def query():
data = json.loads(request.data)
home_number = data["home_number"]
start_timestamp = data['start'] * 1000
end_timestamp = data['end'] * 1000
# Creating a SMAP interface
smap = SMAP(SMAP_URL)
uuid = smap.find_uuid(home_number)
smap_readings = smap.get_readings(uuid, start_timestamp, end_timestamp)
df = to_pd_series(smap_readings)
# Downsample
df = df.resample('1T').dropna()
# Creating nilmtk building
b = Building()
# Attaching mains
b.utility.electric.mains[MainsName(1, 1)] = df
# Instantiating CO disaggregator
disaggregator = CO_1d()
# Loading model
path_to_file = os.path.join(MODEL_PATH, home_number + ".json")
disaggregator.import_model(path_to_file)
# Perform disaggregation
disaggregator.disaggregate(b)
predictions = disaggregator.predictions
return pd_to_higcharts(predictions)
def query_raw():
data = json.loads(request.data)
home_number = data["home_number"]
start_timestamp = data['start'] * 1000
end_timestamp = data['end'] * 1000
# Creating a SMAP interface
smap = SMAP(SMAP_URL)
uuid = smap.find_uuid(home_number)
smap_readings = smap.get_readings(uuid, start_timestamp, end_timestamp)
df = to_pd_series(smap_readings)
# Downsample
df = df.resample('1T').dropna()
df.rename(columns={"poweractive": "total"})
return pd_to_higcharts(df)
from iiit_disagg.smap_interface import SMAP
from iiit_disagg.smap_interface import to_pd_series
from nilmtk.building import Building
from nilmtk.sensors.electricity import MainsName, Measurement
from nilmtk.disaggregate.co_1d import CO_1d
FLAT_NUMBER = '101'
START = 1387996200000
END = 1388341800000
DISAGG_FEATURE = Measurement('power', 'active')
# Making SMAP connection
smap = SMAP("http://nms.iiitd.edu.in:9102/")
# Get UUID
UUID = smap.find_uuid(FLAT_NUMBER)
# Getting data for UUID
smap_readings = smap.get_readings(UUID, START, END)
# Transform to NILMTK Mains
df = to_pd_series(smap_readings)
# Downsample
df = df.resample('1T')
# Creating nilmtk building
b = Building()
# Attaching mains
b.utility.electric.mains[MainsName(1, 1)] = df