def process_household(household, bounds):
"""
Processes each household time series, first by regularizing step
of the raw series to the fifteen minutes time series, then by
aggregating fifteen minutes series to hourly, daily, monthly.
"""
print "Processing household: %s"%(household.alt_codes.all()[0],)
for variable in (VAR_PERIOD, VAR_ENERGY_PERIOD):
raw_series_db = household.timeseries.filter(time_step__isnull=True,
variable__id = {VAR_PERIOD: VAR_CUMULATIVE,
VAR_ENERGY_PERIOD: VAR_ENERGY_CUMULATIVE}[variable])[:1]
if not raw_series_db:
continue
raw_series_db = raw_series_db[0]
fifteen_min_series_db = household.timeseries.get(time_step__id=
TSTEP_FIFTEEN_MINUTES, variable__id=variable)
s1, e1 = timeseries_bounding_dates_from_db(db.connection,
raw_series_db.id)
s2, e2 = timeseries_bounding_dates_from_db(db.connection,
fifteen_min_series_db.id)
# if e2 and (e1-e2).seconds<15*60:
# return
fifteen_min_series = regularize_raw_series(raw_series_db,
fifteen_min_series_db, s1,e1,s2,e2)
if fifteen_min_series.bounding_dates():
bounds[variable]['fifteen_start'] = min(
bounds[variable]['fifteen_start'],
fifteen_min_series.bounding_dates()[0])
bounds[variable]['fifteen_end'] = max(
bounds[variable]['fifteen_end'],
fifteen_min_series.bounding_dates()[1])
result = fifteen_min_series
monthly_series = None
for time_step_id in (TSTEP_HOURLY, TSTEP_DAILY, TSTEP_MONTHLY):
if not result:
break
result = aggregate_household_series(bounds, household,
result, time_step_id, variable)
if time_step_id == TSTEP_MONTHLY:
monthly_series = result
if not monthly_series:
return
# Cost calculation only if monthly_series present
cost_timeseries_db = household.timeseries.get(time_step__id =
TSTEP_MONTHLY, variable__id =
{VAR_PERIOD: VAR_COST,
VAR_ENERGY_PERIOD: VAR_ENERGY_COST
}[variable])
cost_timeseries = calculate_cost_timeseries(monthly_series,
rate = {
VAR_PERIOD: CUBIC_METER_FLAT_RATE,
VAR_ENERGY_PERIOD: KWH_FLAT_RATE}[variable])
cost_timeseries.id = cost_timeseries_db.id
cost_timeseries.write_to_db(db = db.connection, commit=True)
def MultiTimeseriesProcessDb(method, timeseries_arg, out_timeseries_id,
db, read_tstep_func, transaction=None,
commit=True, options={}):
out_timeseries = Timeseries(id = out_timeseries_id)
opts = copy.deepcopy(options)
if 'append_only' in opts and opts['append_only']:
bounds = timeseries_bounding_dates_from_db(db,
id = out_timeseries_id)
opts['start_date'] = bounds[1] if bounds else None;
opts['interval_exclusive'] = True
tseries_arg={}
for key in timeseries_arg:
ts = Timeseries(id=timeseries_arg[key])
if ('append_only' in opts and opts['append_only']) \
and opts['start_date'] is not None:
ts.read_from_db(db, bottom_only=True)
if ts.bounding_dates()[0]>opts['start_date']:
ts.read_from_db(db)
else:
ts.read_from_db(db)
ts.time_step = read_tstep_func(ts.id)
tseries_arg[key] = ts
MultiTimeseriesProcess(method, tseries_arg, out_timeseries, opts)
if 'append_only' in opts and opts['append_only']:
out_timeseries.append_to_db(db=db, transaction=transaction,
commit=commit)
else:
out_timeseries.write_to_db(db=db, transaction=transaction,
commit=commit)
def handle(self, *args, **options):
try:
prefix = args[0]
except IndexError:
prefix = ""
try:
if prefix:
users = User.objects.filter(username__icontains=prefix)
else:
users = User.objects.all()
out = []
print "output for {x} users".format(x=len(users))
for user in users:
