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from typing import Iterable, Optional, Dict, Any
import csv
from chocula.util import (
clean_str,
parse_lang,
gaps_to_spans,
)
from chocula.common import DirectoryLoader
from chocula.database import DirectoryInfo
def truthy(raw: Optional[str]) -> Optional[bool]:
if not raw:
return None
if raw.lower() == 'y':
return True
if raw.lower() == 'n':
return False
return None
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"] = truthy(clean_str(row["Scholarly / Peer-\nReviewed"]))
extra["peer_reviewed"] = truthy(clean_str(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=[lang for lang in [parse_lang(row["Pub Language"])] if lang],
extra=extra,
)
return info
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