def test_repeated_run(self):
# NOTE: this test fails if outfile is not None
#outfile = 'leeway_test.nc'
outfile = None
o = OceanDrift(loglevel=50)
o.set_config('drift:vertical_mixing', False)
o.add_readers_from_list(reader_list)
o.seed_elements(lon=14, lat=67.85,
time=datetime(2016, 2, 2, 12))
o.run(steps=5, outfile=outfile)
lon1 = o.get_property('lon')[0]
# Repeated run with same object
o.seed_elements(lon=14, lat=67.85,
time=datetime(2016, 2, 2, 12))
o.run(steps=5, outfile=outfile)
lon2 = o.get_property('lon')[0]
# Third run, with different config
o.seed_elements(lon=14, lat=67.85,
time=datetime(2016, 2, 2, 12),
wind_drift_factor=.1)
o.run(steps=5)
lon3 = o.get_property('lon')[0]
# Fourth run, with different time
o.reset() # Reset is needed due to new start_time
o.seed_elements(lon=14, lat=67.85,
time=datetime(2016, 2, 2, 13),
wind_drift_factor=.1)
o.run(steps=5, outfile=outfile)
lon4 = o.get_property('lon')[0]
# Check results
self.assertEqual(lon1[-1][0], lon2[-1][0])
self.assertNotEqual(lon3[-1][0], lon2[-1][0])
def test_repeated_run(self):
o = OceanDrift(loglevel=50)
o.add_readers_from_list(reader_list)
o.seed_elements(lon=14, lat=67.85, time=datetime(2016, 2, 2, 12))
o.run(steps=5)
lon1 = o.get_property('lon')[0]
# Repeated run with same object
o.seed_elements(lon=14, lat=67.85, time=datetime(2016, 2, 2, 12))
o.run(steps=5)
lon2 = o.get_property('lon')[0]
# Third run, with different config
o.seed_elements(lon=14,
lat=67.85,
time=datetime(2016, 2, 2, 12),
wind_drift_factor=.1)
o.run(steps=5)
lon3 = o.get_property('lon')[0]
# Fourth run, with different time
o.reset() # Reset is needed due to new start_time
o.seed_elements(lon=14,
lat=67.85,
time=datetime(2016, 2, 2, 13),
wind_drift_factor=.1)
o.run(steps=5)
lon4 = o.get_property('lon')[0]
# Check results
self.assertEqual(lon1[-1][0], lon2[-1][0])
self.assertNotEqual(lon3[-1][0], lon2[-1][0])
def test_adding_readers(self):
o = OceanDrift()
landmask = reader_global_landmask.Reader(
extent=[-1.5, 7, 59, 64])
r = reader_ROMS_native.Reader(o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc')
o.add_reader([r, landmask])
self.assertEqual(o.priority_list['land_binary_mask'],
['roms native', 'global_landmask'])
self.assertEqual(o.priority_list['x_sea_water_velocity'],
['roms native'])
# Switch order
o = OceanDrift()
o.add_reader([landmask, r])
self.assertEqual(o.priority_list['land_binary_mask'],
['global_landmask', 'roms native'])
self.assertEqual(o.priority_list['x_sea_water_velocity'],
['roms native'])
# Test add_readers_from_list
o = OceanDrift()
o.add_readers_from_list(reader_list, lazy=False)
self.assertEqual(o.priority_list['x_sea_water_velocity'],
['roms native'])
self.assertEqual(o.priority_list['x_wind'],
[o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF.nc_20160202_subset'])
def test_adding_readers(self):
o = OceanDrift()
basemap = reader_basemap_landmask.Reader(llcrnrlon=-1.5,
llcrnrlat=59,
urcrnrlon=7,
urcrnrlat=64,
resolution='c')
r = reader_ROMS_native.Reader(
o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc'
)
o.add_reader([r, basemap])
self.assertEqual(o.priority_list['land_binary_mask'],
['roms native', 'basemap_landmask'])
self.assertEqual(o.priority_list['x_sea_water_velocity'],
['roms native'])
# Switch order
o = OceanDrift()
o.add_reader([basemap, r])
self.assertEqual(o.priority_list['land_binary_mask'],
['basemap_landmask', 'roms native'])
self.assertEqual(o.priority_list['x_sea_water_velocity'],
['roms native'])
# Test add_readers_from_list
o = OceanDrift()
o.add_readers_from_list(reader_list, lazy=False)
self.assertEqual(o.priority_list['x_sea_water_velocity'],
['roms native'])
self.assertEqual(o.priority_list['x_wind'], [
o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/AROME_MetCoOp_00_DEF.nc_20160202_subset'
])
def test_get_variables_along_trajectory_and_wind_drift_factor_from_trajectory(
):
o = OceanDrift(loglevel=50)
o.add_readers_from_list([
o.test_data_folder() +
'16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc',
o.test_data_folder() +
'16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc'