print "looking for user %s" % user.username
try:
household = Household.objects.get(user=user)
for variable in (VAR_PERIOD, VAR_ENERGY_PERIOD):
if variable == VAR_PERIOD:
_type = "WATER"
else:
_type = "ENERGY"
raw_series_db = household.timeseries.filter(
time_step__isnull=True,
variable__id={
VAR_PERIOD: VAR_CUMULATIVE,
VAR_ENERGY_PERIOD: VAR_ENERGY_CUMULATIVE
}[variable])[:1]
if not raw_series_db:
continue
raw_series_db = raw_series_db[0]
s1, e1 = timeseries_bounding_dates_from_db(
db.connection, raw_series_db.id)
if s1:
start = s1.strftime("%Y-%m-%d %H:%M:%S")
else:
start = ""
if e1:
end = e1.strftime("%Y-%m-%d %H:%M:%S")
else:
end = ""
out.append([user.username, start, end, _type])
except Exception as e:
print (repr(e))
continue
import time
_outfile = "%s_bounding_dates.csv" % prefix
_path = "data/"
with open(path.join(_path, _outfile), 'w') as of:
a = csv.writer(of, delimiter=',',
quotechar='"',
quoting=csv.QUOTE_ALL)
a.writerows(out)
except Exception as e:
raise CommandError(repr(e))
def parse_and_save_timeseries(device_id, timeseries_id):
"""
Reads a RAW timeseries from REST API and saves in our local
database using the timeseries_id.
``device_id`` will be the ``identifier`` used in other functions,
usualy is the customerID==deviceID
"""
s, e = timeseries_bounding_dates_from_db(db.connection, timeseries_id)
if s or e:
print 'Raw timeseries id=%s has already data, skipping...' % (
timeseries_id, )
return
timeseries = TSeries()
timeseries.id = timeseries_id
for timestamp, value in ibm_restapi.get_raw_timeseries(device_id):
timeseries[timestamp] = value
timeseries.write_to_db(db=db.connection,
transaction=transaction,
commit=False)
def create_objects(dma, household_identifier, series, force=False):
"""
When a household is fully parsed then this command is called to create
database objects thus: user (household owner), household, database time
series placeholders (for raw data and for processed data), to write actual
time series data in database and finally to estimate the household
occupancy.
"""
print "Processing household %s, user username will be %s as well"%(
household_identifier, household_identifier)
# Create user (household owner), household, database series placeholders
user = create_user(household_identifier)
household=create_household(household_identifier, user,
zone=dma.id)
db_series = create_raw_timeseries(household)
create_processed_timeseries(household)
timeseries_data = {}
# Now we will create timeseries.Timeseries() and we will add
# parsed values
for variable in db_series:
if variable not in ('WaterCold', 'Electricity'):
continue
s, e = timeseries_bounding_dates_from_db(db.connection,
db_series[variable].id)
if not force and (s or e):
print 'Raw timeseries id=%s has already data, skipping...'%(
db_series[variable].id,)
continue
timeseries = TSeries()
timeseries.id = db_series[variable].id
total = 0.0
for timestamp, value in series[variable]:
if not math.isnan(value):
total += value
timeseries[timestamp] = total
else:
timeseries[timestamp] = float('NaN')
timeseries_data[variable] = timeseries
timeseries.write_to_db(db=db.connection,
transaction=transaction,
commit=False)
if 'WaterCold' in timeseries_data:
calc_occupancy(timeseries_data['WaterCold'], household)
def parse_and_save_timeseries(device_id, timeseries_id):
"""
Reads a RAW timeseries from REST API and saves in our local
database using the timeseries_id.
