def load_starred_stories(request):
user = get_user(request)
offset = int(request.REQUEST.get('offset', 0))
limit = int(request.REQUEST.get('limit', 10))
page = int(request.REQUEST.get('page', 0))
if page: offset = limit * page
mstories = MStarredStory.objects(user_id=user.pk).order_by('-starred_date')[offset:offset+limit]
stories = Feed.format_stories(mstories)
for story in stories:
story_date = localtime_for_timezone(story['story_date'], user.profile.timezone)
now = localtime_for_timezone(datetime.datetime.now(), 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)
starred_date = localtime_for_timezone(story['starred_date'], user.profile.timezone)
story['starred_date'] = format_story_link_date__long(starred_date, now)
story['read_status'] = 1
story['starred'] = True
story['intelligence'] = {
'feed': 0,
'author': 0,
'tags': 0,
'title': 0,
}
logging.info(" ---> [%s] ~FCLoading starred stories: ~SB%s stories" % (request.user, len(stories)))
return dict(stories=stories)
def test_localtime_for_timezone(self):
self.assertEqual(
localtime_for_timezone(
datetime(2008, 6, 25, 18, 0, 0), "America/Denver"
).strftime("%m/%d/%Y %H:%M:%S"),
"06/25/2008 12:00:00"
)
def load_river_stories(request):
user = get_user(request)
feed_ids = [int(feed_id) for feed_id in request.POST.getlist("feeds")]
offset = int(request.REQUEST.get("offset", 0))
limit = int(request.REQUEST.get("limit", 25))
page = int(request.REQUEST.get("page", 0)) + 1
read_stories = int(request.REQUEST.get("read_stories", 0))
# if page: offset = limit * page
if page:
limit = limit * page - read_stories
def feed_qvalues(feed_id):
feed = UserSubscription.objects.get(feed__pk=feed_id, user=user)
return Q(story_feed_id=feed_id) & Q(story_date__gte=feed.mark_read_date)
feed_last_reads = map(feed_qvalues, feed_ids)
qs = reduce(lambda q1, q2: q1 | q2, feed_last_reads)
read_stories = MUserStory.objects(user_id=user.pk, feed_id__in=feed_ids).only("story")
read_stories = [rs.story.id for rs in read_stories]
mstories = MStory.objects(Q(id__nin=read_stories) & qs)[offset : offset + limit]
stories = Feed.format_stories(mstories)
starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id__in=feed_ids).only(
"story_guid", "starred_date"
)
starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories])
for story in stories:
story_date = localtime_for_timezone(story["story_date"], user.profile.timezone)
story["short_parsed_date"] = format_story_link_date__short(story_date)
story["long_parsed_date"] = format_story_link_date__long(story_date)
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)
story["intelligence"] = {"feed": 0, "author": 0, "tags": 0, "title": 0}
logging.info(
" ---> [%s] ~FCLoading river stories: ~SB%s stories ~SN(%s feeds)" % (request.user, len(stories), len(feed_ids))
)
return dict(stories=stories)
def load_starred_stories(request):
user = get_user(request)
offset = int(request.REQUEST.get("offset", 0))
limit = int(request.REQUEST.get("limit", 10))
page = int(request.REQUEST.get("page", 0))
if page:
offset = limit * page
mstories = MStarredStory.objects(user_id=user.pk).order_by("-starred_date")[offset : offset + limit]
stories = Feed.format_stories(mstories)
for story in stories:
story_date = localtime_for_timezone(story["story_date"], user.profile.timezone)
story["short_parsed_date"] = format_story_link_date__short(story_date)
story["long_parsed_date"] = format_story_link_date__long(story_date)
starred_date = localtime_for_timezone(story["starred_date"], user.profile.timezone)
story["starred_date"] = format_story_link_date__long(starred_date)
story["read_status"] = 1
story["starred"] = True
story["intelligence"] = {"feed": 0, "author": 0, "tags": 0, "title": 0}
logging.info(" ---> [%s] ~FCLoading starred stories: ~SB%s stories" % (request.user, len(stories)))
return dict(stories=stories)
def load_single_feed(request):
user = get_user(request)
offset = int(request.REQUEST.get("offset", 0))
limit = int(request.REQUEST.get("limit", 30))
page = int(request.REQUEST.get("page", 0))
if page:
offset = limit * page
feed_id = int(request.REQUEST.get("feed_id", 0))
if feed_id == 0:
raise Http404
try:
feed = Feed.objects.get(id=feed_id)
except Feed.DoesNotExist:
