def _get_user_usage_data(users,
groups=None,
period_start=None,
period_end=None,
group_id=None):
"""
Returns facility user data, within the given date range.
"""
groups = groups or set([user.group for user in users])
# compute period start and end
# Now compute stats, based on queried data
num_exercises = len(get_node_cache('Exercise'))
user_data = OrderedDict()
group_data = OrderedDict()
# Make queries efficiently
exercise_logs = ExerciseLog.objects.filter(user__in=users, complete=True)
video_logs = VideoLog.objects.filter(user__in=users)
login_logs = UserLogSummary.objects.filter(user__in=users)
# filter results
if period_start:
exercise_logs = exercise_logs.filter(
completion_timestamp__gte=period_start)
video_logs = video_logs.filter(completion_timestamp__gte=period_start)
login_logs = login_logs.filter(start_datetime__gte=period_start)
if period_end:
exercise_logs = exercise_logs.filter(
completion_timestamp__lte=period_end)
video_logs = video_logs.filter(completion_timestamp__lte=period_end)
login_logs = login_logs.filter(end_datetime__lte=period_end)
# Force results in a single query
exercise_logs = list(exercise_logs.values("exercise_id", "user__pk"))
video_logs = list(video_logs.values("video_id", "user__pk"))
login_logs = list(
login_logs.values("activity_type", "total_seconds", "user__pk"))
for user in users:
user_data[user.pk] = OrderedDict()
user_data[user.pk]["id"] = user.pk
user_data[user.pk]["first_name"] = user.first_name
user_data[user.pk]["last_name"] = user.last_name
user_data[user.pk]["username"] = user.username
user_data[user.pk]["group"] = user.group
user_data[user.pk][
"total_report_views"] = 0 #report_stats["count__sum"] or 0
user_data[
user.pk]["total_logins"] = 0 # login_stats["count__sum"] or 0
user_data[user.pk][
"total_hours"] = 0 #login_stats["total_seconds__sum"] or 0)/3600.
user_data[user.pk]["total_exercises"] = 0
user_data[user.pk]["pct_mastery"] = 0.
user_data[user.pk]["exercises_mastered"] = []
user_data[user.pk]["total_videos"] = 0
user_data[user.pk]["videos_watched"] = []
for elog in exercise_logs:
user_data[elog["user__pk"]]["total_exercises"] += 1
user_data[elog["user__pk"]]["pct_mastery"] += 1. / num_exercises
user_data[elog["user__pk"]]["exercises_mastered"].append(
elog["exercise_id"])
for vlog in video_logs:
user_data[vlog["user__pk"]]["total_videos"] += 1
user_data[vlog["user__pk"]]["videos_watched"].append(vlog["video_id"])
for llog in login_logs:
if llog["activity_type"] == UserLog.get_activity_int("coachreport"):
user_data[llog["user__pk"]]["total_report_views"] += 1
elif llog["activity_type"] == UserLog.get_activity_int("login"):
user_data[llog["user__pk"]]["total_hours"] += (
llog["total_seconds"]) / 3600.
user_data[llog["user__pk"]]["total_logins"] += 1
for group in list(groups) + [None] * (group_id == None or _(group_id) == _(
"Ungrouped")): # None for ungrouped, if no group_id passed.
group_pk = getattr(group, "pk", None)
group_name = getattr(group, "name", _("Ungrouped"))
group_data[group_pk] = {
"id": group_pk,
"name": group_name,
"total_logins": 0,
"total_hours": 0,
"total_users": 0,
"total_videos": 0,
"total_exercises": 0,
"pct_mastery": 0,
}
# Add group data. Allow a fake group "Ungrouped"
for user in users:
group_pk = getattr(user.group, "pk", None)
group_data[group_pk]["total_users"] += 1
group_data[group_pk]["total_logins"] += user_data[
user.pk]["total_logins"]
group_data[group_pk]["total_hours"] += user_data[
user.pk]["total_hours"]
group_data[group_pk]["total_videos"] += user_data[
user.pk]["total_videos"]
group_data[group_pk]["total_exercises"] += user_data[
user.pk]["total_exercises"]
total_mastery_so_far = (group_data[group_pk]["pct_mastery"] *
(group_data[group_pk]["total_users"] - 1) +
user_data[user.pk]["pct_mastery"])
group_data[group_pk][
"pct_mastery"] = total_mastery_so_far / group_data[group_pk][
"total_users"]
if len(group_data) == 1 and group_data.has_key(None):
if not group_data[None]["total_users"]:
del group_data[None]
return (user_data, group_data)
def _get_user_usage_data(users, groups=None, period_start=None, period_end=None, group_id=None):
"""
Returns facility user data, within the given date range.
