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import os
import re
import glob
import simplejson as json
import math
import operator
import click
import subprocess
from util import *
DIR_PUBLIC_CITATIONS = "../site/datasets/final"
@click.command()
def s2_final_report():
addresses = AddressBook()
megapixels = load_megapixels_lookup()
ft_lookup = load_ft_lookup()
for key, row in megapixels.items():
print(key)
ft_share = ft_lookup[key]
if ft_share:
paper_data = process_paper(row, addresses)
def process_paper(row, addresses):
aggregate_citations = {}
papers = []
print(row['paper_ids'])
for paper_id in row['paper_ids']:
res = process_single_paper(row, paper_id, addresses, aggregate_citations)
if res:
papers.append(res)
if not len(papers):
return
print(papers)
with open('{}/{}.json'.format(DIR_PUBLIC_CITATIONS, row['key']), 'w') as f:
json.dump({
'id': papers[0]['paper_id'],
'paper': papers[0],
'address': papers[0]['address'],
'additional_papers': papers[1:],
'citations': [aggregate_citations[key] for key in aggregate_citations.keys()],
}, f)
def process_single_paper(row, paper_id, addresses, aggregate_citations):
res = {
'paper_id': '',
'key': '',
'title': '',
# 'journal': '',
'year': '',
'pdf': '',
'address': '',
# 'citation_count': 0,
# 'citations_geocoded': 0,
# 'citations_unknown': 0,
# 'citations_empty': 0,
# 'citations_pdf': 0,
# 'citations_doi': 0,
}
geocoded_citations = []
unknown_citations = []
empty_citations = []
pdf_count = 0
doi_count = 0
address_count = 0
fn = file_path('papers', paper_id, 'paper.json')
with open(fn, 'r') as f:
data = json.load(f)
print('>> {} {}'.format(data['paperId'], row['key']))
paper = load_paper(data['paperId'])
if paper is None:
print("Paper missing! {}".format(data['paperId']))
return
res['key'] = row['key']
res['name'] = row['name']
res['paper_id'] = paper.paper_id
res['title'] = paper.title
# res['journal'] = paper.journal
res['year'] = paper.year
res['pdf'] = paper.pdf_link
# res['authors'] = ', '.join(paper.authors)
# res['citations'] = []
paper_institutions = load_institutions(paper.paper_id)
paper_address = None
for inst in sorted(paper_institutions, key=operator.itemgetter(1)):
# print(inst[1])
institution = inst[1]
if paper_address is None:
paper_address = addresses.findObject(institution)
if paper_address:
# print(paper_address)
res['address'] = paper_address
for cite in data['citations']:
citationId = cite['paperId']
if citationId in aggregate_citations:
continue
seen_here = {}
citation = load_paper(citationId)
has_pdf = os.path.exists(file_path('pdf', citationId, 'paper.txt'))
has_doi = os.path.exists(file_path('doi', citationId, 'paper.doi'))
if has_pdf:
pdf_count += 1
if has_doi:
doi_count += 1
if citation is None or citation.data is None:
print("Citation missing! {}".format(cite['paperId']))
continue
institutions = load_institutions(citationId)
geocoded_addresses = []
geocoded_institutions = []
institution = ''
address = None
for inst in sorted(institutions, key=operator.itemgetter(1)):
address_count += 1
institution = inst[1]
next_address = addresses.findObject(institution)
if next_address and next_address['address'] not in seen_here:
seen_here[next_address['address']] = True
address = next_address
geocoded_addresses.append(next_address)
if not address:
if has_pdf:
headings, found_abstract = read_headings(file_path('pdf', citationId, 'paper.txt'), citation)
heading_string = '\n'.join(headings[0:20])
found_addresses = []
if len(headings):
for heading in headings:
l = heading.lower().strip()
if l:
next_address = addresses.findObject(l)
if next_address and next_address['address'] not in seen_here:
seen_here[next_address['address']] = True
address = next_address
geocoded_addresses.append(next_address)
if address:
if citationId not in aggregate_citations:
aggregate_citations[citationId] = {
'id': citationId,
'title': citation.title,
'addresses': geocoded_addresses,
'year': citation.year,
'pdf': citation.pdf_link,
}
# res['citation_count'] = len(data['citations'])
# res['citations_geocoded'] = len(geocoded_citations)
# res['citations_unknown'] = len(unknown_citations)
# res['citations_empty'] = len(empty_citations)
# res['citations_pdf'] = pdf_count
# res['citations_doi'] = doi_count
return res
def load_ft_lookup():
keys, rows = fetch_google_sheet('datasets')
lookup = {}
for row in rows:
rec = {}
for index, key in enumerate(keys):
rec[key] = row[index]
if rec['ft_share'] == '1' or rec['ft_share'] == 1:
lookup[rec['key']] = True
else:
lookup[rec['key']] = False
return lookup
def load_megapixels_lookup():
keys, rows = read_csv('datasets/citation_lookup.csv')
lookup = {}
for row in rows:
rec = {}
for index, key in enumerate(keys):
rec[key] = row[index]
paper_key = rec['key']
if paper_key not in lookup:
rec['paper_ids'] = []
lookup[paper_key] = rec
lookup[paper_key]['paper_ids'].append(rec['paper_id'])
# recs.append(rec)
return lookup
def load_institutions(paperId):
if os.path.exists(file_path('pdf', paperId, 'institutions.json')):
return read_json(file_path('pdf', paperId, 'institutions.json'))['institutions']
elif os.path.exists(file_path('doi', paperId, 'institutions.json')):
return read_json(file_path('doi', paperId, 'institutions.json'))['institutions']
else:
return []
def data_path(key, paper_id):
return 'datasets/s2/{}/{}/{}'.format(key, paper_id[0:2], paper_id)
def file_path(key, paper_id, fn):
return os.path.join(data_path(key, paper_id), fn)
if __name__ == '__main__':
s2_final_report()
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