o = OceanDrift(loglevel=0) # Set loglevel to 0 for debug information
# Adding nordic reader for coastline
reader_nordic = reader_ROMS_native.Reader(o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc')
reader_nordic.variables = ['land_binary_mask']
reader_osc = reader_oscillating.Reader('x_sea_water_velocity', amplitude=1,
zero_time=reader_nordic.start_time)
o.add_reader([reader_osc]) # Oscillating east-west current component
o.fallback_values['y_sea_water_velocity'] = .2 # Adding northwards drift
o.set_config('general:basemap_resolution', 'i')
##########################################################
# Try different options: 'previous', 'stranding', 'none'
o.set_config('general:coastline_action', 'previous')
##########################################################
time = reader_osc.zero_time
lon = 12.2; lat = 67.7
o.seed_elements(lon, lat, radius=5000, number=15, time=time)
o.run(steps=36*4, time_step=900)
# Print and plot results
print o
o.plot()
#o.plot(background=['x_sea_water_velocity', 'y_sea_water_velocity'])
o.animation()
#%%
# Seeding some particles
lons = np.linspace(10.2, 12.2, 50)
lats = np.linspace(69.8, 70.8, 50)
lons, lats = np.meshgrid(lons, lats)
#%%
# Seed oil elements at defined position and time
o.seed_elements(lons,
lats,
radius=0,
number=2500,
time=reader_norkyst.start_time)
o.set_config('drift:vertical_mixing', False)
#%%
# Running model
o.run(steps=16 * 4, time_step=900, time_step_output=1800)
#%%
# Print and plot results
print(o)
o.animation(fast=True)
#%%
# .. image:: /gallery/animations/example_reader_boundary_0.gif
o.plot(fast=True)
from opendrift.readers import reader_netCDF_CF_generic
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
# Norkyst ocean model
reader_norkyst = reader_netCDF_CF_generic.Reader(o.test_data_folder() + '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')
#o.add_reader([reader_norkyst, reader_arome])
o.add_reader([reader_norkyst])
#%%
# Calculating attracting/backwards FTLE/LCS at 20 hours
lcs = o.calculate_ftle(
time=reader_norkyst.start_time + timedelta(hours=20),
time_step=timedelta(minutes=30),
duration=timedelta(hours=5), delta=1000,
RLCS=False)
#%%
# Simulation from beginning and up to 30 hours (time of LCS)
o.reset()
o.seed_elements(lon=4.4, lat=60.2, number=1000, radius=1000,
time=reader_norkyst.start_time)
o.run(end_time=reader_norkyst.start_time+timedelta(hours=20),
time_step=timedelta(minutes=30))
o.plot(lcs=lcs, vmin=1e-7, vmax=1e-4, colorbar=True, show_elements=True)
reader_basemap = reader_basemap_landmask.Reader(llcrnrlon=-1.5,
llcrnrlat=59,
urcrnrlon=7,
urcrnrlat=64,
resolution='h')
o.add_reader([fake_eddy, reader_basemap])
lon = 2.0
lat = 63.0
# Close to Station M
# First run, with Euler scheme:
o.set_config('drift:scheme', 'euler')
o.seed_elements(lon, lat, radius=0, number=1, time=datetime(2015, 1, 1))
o.run(steps=300, time_step=3600)
# Second run, with Runge-Kutta scheme:
o2 = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
o2.add_reader([fake_eddy, reader_basemap])
o2.set_config('drift:scheme', 'runge-kutta')
o2.seed_elements(lon, lat, radius=0, number=1, time=datetime(2015, 1, 1))
o2.run(steps=300, time_step=3600)
o.plot()
o2.plot()
# Animate and compare the two runs
o.animation(compare=o2,
legend=['Euler scheme', 'Runge-Kutta scheme'],
markersize=15)
'https://thredds.met.no/thredds/dodsC/sea/nordic4km/zdepths1h/aggregate_be'
)
#reader_nordic = reader_ROMS_native.Reader(o.test_data_folder() +
# '2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc')
o.add_reader([reader_nordic])
