import numpy as np
from matplotlib import cm
from netCDF4 import Dataset
meshpath='/gfs1/work/hbkqtang/input/CORE2_final/'
mesh=pf.load_mesh(meshpath,usepickle=False)
m = Basemap(projection='robin',lon_0=0, resolution='c')
x, y = m(mesh.x2, mesh.y2)
# Read the observation data
fl_obs=Dataset('/gfs1/work/hbkqtang/temporary/obs_SST.nc')
# Read the model forecast from the free run
fl_f=Dataset('/gfs1/work/hbkqtang/output/cpl_work_2014_3/thetao_fesom_20140101.nc')
for i in range(730,1096):
level_data_obs, elem_no_nan = pf.get_data(fl_obs.variables['sst'][i-730,:],mesh,000)
level_data_f, elem_no_nan_dummy = pf.get_data(fl_f.variables['thetao'][i,:],mesh,000)
# Calculate the differnece
level_data=level_data_obs-level_data_f
# Plot the figure
fig=plt.figure(figsize=(10,7))
m.drawmapboundary(fill_color='0.9')
m.drawcoastlines()
levels = np.arange(-10., 10., 0.05)
plt.tricontourf(x, y, elem_no_nan[::], level_data, levels = levels, cmap=cm.RdBu_r, extend='both')
cbar = plt.colorbar(orientation='horizontal', pad=0.03);
plt.title('SST_obs-fore'+str(i+1))
plt.tight_layout()
mesh = pf.load_mesh(meshpath, usepickle=False)
dataset = Dataset(
'/home/h/hbkqtang/fesom_echam6_oasis3-mct/run_scripts/hlrn3/work/cpl_work_8sst_testcase/fesom.2016.oce.daexp1.nc'
)
temp_std = dataset.variables['temp_ini'][0:1, 0:126859]
print(temp_std.min())
print(temp_std.max())
temp_std_mean = np.mean(temp_std)
temp_std_std = np.sqrt(temp_std_mean)
print(temp_std_std)
m = Basemap(projection='robin', lon_0=0, resolution='c')
x, y = m(mesh.x2, mesh.y2)
level_data, elem_no_nan = pf.get_data(dataset.variables['temp_ini'][0, :],
mesh, 000)
fig = plt.figure(figsize=(10, 7))
m.drawmapboundary(fill_color='0.9')
m.drawcoastlines()
levels = np.arange(0., 2., .05)
plt.tricontourf(x,
y,
elem_no_nan[::],
level_data,
levels=levels,
cmap=cm.Spectral_r,
extend='both')
cbar = plt.colorbar(orientation='horizontal', pad=0.03)
plt.title('SST_var_model_2')
plt.tight_layout()
def showfile(ifile, variable, depth, meshpath, box, res, influence, timestep,
levels, quiet, ofile, mapproj, abg, clim, cmap, interp, ptype, k):
'''
meshpath - Path to the folder with FESOM1.4 mesh files.
ifile - Path to FESOM1.4 netCDF file.
variable - The netCDF variable to be plotted.
'''
if not quiet:
click.secho('Mesh: {}'.format(meshpath))
click.secho('File: {}'.format(ifile))
click.secho('Variable: {}'.format(variable), fg='red')
click.secho('Depth: {}'.format(depth), fg='red')
click.secho('BOX: {}'.format(box))
click.secho('Resolution: {}'.format(res))
click.secho('Influence raduis: {} meters'.format(influence), fg='red')
click.secho('Timestep: {}'.format(timestep))
if levels:
click.secho('Levels: {}'.format(levels), fg='red')
else:
click.secho('Levels: auto', fg='red')
if cmap:
if cmap in cmo.cmapnames:
colormap = cmo.cmap_d[cmap]
elif cmap in plt.cm.datad:
colormap = plt.get_cmap(cmap)
else:
raise ValueError(
'Get unrecognised name for the colormap `{}`. Colormaps should be from standard matplotlib set of from cmocean package.'
