def load_social_stories(request, user_id, username=None):
start = time.time()
user = get_user(request)
social_user_id = int(user_id)
social_user = get_object_or_404(User, pk=social_user_id)
offset = int(request.REQUEST.get('offset', 0))
limit = int(request.REQUEST.get('limit', 6))
page = request.REQUEST.get('page')
order = request.REQUEST.get('order', 'newest')
read_filter = request.REQUEST.get('read_filter', 'all')
stories = []
if page: offset = limit * (int(page) - 1)
now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone)
UNREAD_CUTOFF = datetime.datetime.utcnow() - datetime.timedelta(days=settings.DAYS_OF_UNREAD)
social_profile = MSocialProfile.get_user(social_user.pk)
try:
socialsub = MSocialSubscription.objects.get(user_id=user.pk, subscription_user_id=social_user_id)
except MSocialSubscription.DoesNotExist:
socialsub = None
mstories = MSharedStory.objects(user_id=social_user.pk).order_by('-shared_date')[offset:offset+limit]
stories = Feed.format_stories(mstories)
if socialsub and (read_filter == 'unread' or order == 'oldest'):
story_ids = socialsub.get_stories(order=order, read_filter=read_filter, offset=offset, limit=limit)
story_date_order = "%sshared_date" % ('' if order == 'oldest' else '-')
if story_ids:
mstories = MSharedStory.objects(user_id=social_user.pk,
story_db_id__in=story_ids).order_by(story_date_order)
stories = Feed.format_stories(mstories)
else:
mstories = MSharedStory.objects(user_id=social_user.pk).order_by('-shared_date')[offset:offset+limit]
stories = Feed.format_stories(mstories)
if not stories:
return dict(stories=[])
checkpoint1 = time.time()
stories, user_profiles = MSharedStory.stories_with_comments_and_profiles(stories, user.pk, check_all=True)
story_feed_ids = list(set(s['story_feed_id'] for s in stories))
usersubs = UserSubscription.objects.filter(user__pk=user.pk, feed__pk__in=story_feed_ids)
usersubs_map = dict((sub.feed_id, sub) for sub in usersubs)
unsub_feed_ids = list(set(story_feed_ids).difference(set(usersubs_map.keys())))
unsub_feeds = Feed.objects.filter(pk__in=unsub_feed_ids)
unsub_feeds = [feed.canonical(include_favicon=False) for feed in unsub_feeds]
date_delta = UNREAD_CUTOFF
if socialsub and date_delta < socialsub.mark_read_date:
date_delta = socialsub.mark_read_date
# Get intelligence classifier for user
classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk, social_user_id=social_user_id))
classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk, social_user_id=social_user_id))
classifier_titles = list(MClassifierTitle.objects(user_id=user.pk, social_user_id=social_user_id))
classifier_tags = list(MClassifierTag.objects(user_id=user.pk, social_user_id=social_user_id))
# Merge with feed specific classifiers
classifier_feeds = classifier_feeds + list(MClassifierFeed.objects(user_id=user.pk, feed_id__in=story_feed_ids))
classifier_authors = classifier_authors + list(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=story_feed_ids))
classifier_titles = classifier_titles + list(MClassifierTitle.objects(user_id=user.pk, feed_id__in=story_feed_ids))
classifier_tags = classifier_tags + list(MClassifierTag.objects(user_id=user.pk, feed_id__in=story_feed_ids))
checkpoint2 = time.time()
story_ids = [story['id'] for story in stories]
userstories_db = MUserStory.objects(user_id=user.pk,
feed_id__in=story_feed_ids,
story_id__in=story_ids).only('story_id')
userstories = set(us.story_id for us in userstories_db)
starred_stories = MStarredStory.objects(user_id=user.pk,
story_feed_id__in=story_feed_ids,
story_guid__in=story_ids).only('story_guid', 'starred_date')
shared_stories = MSharedStory.objects(user_id=user.pk,
story_feed_id__in=story_feed_ids,
story_guid__in=story_ids)\
.only('story_guid', 'shared_date', 'comments')
starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories])
shared_stories = dict([(story.story_guid, dict(shared_date=story.shared_date, comments=story.comments))