o.set_config('general:coastline_action', 'previous')
#%%
# Seeding elements on a grid
lons = np.linspace(12, 14.5, 30)
lats = np.linspace(67.5, 68.5, 30)
lons, lats = np.meshgrid(lons, lats)
lon = lons.ravel()
lat = lats.ravel()
time = reader_nordic.start_time
o.seed_elements(lon, lat, radius=0, number=30 * 30, time=time)
o.run(steps=24 * 2, time_step=3600)
#%%
# Print and plot results
print(o)
o.plot()
#o.plot(background=['x_sea_water_velocity', 'y_sea_water_velocity'])
o.animation()
#%%
# .. image:: /gallery/animations/example_coastline_0.gif
from opendrift.readers import reader_double_gyre
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
o.set_config('environment:fallback:land_binary_mask', 0)
o.set_config('drift:advection_scheme', 'runge-kutta4')
double_gyre = reader_double_gyre.Reader(epsilon=.25, omega=0.628, A=0.1)
print(double_gyre)
o.add_reader(double_gyre)
x = [.9]
y = [.5]
lon, lat = double_gyre.xy2lonlat(x, y)
o.seed_elements(lon,
lat,
radius=.1,
number=5000,
time=double_gyre.initial_time)
o.run(duration=timedelta(seconds=10), time_step=0.1)
o.animation(buffer=0, hide_landmask=True)
#%%
# .. image:: /gallery/animations/example_double_gyre_0.gif
o.plot(buffer=0, hide_landmask=True)
def test_plot(tmpdir):
anifile = os.path.join(tmpdir, 'anim.mp4')
plotfile = os.path.join(tmpdir, 'plot.png')
#anifile = None
#plotfile = None
o = OceanDrift(loglevel=30)
rn = reader_netCDF_CF_generic.Reader(
o.test_data_folder() +
'16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')
o.add_reader(rn)
o.seed_elements(lon=4.8,
lat=60.0,
number=10,
radius=1000,
time=rn.start_time)
o.run(steps=5)
# Making figures/animations
o.plot(filename=plotfile,
fast=True,
background=['x_sea_water_velocity', 'y_sea_water_velocity'])
o.animation(color='lat',
filename=anifile,
show_trajectories=True,
fast=True)
o.animation(density=True,
filename=anifile,
show_trajectories=True,
fast=True)
o.animation(filename=anifile, show_trajectories=True, fast=True)
o.animation(filename=anifile,
fast=True,
background=['x_sea_water_velocity', 'y_sea_water_velocity'])
o.plot(filename=plotfile, fast=True, linecolor='lat')
o.plot(filename=plotfile, fast=False)
# Second run for comparison
o2 = OceanDrift(loglevel=30)
o2.add_reader(rn)
o2.fallback_values['x_wind'] = 15 # Adding wind
o2.fallback_values['y_wind'] = 0
o2.seed_elements(lon=4.8,
lat=60.0,
number=10,
radius=1000,
time=rn.start_time)
o2.run(steps=5)
o.animation(filename=anifile,
compare=o2,
fast=True,
legend=['No wind', '10 m/s wind'])
o.plot(filename=plotfile,
compare=o2,
fast=True,
legend=['No wind', '10 m/s wind'])
# Check that files have been written
assert os.path.exists(anifile)
assert os.path.exists(plotfile)
from datetime import datetime, timedelta
from opendrift.readers import reader_netCDF_CF_generic
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
# Norkyst ocean model
reader_norkyst = reader_netCDF_CF_generic.Reader(o.test_data_folder() + '16Nov2015_NorKyst_z_surface/norkyst800_subset_16Nov2015.nc')
#o.add_reader([reader_norkyst, reader_arome])
o.add_reader([reader_norkyst])
# Calculating attracting/backwards FTLE/LCS at 20 hours
lcs = o.calculate_ftle(
time=reader_norkyst.start_time + timedelta(hours=20),
time_step=timedelta(minutes=30),
duration=timedelta(hours=5), delta=1000,
RLCS=False)
# Simulation from beginning and up to 30 hours (time of LCS)
o.reset()
o.seed_elements(lon=4.4, lat=60.2, number=1000, radius=1000,
time=reader_norkyst.start_time)
