# Copyright The IETF Trust 2017-2020, All Rights Reserved # -*- coding: utf-8 -*- import re import requests from collections import defaultdict from django.conf import settings from django.contrib.auth.models import User import debug # pyflakes:ignore from ietf.stats.models import AffiliationAlias, AffiliationIgnoredEnding, CountryAlias, MeetingRegistration from ietf.name.models import CountryName from ietf.person.models import Person, Email, Alias from ietf.person.name import unidecode_name def compile_affiliation_ending_stripping_regexp(): parts = [] for ending_re in AffiliationIgnoredEnding.objects.values_list("ending", flat=True): try: re.compile(ending_re) except re.error: pass parts.append(ending_re) re_str = ",? *({}) *$".format("|".join(parts)) return re.compile(re_str, re.IGNORECASE) def get_aliased_affiliations(affiliations): """Given non-unique sequence of affiliations, returns dictionary with aliases needed. We employ the following strategies, interleaved: - Stripping company endings like Inc., GmbH etc. from database - Looking up aliases stored directly in the database, like "Examplar International" -> "Examplar" - Case-folding so Examplar and EXAMPLAR is merged with the winner being the one with most occurrences (so input should not be made unique) or most upper case letters in case of ties. Case folding can be overridden by the aliases in the database.""" res = {} ending_re = compile_affiliation_ending_stripping_regexp() known_aliases = { alias.lower(): name for alias, name in AffiliationAlias.objects.values_list("alias", "name") } affiliations_with_case_spellings = defaultdict(set) case_spelling_count = defaultdict(int) for affiliation in affiliations: original_affiliation = affiliation # check aliases from DB name = known_aliases.get(affiliation.lower()) if name is not None: affiliation = name res[original_affiliation] = affiliation # strip ending name = ending_re.sub("", affiliation) if name != affiliation: affiliation = name res[original_affiliation] = affiliation # check aliases from DB name = known_aliases.get(affiliation.lower()) if name is not None: affiliation = name res[original_affiliation] = affiliation affiliations_with_case_spellings[affiliation.lower()].add(original_affiliation) case_spelling_count[affiliation] += 1 def affiliation_sort_key(affiliation): count = case_spelling_count[affiliation] uppercase_letters = sum(1 for c in affiliation if c.isupper()) return (count, uppercase_letters) # now we just need to pick the most popular uppercase/lowercase # spelling for each affiliation with more than one for similar_affiliations in affiliations_with_case_spellings.values(): if len(similar_affiliations) > 1: most_popular = sorted(similar_affiliations, key=affiliation_sort_key, reverse=True)[0] for affiliation in similar_affiliations: if affiliation != most_popular: res[affiliation] = most_popular return res def get_aliased_countries(countries): known_aliases = dict(CountryAlias.objects.values_list("alias", "country__name")) # add aliases for known countries for slug, name in CountryName.objects.values_list("slug", "name"): known_aliases[name.lower()] = name def lookup_alias(possible_alias): name = known_aliases.get(possible_alias) if name is not None: return name name = known_aliases.get(possible_alias.lower()) if name is not None: return name return possible_alias known_re_aliases = { re.compile("\\b{}\\b".format(re.escape(alias))): name for alias, name in known_aliases.items() } # specific hack: check for zip codes from the US since in the # early days, the addresses often didn't include the country us_zipcode_re = re.compile(r"\b(AL|AK|AZ|AR|CA|CO|CT|DE|DC|FL|GA|HI|ID|IL|IN|IA|KS|KY|LA|ME|MD|MA|MI|MN|MS|MO|MT|NE|NV|NH|NJ|NM|NY|NC|ND|OH|OK|OR|PA|RI|SC|SD|TN|TX|UT|VT|VA|WA|WV|WI|WY|AS|GU|MP|PR|VI|UM|FM|MH|PW|Ca|Cal.|California|CALIFORNIA|Colorado|Georgia|Illinois|Ill|Maryland|Ma|Ma.|Mass|Massachuss?etts|Michigan|Minnesota|New Jersey|New York|Ny|N.Y.|North Carolina|NORTH CAROLINA|Ohio|Oregon|Pennsylvania|Tx|Texas|Tennessee|Utah|Vermont|Virginia|Va.|Washington)[., -]*[0-9]{5}\b") us_country_name = CountryName.objects.get(slug="US").name def last_text_part_stripped(split): for t in reversed(split): t = t.strip() if t: return t return "" known_countries = set(CountryName.objects.values_list("name", flat=True)) res = {} for country in countries: if country in res or country in known_countries: continue original_country = country # aliased name country = lookup_alias(country) if country in known_countries: res[original_country] = country continue # contains US zipcode if us_zipcode_re.search(country): res[original_country] = us_country_name continue # do a little bit of cleanup if len(country) > 1 and country[-1] == "." and not country[-2].isupper(): country = country.rstrip(".") country = country.strip("-,").strip() # aliased name country = lookup_alias(country) if country in known_countries: res[original_country] = country continue # country name at end, separated by comma last_part = lookup_alias(last_text_part_stripped(country.split(","))) if last_part in