def plot_cb_pym6(initializer,perc=99):
budgetlist = extract_cb_terms_pym6(initializer)
with mfdset(initializer.fil) as fh, mfdset(initializer.fil2) as fh2:
e = gv('e',budgetlist[0].dom,'hi',fh2).read_array()
plot_kwargs = dict(cmap='RdBu_r')
plotter_kwargs = dict(zcoord=True,z=initializer.z,e=e,isop_mean=True)
fig = Plotter.budget_plot(budgetlist,initializer.meanax,
plot_kwargs=plot_kwargs,ncols=3,
plotter_kwargs=plotter_kwargs,
perc=perc,individual_cbars=False)
return fig
def plotzetaanimation(geofil,
fil,
savfil=None,
fig=None,
wlon=-25,
elon=0,
slat=10,
nlat=60,
zs=0,
ze=1,
fps=5,
bitrate=1000,
**plotkwargs):
fhgeo = dset(geofil)
fh = mfdset(fil)
ani = animatezeta(fhgeo,
fh,
fig=fig,
wlon=wlon,
elon=elon,
slat=slat,
nlat=nlat,
zs=zs,
ze=ze,
**plotkwargs)
if savfil:
mywriter = animation.FFMpegWriter(fps=fps, bitrate=bitrate)
ani.save(savfil + '.mp4', writer=mywriter)
else:
plt.show()
fh.close()
fhgeo.close()
def extract_cb_terms_pym6(initializer):
domain = Domain.Domain(initializer)
with mfdset(initializer.fil) as fh, mfdset(initializer.fil2) as fh2:
vhy = gv('vh',domain,'vl',fh2,fh,plot_loc='hl').ysm().read_array(filled=0).ddx(2,div_by_area=True)
uhx = gv('uh',domain,'ul',fh2,fh,plot_loc='hl').xsm().read_array(filled=0).ddx(3,div_by_area=True)
wd = gv('wd',domain,'hi',fh2).read_array().o1diff(1)
# budgetlist = [-uhx*(1/domain.dyT[uhx._slice[2:]]),-vhy*(1/domain.dxT[vhy._slice[2:]]),-wd]
budgetlist = [-uhx,-vhy,-wd]
lab = [ r'$-(\bar{h}\hat{u})_{\tilde{x}}$',
r'$-(\bar{h}\hat{v})_{\tilde{y}}$',
r'$-(\bar{h}\hat{\varpi})_{\tilde{b}}$']
for i,var in enumerate(budgetlist):
var.name = lab[i]
return budgetlist
def __init__(self, expt, **kwargs):
fh = mfdset(expt.fil0)
self.file_ = expt.fil0
self.filemean_ = expt.fil2
self.x = fh.variables['xh'][:]
self.y = fh.variables['yh'][:]
self.zi = fh.variables['zi'][:]
self.zl = fh.variables['zl'][:]
self.z = kwargs.get('z', np.linspace(-3000, -1))
self.e = fh.variables['e']
self.u = fh.variables['u']
self.v = fh.variables['v']
self.geometry = expt.geometry
#self.toffset = expt.toffset
fhmean = mfdset(expt.fil2)
self.emean = np.mean(fhmean.variables['e'][:], axis=0, keepdims=True)
fhmean.close()
def getuvhe(filename,i):
fh = mfdset(filename)
h = fh.variables['h'][i,:,:,:]
e = fh.variables['e'][i,:,:,:]
el = (e[0:-1,:,:] + e[1:,:,:])/2;
u = fh.variables['u'][i,:,:,:]
v = fh.variables['v'][i,:,:,:]
fh.close()
return (u,v,h,e,el)
def getcontterms(filename,i):
fh = mfdset(filename)
dhdt = fh.variables['dhdt'][i,:,:,:]
uh = fh.variables['uh'][i,:,:,:]
vh = fh.variables['vh'][i,:,:,:]
wd = fh.variables['wd'][i,:,:,:]
uhgm = fh.variables['uhGM'][i,:,:,:]
vhgm = fh.variables['vhGM'][i,:,:,:]
fh.close()
return (dhdt,uh,vh,wd,uhgm,vhgm)
def getoceanstats(filename):
fh = mfdset(filename)
layer = fh.variables['Layer'][:]