o.run(end_time=reader_norkyst.start_time+timedelta(hours=20),
time_step=timedelta(minutes=30))
o.plot(lcs=lcs, vmin=1e-7, vmax=1e-4, colorbar=True, show_particles=True)
['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
o.run(duration=timedelta(hours=24), time_step=1800)
#%%
# Plot results with lines and particles colored by depth
print(o)
o.plot(linecolor='z', buffer=.1, show_elements=False, fast=False)
o.animation(color='z', buffer=.1, fast=True)
#%%
# .. image:: /gallery/animations/example_depth_0.gif
lats=drifter_lats,
times=drifter_times)
wind_drift_factor, azimuth = wind_drift_factor_from_trajectory(t)
o.seed_elements(lon=4,
lat=60,
number=1,
time=ot.readers[list(ot.readers)[0]].start_time,
wind_drift_factor=0.033)
#%%
# New simulation, this time without diffusivity/noise
o.run(duration=timedelta(hours=12), time_step=600)
o.plot(fast=True, legend=True, trajectory_dict=trajectory_dict)
#%%
# The mean retrieved wind_drift_factor is 0.036, slichtly higher than the value 0.033 used for the simulation.
# The difference is due to the "noise" added by horizontal diffusion.
# Note that the retieved wind_drift_factor is linked to the wind and current used for the retrieval,
# other forcing datasets will yeld different value of the wind_drift_factor.
print(wind_drift_factor.mean())
plt.hist(wind_drift_factor, label='Retrieved wind_drift_factor')
plt.axvline(x=0.033, label='Actual wind_drift_factor of 0.033', color='k')
plt.axvline(x=wind_drift_factor.mean(),
label='Mean retieved wind_drift_factor of %.3f' %
wind_drift_factor.mean(),
color='r')
o2.run(end_time=reader_nordic.end_time,
time_step=1800,
time_step_output=3 * 3600)
#%% Prepare cusom colormap/colors for land and ocean
from matplotlib.colors import ListedColormap
import cartopy.feature as cfeature
#cmap = ListedColormap(('blue', 'red'))
cmap = ListedColormap((cfeature.COLORS['water'], cfeature.COLORS['land']))
#%%
# .. _model_landmask_only_model:
#
# To only show the landmask from the model, hide the coastline landmask by doing:
o2.plot(background='land_binary_mask', hide_landmask=True, cmap=cmap)
#%%
# Animation illustrating that red particles strand at ocean model land cells, and black particles strand at GSHHG land polygons
o.animation(compare=o2,
background='land_binary_mask',
cmap=cmap,
legend=['Default GSHHG landmask', 'Ocean model landmask'])
#%%
# .. image:: /gallery/animations/example_model_landmask_0.gif
o.plot(compare=o2,
background='land_binary_mask',
cmap=cmap,
legend=['Default GSHHG landmask', 'Ocean model landmask'])
ax.margins(x=0)
ax.legend()
ax.set_xticks([])
ax.set_xlabel(None)
ax4.set_title('Density of water at Station')
ax4.xaxis.set_major_formatter(DateFormatter("%d %b %H"))
plt.show()
#%%
# Finally, plot the spatial distribution of mean age of water
num = o.get_histogram(pixelsize_m=1500, weights=None, density=False)
num.name = 'number'
num.to_netcdf(histogram_file)
mas = o.get_histogram(pixelsize_m=1500,
weights=o.ds['age_seconds'],
density=False)
mas = mas / 3600 # in hours
mas = mas / num # per area
mas.name = 'mean_age'
mas.to_netcdf(histogram_file, 'a')
mas = mas.mean(dim='time').sum(dim='origin_marker') # Mean time of both rivers
#mas = mas.mean(dim='time').isel(origin_marker=1) # Mean age of a single river
mas.name = 'Mean age of water [hours]'
o.plot(background=mas.where(mas > 0), fast=True, show_elements=False)
# Cleaning up
os.remove(outfile)
os.remove(histogram_file)
# As there are large uncertainties, it makes sense to provide a statistical
# distribution of wind_drift_factors
#
# Using a constant value for all elements:
#wind_drift_factor = 0.03