.format(cmap))
else:
if clim:
colormap = cmo.cmap_d['balance']
else:
colormap = plt.get_cmap('Spectral_r')
sstep = timestep
radius_of_influence = influence
left, right, down, up = box
lonNumber, latNumber = res
mesh = pf.load_mesh(meshpath, abg=abg, usepickle=False, usejoblib=True)
flf = Dataset(ifile)
lonreg = np.linspace(left, right, lonNumber)
latreg = np.linspace(down, up, latNumber)
lonreg2, latreg2 = np.meshgrid(lonreg, latreg)
dind = (abs(mesh.zlevs - depth)).argmin()
realdepth = mesh.zlevs[dind]
level_data, nnn = pf.get_data(flf.variables[variable][sstep], mesh,
realdepth)
if interp == 'nn':
ofesom = pf.fesom2regular(level_data,
mesh,
lonreg2,
latreg2,
radius_of_influence=radius_of_influence)
elif interp == 'idist':
ofesom = pf.fesom2regular(level_data,
mesh,
lonreg2,
latreg2,
radius_of_influence=radius_of_influence,
how='idist',
k=k)
elif interp == 'linear':
points = np.vstack((mesh.x2, mesh.y2)).T
qh = qhull.Delaunay(points)
ofesom = LinearNDInterpolator(qh, level_data)((lonreg2, latreg2))
elif interp == 'cubic':
points = np.vstack((mesh.x2, mesh.y2)).T
qh = qhull.Delaunay(points)
ofesom = CloughTocher2DInterpolator(qh, level_data)((lonreg2, latreg2))
if clim:
if variable == 'temp':
climvar = 'T'
elif variable == 'salt':
climvar = 'S'
else:
raise ValueError(
'You have selected --clim/-c option, but variable `{}` is not in climatology. Acceptable values are `temp` and `salt` only.'
.format(variable))
#os.path.join(os.path.dirname(__file__), "../")
pathToClim = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"../data/")
print(pathToClim)
w = pf.climatology(pathToClim, clim)
xx, yy, oclim = pf.clim2regular(
w,
climvar,
lonreg2,
latreg2,
levels=[realdepth],
radius_of_influence=radius_of_influence)
oclim = oclim[0, :, :]
data = ofesom - oclim
else:
data = ofesom
if mapproj == 'merc':
ax = plt.subplot(111, projection=ccrs.Mercator())
elif mapproj == 'pc':
ax = plt.subplot(111, projection=ccrs.PlateCarree())
elif mapproj == 'np':
ax = plt.subplot(111, projection=ccrs.NorthPolarStereo())
elif mapproj == 'sp':
ax = plt.subplot(111, projection=ccrs.SouthPolarStereo())
elif mapproj == 'rob':
ax = plt.subplot(111, projection=ccrs.Robinson())
ax.set_extent([left, right, down, up], crs=ccrs.PlateCarree())
if levels:
mmin, mmax, nnum = levels
nnum = int(nnum)
else:
mmin = np.nanmin(data)
mmax = np.nanmax(data)
nnum = 40
data_levels = np.linspace(mmin, mmax, nnum)
if ptype == 'cf':
mm = ax.contourf(lonreg,\
latreg,\
data,
levels = data_levels,
transform=ccrs.PlateCarree(),
cmap=colormap,
extend='both')
elif ptype == 'pcm':
data_cyc, lon_cyc = add_cyclic_point(data, coord=lonreg)
mm = ax.pcolormesh(lon_cyc,\
latreg,\
data_cyc,
vmin = mmin,
vmax = mmax,
transform=ccrs.PlateCarree(),
cmap=colormap,
)
else:
raise ValueError('Inknown plot type {}'.format(ptype))
ax.coastlines(resolution='50m', lw=0.5)
ax.add_feature(
cfeature.GSHHSFeature(levels=[1], scale='low', facecolor='lightgray'))
cb = plt.colorbar(mm, orientation='horizontal', pad=0.03)
cb.set_label(flf.variables[variable].units)
plt.title('{} at {}m.'.format(variable, realdepth))
plt.tight_layout()
if ofile:
plt.savefig(ofile, dpi=100)
else:
plt.show()
sys.path.append("/home/h/hbkqtang/pyfesom.git/trunk/")
import pyfesom as pf
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
from matplotlib import cm
from netCDF4 import Dataset
meshpath = '/gfs1/work/hbkqtang/input/CORE2_final/'
mesh = pf.load_mesh(meshpath, usepickle=False)
fl = Dataset('/gfs1/work/hbkqtang/temporary/obs_SST.nc')
m = Basemap(projection='robin', lon_0=0, resolution='c')
x, y = m(mesh.x2, mesh.y2)
for i in range(0, 365):
level_data, elem_no_nan = pf.get_data(fl.variables['sst'][i, :], mesh, 000)
fig = plt.figure(figsize=(10, 7))
m.drawmapboundary(fill_color='0.9')
m.drawcoastlines()
levels = np.arange(-5., 35., .1)
plt.tricontourf(x,
y,
elem_no_nan[::],
level_data,
levels=levels,
cmap=cm.Spectral_r,
extend='both')
cbar = plt.colorbar(orientation='horizontal', pad=0.03)
cbar.set_label("SST")
plt.title('SST_obs_' + str(i + 1))
def showo(meshpath, ifile, variable, depth, box, timestep, minmax, mapproj,
quiet, ofile):
'''
meshpath - Path to the folder with FESOM1.4 mesh files.
