def test_mover_api():
'''
Test the API methods for adding and removing movers to the model.
'''
start_time = datetime(2012, 1, 1, 0, 0)
model = Model()
model.duration = timedelta(hours=12)
model.time_step = timedelta(hours=1)
model.start_time = start_time
mover_1 = SimpleMover(velocity=(1., -1., 0.))
mover_2 = SimpleMover(velocity=(1., -1., 0.))
mover_3 = SimpleMover(velocity=(1., -1., 0.))
mover_4 = SimpleMover(velocity=(1., -1., 0.))
# test our add object methods
model.movers += mover_1
model.movers += mover_2
# test our get object methods
assert model.movers[mover_1.id] == mover_1
assert model.movers[mover_2.id] == mover_2
with raises(KeyError):
temp = model.movers['Invalid']
print temp
# test our iter and len object methods
assert len(model.movers) == 2
assert len([m for m in model.movers]) == 2
for (m1, m2) in zip(model.movers, [mover_1, mover_2]):
assert m1 == m2
# test our add objectlist methods
model.movers += [mover_3, mover_4]
assert [m for m in model.movers] == [mover_1, mover_2, mover_3, mover_4]
# test our remove object methods
del model.movers[mover_3.id]
assert [m for m in model.movers] == [mover_1, mover_2, mover_4]
with raises(KeyError):
# our key should also be gone after the delete
temp = model.movers[mover_3.id]
print temp
# test our replace method
model.movers[mover_2.id] = mover_3
assert [m for m in model.movers] == [mover_1, mover_3, mover_4]
assert model.movers[mover_3.id] == mover_3
with raises(KeyError):
# our key should also be gone after the delete
temp = model.movers[mover_2.id]
print temp
def test_mover_api():
'''
Test the API methods for adding and removing movers to the model.
'''
start_time = datetime(2012, 1, 1, 0, 0)
model = Model()
model.duration = timedelta(hours=12)
model.time_step = timedelta(hours=1)
model.start_time = start_time
mover_1 = SimpleMover(velocity=(1., -1., 0.))
mover_2 = SimpleMover(velocity=(1., -1., 0.))
mover_3 = SimpleMover(velocity=(1., -1., 0.))
mover_4 = SimpleMover(velocity=(1., -1., 0.))
# test our add object methods
model.movers += mover_1
model.movers += mover_2
# test our get object methods
assert model.movers[mover_1.id] == mover_1
assert model.movers[mover_2.id] == mover_2
with raises(KeyError):
temp = model.movers['Invalid']
print temp
# test our iter and len object methods
assert len(model.movers) == 2
assert len([m for m in model.movers]) == 2
for (m1, m2) in zip(model.movers, [mover_1, mover_2]):
assert m1 == m2
# test our add objectlist methods
model.movers += [mover_3, mover_4]
assert [m for m in model.movers] == [mover_1, mover_2, mover_3, mover_4]
# test our remove object methods
del model.movers[mover_3.id]
assert [m for m in model.movers] == [mover_1, mover_2, mover_4]
with raises(KeyError):
# our key should also be gone after the delete
temp = model.movers[mover_3.id]
print temp
# test our replace method
model.movers[mover_2.id] = mover_3
assert [m for m in model.movers] == [mover_1, mover_3, mover_4]
assert model.movers[mover_3.id] == mover_3
with raises(KeyError):
# our key should also be gone after the delete
temp = model.movers[mover_2.id]
print temp
def test_timestep():
model = Model()
ts = timedelta(hours=1)
model.time_step = ts
assert model.time_step == ts.total_seconds()
dur = timedelta(days=3)
model.duration = dur
assert model._duration == dur
def test_timestep():
model = Model()
ts = timedelta(hours=1)
model.time_step = ts
assert model.time_step == ts.total_seconds()
dur = timedelta(days=3)
model.duration = dur
assert model._duration == dur
def test_callback_add_mover():
'Test callback after add mover'
units = 'meter per second'
model = Model()
model.start_time = datetime(2012, 1, 1, 0, 0)
model.duration = timedelta(hours=10)
model.time_step = timedelta(hours=1)
# start_loc = (1.0, 2.0, 0.0) # random non-zero starting points
# add Movers
model.movers += SimpleMover(velocity=(1., -1., 0.))