interface = fh.variables['Interface'][:]
time = fh.variables['Time'][:]
en = fh.variables['En'][:]
ape = fh.variables['APE'][:]
ke = fh.variables['KE'][:]
maxcfltrans = fh.variables['max_CFL_trans'][:]
maxcfllin = fh.variables['max_CFL_lin'][:]
return (layer,interface,time), (en,ape,ke), (maxcfltrans,maxcfllin)
def plot_wf_spectrum_vort(initializer, perc=99):
domain = Domain.Domain(initializer)
with mfdset(initializer.fil) as fh:
e = gv('e', domain, 'hi', fh, plot_loc='qi').xsm().ysm().read_array(
tmean=False,
extend_kwargs=dict(method='mirror')).move_to('ui').move_to('qi')
uy = gv('u', domain, 'ul', fh,
plot_loc='ql').yep().read_array(
tmean=False,
filled=0,
extend_kwargs=dict(method='vorticity')).ddx(2)
vx = gv('v', domain, 'vl', fh,
plot_loc='ql').xep().read_array(
tmean=False,
filled=0,
extend_kwargs=dict(method='vorticity')).ddx(3)
vort = (vx - uy).toz([-1], e=e)
vort.name = ''
fig, ax = plt.subplots(1, 2)
vort.plot('nanmean',
initializer.meanax,
perc=98,
contour=False,
cbar=True,
plot_kwargs=dict(cmap='RdBu_r'),
ax=ax[0])
initializer.ls = 0
initializer.le = None
domain1 = Domain.Domain(initializer)
h = gv('e', domain1, 'hi', fh).read_array().o1diff(1).values
omega = 2 * np.pi * (1 / 24 / 3600 + 1 / 365 / 24 / 3600)
lr = np.sum(
np.sqrt(h * domain.db) / np.pi /
(2 * omega *
np.sin(np.radians(0.5 * (initializer.slat + initializer.nlat)))),
axis=1,
keepdims=True)
lr = np.mean(lr, axis=(0, 1, 2, 3)).squeeze()
sig = vort.mean(axis=2).values.squeeze()
fx, fx1, ft, Rsq = cross_spectrum(sig, 5 * 24 * 3600, 4000)
vmax = np.percentile(Rsq, 99.9)
im = ax[1].pcolormesh(fx1, ft, Rsq, cmap='Reds', vmin=0, vmax=vmax)
fig.colorbar(im, ax=ax[1])
beta = 2 * omega * np.cos(
np.radians(0.5 *
(initializer.slat + initializer.nlat))) / domain.R_earth
w = -beta * fx / (fx**2 + lr**-2)
ax[1].plot(-fx, -w, 'k')
return fig, ax
def getdims(filename):
fh = mfdset(filename)
xq = fh.variables['xq'][:]
yq = fh.variables['yq'][:]
time = fh.variables['Time'][:]
xh = fh.variables['xh'][:]
yh = fh.variables['yh'][:]
zi = fh.variables['zi'][:]
zl = fh.variables['zl'][:]
fh.close()
return (xh,yh), (xq,yq), (zi,zl), time
def getmomxterms(filename,i):
fh = mfdset(filename)
dudt = fh.variables['dudt'][i,:,:,:]
cau = fh.variables['CAu'][i,:,:,:]
pfu = fh.variables['PFu'][i,:,:,:]
dudtvisc = fh.variables['du_dt_visc'][i,:,:,:]
diffu = fh.variables['diffu'][i,:,:,:]
dudtdia = fh.variables['dudt_dia'][i,:,:,:]
gkeu = fh.variables['gKEu'][i,:,:,:]
rvxv = fh.variables['rvxv'][i,:,:,:]
fh.close()
return (dudt,cau,pfu,dudtvisc,diffu,dudtdia,gkeu,rvxv)
def getmomyterms(filename,i):
fh = mfdset(filename)
dvdt = fh.variables['dvdt'][i,:,:,:]
cav = fh.variables['CAv'][i,:,:,:]
pfv = fh.variables['PFv'][i,:,:,:]
dvdtvisc = fh.variables['dv_dt_visc'][i,:,:,:]
diffv = fh.variables['diffv'][i,:,:,:]
