def tabular_view(request, facility, report_type="exercise"):
"""Tabular view also gets data server-side."""
# Get a list of topics (sorted) and groups
topics = get_all_midlevel_topics()
(groups, facilities) = get_accessible_objects_from_logged_in_user(request)
context = plotting_metadata_context(request, facility=facility)
context.update({
"report_types": ("exercise", "video"),
"request_report_type": report_type,
"topics": topics,
})
# get querystring info
topic_id = request.GET.get("topic", "")
# No valid data; just show generic
if not topic_id or not re.match("^[\w\-]+$", topic_id):
return context
group_id = request.GET.get("group", "")
if group_id:
# Narrow by group
users = FacilityUser.objects.filter(group=group_id,
is_teacher=False).order_by(
"last_name", "first_name")
elif facility:
# Narrow by facility
search_groups = [
dict["groups"] for dict in groups
if dict["facility"] == facility.id
]
assert len(search_groups) <= 1, "should only have one or zero matches."
# Return groups and ungrouped
search_groups = search_groups[
0] # make sure to include ungrouped students
users = FacilityUser.objects.filter(
Q(group__in=search_groups) | Q(group=None, facility=facility),
is_teacher=False).order_by("last_name", "first_name")
else:
# Show all (including ungrouped)
for groups_dict in groups:
search_groups += groups_dict["groups"]
users = FacilityUser.objects.filter(
Q(group__in=search_groups) | Q(group=None),
is_teacher=False).order_by("last_name", "first_name")
# We have enough data to render over a group of students
# Get type-specific information
if report_type == "exercise":
# Fill in exercises
exercises = get_topic_exercises(topic_id=topic_id)
exercises = sorted(exercises,
key=lambda e: (e["h_position"], e["v_position"]))
context["exercises"] = exercises
# More code, but much faster
exercise_names = [ex["name"] for ex in context["exercises"]]
# Get students
context["students"] = []
exlogs = ExerciseLog.objects \
.filter(user__in=users, exercise_id__in=exercise_names) \
.order_by("user__last_name", "user__first_name")\
.values("user__id", "struggling", "complete", "exercise_id")
exlog_idx = 0
for user in users:
log_table = {}
while exlog_idx < exlogs.count(
) and exlogs[exlog_idx]["user__id"] == user.id:
log_table[exlogs[exlog_idx]["exercise_id"]] = exlogs[exlog_idx]
exlog_idx += 1
context["students"].append({
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"id": user.id,
"exercise_logs": log_table,
})
elif report_type == "video":
# Fill in videos
context["videos"] = get_topic_videos(topic_id=topic_id)
# More code, but much faster
video_ids = [vid["youtube_id"] for vid in context["videos"]]
# Get students
context["students"] = []
vidlogs = VideoLog.objects \
.filter(user__in=users, youtube_id__in=video_ids) \
.order_by("user__last_name", "user__first_name")\
.values("user__id", "complete", "youtube_id", "total_seconds_watched", "points")
vidlog_idx = 0
for user in users:
log_table = {}
while vidlog_idx < vidlogs.count(
) and vidlogs[vidlog_idx]["user__id"] == user.id:
log_table[vidlogs[vidlog_idx]
["youtube_id"]] = vidlogs[vidlog_idx]
vidlog_idx += 1
context["students"].append({
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"id": user.id,
"video_logs": log_table,
})
else:
raise Http404("Unknown report_type: %s" % report_type)
return context
def student_view(request, xaxis="pct_mastery", yaxis="ex:attempts"):
"""
Student view: data generated on the back-end.
Student view lists a by-topic-summary of their activity logs.
