MARGIN_SHAPE = MatcherPatterns(
'margin_shape',
on_match='efloras.margin.v1',
decoder=COMMON_PATTERNS | {
'margin_shape': {
'ENT_TYPE': 'margin_shape'
},
'shape': {
'ENT_TYPE': {
'IN': SHAPES
}
},
'leader': {
'ENT_TYPE': {
'IN': LEADERS
}
},
'follower': {
'ENT_TYPE': {
'IN': FOLLOWERS
}
},
},
patterns=[
'leader* -* margin_shape+',
'leader* -* margin_shape -* follower*',
'leader* -* margin_shape -* shape? follower+ shape?',
'shape+ -* follower+',
],
)
'ENT_TYPE': 'part_loc'
},
'part/loc': {
'ENT_TYPE': {
'IN': ['part_loc', 'body_part']
}
},
'cconj': {
'POS': 'CCONJ'
},
}
SETAE = MatcherPatterns(
'seta',
decoder=DECODER,
patterns=[
'part* cheta',
'any_part+ cheta',
],
)
SETAE_ABBREV = MatcherPatterns(
'seta_abbrev',
decoder=DECODER,
patterns=['(? cheta_abbrev )?'],
)
SETA_COUNT = MatcherPatterns(
'setae_count',
on_match='anoplura.seta_count.v1',
decoder=DECODER,
patterns=[
'not_count_word': {
'LOWER': {
'IN': NOT_COUNT_WORDS
}
},
'per_count': {
'ENT_TYPE': 'per_count'
},
}
COUNT = MatcherPatterns(
'count',
on_match='efloras.count.v1',
decoder=DECODER,
patterns=[
'99-99 -* per_count?',
'99-99 per_count count_suffix?',
'per_count adp? 99-99 count_suffix?',
'( 99-99 count_suffix? ) per_count',
],
)
COUNT_WORD = MatcherPatterns(
'count_word',
on_match='efloras.count_word.v1',
decoder=DECODER,
patterns=[
'count_word',
],
)
"""Get scientific names."""
import spacy
from spacy.tokens import Token
from traiter.patterns.matcher_patterns import MatcherPatterns
from anoplura.pylib.const import REPLACE
NAMES = ['anoplura', 'mammalia']
SCI_NAME = MatcherPatterns(
'sci_name',
on_match='anoplura.sci_name.v1',
patterns=[[{'ENT_TYPE': {'IN': NAMES}}]],
)
GENUS = MatcherPatterns(
'genus',
on_match='anoplura.genus.v1',
patterns=[[{'ENT_TYPE': 'anoplura_genus'}]],
)
@spacy.registry.misc(SCI_NAME.on_match)
def sci_name(ent):
"""Enrich the match."""
if isinstance(ent, Token):
return
ent._.data = {
'sci_name': REPLACE.get(ent.text.lower(), ent.text.capitalize()),
""".split()
DECODER = COMMON_PATTERNS | {
'part': {'ENT_TYPE': 'part'},
'subpart': {'ENT_TYPE': 'subpart'},
'leader': {'LOWER': {'IN': LOCATION_LEADERS}},
'not_loc': {'ENT_TYPE': {'IN': ['sex', 'location']}},
'sex': {'ENT_TYPE': 'sex'},
'of': {'LOWER': 'of'},
'adj': {'POS': 'ADJ'},
}
PART_AS_LOCATION = MatcherPatterns(
'part_as_loc',
on_match=TEXT_ACTION,
decoder=DECODER,
patterns=[
'leader part',
],
)
SUBPART_AS_LOCATION = MatcherPatterns(
'subpart_location',
on_match='efloras.subpart_location.v1',
decoder=DECODER,
patterns=[
'leader subpart',
'leader subpart of adj? subpart'
],
)
"""Parse count notations."""
import spacy
from traiter.patterns.matcher_patterns import MatcherPatterns
from traiter.util import to_positive_int
SEX_COUNT = MatcherPatterns('sex_count',
on_match='anoplura.sex_count.v1',
patterns=[[
{
'IS_DIGIT': True
},
{
'ENT_TYPE': 'sex'
},
]])
@spacy.registry.misc(SEX_COUNT.on_match)
def sex_count(ent):
"""Enrich the match with data."""
data = {}
for token in ent:
label = token.ent_type_
value = token.lower_
if label == 'sex':
data['sex'] = value
elif (as_int := to_positive_int(value)) is not None:
data['count'] = as_int
DESC = 'description'
WORD_ENTS = [''] + """ sclerotin part_loc sex """.split()
TRIM = re.compile(r'^\W+|\W+$')
DESCRIPTION = MatcherPatterns(
'description',
on_match='anoplura.description.v1',
decoder={
'body_part': {
'ENT_TYPE': 'body_part'
},
'words': {
'ENT_TYPE': {
'IN': WORD_ENTS
}
},
'./;': {
'ENT_TYPE': 'stop'
},
},
patterns=[
'body_part words+ ./;',
'words+ body_part ./;',
'words+ body_part words+ ./;',
],
)
@registry.misc(DESCRIPTION.on_match)
def description(ent):
"""Look for trait descriptions in sentences."""
