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from typing import Iterable, Optional, Dict, Any
import csv
from chocula.util import clean_str, parse_mimetypes, parse_country, parse_lang, PLATFORM_MAP, gaps_to_spans
from chocula.common import DirectoryLoader
from chocula.database import DirectoryInfo, HomepageUrl
class SimLoader(DirectoryLoader):
source_slug = "sim"
def open_file(self) -> Iterable:
return csv.DictReader(open(self.config.sim.filepath))
def parse_record(self, row) -> Optional[DirectoryInfo]:
"""
NA Pub Cat ID
Title
Publisher
ISSN
Impact Rank
Total Cities
Journal Impact Factor
Eigenfact or Score
First Volume
Last Volume
NA Gaps
"Scholarly / Peer-\n Reviewed"
"Peer-\n Reviewed"
Pub Type
Pub Language
Subjects
"""
# TODO: 'Pub Type'
extra: Dict[str, Any] = {}
first_year = row['First Volume']
if first_year:
first_year = int(first_year)
extra['first_year'] = int(row['First Volume'])
else:
first_year = None
last_year = row['Last Volume']
if last_year:
last_year = int(last_year)
extra['last_year'] = last_year
else:
last_year = None
gaps = [int(g) for g in row['NA Gaps'].split(';') if g.strip()]
if gaps:
extra['gaps'] = gaps
if first_year and last_year:
extra['year_spans'] = gaps_to_spans(first_year, last_year, gaps)
extra['scholarly_peer_reviewed'] = row["Scholarly / Peer-\nReviewed"]
extra['peer_reviewed'] = row["Peer-\nReviewed"]
extra['pub_type'] = clean_str(row["Pub Type"])
info = DirectoryInfo(
directory_slug=self.source_slug,
name=clean_str(row['Title']),
publisher=clean_str(row['Publisher']),
raw_issn=row['ISSN'][:9],
custom_id=row.get('NA Pub Cat ID').strip() or None,
langs=[parse_lang(row['Pub Language'])],
extra=extra,
)
return info
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