def get_cmp_authors_cont(ap1: AuthorParam, ap2: AuthorParam, word: str,
field_col: FieldsSet, ngrmpr: Optional[NgrammParam],
probability: Optional[float]) -> list:
assert ap1.only_one() and ap2.only_one()
atype1, name1 = ap1.get_qual_auth()
atype2, name2 = ap2.get_qual_auth()
return get_cmp_authors_ref_cont(atype1, name1, atype2, name2, word,
field_col, ngrmpr, probability)
async def _req_common2authors(
authorParams1: AuthorParam = Depends(depAuthorParamOnlyOne),
authorParams2: AuthorParam = Depends(depAuthorParamOnlyOne2),
ngrmpr: NgrammParam = Depends(depNgrammParamReq),
probability: Optional[float] = .5,
_debug_option: Optional[DebugOption] = None,
slot: Slot = Depends(Slot.req2slot)):
pipelines = get_cmp_authors(authorParams1, authorParams2, ngrmpr,
probability)
if _debug_option == DebugOption.pipeline:
return pipelines
atype1, name1 = authorParams1.get_qual_auth()
atype2, name2 = authorParams2.get_qual_auth()
def_vals = dict(common=1, union=1)
if authorParams1 == authorParams2:
out = dict(author1=dict(atype=atype1, name=name1),
author2=dict(atype=atype2, name=name2),
**{k: def_vals
for k in FieldsSet})
return out
coll: Collection = slot.mdb.publications
if _debug_option == DebugOption.raw_out:
out = {}
for key, pipeline in pipelines.items():
curs = coll.aggregate(pipeline)
out_lst = [doc async for doc in curs]
out[key] = out_lst
return out
vals = {}
for key, pipeline in pipelines.items():
curs = coll.aggregate(pipeline)
cnts1, cnts2 = await collect_cmp_vals(atype1, name1, atype2, name2,
curs)
keys_union = cnts1.keys() | cnts2.keys()
keys_intersect = cnts1.keys() & cnts2.keys()
words = sorted((w, cnts1[w], cnts2[w]) for w in keys_intersect)
if key == FieldsSet.ngram:
len_pref = len(ngrmpr.ltype.value) + 1
words = ((w[len_pref:], c1, c2) for w, c1, c2 in words)
common_words = [
dict(word=w, author1=c1, author2=c2) for w, c1, c2 in words
]
vals[key] = dict(common=len(keys_intersect),
union=len(keys_union),
common_words=common_words)
out = dict(author1=dict(atype=atype1, name=name1),
author2=dict(atype=atype2, name=name2),
**vals)
return out
def get_cmp_authors(ap1: AuthorParam, ap2: AuthorParam, ngrmpr: NgrammParam,
probability: float) -> Dict[str, list]:
assert ap1.only_one() and ap2.only_one()
atype1, name1 = ap1.get_qual_auth()
atype2, name2 = ap2.get_qual_auth()
pipelines = {}
for fld_set in FieldsSet:
# type: fld_set: FieldsSet
pipeline = get_cmp_authors_ref(atype1, name1, atype2, name2, fld_set,
ngrmpr, probability)
pipelines[fld_set] = pipeline
return pipelines
async def _req_pubs_refauthors(
top_auth:Optional[int]=3,
authorParams:AuthorParam=Depends(),
_debug_option:Optional[DebugOption]=None,
slot:Slot=Depends(Slot.req2slot)
):
pipeline = get_refauthors_part(top_auth, AuthorParam())
if _debug_option == DebugOption.pipeline:
return pipeline
publications: Collection = slot.mdb.publications
contexts: Collection = slot.mdb.contexts
out = []
async for pub in publications.find(
# {'uni_authors': 'Sergey-Sinelnikov-Murylev'},
{'name': {'$exists': 1}, **filter_acc_dict(authorParams),},
projection={'_id': True, 'name': True}, sort=[('_id', ASCENDING)]
):
pid = pub['_id']
pub_pipeline = [{'$match': {'pubid': pid}}] + pipeline
ref_authors = []
async for row in contexts.aggregate(pub_pipeline):
row.pop('pos_neg', None)
row.pop('frags', None)
ref_authors.append(row)
pub_out = dict(pubid=pid, name=pub['name'], ref_authors=ref_authors)
out.append(pub_out)
return out
async def _req_common2authors_field(
field: FieldsSet,
authorParams1: AuthorParam,
authorParams2: AuthorParam,
word: Optional[str],
*,
ngrmpr: Optional[NgrammParam] = None,
probability: Optional[float] = None,
slot: Slot,
