def test_fieldset_from_file_subsets(indslon,
indslat,
tmpdir,
filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath = tmpdir.join(filename)
fieldsetfull = FieldSet.from_data(data, dimensions)
fieldsetfull.write(filepath)
indices = {'lon': indslon, 'lat': indslat}
fieldsetsub = FieldSet.from_parcels(filepath, indices=indices)
assert np.allclose(fieldsetsub.U.lon,
fieldsetfull.U.grid.lon[indices['lon']])
assert np.allclose(fieldsetsub.U.lat,
fieldsetfull.U.grid.lat[indices['lat']])
assert np.allclose(fieldsetsub.V.lon,
fieldsetfull.V.grid.lon[indices['lon']])
assert np.allclose(fieldsetsub.V.lat,
fieldsetfull.V.grid.lat[indices['lat']])
ixgrid = np.ix_([0], indices['lat'], indices['lon'])
assert np.allclose(fieldsetsub.U.data, fieldsetfull.U.data[ixgrid])
assert np.allclose(fieldsetsub.V.data, fieldsetfull.V.data[ixgrid])
def test_fieldset_write_curvilinear(tmpdir):
fname = path.join(path.dirname(__file__), 'test_data',
'mask_nemo_cross_180lon.nc')
filenames = {'dx': fname, 'mesh_mask': fname}
variables = {'dx': 'e1u'}
dimensions = {'lon': 'glamu', 'lat': 'gphiu'}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions)
assert fieldset.dx.creation_log == 'from_nemo'
newfile = tmpdir.join('curv_field')
fieldset.write(newfile)
fieldset2 = FieldSet.from_netcdf(filenames=newfile + 'dx.nc',
variables={'dx': 'dx'},
dimensions={
'lon': 'nav_lon',
'lat': 'nav_lat'
})
assert fieldset2.dx.creation_log == 'from_netcdf'
for var in ['lon', 'lat', 'data']:
assert np.allclose(getattr(fieldset2.dx, var),
getattr(fieldset.dx, var))
def test_inversedistance_nearland(pset_mode,
mode,
arrtype,
k_sample_p,
npart=81):
dims = (4, 4, 6)
P = np.random.rand(dims[0], dims[1],
dims[2]) + 2 if arrtype == 'rand' else np.ones(
dims, dtype=np.float32)
P[1, 1:2, 1:6] = np.nan # setting some values to land (NaN)
dimensions = {
'lon': np.linspace(0., 1., dims[2], dtype=np.float32),
'lat': np.linspace(0., 1., dims[1], dtype=np.float32),
'depth': np.linspace(0., 1., dims[0], dtype=np.float32)
}
data = {
'U': np.zeros(dims, dtype=np.float32),
'V': np.zeros(dims, dtype=np.float32),
'P': P
}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
fieldset.P.interp_method = 'linear_invdist_land_tracer'
xv, yv = np.meshgrid(np.linspace(0.1, 0.9, int(np.sqrt(npart))),
np.linspace(0.1, 0.9, int(np.sqrt(npart))))
# combine a pset at 0m with pset at 1m, as meshgrid does not do 3D
pset = pset_type[pset_mode]['pset'](fieldset,
pclass=pclass(mode),
lon=xv.flatten(),
lat=yv.flatten(),
depth=np.zeros(npart))
pset2 = pset_type[pset_mode]['pset'](fieldset,
pclass=pclass(mode),
lon=xv.flatten(),
lat=yv.flatten(),
depth=np.ones(npart))
pset.add(pset2)
pset.execute(k_sample_p, endtime=1, dt=1)
if arrtype == 'rand':
assert np.all((pset.p > 2) & (pset.p < 3))
else:
assert np.allclose(pset.p, 1.0, rtol=1e-5)
success = False
try:
fieldset.U.interp_method = 'linear_invdist_land_tracer'
fieldset.check_complete()
except NotImplementedError:
success = True
assert success
def fieldset_from_globcurrent(chunk_mode):
filenames = path.join(path.dirname(__file__), 'GlobCurrent_example_data',
'200201*-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc')
variables = {'U': 'eastward_eulerian_current_velocity', 'V': 'northward_eulerian_current_velocity'}
dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
chs = False
if chunk_mode == 'auto':
chs = 'auto'
elif chunk_mode == 'specific':
chs = {'U': {'lat': 16, 'lon': 16},
'V': {'lat': 16, 'lon': 16}}
fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, field_chunksize=chs)
return fieldset
def test_fieldset_diffgrids_from_file_data(tmpdir, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath = tmpdir.join(filename)
fieldset_data = FieldSet.from_data(data, dimensions)
fieldset_data.write(filepath)
field_data = fieldset_data.U