"""
user = get_user_from_request(request=request)
topics = get_all_midlevel_topics()
topic_ids = [t['id'] for t in topics]
topics = filter(partial(lambda n, ids: n['id'] in ids, ids=topic_ids), topicdata.NODE_CACHE['Topic'].values()) # real data, like paths
any_data = False # whether the user has any data at all.
exercise_logs = dict()
video_logs = dict()
exercise_sparklines = dict()
stats = dict()
topic_exercises = dict()
for topic in topics:
topic_exercises[topic['id']] = get_topic_exercises(path=topic['path'])
n_exercises = len(topic_exercises[topic['id']])
exercise_logs[topic['id']] = ExerciseLog.objects.filter(user=user, exercise_id__in=[t['name'] for t in topic_exercises[topic['id']]]).order_by("completion_timestamp")
n_exercises_touched = len(exercise_logs[topic['id']])
topic_videos = get_topic_videos(topic_id=topic['id'])
n_videos = len(topic_videos)
video_logs[topic['id']] = VideoLog.objects.filter(user=user, youtube_id__in=[tv['youtube_id'] for tv in topic_videos]).order_by("completion_timestamp")
n_videos_touched = len(video_logs[topic['id']])
exercise_sparklines[topic['id']] = [el.completion_timestamp for el in filter(lambda n: n.complete, exercise_logs[topic['id']])]
# total streak currently a pct, but expressed in max 100; convert to
# proportion (like other percentages here)
stats[topic['id']] = {
"ex:pct_mastery": 0 if not n_exercises_touched else sum([el.complete for el in exercise_logs[topic['id']]]) / float(n_exercises),
"ex:pct_started": 0 if not n_exercises_touched else n_exercises_touched / float(n_exercises),
"ex:average_points": 0 if not n_exercises_touched else sum([el.points for el in exercise_logs[topic['id']]]) / float(n_exercises_touched),
"ex:average_attempts": 0 if not n_exercises_touched else sum([el.attempts for el in exercise_logs[topic['id']]]) / float(n_exercises_touched),
"ex:average_streak": 0 if not n_exercises_touched else sum([el.streak_progress for el in exercise_logs[topic['id']]]) / float(n_exercises_touched) / 100.,
"ex:total_struggling": 0 if not n_exercises_touched else sum([el.struggling for el in exercise_logs[topic['id']]]),
"ex:last_completed": None if not n_exercises_touched else max([el.completion_timestamp or None for el in exercise_logs[topic['id']]]),
"vid:pct_started": 0 if not n_videos_touched else n_videos_touched / float(n_videos),
"vid:pct_completed": 0 if not n_videos_touched else sum([vl.complete for vl in video_logs[topic['id']]]) / float(n_videos),
"vid:total_minutes": 0 if not n_videos_touched else sum([vl.total_seconds_watched for vl in video_logs[topic['id']]]) / 60.,
"vid:average_points": 0. if not n_videos_touched else float(sum([vl.points for vl in video_logs[topic['id']]]) / float(n_videos_touched)),
"vid:last_completed": None if not n_videos_touched else max([vl.completion_timestamp or None for vl in video_logs[topic['id']]]),
}
any_data = any_data or n_exercises_touched > 0 or n_videos_touched > 0
context = plotting_metadata_context(request)
return {
"form": context["form"],
"groups": context["groups"],
"facilities": context["facilities"],
"student": user,
"topics": topics,
"topic_ids": topic_ids,
"exercises": topic_exercises,
"exercise_logs": exercise_logs,
"video_logs": video_logs,
"exercise_sparklines": exercise_sparklines,
"no_data": not any_data,
"stats": stats,
"stat_defs": [ # this order determines the order of display
{"key": "ex:pct_mastery", "title": _("% Mastery"), "type": "pct"},
{"key": "ex:pct_started", "title": _("% Started"), "type": "pct"},
{"key": "ex:average_points", "title": _("Average Points"), "type": "float"},
{"key": "ex:average_attempts", "title": _("Average Attempts"), "type": "float"},
{"key": "ex:average_streak", "title": _("Average Streak"), "type": "pct"},
{"key": "ex:total_struggling", "title": _("Struggling"), "type": "int"},
{"key": "ex:last_completed", "title": _("Last Completed"), "type": "date"},
{"key": "vid:pct_completed", "title": _("% Completed"), "type": "pct"},
{"key": "vid:pct_started", "title": _("% Started"), "type": "pct"},
{"key": "vid:total_minutes", "title": _("Average Minutes Watched"),"type": "float"},
{"key": "vid:average_points", "title": _("Average Points"), "type": "float"},
{"key": "vid:last_completed", "title": _("Last Completed"), "type": "date"},
]
}
def student_view(request, xaxis="pct_mastery", yaxis="ex:attempts"):
"""
Student view: data generated on the back-end.
