def getForecast(self, tseries, units, algorithm, file, default="year"):
highest = 0.0
lowest = 0.0
billdata = {"sum": 0.0, "avg": 0.0, "high": "", "low": ""}
monthdata = {"sum": 0.0, "avg": 0.0, "high": "", "low": ""}
dtlist = []
list2 = []
list1 = []
list3 = []
num = 0.0
dtlist2 = []
data = {}
tserieslength = 0
tseriesend = None
hhold = ihousehold()
series = iseries()
quota = 18
enddate = series.getfinaldate(tseries) #end date of the time series
#file = self.ts.getyearlyArff()
if algorithm == "mlp":
result = self.ts.getForecast(
file, units, self.ts.getMLP(),
"cost") #execute forecast using MLP for 12 units in future
elif algorithm == "lr":
result = self.ts.getForecast(
file, units, self.ts.getLinear(),
"cost") #execute forecast using MLP for 12 units in future
elif algorithm == "gr":
result = self.ts.getForecast(
file, units, self.ts.getGaussian(),
"cost") #execute forecast using MLP for 12 units in future
else:
return None
list2 = result.split(',') #get result in list
if default == "days":
for x in range(0, units):
dt = str(enddate + datetime.timedelta(days=(x + 1)))
dtlist.append(dt)
else:
#get all future dates in list
for x in range(0, units):
dt = str(iutility.addMonths(enddate, x + 1))
dtlist.append(dt)
cost = round(hhold.tariff1(float(list2[x])), 2)
list1.append(cost)
billdata["sum"] = billdata["sum"] + cost
#list1.append(iutility.getDate(dt,'%Y-%m-%d','%d-%m-%Y')) #covert format from YYY-mm-dd to dd-mm-YYYY
highest = max(list1)
lowest = min(list1)
billdata["sum"] = round(billdata["sum"], 2)
billdata["avg"] = round(billdata["sum"] / units, 2)
billdata["high"] = {
"date":
iutility.convertdate(dtlist[list1.index(highest)], '%Y-%m-%d',
'%B-%Y'),
"max":
highest
} #max per nonth
billdata["low"] = {
"date":
iutility.convertdate(dtlist[list1.index(lowest)], '%Y-%m-%d',
'%B-%Y'),
"min":
lowest
} #min per nonth
data["data"] = series.getlistTojsoncost(
dtlist, list1, "Date",
"Cost") #merge two list for passing to client for charting
data["billdata"] = billdata
data["title"] = "NEXT " + str(units) + " MONTHS BILL FORECAST"
'''
price break lines
'''
start = dtlist[0]
end = dtlist[units - 1]
data["price_break"] = [{
"Price Break": "Highest",
"Cost": highest,
"Date": start
}, {
"Price Break": "Highest",
"Cost": highest,
"Date": end
}, {
"Price Break": "Average",
"Cost": billdata["avg"],
"Date": start
}, {
"Price Break": "Average",
"Cost": billdata["avg"],
"Date": end
}, {
"Price Break": "Lowest",
"Cost": lowest,
"Date": start
}, {
"Price Break": "Lowest",
"Cost": lowest,
"Date": end
}]
'''
This is the data for the last "units" months to display with forecasting summary in use case 5.4
'''
tserieslength = len(tseries)
if tserieslength >= units:
tseriesend = tseries[tserieslength - units:]
for d in tseriesend:
dtlist2.append(str(d[0].date()))
num = float(d[1])
if math.isnan(num):
num = 0.0
num = iutility.percentage(num, quota)
cost = round(hhold.tariff1(num), 2)
list3.append(cost)
monthdata["sum"] = monthdata["sum"] + cost
highest = max(list3)
lowest = min(list3)
monthdata["sum"] = round(monthdata["sum"], 2)
monthdata["avg"] = round(monthdata["sum"] / units, 2)
monthdata["high"] = {
"date":
iutility.convertdate(dtlist2[list3.index(highest)], '%Y-%m-%d',
'%B-%Y'),
"max":
highest
} #max per nonth
monthdata["low"] = {
"date":
iutility.convertdate(dtlist2[list3.index(lowest)], '%Y-%m-%d',
'%B-%Y'),
"min":
lowest
} #min per nonth
data["monthdata"] = monthdata
try:
"" #entry.shutGateway()
except:
pass
#data=""
return data
def usecase3_4(self):
'''
Calculating household monthly cost and other statistics
'''
hhold = ihousehold()
hholdstats = hhold.getUsage(self.User)
household = self.User.households.all()[0]
dma = household.dma # dma to calculate user with highest cost
