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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/citations"
DIR_FINAL_CITATIONS = "../site/datasets/final"
DIR_UNKNOWN_CITATIONS = "../site/datasets/unknown"
DIR_VERIFIED_CITATIONS = "../site/datasets/verified"
addresses = AddressBook()
paper_location_lookup = fetch_google_lookup('paper_locations', item_key='paper_id')
@click.command()
def s2_final_report():
megapixels = load_megapixels_lookup()
verified_lookup = fetch_verified_paper_lookup()
items = []
for key, item in megapixels.items():
if 'ft_share' in item['dataset'] and item['dataset']['ft_share'] == 'Y':
if key in verified_lookup:
lookup = verified_lookup[key]
else:
lookup = {}
items.append((item, lookup,))
parallelize(process_paper, items)
# key name_short name_full purpose url
# wild indoor outdoor campus cyberspace parent
# child source usernames names year_start year_end year_published
# ongoing images videos identities img_per_person num_cameras
# faces_persons female male landmarks width height color gray
# derivative_of tags size_gb agreement
# citations_count
# subprocess.call([
# "s3cmd", "put", "-P", "--recursive",
# DIR_PUBLIC_CITATIONS + '/',
# "s3://megapixels/v1/citations/",
# ])
#subprocess.call([
# "s3cmd", "put", "-P", "--recursive",
# DIR_VERIFIED_CITATIONS + '/',
# "s3://megapixels/v1/citations/verified/",
#])
def process_paper(row, verified_lookup):
aggregate_citations = {}
unknown_citations = {}
address = None
address_list = []
papers = []
# print(row['paper_ids'])
for paper_id in row['paper_ids']:
res = process_single_paper(row, paper_id, addresses, aggregate_citations, unknown_citations)
if res:
papers.append(res)
if res['address']:
address_list.append(res['address'])
if not len(papers):
return
paper = papers[0]
with open('{}/{}.json'.format(DIR_FINAL_CITATIONS, row['key']), 'w') as f:
json.dump({
'id': paper['paper_id'],
'dataset': row['dataset'],
'paper': paper,
'addresses': address_list,
'additional_papers': papers[1:],
'citations': [aggregate_citations[key] for key in aggregate_citations.keys()],
}, f)
with open('{}/{}.json'.format(DIR_UNKNOWN_CITATIONS, row['key']), 'w') as f:
json.dump({
'id': papers[0]['paper_id'],
'citations': [unknown_citations[key] for key in unknown_citations.keys()],
}, f)
with open('{}/{}.json'.format(DIR_PUBLIC_CITATIONS, row['key']), 'w') as f:
json.dump({
'id': paper['paper_id'],
'paper': {
'key': row['key'],
'name': row['name'],
'title': paper['title'],
'year': paper['year'],
'addresses': address_list,
},
'citations': [aggregate_citations[key] for key in aggregate_citations.keys()],
}, f)
with open('{}/{}.json'.format(DIR_VERIFIED_CITATIONS, row['key']), 'w') as f:
json.dump({
'id': paper['paper_id'],
'paper': {
'key': row['key'],
'name': row['name'],
'title': paper['title'],
'year': paper['year'],
'addresses': address_list,
},
'citations': [aggregate_citations[key] for key in verified_lookup.keys() if key in aggregate_citations],
}, f)
def process_single_paper(row, paper_id, addresses, aggregate_citations, unknown_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,
}
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_links()
res['doi'] = paper.doi_links()
# res['authors'] = ', '.join(paper.authors)
# res['citations'] = []
paper_institutions = load_institutions(paper.paper_id, paper_location_lookup)
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
elif citationId in unknown_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, paper_location_lookup)
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['name'] not in seen_here:
seen_here[next_address['name']] = 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:
possible_address = heading.lower().strip()
if possible_address:
next_address = addresses.findObject(possible_address)
if next_address and next_address['name'] not in seen_here:
seen_here[next_address['name']] = True
address = next_address
geocoded_addresses.append(next_address)
if address:
aggregate_citations[citationId] = {
'id': citationId,
'title': citation.title,
'addresses': geocoded_addresses,
'year': citation.year,
'pdf': citation.pdf_links(),
'doi': citation.doi_links(),
}
else:
unknown_citations[citationId] = {
'id': citationId,
'title': citation.title,
'year': citation.year,
'pdf': citation.pdf_links(),
'doi': citation.doi_links(),
}
return res
def load_megapixels_lookup():
keys, rows = fetch_google_sheet('citation_lookup')
dataset_lookup = fetch_google_lookup('datasets')
lookup = {}
for row in rows:
rec = {}
for index, key in enumerate(keys):
rec[key] = row[index]
if rec['paper_id'] == "" or (rec['verified'] != 1 and rec['verified'] != '1'):
continue
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'])
if paper_key in dataset_lookup:
lookup[paper_key]['dataset'] = dataset_lookup[paper_key]
else:
print("not in datasets lookup:", paper_key)
lookup[paper_key]['dataset'] = {}
return lookup
if __name__ == '__main__':
s2_final_report()
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