"""
groups = groups or set([user.group for user in users])
# compute period start and end
# Now compute stats, based on queried data
num_exercises = len(get_node_cache('Exercise'))
user_data = OrderedDict()
group_data = OrderedDict()
# Make queries efficiently
exercise_logs = ExerciseLog.objects.filter(user__in=users, complete=True)
video_logs = VideoLog.objects.filter(user__in=users, total_seconds_watched__gt=0)
login_logs = UserLogSummary.objects.filter(user__in=users)
# filter results
login_logs = login_logs.filter(total_seconds__gt=0)
if period_start:
exercise_logs = exercise_logs.filter(completion_timestamp__gte=period_start)
if period_end:
# MUST: Fix the midnight bug where period end covers up to the prior day only because
# period end is datetime(year, month, day, hour=0, minute=0), meaning midnight of previous day.
# Example:
# If period_end == '2014-12-01', we cannot include the records dated '2014-12-01 09:30'.
# So to fix this, we change it to '2014-12-01 23:59.999999'.
period_end = dateutil.parser.parse(period_end)
period_end = period_end + dateutil.relativedelta.relativedelta(days=+1, microseconds=-1)
exercise_logs = exercise_logs.filter(completion_timestamp__lte=period_end)
if period_start and period_end:
exercise_logs = exercise_logs.filter(Q(completion_timestamp__gte=period_start) &
Q(completion_timestamp__lte=period_end))
q1 = Q(completion_timestamp__isnull=False) & \
Q(completion_timestamp__gte=period_start) & \
Q(completion_timestamp__lte=period_end)
q2 = Q(completion_timestamp__isnull=True)
video_logs = video_logs.filter(q1 | q2)
login_q1 = Q(start_datetime__gte=period_start) & Q(start_datetime__lte=period_end) & \
Q(end_datetime__gte=period_start) & Q(end_datetime__lte=period_end)
login_logs = login_logs.filter(login_q1)
# Force results in a single query
exercise_logs = list(exercise_logs.values("exercise_id", "user__pk"))
video_logs = list(video_logs.values("video_id", "user__pk"))
login_logs = list(login_logs.values("activity_type", "total_seconds", "user__pk"))
for user in users:
user_data[user.pk] = OrderedDict()
user_data[user.pk]["id"] = user.pk
user_data[user.pk]["first_name"] = user.first_name
user_data[user.pk]["last_name"] = user.last_name
user_data[user.pk]["username"] = user.username
user_data[user.pk]["group"] = user.group
user_data[user.pk]["total_report_views"] = 0#report_stats["count__sum"] or 0
user_data[user.pk]["total_logins"] =0# login_stats["count__sum"] or 0
user_data[user.pk]["total_hours"] = 0#login_stats["total_seconds__sum"] or 0)/3600.
user_data[user.pk]["total_exercises"] = 0
user_data[user.pk]["pct_mastery"] = 0.
user_data[user.pk]["exercises_mastered"] = []
user_data[user.pk]["total_videos"] = 0
user_data[user.pk]["videos_watched"] = []
for elog in exercise_logs:
user_data[elog["user__pk"]]["total_exercises"] += 1
user_data[elog["user__pk"]]["pct_mastery"] += 1. / num_exercises
user_data[elog["user__pk"]]["exercises_mastered"].append(elog["exercise_id"])
for vlog in video_logs:
user_data[vlog["user__pk"]]["total_videos"] += 1
user_data[vlog["user__pk"]]["videos_watched"].append(vlog["video_id"])
for llog in login_logs:
if llog["activity_type"] == UserLog.get_activity_int("coachreport"):
user_data[llog["user__pk"]]["total_report_views"] += 1
elif llog["activity_type"] == UserLog.get_activity_int("login"):
user_data[llog["user__pk"]]["total_hours"] += (llog["total_seconds"]) / 3600.
