1284 lines
52 KiB
Python
1284 lines
52 KiB
Python
import os
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import calendar
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import datetime
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import email.utils
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import itertools
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import json
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import dateutil.relativedelta
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from collections import defaultdict
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from django.conf import settings
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from django.contrib.auth.decorators import login_required
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from django.core.cache import cache
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from django.db.models import Count, Q
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from django.http import HttpResponseRedirect, HttpResponseForbidden
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from django.shortcuts import get_object_or_404, render
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from django.urls import reverse as urlreverse
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from django.utils.safestring import mark_safe
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import debug # pyflakes:ignore
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from ietf.review.utils import (extract_review_request_data,
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aggregate_raw_period_review_request_stats,
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ReviewRequestData,
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sum_period_review_request_stats,
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sum_raw_review_request_aggregations)
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from ietf.submit.models import Submission
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from ietf.group.models import Role, Group
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from ietf.person.models import Person
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from ietf.name.models import ReviewRequestStateName, ReviewResultName, CountryName, DocRelationshipName
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from ietf.person.name import plain_name
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from ietf.doc.models import DocAlias, Document, State, DocEvent
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from ietf.meeting.models import Meeting
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from ietf.stats.models import MeetingRegistration, CountryAlias
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from ietf.stats.utils import get_aliased_affiliations, get_aliased_countries, compute_hirsch_index
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from ietf.ietfauth.utils import has_role
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from ietf.utils.log import log
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def stats_index(request):
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return render(request, "stats/index.html")
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def generate_query_string(query_dict, overrides):
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query_part = u""
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if query_dict or overrides:
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d = query_dict.copy()
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for k, v in overrides.iteritems():
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if type(v) in (list, tuple):
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if not v:
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if k in d:
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del d[k]
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else:
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d.setlist(k, v)
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else:
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if v is None or v == u"":
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if k in d:
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del d[k]
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else:
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d[k] = v
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if d:
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query_part = u"?" + d.urlencode()
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return query_part
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def get_choice(request, get_parameter, possible_choices, multiple=False):
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# the statistics are built with links to make navigation faster,
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# so we don't really have a form in most cases, so just use this
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# helper instead to select between the choices
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values = request.GET.getlist(get_parameter)
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found = [t[0] for t in possible_choices if t[0] in values]
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if multiple:
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return found
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else:
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if found:
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return found[0]
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else:
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return None
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def add_url_to_choices(choices, url_builder):
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return [ (slug, label, url_builder(slug)) for slug, label in choices]
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def put_into_bin(value, bin_size):
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if value is None:
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return (value, value)
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v = (value // bin_size) * bin_size
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return (v, "{} - {}".format(v, v + bin_size - 1))
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def prune_unknown_bin_with_known(bins):
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# remove from the unknown bin all authors within the
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# named/known bins
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all_known = { n for b, names in bins.iteritems() if b for n in names }
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bins[""] = [name for name in bins[""] if name not in all_known]
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if not bins[""]:
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del bins[""]
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def count_bins(bins):
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return len({ n for b, names in bins.iteritems() if b for n in names })
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def add_labeled_top_series_from_bins(chart_data, bins, limit):
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"""Take bins on the form (x, label): [name1, name2, ...], figure out
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how many there are per label, take the overall top ones and put
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them into sorted series like [(x1, len(names1)), (x2, len(names2)), ...]."""
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aggregated_bins = defaultdict(set)
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xs = set()
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for (x, label), names in bins.iteritems():
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xs.add(x)
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aggregated_bins[label].update(names)
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xs = list(sorted(xs))
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sorted_bins = sorted(aggregated_bins.iteritems(), key=lambda t: len(t[1]), reverse=True)
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top = [ label for label, names in list(sorted_bins)[:limit]]
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for label in top:
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series_data = []
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for x in xs:
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names = bins.get((x, label), set())
