def test():
sim_tz = timezone.FixedTimeZone(-8, 'PST')
init_date = datetime.datetime(1969, 12, 31, 16, tzinfo=sim_tz)
print('{:} {:}'.format(
timezone.datetime_to_epoch(
datetime.datetime(1970, 1, 1, tzinfo=timezone.utc_tz)),
timezone.datetime_to_epoch(init_date)))
print('{:} {:}'.format(
timezone.datetime_to_epoch(
datetime.datetime(2016, 5, 1, 8, tzinfo=timezone.utc_tz)),
timezone.datetime_to_epoch(datetime.datetime(2016, 5, 1,
tzinfo=sim_tz))))
init_date = datetime.datetime(2016, 5, 1, tzinfo=sim_tz)
river_flux_interp = interpolation.NetCDFTimeSeriesInterpolator(
'forcings/stations/bvao3/bvao3.0.A.FLUX/*.nc', ['flux'],
init_date,
scalars=[-1.0],
allow_gaps=True)
print('{:} {:}'.format(timezone.datetime_to_epoch(init_date), init_date,
river_flux_interp(0.)))
ncd = netCDF4.Dataset('forcings/stations/bvao3/bvao3.0.A.FLUX/201605.nc')
t = ncd['time'][:]
v = ncd['flux'][:]
print('{:} {:} {:}'.format(
t[0], datetime.datetime.fromtimestamp(t[0], tz=timezone.utc_tz), v[0]))
def test():
import datetime
mesh2d = Mesh('mesh_cre-plume002.msh')
p1 = FunctionSpace(mesh2d, 'CG', 1)
m2_field = Function(p1, name='elevation')
sim_tz = timezone.FixedTimeZone(-8, 'PST')
init_date = datetime.datetime(2016, 5, 1, tzinfo=sim_tz)
tbnd = TidalBoundaryForcing(m2_field,
init_date,
constituents=['M2'],
boundary_ids=[1, 2, 3])
tbnd.set_tidal_field(0.0)
out = File('tmp/tidal_elev.pvd')
for t in np.linspace(0, 12 * 3600., 60):
tbnd.set_tidal_field(t)
n = norm(m2_field)
if m2_field.function_space().mesh().comm.rank == 0:
print(n)
out.write(m2_field)
def test():
sim_tz = timezone.FixedTimeZone(-8, 'PST')
zero_date = datetime.datetime(1969, 12, 31, 16, tzinfo=sim_tz)
print('Epoch zero: {:} {:}'.format(
timezone.datetime_to_epoch(
datetime.datetime(1970, 1, 1, tzinfo=timezone.pytz.utc)),
timezone.datetime_to_epoch(zero_date)))
init_date = datetime.datetime(2006, 5, 15, tzinfo=sim_tz)
print('sim. start: {:} {:}'.format(
timezone.datetime_to_epoch(
datetime.datetime(2006, 5, 15, 8, tzinfo=timezone.pytz.utc)),
timezone.datetime_to_epoch(init_date)))
river_flux_interp = interpolation.NetCDFTimeSeriesInterpolator(
'forcings/stations/beaverarmy/flux_*.nc', ['flux'],
init_date,
scalars=[-1.0],
allow_gaps=True)
print('interpolated: {:} {:} {:}'.format(
timezone.datetime_to_epoch(init_date), init_date,
river_flux_interp(0.)[0]))
ncd = netCDF4.Dataset('forcings/stations/beaverarmy/flux_2006.nc')
t = ncd['time'][:]
v = ncd['flux'][:]
print('original: {:} {:} {:}'.format(
t[0], datetime.datetime.fromtimestamp(t[0], tz=timezone.pytz.utc),
-v[0]))
dt = 900
for i in range(96):
d = init_date + datetime.timedelta(seconds=i * dt)
print('Time step {:3d}, {:}, flux: {:8.1f}'.format(
i, d,
river_flux_interp(i * dt)[0]))
def test():
mesh2d = Mesh('mesh_cre-plume002.msh')
comm = mesh2d.comm
p1 = FunctionSpace(mesh2d, 'CG', 1)
p1v = VectorFunctionSpace(mesh2d, 'CG', 1)
windstress_2d = Function(p1v, name='wind stress')
atmpressure_2d = Function(p1, name='atm pressure')
sim_tz = timezone.FixedTimeZone(-8, 'PST')
init_date = datetime.datetime(2015, 5, 16, tzinfo=sim_tz)
pattern = 'forcings/atm/wrf/wrf_air.2015_*_*.nc'
wrf = WRFInterpolator(p1, windstress_2d, atmpressure_2d, pattern,
init_date)
# create a naive interpolation for first file
xy = SpatialCoordinate(p1.mesh())
fsx = Function(p1).interpolate(xy[0]).dat.data_with_halos
fsy = Function(p1).interpolate(xy[1]).dat.data_with_halos
mesh_lonlat = []
for node in range(len(fsx)):
lat, lon = to_latlon(fsx[node], fsy[node])
mesh_lonlat.append((lon, lat))
mesh_lonlat = np.array(mesh_lonlat)
ncfile = netCDF4.Dataset('forcings/atm/wrf/wrf_air.2015_05_16.nc')
itime = 10
grid_lat = ncfile['lat'][:].ravel()
grid_lon = ncfile['lon'][:].ravel()
grid_lonlat = np.array((grid_lon, grid_lat)).T
grid_pres = ncfile['prmsl'][itime, :, :].ravel()
pres = scipy.interpolate.griddata(grid_lonlat,
grid_pres,
mesh_lonlat,
method='linear')
grid_uwind = ncfile['uwind'][itime, :, :].ravel()
uwind = scipy.interpolate.griddata(grid_lonlat,
grid_uwind,
mesh_lonlat,
method='linear')
grid_vwind = ncfile['vwind'][itime, :, :].ravel()
vwind = scipy.interpolate.griddata(grid_lonlat,
grid_vwind,
mesh_lonlat,
method='linear')
vrot = coordsys.VectorCoordSysRotation(coordsys.LL_WGS84,
coordsys.SPCS_N_OR,
mesh_lonlat[:, 0], mesh_lonlat[:,
1])
uwind, vwind = vrot(uwind, vwind)
u_stress, v_stress = compute_wind_stress(uwind, vwind)
# compare
wrf.set_fields((itime - 8) * 3600.) # NOTE timezone offset
assert np.allclose(pres, atmpressure_2d.dat.data_with_halos)
assert np.allclose(u_stress, windstress_2d.dat.data_with_halos[:, 0])
# write fields to disk for visualization
out_pres = File('tmp/atm_pressure.pvd')
out_wind = File('tmp/wind_stress.pvd')
hours = 24 * 3
granule = 4
simtime = np.arange(granule * hours) * 3600. / granule
i = 0
for t in simtime:
wrf.set_fields(t)
norm_atm = norm(atmpressure_2d)
norm_wind = norm(windstress_2d)
if comm.rank == 0:
print('{:} {:} {:} {:}'.format(i, t, norm_atm, norm_wind))
out_pres.write(atmpressure_2d)
out_wind.write(windstress_2d)
i += 1
def test():
"""
Tests atmospheric model data interpolation.