series = np.array((model.start_time, (10, 45)),
dtype=datetime_value_2d).reshape((1, ))
model.movers += WindMover(Wind(timeseries=series, units=units))
# this should create a Wind object
new_wind = Wind(timeseries=series, units=units)
model.environment += new_wind
assert new_wind in model.environment
assert len(model.environment) == 2
tide_file = get_datafile(os.path.join(tides_dir, 'CLISShio.txt'))
tide_ = Tide(filename=tide_file)
d_file = get_datafile(os.path.join(lis_dir, 'tidesWAC.CUR'))
model.movers += CatsMover(d_file, tide=tide_)
model.movers += CatsMover(d_file)
for mover in model.movers:
assert mover.active_start == inf_datetime.InfDateTime('-inf')
assert mover.active_stop == inf_datetime.InfDateTime('inf')
if hasattr(mover, 'wind'):
assert mover.wind in model.environment
if hasattr(mover, 'tide'):
if mover.tide is not None:
assert mover.tide in model.environment
# Add a mover with user defined active_start / active_stop values
# - these should not be updated
active_on = model.start_time + timedelta(hours=1)
active_off = model.start_time + timedelta(hours=4)
custom_mover = SimpleMover(velocity=(1., -1., 0.),
active_start=active_on,
active_stop=active_off)
model.movers += custom_mover
assert model.movers[custom_mover.id].active_start == active_on
assert model.movers[custom_mover.id].active_stop == active_off
def test_callback_add_mover():
'Test callback after add mover'
units = 'meter per second'
model = Model()
model.start_time = datetime(2012, 1, 1, 0, 0)
model.duration = timedelta(hours=10)
model.time_step = timedelta(hours=1)
# start_loc = (1.0, 2.0, 0.0) # random non-zero starting points
# add Movers
model.movers += SimpleMover(velocity=(1., -1., 0.))
series = np.array((model.start_time, (10, 45)),
dtype=datetime_value_2d).reshape((1, ))
model.movers += WindMover(Wind(timeseries=series, units=units))
# this should create a Wind object
new_wind = Wind(timeseries=series, units=units)
model.environment += new_wind
assert new_wind in model.environment
assert len(model.environment) == 2
tide_ = Tide(filename=testdata['CatsMover']['tide'])
d_file = testdata['CatsMover']['curr']
model.movers += CatsMover(d_file, tide=tide_)
model.movers += CatsMover(d_file)
for mover in model.movers:
assert mover.active_start == inf_datetime.InfDateTime('-inf')
assert mover.active_stop == inf_datetime.InfDateTime('inf')
if hasattr(mover, 'wind'):
assert mover.wind in model.environment
if hasattr(mover, 'tide'):
if mover.tide is not None:
assert mover.tide in model.environment
# Add a mover with user defined active_start / active_stop values
# - these should not be updated
active_on = model.start_time + timedelta(hours=1)
active_off = model.start_time + timedelta(hours=4)
custom_mover = SimpleMover(velocity=(1., -1., 0.),
active_start=active_on,
active_stop=active_off)
model.movers += custom_mover
assert model.movers[custom_mover.id].active_start == active_on
assert model.movers[custom_mover.id].active_stop == active_off
def test_callback_add_mover():
""" Test callback after add mover """
units = 'meter per second'
model = Model()
model.time_step = timedelta(hours=1)
model.duration = timedelta(hours=10)
model.start_time = datetime(2012, 1, 1, 0, 0)
# start_loc = (1.0, 2.0, 0.0) # random non-zero starting points
# add Movers
model.movers += SimpleMover(velocity=(1., -1., 0.))