#
# Giving each element a unique (random) wind_drift_factor
wind_drift_factor = np.random.uniform(0, 0.06, 2000)
o.seed_elements(4.7, 59.9, radius=3000, number=2000,
time=reader_current.start_time,
wind_drift_factor=wind_drift_factor)
# Prevent mixing elements downwards
o.set_config('drift:vertical_mixing', False)
#%%
# Running model
o.run(time_step=timedelta(minutes=15),
time_step_output=timedelta(minutes=60))
#%%
# Print and plot results
print(o)
o.animation(color='wind_drift_factor', fast=True)
#%%
# .. image:: /gallery/animations/example_drifter_0.gif
# Plot trajectories, colored by the wind_drift_factor of each element
o.plot(linecolor='wind_drift_factor', fast=True)
#%%
# Seeding elements on a grid
lons = np.linspace(12, 14.5, 30)
lats = np.linspace(67.5, 68.5, 30)
lons, lats = np.meshgrid(lons, lats)
lon = lons.ravel()
lat = lats.ravel()
time = reader_nordic.start_time
o.seed_elements(lon, lat, radius=0, number=30 * 30, time=time)
o.run(steps=48 * 2, time_step=3600)
#%%
# Print and plot results
print(o)
ax, _ = o.plot(hide_landmask=True, show=False)
#%%
# Show shapes
ax.add_geometries(reader_natural.polys,
ccrs.PlateCarree(),
facecolor=cfeature.COLORS['land'],
edgecolor='black')
plt.show()
o.animation()
#%%
# .. image:: /gallery/animations/example_shape_landmask_0.gif
rname = "./results_depth/onebyone_Ancud_site_{0}_z{1}.nc".format(j, depths)
if j < 100:
rname = "./results_depth/onebyone_Ancud_site_0{0}_z{1}.nc".format(
j, depths)
if j < 10:
rname = "./results_depth/onebyone_Ancud_site_00{0}_z{1}.nc".format(
j, depths)
#
# Simulacion
#
# 4320 = 30*24*6
o.run(steps=4320,
time_step=timedelta(minutes=10),
time_step_output=timedelta(hours=1),
outfile=rname)
fname = "./Figures/onebyone_Ancud_site_{0}_z{1}.png".format(j, depths)
if j < 100:
fname = "./Figures/onebyone_Ancud_site_0{0}_z{1}.png".format(j, depths)
if j < 10:
fname = "./Figures/onebyone_Ancud_site_00{0}_z{1}.png".format(j, depths)
print(fname)
time.sleep(2)
o.plot(filename=fname)
#o.animation (filename ='./Animations/many_v3.mp4')
o.reset()
cmd = "rm " + rname
os.system(cmd)
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
# Seed oil particles within contours from shapefile
o.seed_from_shapefile(o.test_data_folder() +
'shapefile_spawning_areas/Torsk.shp',
number=2000,
layername=None,
featurenum=[2, 4],
time=datetime.now())
o.fallback_values['x_wind'] = -4 # Constant wind drift
o.fallback_values['y_wind'] = 8
o.set_config('drift:wind_uncertainty', 4) # Adding some diffusion
# Running model
o.run(steps=50, time_step=3600)
# Print and plot results
print(o)
o.plot(fast=True)
if len(argv) > 1:
o.animation(filename=argv[1])
if len(argv) > 2:
try:
o.plot(filename=argv[2])
except:
pass
['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
o.run(duration=timedelta(hours=24), time_step=1800, outfile='openoil.nc')
#%%
# Plot results with lines and particles colored by depth
print(o)
o.plot(linecolor='z', buffer=.1, show_particles=False, fast=False)
o.animation(color='z', buffer=.1, fast=True)
#%%
# .. image:: /gallery/animations/example_depth_0.gif
# This is actual data of SLDMB/Code drifter as used in this study:
# Jones, C.E., Dagestad, K.-F., Breivik, O., Holt, B., Rohrs, J., Christensen, K.H., Espeseth, M.M., Brekke, C., Skrunes, S. (2016): Measurement and modeling of oil slick transport. Journal of Geophysical Research - Oceans, Volume 121, Issue 10, October 2016, Pages 7759-7775. DOI: 10.1002/2016JC012113.
