def plot_org_and_moved(self,lon_range=[20.,130.],lat_range=[-40.,40.],
land_mask=LandMask.read_from_netcdf('input/hycom_landmask.nc'),
plot_mplstyle='plot_tools/plot.mplstyle'):
original = self.read_from_shapefile()
plt.style.use(plot_mplstyle)
fig = plt.figure()
ax = plt.gca(projection=ccrs.PlateCarree())
ax.set_extent([-180,180,-80,80],ccrs.PlateCarree())
ax.add_feature(cftr.COASTLINE,edgecolor='k',zorder=2)
if land_mask is not None:
# plot land mask
cmap_lm = colors.ListedColormap(['#ffffff','#000000'])
norm_lm = colors.BoundaryNorm([0,0.5,1],cmap_lm.N,clip=True)
l_lon = np.logical_and(land_mask.lon>=lon_range[0],land_mask.lon<=lon_range[-1])
l_lat = np.logical_and(land_mask.lat>=lat_range[0],land_mask.lat<=lat_range[-1])
lon_edges,lat_edges = land_mask.get_edges_from_center_points(l_lon=l_lon,l_lat=l_lat)
ax.pcolormesh(lon_edges,lat_edges,land_mask.mask[l_lat,:][:,l_lon],
norm=norm_lm,cmap=cmap_lm,transform=ccrs.PlateCarree(),zorder=1)
# plot source locations with nonzero waste input
i_source = np.where(np.sum(self.waste,axis=1) != 0)[0]
ax.scatter(original.lon[i_source],original.lat[i_source],marker='x',
c='#DC3015',label='original source locations',transform=ccrs.PlateCarree(),
zorder=3)
ax.scatter(self.lon[i_source],self.lat[i_source],marker='o',c='#0C59A6',
label='source locations in ocean',transform=ccrs.PlateCarree(),zorder=4)
# legend
patch_lm = Patch(facecolor='#000000',edgecolor='#000000',label='Hycom landmask')
ax.legend()
ax.set_title('River plastic source locations')
plt.show()
def run_hycom_subset_monthly_release(input_dir,
output_dir,
output_name,
time0,
lon0,
lat0,
start_date,
end_date,
kh=10.,
interp_method='linear',
indices=_get_io_indices_from_netcdf()):
# get paths
ncfiles = get_daily_ncfiles_in_time_range(input_dir, start_date, end_date)
output_path = output_dir + output_name
# create fieldset
filenames = [input_dir + ncfile for ncfile in ncfiles]
variables = {'U': 'u', 'V': 'v'}
dimensions = {'lat': 'lat', 'lon': 'lon', 'time': 'time'}
fset = FieldSet.from_netcdf(filenames,
variables,
dimensions,
indices=indices)
# add constant horizontal diffusivity (zero on land)
lm = LandMask.read_from_netcdf()
kh2D = kh * np.ones(lm.mask.shape)
kh2D[lm.mask.astype('bool')] = 0.0 # diffusion zero on land
kh2D_subset = kh2D[indices['lat'], :][:, indices['lon']]
fset.add_field(
Field('Kh_zonal',
data=kh2D_subset,
lon=fset.U.grid.lon,
lat=fset.U.grid.lat,
mesh='spherical',
interp_method=interp_method))
fset.add_field(
Field('Kh_meridional',
data=kh2D_subset,
lon=fset.U.grid.lon,
lat=fset.U.grid.lat,
mesh='spherical',
interp_method=interp_method))
# montly release
pset = ParticleSet(fieldset=fset,
pclass=JITParticle,
lon=lon0,
lat=lat0,
time=time0)
# execute
run_time = timedelta(days=(end_date - start_date).days)
dt = timedelta(hours=1)
output_interval = 24
kernel = pset.Kernel(AdvectionRK4) + pset.Kernel(DiffusionUniformKh)
output_file = pset.ParticleFile(name=output_path,
outputdt=dt * output_interval)
pset.execute(kernel,
runtime=run_time,
dt=dt,
output_file=output_file,
verbose_progress=True,
recovery={ErrorCode.ErrorOutOfBounds: delete_particle})
def get_kh_value_from_grid_size(lat0,
input_path='input/hycom_landmask.nc') -> float:
lm = LandMask.read_from_netcdf(input_path=input_path)
mean_dx_degree = np.nanmean(
[np.nanmean(np.diff(lm.lon)),
np.nanmean(np.diff(lm.lat))])
dx = get_distance_between_points(113.0, lat0, 113.0 + mean_dx_degree,
lat0) # m
epsilon = 10**(-9) # m^2/s^3
kh = epsilon**(1 / 3) * dx**(4 / 3)
return np.round(kh, 2)
def move_sources_to_ocean(self,land_mask=LandMask.read_from_netcdf('input/hycom_landmask.nc'),
log_file='move_riversources_hycom_landmask.log'):
lon = self.lon
lat = self.lat
lm_halo = land_mask.get_landmask_with_halo()
log.info(log_file,'Increased land mask with 1 grid cell.')
