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)
topic_slugs = [t["id"] for t in get_knowledgemap_topics()]
topics = [NODE_CACHE["Topic"][slug] for slug in topic_slugs]
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("youtube_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:
parents = NODE_CACHE["Exercise"][elog["exercise_id"]]["parents"]
topic = set(parents).intersection(set(topic_slugs))
if not topic:
logging.error(
"Could not find a topic for exercise %s (parents=%s)" %
(elog["exercise_id"], parents))
continue
topic = topic.pop()
if not topic in topic_exercises:
topic_exercises[topic] = get_topic_exercises(
path=NODE_CACHE["Topic"][topic]["path"])
exercises_by_topic[topic] = exercises_by_topic.get(topic, []) + [elog]
# Categorize every video log into a "midlevel" exercise.
for vlog in video_logs:
parents = NODE_CACHE["Video"][ID2SLUG_MAP[
vlog["youtube_id"]]]["parents"]
topic = set(parents).intersection(set(topic_slugs))
if not topic:
logging.error("Could not find a topic for video %s (parents=%s)" %
(vlog["youtube_id"], parents))
continue
topic = topic.pop()
if not topic in topic_videos:
topic_videos[topic] = get_topic_videos(
path=NODE_CACHE["Topic"][topic]["path"])
videos_by_topic[topic] = videos_by_topic.get(topic, []) + [vlog]
# Now compute stats
for topic in topic_slugs: #set(topic_exercises.keys()).union(set(topic_videos.keys())):
n_exercises = len(topic_exercises.get(topic, []))
n_videos = len(topic_videos.get(topic, []))
exercises = exercises_by_topic.get(topic, [])
videos = videos_by_topic.get(topic, [])
n_exercises_touched = len(exercises)
n_videos_touched = len(videos)
exercise_sparklines[topic] = [
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[topic] = {
"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"
},
]
}
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)
topic_slugs = [t["id"] for t in get_knowledgemap_topics()]
topics = [NODE_CACHE["Topic"][slug] for slug in topic_slugs]
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("youtube_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:
parents = NODE_CACHE["Exercise"][elog["exercise_id"]]["parents"]
topic = set(parents).intersection(set(topic_slugs))
if not topic:
logging.error("Could not find a topic for exercise %s (parents=%s)" % (elog["exercise_id"], parents))
continue
topic = topic.pop()
if not topic in topic_exercises:
topic_exercises[topic] = get_topic_exercises(path=NODE_CACHE["Topic"][topic]["path"])
exercises_by_topic[topic] = exercises_by_topic.get(topic, []) + [elog]
# Categorize every video log into a "midlevel" exercise.
for vlog in video_logs:
parents = NODE_CACHE["Video"][ID2SLUG_MAP[vlog["youtube_id"]]]["parents"]
topic = set(parents).intersection(set(topic_slugs))
if not topic:
logging.error("Could not find a topic for video %s (parents=%s)" % (vlog["youtube_id"], parents))
continue
topic = topic.pop()
if not topic in topic_videos:
topic_videos[topic] = get_topic_videos(path=NODE_CACHE["Topic"][topic]["path"])
videos_by_topic[topic] = videos_by_topic.get(topic, []) + [vlog]
# Now compute stats
for topic in topic_slugs:#set(topic_exercises.keys()).union(set(topic_videos.keys())):
n_exercises = len(topic_exercises.get(topic, []))
n_videos = len(topic_videos.get(topic, []))
exercises = exercises_by_topic.get(topic, [])
videos = videos_by_topic.get(topic, [])
n_exercises_touched = len(exercises)
n_videos_touched = len(videos)
exercise_sparklines[topic] = [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[topic] = {
"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"},
]
}
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"},
]
}
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 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"},
]
}
