def tabular_view(request, facility, report_type="exercise"):
"""Tabular view also gets data server-side."""
# Define how students are ordered--used to be as efficient as possible.
student_ordering = ["last_name", "first_name", "username"]
# Get a list of topics (sorted) and groups
topics = [get_node_cache("Topic").get(tid) for tid in get_knowledgemap_topics()]
(groups, facilities) = get_accessible_objects_from_logged_in_user(request, facility=facility)
context = plotting_metadata_context(request, facility=facility)
context.update(
{
# For translators: the following two translations are nouns
"report_types": (_("exercise"), _("video")),
"request_report_type": report_type,
"topics": [{"id": t[0]["id"], "title": t[0]["title"]} for t in topics if t],
}
)
# 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(*student_ordering)
elif facility:
# Narrow by facility
search_groups = [groups_dict["groups"] for groups_dict in groups if groups_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(*student_ordering)
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(
*student_ordering
)
# 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__%s" % field for field in student_ordering])
.values("user__id", "struggling", "complete", "exercise_id")
)
exlogs = list(exlogs) # force the query to be evaluated
exlog_idx = 0
for user in users:
log_table = {}
while exlog_idx < len(exlogs) and exlogs[exlog_idx]["user__id"] == user.id:
log_table[exlogs[exlog_idx]["exercise_id"]] = exlogs[exlog_idx]
exlog_idx += 1
context["students"].append(
{ # this could be DRYer
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"name": user.get_name(),
"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["id"] for vid in context["videos"]]
# Get students
context["students"] = []
vidlogs = (
VideoLog.objects.filter(user__in=users, video_id__in=video_ids)
.order_by(*["user__%s" % field for field in student_ordering])
.values("user__id", "complete", "video_id", "total_seconds_watched", "points")
)
vidlogs = list(vidlogs) # force the query to be executed now
vidlog_idx = 0
for user in users:
log_table = {}
while vidlog_idx < len(vidlogs) and vidlogs[vidlog_idx]["user__id"] == user.id:
log_table[vidlogs[vidlog_idx]["video_id"]] = vidlogs[vidlog_idx]
vidlog_idx += 1
context["students"].append(
{ # this could be DRYer
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"name": user.get_name(),
"id": user.id,
"video_logs": log_table,
}
)
else:
raise Http404(_("Unknown report_type: %(report_type)s") % {"report_type": report_type})
if "facility_user" in request.session:
try:
# Log a "begin" and end here
user = request.session["facility_user"]
UserLog.begin_user_activity(user, activity_type="coachreport")
UserLog.update_user_activity(user, activity_type="login") # to track active login time for teachers
UserLog.end_user_activity(user, activity_type="coachreport")
except ValidationError as e:
# Never report this error; don't want this logging to block other functionality.
logging.error("Failed to update Teacher userlog activity login: %s" % e)
return context
def tabular_view(request, facility, report_type="exercise"):
"""Tabular view also gets data server-side."""
