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_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_simple_run_with_image_output_uncertainty(tmpdir):
'''
Pretty much all this tests is that the model will run and output images
'''
images_dir = tmpdir.mkdir('Test_images2').strpath
if os.path.isdir(images_dir):
shutil.rmtree(images_dir)
os.mkdir(images_dir)
start_time = datetime(2012, 9, 15, 12, 0)
# the land-water map
gmap = gnome.map.MapFromBNA(testdata['MapFromBNA']['testmap'],
refloat_halflife=6) # hours
renderer = gnome.outputters.Renderer(testdata['MapFromBNA']['testmap'],
images_dir, size=(400, 300))
model = Model(start_time=start_time,
time_step=timedelta(minutes=15), duration=timedelta(hours=1),
map=gmap,
uncertain=True, cache_enabled=False,
)
model.outputters += renderer
a_mover = SimpleMover(velocity=(1., -1., 0.))
model.movers += a_mover
N = 10 # a line of ten points
start_points = np.zeros((N, 3), dtype=np.float64)
start_points[:, 0] = np.linspace(-127.1, -126.5, N)
start_points[:, 1] = np.linspace(47.93, 48.1, N)
# print start_points
release = SpatialRelease(start_position=start_points,
release_time=start_time)
model.spills += Spill(release)
# model.add_spill(spill)
model.start_time = release.release_time
# image_info = model.next_image()
model.uncertain = True
num_steps_output = 0
while True:
try:
image_info = model.step()
num_steps_output += 1
print image_info
except StopIteration:
print 'Done with the model run'
break
# there is the zeroth step, too.
calculated_steps = (model.duration.total_seconds() / model.time_step) + 1
assert num_steps_output == calculated_steps
def sample_model():
"""
sample model with no outputter and no spills. Use this as a template for
fixtures to add spills
Uses:
sample_data/MapBounds_Island.bna
Contains: gnome.movers.SimpleMover(velocity=(1.0, -1.0, 0.0))
duration is 1 hour with 15min intervals so 5 timesteps total,
including initial condition,
model is uncertain and cache is not enabled
No spills or outputters defined
To use:
add a spill and run
:returns: It returns a dict -
{'model':model,
'release_start_pos':start_points,
'release_end_pos':end_points}
The release_start_pos and release_end_pos can be used by test to define
the spill's 'start_position' and 'end_position'
"""
release_time = datetime(2012, 9, 15, 12, 0)
# the image output map
mapfile = os.path.join(os.path.dirname(__file__), 'sample_data',
'MapBounds_Island.bna')
# the land-water map
map_ = MapFromBNA(mapfile, refloat_halflife=06) # seconds
model = Model(
time_step=timedelta(minutes=15),
start_time=release_time,
duration=timedelta(hours=1),
map=map_,
uncertain=True,
cache_enabled=False,
)
model.movers += SimpleMover(velocity=(1., -1., 0.0))
model.uncertain = True
start_points = np.zeros((3, ), dtype=np.float64)
end_points = np.zeros((3, ), dtype=np.float64)
start_points[:] = (-127.1, 47.93, 0)
end_points[:] = (-126.5, 48.1, 0)
return {
'model': model,
'release_start_pos': start_points,
'release_end_pos': end_points,
}
def test_release_end_of_step(duration):
'''
- Tests that elements released at end of step are recorded with their
initial conditions with correct timestamp
- Also tests the age is set correctly.
todo: write separate test for checking age
'''
model = Model(time_step=timedelta(minutes=15),
duration=timedelta(hours=duration))
end_release_time = model.start_time + model.duration
model.spills += point_line_release_spill(10, (0.0, 0.0, 0.0),
model.start_time,
end_release_time=end_release_time)
model.movers += SimpleMover(velocity=(1., -1., 0.0))
print '\n---------------------------------------------'
print 'model_start_time: {0}'.format(model.start_time)
prev_rel = len(model.spills.LE('positions'))
for step in model:
new_particles = len(model.spills.LE('positions')) - prev_rel
if new_particles > 0:
assert np.all(
model.spills.LE('positions')[-new_particles:, :] == 0)
assert np.all(model.spills.LE('age')[-new_particles:] == 0)
# assert np.all(model.spills.LE('age')[-new_particles:] ==
# (model.model_time + timedelta(seconds=model.time_step)
# - model.start_time).seconds)
if prev_rel > 0:
assert np.all(model.spills.LE('positions')[:prev_rel, :2] != 0)
assert np.all(model.spills.LE('age')[:prev_rel] >= model.time_step)
prev_rel = len(model.spills.LE('positions'))
print('current_time_stamp: {0}'.format(
model.spills.LE('current_time_stamp')))
print 'particle ID: {0}'.format(model.spills.LE('id'))
print 'positions: \n{0}'.format(model.spills.LE('positions'))
print 'age: \n{0}'.format(model.spills.LE('age'))
print 'just ran: %s' % step
print 'particles released: %s' % new_particles
print '---------------------------------------------'
print '\n==============================================='
def test_simple_run_rewind():
'''
Pretty much all this tests is that the model will run
and the seed is set during first run, then set correctly
after it is rewound and run again
'''
start_time = datetime(2012, 9, 15, 12, 0)
model = Model()
model.map = gnome.map.GnomeMap()
a_mover = SimpleMover(velocity=(1., 2., 0.))