dvdtdia = fh.variables['dvdt_dia'][i,:,:,:]
gkev = fh.variables['gKEv'][i,:,:,:]
rvxu = fh.variables['rvxu'][i,:,:,:]
fh.close()
return (dvdt,cav,pfv,dvdtvisc,diffv,dvdtdia,gkev,rvxu)
def plotsshanim(geofil, fil, desfps, savfil=None):
D, (ah, aq), (dxcu, dycu, dxcv, dycv, dxbu, dybu, dxt,
dyt), f = rdp.getgeom(geofil)
(xh, yh), (xq, yq), (zi, zl), time = rdp.getdims(fil)
print(len(time))
nx = len(xh)
ny = len(yh)
nz = len(zl)
nzp1 = len(zi)
nt = len(time)
fh = mfdset(fil)
e1 = fh.variables['e'][:, 0, :, :]
emax = np.ceil(np.amax(np.abs(e1)))
e = fh.variables['e'][0, :, :, :]
fh.close()
xlim = [-25, 0] # actual values of x in degrees
ylim = [10, 50] # actual values of y in degrees
zlim = [0, 1] # indices of zl or zi
(X, Y, P, Pmn,
Pmx), (xs, xe), (ys, ye) = sliceDomain(e, e, xlim[0], xlim[-1], ylim[0],
ylim[-1], zlim[0], zlim[-1], xq,
yh, zi, 0)
# X,Y,P,Pmn,Pmx = plotrange(e,xlim[0],xlim[-1],
# ylim[0],ylim[-1],
# zlim[0],zlim[-1],xh,yh,zi,e,0)
fig = plt.figure()
ax = plt.axes()
Vctr = np.linspace(-emax, emax, num=12, endpoint=True)
Vcbar = (Vctr[1:] + Vctr[:-1]) / 2
im = ax.contourf(X, Y, P, Vctr, cmap=plt.cm.RdBu_r)
cbar = plt.colorbar(im, ticks=Vcbar)
ani = animation.FuncAnimation(fig,
update_contour_plot,
frames=range(nt),
fargs=(fil, ax, fig, xlim, ylim, zlim, xh,
yh, zi, 0, emax))
if savfil:
ani.save(savfil + '.mp4',
writer="ffmpeg",
fps=desfps,
extra_args=['-vcodec', 'libx264'])
else:
plt.show()
#plt.close(fig)
plt.close('all')
def update_contour_plot(i,fil,ax,fig,xlim,ylim,zlim,x,y,z,meanax):
fh = mfdset(fil)
var = fh.variables['e'][i,:,:,:]
fh.close()
X,Y,P,Pmn,Pmx = plotrange(var,xlim[0],xlim[-1],
ylim[0],ylim[-1],
zlim[0],zlim[-1],x,y,z,var,meanax)
ax.cla()
Vctr = np.linspace(-0.6,0.6,num=12,endpoint=True)
im = ax.contourf(X, Y, P, Vctr, cmap=plt.cm.RdBu_r)
plt.title(str(i))
print(i)
return im,
def extract_northward_transport(geofil,vgeofil,fil,fil2,xstart,xend,ystart,yend,zs,ze,meanax):
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i,)
fhgeo = dset(geofil)
fh = mfdset(fil)
zi = rdp1.getdims(fh)[2][0]
dbl = -np.diff(zi)*9.8/1031
(xs,xe),(ys,ye),dimv = rdp1.getlatlonindx(fh,wlon=xstart,elon=xend,
slat=ystart, nlat=yend,zs=zs,ze=ze,yhyq='yq')
dxcv = rdp1.getgeombyindx(fhgeo,xs,xe,ys,ye)[2][2]
f = rdp1.getgeombyindx(fhgeo,xs,xe,ys,ye)[-1]
nt_const = dimv[0].size
fhgeo.close()
vh = fh.variables['vh'][0:,zs:ze,ys:ye,xs:xe]/dxcv
fh2 = mfdset(fil2)
h = fh2.variables['h_Cv'][0:,zs:ze,ys:ye,xs:xe]
fh2.close()
fh.close()
vh = vh.filled(0)
vhplus = np.where(vh>0,vh,0)
vhminus = np.where(vh<0,vh,0)
R = 6378
#lr = np.sum(np.sqrt(-h*dbl[:,np.newaxis,np.newaxis])/np.pi/f/1e3,axis=1,keepdims=True)/np.radians(R)
lr = np.sum(np.sqrt(-h*dbl[:,np.newaxis,np.newaxis])/np.pi/f/1e3,axis=1,keepdims=True)/np.radians(R*np.cos(np.radians(dimv[2][:,np.newaxis])))