field_data.name = "B"
ufiles = [filepath+'U.nc', ] * 4
vfiles = [filepath+'V.nc', ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
chs = 'auto'
fieldset_file = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, allow_time_extrapolation=True, field_chunksize=chs)
fieldset_file.add_field(field_data, "B")
assert len(fieldset_file.get_fields()) == 3
assert fieldset_file.gridset.size == 2
assert fieldset_file.U.grid != fieldset_file.B.grid
def test_fieldset_celledgesizes_curvilinear(dx, dy):
fname = path.join(path.dirname(__file__), 'test_data', 'mask_nemo_cross_180lon.nc')
filenames = {'dx': fname, 'dy': fname, 'mesh_mask': fname}
variables = {'dx': dx, 'dy': dy}
dimensions = {'dx': {'lon': 'glamu', 'lat': 'gphiu'},
'dy': {'lon': 'glamu', 'lat': 'gphiu'}}
fieldset = FieldSet.from_nemo(filenames, variables, dimensions)
# explicitly setting cell_edge_sizes from e1u and e2u etc
fieldset.dx.grid.cell_edge_sizes['x'] = fieldset.dx.data
fieldset.dx.grid.cell_edge_sizes['y'] = fieldset.dy.data
A = fieldset.dx.cell_areas()
assert np.allclose(A, fieldset.dx.data * fieldset.dy.data)
def test_fieldset_samegrids_from_file(tmpdir, chunksize, filename='test_subsets'):
""" Test for subsetting fieldset from file using indices dict. """
data, dimensions = generate_fieldset(100, 100)
filepath1 = tmpdir.join(filename+'_1')
fieldset1 = FieldSet.from_data(data, dimensions)
fieldset1.write(filepath1)
ufiles = [filepath1+'U.nc', ] * 4
vfiles = [filepath1+'V.nc', ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, allow_time_extrapolation=True, chunksize=chunksize)
if chunksize == 'auto':
assert fieldset.gridset.size == 2
assert fieldset.U.grid != fieldset.V.grid
else:
assert fieldset.gridset.size == 1
assert fieldset.U.grid == fieldset.V.grid
assert fieldset.U.chunksize == fieldset.V.chunksize
def stommel_fieldset(xdim=200, ydim=200):
"""Simulate a periodic current along a western boundary, with significantly
larger velocities along the western edge than the rest of the region
The original test description can be found in: N. Fabbroni, 2009,
Numerical Simulation of Passive tracers dispersion in the sea,
Ph.D. dissertation, University of Bologna
http://amsdottorato.unibo.it/1733/1/Fabbroni_Nicoletta_Tesi.pdf
"""
# Set NEMO fieldset variables
depth = np.zeros(1, dtype=np.float32)
time = np.linspace(0., 100000. * 86400., 2, dtype=np.float64)
# Some constants
A = 100
eps = 0.05
a = 10000
b = 10000
# Coordinates of the test fieldset (on A-grid in deg)
lon = np.linspace(0, a, xdim, dtype=np.float32)
lat = np.linspace(0, b, ydim, dtype=np.float32)
# Define arrays U (zonal), V (meridional), W (vertical) and P (sea
# surface height) all on A-grid
U = np.zeros((lon.size, lat.size, time.size), dtype=np.float32)
V = np.zeros((lon.size, lat.size, time.size), dtype=np.float32)
P = np.zeros((lon.size, lat.size, time.size), dtype=np.float32)
[x, y] = np.mgrid[:lon.size, :lat.size]
l1 = (-1 + math.sqrt(1 + 4 * math.pi**2 * eps**2)) / (2 * eps)
l2 = (-1 - math.sqrt(1 + 4 * math.pi**2 * eps**2)) / (2 * eps)
c1 = (1 - math.exp(l2)) / (math.exp(l2) - math.exp(l1))
c2 = -(1 + c1)
for t in range(time.size):
for i in range(lon.size):
for j in range(lat.size):
xi = lon[i] / a
yi = lat[j] / b
P[i, j, t] = A * (c1*math.exp(l1*xi) + c2*math.exp(l2*xi) + 1) * math.sin(math.pi * yi)
for i in range(lon.size-2):
for j in range(lat.size):
V[i+1, j, t] = (P[i+2, j, t] - P[i, j, t]) / (2 * a / xdim)
for i in range(lon.size):
for j in range(lat.size-2):
U[i, j+1, t] = -(P[i, j+2, t] - P[i, j, t]) / (2 * b / ydim)
data = {'U': U, 'V': V, 'P': P}
dimensions = {'lon': lon, 'lat': lat, 'depth': depth, 'time': time}
return FieldSet.from_data(data, dimensions, mesh='flat')
def brownian_fieldset(
xdim=200,
ydim=200): # Define a flat fieldset of zeros, for simplicity.
