if error != 0:
continue
# grab daily energy, if empty drop through loop
daily_energy, error = og.get_daily_energy_for_circuit_id(c["circuit_id"], date_start, date_end)
# if daily_energy == None:
if error != 0:
continue
# shift daily_energy index by one to line up better
import pandas as p
daily_energy = daily_energy.shift(1, offset=p.DateOffset(days=1))
# get hourly credit
credit = og.get_credit_for_circuit_id(c["circuit_id"], date_start, date_end)
# plot each circuit daily energy values for all time
f, ax = plt.subplots(4, 1, sharex=True, figsize=(8, 12))
# plot energy on axis 0
# ax[0].plot_date(daily_energy.index, daily_energy.values, mfc='#dddddd', ms=15)
ax[0].plot_date(hourly_energy.index, hourly_energy.values, "ko-")
ax[0].set_xlabel("Date")
ax[0].set_ylabel("Daily Watthours")
ax[0].set_xlim((date_start, date_end))
ax[0].set_title(filename)
# plot credit on axis 1
ax[1].plot_date(credit.index, credit.values, "ko-")
ax[1].set_ylabel("Credit Balance (XFCA)")
circuit_dict_list = og.get_circuit_dict_list()
# place time series for credit of each pin in a dictionary
d = {}
for i, c in enumerate(circuit_dict_list):
if not filter_by_meter_list or c['meter_name'] in meter_list:
# generate appropriate dictionary key
if method == 'meter':
label = c['meter_name'] + '-' + c['ip_address'][-2:]
if method == 'pin':
label = c['pin']
# query database and append to dictionary
print 'querying for', i, 'th circuit =', label
credit = og.get_credit_for_circuit_id(c['circuit_id'], date_start, date_end)
sampled_credit = credit[[True if i.hour == 0 else False for i in credit.index]]
d[label] = sampled_credit
# assemble dictionary into dataframe
import pandas as p
df = p.DataFrame(d)
# transpose dataframe and output to CSV
filename = 'credit_daily_' + str(date_start.year) + '-' + str(date_start.month)
filename += '_' + method + '.csv'
df.T.to_csv(filename)
cred_DF = pd.DataFrame(index = datelist)
# Populate the list of all circuits and mains
# AND populate dictionary to translate string name to number name
for ix, row in enumerate(cl):
circ_list.append(row[1].encode('ascii')+'_'+str(int(row[2][-2:])))
circ_dict[circ_list[ix]] = row[0]
# Create a DataFrame of Wh for each main or site
for ix, row in enumerate(circ_list):
wh_col = gw.get_watthours_for_circuit_id(circ_dict[row], date_start, date_end)[0].apply(np.float32)
wh_col.resample('H', how = np.max) # resample options sum, mean, std, max, min, median, first, last, ohlc.
wh_col = pd.Series(wh_col, name = row)
wh_DF = wh_DF.join(wh_col)
cred_col = gw.get_credit_for_circuit_id(circ_dict[row], date_start, date_end).apply(np.float32)
cred_col.resample('H', how = np.min)
cred_col = pd.Series(cred_col, name = row)
cred_DF = cred_DF.join(cred_col)
print row
wh_DF.to_csv('gw_wh.csv', index_label = 'dates' )
cred_DF.to_csv('gw_cred.csv',index_label = 'dates' )
