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to_csv.py
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to_csv.py
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import pandas as pd
from pymongo import MongoClient
from PyLitSurvey.funcs import to_row, abstract_inverted_index2abstract
from PyLitSurvey.config import settings, logger
MED = pd.read_csv('med.csv')
SOUCER = []
SOUCER_ID = []
def filter_by_issn(locations):
# [{"is_oa": false, "landing_page_url": "https://doi.org/10.1146/annurev.ecolsys.28.1.517", "pdf_url": null, "source": {"id": "https://openalex.org/S4210205963", "display_name": "Annual review of ecology and systematics", "issn_l": "0066-4162", "issn": ["0066-4162", "2330-1902"], "is_oa": false, "is_in_doaj": false, "host_organization": "https://openalex.org/P4310320373", "host_organization_name": "Annual Reviews", "host_organization_lineage": ["https://openalex.org/P4310320373"], "host_organization_lineage_names": ["Annual Reviews"], "type": "journal"}, "license": null, "version": null, "is_accepted": false, "is_published": false}]
l_issns = []
display_name = []
souce_id = []
is_med = False
soucer_is_med = []
try:
for location in locations:
try:
souce = location.get('source',{})
if souce is not None:
local_is_med = False
_id = str(souce.get("id",'NAN')).replace( 'https://openalex.org/','')
if len(MED[MED['id'] == _id]) > 0:
is_med = True
local_is_med = True
name = str(souce.get("display_name",'NAN'))
isss = str(souce.get("issn_l",'NAN'))
if not _id in SOUCER_ID:
SOUCER_ID.append(_id)
SOUCER.append({
'id':_id,
'name':name,
'issn_l':isss
})
display_name.append(name)
souce_id.append(_id)
l_issns.append(isss)
soucer_is_med.append(str(local_is_med))
except Exception:
logger.exception(location)
raise Exception('Location fall')
return ', '.join(display_name), ', '.join(souce_id), ', '.join(l_issns), ', '.join(soucer_is_med)
except:
logger.exception('')
raise Exception('error')
def to_csv_all_columns():
BUCKT = 10_000
SKIP = 0
df_all = pd.DataFrame()
while True:
with MongoClient(settings.MONGO_URI) as client:
db = client[f'biblimetry_{settings.VERSION}']
colection = db['works']
all_data = list(colection.find({}).limit(BUCKT).skip(SKIP))
df_all = pd.concat([df_all, pd.DataFrame([to_row(doc) for doc in all_data])])
if not all_data:
break
SKIP += BUCKT
print(f'{SKIP}')
df_all.to_csv('output/all_columns.csv', index=False)
def dict_to_row_short(doc):
""" Converte dict para row
Args:
doc dict: dicionario dos dados
Returns:
list: row
"""
display_name, souce_id, l_issns, is_med = filter_by_issn(doc.get('locations',[]),)
if True:
row = {
'_id': doc.get('_id',''),
'title': doc.get('title',''),
'doi': doc.get('doi',''),
'language':doc.get('language',''),
'type_crossref':doc.get('type_crossref',''),
'ismed': is_med,
'id_sourcer': str(souce_id),
'cited_by_count':doc.get('cited_by_count',0),
'publication_date':doc.get('publication_date',None),
'referenced_works_count':doc.get('referenced_works_count',0),
'relevance_score':doc.get('relevance_score',0),
#'issns':str(l_issns),
'type':doc['type']
}
try:
row['abstract'] = abstract_inverted_index2abstract(doc.get('abstract_inverted_index',{}))
except Exception as e:
...
for i, keyword in enumerate(doc.get('keywords',[])):
row[f'keyword_{i}'] = keyword.get('keyword',' ')
row[f'keyword_score_{i}'] = keyword.get('score',None)
return row
else:
return {}
def to_csv():
BUCKT = 10_000
SKIP = 0
df_all = pd.DataFrame()
while True:
with MongoClient(settings.MONGO_URI) as client:
db = client[f'biblimetry_{settings.VERSION}']
colection = db['works']
all_data = list(colection.find({
'type':'article',
'keywords':{'$ne':[]}
},{
'_id':1,
'title':1,
'doi':1,
'language':1,
'type_crossref':1,
'locations':1,
'type':1,
'cited_by_count':1,
'publication_date':1,
'referenced_works_count':1,
'relevance_score':1,
'keywords':1,
'abstract_inverted_index':1
}).limit(BUCKT).skip(SKIP))
df_all = pd.concat([df_all, pd.DataFrame([dict_to_row_short(doc) for doc in all_data])])
if not all_data:
break
SKIP += BUCKT
print(f'{SKIP}')
df_all.to_csv('output/nomed_filtrado.csv', index=False)
pd.DataFrame(SOUCER).to_csv('output/soucer.csv', index=False)
to_csv()