user_data[llog["user__pk"]]["total_logins"] += 1
for group in list(groups) + [None]*(group_id==None or group_id=="Ungrouped"): # None for ungrouped, if no group_id passed.
group_pk = getattr(group, "pk", None)
group_name = getattr(group, "name", _("Ungrouped"))
group_data[group_pk] = {
"id": group_pk,
"name": group_name,
"total_logins": 0,
"total_hours": 0,
"total_users": 0,
"total_videos": 0,
"total_exercises": 0,
"pct_mastery": 0,
}
# Add group data. Allow a fake group "Ungrouped"
for user in users:
group_pk = getattr(user.group, "pk", None)
if group_pk not in group_data:
logging.error("User %s still in nonexistent group %s!" % (user.id, group_pk))
continue
group_data[group_pk]["total_users"] += 1
group_data[group_pk]["total_logins"] += user_data[user.pk]["total_logins"]
group_data[group_pk]["total_hours"] += user_data[user.pk]["total_hours"]
group_data[group_pk]["total_videos"] += user_data[user.pk]["total_videos"]
group_data[group_pk]["total_exercises"] += user_data[user.pk]["total_exercises"]
total_mastery_so_far = (group_data[group_pk]["pct_mastery"] * (group_data[group_pk]["total_users"] - 1) + user_data[user.pk]["pct_mastery"])
group_data[group_pk]["pct_mastery"] = total_mastery_so_far / group_data[group_pk]["total_users"]
if len(group_data) == 1 and group_data.has_key(None):
if not group_data[None]["total_users"]:
del group_data[None]
return (user_data, group_data)
def _get_user_usage_data(users,
groups=None,
period_start=None,
period_end=None,
group_id=None):
"""
Returns facility user data, within the given date range.
"""
groups = groups or set([user.group for user in users])
# compute period start and end
# Now compute stats, based on queried data
num_exercises = len(get_exercise_cache())
user_data = OrderedDict()
group_data = OrderedDict()
# Make queries efficiently
exercise_logs = ExerciseLog.objects.filter(user__in=users, complete=True)
video_logs = VideoLog.objects.filter(user__in=users,
total_seconds_watched__gt=0)
login_logs = UserLogSummary.objects.filter(user__in=users)
# filter results
login_logs = login_logs.filter(total_seconds__gt=0)
if period_start:
exercise_logs = exercise_logs.filter(
completion_timestamp__gte=period_start)
video_logs = video_logs.filter(completion_timestamp__gte=period_start)
if period_end:
# MUST: Fix the midnight bug where period end covers up to the prior day only because
# period end is datetime(year, month, day, hour=0, minute=0), meaning midnight of previous day.
# Example:
# If period_end == '2014-12-01', we cannot include the records dated '2014-12-01 09:30'.
# So to fix this, we change it to '2014-12-01 23:59.999999'.
period_end = dateutil.parser.parse(period_end)
period_end = period_end + dateutil.relativedelta.relativedelta(
days=+1, microseconds=-1)
exercise_logs = exercise_logs.filter(
completion_timestamp__lte=period_end)
video_logs = video_logs.filter(completion_timestamp__lte=period_end)
if period_start and period_end:
exercise_logs = exercise_logs.filter(
Q(completion_timestamp__gte=period_start)
& Q(completion_timestamp__lte=period_end))
q1 = Q(completion_timestamp__isnull=False) & \
Q(completion_timestamp__gte=period_start) & \
Q(completion_timestamp__lte=period_end)
video_logs = video_logs.filter(q1)
login_q1 = Q(start_datetime__gte=period_start) & Q(start_datetime__lte=period_end) & \
Q(end_datetime__gte=period_start) & Q(end_datetime__lte=period_end)
login_logs = login_logs.filter(login_q1)
# Force results in a single query
exercise_logs = list(exercise_logs.values("exercise_id", "user__pk"))
video_logs = list(video_logs.values("video_id", "user__pk"))
login_logs = list(
login_logs.values("activity_type", "total_seconds", "user__pk"))
for user in users:
user_data[user.pk] = OrderedDict()
user_data[user.pk]["id"] = user.pk
user_data[user.pk]["first_name"] = user.first_name
user_data[user.pk]["last_name"] = user.last_name
user_data[user.pk]["username"] = user.username
user_data[user.pk]["group"] = user.group
user_data[user.pk][
"total_report_views"] = 0 #report_stats["count__sum"] or 0
user_data[
user.pk]["total_logins"] = 0 # login_stats["count__sum"] or 0
user_data[user.pk][
"total_hours"] = 0 #login_stats["total_seconds__sum"] or 0)/3600.
