def updt_fn(stat, cnts):
for dimp in stat:
ts = utils.str2ts(dimp)
ms = utils.month_start(now=ts)
for dims in stat[dimp]:
if dims not in cnts:
cnts[dims] = []
updt_cnts(cnts[dims], stat[dimp][dims], ts, ms)
# get the time stamp.
# for each of the tags, append the time stamp with the cnt and value.
# todo: stat has the timestamp and corresponding [cnt, val] for the
# stats to be updated. find out the corresponding place and update
# them if they exist. else create the place in timeline without any
# gap.
# calculate the index from the timestamp.
return cnts
def get_timeline(cls, proj, typ, tag=None):
# todo: the f**k, how could u hard code these things.
prv_obj = cls.get_obj(proj, utils.slashify(typ, "hour_by_month", utils.month_tag(utils.last_month())))
obj = cls.get_obj(proj, utils.slashify(typ, "hour_by_month", utils.month_tag()))
def fill_entries(lst, ms, hrs):
if len(lst) < last_month_hrs:
lst.extend([str(utils.add_hrs(j, ms)), [0, 0]] for j in range(len(lst), last_month_hrs))
return lst
last_month_hrs = utils.last_month().day * 24
ms = utils.month_start(now=utils.last_month())
def normalize(res, ms, last_month_hrs):
for i in res:
res[i] = fill_entries(res[i], ms, last_month_hrs)
return res
if not prv_obj and not obj:
return {}
if prv_obj:
res = normalize(json.loads(prv_obj.stat), ms, last_month_hrs)
if obj:
nres = json.loads(obj.stat)
if not prv_obj:
res = nres
else:
for t in nres:
if not t in res:
res[t] = fill_entries([], ms, last_month_hrs)
res[t].extend(nres[t])
if tag:
return res[tag]
return res