feed_address = request.REQUEST.get("feed_address")
dupe_feed = DuplicateFeed.objects.filter(duplicate_address=feed_address)
if dupe_feed:
feed = dupe_feed[0].feed
else:
raise Http404
force_update = request.GET.get("force_update", False)
now = datetime.datetime.utcnow()
stories = feed.get_stories(offset, limit)
if force_update:
feed.update(force_update)
# Get intelligence classifier for user
classifier_feeds = MClassifierFeed.objects(user_id=user.pk, feed_id=feed_id)
classifier_authors = MClassifierAuthor.objects(user_id=user.pk, feed_id=feed_id)
classifier_titles = MClassifierTitle.objects(user_id=user.pk, feed_id=feed_id)
classifier_tags = MClassifierTag.objects(user_id=user.pk, feed_id=feed_id)
usersub = UserSubscription.objects.get(user=user, feed=feed)
userstories = []
userstories_db = MUserStory.objects(user_id=user.pk, feed_id=feed.pk, read_date__gte=usersub.mark_read_date)
starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id=feed_id).only("story_guid", "starred_date")
starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories])
for us in userstories_db:
if hasattr(us.story, "story_guid") and isinstance(us.story.story_guid, unicode):
userstories.append(us.story.story_guid)
elif hasattr(us.story, "id") and isinstance(us.story.id, unicode):
userstories.append(us.story.id) # TODO: Remove me after migration from story.id->guid
for story in stories:
classifier_feeds.rewind()
classifier_authors.rewind()
classifier_tags.rewind()
classifier_titles.rewind()
story_date = localtime_for_timezone(story["story_date"], user.profile.timezone)
story["short_parsed_date"] = format_story_link_date__short(story_date)
story["long_parsed_date"] = format_story_link_date__long(story_date)
if story["id"] in userstories:
story["read_status"] = 1
elif not story.get("read_status") and story["story_date"] < usersub.mark_read_date:
story["read_status"] = 1
elif not story.get("read_status") and story["story_date"] > usersub.last_read_date:
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)
story["intelligence"] = {
"feed": apply_classifier_feeds(classifier_feeds, feed),
"author": apply_classifier_authors(classifier_authors, story),
"tags": apply_classifier_tags(classifier_tags, story),
"title": apply_classifier_titles(classifier_titles, story),
}
# Intelligence
feed_tags = json.decode(feed.popular_tags) if feed.popular_tags else []
feed_authors = json.decode(feed.popular_authors) if feed.popular_authors else []
classifiers = get_classifiers_for_user(
user, feed_id, classifier_feeds, classifier_authors, classifier_titles, classifier_tags
)
usersub.feed_opens += 1
usersub.save()
diff = datetime.datetime.utcnow() - now
timediff = float("%s.%s" % (diff.seconds, (diff.microseconds / 1000)))
last_update = relative_timesince(feed.last_update)
logging.info(" ---> [%s] ~FYLoading feed: ~SB%s ~SN(%s seconds)" % (request.user, feed, timediff))
FeedLoadtime.objects.create(feed=feed, loadtime=timediff)
data = dict(
stories=stories,
feed_tags=feed_tags,
feed_authors=feed_authors,
classifiers=classifiers,
last_update=last_update,
feed_id=feed.pk,
)
return data
def load_river_stories(request):
start = datetime.datetime.utcnow()
user = get_user(request)
feed_ids = [int(feed_id) for feed_id in request.POST.getlist('feeds') if feed_id]
original_feed_ids = list(feed_ids)
offset = int(request.REQUEST.get('offset', 0))
limit = int(request.REQUEST.get('limit', 25))
page = int(request.REQUEST.get('page', 0))+1
read_stories_count = int(request.REQUEST.get('read_stories_count', 0))
bottom_delta = datetime.timedelta(days=settings.DAYS_OF_UNREAD)
if not feed_ids:
logging.info(" ---> [%s] ~FCLoading empty river stories: page %s" % (
request.user, page))
return dict(stories=[])
# Fetch all stories at and before the page number.
# Not a single page, because reading stories can move them up in the unread order.
# `read_stories_count` is an optimization, works best when all 25 stories before have been read.
# if page: offset = limit * page
if page: limit = limit * page - read_stories_count
# Read stories to exclude
read_stories = MUserStory.objects(user_id=user.pk, feed_id__in=feed_ids).only('story')
read_stories = [rs.story.id for rs in read_stories]