model.movers += a_mover
assert len(model.movers) == 1
spill = point_line_release_spill(num_elements=10,
start_position=(0., 0., 0.),
release_time=start_time)
model.spills += spill
assert len(model.spills) == 1
# model.add_spill(spill)
model.start_time = spill.release.release_time
# test iterator
for step in model:
print 'just ran time step: %s' % model.current_time_step
assert step['step_num'] == model.current_time_step
pos = np.copy(model.spills.LE('positions'))
# rewind and run again:
print 'rewinding'
model.rewind()
# test iterator is repeatable
for step in model:
print 'just ran time step: %s' % model.current_time_step
assert step['step_num'] == model.current_time_step
assert np.all(model.spills.LE('positions') == pos)
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_simple_run_with_map():
'''
pretty much all this tests is that the model will run
'''
start_time = datetime(2012, 9, 15, 12, 0)
model = Model()
model.map = gnome.map.MapFromBNA(testdata['MapFromBNA']['testmap'],
refloat_halflife=6) # hours
a_mover = SimpleMover(velocity=(1., 2., 0.))
model.movers += a_mover
assert len(model.movers) == 1
spill = point_line_release_spill(num_elements=10,
start_position=(0., 0., 0.),
release_time=start_time)
model.spills += spill
assert len(model.spills) == 1
model.start_time = spill.release.release_time
# test iterator
for step in model:
print 'just ran time step: %s' % step
assert step['step_num'] == model.current_time_step
# reset and run again
model.reset()
# test iterator is repeatable
for step in model:
print 'just ran time step: %s' % step
assert step['step_num'] == model.current_time_step
def make_model(images_dir=os.path.join(base_dir, 'images')):
print 'initializing the model'
# set up the modeling environment
start_time = datetime(2016, 9, 23, 0, 0)
model = Model(start_time=start_time,
duration=timedelta(days=2),
time_step=30 * 60,
uncertain=False)
print 'adding the map'
model.map = GnomeMap() # this is a "water world -- no land anywhere"
# renderere is only top-down view on 2d -- but it's something
renderer = Renderer(output_dir=images_dir,
image_size=(1024, 768),
output_timestep=timedelta(hours=1),
)
renderer.viewport = ((196.14, 71.89), (196.18, 71.93))
print 'adding outputters'
model.outputters += renderer
# Also going to write the results out to a netcdf file
netcdf_file = os.path.join(base_dir, 'script_arctic_plume.nc')
scripting.remove_netcdf(netcdf_file)
model.outputters += NetCDFOutput(netcdf_file,
which_data='most',
# output most of the data associated with the elements
output_timestep=timedelta(hours=2))
print "adding Horizontal and Vertical diffusion"
# Horizontal Diffusion
model.movers += RandomMover(diffusion_coef=500)
# vertical diffusion (different above and below the mixed layer)
model.movers += RandomMover3D(vertical_diffusion_coef_above_ml=5,
vertical_diffusion_coef_below_ml=.11,
mixed_layer_depth=10)
print 'adding Rise Velocity'
# droplets rise as a function of their density and radius
model.movers += TamocRiseVelocityMover()
print 'adding a circular current and eastward current'
fn = 'hycom_glb_regp17_2016092300_subset.nc'
fn_ice = 'hycom-cice_ARCu0.08_046_2016092300_subset.nc'
iconc = IceConcentration.from_netCDF(filename=fn_ice)
ivel = IceVelocity.from_netCDF(filename=fn_ice, grid = iconc.grid)
ic = IceAwareCurrent.from_netCDF(ice_concentration = iconc, ice_velocity= ivel, filename=fn)
model.movers += PyCurrentMover(current = ic)
model.movers += SimpleMover(velocity=(0., 0., 0.))