``device_id`` will be the ``identifier`` used in other functions,
usualy is the customerID==deviceID
"""
s, e = timeseries_bounding_dates_from_db(db.connection,
timeseries_id)
if s or e:
print 'Raw timeseries id=%s has already data, skipping...'%(
timeseries_id,)
return
timeseries = TSeries()
timeseries.id = timeseries_id
for timestamp, value in ibm_restapi.get_raw_timeseries(device_id):
timeseries[timestamp] = value
timeseries.write_to_db(db=db.connection,
transaction=transaction,
commit=False)
def InterpolateDbTimeseries(source_id, dest_id, curve_type, curve_data,
db, data_columns=(0,1), logarithmic=False,
offset=0, append_only=False,
transaction=None, commit=True):
if append_only:
bounds = timeseries_bounding_dates_from_db(db, id = dest_id)
start_date = bounds[1] if bounds else None;
ts = Timeseries(id=source_id)
if append_only and start_date is not None:
ts.read_from_db(db, bottom_only=True)
if ts.bounding_dates()[0]>start_date:
ts.read_from_db(db)
while ts.bounding_dates()[0]<=start_date:
del(ts[ts.bounding_dates()[0]])
if len(ts)==0: return
else:
ts.read_from_db(db)
curve_list = TransientCurveList()
if curve_type=='SingleCurve':
curve_list.add(logarithmic=logarithmic,
offset=CurvePoint(offset, 0))
super(TransientCurve,
curve_list[0]).read_fp(StringIO(curve_data),
data_columns)
elif curve_type=='StageDischargeMulti':
curve_list.read_fp(StringIO(curve_data))
else:
assert(False)
out_timeseries = curve_list.interpolate_ts(ts)
out_timeseries.id = dest_id
if append_only:
out_timeseries.append_to_db(db=db, transaction=transaction,
commit=commit)
else:
out_timeseries.write_to_db(db=db, transaction=transaction,
commit=commit)
def AggregateDbTimeseries(source_id, dest_id, db, read_tstep_func, transaction=None,
commit=True, missing_allowed=0.0,
missing_flag='MISSING', append_only=False,
last_incomplete=False, all_incomplete=False):
source = Timeseries(id=source_id, time_step=read_tstep_func(source_id))
dest_step = read_tstep_func(dest_id)
if append_only:
bounds = timeseries_bounding_dates_from_db(db = db, id = dest_id)
end_date = bounds[1] if bounds else None
source.read_from_db(db)
dest = source.aggregate(target_step=dest_step,
missing_allowed=missing_allowed,
missing_flag=missing_flag,
last_incomplete=last_incomplete,
all_incomplete=all_incomplete)[0]
dest.id = dest_id
if append_only:
d=dest.bounding_dates()
while (d is not None) and (end_date is not None) and d[0]<=end_date:
del dest[d[0]]
d=dest.bounding_dates()
dest.append_to_db(db=db, transaction=transaction, commit=commit)
else:
dest.write_to_db(db=db, transaction=transaction, commit=commit)
def process_household(household):
for variable in (VAR_PERIOD, VAR_ENERGY_PERIOD):
raw_series_db = household \
.timeseries \
.filter(time_step__isnull=True,
variable__id={
VAR_PERIOD: VAR_CUMULATIVE,
VAR_ENERGY_PERIOD: VAR_ENERGY_CUMULATIVE
}[variable])[:1]
if not raw_series_db:
continue
raw_series_db = raw_series_db[0]
fifteen_min_series_db = household \
.timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
variable__id=variable)
s1, e1 = timeseries_bounding_dates_from_db(db.connection,
raw_series_db.id)
s2, e2 = timeseries_bounding_dates_from_db(db.connection,
fifteen_min_series_db.id)
# if e2 and (e1-e2).seconds<15*60:
# return
#log.info("Now regularizing %s with id %s for s1=%s and e1=%s"
# % (raw_series_db, raw_series_db.id, s1, e1))
fifteen_min_series = regularize(raw_series_db, fifteen_min_series_db,
s1, e1)
if fifteen_min_series and fifteen_min_series.bounding_dates():
bounds[variable]['fifteen_start'] = min(
bounds[variable]['fifteen_start'],
fifteen_min_series.bounding_dates()[0])
bounds[variable]['fifteen_end'] = max(
bounds[variable]['fifteen_end'],
fifteen_min_series.bounding_dates()[1])
