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,
image_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 += TamocRiseVelocityMover()
print 'adding a circular current and eastward current'
# This is .3 m/s south
model.movers += PyCurrentMover(current=vg,
default_num_method='RK2',
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
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()
renderer = Renderer(output_dir=images_dir,
image_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 = subsurface_plume_spill(
num_elements=10,
start_position=(-76.126872, 37.680952, 1700.0),
release_time=start_time,
distribution=wd,
amount=90, # default volume_units=m^3
units='m^3',
end_release_time=end_time,
# substance='oil_crude',
density=900,
)
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,
1800.0),
release_time=start_time,
element_type=plume(distribution=wd,
density=900.0))
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
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 += 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 += 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 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_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,
image_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
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_ = Model.deserialize(mdl.serialize('webapi'))
