of the token within the parent
document.
"""
def __get__(self):
return self.c.idx
property cluster:
"""RETURNS (int): Brown cluster ID."""
def __get__(self):
return self.c.lex.cluster
property orth:
"""RETURNS (uint64): ID of the verbatim text content."""
def __get__(self):
return self.c.lex.orth
property lower:
"""RETURNS (uint64): ID of the lowercase token text."""
def __get__(self):
return self.c.lex.lower
property norm:
"""RETURNS (uint64): ID of the token's norm, i.e. a normalised form of
the token text. Usually set in the language's tokenizer exceptions
or norm exceptions.
"""
def __get__(self):
if self.c.norm == 0:
return self.c.lex.norm
else:
return self.c.norm
property shape:
"""RETURNS (uint64): ID of the token's shape, a transform of the
tokens's string, to show orthographic features (e.g. "Xxxx", "dd").
"""
def __get__(self):
return self.c.lex.shape
property prefix:
"""RETURNS (uint64): ID of a length-N substring from the start of the
token. Defaults to `N=1`.
"""
def __get__(self):
return self.c.lex.prefix
property suffix:
"""RETURNS (uint64): ID of a length-N substring from the end of the
token. Defaults to `N=3`.
"""
def __get__(self):
return self.c.lex.suffix
property lemma:
"""RETURNS (uint64): ID of the base form of the word, with no
inflectional suffixes.
"""
def __get__(self):
if self.c.lemma == 0:
lemma_ = self.vocab.morphology.lemmatizer.lookup(self.orth_)
return self.vocab.strings[lemma_]
else:
return self.c.lemma
def __set__(self, attr_t lemma):
self.c.lemma = lemma
property pos:
"""RETURNS (uint64): ID of coarse-grained part-of-speech tag."""
def __get__(self):
return self.c.pos
def __set__(self, pos):
self.c.pos = pos
property tag:
"""RETURNS (uint64): ID of fine-grained part-of-speech tag."""
def __get__(self):
return self.c.tag
def __set__(self, attr_t tag):
self.vocab.morphology.assign_tag(self.c, tag)
property dep:
"""RETURNS (uint64): ID of syntactic dependency label."""
def __get__(self):
return self.c.dep
def __set__(self, attr_t label):
self.c.dep = label
property has_vector:
"""A boolean value indicating whether a word vector is associated with
the object.
RETURNS (bool): Whether a word vector is associated with the object.
"""
def __get__(self):
if 'has_vector' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['has_vector'](self)
if self.vocab.vectors.size == 0 and self.doc.tensor.size != 0:
return True
return self.vocab.has_vector(self.c.lex.orth)
property vector:
"""A real-valued meaning representation.
RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
representing the token's semantics.
"""
def __get__(self):
if 'vector' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['vector'](self)
if self.vocab.vectors.size == 0 and self.doc.tensor.size != 0:
return self.doc.tensor[self.i]
else:
return self.vocab.get_vector(self.c.lex.orth)
property vector_norm:
"""The L2 norm of the token's vector representation.
RETURNS (float): The L2 norm of the vector representation.
"""
def __get__(self):
if 'vector_norm' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['vector_norm'](self)
vector = self.vector
return numpy.sqrt((vector ** 2).sum())
property n_lefts:
"""RETURNS (int): The number of leftward immediate children of the
word, in the syntactic dependency parse.
"""
def __get__(self):
return self.c.l_kids
property n_rights:
"""RETURNS (int): The number of rightward immediate children of the
word, in the syntactic dependency parse.
"""
def __get__(self):
return self.c.r_kids
property sent:
"""RETURNS (Span): The sentence span that the token is a part of."""
def __get__(self):
if 'sent' in self.doc.user_token_hooks:
return self.doc.user_token_hooks['sent'](self)
return self.doc[self.i : self.i+1].sent
property sent_start:
def __get__(self):
# Raising a deprecation warning here causes errors for autocomplete
# Handle broken backwards compatibility case: doc[0].sent_start
# was False.
if self.i == 0:
return False
else:
return self.c.sent_start
def __set__(self, value):
self.is_sent_start = value
property is_sent_start:
"""RETURNS (bool / None): Whether the token starts a sentence.
None if unknown.