lr = np.mean(lr,axis=0).squeeze()
terms = np.ma.concatenate(( vhplus[:,:,:,:,np.newaxis],
vhminus[:,:,:,:,np.newaxis]),axis=4)
termsm = np.ma.apply_over_axes(np.mean, terms, meanax)
termsm = termsm.squeeze()
X = dimv[keepax[1]]
Y = dimv[keepax[0]]
return X,Y, termsm, lr
def update_contour_plot(i, fil, ax, fig, xlim, ylim, zlim, x, y, z, meanax,
emax):
fh = mfdset(fil)
var = fh.variables['e'][i, :, :, :]
fh.close()
(X, Y, P, Pmn,
Pmx), (xs, xe), (ys, ye) = sliceDomain(var, var, xlim[0], xlim[-1],
ylim[0], ylim[-1], zlim[0],
zlim[-1], x, y, z, 0)
# X,Y,P,Pmn,Pmx = plotrange(var,xlim[0],xlim[-1],
# ylim[0],ylim[-1],
# zlim[0],zlim[-1],x,y,z,var,meanax)
ax.cla()
Vctr = np.linspace(-emax, emax, num=12, endpoint=True)
im = ax.contourf(X, Y, P, Vctr, cmap=plt.cm.RdBu_r)
plt.title(str(i))
#print(i)
sys.stdout.write('\r' + str(i))
sys.stdout.flush()
return im,
def plotsshanim(geofil,fil,desfps,savfil=None):
D, (ah,aq), (dxcu,dycu,dxcv,dycv,dxbu,dybu,dxt,dyt) = rdp.getgeom(geofil)
(xh,yh), (xq,yq), (zi,zl), time = rdp.getdims(fil)
print(len(time))
nx = len(xh)
ny = len(yh)
nz = len(zl)
nzp1 = len(zi)
nt = len(time)
fh = mfdset(fil)
e1 = fh.variables['e'][:,0,:,:]
emax = np.ceil(np.amax(np.abs(e1)))
e = fh.variables['e'][0,:,:,:]
fh.close()
xlim = [-25,0] # actual values of x in degrees
ylim = [10,50] # actual values of y in degrees
zlim = [0,1] # indices of zl or zi
X,Y,P,Pmn,Pmx = plotrange(e,xlim[0],xlim[-1],
ylim[0],ylim[-1],
zlim[0],zlim[-1],xh,yh,zi,e,0)
fig = plt.figure()
ax = plt.axes()
Vctr = np.linspace(-emax,emax,num=12,endpoint=True)
Vcbar = (Vctr[1:] + Vctr[:-1])/2
im = ax.contourf(X, Y, P, Vctr, cmap=plt.cm.RdBu_r)
cbar = plt.colorbar(im, ticks=Vcbar)
ani = animation.FuncAnimation(fig,update_contour_plot,frames=range(nt),
fargs=(fil,ax,fig,xlim,ylim,zlim,xh,yh,zi,0))
if savfil:
ani.save(savfil+'.mp4', writer="avconv", fps=desfps,
extra_args=['-vcodec', 'libx264'])
else:
plt.show()
plt.close(fig)
def plot_wf_spectrum(initializer, perc=99):
domain = Domain.Domain(initializer)
with mfdset(initializer.fil) as fh:
e = gv('e', domain, 'hi', fh).read_array(tmean='anom')
fig, ax = plt.subplots(1, 2)
e.plot('nanmean',
initializer.meanax,
ax=ax[0],
cbar=True,
contour=False,
plot_kwargs=dict(cmap='RdBu_r'))
initializer.ls = 0
initializer.le = None
domain1 = Domain.Domain(initializer)
h = gv('e', domain1, 'hi', fh).read_array().o1diff(1).values
omega = 2 * np.pi * (1 / 24 / 3600 + 1 / 365 / 24 / 3600)
lr = np.sum(
np.sqrt(h * domain.db) / np.pi /
(2 * omega *
np.sin(np.radians(0.5 * (initializer.slat + initializer.nlat)))),
axis=1,
keepdims=True)
lr = np.mean(lr, axis=(0, 1, 2, 3)).squeeze()
sig = e.mean(axis=2).values.squeeze()
fx, fx1, ft, Rsq = cross_spectrum(sig, 5 * 24 * 3600, 4000)
vmax = np.percentile(Rsq, 99.9)