ifile - Path to FESOM1.4 netCDF file.
variable - The netCDF variable to be plotted.
'''
if not quiet:
click.secho('Mesh: {}'.format(meshpath))
click.secho('File: {}'.format(ifile))
click.secho('Variable: {}'.format(variable), fg='red')
click.secho('Depth: {}'.format(depth), fg='red')
click.secho('BOX: {}'.format(box))
click.secho('Timestep: {}'.format(timestep))
if minmax:
click.secho('Min/max: {}'.format(minmax), fg='red')
else:
click.secho('Min/max: auto', fg='red')
mesh = pf.load_mesh(meshpath)
flf = Dataset(ifile)
left, right, down, up = box
elem_nonan, no_nan_tri = pf.cut_region(mesh, [left, right, down, up], 0)
if variable == 'topo':
level_data = mesh.topo
else:
level_data, nnn = pf.get_data(flf.variables[variable][timestep, :],
mesh, depth)
if minmax:
mmin, mmax = minmax
else:
mmin = level_data[elem_nonan].min()
mmax = level_data[elem_nonan].max()
plt.figure(figsize=(10, 10))
if mapproj == 'merc':
ax = plt.subplot(111, projection=ccrs.Mercator())
elif mapproj == 'pc':
ax = plt.subplot(111, projection=ccrs.PlateCarree())
elif mapproj == 'np':
ax = plt.subplot(111, projection=ccrs.NorthPolarStereo())
elif mapproj == 'sp':
ax = plt.subplot(111, projection=ccrs.SouthPolarStereo())
elif mapproj == 'rob':
ax = plt.subplot(111, projection=ccrs.Robinson())
ax.set_extent(box, crs=ccrs.PlateCarree())
mm = ax.tripcolor(mesh.x2,
mesh.y2,
elem_nonan,
level_data,
transform=ccrs.PlateCarree(),
edgecolors='k',
cmap=cm.Spectral_r,
vmin=mmin,
vmax=mmax)
ax.coastlines(lw=0.5, resolution='10m')
plt.colorbar(
mm,
orientation='horizontal',
pad=0.03,
)
plt.title('{} at {}'.format(variable, depth), size=20)
if ofile:
plt.savefig(ofile, dpi=200)
else:
plt.show()
import pyfesom as pf
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
from matplotlib import cm
from netCDF4 import Dataset
meshpath='/gfs1/work/hbkqtang/input/CORE2_final/'
mesh=pf.load_mesh(meshpath,usepickle=False)
fl=Dataset('/gfs1/work/hbkqtang/AWI-CM-PDAF/cpl_work_23profile_temp_r106_08/fesom.2016.oce.daexp1.nc')
m = Basemap(projection='robin',lon_0=0, resolution='c')
x, y = m(mesh.x2, mesh.y2)
depth=100
for i in range(3,4):
level_data, elem_no_nan = pf.get_data(fl.variables['salt_f'][i,:],mesh,depth)
fig=plt.figure(figsize=(10,7))
m.drawmapboundary(fill_color='0.9')
m.drawcoastlines()
levels = np.arange(31., 38., .01)
plt.tricontourf(x, y, elem_no_nan[::], level_data, levels = levels, cmap=cm.Spectral_r, extend='both')
cbar = plt.colorbar(orientation='horizontal', pad=0.03);
#plt.title('SST_ini_'+str(i+1))
#plt.title('temp_50_'+str(i+1))
#read observation file
en4=Dataset("/gfs1/work/hbkqtang/temporary/EN.4.2.1.f.profiles.g10.201601.nc",format="NETCDF3_64BIT_OFFSET")
# find out the number of profiles for one day
n_prof=en4.dimensions['N_PROF']
n_prof=n_prof.size
lon=en4.variables['LONGITUDE'][:]