"""
def __get__(self):
if self.c.sent_start == 0:
return None
elif self.c.sent_start < 0:
return False
else:
return True
def __set__(self, value):
if self.doc.is_parsed:
raise ValueError(Errors.E043)
if value is None:
self.c.sent_start = 0
elif value is True:
self.c.sent_start = 1
elif value is False:
self.c.sent_start = -1
else:
raise ValueError(Errors.E044.format(value=value))
property lefts:
"""The leftward immediate children of the word, in the syntactic
dependency parse.
YIELDS (Token): A left-child of the token.
"""
def __get__(self):
cdef int nr_iter = 0
cdef const TokenC* ptr = self.c - (self.i - self.c.l_edge)
while ptr < self.c:
if ptr + ptr.head == self.c:
yield self.doc[ptr - (self.c - self.i)]
ptr += 1
nr_iter += 1
# This is ugly, but it's a way to guard out infinite loops
if nr_iter >= 10000000:
raise RuntimeError(Errors.E045.format(attr='token.lefts'))
property rights:
"""The rightward immediate children of the word, in the syntactic
dependency parse.
YIELDS (Token): A right-child of the token.
"""
def __get__(self):
cdef const TokenC* ptr = self.c + (self.c.r_edge - self.i)
tokens = []
cdef int nr_iter = 0
while ptr > self.c:
if ptr + ptr.head == self.c:
tokens.append(self.doc[ptr - (self.c - self.i)])
ptr -= 1
nr_iter += 1
if nr_iter >= 10000000:
raise RuntimeError(Errors.E045.format(attr='token.rights'))
tokens.reverse()
for t in tokens:
yield t
property children:
"""A sequence of the token's immediate syntactic children.
YIELDS (Token): A child token such that child.head==self
"""
def __get__(self):
yield from self.lefts
yield from self.rights
property subtree:
"""A sequence of all the token's syntactic descendents.
YIELDS (Token): A descendent token such that
`self.is_ancestor(descendent)`.
"""
def __get__(self):
for word in self.lefts:
yield from word.subtree
yield self
for word in self.rights:
yield from word.subtree
property left_edge:
"""The leftmost token of this token's syntactic descendents.
RETURNS (Token): The first token such that `self.is_ancestor(token)`.
"""
def __get__(self):
return self.doc[self.c.l_edge]
property right_edge:
"""The rightmost token of this token's syntactic descendents.
RETURNS (Token): The last token such that `self.is_ancestor(token)`.
"""
def __get__(self):
return self.doc[self.c.r_edge]
property ancestors:
"""A sequence of this token's syntactic ancestors.
YIELDS (Token): A sequence of ancestor tokens such that
`ancestor.is_ancestor(self)`.
"""
def __get__(self):
cdef const TokenC* head_ptr = self.c
# guard against infinite loop, no token can have
# more ancestors than tokens in the tree
cdef int i = 0
while head_ptr.head != 0 and i < self.doc.length:
head_ptr += head_ptr.head
yield self.doc[head_ptr - (self.c - self.i)]
i += 1
def is_ancestor(self, descendant):
"""Check whether this token is a parent, grandparent, etc. of another
in the dependency tree.
descendant (Token): Another token.
RETURNS (bool): Whether this token is the ancestor of the descendant.
"""
if self.doc is not descendant.doc:
return False
return any(ancestor.i == self.i for ancestor in descendant.ancestors)
property head:
"""The syntactic parent, or "governor", of this token.
RETURNS (Token): The token predicted by the parser to be the head of
the current token.
