def serve_historipcal_hvac(bucketsize):
t1 = datetime.now(TZ).replace(microsecond=0)
t0 = get_today()
tstats_df.window = bucketsize
res = dofetch([tstats_view, weather_view], [tstats_df, weather_df], t0, t1)
zones = defaultdict(lambda: defaultdict(dict))
df = res['tstats'].fillna(method='ffill').fillna(method='bfill')
for (tstat, zone, hsp, csp, temp, state) in res.query(
'select tstat, zone, hsp_uuid, csp_uuid, temp_uuid, state_uuid from tstats'
):
zone = zone.split('#')[-1]
zones[zone]['inside'] = json.loads(df[temp].dropna().to_json())
zones[zone]['heating'] = json.loads(df[hsp].dropna().to_json())
zones[zone]['outside'] = json.loads(
res['weather_temp'].max(axis=1).dropna().to_json())
zones[zone]['cooling'] = json.loads(df[csp].dropna().to_json())
zones[zone]['state'] = json.loads(
df[state].dropna().apply(state_to_string).to_json())
for k, values in zones[zone].items():
if len(values) == 0:
fakedates = pd.date_range(t0,
t1,
freq=bucketsize.replace('m', 'T'))
if k != 'state':
fakevals = [0] * len(fakedates)
else:
fakevals = ['off'] * len(fakedates)
zones[zone][k] = json.loads(
pd.DataFrame(fakevals, index=fakedates)[0].to_json())
return jsonify(zones)
def energy_summary(last, bucketsize):
start_date = get_start(last)
if last == 'year' and bucketsize == 'month':
ranges = generate_months(get_today().month - 1)
readings = []
times = []
for t0, t1 in ranges:
meter_df.window = '15m'
res = dofetch([meter_view], [meter_df], t1, t0)
df = res['meters'].copy()
df.columns = ['readings']
df /= 4. # divide by 4 to get 15min (kW) -> kWh
times.append(pd.to_datetime(t1.isoformat()))
readings.append(df['readings'].sum())
df = pd.DataFrame(readings, index=times, columns=['readings'])
# print('\n/api/energy/year/in/month/ df: \n', df)
return df.fillna('myNullVal').to_json()
meter_df.window = '15m'
print('start_date', start_date)
res = dofetch([meter_view], [meter_df], start_date, datetime.now(TZ))
df = res['meters'].tz_convert(TZ).copy()
df.columns = ['readings']
df['readings'] /= 4.
return df.fillna('myNullVal').resample(bucketsize).apply(sum).to_json()
def get_temp_per_zone(bucketsize):
t1 = datetime.now(TZ).replace(microsecond=0)
t0 = get_today()
tstats_df.window = bucketsize
res = dofetch([tstats_view, weather_view], [tstats_df, weather_df], t0, t1)
zones = defaultdict(lambda: defaultdict(dict))
df = res['tstats']
for (zone, temp) in res.query('select zone, temp_uuid from tstats'):
zone = zone.split('#')[-1]
zones[zone] = json.loads(df[temp].fillna('myNullVal').to_json())
return jsonify(zones)
def power_summary(last, bucketsize):
# first, determine the start date from the 'last' argument
start_date = get_start(last)
if last == 'year' and bucketsize == 'month':
ranges = generate_months(get_today().month - 1)
readings = []
times = []
for t0, t1 in ranges:
meter_df.window = '{0}d'.format((t0 - t1).days)
res = dofetch([meter_view], [meter_df], t1, t0)
times.append(t1.tz_convert(TZ).timestamp() * 1000)
readings.append(res['meters'].fillna('myNullVal').values[0][0])
return jsonify({'readings': dict(zip(times, readings))})
# otherwise,
meter_df.window = bucketsize
print('start_date', start_date)
res = dofetch([meter_view], [meter_df], start_date, datetime.now(TZ))
res['meters'].columns = ['readings']
return res['meters'].tz_convert(TZ).fillna('myNullVal').to_json()
def price():
res = xsg.get_price(sites[0], get_today(), get_today() + timedelta(days=1))
return res['price'].to_json()