im = ax[1].pcolormesh(fx1, ft, Rsq, cmap='Reds', vmin=0, vmax=vmax)
fig.colorbar(im, ax=ax[1])
beta = 2 * omega * np.cos(
np.radians(0.5 *
(initializer.slat + initializer.nlat))) / domain.R_earth
w = -beta * fx / (fx**2 + lr**-2)
ax[1].plot(-fx, -w, 'k')
return fig, ax
def get_heddy_coeffs_fromflx(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs=0,
ze=None,
z=np.linspace(-3000, 0, 100),
perc=5,
htol=1e-3):
fhgeo = dset(geofil)
fhvgeo = dset(vgeofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
db = fhvgeo.variables['g'][:]
fhvgeo.close()
dbi = np.append(db, 0)
zi = fh.variables['zi'][:]
zl = fh.variables['zl'][:]
dbl = np.diff(zi) * 9.8 / 1031
dt = fh.variables['average_DT'][:]
dt = dt[:, np.newaxis, np.newaxis, np.newaxis]
sl, dimv = rdp1.getslice(fh, xstart, xend, ystart, yend, yhyq='yq')
slmx = np.s_[:, :, sl[2], (sl[3].start - 1):sl[3].stop]
slpx = np.s_[:, :, sl[2], (sl[3].start + 1):sl[3].stop]
slmxpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1),
(sl[3].start - 1):sl[3].stop]
slpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1), sl[3]]
slmpy = np.s_[:, :, (sl[2].start - 1):(sl[2].stop + 1), sl[3]]
sl2d = sl[2:]
slmx2d = slmx[2:]
slpx2d = slpx[2:]
slpy2d = slpy[2:]
dxbu = fhgeo.variables['dxBu'][slmx2d]
dybu = fhgeo.variables['dyBu'][slmx2d]
dxcv = fhgeo.variables['dxCv'][sl2d]
dycv = fhgeo.variables['dyCv'][sl2d]
dxt = fhgeo.variables['dxT'][slpy2d]
dyt = fhgeo.variables['dyT'][slpy2d]
v, _ = pv.getvtwa(fhgeo, fh, fh2, slmx)
vx = np.diff(v, axis=3) / dxbu
vx = vx[:, :, :, 1:]
e = (fh2.variables['e'][slpy] * dt).sum(axis=0, keepdims=True) / dt.sum(
axis=0, keepdims=True)
eatv = 0.5 * (e[:, :, :-1, :] + e[:, :, 1:, :])
e = fh2.variables['e'][slmxpy]
eatvmx = 0.5 * (e[:, :, :-1, :] + e[:, :, 1:, :])
vh = (fh.variables['vh_masked'][sl] * dt).sum(axis=0,
keepdims=True) / np.sum(dt)
h_cv = (fh.variables['h_Cv'][sl] * dt).sum(axis=0,
keepdims=True) / np.sum(dt)
h_cv[h_cv < htol] = np.nan
sig = h_cv / dbl[:, np.newaxis, np.newaxis]
h_vm = h_cv
vtwa = vh / h_cv / dxcv
vhforxdiff = (fh.variables['vh_masked'][slmx] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
h_cvforxdiff = (fh.variables['h_Cv'][slmx] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
h_cvforxdiff[h_cvforxdiff < htol] = np.nan
vtwaforxdiff = vhforxdiff / h_cvforxdiff
vtwaforxdiff = np.concatenate((vtwaforxdiff, vtwaforxdiff[:, :, :, -1:]),
axis=3)
vtwax = np.diff(vtwaforxdiff, axis=3) / dxbu / dybu
vtwax = 0.5 * (vtwax[:, :, :, :-1] + vtwax[:, :, :, 1:])
uh = (fh.variables['uh_masked'][slmxpy] * dt).filled(0).sum(
axis=0, keepdims=True) / np.sum(dt)
hum = 0.25 * (uh[:, :, :-1, :-1] + uh[:, :, :-1, 1:] + uh[:, :, 1:, :-1] +
uh[:, :, 1:, 1:]) / dycv
huvxphvvym = (fh.variables['twa_huvxpt'][sl] * dt +
fh.variables['twa_hvvymt'][sl] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
