def test_fetchlocation(self):
"""Fetch data from a given location"""
result = export.processExport(houseId=1,
locationIds = [1,],
)
self.assertEqual(result.shape, (2880, 1))
self.assertEqual(result.columns[0], LOC1)
result = export.processExport(houseId=1,
locationIds = [2,],
)
self.assertEqual(result.shape, (2880, 1))
self.assertEqual(result.columns[0], LOC2)
def test_aggregateby(self):
"""Can we add summary statistics?"""
result = export.processExport(houseId=1,
aggregate="1D",
aggregateby=["min"])
#So this will just show the minimum value
self.assertEqual(result.shape, (10, 2))
result = export.processExport(houseId=1,
aggregate="1D",
aggregateby=["min","mean","max"])
#print result.head()
#So this will have 3 readings for each location (6 in total()
self.assertEqual(result.shape, (10, 6))
def test_dates(self):
"""What about if date ranges are given"""
result = export.processExport(houseId=1,
startDate = datetime.datetime(2013, 01, 06) #5 Days
)
self.assertEqual(result.shape, (1440, 2))
self.assertEqual(result.index[0], datetime.datetime(2013, 01, 06))
self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 10, 23, 55))
#Stop at 00:00 on the 5th
result = export.processExport(houseId=1,
endDate = datetime.datetime(2013, 01, 05, 23, 55) #5 Days
)
self.assertEqual(result.shape, (1440, 2))
self.assertEqual(result.index[0], datetime.datetime(2013, 01, 01))
self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 05, 23, 55))
def test_typeids(self):
"""Fetch by a given type"""
result = export.processExport(houseId=1,
locationIds = [1,],
typeIds = [0],
)
self.assertEqual(result.shape, (2880, 1))
#Without a location
result = export.processExport(houseId=1,
typeIds = [0],
)
self.assertEqual(result.shape, (2880, 2))
result = export.processExport(houseId=1,
locationIds = [1,],
typeIds = [2],
)
#We should get an emppty object here
self.assertEqual(result, {})
def test_aggregate(self):
"""Does aggregation work as expected"""
#10 Minute sampleing
result = export.processExport(houseId=1,
aggregate="10Min")
self.assertEqual(result.shape, (1440, 2))
#And the second sample should be 10 minutes in
self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 0, 10, 00))
#1/2 hourly
result = export.processExport(houseId=1,
aggregate="30Min")
#2 * 24 * 10 = 480
self.assertEqual(result.shape, (480, 2))
#And the second sample should be 10 minutes in
self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 0, 30, 00))
#Hourly
result = export.processExport(houseId=1,
aggregate="1H")
self.assertEqual(result.shape, (240, 2))
#And the second sample should be 10 minutes in
self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 1, 00, 00))
#daily
result = export.processExport(houseId=1,
aggregate="1D")
self.assertEqual(result.shape, (10, 2))
#And the second sample should be 10 minutes in
self.assertEqual(result.index[1], datetime.datetime(2013, 01, 02, 0, 00, 00))
def test_fetchall(self):
"""Fetch data from a house"""
result = export.processExport(houseId=1)
#We should have 2 locations * 1 sensor * 10 days of data here
# 2 * 1 * (288 * 10) == 5670
#print result.shape
#result.to_csv("temp.csv")
#Do we get the right object
self.assertEqual(type(result), pandas.DataFrame)
#And is it the right size
self.assertEqual(result.shape, (2880, 2)) #So 2880 samples from two sensors
#And the right range of data
self.assertEqual(result.index[0], datetime.datetime(2013, 01, 01))
self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 10, 23, 55))