def convertTestData(modelDir, data, outputFilename):
watts = []
timeStamps = []
samplePeriod = ''
# parse provided csv files
data = data.split('\n')
firstRow = True
for row in data:
if str(row) != '':
dataPoint = row.split(',')
if firstRow:
samplePeriod = dataPoint[0]
firstRow = False
else:
timeStamps.append(dataPoint[0])
watts.append(dataPoint[1])
# format dataframe data structure and save in nilmtk format
store = get_datastore(outputFilename, 'HDF', mode='w')
df = pd.DataFrame({('power', 'apparent'): watts}, dtype=float)
df.columns.set_names(LEVEL_NAMES, inplace=True)
df.index = pd.to_datetime(timeStamps,
format='%Y-%m-%d %H:%M:%S',
exact=False,
utc=True)
df = df.tz_convert('US/Eastern')
df = df.sort_index()
key = Key(building=1, meter=1)
store.put(str(key), df)
## create the metadata files in accordance with nilmtk guidelines
# building metatdata
if not os.path.exists(pJoin(modelDir, 'test')):
os.makedirs(pJoin(modelDir, 'test'))
f = open(pJoin(modelDir, 'test', 'building1.yaml'), 'w')
f.write('instance: 1\n')
f.write('elec_meters:\n')
f.write(' ' + '1: &generic\n')
f.write(' ' + 'site_meter: true\n')
f.write(' ' + 'device_model: generic\n')
f.write('\nappliances: []')
f.close()
# dataset metadata
f = open(pJoin(modelDir, 'test', 'dataset.yaml'), 'w')
f.write('name: testData\n')
f.close()
# meter device metadata
f = open(pJoin(modelDir, 'test', 'meter_devices.yaml'), 'w')
f.write('generic:\n')
f.write(' ' + 'model: generic\n')
f.write(' ' + 'sample_period: ' + samplePeriod + '\n')
f.write(' ' + 'max_sample_period: ' + samplePeriod + '\n')
f.write(' ' + 'measurements:\n')
f.write(' ' + '- physical_quantity: power\n')
f.write(' ' + 'type: apparent\n')
f.write(' ' + 'upper_limit: 1000000000\n')
f.write(' ' + 'lower_limit: -100000000\n')
f.close()
# save data and metadata
save_yaml_to_datastore(pJoin(modelDir, 'test'), store)
store.close()
return outputFilename
def convertTruthData(modelDir, data, outputFilename):
watts = []
timeStamps = []
appliances = []
samplePeriod = ''
# parse provided csv files
data = data.split('\n')
firstRow = True
for row in data:
if str(row) != '':
dataPoint = row.split(',')
if firstRow:
samplePeriod = dataPoint[0]
firstRow = False
else:
timeStamps.append(dataPoint[0])
watts.append(dataPoint[1])
appliances.append(dataPoint[2])
watts = np.array(watts)
appliances = np.array(appliances)
timeStamps = np.array(timeStamps)
store = get_datastore(outputFilename, 'HDF', mode='w')
# breakdown the data by appliance and set every time point where
# the appliance wasnt used to 0
uniqueAppliances = list(np.unique(appliances))
for index, app in enumerate(uniqueAppliances):
# get the time points where a given appliance is on and
# also where it is off
appIndices = np.where(appliances == app)[0]
nonAppIndices = np.where(appliances != app)[0]
# keep only the data for when the appliance is on
wattsFiltered = np.delete(np.copy(watts), nonAppIndices)
timeFiltered = np.delete(np.copy(timeStamps), nonAppIndices)
# create zeroed data when the appliance is off
timeFiller = np.setdiff1d(np.copy(timeStamps), timeFiltered)
wattsFiller = np.zeros(timeFiller.shape)
# combine the on and off data
timeAll = np.append(timeFiller, timeFiltered)
wattsAll = np.append(wattsFiller, wattsFiltered)
# format dataframe data structure and save in nilmtk format
df = pd.DataFrame({('power', 'apparent'): wattsAll}, dtype=float)
df.index = pd.to_datetime(timeAll,
format='%Y-%m-%d %H:%M:%S',
exact=False,
utc=True)
df.columns.set_names(LEVEL_NAMES, inplace=True)
df = df.tz_convert('US/Eastern')
df = df.sort_index()
key = Key(building=1, meter=index + 1)
store.put(str(key), df)
## create the metadata files in accordance with nilmtk guidelines
# building metatdata
if not os.path.exists(pJoin(modelDir, 'truth')):
os.makedirs(pJoin(modelDir, 'truth'))
f = open(pJoin(modelDir, 'truth', 'building1.yaml'), 'w')
f.write('instance: 1\n')
f.write('elec_meters:\n')
for index, app in enumerate(uniqueAppliances):
if index == 0:
f.write(' ' + '1: &generic\n')
f.write(' ' + 'submeter_of: 0\n')
f.write(' ' + 'device_model: generic\n')
else:
f.write(' ' + str(index + 1) + ': *generic\n')
f.write('\nappliances:')
for index, app in enumerate(uniqueAppliances):
f.write('\n- ' + 'original_name: ' + app + '\n')
f.write(' ' + 'type: unknown\n')
f.write(' ' + 'instance: ' + str(index + 1) + '\n')
f.write(' ' + 'meters: [' + str(index + 1) + ']\n')
f.close()
# dataset metadata
f = open(pJoin(modelDir, 'truth', 'dataset.yaml'), 'w')
f.write('name: truthData\n')
f.close()
# meterdevices metadata
f = open(pJoin(modelDir, 'truth', 'meter_devices.yaml'), 'w')
f.write('generic:\n')
f.write(' ' + 'model: generic\n')
f.write(' ' + 'sample_period: ' + samplePeriod + '\n')
f.write(' ' + 'max_sample_period: ' + samplePeriod + '\n')
f.write(' ' + 'measurements:\n')
f.write(' ' + '- physical_quantity: power\n')
f.write(' ' + 'type: apparent\n')
f.write(' ' + 'upper_limit: 1000000000\n')
f.write(' ' + 'lower_limit: -100000000\n')
f.close()
# save data and metadata
save_yaml_to_datastore(pJoin(modelDir, 'truth'), store)
store.close()
return outputFilename