series = np.array((model.start_time, (10, 45)),
dtype=datetime_value_2d).reshape((1, ))
model.movers += WindMover(Wind(timeseries=series, units=units))
tide_file = get_datafile(os.path.join(os.path.dirname(__file__),
r"sample_data", 'tides', 'CLISShio.txt'))
tide_ = Tide(filename=tide_file)
d_file = get_datafile(os.path.join(datadir,
r"long_island_sound/tidesWAC.CUR"))
model.movers += CatsMover(d_file, tide=tide_)
model.movers += CatsMover(d_file)
for mover in model.movers:
assert mover.active_start == inf_datetime.InfDateTime('-inf')
assert mover.active_stop == inf_datetime.InfDateTime('inf')
if isinstance(mover, WindMover):
assert mover.wind.id in model.environment
if isinstance(mover, CatsMover):
if mover.tide is not None:
assert mover.tide.id in model.environment
# Add a mover with user defined active_start / active_stop values
# - these should not be updated
active_on = model.start_time + timedelta(hours=1)
active_off = model.start_time + timedelta(hours=4)
custom_mover = SimpleMover(velocity=(1., -1., 0.),
active_start=active_on,
active_stop=active_off)
model.movers += custom_mover
assert model.movers[custom_mover.id].active_start == active_on
assert model.movers[custom_mover.id].active_stop == active_off
def test_callback_add_mover_midrun():
'Test callback after add mover called midway through the run'
model = Model()
model.start_time = datetime(2012, 1, 1, 0, 0)
model.duration = timedelta(hours=10)
model.time_step = timedelta(hours=1)
# start_loc = (1.0, 2.0, 0.0) # random non-zero starting points
# model = setup_simple_model()
for i in range(2):
model.step()
assert model.current_time_step > -1
# now add another mover and make sure model rewinds
model.movers += SimpleMover(velocity=(2., -2., 0.))
assert model.current_time_step == -1
def test_callback_add_mover_midrun():
'Test callback after add mover called midway through the run'
model = Model()
model.start_time = datetime(2012, 1, 1, 0, 0)
model.duration = timedelta(hours=10)
model.time_step = timedelta(hours=1)
# start_loc = (1.0, 2.0, 0.0) # random non-zero starting points
# model = setup_simple_model()
for i in range(2):
model.step()
assert model.current_time_step > -1
# now add another mover and make sure model rewinds
model.movers += SimpleMover(velocity=(2., -2., 0.))
assert model.current_time_step == -1
def test_linearity_of_wind_movers(wind_persist):
'''
WindMover is defined as a linear operation - defining a model
with a single WindMover with 15 knot wind is equivalent to defining
a model with three WindMovers each with 5 knot wind. Or any number of
WindMover's such that the sum of their magnitude is 15knots and the
direction of wind is the same for both cases.
Below is an example which defines two models and runs them.
In model2, there are multiple winds defined so the windage parameter
is reset 3 times for one timestep.
Since windage range and persistence do not change, this only has the effect
of doing the same computation 3 times. However, the results are the same.
The mean and variance of the positions for both models are close.
As windage_persist is decreased, the values become closer.
Setting windage_persist=0 gives the large difference between them.
'''
units = 'meter per second'
start_time = datetime(2012, 1, 1, 0, 0)
series1 = np.array((start_time, (15, 45)),
dtype=datetime_value_2d).reshape((1, ))
series2 = np.array((start_time, (6, 45)),
dtype=datetime_value_2d).reshape((1, ))
series3 = np.array((start_time, (3, 45)),
dtype=datetime_value_2d).reshape((1, ))
num_LEs = 1000
element_type = floating(windage_persist=wind_persist)
model1 = Model(name='model1')
model1.duration = timedelta(hours=1)
model1.time_step = timedelta(hours=1)
model1.start_time = start_time
model1.spills += point_line_release_spill(num_elements=num_LEs,
start_position=(1., 2., 0.),
release_time=start_time,
element_type=element_type)
model1.movers += WindMover(Wind(timeseries=series1, units=units),
make_default_refs=False)
model2 = Model(name='model2')
model2.duration = timedelta(hours=10)
model2.time_step = timedelta(hours=1)
model2.start_time = start_time
model2.spills += point_line_release_spill(num_elements=num_LEs,
start_position=(1., 2., 0.),
release_time=start_time,
element_type=element_type)
# todo: CHECK RANDOM SEED
# model2.movers += WindMover(Wind(timeseries=series1, units=units))
model2.movers += WindMover(Wind(timeseries=series2, units=units))
model2.movers += WindMover(Wind(timeseries=series2, units=units))
model2.movers += WindMover(Wind(timeseries=series3, units=units))
model2.set_make_default_refs(False)
while True:
try:
model1.next()
except StopIteration as ex:
# print message
print ex.message
break
while True:
try:
model2.next()
except StopIteration as ex:
# print message
print ex.message
break
# mean and variance at the end should be fairly close
# look at the mean of the position vector. Assume m1 is truth
# and m2 is approximation - look at the absolute error between
# mean position of m2 in the 2 norm.
# rel_mean_error =(np.linalg.norm(np.mean(model2.spills.LE('positions'), 0)
# - np.mean(model1.spills.LE('positions'), 0)))
# assert rel_mean_error <= 0.5
# Similarly look at absolute error in variance of position of m2
# in the 2 norm.
rel_var_error = np.linalg.norm(np.var(model2.spills.LE('positions'), 0) -
np.var(model1.spills.LE('positions'), 0))
assert rel_var_error <= 0.0015
def test_all_movers(start_time, release_delay, duration):
'''
Tests that all the movers at least can be run
Add new ones as they come along!
'''
model = Model()
model.time_step = timedelta(hours=1)
model.duration = timedelta(seconds=model.time_step * duration)
model.start_time = start_time
start_loc = (1., 2., 0.) # random non-zero starting points
# a spill - release after 5 timesteps
release_time = (start_time +
timedelta(seconds=model.time_step * release_delay))
model.spills += point_line_release_spill(num_elements=10,
start_position=start_loc,
release_time=release_time)
# the land-water map
model.map = gnome.map.GnomeMap() # the simplest of maps
# simple mover
model.movers += SimpleMover(velocity=(1., -1., 0.))
assert len(model.movers) == 1
# random mover
model.movers += RandomMover(diffusion_coef=100000)
assert len(model.movers) == 2
# wind mover
series = np.array((start_time, (10, 45)),
dtype=datetime_value_2d).reshape((1, ))
model.movers += WindMover(Wind(timeseries=series,
units='meter per second'))
assert len(model.movers) == 3
# CATS mover
model.movers += CatsMover(testdata['CatsMover']['curr'])
assert len(model.movers) == 4
# run the model all the way...
num_steps_output = 0
for step in model:
num_steps_output += 1
print 'running step:', step
# test release happens correctly for all cases
if release_delay < duration:
# at least one get_move has been called after release
assert np.all(model.spills.LE('positions')[:, :2] != start_loc[:2])
elif release_delay == duration:
# particles are released after last step so no motion,
# only initial _state
assert np.all(model.spills.LE('positions') == start_loc)
else:
# release_delay > duration so nothing released though model ran
assert len(model.spills.LE('positions')) == 0
# there is the zeroth step, too.
calculated_steps = (model.duration.total_seconds() / model.time_step) + 1
assert num_steps_output == calculated_steps
def test_linearity_of_wind_movers(wind_persist):
'''
WindMover is defined as a linear operation - defining a model
with a single WindMover with 15 knot wind is equivalent to defining
a model with three WindMovers each with 5 knot wind. Or any number of
WindMover's such that the sum of their magnitude is 15knots and the
direction of wind is the same for both cases.
Below is an example which defines two models and runs them.
In model2, there are multiple winds defined so the windage parameter
is reset 3 times for one timestep.
Since windage range and persistence do not change, this only has the effect
of doing the same computation 3 times. However, the results are the same.
The mean and variance of the positions for both models are close.
As windage_persist is decreased, the values become closer.
Setting windage_persist=0 gives the large difference between them.