dimensions = {
'lon': np.linspace(0, 600000, xdim, dtype=np.float32),
'lat': np.linspace(0, 600000, ydim, dtype=np.float32)
}
data = {
'U': np.zeros((xdim, ydim), dtype=np.float32),
'V': np.zeros((xdim, ydim), dtype=np.float32)
}
return FieldSet.from_data(data, dimensions, mesh='flat')
def test_advection_zonal(lon, lat, depth, mode, npart=10):
""" Particles at high latitude move geographically faster due to
the pole correction in `GeographicPolar`.
"""
data2D = {
'U': np.ones((lon.size, lat.size), dtype=np.float32),
'V': np.zeros((lon.size, lat.size), dtype=np.float32)
}
data3D = {
'U': np.ones((lon.size, lat.size, depth.size), dtype=np.float32),
'V': np.zeros((lon.size, lat.size, depth.size), dtype=np.float32)
}
dimensions = {'lon': lon, 'lat': lat}
fieldset2D = FieldSet.from_data(data2D,
dimensions,
mesh='spherical',
transpose=True)
pset2D = ParticleSet(fieldset2D,
pclass=ptype[mode],
lon=np.zeros(npart, dtype=np.float32) + 20.,
lat=np.linspace(0, 80, npart, dtype=np.float32))
pset2D.execute(AdvectionRK4, runtime=delta(hours=2), dt=delta(seconds=30))
assert (np.diff(np.array([p.lon for p in pset2D])) > 1.e-4).all()
dimensions['depth'] = depth
fieldset3D = FieldSet.from_data(data3D,
dimensions,
mesh='spherical',
transpose=True)
pset3D = ParticleSet(fieldset3D,
pclass=ptype[mode],
lon=np.zeros(npart, dtype=np.float32) + 20.,
lat=np.linspace(0, 80, npart, dtype=np.float32),
depth=np.zeros(npart, dtype=np.float32) + 10.)
pset3D.execute(AdvectionRK4, runtime=delta(hours=2), dt=delta(seconds=30))
assert (np.diff(np.array([p.lon for p in pset3D])) > 1.e-4).all()
def test_fieldset_from_xarray(tdim):
def generate_dataset(xdim, ydim, zdim=1, tdim=1):
lon = np.linspace(0., 12, xdim, dtype=np.float32)
lat = np.linspace(0., 12, ydim, dtype=np.float32)
depth = np.linspace(0., 20., zdim, dtype=np.float32)
if tdim:
time = np.linspace(0., 10, tdim, dtype=np.float64)
Uxr = np.ones((tdim, zdim, ydim, xdim), dtype=np.float32)
Vxr = np.ones((tdim, zdim, ydim, xdim), dtype=np.float32)
for t in range(Uxr.shape[0]):
Uxr[t, :, :, :] = t / 10.
coords = {'lat': lat, 'lon': lon, 'depth': depth, 'time': time}
dims = ('time', 'depth', 'lat', 'lon')
else:
Uxr = np.ones((zdim, ydim, xdim), dtype=np.float32)
Vxr = np.ones((zdim, ydim, xdim), dtype=np.float32)
for z in range(Uxr.shape[0]):
Uxr[z, :, :] = z / 2.
