importer for atlas of surveillance data export
This commit is contained in:
Ruben van de Ven
parent
1ec45ed016
commit
a4f98a6492
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@ -0,0 +1,453 @@
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from time import sleep
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from typing import Optional
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import urllib.request
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import json
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import logging
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import requests
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import argparse
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import datetime
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import tqdm
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import csv
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from geopy.geocoders import Nominatim
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logger = logging.getLogger('wiki.importer')
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default_categories = [
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'Person',
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'Institution',
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'Technology',
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'Deployments',
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'Dataset',
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'City',
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'Country',
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]
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geocoder = Nominatim(user_agent="tutorial")
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parser = argparse.ArgumentParser(
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description='Turn wiki into nodes & links, usable by d3-force.')
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parser.add_argument('--categories', metavar='categories', default=default_categories, nargs='+',
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help='Categories')
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parser.add_argument('--url', default="https://www.securityvision.io/wiki/api.php",
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help='Wiki API URL')
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parser.add_argument('--output', default="semantic_data.json",
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help='Output JSON file')
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parser.add_argument('--credentials', default="no_credentials.json",
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help="JSON file containing the Bot's credentials")
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parser.add_argument('--csv', default="Atlas of Surveillance-Gunshot Detection,Face Recognition,Real-Time Crime Center,Video Analytics-20220621.csv",
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help="CVS file to import")
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parser.add_argument('--citeref', default="atlasofsurveillance2022",
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help="Bibliography key for imported items")
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parser.add_argument('--dry-run', '-n', action="store_true",
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help="Dry run")
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parser.add_argument('--skip-geolocation', action="store_true",
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help="Skip geolocation fetch, for faster dry-run")
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args = parser.parse_args()
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if args.skip_geolocation and not args.dry_run:
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raise Exception("Cannot do a real run without geolocating cities")
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with open(args.credentials) as fp:
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credentials = json.load(fp)
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username = credentials['user']
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password = credentials['password']
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def get_session():
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S = requests.Session()
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URL = args.url
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# Retrieve login token first
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PARAMS_0 = {
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'action': "query",
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'meta': "tokens",
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'type': "login",
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'format': "json"
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}
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R = S.get(url=URL, params=PARAMS_0)
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DATA = R.json()
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logger.debug(DATA)
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LOGIN_TOKEN = DATA['query']['tokens']['logintoken']
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logger.debug(LOGIN_TOKEN)
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# Send a post request to login. Using the main account for login is not
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# supported. Obtain credentials via Special:BotPasswords
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# (https://www.mediawiki.org/wiki/Special:BotPasswords) for lgname & lgpassword
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PARAMS_1 = {
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'action': "login",
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'lgname': username,
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'lgpassword': password,
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'lgtoken': LOGIN_TOKEN,
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'format': "json"
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}
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R = S.post(URL, data=PARAMS_1)
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DATA = R.json()
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logger.debug(DATA)
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if DATA['login']['result'] != 'Success':
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raise Exception("Failed logging in")
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return S
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# Map columns
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[
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'AOSNUMBER',
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'City', # City_state
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# 'County',
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'State',
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'Agency', # Institution
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'Type of LEA', # Law enforment agency (Institution type?)
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'Summary', # body text
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'Type of Juris', # instution type? (municipal/county/state etc)
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'Technology', # deployment type? face recognition etc
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'Vendor', # empty or clearview ai, veritone etc. (Institution)
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'Link 1',
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# 'Link 1 Snapshot',
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'Link 1 Source',
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# 'Link 1 Type',
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'Link 1 Date',
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'Link 2',
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# 'Link 2 Snapshot',
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'Link 2 Source',
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# 'Link 2 Type',
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'Link 2 Date',
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'Link 3',
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# 'Link 3 Snapshot',
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'Link 3 Source',
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# 'Link 3 Type',
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'Link 3 Date',
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# 'Other Links',
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'Statewide Network of Agency Photos (SNAP)', # single deplyment, aggregrate Used by
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'Face Analysis Comparison & Examination System (FACES)', # single deplyment, aggregrate Used by
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'Maryland Image Repository System', # single deplyment, aggregrate Used by
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#'Clearview AI', # no aggregation
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#'BriefCam', # no aggregation?
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'FACE Services', # create link: Input for
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'Relevant for the Wiki?', # FILTER!!