for story in shared_stories])
for story in stories:
story['social_user_id'] = social_user_id
story_feed_id = story['story_feed_id']
# story_date = localtime_for_timezone(story['story_date'], user.profile.timezone)
shared_date = localtime_for_timezone(story['shared_date'], user.profile.timezone)
story['short_parsed_date'] = format_story_link_date__short(shared_date, now)
story['long_parsed_date'] = format_story_link_date__long(shared_date, now)
if not socialsub:
story['read_status'] = 1
elif story['id'] in userstories:
story['read_status'] = 1
elif story['shared_date'] < date_delta:
story['read_status'] = 1
elif not usersubs_map.get(story_feed_id):
story['read_status'] = 0
elif not story.get('read_status') and story['story_date'] < usersubs_map[story_feed_id].mark_read_date:
story['read_status'] = 1
elif not story.get('read_status') and story['shared_date'] < date_delta:
story['read_status'] = 1
# elif not story.get('read_status') and socialsub and story['shared_date'] > socialsub.last_read_date:
# story['read_status'] = 0
else:
story['read_status'] = 0
if story['id'] in starred_stories:
story['starred'] = True
starred_date = localtime_for_timezone(starred_stories[story['id']], user.profile.timezone)
story['starred_date'] = format_story_link_date__long(starred_date, now)
if story['id'] in shared_stories:
story['shared'] = True
shared_date = localtime_for_timezone(shared_stories[story['id']]['shared_date'],
user.profile.timezone)
story['shared_date'] = format_story_link_date__long(shared_date, now)
story['shared_comments'] = strip_tags(shared_stories[story['id']]['comments'])
story['intelligence'] = {
'feed': apply_classifier_feeds(classifier_feeds, story['story_feed_id'],
social_user_id=social_user_id),
'author': apply_classifier_authors(classifier_authors, story),
'tags': apply_classifier_tags(classifier_tags, story),
'title': apply_classifier_titles(classifier_titles, story),
}
classifiers = sort_classifiers_by_feed(user=user, feed_ids=story_feed_ids,
classifier_feeds=classifier_feeds,
classifier_authors=classifier_authors,
classifier_titles=classifier_titles,
classifier_tags=classifier_tags)
if socialsub:
socialsub.feed_opens += 1
socialsub.save()
diff1 = checkpoint1-start
diff2 = checkpoint2-start
logging.user(request, "~FYLoading ~FMshared stories~FY: ~SB%s%s ~SN(~SB%.4ss/%.4ss~SN)" % (
social_profile.title[:22], ('~SN/p%s' % page) if page > 1 else '', diff1, diff2))
return {
"stories": stories,
"user_profiles": user_profiles,
"feeds": unsub_feeds,
"classifiers": classifiers,
}
def load_river_blurblog(request):
limit = 10
start = time.time()
user = get_user(request)
social_user_ids = [int(uid) for uid in request.REQUEST.getlist("social_user_ids") if uid]
original_user_ids = list(social_user_ids)
page = int(request.REQUEST.get("page", 1))
order = request.REQUEST.get("order", "newest")
read_filter = request.REQUEST.get("read_filter", "unread")
relative_user_id = request.REQUEST.get("relative_user_id", None)
now = localtime_for_timezone(datetime.datetime.now(), user.profile.timezone)
UNREAD_CUTOFF = datetime.datetime.utcnow() - datetime.timedelta(days=settings.DAYS_OF_UNREAD)
if not relative_user_id:
relative_user_id = get_user(request).pk
if not social_user_ids:
socialsubs = MSocialSubscription.objects.filter(user_id=user.pk)
social_user_ids = [s.subscription_user_id for s in socialsubs]
offset = (page - 1) * limit
limit = page * limit - 1
story_ids, story_dates = MSocialSubscription.feed_stories(
user.pk, social_user_ids, offset=offset, limit=limit, order=order, read_filter=read_filter
)
mstories = MStory.objects(id__in=story_ids)
story_id_to_dates = dict(zip(story_ids, story_dates))
def sort_stories_by_id(a, b):
return int(story_id_to_dates[str(b.id)]) - int(story_id_to_dates[str(a.id)])
sorted_mstories = sorted(mstories, cmp=sort_stories_by_id)
stories = Feed.format_stories(sorted_mstories)