household_dma = dma.households.filter(
num_of_occupants=household.num_of_occupants,
property_type=household.property_type)
d = idma()
series = iseries()
obj = {}
list1 = []
data = {}
effuser = None
#effdata = {"year":"","half":"","quarter":"","month":""}
yourdata = {"year": "", "half": "", "quarter": "", "month": ""}
effuser = d.getMostefficient(household_dma, 12)
if effuser:
effdata = effuser["stats"]
for x in range(0, len(effuser["chartdata"])):
list1.append(effuser["chartdata"][x])
for x in range(0, 4):
if x == 0:
period = 12
cal = "year"
elif x == 1:
period = 6
cal = "half"
elif x == 2:
period = 3
cal = "quarter"
elif x == 3:
period = 1
cal = "month"
obj = {}
dic = {}
dic = hholdstats[cal]
yourdata[cal] = dic
#print dic
obj["Units"] = str(dic["sum_units"])
obj["Cost"] = round(hhold.tariff1(dic["sum_units"]), 2)
#obj["Cost"] = str(series.getCost(dic["sum_units"]))
obj["Period"] = str(period) + " Month"
obj["Data"] = "You"
dic = {}
list1.append(obj)
obj = {}
data["chart"] = list1
data["effdata"] = effdata
data["yourdata"] = yourdata
# Added by Chris Pantazis to send the currency to the graphs
# see: uc32barplot1 javascript function in base.js
if self.User.username.startswith("GB"):
data["currency"] = u"£"
else:
data["currency"] = u"€"
else:
data = None
return data
def usecase5_4(self):
series = iseries()
hhold = ihousehold()
household = self.User.households.all()[0]
tsmonthly = series.readseries(
series.getmonthlyseries(household)) #get timeseries
quota = 18
num = 0.0
try:
forecast = ElectricForecast.objects.get(
user=self.User) #get the current user forecast model object
if not forecast.yearfile:
if len(tsmonthly) > 0:
gateway = JavaGateway() #connect to JVM
entry = gateway.entry_point
ts = entry.getTimeSeries(
str(self.User.id
)) #get JAVA timeseries Object for this user
path = ts.getelectricArff()
'''
The method runs in a separate JAVA thread so it will not block the application, however if the thread runs for very long time
for file writing and Iforecast class request the forecasting athen code needs to be block until the thread completes writing the file.
'''
ts.writelectricArff(
json.dumps(
series.getseriesToelectricjson(
tsmonthly, quota, "cost")), path)
'''
End of thread
'''
#write path to db
forecast.yearfile = path
forecast.yeardate = series.getfinaldate(tsmonthly)
forecast.save()
else:
#if newer data then append that month to file
seriesdate = series.getfinaldate(tsmonthly)
dbdate = forecast.yeardate
if seriesdate > dbdate:
diff = iutility.diffmonth(
seriesdate, dbdate
) #get the mumber of months to append series to file
list = tsmonthly[len(tsmonthly) - diff:]
line = ""
for i in list:
num = float(i[1])
if math.isnan(num):
num = 0.0
if num > 0.0:
num = iutility.percentage(num, quota)
#print (tseries[len(tseries)-1:][0][0]).date()
line = line + str(i[0].date()) + "," + str(num) + "\n"
f = ifile(forecast.yearfile, "a")
f.write(line)
f.close()
forecast.yeardate = seriesdate
forecast.save()
except: #exception result if no record exists
gateway = JavaGateway() #connect to JVM
entry = gateway.entry_point
ts = entry.getTimeSeries(str(
self.User.id)) #get JAVA timeseries Object for this user
# get_or_create: Added by Chris Pantazis because sometimes
# two forecast keys are made for a user
forecast, created = ElectricForecast.objects.get_or_create(
user=self.User)
#ts.safeThread();
#entry.shutGateway()
if len(tsmonthly) > 0:
path = ts.getelectricArff()
'''
The method runs in a separate JAVA thread so it will not block the application, however if the thread runs for very long time
for file writing and Iforecast class request the forecasting athen code needs to be block until the thread completes writing the file.