Student view lists a by-topic-summary of their activity logs.
"""
user = get_user_from_request(request=request)
topics = get_all_midlevel_topics()
topic_ids = [t['id'] for t in topics]
topics = filter(
partial(lambda n, ids: n['id'] in ids, ids=topic_ids),
topicdata.NODE_CACHE['Topic'].values()) # real data, like paths
any_data = False # whether the user has any data at all.
exercise_logs = dict()
video_logs = dict()
exercise_sparklines = dict()
stats = dict()
topic_exercises = dict()
for topic in topics:
topic_exercises[topic['id']] = get_topic_exercises(path=topic['path'])
n_exercises = len(topic_exercises[topic['id']])
exercise_logs[topic['id']] = ExerciseLog.objects.filter(
user=user,
exercise_id__in=[t['name'] for t in topic_exercises[topic['id']]
]).order_by("completion_timestamp")
n_exercises_touched = len(exercise_logs[topic['id']])
topic_videos = get_topic_videos(topic_id=topic['id'])
n_videos = len(topic_videos)
video_logs[topic['id']] = VideoLog.objects.filter(
user=user,
youtube_id__in=[tv['youtube_id'] for tv in topic_videos
]).order_by("completion_timestamp")
n_videos_touched = len(video_logs[topic['id']])
exercise_sparklines[topic['id']] = [
el.completion_timestamp
for el in filter(lambda n: n.complete, exercise_logs[topic['id']])
]
# total streak currently a pct, but expressed in max 100; convert to
# proportion (like other percentages here)
stats[topic['id']] = {
"ex:pct_mastery":
0 if not n_exercises_touched else
sum([el.complete
for el in exercise_logs[topic['id']]]) / float(n_exercises),
"ex:pct_started":
0 if not n_exercises_touched else n_exercises_touched /
float(n_exercises),
"ex:average_points":
0 if not n_exercises_touched else
sum([el.points for el in exercise_logs[topic['id']]]) /
float(n_exercises_touched),
"ex:average_attempts":
0 if not n_exercises_touched else
sum([el.attempts for el in exercise_logs[topic['id']]]) /
float(n_exercises_touched),
"ex:average_streak":
0 if not n_exercises_touched else
sum([el.streak_progress for el in exercise_logs[topic['id']]]) /
float(n_exercises_touched) / 100.,
"ex:total_struggling":
0 if not n_exercises_touched else sum(
[el.struggling for el in exercise_logs[topic['id']]]),
"ex:last_completed":
None if not n_exercises_touched else max_none([
el.completion_timestamp or None
for el in exercise_logs[topic['id']]
]),
"vid:pct_started":
0 if not n_videos_touched else n_videos_touched / float(n_videos),
"vid:pct_completed":
0 if not n_videos_touched else
sum([vl.complete
for vl in video_logs[topic['id']]]) / float(n_videos),
"vid:total_minutes":
0 if not n_videos_touched else
sum([vl.total_seconds_watched
for vl in video_logs[topic['id']]]) / 60.,
"vid:average_points":
0. if not n_videos_touched else float(
sum([vl.points for vl in video_logs[topic['id']]]) /
float(n_videos_touched)),
"vid:last_completed":
None if not n_videos_touched else max_none([
vl.completion_timestamp or None
for vl in video_logs[topic['id']]
]),
}
any_data = any_data or n_exercises_touched > 0 or n_videos_touched > 0
context = plotting_metadata_context(request)
return {
"form":
context["form"],
"groups":
context["groups"],
"facilities":
context["facilities"],
"student":
user,
"topics":
topics,
"topic_ids":
topic_ids,
"exercises":
topic_exercises,
"exercise_logs":
exercise_logs,
"video_logs":
video_logs,
"exercise_sparklines":
exercise_sparklines,
"no_data":
not any_data,
"stats":
stats,
"stat_defs": [ # this order determines the order of display
{
"key": "ex:pct_mastery",
"title": _("% Mastery"),
"type": "pct"
},
{
"key": "ex:pct_started",
"title": _("% Started"),
"type": "pct"
},
{
"key": "ex:average_points",
"title": _("Average Points"),
"type": "float"
},
{
"key": "ex:average_attempts",
"title": _("Average Attempts"),
"type": "float"
},
{
"key": "ex:average_streak",
"title": _("Average Streak"),
"type": "pct"
},
{
"key": "ex:total_struggling",
"title": _("Struggling"),
"type": "int"
},
{
"key": "ex:last_completed",
"title": _("Last Completed"),
"type": "date"
},
{
"key": "vid:pct_completed",
"title": _("% Completed"),
"type": "pct"
},
{
"key": "vid:pct_started",
"title": _("% Started"),
"type": "pct"
},
{
"key": "vid:total_minutes",
"title": _("Average Minutes Watched"),
"type": "float"
},
{
"key": "vid:average_points",
"title": _("Average Points"),
"type": "float"
},
{
"key": "vid:last_completed",
"title": _("Last Completed"),
"type": "date"
},
]
}
def tabular_view(request, facility, report_type="exercise"):
"""Tabular view also gets data server-side."""
