# Copyright The IETF Trust 2017-2020, All Rights Reserved # -*- coding: utf-8 -*- import re import requests from collections import defaultdict, namedtuple from django.conf import settings from django.db.models import Q 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 from ietf.utils.log import log import logging logger = logging.getLogger('django') 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. MeetingRegistration records are created in realtime as people register for a meeting. This function serves as an audit / reconciliation. Most records are expected to already exist. The function has been optimized with this in mind. """ num_created = 0 num_processed = 0 try: response = requests.get( settings.STATS_REGISTRATION_ATTENDEES_JSON_URL.format(number=meeting.number), timeout=settings.DEFAULT_REQUESTS_TIMEOUT, ) except requests.Timeout as exc: log(f'GET request timed out for [{settings.STATS_REGISTRATION_ATTENDEES_JSON_URL}]: {exc}') raise RuntimeError("Timeout retrieving data from registrations API") from exc if response.status_code == 200: decoded = [] try: decoded = response.json() except ValueError: if response.content.strip() == 'Invalid meeting': logger.info('Invalid meeting: {}'.format(meeting.number)) return (0,0,0) else: raise RuntimeError("Could not decode response from registrations API: '%s...'" % (response.content[:64], )) records = MeetingRegistration.objects.filter(meeting_id=meeting.pk).select_related('person') meeting_registrations = {(r.email, r.reg_type):r for r in records} 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() reg_type = registration['RegType'].strip() ticket_type = registration['TicketType'].strip() checkedin = bool(registration['CheckedIn'].strip().lower() == 'true') if (address, reg_type) in meeting_registrations: object = meeting_registrations.pop((address, reg_type)) created = False else: object, created = MeetingRegistration.objects.get_or_create( meeting_id=meeting.pk, email=address, reg_type=reg_type) if (object.first_name != first_name[:200] or object.last_name != last_name[:200] or object.affiliation != affiliation or object.country_code != country_code or object.ticket_type != ticket_type or object.checkedin != checkedin): object.first_name=first_name[:200] object.last_name=last_name[:200] object.affiliation=affiliation object.country_code=country_code object.ticket_type=ticket_type object.checkedin=checkedin 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: logger.error("No Person record for registration. email={}".format(address)) # update the person object to an actual value object.person = person object.save() if created: num_created += 1 num_processed += 1 # any registrations left in meeting_registrations no longer exist in reg # so must have been deleted for r in meeting_registrations: try: MeetingRegistration.objects.get(meeting=meeting,email=r[0],reg_type=r[1]).delete() logger.info('Removing deleted registration. email={}, reg_type={}'.format(r[0], r[1])) except MeetingRegistration.DoesNotExist: pass else: raise RuntimeError("Bad response from registrations API: %s, '%s'" % (response.status_code, response.content)) num_total = MeetingRegistration.objects.filter( meeting_id=meeting.pk, reg_type__in=['onsite', 'remote'] ).filter( Q(attended=True) | Q(checkedin=True) ).count() if meeting.attendees is None or num_total > meeting.attendees: meeting.attendees = num_total meeting.save() return num_created, num_processed, num_total def repair_meetingregistration_person(meetings=None): repaired_records = 0 qs = MeetingRegistration.objects.all() if meetings: qs = qs.filter(meeting__number__in=meetings) for mr in qs: if mr.email and not mr.person: email_person = Person.objects.filter(email__address=mr.email).first() if email_person: mr.person = email_person mr.save() repaired_records += 1 return repaired_records class MeetingRegistrationIssuesSummary(object): pass def find_meetingregistration_person_issues(meetings=None): summary = MeetingRegistrationIssuesSummary() summary.could_be_fixed = set() summary.maybe_address = set() summary.different_person = set() summary.no_person = set() summary.maybe_person = set() summary.no_email = set() summary.ok_records = 0 qs = MeetingRegistration.objects.all() if meetings: qs = qs.filter(meeting__number__in=meetings) for mr in qs: if mr.person and mr.email and mr.email in mr.person.email_set.values_list('address',flat=True): summary.ok_records += 1 elif mr.email: email_person = Person.objects.filter(email__address=mr.email).first() if mr.person: if not email_person: summary.maybe_address.add(f'{mr.email} is not present in any Email object. The MeetingRegistration object implies this is an address for {mr.person} ({mr.person.pk})') elif email_person != mr.person: summary.different_person.add(f'{mr} ({mr.pk}) has person {mr.person} ({mr.person.pk}) but an email {mr.email} attached to a different person {email_person} ({email_person.pk}).') elif email_person: summary.could_be_fixed.add(f'{mr} ({mr.pk}) has no person, but email {mr.email} matches {email_person} ({email_person.pk})') else: maybe_person_qs = Person.objects.filter(name__icontains=mr.last_name).filter(name__icontains=mr.first_name) if maybe_person_qs.exists(): summary.maybe_person.add(f'{mr} ({mr.pk}) has email address {mr.email} which cannot be associated with any Person. Consider these possible people {[(p,p.pk) for p in maybe_person_qs]}') else: summary.no_person.add(f'{mr} ({mr.pk}) has email address {mr.email} which cannot be associated with any Person') else: summary.no_email.add(f'{mr} ({mr.pk}) provides no email address') return summary FetchStats = namedtuple("FetchStats", "added processed total") def fetch_attendance_from_meetings(meetings): stats = [ FetchStats(*get_meeting_registration_data(meeting)) for meeting in meetings ] return stats