'''
ts.writelectricArff(
json.dumps(
series.getseriesToelectricjson(tsmonthly, quota,
"cost")), path)
'''
End of thread
'''
#write path to db
forecast.yearfile = path
forecast.yeardate = series.getfinaldate(tsmonthly)
forecast.save()
return
def usecase3_2(self):
data = {}
household = self.User.households.all()[0]
dma = household.dma
series = iseries()
d = idma()
'''
start of all user statistics in DMA
'''
#dict = {"sum_households":"","avg_occupants":""}
#dict["sum_households"] = Household.objects.filter(dma__pk=dma.pk).count() #total household
#dict["avg_occupants"] = Household.objects.filter(dma__pk=dma.pk).aggregate(Sum("num_of_occupants"))
#dict["avg_occupants"] = dict["avg_occupants"]["num_of_occupants__sum"]/dict["sum_households"]
#print dict
household_dma = dma.households.all(
) #get all households in logged user DMA
'''#start
dmastats = DMAstats.objects.filter(dma__pk=dma.pk)
#dmasummary = d.getStats(household_dma,0)
#print dict
if not dmastats: #if no statistics ever computed before for this DMA
self.__savedbdmastats(household_dma,dma,12)
self.__savedbdmastats(household_dma,dma,6)
self.__savedbdmastats(household_dma,dma,4)
self.__savedbdmastats(household_dma,dma,1)
else: #if stats exists then check it is updated in the last 24 hours
for st in dmastats:
if datetime.date.today() > st.statsdate:
stats = d.getStats(household_dma,st.statsperiod)
st.statsdate = datetime.date.today()
st.sumhouseholds=stats["sum_households"]
st.sumoccupants=stats["sum_occupants"]
st.sumunits=stats["sum_units"]
st.maxoccupants=stats["max_occupants"]
st.avgoccupants=stats["avg_occupants"]
st.minoccupants=stats["min_occupants"]
st.maxunits=stats["max_units"]
st.avgunits=stats["avg_units"]
st.minunits=stats["min_units"]
st.dma=dma
st.save();
'''#end
'''
End of all user statistics in DMA
'''
'''
Start of DMA user statistics based on filter information
1) Based on number of occupants
'''
'''#start
dmastats = userDMAstats.objects.filter(household=household)
#household_dma = dma.households.exclude(user=self.User) #exclude logged user from DMA
if not dmastats:
household_dma = dma.households.exclude(user=self.User) #exclude logged user from DMA
household_dma = household_dma.filter(num_of_occupants=household.num_of_occupants) #get households with the same of household as like logged user
self.__savedbuserdmastats(household_dma,12,1)
self.__savedbuserdmastats(household_dma,6,1)
self.__savedbuserdmastats(household_dma,4,1)
self.__savedbuserdmastats(household_dma,1,1)
household_dma = dma.households.exclude(user=self.User) #exclude logged user from DMA
household_dma = household_dma.filter(property_type=household.property_type) #get households with the same of household as like logged user
self.__savedbuserdmastats(household_dma,12,2)
self.__savedbuserdmastats(household_dma,6,2)
self.__savedbuserdmastats(household_dma,4,2)
self.__savedbuserdmastats(household_dma,1,2)
household_dma = dma.households.exclude(user=self.User) #exclude logged user from DMA
household_dma = household_dma.filter(num_of_occupants=household.num_of_occupants,property_type=household.property_type) #get households with the same of household as like logged user
self.__savedbuserdmastats(household_dma,12,3)
self.__savedbuserdmastats(household_dma,6,3)
self.__savedbuserdmastats(household_dma,4,3)
self.__savedbuserdmastats(household_dma,1,3)
else: #if stats exists then check it is updated in the last 24 hours