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series_data.append((x, len(names)))
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chart_data.append({
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"data": series_data,
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"name": label
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})
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def document_stats(request, stats_type=None):
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def build_document_stats_url(stats_type_override=Ellipsis, get_overrides={}):
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kwargs = {
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"stats_type": stats_type if stats_type_override is Ellipsis else stats_type_override,
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}
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return urlreverse(document_stats, kwargs={ k: v for k, v in kwargs.iteritems() if v is not None }) + generate_query_string(request.GET, get_overrides)
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cache_key = ("stats:document_stats:%s:%s" % (stats_type, request.META.get('QUERY_STRING','')))
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data = cache.get(cache_key)
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if not data:
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names_limit = settings.STATS_NAMES_LIMIT
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# statistics types
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possible_document_stats_types = add_url_to_choices([
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("authors", "Number of authors"),
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("pages", "Pages"),
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("words", "Words"),
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("format", "Format"),
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("formlang", "Formal languages"),
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], lambda slug: build_document_stats_url(stats_type_override=slug))
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possible_author_stats_types = add_url_to_choices([
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("author/documents", "Number of documents"),
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("author/affiliation", "Affiliation"),
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("author/country", "Country"),
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("author/continent", "Continent"),
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("author/citations", "Citations"),
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("author/hindex", "h-index"),
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], lambda slug: build_document_stats_url(stats_type_override=slug))
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possible_yearly_stats_types = add_url_to_choices([
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("yearly/affiliation", "Affiliation"),
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("yearly/country", "Country"),
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("yearly/continent", "Continent"),
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], lambda slug: build_document_stats_url(stats_type_override=slug))
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if not stats_type:
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return HttpResponseRedirect(build_document_stats_url(stats_type_override=possible_document_stats_types[0][0]))
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possible_document_types = add_url_to_choices([
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("", "All"),
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("rfc", "RFCs"),
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("draft", "Drafts"),
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], lambda slug: build_document_stats_url(get_overrides={ "type": slug }))
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document_type = get_choice(request, "type", possible_document_types) or ""
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possible_time_choices = add_url_to_choices([
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("", "All time"),
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("5y", "Past 5 years"),
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], lambda slug: build_document_stats_url(get_overrides={ "time": slug }))
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time_choice = request.GET.get("time") or ""
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from_time = None
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if "y" in time_choice:
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try:
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y = int(time_choice.rstrip("y"))
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from_time = datetime.datetime.today() - dateutil.relativedelta.relativedelta(years=y)
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except ValueError:
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pass
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chart_data = []
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table_data = []
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stats_title = ""
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template_name = stats_type.replace("/", "_")
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bin_size = 1
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alias_data = []
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eu_countries = None
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if any(stats_type == t[0] for t in possible_document_stats_types):
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# filter documents
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docalias_filters = Q(document__type="draft")
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rfc_state = State.objects.get(type="draft", slug="rfc")
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if document_type == "rfc":
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docalias_filters &= Q(document__states=rfc_state)
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elif document_type == "draft":
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docalias_filters &= ~Q(document__states=rfc_state)
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if from_time:
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# this is actually faster than joining in the database,
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# despite the round-trip back and forth
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docs_within_time_constraint = list(Document.objects.filter(
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type="draft",
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docevent__time__gte=from_time,
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docevent__type__in=["published_rfc", "new_revision"],
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).values_list("pk"))
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docalias_filters &= Q(document__in=docs_within_time_constraint)
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docalias_qs = DocAlias.objects.filter(docalias_filters)
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if document_type == "rfc":
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doc_label = "RFC"
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elif document_type == "draft":
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doc_label = "draft"
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else:
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doc_label = "document"
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total_docs = docalias_qs.values_list("document").distinct().count()
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def generate_canonical_names(docalias_qs):
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for doc_id, ts in itertools.groupby(docalias_qs.order_by("document"), lambda t: t[0]):
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chosen = None
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for t in ts:
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if chosen is None:
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chosen = t
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else:
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if t[1].startswith("rfc"):
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chosen = t
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elif t[1].startswith("draft") and not chosen[1].startswith("rfc"):
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chosen = t