hvv = (fh.variables['hvv_Cv'][slmpy] * dt).sum(axis=0,
keepdims=True) / np.sum(dt)
hvvym = np.diff(hvv, axis=2) / dxt / dyt
hvvym = 0.5 * (hvvym[:, :, :-1, :] + hvvym[:, :, 1:, :])
huvxm = -(huvxphvvym + hvvym)
advx = hum * vtwax / h_vm
humx = np.diff(np.nan_to_num(uh), axis=3) / dxt / dyt
humx = 0.5 * (humx[:, :, :-1, :] + humx[:, :, 1:, :])
xdivep1 = -huvxm / h_vm
xdivep2 = advx
xdivep3 = vtwa * humx / h_vm
xdivep = (xdivep1 + xdivep2 + xdivep3)
xdivep *= sig
uv = sint.cumtrapz(
xdivep[:, :, :, ::-1], dx=-dxbu[:, 1:-1], axis=3,
initial=0)[:, :, :, ::-1] / sig
uvm = np.apply_over_axes(np.nanmean, uv, (0, 2))
vxm = np.apply_over_axes(np.nanmean, vx, (0, 2))
uvm = getvaratzc(uvm.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
vxm = getvaratzc(vxm.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 3))
cmax = np.fabs(np.percentile(uvm, (1, 99))).max()
im = ax[0].pcolormesh(dimv[3],
z,
uvm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
cmax = np.fabs(np.percentile(vxm, (1, 99))).max()
im = ax[1].pcolormesh(dimv[3],
z,
vxm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
for axc in ax:
xdegtokm(axc, 0.5 * (ystart + yend))
axc.set_xlabel('x (km)')
axc.grid()
ax[0].set_ylabel('z (m)')
fig2, ax = plt.subplots(1, 1, sharey=True)
ax.linfit(vx, uv)
ax.set_xlabel('v_x (m/s)')
ax.set_ylabel('RS (m2/s2)')
ax.grid()
return fig, fig2
def get_deddy_coeffs(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs=0,
ze=None,
z=np.linspace(-1500, 0, 100),
perc=5):
fhgeo = dset(geofil)
fhvgeo = dset(vgeofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
db = fhvgeo.variables['g'][:]
fhvgeo.close()
dbi = np.append(db, 0)
zi = fh.variables['zi'][:]
zl = fh.variables['zl'][:]
dbl = np.diff(zi) * 9.8 / 1031
sl, dimv = rdp1.getslice(fh, xstart, xend, ystart, yend, yhyq='yq')
sl2d = sl[2:]
slpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1), sl[3]]
f = fhgeo.variables['f'][sl2d]
e = fh2.variables['e'][slpy]
eatv = 0.5 * (e[:, :, :-1, :] + e[:, :, 1:, :])
v, h = pv.getvtwa(fhgeo, fh, fh2, sl)
v = v[:, :, :, :-1]
h = h[:, :, :, :-1]
sig = h / dbl[:, np.newaxis, np.newaxis]
sigaz = getvaratzc(sig.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
vb = -np.diff(v, axis=1) / dbi[1:-1, np.newaxis, np.newaxis]
vb = np.concatenate((vb[:, :1, :, :], vb, vb[:, -1:, :, :]), axis=1)
vbb = -np.diff(vb, axis=1) / dbl[:, np.newaxis, np.newaxis]
vbb = getvaratzc(vbb.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
vi = np.concatenate((v[:, :1, :, :], v, -v[:, -1:, :, :]), axis=1)
vi = 0.5 * (vi[:, :-1, :, :] + vi[:, 1:, :, :])
vib = -np.diff(vi, axis=1) / dbl[:, np.newaxis, np.newaxis]
svib = sig * vib
svibb = -np.diff(svib, axis=1) / dbi[1:-1, np.newaxis, np.newaxis]
svibb = np.concatenate((svibb[:, :1, :, :], svibb, svibb[:, -1:, :, :]),