result = fifteen_min_series
monthly_series = None
# Stefanos wants to aggregate using previous results
# Why? The problem is that Monthly series is not inserted.
# Was this on purpose?
for time_step_id in (TSTEP_HOURLY, TSTEP_DAILY, TSTEP_MONTHLY):
result = aggregate(household, result, time_step_id, variable)
if time_step_id == TSTEP_MONTHLY:
monthly_series = result
if not monthly_series:
return
# Cost calculation only if monthly_series present
try:
cost_timeseries_db = household \
.timeseries.get(time_step__id=TSTEP_MONTHLY,
variable__id={
VAR_PERIOD: VAR_COST,
VAR_ENERGY_PERIOD: VAR_ENERGY_COST
}[variable])
cost_timeseries = calculate_cost(monthly_series,
rate={
VAR_PERIOD:
CUBIC_METER_FLAT_RATE,
VAR_ENERGY_PERIOD:
KWH_FLAT_RATE
}[variable])
cost_timeseries.id = cost_timeseries_db.id
cost_timeseries.write_to_db(db=db.connection, commit=True)
except Exception as e:
log.info("Error in monthly calculation %s. Skipping!" %
repr(e))
continue
def create_objects(data, usernames, force, z_names, z_dict):
"""
:param data: meter_id -> consumption_type -> [timestamp, volume]
:param force: True to overwrite
:return: True for success
"""
households = []
# Create user (household owner), household, database series placeholders
hh_ids = sorted(data.keys())
found = False
for hh_id in hh_ids:
username = usernames[hh_id]
if username == "PT94993":
pass
try:
zone_name = z_dict[username]
except KeyError:
zone_name = z_names[0]
zone = DMA.objects.get(name=zone_name)
user, created = create_user(username, hh_id)
household, found = create_household(hh_id, user, zone.id)
households.append(household)
db_series = create_raw_timeseries(household)
create_processed_timeseries(household)
timeseries_data = {}
# Now we will create timeseries.Timeseries() and we will add
# parsed values
for variable in db_series:
if variable not in ('WaterCold', 'Electricity'):
continue
exists = False
s, e = timeseries_bounding_dates_from_db(db.connection,
db_series[variable].id)
latest_ts = e
ts_id = db_series[variable].id
# checking to see if timeseries records already exist in order
# to append
# d = read_timeseries_tail_from_db(db.connection, ts_id)
total = 0.0
# if s or e:
# exists = True
# timeseries = TSeries(ts_id)
# timeseries.read_from_db(db.connection)
# else:
# timeseries = TSeries()
# timeseries.id = ts_id
_dict = data[hh_id]
arr = _dict[variable]
series = arr
if not series:
continue
earlier = []
if (not latest_ts) or (latest_ts < series[0][0]): # append
timeseries = TSeries()
timeseries.id = ts_id
try:
tail = read_timeseries_tail_from_db(db.connection, ts_id)
total = float(tail[1]) # keep up from last value
except Exception as e:
log.debug(repr(e))
total = 0
for timestamp, value in series:
if (not latest_ts) or (timestamp > latest_ts):
if not isnan(value):
total += value
timeseries[timestamp] = total
else:
timeseries[timestamp] = float('NaN')
elif timestamp < latest_ts:
earlier.append((timestamp, value))
timeseries.append_to_db(db=db.connection,
transaction=transaction,
commit=True)
elif latest_ts >= series[0][0]:
if not force: # ignore
continue
else: # insert
for timestamp, value in series:
if timestamp < latest_ts:
earlier.append((timestamp, value))
if earlier and ("GR" in username
or "GBA" in username): # insert (only for athens)
# print "appending %s items for %s" % (len(earlier), username)
if variable == "WaterCold":
ts15 = household \
.timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
variable__id=VAR_PERIOD)
