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 author_stats_table(scope, limit=None):
"""
this drives the author tables, both ranged and non-ranged, accessed off the main repo list.
the interval 'LF' shows lifetime stats, but elsewhere we just do daily roundups, so this parameter
should really be a boolean. The limit parameter is not yet used.
"""
# FIXME: this performs one query PER author and could be rewritten to be a LOT more intelligent.
(repos, authors) = scope.standardize_repos_and_authors()
interval = 'DY'
stats = Statistic.queryset_for_range(repos=repos,
authors=authors,
start=scope.start,
end=scope.end,
interval=interval)
data = None
if not scope.author:
stats = Statistic.annotate(
stats.values('author__email')).order_by('author__email')
data = _annotations_to_table(stats, 'author', 'author__email')
else:
stats = Statistic.annotate(
stats.values('repo__name')).order_by('repo__name')
data = _annotations_to_table(stats, 'repo', 'repo__name')
return data
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.
"""
results = []
for repo in scope.repos:
stats = Statistic.queryset_for_range(repo,
author=None,
interval='DY',
start=scope.start,
end=scope.end)
stat2 = Statistic.compute_interval_statistic(stats,
interval='DY',
repo=repo,
author=None,
start=scope.start,
end=scope.end)
stat2 = stat2.to_dict()
stat2['name'] = repo.name
# providing pk's for link columns in the repo chart
for x in ['details1', 'details2', 'details3']:
stat2[x] = repo.pk
results.append(stat2)
results = sorted(results, key=lambda x: x['name'])
return json.dumps(results)
def compute_interval_rollup(cls, repo=None, author=None, interval=None, start_day=None, total_instances=None):
"""
Use the daily team stats to generate weekly or monthly rollup stats.
start_day is the beginning of that period
"""
# FIXME: all this code should be cleaned up.
# IF in weekly mode, and start_day is this week, we need to delete the current stat
# IF in monthly mode, and start_day is this month, we need to delete the current stat
assert repo is not None
assert interval in [ 'WK', 'MN', 'LF']
(start_day, end_date) = cls.start_and_end_dates_for_interval(repo=repo, author=author, start=start_day, interval=interval)
if start_day is None and interval != 'LF':
print("**** GLITCH: Author has no commits? ", author)
return
days = cls._queryset_for_interval_rollup(repo=repo, author=author, interval=interval, start_day=start_day, end_date=end_date)
if days.count() == 0:
# probably just a merge commit today, be cool about it and skip this one.
return
stat = Statistic.compute_interval_statistic(days, repo=repo, interval=interval, author=author, start=start_day, end=end_date, for_update=True)
cls.smart_bulk_update(repo=repo, start_day=start_day, author=author, interval=interval, stat=stat, total_instances=total_instances)
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)
def add_stat(author, repo):
stat1 = Statistic.queryset_for_range(repo,
author=author,
start=scope.start,
end=scope.end,
interval=scope.interval)
stat2 = Statistic.compute_interval_statistic(stat1,
interval=scope.interval,
repo=repo,
author=author,
start=scope.start,
end=scope.end)
stat2 = stat2.to_dict()
stat2['author'] = author.email
stat2['repo'] = repo.name
if stat2['lines_changed']:
# skip authors with no contribution in the time range
results.append(stat2)
def compute_interval_rollup(cls,
repo=None,
author=None,
interval=None,
start_day=None,
total_instances=None):
"""
Use the daily team stats to generate weekly or monthly rollup stats.
start_day is the beginning of that period
"""
# IF in weekly mode, and start_day is this week, we need to delete the current stat
# IF in monthly mode, and start_day is this month, we need to delete the current stat
end_date = cls.get_end_day(start_day, interval)
days = None
if not author:
days = Statistic.objects.filter(
author__isnull=True,
interval=DAY,
repo=repo,
start_date__range=(cls.aware(start_day), cls.aware(end_date)))
else:
days = Statistic.objects.filter(
author=author,
interval=DAY,
repo=repo,
start_date__range=(cls.aware(start_day), cls.aware(end_date)))
# aggregates total stats for the interval
data = days.aggregate(lines_added=Sum("lines_added"),
lines_removed=Sum("lines_removed"),
lines_changed=Sum("lines_changed"),
commit_total=Sum("commit_total"),
files_changed=Sum("files_changed"),
author_total=Max("author_total"))
stat = Statistic(start_date=cls.aware(start_day),
interval=interval,
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=data['author_total'])
cls.smart_bulk_update(repo=repo,
start_day=start_day,
author=author,
interval=interval,
stat=stat,
total_instances=total_instances)
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
"""
file_changes = None
if not author:
file_changes = FileChange.objects.select_related(
'commit', 'author').filter(
commit__commit_date__year=start_day.year,
commit__commit_date__month=start_day.month,
commit__commit_date__day=start_day.day,
)
else:
file_changes = FileChange.objects.select_related(
'commit',
'author').filter(commit__commit_date__year=start_day.year,
commit__commit_date__month=start_day.month,
commit__commit_date__day=start_day.day,
commit__author=author)
if file_changes.count() == 0:
return
# FIXME: probably not the most efficient way to do this
commits = file_changes.values_list('commit',
flat=True).distinct().all()
files = file_changes.values_list('file', flat=True).distinct().all()
authors = Commit.objects.filter(pk__in=commits).values_list(
'author', flat=True).distinct().all()
authors_count = len(authors)
# Aggregate values from query set for rollup
data = file_changes.aggregate(lines_added=Sum("lines_added"),
lines_removed=Sum("lines_removed"))
commits_total = len(commits)
files_changed = len(files)
lines_added = data['lines_added']
lines_removed = data['lines_removed']
lines_changed = lines_added + lines_removed
# FIXME: if start_day is today, we need to delete the current stat
# Create total rollup row for the day
stat = Statistic(start_date=cls.aware(start_day),
interval=DAY,
repo=repo,
author=author,
lines_added=lines_added,
lines_removed=lines_removed,
lines_changed=lines_changed,
commit_total=commits_total,
files_changed=files_changed,
author_total=authors_count)
cls.smart_bulk_update(repo=repo,
start_day=start_day,
author=author,
interval=DAY,
stat=stat,
total_instances=total_instances)