coords = {'lat': lat, 'lon': lon, 'depth': depth}
dims = ('depth', 'lat', 'lon')
return xr.Dataset({
'Uxr': xr.DataArray(Uxr, coords=coords, dims=dims),
'Vxr': xr.DataArray(Vxr, coords=coords, dims=dims)
})
ds = generate_dataset(3, 3, 2, tdim)
variables = {'U': 'Uxr', 'V': 'Vxr'}
if tdim:
dimensions = {
'lat': 'lat',
'lon': 'lon',
'depth': 'depth',
'time': 'time'
}
else:
dimensions = {'lat': 'lat', 'lon': 'lon', 'depth': 'depth'}
fieldset = FieldSet.from_xarray_dataset(ds,
variables,
dimensions,
mesh='flat')
assert fieldset.U.creation_log == 'from_xarray_dataset'
pset = ParticleSet(fieldset, JITParticle, 0, 0, depth=20)
pset.execute(AdvectionRK4, dt=1, runtime=10)
if tdim == 10:
assert np.allclose(pset.lon[0], 4.5) and np.allclose(pset.lat[0], 10)
else:
assert np.allclose(pset.lon[0], 5.0) and np.allclose(pset.lat[0], 10)
def test_EOSseawaterproperties_kernels(pset_mode, mode):
fieldset = FieldSet.from_data(data={
'U': 0,
'V': 0,
'psu_salinity': 40,
'temperature': 40,
'potemperature': 36.89073
},
dimensions={
'lat': 0,
'lon': 0,
'depth': 0
})
fieldset.add_constant('refpressure', np.float(0))
class PoTempParticle(ptype[mode]):
potemp = Variable('potemp', dtype=np.float32)
pressure = Variable('pressure', dtype=np.float32, initial=10000)
pset = pset_type[pset_mode]['pset'](fieldset,
pclass=PoTempParticle,
lon=5,
lat=5,
depth=1000)
pset.execute(PtempFromTemp, runtime=0, dt=0)
assert np.allclose(pset[0].potemp, 36.89073)
class TempParticle(ptype[mode]):
temp = Variable('temp', dtype=np.float32)
pressure = Variable('pressure', dtype=np.float32, initial=10000)
pset = pset_type[pset_mode]['pset'](fieldset,
pclass=TempParticle,
lon=5,
lat=5,
depth=1000)
pset.execute(TempFromPtemp, runtime=0, dt=0)
assert np.allclose(pset[0].temp, 40)
class TPressureParticle(ptype[mode]):
pressure = Variable('pressure', dtype=np.float32)
pset = pset_type[pset_mode]['pset'](fieldset,
pclass=TempParticle,
lon=5,
lat=30,
depth=7321.45)
pset.execute(PressureFromLatDepth, runtime=0, dt=0)
assert np.allclose(pset[0].pressure, 7500, atol=1e-2)
def simulate_parcels(source_url, output_filename, lat, lon,
wind_drift_factor= 0.0,
velocity_average= True, duration=23):
"""
source_url: local file or list of local files with fielddata in NetCDF-format
output_filename: name of file in which to save calculated trajectory
lat, lon: initial coordinates of single drifter or lists with coordinates for multiple drifters
wind_drift_factor: fraction of wind-speed at which objects will be advected. Default is 0 (no direct wind-drift)
velocity_average: Boolean variable deciding whether averaged horisontal velocities or surface velocities will be used.
Default is average which is consistent with GPU Ocean
duration: duration of the simulation in hours. Default is 24 hours.
TODO: Add functionality to start drifters at a later time in the simulation like in GPU Ocean.
"""
filenames = {'U' : source_url, 'V': source_url}
dimensions = {'lat': 'lat','lon': 'lon','time': 'time'}
if velocity_average:
variables = {'U': 'ubar', 'V': 'vbar'}
else:
variables = {'U': 'u', 'V': 'v'}
fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, interp_method = 'cgrid_velocity')
if wind_drift_factor:
Uwind = Field.from_netcdf(source_url, ('U', 'Uwind'), dimensions, field_chunksize='auto', interp_method = 'cgrid_velocity')
Vwind = Field.from_netcdf(source_url, ('V', 'Vwind'), dimensions, field_chunksize='auto', interp_method = 'cgrid_velocity')
Uwind.set_scaling_factor(wind_drift_factor)
Vwind.set_scaling_factor(wind_drift_factor)
fieldset = FieldSet(U = fieldset.U+ Uwind,V = fieldset.V+ Vwind)
pset = ParticleSet.from_list(fieldset = fieldset, pclass = JITParticle, lon=lon, lat=lat)
output_file = pset.ParticleFile(name = output_filename, outputdt = timedelta(minutes=5))
pset.execute(AdvectionRK4, runtime = timedelta(hours = duration), dt = timedelta(minutes=5), output_file = output_file)
output_file.export()
def test_nearest_neighbour_interpolation2D(pset_mode, mode, k_sample_p, npart=81):
dims = (2, 2)
dimensions = {'lon': np.linspace(0., 1., dims[0], dtype=np.float32),
'lat': np.linspace(0., 1., dims[1], dtype=np.float32)}
data = {'U': np.zeros(dims, dtype=np.float32),
'V': np.zeros(dims, dtype=np.float32),
'P': np.zeros(dims, dtype=np.float32)}
data['P'][0, 1] = 1.
