# -*- coding: utf-8 -*- from __future__ import absolute_import from zerver.models import UserProfile, UserActivity, UserActivityInterval, Message from django.utils.timezone import utc from datetime import timedelta from itertools import chain def median(data): data = sorted(data) size = len(data) if size % 2 == 1: return data[size//2] else: before = size//2 - 1 after = size//2 return (data[before] + data[after]) / 2.0 users_who_sent_query = Message.objects.select_related("sender") \ .exclude(sending_client__name__contains="mirror") \ .exclude(sending_client__name__contains="API") def active_users(): # Return a list of active users we want to count towards various # statistics. return UserProfile.objects.filter(is_bot=False, is_active=True).select_related() def users_who_sent_between(begin, end): sender_objs = users_who_sent_query.filter(pub_date__gt=begin, pub_date__lt=end) \ .values("sender__id") return set(s["sender__id"] for s in sender_objs) def users_who_sent_ever(): return set(s["sender__id"] for s in users_who_sent_query.values("sender__id")) def active_users_to_measure(): senders = users_who_sent_ever() return [u for u in active_users() if u.id in senders] def active_users_who_sent_between(begin, end): senders = users_who_sent_between(begin, end) return [u for u in active_users() if u.id in senders] # Return the amount of Zulip usage for this user between the two # given dates def seconds_usage_between(user_profile, begin, end): intervals = UserActivityInterval.objects.filter(user_profile=user_profile, end__gte=begin, start__lte=end) duration = timedelta(0) for interval in intervals: start = max(begin, interval.start) finish = min(end, interval.end) duration += finish-start return duration # Return a list of how many seconds each user has been engaging with the app on a given day def seconds_active_during_day(day): begin_day = day.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=utc) end_day = day.replace(hour=23, minute=59, second=59, microsecond=0, tzinfo=utc) active_users = active_users_to_measure() return [seconds_usage_between(user, begin_day, end_day).total_seconds() for user in active_users] def users_active_nosend_during_day(day): begin_day = day.replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=utc) end_day = day.replace(hour=23, minute=59, second=59, microsecond=0, tzinfo=utc) active_users = active_users_to_measure() today_senders = users_who_sent_between(begin_day, end_day) today_users = [] for user_profile in active_users: intervals = UserActivityInterval.objects.filter(user_profile=user_profile, end__gte=begin_day, start__lte=end_day) if len(intervals) != 0: today_users.append(user_profile) return [u for u in today_users if not u.id in today_senders] def calculate_stats(data, all_users): if len(data) == 0: return {"# data points": 0} active_user_count = len([x for x in data if x > 1]) mean_data = sum(data) / active_user_count median_data = median([x for x in data if x > 1]) return {'active users': active_user_count, 'total users': len(all_users), 'mean': str(timedelta(seconds=mean_data)), 'median': str(timedelta(seconds=median_data)), '# data points': len(data)} # Return an info dict {mean: , median} containing the mean/median seconds users were active on a given day def activity_averages_during_day(day): users_to_measure = active_users_to_measure() seconds_active = seconds_active_during_day(day) return calculate_stats(seconds_active, all_users=users_to_measure) # Returns an info dict {mean: , median} with engagement numbers for all users according # to active_users_to_measure. This will ignore weekends, and ignore users.customer4.invalid # on Fridays def activity_averages_between(begin, end, by_day=True): seconds_active = {} users_to_measure = active_users_to_measure() for i in range((end - begin).days): day = begin + timedelta(days=i) # Ignore weekends if day.weekday() in [5, 6]: continue seconds_active[day] = seconds_active_during_day(day) if by_day: return dict((day, calculate_stats(values, all_users=users_to_measure)) for day, values in seconds_active.iteritems()) else: return calculate_stats(list(chain.from_iterable(seconds_active.values())), all_users=users_to_measure)