user_data[user.pk]["total_exercises"] = 0
user_data[user.pk]["pct_mastery"] = 0.
user_data[user.pk]["exercises_mastered"] = []
user_data[user.pk]["total_videos"] = 0
user_data[user.pk]["videos_watched"] = []
for elog in exercise_logs:
user_data[elog["user__pk"]]["total_exercises"] += 1
user_data[elog["user__pk"]]["pct_mastery"] += 1. / num_exercises
user_data[elog["user__pk"]]["exercises_mastered"].append(
elog["exercise_id"])
for vlog in video_logs:
user_data[vlog["user__pk"]]["total_videos"] += 1
user_data[vlog["user__pk"]]["videos_watched"].append(vlog["video_id"])
for llog in login_logs:
if llog["activity_type"] == UserLog.get_activity_int("coachreport"):
user_data[llog["user__pk"]]["total_report_views"] += 1
elif llog["activity_type"] == UserLog.get_activity_int("login"):
user_data[llog["user__pk"]]["total_hours"] += (
llog["total_seconds"]) / 3600.
user_data[llog["user__pk"]]["total_logins"] += 1
for group in list(groups) + [None] * (
group_id == None or group_id
== UNGROUPED): # None for ungrouped, if no group_id passed.
group_pk = getattr(group, "pk", None)
group_name = getattr(group, "name", _(UNGROUPED))
group_title = getattr(group, "title", _(UNGROUPED))
group_data[group_pk] = {
"id": group_pk,
"name": group_name,
"title": group_title,
"total_logins": 0,
"total_hours": 0,
"total_users": 0,
"total_videos": 0,
"total_exercises": 0,
"pct_mastery": 0,
}
# Add group data. Allow a fake group UNGROUPED
for user in users:
group_pk = getattr(user.group, "pk", None)
if group_pk not in group_data:
logging.error("User %s still in nonexistent group %s!" %
(user.id, group_pk))
continue
group_data[group_pk]["total_users"] += 1
group_data[group_pk]["total_logins"] += user_data[
user.pk]["total_logins"]
group_data[group_pk]["total_hours"] += user_data[
user.pk]["total_hours"]
group_data[group_pk]["total_videos"] += user_data[
user.pk]["total_videos"]
group_data[group_pk]["total_exercises"] += user_data[
user.pk]["total_exercises"]
total_mastery_so_far = (group_data[group_pk]["pct_mastery"] *
(group_data[group_pk]["total_users"] - 1) +
user_data[user.pk]["pct_mastery"])
group_data[group_pk][
"pct_mastery"] = total_mastery_so_far / group_data[group_pk][
"total_users"]
if len(group_data) == 1 and group_data.has_key(None):
if not group_data[None]["total_users"]:
del group_data[None]
return (user_data, group_data)
def _get_user_usage_data(users, groups=None, period_start=None, period_end=None, group_id=None):
"""
Returns facility user data, within the given date range.