known_countries: res[original_country] = last_part continue # country name at end, separated by whitespace last_part = lookup_alias(last_text_part_stripped(country.split())) if last_part in known_countries: res[original_country] = last_part continue # country name anywhere country_lower = country.lower() found = False for alias_re, name in known_re_aliases.items(): if alias_re.search(country) or alias_re.search(country_lower): res[original_country] = name found = True break if found: continue # unknown country res[original_country] = "" return res def clean_country_name(country_name): if country_name: country_name = get_aliased_countries([country_name]).get(country_name, country_name) if country_name and CountryName.objects.filter(name=country_name).exists(): return country_name return "" def compute_hirsch_index(citation_counts): """Computes the h-index given a sequence containing the number of citations for each document.""" i = 0 for count in sorted(citation_counts, reverse=True): if i + 1 > count: break i += 1 return i def get_meeting_registration_data(meeting): """"Retrieve registration attendee data and summary statistics. Returns number of Registration records created.""" num_created = 0 num_processed = 0 response = requests.get(settings.STATS_REGISTRATION_ATTENDEES_JSON_URL.format(number=meeting.number)) if response.status_code == 200: decoded = [] try: decoded = response.json() except ValueError: if response.content.strip() == 'Invalid meeting': pass else: raise RuntimeError("Could not decode response from registrations API: '%s...'" % (response.content[:64], )) # for each user identified in the Registration system # Create a DataTracker MeetingRegistration object for registration in decoded: person = None # capture the stripped registration values for later use first_name = registration['FirstName'].strip() last_name = registration['LastName'].strip() affiliation = registration['Company'].strip() country_code = registration['Country'].strip() address = registration['Email'].strip() matching = MeetingRegistration.objects.filter(meeting_id=meeting.pk, email=address) if matching.exists(): object = matching.first() created = False else: object = MeetingRegistration.objects.create(meeting_id=meeting.pk, email=address) created = True object.first_name=first_name[:200] object.last_name=last_name[:200] object.affiliation=affiliation object.country_code=country_code object.attended = True object.save() # Add a Person object to MeetingRegistration object # if valid email is available if object and not object.person and address: # If the person already exists do not try to create a new one emails = Email.objects.filter(address=address) # there can only be on Email object with a unique email address (primary key) if emails.exists(): person = emails.first().person # Create a new Person object else: try: # Normalize all-caps or all-lower entries. Don't touch # others, there might be names properly spelled with # internal uppercase letters. if ( ( first_name == first_name.upper() or first_name == first_name.lower() ) and ( last_name == last_name.upper() or last_name == last_name.lower() ) ): first_name = first_name.capitalize() last_name = last_name.capitalize() regname = "%s %s" % (first_name, last_name) # if there are any unicode characters decode the string to ascii ascii_name = unidecode_name(regname) # Create a new user object if it does not exist already # if the user already exists do not try to create a new one users = User.objects.filter(username=address) if users.exists(): user = users.first() else: # Create a new user. user = User.objects.create( first_name=first_name[:30], last_name=last_name[:30], username=address, email=address, ) try: person = user.person except Person.DoesNotExist: aliases = Alias.objects.filter(name=regname) if aliases.exists(): person = aliases.first().person else: # Create the new Person object. person = Person.objects.create( name=regname, ascii=ascii_name, user=user, ) # Create an associated Email address for this Person try: email = Email.objects.get(person=person, address=address[:64]) except Email.DoesNotExist: email = Email.objects.create(person=person, address=address[:64], origin='registration: ietf-%s'%meeting.number) # If this is the only email address, set primary to true. # If the person already existed (found through Alias) and # had email addresses, we don't do this. if Email.objects.filter(person=person).count() == 1: email.primary = True email.save() except: debug.show('first_name') debug.show('last_name') debug.show('regname') debug.show('user') debug.show('aliases') raise # update the person object to an actual value object.person = person object.save() if created: num_created += 1 num_processed += 1 else: raise RuntimeError("Bad response from registrations API: %s, '%s'" % (response.status_code, response.content)) num_total = MeetingRegistration.objects.filter(meeting_id=meeting.pk).count() if meeting.attendees is None or num_total > meeting.attendees: meeting.attendees = num_total meeting.save() return num_created, num_processed, num_total