def test_full_run():
'''
test a wind series that has a constant average
'''
ts = [(datetime(2015, 1, 1, 1, 0), (10, 0)),
(datetime(2015, 1, 1, 2, 0), (20, 0)),
(datetime(2015, 1, 1, 3, 0), (10, 0)),
(datetime(2015, 1, 1, 4, 0), (20, 0)),
(datetime(2015, 1, 1, 5, 0), (10, 0)),
(datetime(2015, 1, 1, 6, 0), (20, 0)),
(datetime(2015, 1, 1, 7, 0), (10, 0)),
(datetime(2015, 1, 1, 8, 0), (20, 0)),
(datetime(2015, 1, 1, 9, 0), (10, 0)),
(datetime(2015, 1, 1, 10, 0), (20, 0))]
start_time = datetime(2015, 1, 1, 1)
model_time = start_time
running_av = RunningAverage(timeseries=ts)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av"
print running_av.ossm.timeseries[:]
model_time = datetime(2015, 1, 1, 4, 0)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av2"
print running_av.ossm.timeseries[:]
assert np.all(running_av.ossm.timeseries['value']['u'][3:9] == 15)
def test_past_hours_to_average():
"""
just make sure there are no errors
"""
wm = Wind(filename=wind_file)
av = RunningAverage(wm)
assert av.past_hours_to_average == 3
av.past_hours_to_average = 6
assert av.past_hours_to_average == 6
def test_serialize_deserialize():
'test serialize/deserialize for webapi'
wind = constant_wind(1., 0)
av = RunningAverage(wind)
json_ = av.serialize()
# deserialize and ensure the dict's are correct
d_av = RunningAverage.deserialize(json_)
assert d_av == av
def test_serialize_deserialize():
'test serialize/deserialize for webapi'
wind = constant_wind(1., 0)
av = RunningAverage(wind)
json_ = av.serialize()
# deserialize and ensure the dict's are correct
d_av = RunningAverage.deserialize(json_)
assert d_av == av
def test_past_hours_to_average():
"""
just make sure there are no errors
"""
wm = Wind(filename=wind_file)
av = RunningAverage(wm)
assert av.past_hours_to_average == 3
av.past_hours_to_average = 6
assert av.past_hours_to_average == 6
def test_full_run():
'''
test a wind series that has a constant average
'''
ts = [(datetime(2015, 1, 1, 1, 0), (10, 0)),
(datetime(2015, 1, 1, 2, 0), (20, 0)),
(datetime(2015, 1, 1, 3, 0), (10, 0)),
(datetime(2015, 1, 1, 4, 0), (20, 0)),
(datetime(2015, 1, 1, 5, 0), (10, 0)),
(datetime(2015, 1, 1, 6, 0), (20, 0)),
(datetime(2015, 1, 1, 7, 0), (10, 0)),
(datetime(2015, 1, 1, 8, 0), (20, 0)),
(datetime(2015, 1, 1, 9, 0), (10, 0)),
(datetime(2015, 1, 1, 10, 0), (20, 0))]
start_time = datetime(2015, 1, 1, 1)
model_time = start_time
running_av = RunningAverage(timeseries=ts)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av"
print running_av.ossm.timeseries[:]
model_time = datetime(2015, 1, 1, 4, 0)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av2"
print running_av.ossm.timeseries[:]
assert np.all(running_av.ossm.timeseries['value']['u'][3:9] == 15)
def test_serialize_deserialize():
'test serialize/deserialize for webapi'
wind = constant_wind(1., 0)
av = RunningAverage(wind)
json_ = av.serialize()
json_['wind'] = wind.serialize()
# deserialize and ensure the dict's are correct
d_av = RunningAverage.deserialize(json_)
assert d_av['wind'] == Wind.deserialize(json_['wind'])
d_av['wind'] = wind
av.update_from_dict(d_av)
assert av.wind is wind
def test_av_from_variable_wind():
'''
test variable wind to running average
'''
# ts = np.zeros((4,), dtype=datetime_value_2d)
# ts[:] = [(datetime(2012, 9, 7, 8, 0), (10, 270)),
# (datetime(2012, 9, 7, 14, 0), (28, 270)),
# (datetime(2012, 9, 7, 20, 0), (28, 270)),
# (datetime(2012, 9, 8, 02, 0), (10, 270))]
# wm = Wind(timeseries=ts, units='m/s')
wm = Wind(timeseries=[(datetime(2012, 9, 7, 8, 0), (10, 270)),
(datetime(2012, 9, 7, 14, 0), (28, 270)),
(datetime(2012, 9, 7, 20, 0), (28, 270)),
(datetime(2012, 9, 8, 02, 0), (10, 270))],
units='m/s')
# wm = Wind(filename=wind_file)
av = RunningAverage(wm)
# print "wm.ossm.timeseries"
# print wm.ossm.timeseries[:]
# print "av.ossm.timeseries"
# print av.ossm.timeseries[:]
assert av.ossm.timeseries['time'][0] == wm.ossm.timeseries['time'][0]
assert av.ossm.timeseries['value']['u'][0] == wm.ossm.timeseries['value'][
'u'][0]
assert av.ossm.timeseries['value']['u'][1] == 10.5
def test_full_run_extended():