axis=1)
svibb = 0.5 * (svibb[:, :-1, :, :] + svibb[:, 1:, :, :])
svibb = getvaratzc(svibb.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
sv = sig * v
svb = -np.diff(sv, axis=1) / dbi[1:-1, np.newaxis, np.newaxis]
svb = np.concatenate((svb[:, :1, :, :], svb, svb[:, -1:, :, :]), axis=1)
svbb = -np.diff(svb, axis=1) / dbl[:, np.newaxis, np.newaxis]
svbb = getvaratzc(svbb.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
x, y, P, _, _ = py.extract_twamomy_terms(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze, (0, ),
z=z)
fdrag = P[:, :, :, :, 7]
vbbm = np.apply_over_axes(np.nanmean, vbb, (0, 2))
svibbm = np.apply_over_axes(np.nanmean, svibb, (0, 2))
svbbm = np.apply_over_axes(np.nanmean, svbb, (0, 2))
fdragm = np.apply_over_axes(np.nanmean, fdrag, (0, 2))
fig, ax = plt.subplots(1, 4, sharex=True, sharey=True, figsize=(12, 3))
cmax = np.fabs(np.percentile(vbbm, (1, 99))).max()
im = ax[0].pcolormesh(dimv[3],
z,
vbbm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[0])
cmax = np.fabs(np.percentile(svibbm, (1, 99))).max()
im = ax[1].pcolormesh(dimv[3],
z,
svibbm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[1])
cmax = np.fabs(np.percentile(svbbm, (1, 99))).max()
im = ax[2].pcolormesh(dimv[3],
z,
svbbm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[2])
cmax = np.fabs(np.percentile(fdragm, (1, 99))).max()
im = ax[3].pcolormesh(dimv[3],
z,
fdragm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[3])
fig2, ax = plt.subplots(1, 3, sharey=True, figsize=(12, 3))
ax[0].linfit(vbb, sigaz * fdrag / f**2)
ax[0].set_xlabel(r'$v_{bb}$')
ax[1].linfit(svibb, sigaz * fdrag / f**2)
ax[1].set_xlabel(r'$(\sigma v_b)_b$')
ax[2].linfit(svbb, sigaz * fdrag / f**2)
ax[2].set_xlabel(r'$(\sigma v)_{bb}$')
ax[0].set_ylabel('Form Drag')
for axc in ax:
axc.grid()
return fig, fig2
def plot_sst_vort(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
ls,
le,
fil3=None,
z=np.linspace(-10, -9, 4),
htol=1e-3,
whichterms=None):
domain = Domain.Domain2(geofil,
vgeofil,
xstart,
xend,
ystart,
yend,
ls=ls,
le=le,
ts=450,
te=451)
with mfdset(fil2) as fh2:
rho = gv('e', domain, 'hi', fh2).read_array(tmean=False).toz(z[0],
rho=True)
T = (-rho + 1031) * (1 / 0.2)
T.name = ''
fig, ax = plt.subplots(1, 2, sharex=True, sharey=True)
T.plot('nanmean', (0, 1),
contour=False,
cbar=True,
plot_kwargs=dict(cmap='RdYlBu_r', vmax=20, vmin=10),
ax=ax[0])
rho = gv('e', domain, 'hi', fh2).read_array(tmean=False).toz(z[1],
rho=True)
T = (-rho + 1031) * (1 / 0.2)
T.name = ''
T.plot('nanmean', (0, 1),
contour=False,
cbar=True,
plot_kwargs=dict(cmap='RdYlBu_r', vmax=10, vmin=6),
ax=ax[1])
