def get(self, uid):
tp_10_data_blocks = DataBlock.objects(channel_name='tp10').limit(2000)
lists = (x.data for x in tp_10_data_blocks)
tp10_values = reduce(lambda x, y: x+y, lists)
tp_9_data_blocks = DataBlock.objects(channel_name='tp9').limit(2000)
lists = (x.data for x in tp_9_data_blocks)
tp9_values = reduce(lambda x, y: x+y, lists)
fp1_data_blocks = DataBlock.objects(channel_name='fp1').limit(2000)
lists = (x.data for x in fp1_data_blocks)
fp1_values = reduce(lambda x, y: x+y, lists)
fp2_data_blocks = DataBlock.objects(channel_name='fp2').limit(2000)
lists = (x.data for x in fp2_data_blocks)
fp2_values = reduce(lambda x, y: x+y, lists)
person = None
dummy_data = {
"tp9": list(tp9_values),
"tp10": list(tp10_values),
"fp1": list(fp1_values),
"fp2": list(fp2_values),
'timestamps': list(x for x in range(1, len(fp2_values))),
}
return render_template('person/history.html',
person=person, page='history',
data=json.dumps(dummy_data))
def post(self, todo_id):
if request.data:
red.publish("patient1", request.data)
data = json.loads(request.data)
timestamp_data = data.pop("timestamps", [])
data_type = data.pop("type", None)
return_message = MESSAGE
connect('cognisense')
for key, value in data.iteritems():
d = DataBlockModel(
source_timestamp=map(unix_to_datetime, timestamp_data),
channel_name=key,
channel_type=data_type,
data=value)
d.save()
if key == "fp1":
if max((float(v) for v in value)) > 900:
return_message = PANIC
return return_message, 201