series15 = TSeries(id=ts15.id)
elif variable == "Electricity":
ts15 = household \
.timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
variable__id=VAR_ENERGY_PERIOD)
series15 = TSeries(id=ts15.id)
series15.read_from_db(db.connection)
for ts, value in earlier:
series15[ts] = value
series15.write_to_db(db=db.connection,
transaction=transaction,
commit=True)
raw_ts = TSeries(ts_id) # read existing ts raw data
raw_ts.read_from_db(db.connection)
total = get_consumption_totals(household, earlier[0][0],
variable)
init = total
for timestamp, value in earlier:
if not isnan(value):
total += value
raw_ts[timestamp] = total
else:
raw_ts[timestamp] = float('NaN')
# correct later values, too
diff = total - init
all_ts = sorted(raw_ts.keys())
for ts in all_ts:
if ts <= timestamp:
continue
curr = raw_ts[ts]
raw_ts[ts] = curr + diff
raw_ts.write_to_db(db=db.connection,
transaction=transaction,
commit=True)
if 'WaterCold' in timeseries_data and not found: # only for new HH
calc_occupancy(timeseries_data['WaterCold'], household)
return households
def process_household(household):
for variable in (VAR_PERIOD, VAR_ENERGY_PERIOD):
raw_series_db = household \
.timeseries \
.filter(time_step__isnull=True,
variable__id={
VAR_PERIOD: VAR_CUMULATIVE,
VAR_ENERGY_PERIOD: VAR_ENERGY_CUMULATIVE
}[variable])[:1]
if not raw_series_db:
continue
raw_series_db = raw_series_db[0]
fifteen_min_series_db = household \
.timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
variable__id=variable)
s1, e1 = timeseries_bounding_dates_from_db(db.connection,
raw_series_db.id)
s2, e2 = timeseries_bounding_dates_from_db(db.connection,
fifteen_min_series_db.id)
# if e2 and (e1-e2).seconds<15*60:
# return
#log.info("Now regularizing %s with id %s for s1=%s and e1=%s"
# % (raw_series_db, raw_series_db.id, s1, e1))
fifteen_min_series = regularize(raw_series_db, fifteen_min_series_db,
s1, e1)
if fifteen_min_series and fifteen_min_series.bounding_dates():
bounds[variable]['fifteen_start'] = min(
bounds[variable]['fifteen_start'],
fifteen_min_series.bounding_dates()[0])
bounds[variable]['fifteen_end'] = max(
bounds[variable]['fifteen_end'],
fifteen_min_series.bounding_dates()[1])
result = fifteen_min_series
monthly_series = None
# Stefanos wants to aggregate using previous results
# Why? The problem is that Monthly series is not inserted.
# Was this on purpose?
for time_step_id in (TSTEP_HOURLY, TSTEP_DAILY, TSTEP_MONTHLY):
result = aggregate(household, result, time_step_id, variable)
if time_step_id == TSTEP_MONTHLY:
monthly_series = result
if not monthly_series:
return
# Cost calculation only if monthly_series present
try:
cost_timeseries_db = household \
.timeseries.get(time_step__id=TSTEP_MONTHLY,
variable__id={
VAR_PERIOD: VAR_COST,
VAR_ENERGY_PERIOD: VAR_ENERGY_COST
}[variable])
cost_timeseries = calculate_cost(monthly_series,
rate={
VAR_PERIOD: CUBIC_METER_FLAT_RATE,
VAR_ENERGY_PERIOD: KWH_FLAT_RATE
}[variable])
cost_timeseries.id = cost_timeseries_db.id
cost_timeseries.write_to_db(db=db.connection, commit=True)
except Exception as e:
log.info("Error in monthly calculation %s. Skipping!" % repr(e))
continue
def create_objects(data, usernames, force, z_names, z_dict):
"""
:param data: meter_id -> consumption_type -> [timestamp, volume]
:param force: True to overwrite
:return: True for success
"""
households = []
# Create user (household owner), household, database series placeholders
hh_ids = sorted(data.keys())
found = False