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yield chosen
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if stats_type == "authors":
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stats_title = "Number of authors for each {}".format(doc_label)
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bins = defaultdict(set)
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for name, canonical_name, author_count in generate_canonical_names(docalias_qs.values_list("document", "name").annotate(Count("document__documentauthor"))):
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bins[author_count].add(canonical_name)
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series_data = []
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for author_count, names in sorted(bins.iteritems(), key=lambda t: t[0]):
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percentage = len(names) * 100.0 / (total_docs or 1)
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series_data.append((author_count, percentage))
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table_data.append((author_count, percentage, len(names), list(names)[:names_limit]))
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chart_data.append({ "data": series_data })
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elif stats_type == "pages":
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stats_title = "Number of pages for each {}".format(doc_label)
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bins = defaultdict(set)
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for name, canonical_name, pages in generate_canonical_names(docalias_qs.values_list("document", "name", "document__pages")):
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bins[pages].add(canonical_name)
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series_data = []
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for pages, names in sorted(bins.iteritems(), key=lambda t: t[0]):
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percentage = len(names) * 100.0 / (total_docs or 1)
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if pages is not None:
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series_data.append((pages, len(names)))
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table_data.append((pages, percentage, len(names), list(names)[:names_limit]))
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chart_data.append({ "data": series_data })
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elif stats_type == "words":
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stats_title = "Number of words for each {}".format(doc_label)
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bin_size = 500
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bins = defaultdict(set)
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for name, canonical_name, words in generate_canonical_names(docalias_qs.values_list("document", "name", "document__words")):
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bins[put_into_bin(words, bin_size)].add(canonical_name)
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series_data = []
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for (value, words), names in sorted(bins.iteritems(), key=lambda t: t[0][0]):
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percentage = len(names) * 100.0 / (total_docs or 1)
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if words is not None:
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series_data.append((value, len(names)))
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table_data.append((words, percentage, len(names), list(names)[:names_limit]))
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chart_data.append({ "data": series_data })
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elif stats_type == "format":
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stats_title = "Submission formats for each {}".format(doc_label)
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bins = defaultdict(set)
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# on new documents, we should have a Submission row with the file types
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submission_types = {}
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for doc_name, file_types in Submission.objects.values_list("draft", "file_types").order_by("submission_date", "id"):
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submission_types[doc_name] = file_types
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doc_names_with_missing_types = {}
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for doc_name, canonical_name, rev in generate_canonical_names(docalias_qs.values_list("document", "name", "document__rev")):
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types = submission_types.get(doc_name)
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if types:
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for dot_ext in types.split(","):
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bins[dot_ext.lstrip(".").upper()].add(canonical_name)
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else:
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if canonical_name.startswith("rfc"):
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filename = canonical_name
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else:
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filename = canonical_name + "-" + rev
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doc_names_with_missing_types[filename] = canonical_name
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# look up the remaining documents on disk
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for filename in itertools.chain(os.listdir(settings.INTERNET_ALL_DRAFTS_ARCHIVE_DIR), os.listdir(settings.RFC_PATH)):
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t = filename.split(".", 1)
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if len(t) != 2:
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continue
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basename, ext = t
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ext = ext.lower()
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if not any(ext==whitelisted_ext for whitelisted_ext in settings.DOCUMENT_FORMAT_WHITELIST):
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continue
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canonical_name = doc_names_with_missing_types.get(basename)
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if canonical_name:
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bins[ext.upper()].add(canonical_name)
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series_data = []
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for fmt, names in sorted(bins.iteritems(), key=lambda t: t[0]):
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percentage = len(names) * 100.0 / (total_docs or 1)
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series_data.append((fmt, len(names)))
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table_data.append((fmt, percentage, len(names), list(names)[:names_limit]))
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chart_data.append({ "data": series_data })
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elif stats_type == "formlang":
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stats_title = "Formal languages used for each {}".format(doc_label)
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bins = defaultdict(set)
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for name, canonical_name, formal_language_name in generate_canonical_names(docalias_qs.values_list("document", "name", "document__formal_languages__name")):
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bins[formal_language_name].add(canonical_name)
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series_data = []
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for formal_language, names in sorted(bins.iteritems(), key=lambda t: t[0]):
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percentage = len(names) * 100.0 / (total_docs or 1)
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if formal_language is not None:
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series_data.append((formal_language, len(names)))
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table_data.append((formal_language, percentage, len(names), list(names)[:names_limit]))
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chart_data.append({ "data": series_data })
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elif any(stats_type == t[0] for t in possible_author_stats_types):