# Determine mark_as_read dates for all feeds to ignore all stories before this date.
# max_feed_count = 0
feed_counts = {}
feed_last_reads = {}
for feed_id in feed_ids:
usersub = UserSubscription.objects.get(feed__pk=feed_id, user=user)
feed_counts[feed_id] = (usersub.unread_count_negative * 1+
usersub.unread_count_neutral * 10+
usersub.unread_count_positive * 20)
# if feed_counts[feed_id] > max_feed_count:
# max_feed_count = feed_counts[feed_id]
feed_last_reads[feed_id] = int(time.mktime(usersub.mark_read_date.timetuple()))
feed_counts = sorted(feed_counts.items(), key=itemgetter(1))[:25]
feed_ids = [f[0] for f in feed_counts]
feed_last_reads = dict([(str(feed_id), feed_last_reads[feed_id]) for feed_id in feed_ids])
feed_counts = dict(feed_counts)
# After excluding read stories, all that's left are stories
# past the mark_read_date. Everything returned is guaranteed to be unread.
mstories = MStory.objects(
id__nin=read_stories,
story_feed_id__in=feed_ids,
story_date__gte=start - bottom_delta
).map_reduce("""function() {
var d = feed_last_reads[this.story_feed_id];
if (this.story_date.getTime()/1000 > d) {
emit(this._id, this);
}
}""",
"""function(key, values) {
return values[0];
}""",
scope={
'feed_last_reads': feed_last_reads
}
)
mstories = [story.value for story in mstories]
mstories = sorted(mstories, cmp=lambda x, y: cmp(story_score(y, bottom_delta), story_score(x, bottom_delta)))
# story_feed_counts = defaultdict(int)
# mstories_pruned = []
# for story in mstories:
# print story['story_title'], story_feed_counts[story['story_feed_id']]
# if story_feed_counts[story['story_feed_id']] >= 3: continue
# mstories_pruned.append(story)
# story_feed_counts[story['story_feed_id']] += 1
stories = []
for i, story in enumerate(mstories):
if i < offset: continue
if i >= offset + limit: break
stories.append(bunch(story))
stories = Feed.format_stories(stories)
found_feed_ids = list(set([story['story_feed_id'] for story in stories]))
# Find starred stories
starred_stories = MStarredStory.objects(
user_id=user.pk,
story_feed_id__in=found_feed_ids
).only('story_guid', 'starred_date')
starred_stories = dict([(story.story_guid, story.starred_date)
for story in starred_stories])
# Intelligence classifiers for all feeds involved
def sort_by_feed(classifiers):
feed_classifiers = defaultdict(list)
for classifier in classifiers:
feed_classifiers[classifier.feed_id].append(classifier)
return feed_classifiers
classifier_feeds = sort_by_feed(MClassifierFeed.objects(user_id=user.pk, feed_id__in=found_feed_ids))
classifier_authors = sort_by_feed(MClassifierAuthor.objects(user_id=user.pk, feed_id__in=found_feed_ids))
classifier_titles = sort_by_feed(MClassifierTitle.objects(user_id=user.pk, feed_id__in=found_feed_ids))
classifier_tags = sort_by_feed(MClassifierTag.objects(user_id=user.pk, feed_id__in=found_feed_ids))
# Just need to format stories
for story in stories:
story_date = localtime_for_timezone(story['story_date'], user.profile.timezone)
now = localtime_for_timezone(datetime.datetime.now(), 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)
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)
story['intelligence'] = {
'feed': apply_classifier_feeds(classifier_feeds[story['story_feed_id']], story['story_feed_id']),
'author': apply_classifier_authors(classifier_authors[story['story_feed_id']], story),
'tags': apply_classifier_tags(classifier_tags[story['story_feed_id']], story),
'title': apply_classifier_titles(classifier_titles[story['story_feed_id']], story),
}
diff = datetime.datetime.utcnow() - start
timediff = float("%s.%.2s" % (diff.seconds, (diff.microseconds / 1000)))
logging.info(" ---> [%s] ~FCLoading river stories: page %s - ~SB%s/%s stories ~SN(%s/%s/%s feeds) ~FB(%s seconds)" % (
request.user, page, len(stories), len(mstories), len(found_feed_ids), len(feed_ids), len(original_feed_ids), timediff))
return dict(stories=stories)
def localtime(value, timezone):
return localtime_for_timezone(value, timezone)
def load_single_feed(request):