for st in dmastats:
if datetime.date.today() > st.statsdate:
stats = d.gethouseholdstats(household_dma,st.statsperiod)
st.statsdate = datetime.date.today()
st.sumhouseholds=stats["sum_households"]
st.sumoccupants=stats["sum_occupants"]
st.sumunits=stats["sum_units"]
st.maxunits=stats["max_units"]
st.avgunits=stats["avg_units"]
st.minunits=stats["min_units"]
st.household=household
st.save();
'''#end
hhold = ihousehold()
hholdstats = hhold.getUsage(self.User)
#print hholdstats
#household = self.User.households.all()[0]
#dma = DMA.objects.get(pk=10) #chosing other DMA for comparison, we will change this when we will get the different demographic database
#household_dma = dma.households.all()
#d = idma()
#series = iseries()
obj = {}
list1 = []
areadata = {"year": "", "half": "", "quarter": "", "month": ""}
yourdata = {"year": "", "half": "", "quarter": "", "month": ""}
#getting consumer with same socio-demographic status for the average unit/bill
for x in range(0, 4):
if x == 0:
period = 12
cal = "year"
elif x == 1:
period = 6
cal = "half"
elif x == 2:
period = 3
cal = "quarter"
elif x == 3:
period = 1
cal = "month"
dic = d.getStats(household_dma, period)
areadata[cal] = dic
obj["Units"] = dic["avg_units"]
obj["Cost"] = round(hhold.tariff1(dic["avg_units"]), 2)
#obj["Cost"] = str(series.getCost(dic["avg_units"]))
obj["Period"] = str(period) + " Month"
obj["Data"] = "Area"
list1.append(obj)
obj = {}
dic = {}
dic = hholdstats[cal]
yourdata[cal] = dic
#print dic
obj["Units"] = str(dic["sum_units"])
obj["Cost"] = round(hhold.tariff1(dic["sum_units"]), 2)
#obj["Cost"] = str(series.getCost(dic["sum_units"]))
obj["Period"] = str(period) + " Month"
obj["Data"] = "You"
dic = {}
list1.append(obj)
obj = {}
data = {}
data["chart"] = list1
data["areadata"] = areadata
data["yourdata"] = yourdata
# Added by Chris Pantazis to send the currency to the graphs
# see: uc32barplot1 javascript function in base.js
if self.User.username.startswith("GB"):
data["currency"] = u"£"
else:
data["currency"] = u"€"
return data
def usecase5_2(self):
'''
Declare data
'''
dtlist = []
list1 = []
tariff1data = {"sum": 0.0, "avg": 0.0, "high": 0.0, "low": 0.0}
tariff2data = {"sum": 0.0, "avg": 0.0, "high": 0.0, "low": 0.0}
comparechart = [{
"Cost": "",
"Data": "Tariff1"
}, {
"Cost": "",
"Data": "Tariff2"
}]
stdate = ""
endate = ""
data = {}
household = self.User.households.all()[0]
hhold = ihousehold()
series = iseries()
ts_monthly = series.readseries(
series.getmonthlyseries(household)) #get timeseries
if len(ts_monthly) >= 2: #at least 2 months data
dates, units = IT.izip(
*ts_monthly) #much better for longer data, returning tuples
pdf = pd.DataFrame(
list(units), index=list(dates), columns=["units"]
) #create pandas Series (time series using two different list for timeseries data analysis
pdf.index.name = "dates"
#list of dates (chosen the latest two months of the time series)
stdate = str(dates[len(dates) - 2].date())
endate = str(dates[len(dates) - 1].date())
dtlist.append(stdate)
dtlist.append(endate)
list1.append(round(hhold.tariff1(units[len(units) - 2]), 2))
list1.append(round(hhold.tariff1(units[len(units) - 1]), 2))
data["tariff1"] = series.getlistTojson(dtlist, list1, "Date",
"Cost")
tariff1data["sum"] = list1[0] + list1[
1] # sum over month for this tariff
tariff1data["avg"] = round(tariff1data["sum"] / len(list1),
2) #average per nonth