],
lazy=False)
t = o.get_variables_along_trajectory(
variables=[
'x_sea_water_velocity', 'y_sea_water_velocity', 'x_wind', 'y_wind'
],
lons=np.array([3.5, 4, 4.5]),
lats=np.array([59.7, 60, 60.3]),
times=[
o.readers[list(o.readers)[0]].start_time + i * timedelta(hours=3)
for i in range(3)
])
np.testing.assert_array_almost_equal(t['x_sea_water_velocity'],
[-0.078685, -0.106489, -0.058386])
np.testing.assert_array_almost_equal(t['x_wind'],
[-8.308249, -13.063459, -11.09289])
wdf, azimuth = wind_drift_factor_from_trajectory(t)
np.testing.assert_array_almost_equal(wdf, [0.27189012, 0.20492421])
np.testing.assert_array_almost_equal(azimuth, [73.0112213, 82.39749185])
#!/usr/bin/env python
"""
Thredds (global)
==================================
"""
from datetime import datetime, timedelta
from opendrift.models.oceandrift import OceanDrift
# Basic ocean drift module: current + 2% of wind
o = OceanDrift()
# Adding readers for global Thredds datasets:
# - Ocean forecast from global HYCOM model
# - Weather forecast from NOAA/NCEP
o.add_readers_from_list([
'https://tds.hycom.org/thredds/dodsC/GLBy0.08/latest',
'https://pae-paha.pacioos.hawaii.edu/thredds/dodsC/ncep_global/NCEP_Global_Atmospheric_Model_best.ncd'
])
o.seed_elements(lat=24,
lon=-81,
time=datetime.utcnow(),
number=5000,
radius=10000)
o.run(time_step=timedelta(minutes=30), duration=timedelta(days=5))
o.animation()
#!/usr/bin/env python
"""
Drift at different depths
==========================
"""
from datetime import datetime, timedelta
import numpy as np
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
#%%
# Using live data from Thredds
o.add_readers_from_list([
'https://thredds.met.no/thredds/dodsC/sea/norkyst800m/1h/aggregate_be'])
#%%
# Seed 1000 elements at random depths
z = -np.random.rand(2000)*50
o.seed_elements(lon=4.8, lat=60.0, z=z, radius=0, number=2000,
time=datetime.utcnow())
print(o)
#%%
# Adding some diffusion
o.set_config('drift:horizontal_diffusivity', 10) # m2/s
#%%
# Running model
from opendrift.models.physics_methods import wind_drift_factor_from_trajectory, distance_between_trajectories, skillscore_liu_weissberg
#%%
# A very simple drift model is: current + wind_drift_factor*wind
# where wind_drift_factor for surface drift is typically
# 0.033 if Stokes drift included, and 0.02 in addition to Stokes drift.
# This example illustrates how a best-fit wind_drift_factor
# can be calculated from an observed trajectory, using two different methods.
#%%
# First we simulate a synthetic drifter trajectory
ot = OceanDrift(loglevel=50)
ot.add_readers_from_list([
ot.test_data_folder() +
'16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc',
ot.test_data_folder() +
'16Nov2015_NorKyst_z_surface/arome_subset_16Nov2015.nc'
],
lazy=False)
#%%
# Using a wind_drift_factor of 0.33 i.e. drift is current + 3.3% of wind speed
ot.seed_elements(lon=4,
lat=60,
number=1,
time=ot.readers[list(ot.readers)[0]].start_time,
wind_drift_factor=0.033)
#%%
# Adding some horizontal diffusivity as "noise"
ot.set_config('drift:horizontal_diffusivity', 10)
#%%
# Seed information
lon = 4.8
lat = 60 # Bergen, Norway
lon = -89
lat = 29.8 # New Orleans
lon = 107
lat = 10 # Ho Chi Minh
lon = 123
lat = -16.3 # Australia
o = OceanDrift()
o.add_readers_from_list([
'https://nrt.cmems-du.eu/thredds/dodsC/cmems_mod_glo_phy_anfc_merged-uv_PT1H-i'
])
o.seed_elements(lon=lon,
lat=lat,
number=5000,
radius=1000,
time=datetime.utcnow())
o.run(duration=timedelta(days=3))
o.animation(fast=True,
clabel='Ocean current [m/s]',
background=['x_sea_water_velocity', 'y_sea_water_velocity'])
#%%
# .. image:: /gallery/animations/example_cmems_0.gif
#!/usr/bin/env python
from datetime import datetime, timedelta
from opendrift.models.oceandrift import OceanDrift
# Basic ocean drift module: current + 2% of wind
o = OceanDrift()
# Adding readers for global Thredds datasets:
# - Ocean forecast from UK Met Office (FOAM model)
# - Weather forecast from NOAA/NCEP
o.add_readers_from_list([
'http://data.ncof.co.uk/thredds/dodsC/METOFFICE-GLO-AF-PHYS-HOURLY-CUR',