_debug_option: Optional[DebugOption] = None,
):
pipeline = get_cmp_authors_cont(authorParams1, authorParams2, word, field,
ngrmpr, probability)
if _debug_option == DebugOption.pipeline:
return pipeline
coll: Collection = slot.mdb.publications
curs = coll.aggregate(pipeline, allowDiskUse=True)
if _debug_option == DebugOption.raw_out:
out = [doc async for doc in curs]
return out
atype1, name1 = authorParams1.get_qual_auth()
atype2, name2 = authorParams2.get_qual_auth()
(set1, conts1), (set2, conts2) = await collect_cmp_vals_conts(
atype1, name1, atype2, name2, curs)
keys_union = set1.keys() | set2.keys()
keys_intersect = set1.keys() & set2.keys()
words = sorted((w, set1[w], set2[w]) for w in keys_intersect)
len_pref = len(ngrmpr.ltype.value) + 1 if field == FieldsSet.ngram else 0
common_words = [
dict(word=w[len_pref:],
author1=dict(cnt=c1, conts=sorted(conts1.get(w, ()))),
author2=dict(cnt=c2, conts=sorted(conts2.get(w, ()))))
for w, c1, c2 in words
]
out = dict(author1=dict(atype=atype1, name=name1),
author2=dict(atype=atype2, name=name2),
common=len(keys_intersect),
union=len(keys_union),
common_words=common_words)
return out
async def _req_compare2authors(
authorParams1: AuthorParam = Depends(depAuthorParamOnlyOne),
authorParams2: AuthorParam = Depends(depAuthorParamOnlyOne2),
ngrmpr: NgrammParam = Depends(depNgrammParamReq),
probability: Optional[float] = .5,
_debug_option: Optional[DebugOption] = None,
slot: Slot = Depends(Slot.req2slot)):
pipelines = get_cmp_authors(authorParams1, authorParams2, ngrmpr,
probability)
if _debug_option == DebugOption.pipeline:
return pipelines
atype1, name1 = authorParams1.get_qual_auth()
atype2, name2 = authorParams2.get_qual_auth()
def_vals = dict(common=1, union=1, yaccard=1, jensen_shannon=0)
if authorParams1 == authorParams2:
out = dict(author1=dict(atype=atype1, name=name1),
author2=dict(atype=atype2, name=name2),
**{k: def_vals
for k in FieldsSet})
return out
coll: Collection = slot.mdb.publications
if _debug_option == DebugOption.raw_out:
out = {}
for key, pipeline in pipelines.items():
curs = coll.aggregate(pipeline)
out_lst = [doc async for doc in curs]
out[key] = out_lst
return out
vals = {}
for key, pipeline in pipelines.items():
curs = coll.aggregate(pipeline)
calc_vals = await calc_cmp_vals(atype1, name1, atype2, name2, curs,
key)
vals[key] = calc_vals
out = dict(author1=dict(atype=atype1, name=name1),
author2=dict(atype=atype2, name=name2),
**vals)
return out
def filter_acc_dict(ap: AuthorParam) -> Dict[str, str]:
"""Фильтр по author, cited, citing"""
if ap.is_empty():
return {}
match = {
f'uni_{key}': val for key, val in
(('authors', ap.author), ('cited', ap.cited), ('citing', ap.citing))
if val}
return match
def filter_by_pubs_acc(authParams: AuthorParam) -> List[dict]:
if authParams.is_empty():
return []
match = filter_acc_dict(authParams)
pipeline = [
{'$lookup': {
'from': 'publications', 'localField': 'pubid', 'foreignField': '_id',
'as': 'pub'}},
{'$unwind': '$pub'},
# {'$match': {'pub.uni_authors': {'$exists': 1}}},
{'$match': {f'pub.{key}': val for key, val in match.items()}},
]
return pipeline
def filter_by_topic(
ap: AuthorParam, *, as_field:str= 'topic'
):
if ap.is_empty():
return []
pipeline = [
{
'$match': {
"$or": [
{f'{as_field}.uni_{fld}': val} for fld, val in
(('author', ap.author), ('cited', ap.cited), ('citing', ap.citing),)
if val]}}, ]
return pipeline
def depAuthorParamOnlyOne2(
ap:AuthorParamOnlyOne2=Depends()
) -> AuthorParam:
params = AuthorParam(author=ap.author2, cited=ap.cited2, citing=ap.citing2)
return params
def depAuthorParamOnlyOne( authorParams:AuthorParamOnlyOne=Depends() ) -> AuthorParam: res = AuthorParam(**authorParams.dict()) return res