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person_filters = Q(documentauthor__document__type="draft")
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# filter persons
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rfc_state = State.objects.get(type="draft", slug="rfc")
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if document_type == "rfc":
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person_filters &= Q(documentauthor__document__states=rfc_state)
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elif document_type == "draft":
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person_filters &= ~Q(documentauthor__document__states=rfc_state)
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if from_time:
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# this is actually faster than joining in the database,
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# despite the round-trip back and forth
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docs_within_time_constraint = set(Document.objects.filter(
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type="draft",
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docevent__time__gte=from_time,
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docevent__type__in=["published_rfc", "new_revision"],
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).values_list("pk"))
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person_filters &= Q(documentauthor__document__in=docs_within_time_constraint)
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person_qs = Person.objects.filter(person_filters)
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if document_type == "rfc":
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doc_label = "RFC"
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elif document_type == "draft":
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doc_label = "draft"
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else:
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doc_label = "document"
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if stats_type == "author/documents":
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stats_title = "Number of {}s per author".format(doc_label)
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bins = defaultdict(set)
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person_qs = Person.objects.filter(person_filters)
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for name, document_count in person_qs.values_list("name").annotate(Count("documentauthor")):
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bins[document_count].add(name)
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total_persons = count_bins(bins)
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series_data = []
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for document_count, names in sorted(bins.iteritems(), key=lambda t: t[0]):
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percentage = len(names) * 100.0 / (total_persons or 1)
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series_data.append((document_count, percentage))
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plain_names = [ plain_name(n) for n in names ]
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table_data.append((document_count, percentage, len(plain_names), list(plain_names)[:names_limit]))
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chart_data.append({ "data": series_data })
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elif stats_type == "author/affiliation":
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stats_title = "Number of {} authors per affiliation".format(doc_label)
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|
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bins = defaultdict(set)
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person_qs = Person.objects.filter(person_filters)
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# Since people don't write the affiliation names in the
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# same way, and we don't want to go back and edit them
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# either, we transform them here.
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|
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name_affiliation_set = {
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(name, affiliation)
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for name, affiliation in person_qs.values_list("name", "documentauthor__affiliation")
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}
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aliases = get_aliased_affiliations(affiliation for _, affiliation in name_affiliation_set)
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|
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for name, affiliation in name_affiliation_set:
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bins[aliases.get(affiliation, affiliation)].add(name)
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|
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prune_unknown_bin_with_known(bins)
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total_persons = count_bins(bins)
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|
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series_data = []
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for affiliation, names in sorted(bins.iteritems(), key=lambda t: t[0].lower()):
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percentage = len(names) * 100.0 / (total_persons or 1)
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if affiliation:
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series_data.append((affiliation, len(names)))
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plain_names = [ plain_name(n) for n in names ]
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table_data.append((affiliation, percentage, len(plain_names), list(plain_names)[:names_limit]))
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series_data.sort(key=lambda t: t[1], reverse=True)
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series_data = series_data[:30]
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chart_data.append({ "data": series_data })
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for alias, name in sorted(aliases.iteritems(), key=lambda t: t[1]):
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alias_data.append((name, alias))
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|
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elif stats_type == "author/country":
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stats_title = "Number of {} authors per country".format(doc_label)
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|
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bins = defaultdict(set)
|
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|
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person_qs = Person.objects.filter(person_filters)
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|
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# Since people don't write the country names in the
|
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# same way, and we don't want to go back and edit them
|
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# either, we transform them here.
|
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|
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name_country_set = {
|
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(name, country)
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for name, country in person_qs.values_list("name", "documentauthor__country")
|
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}
|
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|
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aliases = get_aliased_countries(country for _, country in name_country_set)
|
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|
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countries = { c.name: c for c in CountryName.objects.all() }
|
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eu_name = "EU"
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eu_countries = { c for c in countries.itervalues() if c.in_eu }
|
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|
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for name, country in name_country_set:
|
|
country_name = aliases.get(country, country)
|
|
bins[country_name].add(name)
|
|
|
|
c = countries.get(country_name)
|
|
if c and c.in_eu:
|
|
bins[eu_name].add(name)
|
|
|
|
prune_unknown_bin_with_known(bins)
|
|
total_persons = count_bins(bins)
|
|
|
|
series_data = []