fieldset = FieldSet.from_data(data, dimensions, mesh='flat', transpose=True)
fieldset.P.interp_method = 'nearest'
xv, yv = np.meshgrid(np.linspace(0., 1.0, int(np.sqrt(npart))), np.linspace(0., 1.0, int(np.sqrt(npart))))
pset = pset_type[pset_mode]['pset'](fieldset, pclass=pclass(mode), lon=xv.flatten(), lat=yv.flatten())
pset.execute(k_sample_p, endtime=1, dt=1)
assert np.allclose(pset.p[(pset.lon < 0.5) & (pset.lat > 0.5)], 1.0, rtol=1e-5)
assert np.allclose(pset.p[(pset.lon > 0.5) | (pset.lat < 0.5)], 0.0, rtol=1e-5)
def test_from_netcdf_chunking(mode, time_periodic, chunksize, deferLoad):
fnameU = path.join(path.dirname(__file__), 'test_data', 'perlinfieldsU.nc')
fnameV = path.join(path.dirname(__file__), 'test_data', 'perlinfieldsV.nc')
ufiles = [fnameU, ] * 4
vfiles = [fnameV, ] * 4
timestamps = np.arange(0, 4, 1) * 86400.0
timestamps = np.expand_dims(timestamps, 1)
files = {'U': ufiles, 'V': vfiles}
variables = {'U': 'vozocrtx', 'V': 'vomecrty'}
dimensions = {'lon': 'nav_lon', 'lat': 'nav_lat'}
fieldset = FieldSet.from_netcdf(files, variables, dimensions, timestamps=timestamps, time_periodic=time_periodic, deferred_load=deferLoad, allow_time_extrapolation=True if time_periodic in [False, None] else False, chunksize=chunksize)
pset = ParticleSet.from_line(fieldset, size=1, pclass=ptype[mode],
start=(0.5, 0.5), finish=(0.5, 0.5))
pset.execute(AdvectionRK4, dt=1, runtime=1)
def test_advect_nemo(mode):
data_path = path.join(path.dirname(__file__), 'test_data/')
filenames = {'U': data_path + 'Uu_eastward_nemo_cross_180lon.nc',
'V': data_path + 'Vv_eastward_nemo_cross_180lon.nc',
'mesh_mask': data_path + 'mask_nemo_cross_180lon.nc'}
variables = {'U': 'U', 'V': 'V'}
dimensions = {'lon': 'glamf', 'lat': 'gphif'}
field_set = FieldSet.from_nemo(filenames, variables, dimensions)
lonp = 175.5
latp = 81.5
pset = ParticleSet.from_list(field_set, ptype[mode], lon=[lonp], lat=[latp])
pset.execute(AdvectionRK4, runtime=delta(days=2), dt=delta(hours=6))
assert abs(pset[0].lat - latp) < 1e-3
def test_peninsula_file(mode, mesh, tmpdir):
"""Open fieldset files and execute"""
gc.collect()
fieldset = FieldSet.from_parcels(fieldsetfile(mesh, tmpdir),
extra_fields={'P': 'P'},
allow_time_extrapolation=True)
outfile = tmpdir.join("Peninsula")
pset = peninsula_example(fieldset, outfile, 5, mode=mode, degree=1)
# Test advection accuracy by comparing streamline values
err_adv = np.array([abs(p.p_start - p.p) for p in pset])
assert (err_adv <= 1.e-3).all()
# Test Field sampling accuracy by comparing kernel against Field sampling
err_smpl = np.array(
[abs(p.p - pset.fieldset.P[0., p.depth, p.lat, p.lon]) for p in pset])
assert (err_smpl <= 1.e-3).all()
def generate_testfieldset(xdim, ydim, zdim, tdim):
lon = np.linspace(0., 2., xdim, dtype=np.float32)
lat = np.linspace(0., 1., ydim, dtype=np.float32)
depth = np.linspace(0., 0.5, zdim, dtype=np.float32)
time = np.linspace(0., tdim, tdim, dtype=np.float64)
U = np.ones((xdim, ydim, zdim, tdim), dtype=np.float32)
V = np.zeros((xdim, ydim, zdim, tdim), dtype=np.float32)
P = 2. * np.ones((xdim, ydim, zdim, tdim), dtype=np.float32)
data = {'U': U, 'V': V, 'P': P}
dimensions = {'lon': lon, 'lat': lat, 'depth': depth, 'time': time}
fieldset = FieldSet.from_data(data,
dimensions,
mesh='flat',
transpose=True)
fieldset.write('testfields')