'ENT_TYPE': 'sex'
},
'x': {
'LOWER': {
'IN': CROSS
}
},
}
SIZE = MatcherPatterns(
'size',
on_match='efloras.size.v1',
decoder=DECODER,
patterns=[
'about? 99.9-99.9 cm follow*',
(' about? 99.9-99.9 cm? follow* '
'x to? about? 99.9-99.9 cm follow*'),
(' about? 99.9-99.9 cm? follow* '
'x to? about? 99.9-99.9 cm? follow* '
'x to? about? 99.9-99.9 cm follow*'),
],
)
SIZE_HIGH_ONLY = MatcherPatterns(
'size.high_only',
on_match='efloras.size_high_only.v1',
decoder=DECODER,
patterns=[
'to about? 99.9 [?]? cm follow*',
],
)
MISSING_RE = re.compile(MISSING_RE, flags=re.IGNORECASE)
BODY_PART = MatcherPatterns(
'body_part',
on_match='anoplura.body_part.v1',
decoder=COMMON_PATTERNS | {
'seg': {
'ENT_TYPE': 'segmented'
},
'ord': {
'ENT_TYPE': {
'IN': ['ordinal', 'number_word']
}
},
},
patterns=[
'missing part+',
'missing? any_part* part',
'part+ &/,/or* part* &/,/or* part+',
'part+ ord -? ord',
'part+ 99? -? 99',
'part+ ord?',
'part+ 99?',
'part+ ord -? seg',
'part+ 99 -? seg',
'ord? -? seg? part+',
'99 - seg part+',
],
)
'max': {
'LOWER': {
'IN': MAXIMUM
}
},
'width': {
'LOWER': {
'IN': WIDTH
}
},
}
MEASUREMENT = MatcherPatterns(
'measurement',
decoder=DECODER,
patterns=[
'99.9 cm',
'99.9 - 99.9 cm',
],
)
MEAN = MatcherPatterns(
'mean',
decoder=DECODER,
patterns=['mean_word punct? 99.9 cm?'],
)
SAMPLE = MatcherPatterns(
'sample',
decoder=DECODER,
patterns=['n = 99'],
)
'IN': ['shape', 'shape_leader']
}
},
'angular': {
'LOWER': {
'IN': ['angular', 'angulate']
}
},
}
SHAPE = MatcherPatterns(
'shape',
on_match='efloras.shape.v1',
decoder=DECODER,
patterns=[
'shape_loc* -* shape+',
'shape_loc* -* shape -* shape+',
'shape_leader -/to shape_word+ -* shape+',
'shape_word+ -* shape+',
],
)
N_SHAPE = MatcherPatterns(
'n_shape',
on_match='efloras.n_shape.v1',
decoder=DECODER,
patterns=[
'shape_loc* 9 - angular',
],
)
from traiter.patterns.matcher_patterns import MatcherPatterns
from efloras.pylib.const import COMMON_PATTERNS
DECODER = COMMON_PATTERNS | {
'ambiguous': {
'LOWER': {
'IN': ['few', 'many']
}
},
}
RANGE_LOW = MatcherPatterns(
'range.low',
decoder=DECODER,
patterns=[
'99.9',
'( 99.9 -/or ) ambiguous ( -/to ambiguous )',
],
)
RANGE_MIN_LOW = MatcherPatterns(
'range.min.low',
decoder=DECODER,
patterns=[
'( 99.9 -/or ) 99.9',
'( 99.9 -/to ) 99.9',
],
)
RANGE_LOW_HIGH = MatcherPatterns(
'range.low.high',
from efloras.pylib.const import COMMON_PATTERNS, MISSING, REMOVE, REPLACE
MULTIPLE_DASHES = ['\\' + c for c in DASH_CHAR]
MULTIPLE_DASHES = fr'\s*[{"".join(MULTIPLE_DASHES)}]{{2,}}\s*'
SKIP = DASH + MISSING
COLOR = MatcherPatterns(
'color',
on_match='efloras.color.v1',
decoder=COMMON_PATTERNS | {
'color_words': {
'ENT_TYPE': {
'IN': ['color', 'color_mod']
}
},
'color': {
'ENT_TYPE': 'color'
},
},
patterns=[
'missing? color_words* -* color+ -* color_words*',
],
)
@registry.misc(COLOR.on_match)
def color(ent):
"""Enrich a phrase match."""
parts = {
r: 1