|
|
for country, names in sorted(bins.iteritems(), key=lambda t: t[0].lower()):
|
|
percentage = len(names) * 100.0 / (total_persons or 1)
|
|
if country:
|
|
series_data.append((country, len(names)))
|
|
plain_names = [ plain_name(n) for n in names ]
|
|
table_data.append((country, percentage, len(plain_names), list(plain_names)[:names_limit]))
|
|
|
|
series_data.sort(key=lambda t: t[1], reverse=True)
|
|
series_data = series_data[:30]
|
|
|
|
chart_data.append({ "data": series_data })
|
|
|
|
for alias, country_name in aliases.iteritems():
|
|
alias_data.append((country_name, alias, countries.get(country_name)))
|
|
|
|
alias_data.sort()
|
|
|
|
elif stats_type == "author/continent":
|
|
stats_title = "Number of {} authors per continent".format(doc_label)
|
|
|
|
bins = defaultdict(set)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
name_country_set = {
|
|
(name, country)
|
|
for name, country in person_qs.values_list("name", "documentauthor__country")
|
|
}
|
|
|
|
aliases = get_aliased_countries(country for _, country in name_country_set)
|
|
|
|
country_to_continent = dict(CountryName.objects.values_list("name", "continent__name"))
|
|
|
|
for name, country in name_country_set:
|
|
country_name = aliases.get(country, country)
|
|
continent_name = country_to_continent.get(country_name, "")
|
|
bins[continent_name].add(name)
|
|
|
|
prune_unknown_bin_with_known(bins)
|
|
total_persons = count_bins(bins)
|
|
|
|
series_data = []
|
|
for continent, names in sorted(bins.iteritems(), key=lambda t: t[0].lower()):
|
|
percentage = len(names) * 100.0 / (total_persons or 1)
|
|
if continent:
|
|
series_data.append((continent, len(names)))
|
|
plain_names = [ plain_name(n) for n in names ]
|
|
table_data.append((continent, percentage, len(plain_names), list(plain_names)[:names_limit]))
|
|
|
|
series_data.sort(key=lambda t: t[1], reverse=True)
|
|
|
|
chart_data.append({ "data": series_data })
|
|
|
|
elif stats_type == "author/citations":
|
|
stats_title = "Number of citations of {}s written by author".format(doc_label)
|
|
|
|
bins = defaultdict(set)
|
|
|
|
cite_relationships = list(DocRelationshipName.objects.filter(slug__in=['refnorm', 'refinfo', 'refunk', 'refold']))
|
|
person_filters &= Q(documentauthor__document__docalias__relateddocument__relationship__in=cite_relationships)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
for name, citations in person_qs.values_list("name").annotate(Count("documentauthor__document__docalias__relateddocument")):
|
|
bins[citations].add(name)
|
|
|
|
total_persons = count_bins(bins)
|
|
|
|
series_data = []
|
|
for citations, names in sorted(bins.iteritems(), key=lambda t: t[0], reverse=True):
|
|
percentage = len(names) * 100.0 / (total_persons or 1)
|
|
series_data.append((citations, percentage))
|
|
plain_names = [ plain_name(n) for n in names ]
|
|
table_data.append((citations, percentage, len(plain_names), list(plain_names)[:names_limit]))
|
|
|
|
chart_data.append({ "data": sorted(series_data, key=lambda t: t[0]) })
|
|
|
|
elif stats_type == "author/hindex":
|
|
stats_title = "h-index for {}s written by author".format(doc_label)
|
|
|
|
bins = defaultdict(set)
|
|
|
|
cite_relationships = list(DocRelationshipName.objects.filter(slug__in=['refnorm', 'refinfo', 'refunk', 'refold']))
|
|
person_filters &= Q(documentauthor__document__docalias__relateddocument__relationship__in=cite_relationships)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
values = person_qs.values_list("name", "documentauthor__document").annotate(Count("documentauthor__document__docalias__relateddocument"))
|
|
for name, ts in itertools.groupby(values.order_by("name"), key=lambda t: t[0]):
|
|
h_index = compute_hirsch_index([citations for _, document, citations in ts])
|
|
bins[h_index].add(name)
|
|
|
|
total_persons = count_bins(bins)
|
|
|
|
series_data = []
|
|
for citations, names in sorted(bins.iteritems(), key=lambda t: t[0], reverse=True):
|
|
percentage = len(names) * 100.0 / (total_persons or 1)
|
|
series_data.append((citations, percentage))
|
|
plain_names = [ plain_name(n) for n in names ]
|
|
table_data.append((citations, percentage, len(plain_names), list(plain_names)[:names_limit]))
|
|
|
|
chart_data.append({ "data": sorted(series_data, key=lambda t: t[0]) })
|
|
|
|
elif any(stats_type == t[0] for t in possible_yearly_stats_types):
|
|
|
|
person_filters = Q(documentauthor__document__type="draft")
|
|
|
|
# filter persons
|
|
rfc_state = State.objects.get(type="draft", slug="rfc")
|
|
if document_type == "rfc":
|
|
person_filters &= Q(documentauthor__document__states=rfc_state)
|
|
elif document_type == "draft":
|
|
person_filters &= ~Q(documentauthor__document__states=rfc_state)
|
|
|
|
doc_years = defaultdict(set)
|
|
|
|
docevent_qs = DocEvent.objects.filter(
|
|
doc__type="draft",
|
|
type__in=["published_rfc", "new_revision"],
|
|
).values_list("doc", "time").order_by("doc")
|
|
|
|
for doc, time in docevent_qs.iterator():
|
|
doc_years[doc].add(time.year)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
if document_type == "rfc":
|
|
doc_label = "RFC"
|
|
elif document_type == "draft":
|
|
doc_label = "draft"
|
|
else:
|
|
doc_label = "document"
|
|
|
|
template_name = "yearly"
|
|
|
|
years_from = from_time.year if from_time else 1
|
|
years_to = datetime.date.today().year - 1
|
|
|
|
|
|
if stats_type == "yearly/affiliation":
|
|
stats_title = "Number of {} authors per affiliation over the years".format(doc_label)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
name_affiliation_doc_set = {
|
|
(name, affiliation, doc)
|
|
for name, affiliation, doc in person_qs.values_list("name", "documentauthor__affiliation", "documentauthor__document")
|
|
}
|
|
|
|
aliases = get_aliased_affiliations(affiliation for _, affiliation, _ in name_affiliation_doc_set)
|
|
|
|
bins = defaultdict(set)
|
|
for name, affiliation, doc in name_affiliation_doc_set:
|
|
a = aliases.get(affiliation, affiliation)
|
|
if a:
|
|
for year in doc_years.get(doc):
|
|
if years_from <= year <= years_to:
|
|
bins[(year, a)].add(name)
|
|
|
|
add_labeled_top_series_from_bins(chart_data, bins, limit=8)
|
|
|
|
elif stats_type == "yearly/country":
|
|
stats_title = "Number of {} authors per country over the years".format(doc_label)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
name_country_doc_set = {
|
|
(name, country, doc)
|
|
for name, country, doc in person_qs.values_list("name", "documentauthor__country", "documentauthor__document")
|
|
}
|
|
|
|
aliases = get_aliased_countries(country for _, country, _ in name_country_doc_set)
|
|
|
|
countries = { c.name: c for c in CountryName.objects.all() }
|
|
eu_name = "EU"
|
|
eu_countries = { c for c in countries.itervalues() if c.in_eu }
|
|
|
|
bins = defaultdict(set)
|
|
|
|
for name, country, doc in name_country_doc_set:
|
|
country_name = aliases.get(country, country)
|
|
c = countries.get(country_name)
|
|
|
|
years = doc_years.get(doc)
|
|
if country_name and years:
|
|
for year in years:
|
|
if years_from <= year <= years_to:
|
|
bins[(year, country_name)].add(name)
|
|
|
|
if c and c.in_eu:
|
|
bins[(year, eu_name)].add(name)
|
|
|
|
add_labeled_top_series_from_bins(chart_data, bins, limit=8)
|
|
|
|
|
|
elif stats_type == "yearly/continent":
|
|
stats_title = "Number of {} authors per continent".format(doc_label)
|
|
|
|
person_qs = Person.objects.filter(person_filters)
|
|
|
|
name_country_doc_set = {
|
|
(name, country, doc)
|
|
for name, country, doc in person_qs.values_list("name", "documentauthor__country", "documentauthor__document")
|
|
}
|
|
|
|
aliases = get_aliased_countries(country for _, country, _ in name_country_doc_set)
|
|
|
|
country_to_continent = dict(CountryName.objects.values_list("name", "continent__name"))
|
|
|
|
bins = defaultdict(set)
|
|
|
|
for name, country, doc in name_country_doc_set:
|
|
country_name = aliases.get(country, country)
|
|
continent_name = country_to_continent.get(country_name, "")
|
|
|
|
if continent_name:
|
|
for year in doc_years.get(doc):
|
|
if years_from <= year <= years_to:
|
|
bins[(year, continent_name)].add(name)
|
|
|
|
add_labeled_top_series_from_bins(chart_data, bins, limit=8)
|
|
|
|
data = {
|
|
"chart_data": mark_safe(json.dumps(chart_data)),
|
|
"table_data": table_data,
|
|
"stats_title": stats_title,
|
|
"possible_document_stats_types": possible_document_stats_types,
|
|
"possible_author_stats_types": possible_author_stats_types,
|
|
"possible_yearly_stats_types": possible_yearly_stats_types,
|
|
"stats_type": stats_type,
|
|
"possible_document_types": possible_document_types,
|
|
"document_type": document_type,
|
|
"possible_time_choices": possible_time_choices,
|
|
"time_choice": time_choice,
|
|
"doc_label": doc_label,
|
|
"bin_size": bin_size,
|
|
"show_aliases_url": build_document_stats_url(get_overrides={ "showaliases": "1" }),