drifterlons = [2.407376, 2.405140, 2.403248, 2.401872, 2.400152, 2.398518, 2.397056, 2.395766, 2.394476, 2.393358, 2.392584, 2.391810, 2.390606, 2.389316, 2.388628, 2.388370, 2.387940, 2.387510, 2.387338, 2.387166, 2.387252, 2.387338, 2.387682, 2.387854, 2.388284, 2.388628, 2.389230, 2.390004, 2.390434, 2.390692, 2.391380, 2.391896, 2.392068, 2.392154, 2.392068, 2.391896, 2.391896, 2.391896, 2.391638, 2.391380, 2.391208, 2.391036, 2.390692, 2.390090, 2.389660, 2.389058, 2.388628]
drifterlats = [60.034740, 60.033880, 60.033106, 60.032246, 60.031300, 60.030182, 60.028892, 60.027602, 60.026656, 60.025538, 60.024420, 60.023388, 60.022442, 60.021496, 60.020378, 60.019346, 60.018572, 60.017626, 60.016852, 60.016164, 60.015734, 60.015304, 60.014616, 60.014100, 60.013670, 60.013412, 60.013240, 60.013068, 60.013154, 60.013412, 60.013584, 60.013842, 60.014186, 60.014616, 60.015218, 60.015820, 60.016594, 60.017454, 60.018400, 60.019346, 60.020464, 60.021410, 60.022442, 60.023474, 60.024678, 60.025882, 60.026914]
drifterlats = drifterlats[::-1]
drifterlons = drifterlons[::-1]
driftertimes = [datetime(2015, 6, 10, 5, 50) +
timedelta(minutes=10)*i for i in range(len(drifterlons))]
r = reader_current_from_drifter.Reader(
lons=drifterlons, lats=drifterlats, times=driftertimes)
o.add_reader(r)
# We seed elements within polygon, as could have been extracted
# from remote sensing imagery
lons = [2.39, 2.391, 2.392, 2.393, 2.394, 2.393, 2.392, 2.391, 2.39]
lats = [60.02, 60.02, 60.019, 60.02, 60.021, 60.022, 60.021, 60.021, 60.02]
o.seed_within_polygon(lons=lons, lats=lats,
number=1000, time=r.start_time)
# Finally running simulation
o.run(end_time=r.end_time, time_step=r.time_step)
o.animation(buffer=.01, filename='current_from_drifter.gif')
#%%
# .. image:: /gallery/animations/current_from_drifter.gif
o.plot(buffer=.01)
from opendrift.readers import reader_ROMS_native
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
#%%
# Creating and adding reader for Nordic 4km current dataset
nordic_native = reader_ROMS_native.Reader(
o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc')
o.add_reader(nordic_native)
#%%
# Seed elements at defined positions, depth and time
o.seed_elements(lon=12.0,
lat=68.3,
radius=0,
number=10,
z=np.linspace(0, -150, 10),
time=nordic_native.start_time)
#%%
# Running model
o.run(time_step=3600)
#%%
# Print and plot results, with lines colored by particle depth
print(o)
o.plot(linecolor='z', fast=True)
#o.animation()
=============
"""
from datetime import datetime, timedelta
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
#%%
# Adding no input models, but instead constant northwards current of 1 m/s
o.fallback_values['x_sea_water_velocity'] = 0
o.fallback_values['y_sea_water_velocity'] = 1
o.fallback_values['land_binary_mask'] = 0
#%%
# Seed elements at defined position and time
o.seed_elements(lon=4.0,
lat=60.0,
radius=5000,
number=100,
time=datetime(2015, 9, 22, 6, 0, 0))
#%%
# Running model for 50 hours
o.run(duration=timedelta(hours=50))
#%%
# Print and plot results
print(o)
o.plot(fast=True, buffer=1)
lats=drifterlats,
times=driftertimes)
o.add_reader(r)
#%%
# We seed elements within polygon, as could have been extracted
# from remote sensing imagery
lons = [2.39, 2.391, 2.392, 2.393, 2.394, 2.393, 2.392, 2.391, 2.39]
lats = [60.02, 60.02, 60.019, 60.02, 60.021, 60.022, 60.021, 60.021, 60.02]
o.seed_within_polygon(lons=lons, lats=lats, number=1000, time=r.start_time)
#%%
# Finally running simulation
o.run(end_time=r.end_time, time_step=r.time_step)
o.animation(buffer=.01, fast=True)
#%%
# .. image:: /gallery/animations/example_current_from_drifter_0.gif
#%%
# Drifter track is shown in red, and simulated trajectories are shown in gray. Oil spill is displaced relative to drifter, but drifter current is assumed to be spatially homogeneous.