.. note::
The following files must be present
forcings/atm/wrf/wrf_air.2015_05_16.nc
forcings/atm/wrf/wrf_air.2015_05_17.nc
forcings/atm/nam/nam_air.local.2006_05_01.nc
forcings/atm/nam/nam_air.local.2006_05_02.nc
"""
mesh2d = Mesh('mesh_cre-plume_03_normal.msh')
comm = mesh2d.comm
p1 = get_functionspace(mesh2d, 'CG', 1)
p1v = get_functionspace(mesh2d, 'CG', 1, vector=True)
windstress_2d = Function(p1v, name='wind stress')
atmpressure_2d = Function(p1, name='atm pressure')
sim_tz = timezone.FixedTimeZone(-8, 'PST')
# WRF
# init_date = datetime.datetime(2015, 5, 16, tzinfo=sim_tz)
# pattern = 'forcings/atm/wrf/wrf_air.2015_*_*.nc'
# atm_time_step = 3600. # for verification only
# test_atm_file = 'forcings/atm/wrf/wrf_air.2015_05_16.nc'
# NAM
init_date = datetime.datetime(2006, 5, 1, tzinfo=sim_tz)
pattern = 'forcings/atm/nam/nam_air.local.2006_*_*.nc'
atm_time_step = 3*3600.
test_atm_file = 'forcings/atm/nam/nam_air.local.2006_05_01.nc'
atm_interp = ATMInterpolator(p1, windstress_2d, atmpressure_2d,
to_latlon,
pattern, init_date, COORDSYS, verbose=True)
# create a naive interpolation for first file
xy = SpatialCoordinate(p1.mesh())
fsx = Function(p1).interpolate(xy[0]).dat.data_with_halos
fsy = Function(p1).interpolate(xy[1]).dat.data_with_halos
mesh_lonlat = []
for node in range(len(fsx)):
lat, lon = to_latlon(fsx[node], fsy[node])
mesh_lonlat.append((lon, lat))
mesh_lonlat = np.array(mesh_lonlat)
ncfile = netCDF4.Dataset(test_atm_file)
itime = 6
grid_lat = ncfile['lat'][:].ravel()
grid_lon = ncfile['lon'][:].ravel()
grid_lonlat = np.array((grid_lon, grid_lat)).T
grid_pres = ncfile['prmsl'][itime, :, :].ravel()
pres = scipy.interpolate.griddata(grid_lonlat, grid_pres, mesh_lonlat, method='linear')
grid_uwind = ncfile['uwind'][itime, :, :].ravel()
uwind = scipy.interpolate.griddata(grid_lonlat, grid_uwind, mesh_lonlat, method='linear')
grid_vwind = ncfile['vwind'][itime, :, :].ravel()
vwind = scipy.interpolate.griddata(grid_lonlat, grid_vwind, mesh_lonlat, method='linear')
vrot = coordsys.VectorCoordSysRotation(coordsys.LL_WGS84, COORDSYS, mesh_lonlat[:, 0], mesh_lonlat[:, 1])
uwind, vwind = vrot(uwind, vwind)
u_stress, v_stress = compute_wind_stress(uwind, vwind)
# compare
atm_interp.set_fields(itime*atm_time_step - 8*3600.) # NOTE timezone offset
assert np.allclose(pres, atmpressure_2d.dat.data_with_halos)
assert np.allclose(u_stress, windstress_2d.dat.data_with_halos[:, 0])
# write fields to disk for visualization
pres_fn = 'tmp/AtmPressure2d.pvd'
wind_fn = 'tmp/WindStress2d.pvd'
print('Saving output to {:} {:}'.format(pres_fn, wind_fn))
out_pres = File(pres_fn)
out_wind = File(wind_fn)
hours = 24*1.5
granule = 4
simtime = np.arange(granule*hours)*3600./granule
i = 0
for t in simtime:
atm_interp.set_fields(t)
norm_atm = norm(atmpressure_2d)
norm_wind = norm(windstress_2d)
if comm.rank == 0:
print('{:} {:} {:} {:}'.format(i, t, norm_atm, norm_wind))
out_pres.write(atmpressure_2d)
out_wind.write(windstress_2d)
i += 1