|
|
"hide_aliases_url": build_document_stats_url(get_overrides={ "showaliases": None }),
|
|
"alias_data": alias_data,
|
|
"eu_countries": sorted(eu_countries or [], key=lambda c: c.name),
|
|
"content_template": "stats/document_stats_{}.html".format(template_name),
|
|
}
|
|
log("Cache miss for '%s'. Data size: %sk" % (cache_key, len(str(data))/1000))
|
|
cache.set(cache_key, data, 24*60*60)
|
|
return render(request, "stats/document_stats.html", data)
|
|
|
|
def known_countries_list(request, stats_type=None, acronym=None):
|
|
countries = CountryName.objects.prefetch_related("countryalias_set")
|
|
for c in countries:
|
|
# the sorting is a bit of a hack - it puts the ISO code first
|
|
# since it was added in a migration
|
|
c.aliases = sorted(c.countryalias_set.all(), key=lambda a: a.pk)
|
|
|
|
return render(request, "stats/known_countries_list.html", {
|
|
"countries": countries,
|
|
"ticket_email_address": settings.SECRETARIAT_TICKET_EMAIL,
|
|
})
|
|
|
|
def meeting_stats(request, num=None, stats_type=None):
|
|
meeting = None
|
|
if num is not None:
|
|
meeting = get_object_or_404(Meeting, number=num, type="ietf")
|
|
|
|
def build_meeting_stats_url(number=None, stats_type_override=Ellipsis, get_overrides={}):
|
|
kwargs = {
|
|
"stats_type": stats_type if stats_type_override is Ellipsis else stats_type_override,
|
|
}
|
|
|
|
if number is not None:
|
|
kwargs["num"] = number
|
|
|
|
return urlreverse(meeting_stats, kwargs={ k: v for k, v in kwargs.iteritems() if v is not None }) + generate_query_string(request.GET, get_overrides)
|
|
|
|
cache_key = "stats:meeting_stats:%s:%s:%s" % (num, stats_type, request.META.get('QUERY_STRING',''))
|
|
data = cache.get(cache_key)
|
|
if not data:
|
|
names_limit = settings.STATS_NAMES_LIMIT
|
|
# statistics types
|
|
if meeting:
|
|
possible_stats_types = add_url_to_choices([
|
|
("country", "Country"),
|
|
("continent", "Continent"),
|
|
], lambda slug: build_meeting_stats_url(number=meeting.number, stats_type_override=slug))
|
|
else:
|
|
possible_stats_types = add_url_to_choices([
|
|
("overview", "Overview"),
|
|
("country", "Country"),
|
|
("continent", "Continent"),
|
|
], lambda slug: build_meeting_stats_url(number=None, stats_type_override=slug))
|
|
|
|
if not stats_type:
|
|
return HttpResponseRedirect(build_meeting_stats_url(number=num, stats_type_override=possible_stats_types[0][0]))
|
|
|
|
chart_data = []
|
|
piechart_data = []
|
|
table_data = []
|
|
stats_title = ""
|
|
template_name = stats_type
|
|
bin_size = 1
|
|
eu_countries = None
|
|
|
|
def get_country_mapping(attendees):
|
|
return {
|
|
alias.alias: alias.country
|
|
for alias in CountryAlias.objects.filter(alias__in=set(r.country_code for r in attendees)).select_related("country", "country__continent")
|
|
if alias.alias.isupper()
|
|
}
|
|
|
|
def reg_name(r):
|
|
return email.utils.formataddr(((r.first_name + u" " + r.last_name).strip(), r.email))
|
|
|
|
if meeting and any(stats_type == t[0] for t in possible_stats_types):
|
|
attendees = MeetingRegistration.objects.filter(meeting=meeting)
|
|
|
|
if stats_type == "country":
|
|
stats_title = "Number of attendees for {} {} per country".format(meeting.type.name, meeting.number)
|
|
|
|
bins = defaultdict(set)
|
|
|
|
country_mapping = get_country_mapping(attendees)
|
|
|
|
eu_name = "EU"
|
|
eu_countries = set(CountryName.objects.filter(in_eu=True))
|
|
|
|
for r in attendees:
|
|
name = reg_name(r)
|
|
c = country_mapping.get(r.country_code)
|
|
bins[c.name if c else ""].add(name)
|
|
|
|
if c and c.in_eu:
|
|
bins[eu_name].add(name)
|
|
|
|
prune_unknown_bin_with_known(bins)
|
|
total_attendees = count_bins(bins)
|
|
|
|
series_data = []
|
|
for country, names in sorted(bins.iteritems(), key=lambda t: t[0].lower()):
|
|
percentage = len(names) * 100.0 / (total_attendees or 1)
|
|
if country:
|
|
series_data.append((country, len(names)))
|
|
table_data.append((country, percentage, len(names), list(names)[:names_limit]))
|
|
|
|
if country and country != eu_name:
|
|
piechart_data.append({ "name": country, "y": percentage })
|
|
|
|
series_data.sort(key=lambda t: t[1], reverse=True)
|
|
series_data = series_data[:20]
|
|
|
|
piechart_data.sort(key=lambda d: d["y"], reverse=True)
|
|
pie_cut_off = 8
|
|
piechart_data = piechart_data[:pie_cut_off] + [{ "name": "Other", "y": sum(d["y"] for d in piechart_data[pie_cut_off:])}]
|
|
|
|
chart_data.append({ "data": series_data })
|
|
|
|
elif stats_type == "continent":
|
|
stats_title = "Number of attendees for {} {} per continent".format(meeting.type.name, meeting.number)
|
|
|
|
bins = defaultdict(set)
|
|
|
|
country_mapping = get_country_mapping(attendees)
|
|
|
|
for r in attendees:
|
|
name = reg_name(r)
|
|
c = country_mapping.get(r.country_code)
|
|
bins[c.continent.name if c else ""].add(name)
|
|
|
|
prune_unknown_bin_with_known(bins)
|
|
total_attendees = count_bins(bins)
|
|
|
|
series_data = []
|
|
for continent, names in sorted(bins.iteritems(), key=lambda t: t[0].lower()):
|
|
percentage = len(names) * 100.0 / (total_attendees or 1)
|
|
if continent:
|
|
series_data.append((continent, len(names)))
|
|
table_data.append((continent, percentage, len(names), list(names)[:names_limit]))
|
|
|
|
series_data.sort(key=lambda t: t[1], reverse=True)
|
|
|
|
chart_data.append({ "data": series_data })
|
|
|
|
|
|
elif not meeting and any(stats_type == t[0] for t in possible_stats_types):
|
|
template_name = "overview"
|
|
|
|
attendees = MeetingRegistration.objects.filter(meeting__type="ietf").select_related('meeting')
|
|
|
|
if stats_type == "overview":
|
|
stats_title = "Number of attendees per meeting"
|
|
|
|
continents = {}
|
|
|
|
meetings = Meeting.objects.filter(type='ietf', date__lte=datetime.date.today()).order_by('number')
|
|
for m in meetings:
|
|
country = CountryName.objects.get(slug=m.country)
|
|
continents[country.continent.name] = country.continent.name
|
|
|
|
bins = defaultdict(set)
|
|
|
|
for r in attendees:
|
|
meeting_number = int(r.meeting.number)
|
|
name = reg_name(r)
|
|
bins[meeting_number].add(name)
|
|
|
|
series_data = {}
|
|
for continent in continents.keys():
|
|
series_data[continent] = []
|
|
|
|
for m in meetings:
|
|
country = CountryName.objects.get(slug=m.country)
|
|
url = build_meeting_stats_url(number=m.number,
|
|
stats_type_override="country")
|
|
for continent in continents.keys():
|
|
if continent == country.continent.name:
|
|
d = {
|
|
"name": "IETF {} - {}, {}".format(int(m.number), m.city, country),
|
|
"x": int(m.number),
|
|
"y": m.attendees,
|
|
"date": m.date.strftime("%d %b %Y"),
|
|
"url": url,
|
|
}
|
|
else:
|
|
d = {
|
|
"x": int(m.number),
|
|
"y": 0,
|
|
}
|
|
series_data[continent].append(d)
|
|
table_data.append((m, url,
|
|
m.attendees, country))
|
|
|
|
for continent in continents.keys():
|
|
# series_data[continent].sort(key=lambda t: t[0]["x"])
|
|
chart_data.append( { "name": continent,
|
|
"data": series_data[continent] })
|
|
|
|
table_data.sort(key=lambda t: int(t[0].number), reverse=True)
|
|
|
|
elif stats_type == "country":
|
|
stats_title = "Number of attendees per country across meetings"
|
|
|
|
country_mapping = get_country_mapping(attendees)
|
|
|
|
eu_name = "EU"
|
|
eu_countries = set(CountryName.objects.filter(in_eu=True))
|
|
|
|
bins = defaultdict(set)
|
|
|
|
for r in attendees:
|
|
meeting_number = int(r.meeting.number)
|
|
name = reg_name(r)
|
|
c = country_mapping.get(r.country_code)
|
|
|
|
if c:
|
|
bins[(meeting_number, c.name)].add(name)
|
|
if c.in_eu:
|
|
bins[(meeting_number, eu_name)].add(name)
|
|
|
|
add_labeled_top_series_from_bins(chart_data, bins, limit=8)
|
|
|
|
|
|
elif stats_type == "continent":
|
|
stats_title = "Number of attendees per continent across meetings"
|
|
|
|
country_mapping = get_country_mapping(attendees)
|
|
|
|
bins = defaultdict(set)
|
|
|
|
for r in attendees:
|
|
meeting_number = int(r.meeting.number)
|
|
name = reg_name(r)
|
|
c = country_mapping.get(r.country_code)
|
|
|
|
if c:
|
|
bins[(meeting_number, c.continent.name)].add(name)
|
|
|
|
add_labeled_top_series_from_bins(chart_data, bins, limit=8)
|
|
data = {
|
|
"chart_data": mark_safe(json.dumps(chart_data)),
|
|
"piechart_data": mark_safe(json.dumps(piechart_data)),
|
|
"table_data": table_data,
|
|
"stats_title": stats_title,
|
|
"possible_stats_types": possible_stats_types,
|
|
"stats_type": stats_type,
|
|
"bin_size": bin_size,
|
|
"meeting": meeting,
|
|
"eu_countries": sorted(eu_countries or [], key=lambda c: c.name),
|
|
"content_template": "stats/meeting_stats_{}.html".format(template_name),
|
|
}
|
|
log("Cache miss for '%s'. Data size: %sk" % (cache_key, len(str(data))/1000))
|
|
cache.set(cache_key, data, 24*60*60)
|
|
#
|
|
return render(request, "stats/meeting_stats.html", data)
|
|
|
|
|
|
@login_required
|
|
def review_stats(request, stats_type=None, acronym=None):