"""
def __get__(self):
return self.doc[self.i + self.c.head]
def __set__(self, Token new_head):
# this function sets the head of self to new_head
# and updates the counters for left/right dependents
# and left/right corner for the new and the old head
# do nothing if old head is new head
if self.i + self.c.head == new_head.i:
return
cdef Token old_head = self.head
cdef int rel_newhead_i = new_head.i - self.i
# is the new head a descendant of the old head
cdef bint is_desc = old_head.is_ancestor(new_head)
cdef int new_edge
cdef Token anc, child
# update number of deps of old head
if self.c.head > 0: # left dependent
old_head.c.l_kids -= 1
if self.c.l_edge == old_head.c.l_edge:
# the token dominates the left edge so the left edge of
# the head may change when the token is reattached, it may
# not change if the new head is a descendant of the current
# head
new_edge = self.c.l_edge
# the new l_edge is the left-most l_edge on any of the
# other dependents where the l_edge is left of the head,
# otherwise it is the head
if not is_desc:
new_edge = old_head.i
for child in old_head.children:
if child == self:
continue
if child.c.l_edge < new_edge:
new_edge = child.c.l_edge
old_head.c.l_edge = new_edge
# walk up the tree from old_head and assign new l_edge to
# ancestors until an ancestor already has an l_edge that's
# further left
for anc in old_head.ancestors:
if anc.c.l_edge <= new_edge:
break
anc.c.l_edge = new_edge
elif self.c.head < 0: # right dependent
old_head.c.r_kids -= 1
# do the same thing as for l_edge
if self.c.r_edge == old_head.c.r_edge:
new_edge = self.c.r_edge
if not is_desc:
new_edge = old_head.i
for child in old_head.children:
if child == self:
continue
if child.c.r_edge > new_edge:
new_edge = child.c.r_edge
old_head.c.r_edge = new_edge
for anc in old_head.ancestors:
if anc.c.r_edge >= new_edge:
break
anc.c.r_edge = new_edge
# update number of deps of new head
if rel_newhead_i > 0: # left dependent
new_head.c.l_kids += 1
# walk up the tree from new head and set l_edge to self.l_edge
# until you hit a token with an l_edge further to the left
if self.c.l_edge < new_head.c.l_edge:
new_head.c.l_edge = self.c.l_edge
for anc in new_head.ancestors:
if anc.c.l_edge <= self.c.l_edge:
break
anc.c.l_edge = self.c.l_edge
elif rel_newhead_i < 0: # right dependent
new_head.c.r_kids += 1
# do the same as for l_edge
if self.c.r_edge > new_head.c.r_edge:
new_head.c.r_edge = self.c.r_edge
for anc in new_head.ancestors:
if anc.c.r_edge >= self.c.r_edge:
break
anc.c.r_edge = self.c.r_edge
# set new head
self.c.head = rel_newhead_i
property conjuncts:
"""A sequence of coordinated tokens, including the token itself.
YIELDS (Token): A coordinated token.
"""
def __get__(self):
"""Get a list of conjoined words."""
cdef Token word
if 'conjuncts' in self.doc.user_token_hooks:
yield from self.doc.user_token_hooks['conjuncts'](self)
else:
if self.dep_ != 'conj':
for word in self.rights:
if word.dep_ == 'conj':
yield word
yield from word.conjuncts
property ent_type:
"""RETURNS (uint64): Named entity type."""
def __get__(self):
return self.c.ent_type
def __set__(self, ent_type):
self.c.ent_type = ent_type
property ent_iob:
"""IOB code of named entity tag. `1="I", 2="O", 3="B"`. 0 means no tag
is assigned.
RETURNS (uint64): IOB code of named entity tag.
"""
def __get__(self):
return self.c.ent_iob
property ent_type_:
"""RETURNS (unicode): Named entity type."""
def __get__(self):
return self.vocab.strings[self.c.ent_type]
def __set__(self, ent_type):
self.c.ent_type = self.vocab.strings.add(ent_type)
property ent_iob_:
"""IOB code of named entity tag. "B" means the token begins an entity,
"I" means it is inside an entity, "O" means it is outside an entity,
and "" means no entity tag is set.
RETURNS (unicode): IOB code of named entity tag.
"""
def __get__(self):
iob_strings = ('', 'I', 'O', 'B')
return iob_strings[self.c.ent_iob]
property ent_id:
"""RETURNS (uint64): ID of the entity the token is an instance of,
if any.
"""
def __get__(self):
return self.c.ent_id
def __set__(self, hash_t key):
self.c.ent_id = key
property ent_id_:
"""RETURNS (unicode): ID of the entity the token is an instance of,
if any.
"""
def __get__(self):
return self.vocab.strings[self.c.ent_id]
def __set__(self, name):
self.c.ent_id = self.vocab.strings.add(name)
property whitespace_:
"""RETURNS (unicode): The trailing whitespace character, if present.
"""
def __get__(self):
return ' ' if self.c.spacy else ''
property orth_:
"""RETURNS (unicode): Verbatim text content (identical to
`Token.text`). Exists mostly for consistency with the other
attributes.
"""
def __get__(self):
return self.vocab.strings[self.c.lex.orth]
property lower_:
"""RETURNS (unicode): The lowercase token text. Equivalent to
`Token.text.lower()`.
"""
def __get__(self):
return self.vocab.strings[self.c.lex.lower]
property norm_:
"""RETURNS (unicode): The token's norm, i.e. a normalised form of the
token text. Usually set in the language's tokenizer exceptions or
norm exceptions.
