def load_andremap_MLD(firstyear, lastyear, mesh, filetmp):
"Load and compute (mean) mixed layer depth for the period (firstyear---lastyear)."
# load the files into one dataset
files = [filetmp.format(d) for d in range(firstyear, lastyear + 1, 1)]
fl = MFDataset(files)
print 'Computing mean, max, min ...'
MLDmean = fl.variables['mixlay'][:, :].mean(axis=0) # 1x 2D field
MLDmax = fl.variables['mixlay'][:, :].max(axis=0) # 1x 2D field
MLDmin = fl.variables['mixlay'][:, :].min(axis=0) # 1x 2D field
print 'Done.'
# load climatology
climpath = '/mnt/lustre01/work/bm0944/a270046/DATA/climatology/'
clim = pf.climatology(climpath, climname='phc') # climname='phc'|'woa05'
# map fesom data to PHC climatology grid
xx, yy = np.meshgrid(clim.x, clim.y)
zz_MLDmean = np.zeros((clim.T.shape[1], clim.T.shape[2]))
zz_MLDmax = np.zeros((clim.T.shape[1], clim.T.shape[2]))
zz_MLDmin = np.zeros((clim.T.shape[1], clim.T.shape[2]))
distances, inds = pf.create_indexes_and_distances(mesh,
xx,
yy,
k=10,
n_jobs=2)
# remap mean
zz_MLDmean[:,:] = pf.fesom2regular(MLDmean, mesh, xx, yy, distances=distances,\
inds=inds, how='idist', k=10,\
radius_of_influence=200000)
zz_MLDmax[:,:] = pf.fesom2regular(MLDmax , mesh, xx, yy, distances=distances,\
inds=inds, how='idist', k=10,\
radius_of_influence=200000)
zz_MLDmin[:,:] = pf.fesom2regular(MLDmin , mesh, xx, yy, distances=distances,\
inds=inds, how='idist', k=10,\
radius_of_influence=200000)
# set land to NaN
zz_MLDmean[np.isnan(clim.T[0, :, :])] = np.nan
zz_MLDmax[np.isnan(clim.T[0, :, :])] = np.nan
zz_MLDmin[np.isnan(clim.T[0, :, :])] = np.nan
return xx, yy, zz_MLDmean, zz_MLDmax, zz_MLDmin
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()
def load_andcompute_TSRho_split(firstyear, lastyear, mesh, filetmp):
"Load and compute mean T, S, and density (sigma1) for the period (firstyear---lastyear). Split the computation in parts if you encounter memory errors."
## load the files into one dataset
#files = [filetmp.format(d) for d in range(firstyear,lastyear+1,1)]
#fl = MFDataset(files)
#
#print 'Computing mean ...'
#temp = fl.variables['temp'][:,:].mean(axis=0) # 1x 3D field
#salt = fl.variables['salt'][:,:].mean(axis=0) # 1x 3D field
#print 'Done.'
# load the files into one dataset
files = [filetmp.format(d) for d in range(firstyear, lastyear + 1, 1)]
fl = MFDataset(files)
# initialize
dim3D = np.shape(fl.variables['temp'][0, :])[0]
temp = np.zeros(dim3D)
salt = np.zeros(dim3D)
print 'Computing mean ...'
temp[0:dim3D / 2] = fl.variables['temp'][:, 0:dim3D / 2].mean(
axis=0) # 1x 3D field
temp[dim3D / 2:] = fl.variables['temp'][:, dim3D / 2:].mean(
axis=0) # 1x 3D field
salt[0:dim3D / 2] = fl.variables['salt'][:, 0:dim3D / 2].mean(
axis=0) # 1x 3D field
salt[dim3D / 2:] = fl.variables['salt'][:, dim3D / 2:].mean(
axis=0) # 1x 3D field
print 'Done.'
# load climatology
climpath = '/mnt/lustre01/work/bm0944/a270046/DATA/climatology/'
clim = pf.climatology(climpath, climname='phc') # climname='phc'|'woa05'
# map fesom data to PHC climatology grid
xx, yy, zz_temp_pot = pf.fesom2clim(temp,
mesh,
clim,
verbose=False,
how='idist',
k_neighbors=10,
radius_of_influence=200000)
xx, yy, zz_salt = pf.fesom2clim(salt,
mesh,
clim,
verbose=False,
how='idist',
k_neighbors=10,
radius_of_influence=200000)
# initialize additional variables
zz_temp_insitu = np.copy(zz_temp_pot)
zz_sigma_1 = np.zeros_like(zz_temp_pot)
PHC_temp_insitu = np.zeros_like(zz_temp_pot)
PHC_sigma_1 = np.zeros_like(zz_temp_pot)
# for every layer (0 ... 32):
for ilevel in np.arange(np.shape(clim.z)[0]):
# FESOM PART
# calculates temperature from potential temperature at the reference pressure PR and in situ pressure P
zz_temp_insitu[ilevel, :, :] = sw.eos80.temp(zz_salt[ilevel, :, :],
zz_temp_pot[ilevel, :, :],
clim.z[ilevel],
pr=0)
# density of Sea Water using UNESCO 1983 (EOS 80) polynomial
zz_sigma_1[ilevel, :, :] = sw.eos80.pden(zz_salt[ilevel, :, :],
zz_temp_insitu[ilevel, :, :],
clim.z[ilevel],
pr=1000.) - 1000.
# PHC PART (pot. 'clim' values are loaded in newest version of pyfesom)
PHC_temp_insitu[ilevel, :, :] = sw.eos80.temp(clim.S[ilevel, :, :],
clim.T[ilevel, :, :],
clim.z[ilevel],
pr=0)
PHC_sigma_1[ilevel, :, :] = sw.eos80.pden(
clim.S[ilevel, :, :],
PHC_temp_insitu[ilevel, :, :],
clim.z[ilevel],
pr=1000.) - 1000.
return xx, yy, zz_temp_pot, zz_salt, clim, zz_sigma_1, PHC_sigma_1