|
|
# This view is a bit complex because we want to show a bunch of
|
|
# tables with various filtering options, and both a team overview
|
|
# and a reviewers-within-team overview - and a time series chart.
|
|
# And in order to make the UI quick to navigate, we're not using
|
|
# one big form but instead presenting a bunch of immediate
|
|
# actions, with a URL scheme where the most common options (level
|
|
# and statistics type) are incorporated directly into the URL to
|
|
# be a bit nicer.
|
|
|
|
def build_review_stats_url(stats_type_override=Ellipsis, acronym_override=Ellipsis, get_overrides={}):
|
|
kwargs = {
|
|
"stats_type": stats_type if stats_type_override is Ellipsis else stats_type_override,
|
|
}
|
|
acr = acronym if acronym_override is Ellipsis else acronym_override
|
|
if acr:
|
|
kwargs["acronym"] = acr
|
|
|
|
return urlreverse(review_stats, kwargs=kwargs) + generate_query_string(request.GET, get_overrides)
|
|
|
|
# which overview - team or reviewer
|
|
if acronym:
|
|
level = "reviewer"
|
|
else:
|
|
level = "team"
|
|
|
|
# statistics type - one of the tables or the chart
|
|
possible_stats_types = [
|
|
("completion", "Completion status"),
|
|
("results", "Review results"),
|
|
("states", "Request states"),
|
|
]
|
|
|
|
if level == "team":
|
|
possible_stats_types.append(("time", "Changes over time"))
|
|
|
|
possible_stats_types = add_url_to_choices(possible_stats_types,
|
|
lambda slug: build_review_stats_url(stats_type_override=slug))
|
|
|
|
if not stats_type:
|
|
return HttpResponseRedirect(build_review_stats_url(stats_type_override=possible_stats_types[0][0]))
|
|
|
|
# what to count
|
|
possible_count_choices = add_url_to_choices([
|
|
("", "Review requests"),
|
|
("pages", "Reviewed pages"),
|
|
], lambda slug: build_review_stats_url(get_overrides={ "count": slug }))
|
|
|
|
count = get_choice(request, "count", possible_count_choices) or ""
|
|
|
|
# time range
|
|
def parse_date(s):
|
|
if not s:
|
|
return None
|
|
try:
|
|
return datetime.datetime.strptime(s.strip(), "%Y-%m-%d").date()
|
|
except ValueError:
|
|
return None
|
|
|
|
today = datetime.date.today()
|
|
from_date = parse_date(request.GET.get("from")) or today - dateutil.relativedelta.relativedelta(years=1)
|
|
to_date = parse_date(request.GET.get("to")) or today
|
|
|
|
from_time = datetime.datetime.combine(from_date, datetime.time.min)
|
|
to_time = datetime.datetime.combine(to_date, datetime.time.max)
|
|
|
|
# teams/reviewers
|
|
teams = list(Group.objects.exclude(reviewrequest=None).distinct().order_by("name"))
|
|
|
|
reviewer_filter_args = {}
|
|
|
|
# - interlude: access control
|
|
if has_role(request.user, ["Secretariat", "Area Director"]):
|
|
pass
|
|
else:
|
|
secr_access = set()
|
|
reviewer_only_access = set()
|
|
|
|
for r in Role.objects.filter(person__user=request.user, name__in=["secr", "reviewer"], group__in=teams).distinct():
|
|
if r.name_id == "secr":
|
|
secr_access.add(r.group_id)
|
|
reviewer_only_access.discard(r.group_id)
|
|
elif r.name_id == "reviewer":
|
|
if not r.group_id in secr_access:
|
|
reviewer_only_access.add(r.group_id)
|
|
|
|
if not secr_access and not reviewer_only_access:
|
|
return HttpResponseForbidden("You do not have the necessary permissions to view this page")
|
|
|
|
teams = [t for t in teams if t.pk in secr_access or t.pk in reviewer_only_access]
|
|
|
|
for t in reviewer_only_access:
|
|
reviewer_filter_args[t] = { "user": request.user }
|
|
|
|
reviewers_for_team = None
|
|
|
|
if level == "team":
|
|
for t in teams:
|
|
t.reviewer_stats_url = build_review_stats_url(acronym_override=t.acronym)
|
|
|
|
query_teams = teams
|
|
query_reviewers = None
|
|
|
|
group_by_objs = { t.pk: t for t in query_teams }
|
|
group_by_index = ReviewRequestData._fields.index("team")
|
|
|
|
elif level == "reviewer":
|
|
for t in teams:
|
|
if t.acronym == acronym:
|
|
reviewers_for_team = t
|
|
break
|
|
else:
|
|
return HttpResponseRedirect(urlreverse(review_stats))
|
|
|
|
query_reviewers = list(Person.objects.filter(
|
|
email__reviewrequest__time__gte=from_time,
|
|
email__reviewrequest__time__lte=to_time,
|
|
email__reviewrequest__team=reviewers_for_team,
|
|
**reviewer_filter_args.get(t.pk, {})
|
|
).distinct())
|
|
query_reviewers.sort(key=lambda p: p.last_name())
|
|
|
|
query_teams = [t]
|
|
|
|
group_by_objs = { r.pk: r for r in query_reviewers }
|
|
group_by_index = ReviewRequestData._fields.index("reviewer")
|
|
|
|
# now filter and aggregate the data
|
|
possible_teams = possible_completion_types = possible_results = possible_states = None
|
|
selected_teams = selected_completion_type = selected_result = selected_state = None
|
|
|
|
if stats_type == "time":
|
|
possible_teams = [(t.acronym, t.acronym) for t in teams]
|
|
selected_teams = get_choice(request, "team", possible_teams, multiple=True)
|
|
|
|
def add_if_exists_else_subtract(element, l):
|
|
if element in l:
|
|
return [x for x in l if x != element]
|
|
else:
|
|
return l + [element]
|
|
|
|
possible_teams = add_url_to_choices(
|
|
possible_teams,
|
|
lambda slug: build_review_stats_url(get_overrides={
|
|