'''
units = 'meter per second'
start_time = datetime(2012, 1, 1, 0, 0)
series1 = np.array((start_time, (15, 45)),
dtype=datetime_value_2d).reshape((1, ))
series2 = np.array((start_time, (6, 45)), dtype=datetime_value_2d).reshape(
(1, ))
series3 = np.array((start_time, (3, 45)), dtype=datetime_value_2d).reshape(
(1, ))
num_LEs = 1000
element_type = floating(windage_persist=wind_persist)
model1 = Model(name='model1')
model1.duration = timedelta(hours=1)
model1.time_step = timedelta(hours=1)
model1.start_time = start_time
model1.spills += point_line_release_spill(num_elements=num_LEs,
start_position=(1., 2., 0.),
release_time=start_time,
element_type=element_type)
model1.movers += WindMover(Wind(timeseries=series1, units=units),
make_default_refs=False)
model2 = Model(name='model2')
model2.duration = timedelta(hours=10)
model2.time_step = timedelta(hours=1)
model2.start_time = start_time
model2.spills += point_line_release_spill(num_elements=num_LEs,
start_position=(1., 2., 0.),
release_time=start_time,
element_type=element_type)
# todo: CHECK RANDOM SEED
# model2.movers += WindMover(Wind(timeseries=series1, units=units))
model2.movers += WindMover(Wind(timeseries=series2, units=units))
model2.movers += WindMover(Wind(timeseries=series2, units=units))
model2.movers += WindMover(Wind(timeseries=series3, units=units))
model2.set_make_default_refs(False)
while True:
try:
model1.next()
except StopIteration as ex:
# print message
print ex.message
break
while True:
try:
model2.next()
except StopIteration as ex:
# print message
print ex.message
break
# mean and variance at the end should be fairly close
# look at the mean of the position vector. Assume m1 is truth
# and m2 is approximation - look at the absolute error between
# mean position of m2 in the 2 norm.
# rel_mean_error =(np.linalg.norm(np.mean(model2.spills.LE('positions'), 0)
# - np.mean(model1.spills.LE('positions'), 0)))
# assert rel_mean_error <= 0.5
# Similarly look at absolute error in variance of position of m2
# in the 2 norm.
rel_var_error = np.linalg.norm(
np.var(model2.spills.LE('positions'), 0) -
np.var(model1.spills.LE('positions'), 0))
assert rel_var_error <= 0.0015
def test_all_movers(start_time, release_delay, duration):
'''
Tests that all the movers at least can be run
Add new ones as they come along!
'''
model = Model()
model.time_step = timedelta(hours=1)
model.duration = timedelta(seconds=model.time_step * duration)
model.start_time = start_time
start_loc = (1., 2., 0.) # random non-zero starting points
# a spill - release after 5 timesteps
release_time = (start_time +
timedelta(seconds=model.time_step * release_delay))
model.spills += point_line_release_spill(num_elements=10,
start_position=start_loc,
release_time=release_time)
# the land-water map
model.map = gnome.map.GnomeMap() # the simplest of maps
# simple mover
model.movers += SimpleMover(velocity=(1., -1., 0.))
assert len(model.movers) == 1
# random mover
model.movers += RandomMover(diffusion_coef=100000)
assert len(model.movers) == 2
# wind mover
series = np.array((start_time, (10, 45)), dtype=datetime_value_2d).reshape(
(1, ))
model.movers += WindMover(Wind(timeseries=series,
units='meter per second'))
assert len(model.movers) == 3
# CATS mover
model.movers += CatsMover(testdata['CatsMover']['curr'])
assert len(model.movers) == 4
# run the model all the way...
num_steps_output = 0
for step in model:
num_steps_output += 1
print 'running step:', step
# test release happens correctly for all cases
if release_delay < duration:
# at least one get_move has been called after release
assert np.all(model.spills.LE('positions')[:, :2] != start_loc[:2])
elif release_delay == duration:
# particles are released after last step so no motion,
# only initial _state
assert np.all(model.spills.LE('positions') == start_loc)
else:
# release_delay > duration so nothing released though model ran
assert len(model.spills.LE('positions')) == 0
# there is the zeroth step, too.
calculated_steps = (model.duration.total_seconds() / model.time_step) + 1
assert num_steps_output == calculated_steps