for hh_id in hh_ids:
username = usernames[hh_id]
if username == "PT94993":
pass
try:
zone_name = z_dict[username]
except KeyError:
zone_name = z_names[0]
zone = DMA.objects.get(name=zone_name)
user, created = create_user(username, hh_id)
household, found = create_household(hh_id, user, zone.id)
households.append(household)
db_series = create_raw_timeseries(household)
create_processed_timeseries(household)
timeseries_data = {}
# Now we will create timeseries.Timeseries() and we will add
# parsed values
for variable in db_series:
if variable not in ('WaterCold', 'Electricity'):
continue
exists = False
s, e = timeseries_bounding_dates_from_db(db.connection,
db_series[variable].id)
latest_ts = e
ts_id = db_series[variable].id
# checking to see if timeseries records already exist in order
# to append
# d = read_timeseries_tail_from_db(db.connection, ts_id)
total = 0.0
# if s or e:
# exists = True
# timeseries = TSeries(ts_id)
# timeseries.read_from_db(db.connection)
# else:
# timeseries = TSeries()
# timeseries.id = ts_id
_dict = data[hh_id]
arr = _dict[variable]
series = arr
if not series:
continue
earlier = []
if (not latest_ts) or (latest_ts < series[0][0]): # append
timeseries = TSeries()
timeseries.id = ts_id
try:
tail = read_timeseries_tail_from_db(db.connection, ts_id)
total = float(tail[1]) # keep up from last value
except Exception as e:
log.debug(repr(e))
total = 0
for timestamp, value in series:
if (not latest_ts) or (timestamp > latest_ts):
if not isnan(value):
total += value
timeseries[timestamp] = total
else:
timeseries[timestamp] = float('NaN')
elif timestamp < latest_ts:
earlier.append((timestamp, value))
timeseries.append_to_db(db=db.connection,
transaction=transaction,
commit=True)
elif latest_ts >= series[0][0]:
if not force: # ignore
continue
else: # insert
for timestamp, value in series:
if timestamp < latest_ts:
earlier.append((timestamp, value))
if earlier and ("GR" in username or "GBA" in username): # insert (only for athens)
# print "appending %s items for %s" % (len(earlier), username)
if variable == "WaterCold":
ts15 = household \
.timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
variable__id=VAR_PERIOD)
series15 = TSeries(id=ts15.id)
elif variable == "Electricity":
ts15 = household \
.timeseries.get(time_step__id=TSTEP_FIFTEEN_MINUTES,
variable__id=VAR_ENERGY_PERIOD)
series15 = TSeries(id=ts15.id)
series15.read_from_db(db.connection)
for ts, value in earlier:
series15[ts] = value
series15.write_to_db(db=db.connection,
transaction=transaction,
commit=True)
raw_ts = TSeries(ts_id) # read existing ts raw data
raw_ts.read_from_db(db.connection)
total = get_consumption_totals(household, earlier[0][0],
variable)
init = total
for timestamp, value in earlier:
if not isnan(value):
total += value
raw_ts[timestamp] = total
else:
raw_ts[timestamp] = float('NaN')
# correct later values, too
diff = total - init
all_ts = sorted(raw_ts.keys())
for ts in all_ts:
if ts <= timestamp:
continue
curr = raw_ts[ts]
raw_ts[ts] = curr + diff
raw_ts.write_to_db(db=db.connection,
transaction=transaction,
commit=True)
if 'WaterCold' in timeseries_data and not found: # only for new HH
calc_occupancy(timeseries_data['WaterCold'], household)
return households
def process_household(household, bounds):
"""
Processes each household time series, first by regularizing step
of the raw series to the fifteen minutes time series, then by
aggregating fifteen minutes series to hourly, daily, monthly.
