def del_raw(viewer, start, end):
start_time = str_to_datetime(start, UTC)
end_time = str_to_datetime(end, UTC)
raws = Raw.objects.filter(subject_id=viewer,
start_time__range=(start_time, end_time))
print raws[0].subject_id, raws[0].start_time, raws[0].end_time
raws.delete()
def add_session(start, end, content_name, viewers):
start_time = str_to_datetime(start, UTC)
end_time = str_to_datetime(end, UTC)
duration = end_time - start_time
content = Content.objects.get(name=content_name)
session_name = content_name + str(
len(Session.objects.filter(content=content)))
session = Session.objects.create(content=content,
name=session_name,
start_time=start_time,
end_time=end_time,
content_start_sec=0,
content_end_sec=duration.total_seconds())
viewers = Viewer.objects.filter(user__username__in=viewers)
session.viewers.add(*viewers)
def create_tags_from_file(f):
viewers = {}
label_types = {}
reader = csv.reader(f, delimiter=",")
reader.next()
for row in reader:
viewer_name, start, end, label_type_name, label_str = row
start = str_to_datetime(start.strip(), UTC)
end = str_to_datetime(end.strip(), UTC)
if not viewer_name in viewers:
print viewer_name
viewers[viewer_name] = Viewer.objects.get(user__username=viewer_name.strip())
if not label_type_name in label_types:
label_types[label_type_name] = LabelType.objects.get(name=label_type_name.strip())
tag=Tag.objects.create(subject=viewers[viewer_name], start_time=start, end_time=end)
Label.objects.create(label_type=label_types[label_type_name], true=-1, predicted=int(label_str), tag=tag)
def fill_db(now=None):
# fill in default params
if now is None:
now = timefixer.now()
# clear the db
LabelType.objects.all().delete()
Label.objects.all().delete()
Tag.objects.all().delete()
Raw.objects.all().delete()
User.objects.all().delete()
ContentGroup.objects.all().delete()
Content.objects.all().delete()
VideoContent.objects.all().delete()
Session.objects.all().delete()
# create label type
confusion_label_type = LabelType.objects.create(name='confusion')
# create prof
user = User.objects.create(username='******')
user.set_password('essor')
user.save()
owner = Owner.objects.create(user=user, marketer=False)
# create students
viewer_names = ['bic1', 'alice']
viewers = []
for name in viewer_names:
user = User.objects.create(username=name)
user.set_password('thomas')
user.save()
viewer = Viewer.objects.create(user=user)
viewers.append(viewer)
# create classes
content_group_names = ['software_management', 'bic_capstone']
lecture_length = 60 * 2
for content_group_name in content_group_names:
content_group = ContentGroup.objects.create(name=content_group_name)
content_group.owners.add(owner)
content_group.viewers.add(*viewers)
content_group.save()
# create lecture 1
content1 = Content.objects.create(group=content_group,
duration=lecture_length,
name='lecture 1')
content1.invited_viewers.add(*viewers)
content1.save()
d = now + datetime.timedelta(minutes=1)
session1 = Session.objects.create(
content=content1,
start_time=d,
end_time=d + datetime.timedelta(seconds=lecture_length),
content_start_sec=0,
content_end_sec=lecture_length)
session1.viewers.add(*viewers)
session1.save()
# create lecture 2
content2 = Content.objects.create(group=content_group,
duration=lecture_length,
name='lecture 2')
content1.invited_viewers.add(*viewers)
content2.save()
start_18 = timefixer.str_to_datetime('2018-01-01 00:00:00')
end_18 = start_18 + datetime.timedelta(lecture_length)
session2 = Session.objects.create(content=content2,
start_time=start_18,
end_time=end_18,
content_start_sec=0,
content_end_sec=lecture_length)
session2.viewers.add(*viewers)
session2.save()
# gen data for each student
for viewer in viewers:
step = 10 # seconds
for i, t in enumerate(range(0, 300, step)):
d = session1.start_time
start_time = d + datetime.timedelta(seconds=t)
end_time = start_time + datetime.timedelta(seconds=step)
confusion = i % 2
predicted_confusion = 1 - i
tag = Tag.objects.create(subject=viewer,
start_time=start_time,
end_time=end_time)
label = Label.objects.create(label_type=confusion_label_type,
tag=tag,
true=confusion,
predicted=predicted_confusion)
raw_data = Raw.objects.create(subject=viewer,
start_time=start_time,
end_time=end_time,
sensor="center",
rawwave='1 2 3 4 5')
def input(request):
"""
:Args:
| request:Http post request storing student's confusion information
:Returns:
| returns "EEG" if OK
"""
POST = request.POST
ok = True
student = POST.get('student')
viewer = Viewer.objects.filter(user__username=student).get()
start_time = str_to_datetime(POST.get("start_time"), UTC)
end_time = str_to_datetime(POST.get("end_time"), UTC)
sensor = POST.get("sensor")
raw = POST.get("raw")
label_names = POST.get("label_names")
label_values = POST.get("label_values")
print start_time
if label_names is not None:
label_names = json.loads(label_names)
label_values = json.loads(label_values)
for ln, lv in zip(label_names, label_values):
label_type = LabelType.objects.filter(name=ln).get()
tag = Tag.objects.create(subject=viewer,
start_time=start_time,
end_time=end_time)
label = Label.objects.create(label_type=label_type,
tag=tag,
true=lv,
predicted=-1)
# uncomment to apply classifier
# task_qs = ConfusionTask.objects.get(pk=conf_data.pk)
# classify.classifySingleTask(task_qs, 'default.model', 'confusion')
elif raw is not None:
rawToks = raw.split(' ')
waves = []
att = -1
sig = -1
for raw in rawToks:
if raw[0] == 'S':
sig = int(raw[3:])
elif raw[0] == 'A':
att = int(raw[3:])
else:
waves.append(raw)
waves = ' '.join(waves)
raw_data = Raw(subject=viewer,
start_time=start_time,
end_time=end_time,
sensor=sensor,
attention=att,
sigqual=sig,
rawwave=waves)
if sig == 200:
ok = False
raw_data.save()
return HttpResponse('ok' if ok else 'not ok')