'http://oos.soest.hawaii.edu/thredds/dodsC/hioos/model/atm/ncep_global/NCEP_Global_Atmospheric_Model_best.ncd'])
o.seed_elements(lat=24, lon=-81, time=datetime.now(),
number=5000, radius=10000)
o.run(time_step=timedelta(minutes=30),
duration=timedelta(days=5))
o.animation()
#!/usr/bin/env python
from datetime import datetime, timedelta
from opendrift.models.oceandrift import OceanDrift
# Basic ocean drift module: current + 2% of wind
o = OceanDrift()
# Adding readers for global Thredds datasets:
# - Ocean forecast from UK Met Office (FOAM model)
# - Weather forecast from NOAA/NCEP
o.add_readers_from_list([
'http://data.ncof.co.uk/thredds/dodsC/METOFFICE-GLO-AF-PHYS-HOURLY-CUR',
'http://oos.soest.hawaii.edu/thredds/dodsC/hioos/model/atm/ncep_global/NCEP_Global_Atmospheric_Model_best.ncd'
])
o.seed_elements(lat=24,
lon=-81,
time=datetime.now(),
number=5000,
radius=10000)
o.run(time_step=timedelta(minutes=30), duration=timedelta(days=5))
o.animation()
def test_dateline(self):
# Make synthetic netCDF file with currents from 0 to 360 deg longitude
fc = 'opendrift_test_current_0_360.nc'
lon = np.arange(0, 360)
lat = np.arange(-88, 89)
start_time = datetime(2021, 1, 1)
time = [start_time + i * timedelta(hours=24) for i in range(3)]
t, xcurr, ycurr = np.meshgrid(time,
np.zeros(lat.shape),
np.zeros(lon.shape),
indexing='ij')
xcurr[:, :, 0:180] = 1 # eastward
xcurr[:, :, 180:] = -1 # westward, i.e. current divergence at lon=0
ds = xr.Dataset(
{
"xcurr": (("time", "lat", "lon"), xcurr, {
'standard_name': 'x_sea_water_velocity'
}),
"ycurr": (("time", "lat", "lon"), ycurr, {
'standard_name': 'y_sea_water_velocity'
})
},
coords={
"lon": lon,
"lat": lat,
"time": time
})
ds.to_netcdf(fc)
# Make synthetic netCDF file with winds from -180 to 180 deg longitude
fw = 'opendrift_test_winds_180_180.nc'
lon = np.arange(-180, 180)
t, xwind, ywind = np.meshgrid(time,
np.zeros(lat.shape),
np.zeros(lon.shape),
indexing='ij')
ywind[:, :, 0:180] = 1 # northward
ywind[:, :, 180:] = -1 # southward, i.e. wind divergence at lon=180
ds = xr.Dataset(
{
"xwind": (("time", "lat", "lon"), xwind, {
'standard_name': 'x_wind'
}),
"ywind": (("time", "lat", "lon"), ywind, {
'standard_name': 'y_wind'
})
},
coords={
"lon": lon,
"lat": lat,
"time": time
})
ds.to_netcdf(fw)
reader_current = reader_netCDF_CF_generic.Reader(fc)
reader_wind = reader_netCDF_CF_generic.Reader(fw)
# Simulation across 0 meridian
o = OceanDrift(loglevel=30)
o.add_readers_from_list([fc, fw])
o.seed_elements(lon=[-2, 2],
lat=[60, 60],
time=start_time,
wind_drift_factor=.1)
o.run(steps=2)
# Check that current give divergence, and that
# wind is northwards east of 0 and southwards to the east
np.testing.assert_array_almost_equal(o.elements.lon, [-2.129, 2.129],
decimal=3)
np.testing.assert_array_almost_equal(o.elements.lat, [60.006, 59.994],
decimal=3)
# Simulation across dateline (180 E/W)
o = OceanDrift(loglevel=30)
o.add_readers_from_list([fc, fw])
o.seed_elements(lon=[-175, 175],
lat=[60, 60],
time=start_time,
wind_drift_factor=.1)
o.run(steps=2)
#o.plot(fast=True)
# Check that current give convergence, and that
# wind is northwards east of 180 and southwards to the west
np.testing.assert_array_almost_equal(o.elements.lon,
[-175.129, 175.129],
decimal=3)
np.testing.assert_array_almost_equal(o.elements.lat, [60.006, 59.994],
decimal=3)
# Cleaning up
os.remove(fw)
os.remove(fc)
#!/usr/bin/env python
from datetime import datetime, timedelta
from opendrift.models.oceandrift import OceanDrift
# Basic ocean drift module: current + 2% of wind
o = OceanDrift()
# Adding readers for global Thredds datasets:
# - Ocean forecast from UK Met Office (FOAM model)
# - Weather forecast from NOAA/NCEP
o.add_readers_from_list([
'http://tds.hycom.org/thredds/dodsC/GLBy0.08/latest',
'http://oos.soest.hawaii.edu/thredds/dodsC/hioos/model/atm/ncep_global/NCEP_Global_Atmospheric_Model_best.ncd'
])
o.seed_elements(lat=24,
lon=-81,
time=datetime.now(),
number=5000,
radius=10000)
o.run(time_step=timedelta(minutes=30), duration=timedelta(days=5))
o.animation()