"""
groups = groups or set([user.group for user in users])
# compute period start and end
# Now compute stats, based on queried data
num_exercises = len(get_node_cache('Exercise'))
user_data = OrderedDict()
group_data = OrderedDict()
# Make queries efficiently
exercise_logs = ExerciseLog.objects.filter(user__in=users, complete=True)
video_logs = VideoLog.objects.filter(user__in=users)
login_logs = UserLogSummary.objects.filter(user__in=users)
# filter results
if period_start:
exercise_logs = exercise_logs.filter(completion_timestamp__gte=period_start)
video_logs = video_logs.filter(completion_timestamp__gte=period_start)
login_logs = login_logs.filter(start_datetime__gte=period_start)
if period_end:
exercise_logs = exercise_logs.filter(completion_timestamp__lte=period_end)
video_logs = video_logs.filter(completion_timestamp__lte=period_end)
login_logs = login_logs.filter(end_datetime__lte=period_end)
# Force results in a single query
exercise_logs = list(exercise_logs.values("exercise_id", "user__pk"))
video_logs = list(video_logs.values("video_id", "user__pk"))
login_logs = list(login_logs.values("activity_type", "total_seconds", "user__pk"))
for user in users:
user_data[user.pk] = OrderedDict()
user_data[user.pk]["id"] = user.pk
user_data[user.pk]["first_name"] = user.first_name
user_data[user.pk]["last_name"] = user.last_name
user_data[user.pk]["username"] = user.username
user_data[user.pk]["group"] = user.group
user_data[user.pk]["total_report_views"] = 0#report_stats["count__sum"] or 0
user_data[user.pk]["total_logins"] =0# login_stats["count__sum"] or 0
user_data[user.pk]["total_hours"] = 0#login_stats["total_seconds__sum"] or 0)/3600.
user_data[user.pk]["total_exercises"] = 0
user_data[user.pk]["pct_mastery"] = 0.
user_data[user.pk]["exercises_mastered"] = []
user_data[user.pk]["total_videos"] = 0
user_data[user.pk]["videos_watched"] = []
for elog in exercise_logs:
user_data[elog["user__pk"]]["total_exercises"] += 1
user_data[elog["user__pk"]]["pct_mastery"] += 1. / num_exercises
user_data[elog["user__pk"]]["exercises_mastered"].append(elog["exercise_id"])
for vlog in video_logs:
user_data[vlog["user__pk"]]["total_videos"] += 1
user_data[vlog["user__pk"]]["videos_watched"].append(vlog["video_id"])
for llog in login_logs:
if llog["activity_type"] == UserLog.get_activity_int("coachreport"):
user_data[llog["user__pk"]]["total_report_views"] += 1
elif llog["activity_type"] == UserLog.get_activity_int("login"):
user_data[llog["user__pk"]]["total_hours"] += (llog["total_seconds"]) / 3600.
user_data[llog["user__pk"]]["total_logins"] += 1
for group in list(groups) + [None]*(group_id==None or group_id=="Ungrouped"): # None for ungrouped, if no group_id passed.
group_pk = getattr(group, "pk", None)
group_name = getattr(group, "name", _("Ungrouped"))
group_data[group_pk] = {
"id": group_pk,
"name": group_name,
"total_logins": 0,
"total_hours": 0,
"total_users": 0,
"total_videos": 0,
"total_exercises": 0,
"pct_mastery": 0,
}
# Add group data. Allow a fake group "Ungrouped"
for user in users:
group_pk = getattr(user.group, "pk", None)
if group_pk not in group_data:
logging.error("User %s still in nonexistent group %s!" % (user.id, group_pk))
continue
group_data[group_pk]["total_users"] += 1
group_data[group_pk]["total_logins"] += user_data[user.pk]["total_logins"]
group_data[group_pk]["total_hours"] += user_data[user.pk]["total_hours"]
group_data[group_pk]["total_videos"] += user_data[user.pk]["total_videos"]
group_data[group_pk]["total_exercises"] += user_data[user.pk]["total_exercises"]
total_mastery_so_far = (group_data[group_pk]["pct_mastery"] * (group_data[group_pk]["total_users"] - 1) + user_data[user.pk]["pct_mastery"])
group_data[group_pk]["pct_mastery"] = total_mastery_so_far / group_data[group_pk]["total_users"]
if len(group_data) == 1 and group_data.has_key(None):
if not group_data[None]["total_users"]:
del group_data[None]
return (user_data, group_data)