def test_fieldset_defer_loading_with_diff_time_origin(
tmpdir, fail, filename='test_parcels_defer_loading'):
filepath = tmpdir.join(filename)
data0, dims0 = generate_fieldset(10, 10, 1, 10)
dims0['time'] = np.arange(0, 10, 1) * 3600
fieldset_out = FieldSet.from_data(data0, dims0)
fieldset_out.U.grid.time_origin = TimeConverter(
np.datetime64('2018-04-20'))
fieldset_out.V.grid.time_origin = TimeConverter(
np.datetime64('2018-04-20'))
data1, dims1 = generate_fieldset(10, 10, 1, 10)
if fail:
dims1['time'] = np.arange(0, 10, 1) * 3600
else:
dims1['time'] = np.arange(0, 10, 1) * 1800 + (24 + 25) * 3600
if fail:
Wtime_origin = TimeConverter(np.datetime64('2018-04-22'))
else:
Wtime_origin = TimeConverter(np.datetime64('2018-04-18'))
gridW = RectilinearZGrid(dims1['lon'],
dims1['lat'],
dims1['depth'],
dims1['time'],
time_origin=Wtime_origin)
fieldW = Field('W', np.zeros(data1['U'].shape), grid=gridW)
fieldset_out.add_field(fieldW)
fieldset_out.write(filepath)
fieldset = FieldSet.from_parcels(filepath, extra_fields={'W': 'W'})
assert fieldset.U.creation_log == 'from_parcels'
pset = ParticleSet.from_list(fieldset,
pclass=JITParticle,
lon=[0.5],
lat=[0.5],
depth=[0.5],
time=[datetime.datetime(2018, 4, 20, 1)])
pset.execute(AdvectionRK4_3D, runtime=delta(hours=4), dt=delta(hours=1))
def set_globcurrent_fieldset(filename=None, indices=None, full_load=False):
if filename is None:
filename = path.join(
path.dirname(__file__), 'GlobCurrent_example_data',
'20*-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc')
variables = {
'U': 'eastward_eulerian_current_velocity',
'V': 'northward_eulerian_current_velocity'
}
dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
return FieldSet.from_netcdf(filename,
variables,
dimensions,
indices,
full_load=full_load)
def test_advection_meridional(lon, lat, mode, npart=10):
""" Particles at high latitude move geographically faster due to
the pole correction in `GeographicPolar`.
"""
data = {'U': np.zeros((lon.size, lat.size), dtype=np.float32),
'V': np.ones((lon.size, lat.size), dtype=np.float32)}
dimensions = {'lon': lon, 'lat': lat}
fieldset = FieldSet.from_data(data, dimensions, mesh='spherical', transpose=True)
pset = ParticleSet(fieldset, pclass=ptype[mode],
lon=np.linspace(-60, 60, npart, dtype=np.float32),
lat=np.linspace(0, 30, npart, dtype=np.float32))
delta_lat = np.diff(np.array([p.lat for p in pset]))
pset.execute(AdvectionRK4, runtime=delta(hours=2), dt=delta(seconds=30))
assert np.allclose(np.diff(np.array([p.lat for p in pset])), delta_lat, rtol=1.e-4)
def fieldset_geometric_polar(xdim=200, ydim=100):
""" Standard earth fieldset with U and V equivalent to lon/lat in m
and the inversion of the pole correction applied to U.
"""
lon = np.linspace(-180, 180, xdim, dtype=np.float32)
lat = np.linspace(-90, 90, ydim, dtype=np.float32)
U, V = np.meshgrid(lat, lon)
# Apply inverse of pole correction to U
for i, y in enumerate(lat):
U[:, i] *= cos(y * pi / 180)
U *= 1000. * 1.852 * 60.
V *= 1000. * 1.852 * 60.
data = {'U': U, 'V': V}
dimensions = {'lon': lon, 'lat': lat}
return FieldSet.from_data(data, dimensions, mesh='spherical', transpose=True)
def periodicfields(xdim, ydim, uvel, vvel):
dimensions = {
'lon': np.linspace(0., 1., xdim + 1, dtype=np.float32)
[1:], # don't include both 0 and 1, for periodic b.c.