'''
test algorithm resets two day running average with a new time
'''
ts = np.zeros((20, ), dtype=datetime_value_2d)
ts[:] = [(datetime(2015, 1, 1, 1, 0), (10, 0)),
(datetime(2015, 1, 1, 2, 0), (20, 0)),
(datetime(2015, 1, 1, 3, 0), (10, 0)),
(datetime(2015, 1, 1, 4, 0), (20, 0)),
(datetime(2015, 1, 1, 5, 0), (10, 0)),
(datetime(2015, 1, 1, 6, 0), (20, 0)),
(datetime(2015, 1, 1, 7, 0), (10, 0)),
(datetime(2015, 1, 1, 8, 0), (20, 0)),
(datetime(2015, 1, 1, 9, 0), (10, 0)),
(datetime(2015, 1, 1, 10, 0), (20, 0)),
(datetime(2015, 1, 5, 1, 0), (10, 0)),
(datetime(2015, 1, 5, 2, 0), (20, 0)),
(datetime(2015, 1, 5, 3, 0), (10, 0)),
(datetime(2015, 1, 5, 4, 0), (20, 0)),
(datetime(2015, 1, 5, 5, 0), (10, 0)),
(datetime(2015, 1, 5, 6, 0), (20, 0)),
(datetime(2015, 1, 5, 7, 0), (10, 0)),
(datetime(2015, 1, 5, 8, 0), (20, 0)),
(datetime(2015, 1, 5, 9, 0), (10, 0)),
(datetime(2015, 1, 5, 10, 0), (20, 0))]
wm = Wind(filename=wind_file, units='mps')
start_time = datetime(2015, 1, 1, 1)
model_time = start_time
running_av = RunningAverage(wind=wm)
# running_av = RunningAverage(timeseries=ts)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av"
print running_av.ossm.timeseries[:]
model_time = datetime(2015, 1, 5, 4, 0)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av2"
print running_av.ossm.timeseries[:]
assert np.all(running_av.ossm.timeseries['value']['u'][:] == 15)
def test_av_from_timeseries():
'''
doesn't do any unit or format conversion
assume m/s and uv
'''
ts = np.zeros((3, ), dtype=datetime_value_2d)
ts[:] = [(datetime(2014, 1, 1, 10, 10, 00), (10, 0)),
(datetime(2014, 1, 1, 11, 10, 00), (20, 0)),
(datetime(2014, 1, 1, 12, 10), (10, 0))]
av = RunningAverage(timeseries=ts)
def test_full_run_extended():
'''
test algorithm resets two day running average with a new time
'''
ts = np.zeros((20,), dtype=datetime_value_2d)
ts[:] = [(datetime(2015, 1, 1, 1, 0), (10, 0)),
(datetime(2015, 1, 1, 2, 0), (20, 0)),
(datetime(2015, 1, 1, 3, 0), (10, 0)),
(datetime(2015, 1, 1, 4, 0), (20, 0)),
(datetime(2015, 1, 1, 5, 0), (10, 0)),
(datetime(2015, 1, 1, 6, 0), (20, 0)),
(datetime(2015, 1, 1, 7, 0), (10, 0)),
(datetime(2015, 1, 1, 8, 0), (20, 0)),
(datetime(2015, 1, 1, 9, 0), (10, 0)),
(datetime(2015, 1, 1, 10, 0), (20, 0)),
(datetime(2015, 1, 5, 1, 0), (10, 0)),
(datetime(2015, 1, 5, 2, 0), (20, 0)),
(datetime(2015, 1, 5, 3, 0), (10, 0)),
(datetime(2015, 1, 5, 4, 0), (20, 0)),
(datetime(2015, 1, 5, 5, 0), (10, 0)),
(datetime(2015, 1, 5, 6, 0), (20, 0)),
(datetime(2015, 1, 5, 7, 0), (10, 0)),
(datetime(2015, 1, 5, 8, 0), (20, 0)),
(datetime(2015, 1, 5, 9, 0), (10, 0)),
(datetime(2015, 1, 5, 10, 0), (20, 0))]
wm = Wind(filename=wind_file, units='mps')
start_time = datetime(2015, 1, 1, 1)
model_time = start_time
running_av = RunningAverage(wind=wm)
# running_av = RunningAverage(timeseries=ts)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av"
print running_av.ossm.timeseries[:]
model_time = datetime(2015, 1, 5, 4, 0)
running_av.prepare_for_model_run(model_time)
running_av.prepare_for_model_step(model_time)
print "running_av2"
print running_av.ossm.timeseries[:]
assert np.all(running_av.ossm.timeseries['value']['u'][:] == 15)
def test_av_from_constant_wind():
'''
test constant wind gives constant running average
'''
wind = constant_wind(1., 270.)
av = RunningAverage(wind)
# print "wind.ossm.timeseries"
# print wind.ossm.timeseries[:]
# print "av.ossm.timeseries"
# print av.ossm.timeseries[:]
assert av.ossm.timeseries[0] == wind.ossm.timeseries[0]
def test_serialize_deserialize():
'test serialize/deserialize for webapi'
wind = constant_wind(1., 0)
av = RunningAverage(wind)
json_ = av.serialize()
json_['wind'] = wind.serialize()
# deserialize and ensure the dict's are correct
d_av = RunningAverage.deserialize(json_)
assert d_av['wind'] == Wind.deserialize(json_['wind'])
d_av['wind'] = wind
av.update_from_dict(d_av)
assert av.wind is wind
def test_default_init():
av = RunningAverage()
assert av.timeseries == np.array([(sec_to_date(zero_time()), [0.0, 0.0])],
dtype=datetime_value_2d)
assert av.units == 'mps'