def featured_works(self, use_materialized_works=True):
if not use_materialized_works:
qu = self.works()
else:
qu = self.materialized_works()
# Aliasing Edition here allows this query to function
# regardless of work_model and existing joins.
work_edition = aliased(Edition)
qu = qu.join(work_edition).order_by(work_edition.series_position, work_edition.title)
target_size = Configuration.featured_lane_size()
qu = qu.limit(target_size)
return qu.all()
def randomized_sample_works(self, query, use_min_size=False):
"""Find a random sample of works for a feed"""
offset = 0
target_size = Configuration.featured_lane_size()
smallest_sample_size = target_size
if use_min_size:
smallest_sample_size = self.MINIMUM_SAMPLE_SIZE or (target_size -
5)
total_size = query.count()
if total_size < smallest_sample_size:
# There aren't enough works here. Ignore the lane.
return []
if total_size > target_size:
# We have enough results to randomly offset the selection.
offset = random.randint(0, total_size - target_size)
works = query.offset(offset).limit(target_size).all()
random.shuffle(works)
return works