"""
print "Processing household: %s" % (household.alt_codes.all()[0], )
for variable in (VAR_PERIOD, VAR_ENERGY_PERIOD):
raw_series_db = household.timeseries.filter(time_step__isnull=True,
variable__id={
VAR_PERIOD:
VAR_CUMULATIVE,
VAR_ENERGY_PERIOD:
VAR_ENERGY_CUMULATIVE
}[variable])[:1]
if not raw_series_db:
continue
raw_series_db = raw_series_db[0]
fifteen_min_series_db = household.timeseries.get(
time_step__id=TSTEP_FIFTEEN_MINUTES, variable__id=variable)
s1, e1 = timeseries_bounding_dates_from_db(db.connection,
raw_series_db.id)
s2, e2 = timeseries_bounding_dates_from_db(db.connection,
fifteen_min_series_db.id)
# if e2 and (e1-e2).seconds<15*60:
# return
fifteen_min_series = regularize_raw_series(raw_series_db,
fifteen_min_series_db, s1,
e1, s2, e2)
if fifteen_min_series.bounding_dates():
bounds[variable]['fifteen_start'] = min(
bounds[variable]['fifteen_start'],
fifteen_min_series.bounding_dates()[0])
bounds[variable]['fifteen_end'] = max(
bounds[variable]['fifteen_end'],
fifteen_min_series.bounding_dates()[1])
result = fifteen_min_series
monthly_series = None
for time_step_id in (TSTEP_HOURLY, TSTEP_DAILY, TSTEP_MONTHLY):
if not result:
break
result = aggregate_household_series(bounds, household, result,
time_step_id, variable)
if time_step_id == TSTEP_MONTHLY:
monthly_series = result
if not monthly_series:
return
# Cost calculation only if monthly_series present
cost_timeseries_db = household.timeseries.get(
time_step__id=TSTEP_MONTHLY,
variable__id={
VAR_PERIOD: VAR_COST,
VAR_ENERGY_PERIOD: VAR_ENERGY_COST
}[variable])
cost_timeseries = calculate_cost_timeseries(monthly_series,
rate={
VAR_PERIOD:
CUBIC_METER_FLAT_RATE,
VAR_ENERGY_PERIOD:
KWH_FLAT_RATE
}[variable])
cost_timeseries.id = cost_timeseries_db.id
cost_timeseries.write_to_db(db=db.connection, commit=True)
def usage_data(request, ctr_code):
user = request.user
if user.is_superuser:
users = User.objects.filter(username__startswith=ctr_code)
u_data = {}
user_pages = {}
for user in users:
arr = [user.username]
try:
household = Household.objects.get(user=user)
except Household.DoesNotExist:
continue
for variable in (VAR_PERIOD, VAR_ENERGY_PERIOD):
if variable == VAR_PERIOD:
_type = "WATER"
else:
_type = "ENERGY"
raw_series_db = household.timeseries.filter(
time_step__isnull=True,
variable__id={
VAR_PERIOD: VAR_CUMULATIVE,
VAR_ENERGY_PERIOD: VAR_ENERGY_CUMULATIVE
}[variable])[:1]
if not raw_series_db:
continue
raw_series_db = raw_series_db[0]
s1, e1 = timeseries_bounding_dates_from_db(
db.connection, raw_series_db.id)
if s1:
start = s1.strftime("%Y-%m-%d %H:%M")
else:
start = ""
if e1:
end = e1.strftime("%Y-%m-%d %H:%M")
else:
end = ""
arr.extend([start, end])
ud = UsageData.objects.filter(user=user)
if ud:
ets = ud[0].enter_ts
lts = ud[0].exit_ts
arr.append(ets.strftime("%Y-%m-%d %H:%M"))
arr.append(lts.strftime("%Y-%m-%d %H:%M"))
# duration
arr.append(int(time.mktime(lts.timetuple())
- time.mktime(ets.timetuple())) / 60)
else:
arr.extend([0, 0, 0])
upvs = UserPageView.objects.filter(user=user).order_by('-added')
if upvs:
arr.append(upvs[0].added)
else:
arr.append("-")
arr.append(len(ud))
u_data[user.id] = arr
pages = {}
for upv in upvs:
page = upv.page
count = pages.get(page, 0)
count += 1
pages[page] = count
keys = pages.keys()
page_tuples = []
for k in keys:
page_tuples.append((k, pages[k]))
user_pages[user.id] = page_tuples
data = {
"users": users,
"data": u_data,
"pages": user_pages,
}
variables = RequestContext(request, data)
return render_to_response("_usage_data.html", variables)
else:
return HttpResponseRedirect(reverse('dashboard'))
