Python UserLog.get_activity_int方法代码示例

# 需要导入模块: from kalite.main.models import UserLog [as 别名]
# 或者: from kalite.main.models.UserLog import get_activity_int [as 别名]
def generate_fake_coachreport_logs(password="hellothere"):
    try:
        t = FacilityUser.objects.get(facility=Facility.objects.all()[0], username=random.choice(firstnames))
    except FacilityUser.DoesNotExist as e:
        t = FacilityUser(facility=Facility.objects.all()[0], username=random.choice(firstnames))
        t.set_password(password)
        t.save()

    # TODO: create flags later
    num_logs = 20
    logs = []
    for _ in xrange(num_logs):
        date_logged_in = datetime.datetime.now() - datetime.timedelta(days=random.randint(1, 10))
        date_viewed_coachreport = date_logged_in + datetime.timedelta(minutes=random.randint(0, 30))
        date_logged_out = date_viewed_coachreport + datetime.timedelta(minutes=random.randint(0, 30))
        login_log = UserLog.objects.create(
            user=t,
            activity_type=UserLog.get_activity_int("login"),
            start_datetime=date_logged_in,
            last_active_datetime=date_viewed_coachreport,
            end_datetime=date_logged_out,
        )
        logging.info("created login log for teacher %s" % t.username)
        coachreport_log = UserLog.objects.create(
            user=t,
            activity_type=UserLog.get_activity_int("coachreport"),
            start_datetime=date_viewed_coachreport,
            last_active_datetime=date_viewed_coachreport,
            end_datetime=date_viewed_coachreport,
        )
        logs.append((login_log, coachreport_log))
        logging.info("created coachreport log for teacher %s" % t.username)
    return logs

# 需要导入模块: from kalite.main.models import UserLog [as 别名]
# 或者: from kalite.main.models.UserLog import get_activity_int [as 别名]
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"]
#.........这里部分代码省略.........