model.movers += constant_wind_mover(20, 315, units='knots')
# Now to add in the TAMOC "spill"
print "Adding TAMOC spill"
model.spills += tamoc_spill.TamocSpill(release_time=start_time,
start_position=(196.16, 71.91, 40.0),
num_elements=1000,
end_release_time=start_time + timedelta(days=1),
name='TAMOC plume',
TAMOC_interval=None, # how often to re-run TAMOC
)
model.spills[0].data_sources['currents'] = ic
return model
def make_model(images_dir=os.path.join(base_dir, 'images')):
print 'initializing the model'
start_time = datetime(2004, 12, 31, 13, 0)
model = Model(start_time=start_time,
duration=timedelta(days=3),
time_step=30 * 60,
uncertain=False)
print 'adding the map'
model.map = GnomeMap()
# draw_ontop can be 'uncertain' or 'forecast'
# 'forecast' LEs are in black, and 'uncertain' are in red
# default is 'forecast' LEs draw on top
renderer = Renderer(
output_dir=images_dir,
# size=(800, 600),
output_timestep=timedelta(hours=1),
draw_ontop='uncertain')
renderer.viewport = ((-76.5, 37.), (-75.8, 38.))
print 'adding outputters'
model.outputters += renderer
netcdf_file = os.path.join(base_dir, 'script_plume.nc')
scripting.remove_netcdf(netcdf_file)
model.outputters += NetCDFOutput(netcdf_file,
which_data='most',
output_timestep=timedelta(hours=2))
print 'adding two spills'
# Break the spill into two spills, first with the larger droplets
# and second with the smaller droplets.
# Split the total spill volume (100 m^3) to have most
# in the larger droplet spill.
# Smaller droplets start at a lower depth than larger
wd = WeibullDistribution(alpha=1.8, lambda_=.00456,
min_=.0002) # 200 micron min
end_time = start_time + timedelta(hours=24)
# spill = point_line_release_spill(num_elements=10,
# amount=90, # default volume_units=m^3
# units='m^3',
# start_position=(-76.126872, 37.680952,
# 1700),
# release_time=start_time,
# end_release_time=end_time,
# element_type=plume(distribution=wd,
# density=600)
# )
spill = subsurface_plume_spill(
num_elements=10,
start_position=(-76.126872, 37.680952, 1700),
release_time=start_time,
distribution=wd,
amount=90, # default volume_units=m^3
units='m^3',
end_release_time=end_time,
density=600)
model.spills += spill
wd = WeibullDistribution(alpha=1.8, lambda_=.00456,
max_=.0002) # 200 micron max
spill = point_line_release_spill(
num_elements=10,
amount=90,
units='m^3',
start_position=(-76.126872, 37.680952, 1800),
release_time=start_time,
element_type=plume(distribution=wd, substance_name='oil_crude'))
model.spills += spill
print 'adding a RandomMover:'
model.movers += RandomMover(diffusion_coef=50000)
print 'adding a RiseVelocityMover:'
model.movers += RiseVelocityMover()
print 'adding a RandomVerticalMover:'
model.movers += RandomVerticalMover(vertical_diffusion_coef_above_ml=5,
vertical_diffusion_coef_below_ml=.11,
mixed_layer_depth=10)
# print 'adding a wind mover:'
# series = np.zeros((2, ), dtype=gnome.basic_types.datetime_value_2d)
# series[0] = (start_time, (30, 90))
# series[1] = (start_time + timedelta(hours=23), (30, 90))
# wind = Wind(timeseries=series, units='knot')
#
# default is .4 radians
# w_mover = gnome.movers.WindMover(wind, uncertain_angle_scale=0)
#
# model.movers += w_mover
print 'adding a simple mover:'
s_mover = SimpleMover(velocity=(0.0, -.3, 0.0))
model.movers += s_mover
return model
from gnome.movers import RandomMover
l_spills = [point_line_release_spill(10, (0, 0, 0),
datetime.now().replace(microsecond=0),
name='sp1'),
point_line_release_spill(15, (0, 0, 0),
datetime.now().replace(microsecond=0),
name='sp2'),
point_line_release_spill(20, (0, 0, 0),
datetime.now().replace(microsecond=0),
name='sp3'),
point_line_release_spill(5, (0, 0, 0),
datetime.now().replace(microsecond=0),
