def test_convert_random_dataset():
input_filepath = r'C:\Users\CVLab\Documents\nilm\nilmtk\data\random.h5'
output_filepath = r'C:\Users\CVLab\Documents\nilm\nilmtk\data\random_csv'
if os.path.isdir(output_filepath):
shutil.rmtree(output_filepath)
input_store = HDFDataStore(input_filepath)
output_store = CSVDataStore(output_filepath)
convert_datastore(input_store, output_store)
input_store.close()
output_store.close()
def test_convert_random_dataset():
input_filepath = 'data/random.h5'
output_filepath = 'data/random_csv'
if os.path.isdir(output_filepath):
shutil.rmtree(output_filepath)
input_store = HDFDataStore(input_filepath)
output_store = CSVDataStore(output_filepath)
convert_datastore(input_store, output_store)
input_store.close()
output_store.close()
def test_convert_random_dataset():
input_filepath = 'data/random.h5'
output_filepath = 'data/random_csv'
if os.path.isdir(output_filepath):
shutil.rmtree(output_filepath)
input_store=HDFDataStore(input_filepath)
output_store=CSVDataStore(output_filepath)
convert_datastore(input_store, output_store)
input_store.close()
output_store.close()
def test_co_correctness(self):
elec = self.dataset.buildings[1].elec
co = CombinatorialOptimisation()
co.train(elec)
mains = elec.mains()
output = HDFDataStore("output.h5", "w")
co.disaggregate(mains, output, resample_seconds=1)
for meter in range(2, 4):
df1 = output.store.get("/building1/elec/meter{}".format(meter))
df2 = self.dataset.store.store.get("/building1/elec/meter{}".format(meter))
self.assertEqual((df1 == df2).sum().values[0], len(df1.index))
self.assertEqual(len(df1.index), len(df2.index))
output.close()
rm("output.h5")
def test_co_correctness(self):
elec = self.dataset.buildings[1].elec
co = CombinatorialOptimisation()
co.train(elec)
mains = elec.mains()
output = HDFDataStore('output.h5', 'w')
co.disaggregate(mains, output, resample_seconds=1)
for meter in range(2, 4):
df1 = output.store.get('/building1/elec/meter{}'.format(meter))
df2 = self.dataset.store.store.get(
'/building1/elec/meter{}'.format(meter))
self.assertEqual((df1 == df2).sum().values[0], len(df1.index))
self.assertEqual(len(df1.index), len(df2.index))
output.close()
remove("output.h5")
def test_fhmm_correctness(self):
elec = self.dataset.buildings[1].elec
fhmm = FHMM()
fhmm.train(elec)
mains = elec.mains()
output = HDFDataStore('output.h5', 'w')
fhmm.disaggregate(mains, output, sample_period=1)
for meter in range(2, 4):
df1 = output.store.get('/building1/elec/meter{}'.format(meter))
df2 = self.dataset.store.store.get(
'/building1/elec/meter{}'.format(meter))
self.assertEqual((df1 == df2).sum().values[0], len(df1.index))
self.assertEqual(len(df1.index), len(df2.index))
output.close()
remove("output.h5")
def test_fhmm_correctness(self):
elec = self.dataset.buildings[1].elec
fhmm = FHMM()
fhmm.train(elec)
mains = elec.mains()
output = HDFDataStore('output.h5', 'w')
fhmm.disaggregate(mains, output, sample_period=1)
for meter in range(2, 4):
df1 = output.store.get('/building1/elec/meter{}'.format(meter))
df2 = self.dataset.store.store.get(
'/building1/elec/meter{}'.format(meter))
self.assertEqual((df1 == df2).sum().values[0], len(df1.index))
self.assertEqual(len(df1.index), len(df2.index))
output.close()
remove("output.h5")
disag_filename = 'disag-out.h5'
pred_df1 = None
if not Path("disag-out.h5").exists():
from nilmtk.datastore import HDFDataStore
output = HDFDataStore(disag_filename, 'w')
# test_mains: The aggregated signal meter
# output: The output datastore
# train_meter: This is used in order to copy the metadata of the train meter into the datastore
pred_df1 = gru.disaggregate(test_mains,
output,
train_meter,
sample_period=1)
output.close()
result = DataSet(disag_filename)
res_elec = result.buildings[1].elec
predicted = res_elec['fridge']
ground_truth = test_elec['fridge']
import matplotlib.pyplot as plt
predicted.plot()
plt.show()
ground_truth.plot()
plt.show()
test_elec.mains().plot()
plt.show()
pred_df1.plot()
plt.show()
from nilmtk.datastore import HDFDataStore, CSVDataStore
from nilmtk.datastore.datastore import convert_datastore
import os
import shutil
input_filepath = 'data/random.h5'
output_filepath = 'data/random_csv'
if os.path.isdir(output_filepath):
shutil.rmtree(output_filepath)
input_store=HDFDataStore(input_filepath)
output_store=CSVDataStore(output_filepath)
convert_datastore(input_store, output_store)
input_store.close()
output_store.close()
from nilmtk.datastore import HDFDataStore, CSVDataStore
from nilmtk.datastore.datastore import convert_datastore
import os
import shutil
input_filepath = 'data/random.h5'
output_filepath = 'data/random_csv'
if os.path.isdir(output_filepath):
shutil.rmtree(output_filepath)
input_store = HDFDataStore(input_filepath)
output_store = CSVDataStore(output_filepath)
convert_datastore(input_store, output_store)
input_store.close()
output_store.close()