o.plot(buffer=.01,
fast=True,
trajectory_dict={
'lon': drifterlons,
'lat': drifterlats,
'time': driftertimes,
'linestyle': 'r-'
})
o = OceanDrift(loglevel=0) # Set loglevel to 0 for debug information
# Nordic 4km
nordic_native = reader_ROMS_native.Reader(o.test_data_folder() +
'2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc')
# Landmask (Basemap)
reader_basemap = reader_basemap_landmask.Reader(
llcrnrlon=11.0, llcrnrlat=67.5,
urcrnrlon=16.0, urcrnrlat=69.0,
resolution='h', projection='merc')
o.add_reader([reader_basemap, nordic_native])
# Seeding some particles
time = nordic_native.start_time
lon = 12.0; lat = 68.3;
# Seed oil elements at defined position and time
o.seed_elements(lon, lat, radius=0, number=10, z=np.linspace(0, -150, 10), time=time)
print o
# Running model
o.run(time_step=3600)
# Print and plot results
print o
o.plot(linecolor='z')
#o.animation()
# - 0.035 (3.5 %) for oil and iSphere driftes
# - 0.01 (1 %) for CODE drifters partly submerged ~0.5 m
# As there are large uncertainties, it makes sence to provide a statistical
# distribution of wind_drift_factors
# Using a constant value for all elements:
#wind_drift_factor = 0.03
# Giving each element a unique (random) wind_drift_factor
wind_drift_factor = np.random.uniform(0, 0.06, 2000)
o.seed_elements(4.7,
59.9,
radius=3000,
number=2000,
time=reader_current.start_time,
wind_drift_factor=wind_drift_factor)
#######################
# Running model
#######################
o.run(time_step=timedelta(minutes=15), time_step_output=timedelta(minutes=60))
###########################
# Print and plot results
###########################
print o
o.animation()
# Plot trajectories, colored by the wind_drift_factor of each element
o.plot(linecolor='wind_drift_factor')
lat = 60.0
# Outside Bergen
time = [reader_arome.start_time, reader_arome.start_time + timedelta(hours=30)]
#%%
# Seed oil elements at defined position and time
o.seed_elements(lon, lat, radius=50, number=5000, time=time)
#%%
# Using windspeed relative to moving ocean (current)
o.set_config('drift:relative_wind', False)
o.run(steps=48 * 2, time_step=1800, time_step_output=3600 * 2)
#%%
# Second run, for comparison
o2 = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
o2.add_reader([reader_norkyst, reader_arome])
o2.seed_elements(lon, lat, radius=50, number=5000, time=time)
o2.set_config('drift:relative_wind', True)
o2.run(steps=48 * 2, time_step=1800, time_step_output=3600 * 2)
#%%
# Animate and compare the two runs
o.animation(compare=o2, legend=['Absolute wind', 'Relative wind'])
#%%
# .. image:: /gallery/animations/example_relative_0.gif
o.plot(compare=o2, legend=['Absolute wind', 'Relative wind'], fast=True)
from opendrift.readers import reader_ArtificialOceanEddy
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=0) # Set loglevel to 0 for debug information
fake_eddy = reader_ArtificialOceanEddy.Reader(2, 62)
reader_basemap = reader_basemap_landmask.Reader(
llcrnrlon=-1.5, llcrnrlat=59,
urcrnrlon=7, urcrnrlat=64, resolution='h')
o.add_reader([fake_eddy, reader_basemap])
lon = 2.0; lat = 63.0; # Close to Station M
# First run, with Euler scheme:
o.set_config('drift:scheme', 'euler')