name='sp4')]
l_mv = [SimpleMover(velocity=(1, 2, 3)), RandomMover()]
def define_mdl(test=0):
'''
WebAPI will update/replace nested objects so do that for the test as well
Setup some test cases:
0 - empty model w/ no changes
1 - model with l_mv and l_spills but no changes
2 - empty model but add l_mv and l_spills to json_ so it changed
3 - add l_mv and l_spills to model, then delete some elements and update
via json
'''
def get_json(mdl):
json_ = mdl.serialize()
# For WindMover test_save_load in test_wind_mover
g_objects = (
GridCurrent.from_netCDF(testdata['GridCurrentMover']['curr_tri']),
Tide(testdata['CatsMover']['tide']),
Wind(filename=testdata['ComponentMover']['wind']),
Wind(timeseries=(sec_to_date(24 * 60 * 60), (0, 0)), units='mps'),
Water(temperature=273),
RandomMover(),
CatsMover(testdata['CatsMover']['curr']),
CatsMover(testdata['CatsMover']['curr'],
tide=Tide(testdata['CatsMover']['tide'])),
ComponentMover(testdata['ComponentMover']['curr']),
ComponentMover(testdata['ComponentMover']['curr'],
wind=Wind(filename=testdata['ComponentMover']['wind'])),
RandomMover3D(),
SimpleMover(velocity=(10.0, 10.0, 0.0)),
map.MapFromBNA(testdata['MapFromBNA']['testmap'], 6),
NetCDFOutput(os.path.join(base_dir, u'xtemp.nc')),
Renderer(testdata['Renderer']['bna_sample'],
os.path.join(base_dir, 'output_dir')),
WeatheringOutput(),
spill.PointLineRelease(release_time=datetime.now(),
num_elements=10,
start_position=(0, 0, 0)),
spill.point_line_release_spill(10, (0, 0, 0), datetime.now()),
spill.substance.Substance(windage_range=(0.05, 0.07)),
spill.substance.GnomeOil(test_oil, windage_range=(0.05, 0.07)),
spill.substance.NonWeatheringSubstance(windage_range=(0.05, 0.07)),
Skimmer(amount=100,
efficiency=0.3,
active_range=(datetime(2014, 1, 1, 0,
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 make_model(images_dir=os.path.join(base_dir, 'images')):
print 'initializing the model'
# set up the modeling environment
start_time = datetime(2004, 12, 31, 13, 0)
model = Model(start_time=start_time,
duration=timedelta(days=3),
time_step=30 * 60,
uncertain=False)
print 'adding the map'
model.map = GnomeMap() # this is a "water world -- no land anywhere"
# renderere is only top-down view on 2d -- but it's something
renderer = Renderer(
output_dir=images_dir,
size=(1024, 768),
output_timestep=timedelta(hours=1),
)
renderer.viewport = ((-.15, -.35), (.15, .35))
print 'adding outputters'
model.outputters += renderer
# Also going to write the results out to a netcdf file
netcdf_file = os.path.join(base_dir, 'script_plume.nc')
scripting.remove_netcdf(netcdf_file)
model.outputters += NetCDFOutput(
netcdf_file,
which_data='most',
# output most of the data associated with the elements
output_timestep=timedelta(hours=2))
print "adding Horizontal and Vertical diffusion"
# Horizontal Diffusion
# model.movers += RandomMover(diffusion_coef=5)
# vertical diffusion (different above and below the mixed layer)
model.movers += RandomVerticalMover(vertical_diffusion_coef_above_ml=5,
vertical_diffusion_coef_below_ml=.11,
mixed_layer_depth=10)
print 'adding Rise Velocity'
# droplets rise as a function of their density and radius
model.movers += RiseVelocityMover()
print 'adding a circular current and eastward current'
# This is .3 m/s south
model.movers += PyCurrentMover(current=vg,
default_num_method='Trapezoid',
extrapolate=True)
model.movers += SimpleMover(velocity=(0., -0.1, 0.))
# Now to add in the TAMOC "spill"
print "Adding TAMOC spill"
model.spills += tamoc_spill.TamocSpill(
release_time=start_time,
start_position=(0, 0, 1000),
num_elements=1000,
end_release_time=start_time + timedelta(days=1),
name='TAMOC plume',
TAMOC_interval=None, # how often to re-run TAMOC
)
return model