start = datetime.datetime.utcnow()
user = get_user(request)
offset = int(request.REQUEST.get('offset', 0))
limit = int(request.REQUEST.get('limit', 30))
page = int(request.REQUEST.get('page', 0))
if page:
offset = limit * page
feed_id = None
try:
feed_id = int(request.REQUEST.get('feed_id', 0))
except ValueError:
feed_id_matches = re.search(r'(\d+)', request.REQUEST['feed_id'])
if feed_id_matches: feed_id = int(feed_id_matches.group(1))
dupe_feed_id = None
if not feed_id:
raise Http404
try:
feed = Feed.objects.get(id=feed_id)
except Feed.DoesNotExist:
feed_address = request.REQUEST.get('feed_address')
dupe_feed = DuplicateFeed.objects.filter(duplicate_address=feed_address)
if dupe_feed:
feed = dupe_feed[0].feed
dupe_feed_id = feed_id
else:
raise Http404
force_update = request.GET.get('force_update', False)
stories = feed.get_stories(offset, limit)
if force_update:
feed.update(force_update)
# Get intelligence classifier for user
classifier_feeds = MClassifierFeed.objects(user_id=user.pk, feed_id=feed_id)
classifier_authors = MClassifierAuthor.objects(user_id=user.pk, feed_id=feed_id)
classifier_titles = MClassifierTitle.objects(user_id=user.pk, feed_id=feed_id)
classifier_tags = MClassifierTag.objects(user_id=user.pk, feed_id=feed_id)
usersub = UserSubscription.objects.get(user=user, feed=feed)
userstories = []
if usersub:
userstories_db = MUserStory.objects(user_id=user.pk,
feed_id=feed.pk,
read_date__gte=usersub.mark_read_date)
starred_stories = MStarredStory.objects(user_id=user.pk, story_feed_id=feed_id).only('story_guid', 'starred_date')
starred_stories = dict([(story.story_guid, story.starred_date) for story in starred_stories])
for us in userstories_db:
if hasattr(us.story, 'story_guid') and isinstance(us.story.story_guid, unicode):
userstories.append(us.story.story_guid)
elif hasattr(us.story, 'id') and isinstance(us.story.id, unicode):
userstories.append(us.story.id) # TODO: Remove me after migration from story.id->guid
for story in stories:
[x.rewind() for x in [classifier_feeds, classifier_authors, classifier_tags, classifier_titles]]
story_date = localtime_for_timezone(story['story_date'], user.profile.timezone)
now = localtime_for_timezone(datetime.datetime.now(), 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 usersub:
if story['id'] in userstories:
story['read_status'] = 1
elif not story.get('read_status') and story['story_date'] < usersub.mark_read_date:
story['read_status'] = 1
elif not story.get('read_status') and story['story_date'] > usersub.last_read_date:
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)
else:
story['read_status'] = 1
story['intelligence'] = {
'feed': apply_classifier_feeds(classifier_feeds, feed),
'author': apply_classifier_authors(classifier_authors, story),
'tags': apply_classifier_tags(classifier_tags, story),
'title': apply_classifier_titles(classifier_titles, story),
}
# Intelligence
feed_tags = json.decode(feed.data.popular_tags) if feed.data.popular_tags else []
feed_authors = json.decode(feed.data.popular_authors) if feed.data.popular_authors else []
classifiers = get_classifiers_for_user(user, feed_id, classifier_feeds,
classifier_authors, classifier_titles, classifier_tags)
if usersub:
usersub.feed_opens += 1
usersub.save()
diff = datetime.datetime.utcnow()-start
timediff = float("%s.%.2s" % (diff.seconds, (diff.microseconds / 1000)))
last_update = relative_timesince(feed.last_update)
logging.user(request.user, "~FYLoading feed: ~SB%s ~SN(%s seconds)" % (feed, timediff))
FeedLoadtime.objects.create(feed=feed, loadtime=timediff)
data = dict(stories=stories,
feed_tags=feed_tags,
feed_authors=feed_authors,
classifiers=classifiers,
last_update=last_update,
feed_id=feed.pk)
if dupe_feed_id: data['dupe_feed_id'] = dupe_feed_id
if not usersub:
data.update(feed.canonical())
return data
def test_localtime_for_timezone(self):
self.assertEqual(
localtime_for_timezone(
datetime(2008, 6, 25, 18, 0,
0), "America/Denver").strftime("%m/%d/%Y %H:%M:%S"),
"06/25/2008 12:00:00")