date_start = dt.datetime(2011, 9, 1)
date_end = dt.datetime(2012, 2, 1)
if __name__ == '__main__':
import offline_gateway as og
cdl = og.get_circuit_dict_list(mains=True)
#cdl = cdl[:1]
for i, c in enumerate(cdl):
filename = 'pcde-' + c['meter_name'] + '-' + c['ip_address'][-3:] + '.pdf'
print 'querying for ' + filename
daily_energy = og.get_daily_energy_for_circuit_id(c['circuit_id'], date_start, date_end)
if daily_energy == None:
continue
# plot each circuit daily energy values for all time
f, ax = plt.subplots(2, 1, sharex=True)
# fit linear slope to watthour data
import numpy as np
dates = daily_energy.index
watthours = daily_energy.values
date_max = max(dates)
date_min = min(dates)
#cdl = cdl[:1]
for i, c in enumerate(cdl):
filename = 'pche-' + c['meter_name'] + '-' + c['ip_address'][
-3:] + '.pdf'
print 'querying for ' + filename
# grab hourly energy, if empty drop through loop
hourly_energy = og.get_watthours_for_circuit_id(
c['circuit_id'], date_start, date_end)
if hourly_energy == None:
continue
# grab daily energy, if empty drop through loop
daily_energy = og.get_daily_energy_for_circuit_id(
c['circuit_id'], date_start, date_end)
if daily_energy == None:
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(2, 1, sharex=True)
# plot energy on axis 0
cdl = og.get_circuit_dict_list(mains=True)
# cdl = cdl[:10]
for i, c in enumerate(cdl):
filename = "pcha-" + c["meter_name"] + "-" + c["ip_address"][-3:] + ".pdf"
print "querying for " + filename
# grab hourly energy, if empty drop through loop
hourly_energy, error = og.get_watthours_for_circuit_id(c["circuit_id"], date_start, date_end)
# if hourly_energy == -1:
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))
# 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
if non_resetting_data:
d[label], error = og.get_daily_energy_for_circuit_id_nr(c['circuit_id'], date_start, date_end)
else:
d[label], error = og.get_daily_energy_for_circuit_id(c['circuit_id'], date_start, date_end)
# assemble dictionary into dataframe
import pandas as p
df = p.DataFrame(d)
# transpose dataframe and output to CSV
filename = 'energy_daily_' + str(date_start.year) + '-' + str(date_start.month)
filename += '_' + method + '.csv'
if columns == 'dates':
df.T.to_csv(filename)
if columns == 'circuits':
df.to_csv(filename)
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
if non_resetting_data:
d[label], error = og.get_daily_energy_for_circuit_id_nr(
c['circuit_id'], date_start, date_end)
else:
d[label], error = og.get_daily_energy_for_circuit_id(
c['circuit_id'], date_start, date_end)
# assemble dictionary into dataframe
import pandas as p
df = p.DataFrame(d)
# transpose dataframe and output to CSV
filename = 'energy_daily_' + str(date_start.year) + '-' + str(date_start.month)
filename += '_' + method + '.csv'
if columns == 'dates':
df.T.to_csv(filename)
if columns == 'circuits':
df.to_csv(filename)