# Get a list of topics (sorted) and groups
topics = get_all_midlevel_topics()
(groups, facilities) = get_accessible_objects_from_logged_in_user(request)
context = plotting_metadata_context(request, facility=facility)
context.update({
"report_types": ("exercise", "video"),
"request_report_type": report_type,
"topics": topics,
})
# get querystring info
topic_id = request.GET.get("topic", "")
# No valid data; just show generic
if not topic_id or not re.match("^[\w\-]+$", topic_id):
return context
group_id = request.GET.get("group", "")
if group_id:
# Narrow by group
users = FacilityUser.objects.filter(
group=group_id, is_teacher=False).order_by("last_name", "first_name")
elif facility:
# Narrow by facility
search_groups = [dict["groups"] for dict in groups if dict["facility"] == facility.id]
assert len(search_groups) <= 1, "should only have one or zero matches."
# Return groups and ungrouped
search_groups = search_groups[0] # make sure to include ungrouped students
users = FacilityUser.objects.filter(
Q(group__in=search_groups) | Q(group=None, facility=facility), is_teacher=False).order_by("last_name", "first_name")
else:
# Show all (including ungrouped)
for groups_dict in groups:
search_groups += groups_dict["groups"]
users = FacilityUser.objects.filter(
Q(group__in=search_groups) | Q(group=None), is_teacher=False).order_by("last_name", "first_name")
# We have enough data to render over a group of students
# Get type-specific information
if report_type == "exercise":
# Fill in exercises
exercises = get_topic_exercises(topic_id=topic_id)
exercises = sorted(exercises, key=lambda e: (e["h_position"], e["v_position"]))
context["exercises"] = exercises
# More code, but much faster
exercise_names = [ex["name"] for ex in context["exercises"]]
# Get students
context["students"] = []
exlogs = ExerciseLog.objects \
.filter(user__in=users, exercise_id__in=exercise_names) \
.order_by("user__last_name", "user__first_name")\
.values("user__id", "struggling", "complete", "exercise_id")
exlog_idx = 0
for user in users:
log_table = {}
while exlog_idx < exlogs.count() and exlogs[exlog_idx]["user__id"] == user.id:
log_table[exlogs[exlog_idx]["exercise_id"]] = exlogs[exlog_idx]
exlog_idx += 1
context["students"].append({
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"id": user.id,
"exercise_logs": log_table,
})
elif report_type == "video":
# Fill in videos
context["videos"] = get_topic_videos(topic_id=topic_id)
# More code, but much faster
video_ids = [vid["youtube_id"] for vid in context["videos"]]
# Get students
context["students"] = []
vidlogs = VideoLog.objects \
.filter(user__in=users, youtube_id__in=video_ids) \
.order_by("user__last_name", "user__first_name")\
.values("user__id", "complete", "youtube_id", "total_seconds_watched", "points")
vidlog_idx = 0
for user in users:
log_table = {}
while vidlog_idx < vidlogs.count() and vidlogs[vidlog_idx]["user__id"] == user.id:
log_table[vidlogs[vidlog_idx]["youtube_id"]] = vidlogs[vidlog_idx]
vidlog_idx += 1
context["students"].append({
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"id": user.id,
"video_logs": log_table,
})
else:
raise Http404("Unknown report_type: %s" % report_type)
return context