# Define how students are ordered--used to be as efficient as possible.
student_ordering = ["last_name", "first_name", "username"]
# Get a list of topics (sorted) and groups
topics = [
get_node_cache("Topic").get(tid) for tid in get_knowledgemap_topics()
]
(groups, facilities) = get_accessible_objects_from_logged_in_user(
request, facility=facility)
context = plotting_metadata_context(request, facility=facility)
context.update({
# For translators: the following two translations are nouns
"report_types": (_("exercise"), _("video")),
"request_report_type":
report_type,
"topics": [{
"id": t[0]["id"],
"title": t[0]["title"]
} for t in topics if t],
})
# 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(*student_ordering)
elif facility:
# Narrow by facility
search_groups = [
groups_dict["groups"] for groups_dict in groups
if groups_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(*student_ordering)
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(*student_ordering)
# 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__%s" % field for field in student_ordering]) \
.values("user__id", "struggling", "complete", "exercise_id")
exlogs = list(exlogs) # force the query to be evaluated
exlog_idx = 0
for user in users:
log_table = {}
while exlog_idx < len(
exlogs) and exlogs[exlog_idx]["user__id"] == user.id:
log_table[exlogs[exlog_idx]["exercise_id"]] = exlogs[exlog_idx]
exlog_idx += 1
context["students"].append({ # this could be DRYer
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"name": user.get_name(),
"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["id"] for vid in context["videos"]]
# Get students
context["students"] = []
vidlogs = VideoLog.objects \
.filter(user__in=users, video_id__in=video_ids) \
.order_by(*["user__%s" % field for field in student_ordering])\
.values("user__id", "complete", "video_id", "total_seconds_watched", "points")
vidlogs = list(vidlogs) # force the query to be executed now
vidlog_idx = 0
for user in users:
log_table = {}
while vidlog_idx < len(
vidlogs) and vidlogs[vidlog_idx]["user__id"] == user.id:
log_table[vidlogs[vidlog_idx]
["video_id"]] = vidlogs[vidlog_idx]
vidlog_idx += 1
context["students"].append({ # this could be DRYer
"first_name": user.first_name,
"last_name": user.last_name,
"username": user.username,
"name": user.get_name(),
"id": user.id,
"video_logs": log_table,
})
else:
raise Http404(
_("Unknown report_type: %(report_type)s") %
{"report_type": report_type})
if "facility_user" in request.session:
try:
# Log a "begin" and end here
user = request.session["facility_user"]
UserLog.begin_user_activity(user, activity_type="coachreport")
UserLog.update_user_activity(
user, activity_type="login"
) # to track active login time for teachers
UserLog.end_user_activity(user, activity_type="coachreport")
except ValidationError as e:
# Never report this error; don't want this logging to block other functionality.
logging.error(
"Failed to update Teacher userlog activity login: %s" % e)
return context
def student_view_context(request, xaxis="pct_mastery", yaxis="ex:attempts"):
"""
Context done separately, to be importable for similar pages.
"""
user = get_user_from_request(request=request)
if not user:
raise Http404("User not found.")
node_cache = get_node_cache()
topic_ids = get_knowledgemap_topics()
topic_ids += [ch["id"] for node in get_topic_tree()["children"] for ch in node["children"] if node["id"] != "math"]
topics = [node_cache["Topic"][id][0] for id in topic_ids]
user_id = user.id
exercise_logs = list(
ExerciseLog.objects.filter(user=user).values(
"exercise_id", "complete", "points", "attempts", "streak_progress", "struggling", "completion_timestamp"
)
)
video_logs = list(
VideoLog.objects.filter(user=user).values(
"video_id", "complete", "total_seconds_watched", "points", "completion_timestamp"
)
)
exercise_sparklines = dict()
stats = dict()
topic_exercises = dict()
topic_videos = dict()
exercises_by_topic = dict()
videos_by_topic = dict()
# Categorize every exercise log into a "midlevel" exercise
for elog in exercise_logs:
if not elog["exercise_id"] in node_cache["Exercise"]:
# Sometimes KA updates their topic tree and eliminates exercises;
# we also want to support 3rd party switching of trees arbitrarily.
logging.debug("Skip unknown exercise log for %s/%s" % (user_id, elog["exercise_id"]))
continue
parent_ids = [topic for ex in node_cache["Exercise"][elog["exercise_id"]] for topic in ex["ancestor_ids"]]
topic = set(parent_ids).intersection(set(topic_ids))
if not topic:
logging.error("Could not find a topic for exercise %s (parents=%s)" % (elog["exercise_id"], parent_ids))
continue
topic = topic.pop()
if not topic in topic_exercises:
topic_exercises[topic] = get_topic_exercises(path=node_cache["Topic"][topic][0]["path"])
exercises_by_topic[topic] = exercises_by_topic.get(topic, []) + [elog]
# Categorize every video log into a "midlevel" exercise.
for vlog in video_logs:
if not vlog["video_id"] in node_cache["Video"]:
# Sometimes KA updates their topic tree and eliminates videos;
# we also want to support 3rd party switching of trees arbitrarily.
logging.debug("Skip unknown video log for %s/%s" % (user_id, vlog["video_id"]))
continue
parent_ids = [topic for vid in node_cache["Video"][vlog["video_id"]] for topic in vid["ancestor_ids"]]
topic = set(parent_ids).intersection(set(topic_ids))
if not topic:
logging.error("Could not find a topic for video %s (parents=%s)" % (vlog["video_id"], parent_ids))
continue
topic = topic.pop()
if not topic in topic_videos:
topic_videos[topic] = get_topic_videos(path=node_cache["Topic"][topic][0]["path"])
videos_by_topic[topic] = videos_by_topic.get(topic, []) + [vlog]
# Now compute stats
for id in topic_ids: # set(topic_exercises.keys()).union(set(topic_videos.keys())):
n_exercises = len(topic_exercises.get(id, []))
n_videos = len(topic_videos.get(id, []))
exercises = exercises_by_topic.get(id, [])
videos = videos_by_topic.get(id, [])
n_exercises_touched = len(exercises)
n_videos_touched = len(videos)
exercise_sparklines[id] = [el["completion_timestamp"] for el in filter(lambda n: n["complete"], exercises)]
# total streak currently a pct, but expressed in max 100; convert to
# proportion (like other percentages here)
stats[id] = {
"ex:pct_mastery": 0
if not n_exercises_touched
else sum([el["complete"] for el in exercises]) / 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 exercises]) / float(n_exercises_touched),
"ex:average_attempts": 0
if not n_exercises_touched
else sum([el["attempts"] for el in exercises]) / float(n_exercises_touched),
"ex:average_streak": 0
if not n_exercises_touched
else sum([el["streak_progress"] for el in exercises]) / float(n_exercises_touched) / 100.0,
"ex:total_struggling": 0 if not n_exercises_touched else sum([el["struggling"] for el in exercises]),
"ex:last_completed": None
if not n_exercises_touched
else max_none([el["completion_timestamp"] or None for el in exercises]),
"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 videos]) / float(n_videos),
"vid:total_minutes": 0
if not n_videos_touched
else sum([vl["total_seconds_watched"] for vl in videos]) / 60.0,
"vid:average_points": 0.0
if not n_videos_touched
else float(sum([vl["points"] for vl in videos]) / float(n_videos_touched)),
"vid:last_completed": None
if not n_videos_touched
else max_none([vl["completion_timestamp"] or None for vl in videos]),
}
context = plotting_metadata_context(request)
return {
"form": context["form"],
"groups": context["groups"],
"facilities": context["facilities"],
"student": user,
"topics": topics,
"exercises": topic_exercises,
"exercise_logs": exercises_by_topic,
"video_logs": videos_by_topic,
"exercise_sparklines": exercise_sparklines,
"no_data": not exercise_logs and not video_logs,
"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_context(request, xaxis="pct_mastery", yaxis="ex:attempts"):
"""
Context done separately, to be importable for similar pages.
"""
user = get_user_from_request(request=request)
if not user:
raise Http404("User not found.")
node_cache = get_node_cache()
topic_ids = get_knowledgemap_topics()
topic_ids += [
ch["id"] for node in get_topic_tree()["children"]
for ch in node["children"] if node["id"] != "math"
]
topics = [node_cache["Topic"][id][0] for id in topic_ids]
user_id = user.id
exercise_logs = list(ExerciseLog.objects \
.filter(user=user) \
.values("exercise_id", "complete", "points", "attempts", "streak_progress", "struggling", "completion_timestamp"))
video_logs = list(VideoLog.objects \
.filter(user=user) \
.values("video_id", "complete", "total_seconds_watched", "points", "completion_timestamp"))
exercise_sparklines = dict()
stats = dict()
topic_exercises = dict()
topic_videos = dict()
exercises_by_topic = dict()
videos_by_topic = dict()
# Categorize every exercise log into a "midlevel" exercise
for elog in exercise_logs:
if not elog["exercise_id"] in node_cache["Exercise"]:
# Sometimes KA updates their topic tree and eliminates exercises;
# we also want to support 3rd party switching of trees arbitrarily.
logging.debug("Skip unknown exercise log for %s/%s" %
(user_id, elog["exercise_id"]))
continue
parent_ids = [
topic for ex in node_cache["Exercise"][elog["exercise_id"]]
for topic in ex["ancestor_ids"]
]
topic = set(parent_ids).intersection(set(topic_ids))
if not topic:
logging.error(
"Could not find a topic for exercise %s (parents=%s)" %
(elog["exercise_id"], parent_ids))
continue
topic = topic.pop()
if not topic in topic_exercises:
topic_exercises[topic] = get_topic_exercises(
path=node_cache["Topic"][topic][0]["path"])
exercises_by_topic[topic] = exercises_by_topic.get(topic, []) + [elog]
# Categorize every video log into a "midlevel" exercise.
for vlog in video_logs:
if not vlog["video_id"] in node_cache["Video"]:
# Sometimes KA updates their topic tree and eliminates videos;
# we also want to support 3rd party switching of trees arbitrarily.
logging.debug("Skip unknown video log for %s/%s" %
(user_id, vlog["video_id"]))
continue
parent_ids = [
topic for vid in node_cache["Video"][vlog["video_id"]]
for topic in vid["ancestor_ids"]
]
topic = set(parent_ids).intersection(set(topic_ids))
if not topic:
logging.error("Could not find a topic for video %s (parents=%s)" %
(vlog["video_id"], parent_ids))
continue
topic = topic.pop()
if not topic in topic_videos:
topic_videos[topic] = get_topic_videos(
path=node_cache["Topic"][topic][0]["path"])
videos_by_topic[topic] = videos_by_topic.get(topic, []) + [vlog]
# Now compute stats
for id in topic_ids: #set(topic_exercises.keys()).union(set(topic_videos.keys())):
n_exercises = len(topic_exercises.get(id, []))
n_videos = len(topic_videos.get(id, []))
exercises = exercises_by_topic.get(id, [])
videos = videos_by_topic.get(id, [])
n_exercises_touched = len(exercises)
n_videos_touched = len(videos)
exercise_sparklines[id] = [
el["completion_timestamp"]
for el in filter(lambda n: n["complete"], exercises)
]
# total streak currently a pct, but expressed in max 100; convert to
# proportion (like other percentages here)
stats[id] = {
"ex:pct_mastery":
0 if not n_exercises_touched else
sum([el["complete"] for el in exercises]) / 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 exercises]) / float(n_exercises_touched),
"ex:average_attempts":
0 if not n_exercises_touched else
sum([el["attempts"]
for el in exercises]) / float(n_exercises_touched),
"ex:average_streak":
0 if not n_exercises_touched else
sum([el["streak_progress"]
for el in exercises]) / float(n_exercises_touched) / 100.,
"ex:total_struggling":
0 if not n_exercises_touched else sum(
[el["struggling"] for el in exercises]),
"ex:last_completed":
None if not n_exercises_touched else max_none(
[el["completion_timestamp"] or None for el in exercises]),
"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 videos]) / float(n_videos),
"vid:total_minutes":
0 if not n_videos_touched else
sum([vl["total_seconds_watched"] for vl in videos]) / 60.,
"vid:average_points":
0. if not n_videos_touched else float(
sum([vl["points"]
for vl in videos]) / float(n_videos_touched)),
"vid:last_completed":
None if not n_videos_touched else max_none(
[vl["completion_timestamp"] or None for vl in videos]),
}
context = plotting_metadata_context(request)
return {
"form":
context["form"],
"groups":
context["groups"],
"facilities":
context["facilities"],
"student":
user,
"topics":
topics,
"exercises":
topic_exercises,
"exercise_logs":
exercises_by_topic,
"video_logs":
videos_by_topic,
"exercise_sparklines":
exercise_sparklines,
"no_data":
not exercise_logs and not video_logs,
"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"
},
]
}