'lat': np.linspace(0., 1., ydim + 1, dtype=np.float32)[1:]
}
data = {
'U': uvel * np.ones((xdim, ydim), dtype=np.float32),
'V': vvel * np.ones((xdim, ydim), dtype=np.float32)
}
return FieldSet.from_data(data,
dimensions,
mesh='spherical',
transpose=True)
def timeoscillation_fieldset(xdim, ydim):
time = np.arange(0., 4. * 86400., 60. * 5., dtype=np.float64)
lon = np.linspace(-20000, 20000, xdim, dtype=np.float32)
lat = np.linspace(0, 40000, ydim, dtype=np.float32)
U = np.zeros((time.size, lat.size, lon.size), dtype=np.float32)
V = np.zeros((time.size, lat.size, lon.size), dtype=np.float32)
for t in range(time.size):
U[t, :, :] = A * np.cos(omega * time[t])
V[t, :, :] = A
data = {'U': U, 'V': V}
dimensions = {'lon': lon, 'lat': lat, 'time': time}
return FieldSet.from_data(data, dimensions, mesh='flat')
def main(gc_dir, output_file, num_paths, runtime, dt):
filepaths = "%s/*.nc" % gc_dir
filenames = {'U': filepaths, 'V': filepaths}
variables = {
'U': 'eastward_eulerian_current_velocity',
'V': 'northward_eulerian_current_velocity'
}
dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
fieldset = FieldSet.from_netcdf(filenames,
variables,
dimensions,
allow_time_extrapolation=True)
output = {}
for (loc_name, loc) in locations.iteritems():
lat_range = get_range(loc[0], second_largest_divisor(num_paths), 100.0)
lon_range = get_range(loc[1],
num_paths / second_largest_divisor(num_paths),
100.0)
lats, lons = np.meshgrid(lat_range, lon_range)
pset = ParticleSet(fieldset=fieldset,
pclass=JITParticle,
lon=lons,
lat=lats)
#pset.show()
paths = [[] for i in range(num_paths)]
for d in range(runtime / dt):
pset.execute(
AdvectionRK4,
runtime=timedelta(days=dt),
dt=timedelta(days=dt),
recovery={ErrorCode.ErrorOutOfBounds: delete_particle})
for (i, particle) in enumerate(pset.particles):
paths[i].append(
(trunc_float(particle.lat), trunc_float(particle.lon)))
i += 1
output[loc_name] = paths
pset.show(savefile=posixpath.join("../path_images", loc_name))
out = open(output_file, 'w')
out.write(json.dumps(output))
def stommel_fieldset(xdim=200, ydim=200, grid_type='A'):
"""Simulate a periodic current along a western boundary, with significantly
larger velocities along the western edge than the rest of the region
The original test description can be found in: N. Fabbroni, 2009,
Numerical Simulation of Passive tracers dispersion in the sea,
Ph.D. dissertation, University of Bologna
http://amsdottorato.unibo.it/1733/1/Fabbroni_Nicoletta_Tesi.pdf
"""
a = b = 10000 * 1e3
scalefac = 0.05 # to scale for physically meaningful velocities
dx, dy = a / xdim, b / ydim
# Coordinates of the test fieldset (on A-grid in deg)
lon = np.linspace(0, a, xdim, dtype=np.float32)
lat = np.linspace(0, b, ydim, dtype=np.float32)
# Define arrays U (zonal), V (meridional) and P (sea surface height)
U = np.zeros((lat.size, lon.size), dtype=np.float32)
V = np.zeros((lat.size, lon.size), dtype=np.float32)
P = np.zeros((lat.size, lon.size), dtype=np.float32)
beta = 2e-11
r = 1 / (11.6 * 86400)
es = r / (beta * a)
for j in range(lat.size):
for i in range(lon.size):
xi = lon[i] / a
yi = lat[j] / b
P[j, i] = (1 - math.exp(-xi / es) - xi) * math.pi * np.sin(
math.pi * yi) * scalefac
if grid_type == 'A':
U[j, i] = -(1 - math.exp(-xi / es) - xi) * math.pi**2 * np.cos(
math.pi * yi) * scalefac
V[j, i] = (math.exp(-xi / es) / es - 1) * math.pi * np.sin(
math.pi * yi) * scalefac
if grid_type == 'C':
V[:, 1:] = (P[:, 1:] - P[:, 0:-1]) / dx * a
U[1:, :] = -(P[1:, :] - P[0:-1, :]) / dy * b
data = {'U': U, 'V': V, 'P': P}
dimensions = {'lon': lon, 'lat': lat}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
if grid_type == 'C':
fieldset.U.interp_method = 'cgrid_velocity'
fieldset.V.interp_method = 'cgrid_velocity'
return fieldset
def test_recursive_errorhandling(mode, xdim=2, ydim=2):
"""Example script to show how recursaive error handling can work.