o.seed_elements(lon, lat, radius=0, number=1, time=datetime(2015,1,1))
o.run(steps=300, time_step=3600)
# Second run, with Runge-Kutta scheme:
o2 = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
o2.add_reader([fake_eddy, reader_basemap])
o2.set_config('drift:scheme', 'runge-kutta')
o2.seed_elements(lon, lat, radius=0, number=1, time=datetime(2015,1,1))
o2.run(steps=300, time_step=3600)
o.plot(compare=o2, legend=['Euler scheme', 'Runge-Kutta scheme'])
#o2.plot()
# Animate and compare the two runs
o.animation(compare=o2, legend=['Euler scheme', 'Runge-Kutta scheme'])
o.set_config('environment:fallback:x_sea_water_velocity', 0)
o.set_config('environment:fallback:y_sea_water_velocity', 0)
o.set_config('drift:wind_uncertainty', 0)
o.set_config('drift:current_uncertainty', 0)
time = datetime(2016, 1, 20, 12, 30, 0)
#%%
# Seeding a single element at a point
print('\n' + '=' * 70)
print('Seeding a single element at a point:')
print('o.seed_elements(lon=4, lat=60, time=time)')
print('=' * 70)
o.seed_elements(lon=4, lat=60, time=time)
#o.run(steps=1)
o.plot(buffer=.7, fast=True)
#%%
# Seeding 100 elements within a radius of 1000 m
print('\n' + '=' * 70)
print('Seeding 100 elements within a radius of 1000 m:')
print('o.seed_elements(lon=4, lat=60, number=100, radius=1000, time=time)')
print('=' * 70)
o.seed_elements(lon=4, lat=60, number=100, radius=1000, time=time)
#o.run(steps=1)
o.plot(buffer=.7, fast=True)
#%%
# Seeding 100 elements within a radius of 1000 m and specifying a property (here z/depth) as an array
print('\n' + '=' * 70)
print(
o.set_config('general:coastline_action', 'previous')
shyfem = shyfem.Reader('https://iws.ismar.cnr.it/thredds/dodsC/emerge/shyfem_unstructured_adriatic.nc')
o.add_reader(shyfem)
print(shyfem)
# Seed elements at defined positions, depth and time
N = 1000
z = -15*np.random.uniform(0, 1, N)
o.seed_elements(lon=12.4, lat=45.25, radius=1000, number=N,
z=z, time=shyfem.start_time)
#%%
# Running model
o.run(time_step=1800, duration=timedelta(hours=12))
#%%
# Print and plot results
print(o)
#%%
# Animations
o.animation(color='z', markersize=5, corners=[12.2, 12.6, 45.1, 45.5])
o.animation_profile(color='z', markersize=5)
#%%
# .. image:: /gallery/animations/example_shyfem_0.gif
o.plot(fast=True, buffer = 1., corners=[12.2, 12.6, 45.1, 45.5])
#%% # Forward run # Seeding some particles lon = 4.2 lat = 60.1 time = reader_norkyst.start_time o.seed_elements(lon, lat, radius=1000, number=100, time=time) o.disable_vertical_motion() o.run(steps=50 * 4, time_step=900, time_step_output=3600, outfile=ncfile) #%% # Print and plot results print(o) o.plot(buffer=.2, fast=True) ##%% # Backward run: # Import forward run, and seed elements at final positions and time o = opendrift.open(ncfile) elements_final = o.elements time_final = o.time del o o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information o.fallback_values['land_binary_mask'] = 0 o.add_reader(reader_norkyst) o.schedule_elements(elements_final, time_final) o.disable_vertical_motion() #%%