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]
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# title: Use of [VENDOR ]TECHNOLOGY by AGENCY
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# City: CITY (STATE) or aggregated
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# Country: USA
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# Software Used: VENDOR TECHNOLOGY
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# Used by: AGENCY
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# Information Certainty: Documented
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# Input for: [FACE Services]
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# body: <blockquote> SUMMARY</blockquote>
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# Additional properties:
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# Original Sources: url date (link 1, 2, 3)
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# AOSNUMBER
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# CiteRef: args.citeref
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# title: AGENCY
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# City: CITY (STATE)
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# Institution Type: Law Enforcement
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# aggregate agencies when these columns are 'yes'
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aggregates = {
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'Statewide Network of Agency Photos (SNAP)': [],
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'Face Analysis Comparison & Examination System (FACES)': [],
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'Maryland Image Repository System': [],
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}
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institutions = {}
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cities = {}
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technologies = {}
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def mapEntry(entry) -> Optional[dict]:
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if entry['Relevant for the Wiki?'] != 'Yes':
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logger.warning(f'Ignore entry {entry["AOSNUMBER"]}')
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return None
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else:
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hasAggregated = False
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for field in aggregates.keys():
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if entry[field] == 'Yes':
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aggregates[field].append(entry)
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hasAggregated = True
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if hasAggregated:
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return None
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return mapDeployment(entry)
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def mapTechnology(entry):
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entry['Vendor'] = entry['Vendor'].strip()
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tech = {
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'title': f"{entry['Vendor'] if entry['Vendor'] else 'Unknown'} {entry['Technology']}",
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"@type": "Products",
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'properties': {
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"Developed by": entry['Vendor'],
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},
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"additionalProperties": {
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"Technology Type": entry['Technology'],
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"Needs processing of the title": "Yes",
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'CiteRef': args.citeref,
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}
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}
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technologies[tech['title']] = tech
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return tech
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def mapDeployment(entry):
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global args
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city = mapCity(entry)
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mapInstitution(entry)
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tech = mapTechnology(entry)
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return {
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'title': f"{entry['Vendor']} {entry['Technology']} used by {entry['Agency']}".replace(' '," ").strip(),
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'@type': 'Deployments',
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'properties': {
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"Keywords": [entry['Technology']],
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"used by": entry['Agency'],
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"Software Deployed": tech['title'],
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"City": city['title'],
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"Information Certainty": "Documented",
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},
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"additionalProperties": {
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"URL": [
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"https://atlasofsurveillance.org/es/a/"+entry['AOSNUMBER'],
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entry['Link 1'],
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entry['Link 2'],
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entry['Link 3'],
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],
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'CiteRef': args.citeref,
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"Input for": "FACES (FBI) Dataset" if entry['FACE Services'] == 'Yes' else None,
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},
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"body": [entry['Summary']]
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}
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# Type of LEA: Court gives Institution Type::Government Instititution Sector::Justice,
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# Police/Sheriff/State Police/District Attorney/Attorney General/Prosecutor/School Police/Constables/DHS/Fusion Center/Juvenile/Security/Transit Police Type::Law Enfrocement Instititution Sector::Security
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# DMV/Emergency Services/Parks/State Agency/Transit Institution Type::Government Institution Sector::Civil Administration
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# Medical Examiner Institution Type::Government Institution Sector::Health
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# School District Institution Type::Local Government Institution Sector::Education
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# State-Local Partnership Institution Type::State-Local Partnership Institution Sector::Security
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institution_type_sector = {
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"Court": ("Government", "Justice"),
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"Police": ("Law Enforcement", "Security"),
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"Sheriff": ("Law Enforcement", "Security"),
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"State Police": ("Law Enforcement", "Security"),
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"District Attorney": ("Law Enforcement", "Security"),
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"Attorney General": ("Law Enforcement", "Security"),
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"Prosecutor": ("Law Enforcement", "Security"),
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"School Police": ("Law Enforcement", "Security"),
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"Constables": ("Law Enforcement", "Security"),
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"DHS": ("Law Enforcement", "Security"),
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"Fusion Center": ("Law Enforcement", "Security"),
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"Juvenile": ("Law Enforcement", "Security"),
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"Security": ("Law Enforcement", "Security"),
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"Transit Police": ("Law Enforcement", "Security"),
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"Corrections": ("Law Enforcement", "Security"),
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"Clemis": ("Law Enforcement", "Security"),
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"DMV": ("Government", "Civil Administration"),
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"Emergency Services": ("Government", "Civil Administration"),
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"Parks": ("Government", "Civil Administration"),
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"State Agency": ("Government", "Civil Administration"),
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"Transit": ("Government", "Civil Administration"),
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"Medical Examiner": ("Local Government", "Health"),
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"School District": ("Local Government", "Education"),
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"State-Local Partnership": ("State-Local Partnership", "Security"),
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}
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def mapInstitution(entry):
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# aggregate agencies as institutions from entries
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global args
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type, sector = institution_type_sector[entry['Type of LEA']]
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info = {
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'title': entry['Agency'],
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'@type': 'Institution',
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'properties': {
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"Institution Type": type,
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"Institution Sector": sector,
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'City': mapCity(entry)['title'],
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},
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"additionalProperties": {
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"Type of Juris": entry['Type of Juris'],
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"URL": [
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"https://atlasofsurveillance.org/es/a/"+entry['AOSNUMBER'],
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entry['Link 1'],
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entry['Link 2'],