In this example, a set of Particles is started at Longitude 0.5.
These are run through a Kernel that throws an error if the
Longitude is smaller than 0.7.
The error Kernel then draws a new random number between 0 and 1
Importantly, the 'normal' Kernel and Error Kernel keep iterating
until a particle does have a longitude larger than 0.7.
This behaviour can be useful if particles need to be 'pushed out'
from e.g. land. Note however that current under-the-hood
implementation is not extremely efficient, so code could be slow."""
dimensions = {'lon': np.linspace(0., 1., xdim, dtype=np.float32),
'lat': np.linspace(0., 1., ydim, dtype=np.float32)}
data = {'U': np.zeros((ydim, xdim), dtype=np.float32),
'V': np.zeros((ydim, xdim), dtype=np.float32)}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
# Set minimum value for valid longitudes (i.e. all longitudes < minlon are 'land')
fieldset.add_constant('minlon', 0.7)
# create a ParticleSet with all particles starting at centre of Field
pset = ParticleSet.from_line(fieldset=fieldset, pclass=ptype[mode],
start=(0.5, 0.5), finish=(0.5, 0.5), size=10)
def TestLon(particle, fieldset, time):
"""Kernel to check whether a longitude is larger than fieldset.minlon.
If not, the Kernel throws an error"""
if particle.lon <= fieldset.minlon:
return ErrorCode.Error
def Error_RandomiseLon(particle, fieldset, time):
"""Error handling kernel that draws a new longitude.
Note that this new longitude can be smaller than fieldset.minlon"""
particle.lon = ParcelsRandom.uniform(0., 1.)
ParcelsRandom.seed(123456)
# The .execute below is only run for one timestep. Yet the
# recovery={ErrorCode.Error: Error_RandomiseLon} assures Parcels keeps
# attempting to move all particles beyond 0.7 longitude
pset.execute(pset.Kernel(TestLon), runtime=1, dt=1,
recovery={ErrorCode.Error: Error_RandomiseLon})
assert (pset.lon > fieldset.minlon).all()
def test_add_duplicate_field(dupobject):
data, dimensions = generate_fieldset(100, 100)
fieldset = FieldSet.from_data(data, dimensions)
field = Field('newfld', fieldset.U.data, lon=fieldset.U.lon, lat=fieldset.U.lat)
fieldset.add_field(field)
error_thrown = False
try:
if dupobject == 'same':
fieldset.add_field(field)
elif dupobject == 'new':
field2 = Field('newfld', np.ones((2, 2)), lon=np.array([0, 1]), lat=np.array([0, 2]))
fieldset.add_field(field2)
except RuntimeError:
error_thrown = True
assert error_thrown
def test_fieldset_constant(mode):
data, dimensions = generate_fieldset(100, 100)
fieldset = FieldSet.from_data(data, dimensions)
westval = -0.2
eastval = 0.3
fieldset.add_constant('movewest', westval)
fieldset.add_constant('moveeast', eastval)
assert fieldset.movewest == westval
pset = ParticleSet.from_line(fieldset,
size=1,
pclass=ptype[mode],
start=(0.5, 0.5),
finish=(0.5, 0.5))
pset.execute(pset.Kernel(addConst), dt=1, runtime=1)
assert abs(pset[0].lon - (0.5 + westval + eastval)) < 1e-4
def test_advection_3D_outofbounds(mode):
xdim = ydim = zdim = 2
dimensions = {'lon': np.linspace(0., 1, xdim, dtype=np.float32),
'lat': np.linspace(0., 1, ydim, dtype=np.float32),
'depth': np.linspace(0., 1, zdim, dtype=np.float32)}
data = {'U': np.zeros((xdim, ydim, zdim), dtype=np.float32),
'V': np.zeros((xdim, ydim, zdim), dtype=np.float32),
'W': np.ones((xdim, ydim, zdim), dtype=np.float32)}
fieldset = FieldSet.from_data(data, dimensions, mesh='flat')
def DeleteParticle(particle, fieldset, time):
particle.delete()
pset = ParticleSet(fieldset=fieldset, pclass=ptype[mode], lon=0.5, lat=0.5, depth=0.9)
pset.execute(AdvectionRK4_3D, runtime=1., dt=1,
recovery={ErrorCode.ErrorOutOfBounds: DeleteParticle})