"team": add_if_exists_else_subtract(slug, selected_teams)
|
|
})
|
|
)
|
|
query_teams = [t for t in query_teams if t.acronym in selected_teams]
|
|
|
|
extracted_data = extract_review_request_data(query_teams, query_reviewers, from_time, to_time)
|
|
|
|
req_time_index = ReviewRequestData._fields.index("req_time")
|
|
|
|
def time_key_fn(t):
|
|
d = t[req_time_index].date()
|
|
#d -= datetime.timedelta(days=d.weekday()) # weekly
|
|
d -= datetime.timedelta(days=d.day) # monthly
|
|
return d
|
|
|
|
found_results = set()
|
|
found_states = set()
|
|
aggrs = []
|
|
for d, request_data_items in itertools.groupby(extracted_data, key=time_key_fn):
|
|
raw_aggr = aggregate_raw_period_review_request_stats(request_data_items, count=count)
|
|
aggr = sum_period_review_request_stats(raw_aggr)
|
|
|
|
aggrs.append((d, aggr))
|
|
|
|
for slug in aggr["result"]:
|
|
found_results.add(slug)
|
|
for slug in aggr["state"]:
|
|
found_states.add(slug)
|
|
|
|
results = ReviewResultName.objects.filter(slug__in=found_results)
|
|
states = ReviewRequestStateName.objects.filter(slug__in=found_states)
|
|
|
|
# choice
|
|
|
|
possible_completion_types = add_url_to_choices([
|
|
("completed_in_time", "Completed in time"),
|
|
("completed_late", "Completed late"),
|
|
("not_completed", "Not completed"),
|
|
("average_assignment_to_closure_days", "Avg. compl. days"),
|
|
], lambda slug: build_review_stats_url(get_overrides={ "completion": slug, "result": None, "state": None }))
|
|
|
|
selected_completion_type = get_choice(request, "completion", possible_completion_types)
|
|
|
|
possible_results = add_url_to_choices(
|
|
[(r.slug, r.name) for r in results],
|
|
lambda slug: build_review_stats_url(get_overrides={ "completion": None, "result": slug, "state": None })
|
|
)
|
|
|
|
selected_result = get_choice(request, "result", possible_results)
|
|
|
|
possible_states = add_url_to_choices(
|
|
[(s.slug, s.name) for s in states],
|
|
lambda slug: build_review_stats_url(get_overrides={ "completion": None, "result": None, "state": slug })
|
|
)
|
|
|
|
selected_state = get_choice(request, "state", possible_states)
|
|
|
|
if not selected_completion_type and not selected_result and not selected_state:
|
|
selected_completion_type = "completed_in_time"
|
|
|
|
series_data = []
|
|
if selected_completion_type == "completed_combined":
|
|
pass
|
|
else:
|
|
for d, aggr in aggrs:
|
|
v = 0
|
|
if selected_completion_type is not None:
|
|
v = aggr[selected_completion_type]
|
|
elif selected_result is not None:
|
|
v = aggr["result"][selected_result]
|
|
elif selected_state is not None:
|
|
v = aggr["state"][selected_state]
|
|
|
|
series_data.append((calendar.timegm(d.timetuple()) * 1000, v))
|
|
data = json.dumps([{
|
|
"data": series_data
|
|
}])
|
|
|
|
else: # tabular data
|
|
extracted_data = extract_review_request_data(query_teams, query_reviewers, from_time, to_time, ordering=[level])
|
|
|
|
data = []
|
|
|
|
found_results = set()
|
|
found_states = set()
|
|
raw_aggrs = []
|
|
for group_pk, request_data_items in itertools.groupby(extracted_data, key=lambda t: t[group_by_index]):
|
|
raw_aggr = aggregate_raw_period_review_request_stats(request_data_items, count=count)
|
|
raw_aggrs.append(raw_aggr)
|
|
|
|
aggr = sum_period_review_request_stats(raw_aggr)
|
|
|
|
# skip zero-valued rows
|
|
if aggr["open"] == 0 and aggr["completed"] == 0 and aggr["not_completed"] == 0:
|
|
continue
|
|
|
|
aggr["obj"] = group_by_objs.get(group_pk)
|
|
|
|
for slug in aggr["result"]:
|
|
found_results.add(slug)
|
|
for slug in aggr["state"]:
|
|
found_states.add(slug)
|
|
|
|
data.append(aggr)
|
|
|
|
# add totals row
|
|
if len(raw_aggrs) > 1:
|
|
totals = sum_period_review_request_stats(sum_raw_review_request_aggregations(raw_aggrs))
|
|
totals["obj"] = "Totals"
|
|
data.append(totals)
|
|
|
|
results = ReviewResultName.objects.filter(slug__in=found_results)
|
|
states = ReviewRequestStateName.objects.filter(slug__in=found_states)
|
|
|
|
# massage states/results breakdowns for template rendering
|
|
for aggr in data:
|
|
aggr["state_list"] = [aggr["state"].get(x.slug, 0) for x in states]
|
|
aggr["result_list"] = [aggr["result"].get(x.slug, 0) for x in results]
|
|
|
|
|
|
return render(request, 'stats/review_stats.html', {
|
|
"team_level_url": build_review_stats_url(acronym_override=None),
|
|
"level": level,
|
|
"reviewers_for_team": reviewers_for_team,
|
|
"teams": teams,
|
|
"data": data,
|
|
"states": states,
|
|
"results": results,
|
|
|
|
# options
|
|
"possible_stats_types": possible_stats_types,
|
|
"stats_type": stats_type,
|
|
|
|
"possible_count_choices": possible_count_choices,
|
|
"count": count,
|
|
|
|
"from_date": from_date,
|
|
"to_date": to_date,
|
|
"today": today,
|
|
|
|
# time options
|
|
"possible_teams": possible_teams,
|
|
"selected_teams": selected_teams,
|
|
"possible_completion_types": possible_completion_types,
|
|
"selected_completion_type": selected_completion_type,
|
|
"possible_results": possible_results,
|
|
"selected_result": selected_result,
|
|
"possible_states": possible_states,
|
|
"selected_state": selected_state,
|
|
})
|