def repo_table(scope): # repos, start, end):
#this drives the list of all repos within an organization, showing the statistics for them within the selected
#time range, along with navigation links.
(repos, authors) = scope.standardize_repos_and_authors()
interval = 'DY'
# FIXME: explain
repos = [x.pk for x in scope.available_repos.all()]
stats = Statistic.queryset_for_range(repos=repos,
authors=authors,
start=scope.start,
end=scope.end,
interval=interval)
stats = Statistic.annotate(
stats.values('repo__name')).order_by('repo__name')
data = _annotations_to_table(stats, 'repo', 'repo__name')
# FIXME: insert in author count, which is ... complicated ... this can be optimized later
# we should be able to grab every repo and annotate it with the author count in one extra query tops
# but it might require manually writing it.
for d in data:
repo = d['repo']
author_count = Author.author_count(repo,
start=scope.start,
end=scope.end)
d['author_count'] = author_count
# some repos won't have been scanned, and this requires a second query to fill them into the table
repos = Repository.objects.filter(last_scanned=None,
organization=scope.org)
for unscanned in repos:
data.append(dict(repo=unscanned.name))
return data
def compute_daily_rollup(cls, repo=None, author=None, start_day=None, total_instances=None):
"""
Generate rollup stats for everything the team did on a given day
"""
end_date = cls.get_end_day(start_day, DAY)
file_change_count = FileChange.change_count(repo, author=author, start=start_day, end=end_date)
if file_change_count == 0:
# this looks like a merge commit, FIXME: it would be a good idea to validate that this is 100% true.
# print("-- skipping potential merge commit --")
return
if not author:
authors_count = Author.author_count(repo, start=start_day, end=end_date)
else:
authors_count = 1
# Aggregate values from query set for rollup
data = FileChange.aggregate_stats(repo, author=author, start=start_day, end=end_date)
# FIXME: if start_day is today, we need to UPDATE the current stat? - verify if the bulk_update code deals with this?
# FIXME: model code method below is rather inefficient, does this matter?
# Create total rollup row for the day
stat = Statistic(
start_date=start_day,
interval=DAY,
repo=repo,
author=author,
lines_added=data['lines_added'],
lines_removed=data['lines_removed'],
lines_changed=data['lines_changed'],
commit_total= data['commit_total'],
files_changed=data['files_changed'],
author_total=authors_count,
days_active=1,
)
stat.compute_derived_values()
cls.smart_bulk_update(repo=repo, start_day=start_day, author=author, interval=DAY, stat=stat, total_instances=total_instances)