log.info(log_file,'Getting indices of points on land...')
p_land = self._get_index_of_sources_on_land(lm_halo)
log.info(log_file,f'Found {str(len(p_land))} sources on land.')
for i,p in enumerate(p_land):
log.info(log_file,f'Trying to move source {str(i+1)}/{str(len(p_land))} , p = {str(p)}:')
lon[p],lat[p] = lm_halo.get_closest_ocean_point(self.lon[p],self.lat[p],log_file=log_file)
return RiverSources(lon,lat,self.time,self.waste,self.waste_min,self.waste_max)
def get_release_time_lon_lat():
lon_org = 79.757667
lat_org = 7.071556
start_date = datetime(2008, 5, 22) # first explosion
end_date = datetime(2008, 6, 2) # stern sunk
n_particles = 1000 # per release
release_interval_hours = 3
n_releases = ((end_date - start_date).days + 1) * int(
24 / release_interval_hours) # 3 hourly release
lm = LandMask.read_from_netcdf()
lm_halo = lm.get_landmask_with_halo()
lon, lat = lm_halo.get_closest_ocean_point(lon_org, lat_org)
time = np.array([
start_date + timedelta(hours=i * release_interval_hours)
for i in range(n_releases)
])
time0 = np.repeat(time, n_particles)
lon0 = np.repeat(lon, len(time0))
lat0 = np.repeat(lat, len(time0))
return time0, lon0, lat0
def plot(self,t=0,lon_range=None,lat_range=None,
plot_mplstyle='plot_tools/plot.mplstyle',
land_mask=LandMask.read_from_netcdf('input/hycom_landmask.nc')):
plastic = np.round(self.waste[:,0])
plastic[plastic==0] = np.nan
plastic_log = np.log10(plastic)
month = datetime.strptime(str(t+1),'%m').strftime('%b')
plt.style.use(plot_mplstyle)
fig = plt.figure()
ax = plt.gca(projection=ccrs.PlateCarree())
# map
if not lon_range and not lat_range:
lon_range = [-180,180]
lat_range = [-80,80]
ax.set_extent([lon_range[0],lon_range[1],lat_range[0],lat_range[1]],ccrs.PlateCarree())
if land_mask is not None:
# plot land mask
cmap_lm = colors.ListedColormap(['#ffffff','#000000'])
norm_lm = colors.BoundaryNorm([0,0.5,1],cmap_lm.N,clip=True)
l_lon = np.logical_and(land_mask.lon>=lon_range[0],land_mask.lon<=lon_range[-1])
l_lat = np.logical_and(land_mask.lat>=lat_range[0],land_mask.lat<=lat_range[-1])
lon_edges,lat_edges = land_mask.get_edges_from_center_points(l_lon=l_lon,l_lat=l_lat)
ax.pcolormesh(lon_edges,lat_edges,land_mask.mask[l_lat,:][:,l_lon],
norm=norm_lm,cmap=cmap_lm,transform=ccrs.PlateCarree(),zorder=1)
else:
ax.add_feature(cftr.COASTLINE,edgecolor='k',zorder=2)
# set plot ranges and custom colormap
ticks = np.array([1,10,10**2,10**3,10**4,10**5,10**6])
ticks_str = ['1','10','10$^2$','10$^3$','10$^4$','10$^5$','10$^6$']
levels = np.log10(ticks)
cmap = colors.ListedColormap(['#63676b','#106bb8','#f1d435','#d50000','#640000','#470000'])
norm = colors.BoundaryNorm(levels, cmap.N, clip=True)
c = ax.scatter(self.lon,self.lat,c=plastic_log,cmap=cmap,norm=norm,s=30,
transform=ccrs.PlateCarree(),zorder=3)
# colorbar
cbar = plt.colorbar(c,ticks=levels)
cbar.ax.set_yticklabels(ticks_str)
cbar.set_label('Plastic waste input [tonnes]')
ax.set_title('River plastic input in '+month)
plt.show()