o.add_reader([reader_norkyst, reader_basemap]) ################ # Forward run ################ # Seeding some particles lon = 4.2 lat = 60.1 time = reader_norkyst.start_time o.seed_elements(lon, lat, radius=1000, number=100, time=time) o.run(steps=50 * 4, time_step=900, outfile=ncfile) # Print and plot results print(o) o.plot(buffer=.2, filename='forward.png') ################# ## Backward run ################# o = OceanDrift(loglevel=0) # Set loglevel to 0 for debug information #o.add_reader([reader_norkyst, reader_basemap]) # Import forward run, and seed elements at final positions and time o.io_import_file(ncfile) elements_final = o.elements time_final = o.time del o o = OceanDrift(loglevel=0) # Set loglevel to 0 for debug information o.add_reader([reader_norkyst, reader_basemap]) o.schedule_elements(elements_final, time_final)
from opendrift.readers import reader_double_gyre
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
o.fallback_values['land_binary_mask'] = 0
o.set_config('drift:scheme', 'runge-kutta4')
double_gyre = reader_double_gyre.Reader(epsilon=.25, omega=0.628, A=0.1)
print(double_gyre)
o.add_reader(double_gyre)
x = [.9]
y = [.5]
lon, lat = double_gyre.xy2lonlat(x, y)
o.seed_elements(lon,
lat,
radius=.1,
number=1000,
time=double_gyre.initial_time)
o.run(duration=timedelta(seconds=10), time_step=0.1)
o.animation(buffer=0)
#%%
# .. image:: /gallery/animations/example_double_gyre_0.gif
o.plot(buffer=0)
# of the elements. Typical empirical values are:
# - 0.035 (3.5 %) for oil and iSphere driftes
# - 0.01 (1 %) for CODE drifters partly submerged ~0.5 m
# As there are large uncertainties, it makes sence to provide a statistical
# distribution of wind_drift_factors
# Using a constant value for all elements:
#wind_drift_factor = 0.03
# Giving each element a unique (random) wind_drift_factor
wind_drift_factor = np.random.uniform(0, 0.06, 2000)
o.seed_elements(4.7, 59.9, radius=3000, number=2000,
time=reader_current.start_time,
wind_drift_factor=wind_drift_factor)
#######################
# Running model
#######################
o.run(time_step=timedelta(minutes=15),
time_step_output=timedelta(minutes=60))
###########################
# Print and plot results
###########################
print o
o.animation()
# Plot trajectories, colored by the wind_drift_factor of each element
o.plot(linecolor='wind_drift_factor')
from opendrift.models.oceandrift import OceanDrift
o = OceanDrift(loglevel=0) # Set loglevel to 0 for debug information
fake_eddy = reader_ArtificialOceanEddy.Reader(2, 62)
reader_basemap = reader_basemap_landmask.Reader(
llcrnrlon=-1.5, llcrnrlat=59,
urcrnrlon=7, urcrnrlat=64, resolution='h')
o.add_reader([fake_eddy, reader_basemap])
lon = 2.0; lat = 63.0; # Close to Station M
# First run, with Euler scheme:
o.set_config('drift:scheme', 'euler')
o.seed_elements(lon, lat, radius=0, number=1, time=datetime(2015,1,1))
o.run(steps=300, time_step=3600)
# Second run, with Runge-Kutta scheme:
o2 = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information
o2.add_reader([fake_eddy, reader_basemap])
o2.set_config('drift:scheme', 'runge-kutta')
o2.seed_elements(lon, lat, radius=0, number=1, time=datetime(2015,1,1))
o2.run(steps=300, time_step=3600)
o.plot()
o2.plot()
# Animate and compare the two runs
o.animation(compare=o2, legend=['Euler scheme', 'Runge-Kutta scheme'],
markersize=15)