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entry['Link 3'],
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],
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'CiteRef': args.citeref,
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},
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"body": [entry['Type of LEA']],
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}
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if entry['Agency'] in institutions:
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logger.warning(f'Ignore duplicate {entry["Agency"]}')
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else:
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institutions[entry['Agency']] = info
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def mapCity(entry):
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title = f"{entry['City']} ({entry['State']})"
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if title not in cities:
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info = {
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'title': title,
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'@type': 'City',
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'properties': {
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"is in Country": "USA",
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},
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"additionalProperties": {
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'CiteRef': args.citeref,
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}
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}
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if not args.skip_geolocation:
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location_response = geocoder.geocode(title + " USA")
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sleep(1) # free tier of location geocode requires 1 sec delay
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if location_response:
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location = location_response.raw
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info["properties"]["Has Coordinates"] = f"{location['lat']}, {location['lon']}"
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info["body"] = [location['display_name']]
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else:
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logger.warning(f"No location data for {title} USA")
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cities[title] = info
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return cities[title]
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def mapAggregate(title, data):
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urls = [ "https://atlasofsurveillance.org/es/a/"+entry['AOSNUMBER'] for entry in data]
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urls.extend([entry['Link 1'] for entry in data])
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urls.extend([entry['Link 2'] for entry in data])
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urls.extend([entry['Link 3'] for entry in data])
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urls = list(dict.fromkeys(urls)) # unique
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urls = list(filter(lambda url: url and len(url) > 0, urls))
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for entry in data:
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mapInstitution(entry)
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return {
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"title": title,
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'@type': 'Deployments',
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'properties': {
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"Information Certainty": "Documented",
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"used by": [entry['Agency'] for entry in data]
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},
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"additionalProperties": {
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"URL": urls,
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'CiteRef': args.citeref,
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}
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}
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def renderPage(data):
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global args
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page = f"{{{{{data['@type']}"
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for key, value in data['properties'].items():
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page += f"\n|{key}=" + (', '.join(value) if isinstance(value, list) else value)
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page += "}}\n\n"
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if 'body' in data:
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for b in data['body']:
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if b and len(b):
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page += f"<blockquote>{b} [[CiteRef::{args.citeref}]]</blockquote>\n\n"
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if len(data['additionalProperties']):
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page += "=== Additional properties ===\n\n"
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for key, value in data['additionalProperties'].items():
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if not isinstance(value, list):
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value = [value]
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for v in value:
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if v:
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page += f"* {key} [[{key}::{v}]]\n"
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return page
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def saveIfNotExists(data, page, session, token):
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# https://en.wikipedia.org/w/api.php?action=query&prop=info&titles=New%20York%20Yankeesdfsdf
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# baseurl = f"{args.url}?action=query&list=categorymembers&cmtitle=Category:{category}&format=json"
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params = {
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'action': 'edit',
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'createonly': '1',
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'title': data['title'].strip(),
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'contentformat': 'text/x-wiki',
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'text': page,
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'format': 'json',
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'token': token,
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}
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logger.debug(args.url, params)
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if not args.dry_run:
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logger.warning(f"Creating '{data['title'].strip()}' type {data['@type']}")
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response = session.post(args.url, data=params)
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resp = response.json()
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if 'warnings' in resp:
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logger.warning(resp)
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logger.debug(resp)
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else:
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logger.warning(f"'{data['title'].strip()}' type {data['@type']}")
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def getEditToken(session):
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params = {
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'action': "query",
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'meta': "tokens",
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'type': "csrf",
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'format': "json"
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}
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R = session.get(args.url, params=params)
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DATA = R.json()
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logger.debug(DATA)
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return DATA['query']['tokens']['csrftoken']
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if __name__ == "__main__":
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logger.setLevel(logging.DEBUG)
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session = get_session()
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token = getEditToken(session)
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parsedData=[]
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with open(args.csv, newline='') as csvfile:
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csvreader = csv.DictReader(csvfile, delimiter=',')
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for row in csvreader:
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data = mapEntry(row)
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if data is None:
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continue
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parsedData.append(data)
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parsedData.extend([mapAggregate(title, a) for title, a in aggregates.items()])
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parsedData.extend(cities.values())
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parsedData.extend(technologies.values())
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parsedData.extend(institutions.values())
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# print(parsedData)
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for i, data in enumerate(parsedData):
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page = renderPage(data)
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# if data['@type'] == "Institution":
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# print(data['title'])
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# print(page)
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# break
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saveIfNotExists(data, page, session, token)
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# if i > 5:
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# break
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print(f"total: {len(parsedData)} items (of which {len(institutions)} institutions, {len(cities)} cities, {len(technologies)} products)")
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print (len(parsedData) - len(institutions) - len(cities) - len(technologies), "deployments" )
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#
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# Title: vendor/unknown
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# Postprocessing: make sure unknown are numbered, and multiple related deployments individual unknowns are created.
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