tariff1data["high"] = {
"date":
iutility.convertdate(dtlist[list1.index(max(list1))],
'%Y-%m-%d', '%B-%Y'),
"max":
max(list1)
} #max per nonth
tariff1data["low"] = {
"date":
iutility.convertdate(dtlist[list1.index(min(list1))],
'%Y-%m-%d', '%B-%Y'),
"min":
min(list1)
} #min per nonth
data["tariff1data"] = tariff1data
comparechart[0]["Cost"] = tariff1data["sum"]
list1 = []
list1.append(round(hhold.tariff2(units[len(units) - 2]), 2))
list1.append(round(hhold.tariff2(units[len(units) - 1]), 2))
data["tariff2"] = series.getlistTojson(dtlist, list1, "Date",
"Cost")
tariff2data["sum"] = list1[0] + list1[
1] # sum over month for this tariff
tariff2data["avg"] = round(tariff2data["sum"] / len(list1),
2) #average per nonth
tariff2data["high"] = {
"date":
iutility.convertdate(dtlist[list1.index(max(list1))],
'%Y-%m-%d', '%B-%Y'),
"max":
max(list1)
} #max per nonth
tariff2data["low"] = {
"date":
iutility.convertdate(dtlist[list1.index(min(list1))],
'%Y-%m-%d', '%B-%Y'),
"min":
min(list1)
} #min per nonth
data["tariff2data"] = tariff2data
comparechart[1]["Cost"] = tariff2data["sum"]
data["comparechart"] = comparechart
'''
add title to be displayed on top of graph
'''
#convert date from to easily read at client side (eg. December 2009
stdate = iutility.convertdate(str(stdate), '%Y-%m-%d', '%B-%Y')
endate = iutility.convertdate(str(endate), '%Y-%m-%d', '%B-%Y')
data["title"] = _("TARIFF COMPARISON FROM ") + stdate + _(
" TO ") + endate
if self.User.username.startswith("GB"):
data["currency"] = u"£"
else:
data["currency"] = u"€"
else:
data = None
#print data
return data
'''
def usecase3_3(self):
'''
Calculating household monthly cost and other statistics
'''
hhold = ihousehold()
hholdstats = hhold.getUsage(self.User)
#print hholdstats
household = self.User.households.all()[0]
# dma_id added by Chris Pantazis. Used to be int(3). Don't know why.
dma_id = household.dma.id
dma = DMA.objects.get(
pk=dma_id
) #chosing other DMA for comparison, we will change this when we will get the different demographic database
household_dma = dma.households.all()
d = idma()
series = iseries()
obj = {}
list1 = []
areadata = {"year": "", "half": "", "quarter": "", "month": ""}
yourdata = {"year": "", "half": "", "quarter": "", "month": ""}
#getting consumer with same socio-demographic status for the average unit/bill
for x in range(0, 4):
if x == 0:
period = 12
cal = "year"
elif x == 1:
period = 6
cal = "half"
elif x == 2:
period = 3
cal = "quarter"
elif x == 3:
period = 1
cal = "month"
dic = d.getStats(household_dma, period)
areadata[cal] = dic
obj["Units"] = dic["avg_units"]
obj["Cost"] = round(hhold.tariff1(dic["avg_units"]), 2)
#obj["Cost"] = str(series.getCost(dic["avg_units"]))
obj["Period"] = str(period) + " Month"
obj["Data"] = "Area"
list1.append(obj)
obj = {}
dic = {}
dic = hholdstats[cal]
yourdata[cal] = dic
#print dic
obj["Units"] = str(dic["sum_units"])
obj["Cost"] = round(hhold.tariff1(dic["sum_units"]), 2)
#obj["Cost"] = str(series.getCost(dic["sum_units"]))
obj["Period"] = str(period) + " Month"
obj["Data"] = "You"
dic = {}
list1.append(obj)
obj = {}
data = {}
data["chart"] = list1
data["areadata"] = areadata
data["yourdata"] = yourdata
# Added by Chris Pantazis to send the currency to the graphs
# see: uc32barplot1 javascript function in base.js
if self.User.username.startswith("GB"):
data["currency"] = u"£"
else:
data["currency"] = u"€"
return data