"""
def __get__(self):
return self.vocab.strings[self.norm]
def __set__(self, unicode norm_):
self.c.norm = self.vocab.strings.add(norm_)
property shape_:
"""RETURNS (unicode): Transform of the tokens's string, to show
orthographic features. For example, "Xxxx" or "dd".
"""
def __get__(self):
return self.vocab.strings[self.c.lex.shape]
property prefix_:
"""RETURNS (unicode): A length-N substring from the start of the token.
Defaults to `N=1`.
"""
def __get__(self):
return self.vocab.strings[self.c.lex.prefix]
property suffix_:
"""RETURNS (unicode): A length-N substring from the end of the token.
Defaults to `N=3`.
"""
def __get__(self):
return self.vocab.strings[self.c.lex.suffix]
property lang_:
"""RETURNS (unicode): Language of the parent document's vocabulary,
e.g. 'en'.
"""
def __get__(self):
return self.vocab.strings[self.c.lex.lang]
property lemma_:
"""RETURNS (unicode): The token lemma, i.e. the base form of the word,
with no inflectional suffixes.
"""
def __get__(self):
if self.c.lemma == 0:
return self.vocab.morphology.lemmatizer.lookup(self.orth_)
else:
return self.vocab.strings[self.c.lemma]
def __set__(self, unicode lemma_):
self.c.lemma = self.vocab.strings.add(lemma_)
property pos_:
"""RETURNS (unicode): Coarse-grained part-of-speech tag."""
def __get__(self):
return parts_of_speech.NAMES[self.c.pos]
def __set__(self, pos_name):
self.c.pos = parts_of_speech.IDS[pos_name]
property tag_:
"""RETURNS (unicode): Fine-grained part-of-speech tag."""
def __get__(self):
return self.vocab.strings[self.c.tag]
def __set__(self, tag):
self.tag = self.vocab.strings.add(tag)
property dep_:
"""RETURNS (unicode): The syntactic dependency label."""
def __get__(self):
return self.vocab.strings[self.c.dep]
def __set__(self, unicode label):
self.c.dep = self.vocab.strings.add(label)
property is_oov:
"""RETURNS (bool): Whether the token is out-of-vocabulary."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_OOV)
property is_stop:
"""RETURNS (bool): Whether the token is a stop word, i.e. part of a
"stop list" defined by the language data.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_STOP)
property is_alpha:
"""RETURNS (bool): Whether the token consists of alpha characters.
Equivalent to `token.text.isalpha()`.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_ALPHA)
property is_ascii:
"""RETURNS (bool): Whether the token consists of ASCII characters.
Equivalent to `[any(ord(c) >= 128 for c in token.text)]`.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_ASCII)
property is_digit:
"""RETURNS (bool): Whether the token consists of digits. Equivalent to
`token.text.isdigit()`.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_DIGIT)
property is_lower:
"""RETURNS (bool): Whether the token is in lowercase. Equivalent to
`token.text.islower()`.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_LOWER)
property is_upper:
"""RETURNS (bool): Whether the token is in uppercase. Equivalent to
`token.text.isupper()`
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_UPPER)
property is_title:
"""RETURNS (bool): Whether the token is in titlecase. Equivalent to
`token.text.istitle()`.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_TITLE)
property is_punct:
"""RETURNS (bool): Whether the token is punctuation."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_PUNCT)
property is_space:
"""RETURNS (bool): Whether the token consists of whitespace characters.
Equivalent to `token.text.isspace()`.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_SPACE)
property is_bracket:
"""RETURNS (bool): Whether the token is a bracket."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_BRACKET)
property is_quote:
"""RETURNS (bool): Whether the token is a quotation mark."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_QUOTE)
property is_left_punct:
"""RETURNS (bool): Whether the token is a left punctuation mark."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_LEFT_PUNCT)
property is_right_punct:
"""RETURNS (bool): Whether the token is a left punctuation mark."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_RIGHT_PUNCT)
property is_currency:
"""RETURNS (bool): Whether the token is a currency symbol."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, IS_CURRENCY)
property like_url:
"""RETURNS (bool): Whether the token resembles a URL."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, LIKE_URL)
property like_num:
"""RETURNS (bool): Whether the token resembles a number, e.g. "10.9",
"10", "ten", etc.
"""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, LIKE_NUM)
property like_email:
"""RETURNS (bool): Whether the token resembles an email address."""
def __get__(self):
return Lexeme.c_check_flag(self.c.lex, LIKE_EMAIL)
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