for s, story in enumerate(stories):
story["story_date"] = datetime.datetime.fromtimestamp(story_dates[s])
stories, user_profiles = MSharedStory.stories_with_comments_and_profiles(stories, relative_user_id, check_all=True)
story_feed_ids = list(set(s["story_feed_id"] for s in stories))
usersubs = UserSubscription.objects.filter(user__pk=user.pk, feed__pk__in=story_feed_ids)
usersubs_map = dict((sub.feed_id, sub) for sub in usersubs)
unsub_feed_ids = list(set(story_feed_ids).difference(set(usersubs_map.keys())))
unsub_feeds = Feed.objects.filter(pk__in=unsub_feed_ids)
unsub_feeds = [feed.canonical(include_favicon=False) for feed in unsub_feeds]
# Find starred stories
if story_feed_ids:
story_ids = [story["id"] for story in stories]
starred_stories = MStarredStory.objects(user_id=user.pk, story_guid__in=story_ids).only(
"story_guid", "starred_date"
)
starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories])
shared_stories = MSharedStory.objects(user_id=user.pk, story_guid__in=story_ids).only(
"story_guid", "shared_date", "comments"
)
shared_stories = dict(
[
(story.story_guid, dict(shared_date=story.shared_date, comments=story.comments))
for story in shared_stories
]
)
userstories_db = MUserStory.objects(user_id=user.pk, feed_id__in=story_feed_ids, story_id__in=story_ids).only(
"story_id"
)
userstories = set(us.story_id for us in userstories_db)
else:
starred_stories = {}
shared_stories = {}
userstories = []
# Intelligence classifiers for all feeds involved
if story_feed_ids:
classifier_feeds = list(MClassifierFeed.objects(user_id=user.pk, feed_id__in=story_feed_ids))
classifier_authors = list(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=story_feed_ids))
classifier_titles = list(MClassifierTitle.objects(user_id=user.pk, feed_id__in=story_feed_ids))
classifier_tags = list(MClassifierTag.objects(user_id=user.pk, feed_id__in=story_feed_ids))
else:
classifier_feeds = []
classifier_authors = []
classifier_titles = []
classifier_tags = []
classifiers = sort_classifiers_by_feed(
user=user,
feed_ids=story_feed_ids,
classifier_feeds=classifier_feeds,
classifier_authors=classifier_authors,
classifier_titles=classifier_titles,
classifier_tags=classifier_tags,
)
# Just need to format stories
for story in stories:
if story["id"] in userstories:
story["read_status"] = 1
elif story["story_date"] < UNREAD_CUTOFF:
story["read_status"] = 1
else:
story["read_status"] = 0
story_date = localtime_for_timezone(story["story_date"], user.profile.timezone)
story["short_parsed_date"] = format_story_link_date__short(story_date, now)
story["long_parsed_date"] = format_story_link_date__long(story_date, now)
if story["id"] in starred_stories:
story["starred"] = True
starred_date = localtime_for_timezone(starred_stories[story["id"]], user.profile.timezone)
story["starred_date"] = format_story_link_date__long(starred_date, now)
story["intelligence"] = {
"feed": apply_classifier_feeds(classifier_feeds, story["story_feed_id"]),
"author": apply_classifier_authors(classifier_authors, story),
"tags": apply_classifier_tags(classifier_tags, story),
"title": apply_classifier_titles(classifier_titles, story),
}
if story["id"] in shared_stories:
story["shared"] = True
shared_date = localtime_for_timezone(shared_stories[story["id"]]["shared_date"], user.profile.timezone)
story["shared_date"] = format_story_link_date__long(shared_date, now)
story["shared_comments"] = strip_tags(shared_stories[story["id"]]["comments"])
diff = time.time() - start
timediff = round(float(diff), 2)
logging.user(
request,
"~FYLoading ~FCriver blurblogs stories~FY: ~SBp%s~SN (%s/%s "
"stories, ~SN%s/%s/%s feeds)"
% (page, len(stories), len(mstories), len(story_feed_ids), len(social_user_ids), len(original_user_ids)),
)
return {
"stories": stories,
"user_profiles": user_profiles,
"feeds": unsub_feeds,
"classifiers": classifiers,
"elapsed_time": timediff,
}