# e = gv('e',domain,'hi',fh2,plot_loc='qi').xsm().ysm().read_array(tmean=False,
# extend_kwargs=dict(method='mirror')).move_to('ui').move_to('qi')
# uy = gv('u',domain,'ul',fh2,plot_loc='ql').yep().read_array(tmean=False,
# extend_kwargs=dict(method='vorticity')).ddx(2)
# vx = gv('v',domain,'vl',fh2,plot_loc='ql').xep().read_array(tmean=False,
# extend_kwargs=dict(method='vorticity')).ddx(3)
# vort = (vx - uy)
# vort.name = ''
# vort.plot('nanmean',(0,1),perc=98,contour=False,cbar=True,plot_kwargs=dict(cmap='RdBu_r'),ax=ax[1])
# ax[1].set_ylabel('')
#
# vortz = vort.toz([-1],e=e)
# vortz.name = ''
# vortz.plot('nanmean',(0,1),perc=98,contour=False,cbar=True,plot_kwargs=dict(cmap='RdBu_r'),ax=ax[2])
# vmax = np.nanpercentile(np.fabs(vortz.values),98)
# xx,yy = np.meshgrid(vortz.dom.lonq[vortz._plot_slice[3,0]:vortz._plot_slice[3,1]],
# vortz.dom.latq[vortz._plot_slice[2,0]:vortz._plot_slice[2,1]])
# print(xx.shape, yy.shape, vortz.values[0,0,:,:].shape)
# im = ax[2].pcolormesh(xx,yy,
# vortz.values[0,0,:,:],cmap='RdBu_r',vmin=-vmax,vmax=vmax)
# print(e.values.shape)
# im = ax[2].pcolormesh(e.dom.lonq[e._plot_slice[3,0]:e._plot_slice[3,1]],
# e.dom.latq[e._plot_slice[2,0]:e._plot_slice[2,1]],
# e.values[0,0,:,:],cmap='RdBu_r')
# cbar = fig.colorbar(im,ax=ax[2])
# cbar.formatter.set_powerlimits((-2,2))
# cbar.update_ticks()
# ax[2].set_ylabel('')
domain = Domain.Domain2(geofil,
vgeofil,
xstart,
xend,
ystart,
yend,
ls=0,
le=1)
with mfdset(fil) as fh:
e = gv('e', domain, 'hi', fh).read_array()
cs = ax[0].contour(e.dom.lonh[e._plot_slice[3, 0]:e._plot_slice[3, 1]],
e.dom.lath[e._plot_slice[2, 0]:e._plot_slice[2, 1]],
e.values.squeeze(),
levels=[-0.4, -0.3, -0.15, 0, 0.15, 0.3, 0.4],
colors='k')
cs.clabel(inline=True)
return fig
def extractT(geofil,
fil,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
ts=0,
te=None,
z=None,
drhodt=-0.2,
rho0=1031.0,
savfil=None,
plotit=True,
loop=True):
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fh = mfdset(fil)
(xs, xe), (ys, ye), dimh = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze)
fhgeo = dset(geofil)
D = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[0]
fhgeo.close()
nt = dimh[0].size
t0 = time.time()
zl = rdp1.getdims(fh)[2][1]
if loop:
print('Reading data in loop...')
e = fh.variables['e'][0:1, zs:ze, ys:ye, xs:xe] / nt
for i in range(nt):
e += fh.variables['e'][i:i + 1, zs:ze, ys:ye, xs:xe] / nt
sys.stdout.write('\r' + str(int((i + 1) / nt * 100)) + '% done...')
sys.stdout.flush()
print('Time taken for data reading: {}s'.format(time.time() - t0))
else:
e = fh.variables['e'][ts:te, zs:ze, ys:ye, xs:xe]
X = dimh[keepax[1]]
Y = dimh[keepax[0]]
if 1 in keepax:
Y = z
if z == None:
z = np.linspace(-np.nanmax(D), -1, num=50)
Y = z
T = getTatz(zl, z, e)
T = (T - rho0) / drhodt
T = np.ma.apply_over_axes(np.nanmean, T, meanax)
P = T.squeeze()
data = (X, Y, P)
if plotit:
Pmax = np.nanmax(P)
Pmin = np.nanmin(P)
im = m6plot(data,
vmax=Pmax,
vmin=Pmin,
title=r'T at 40N ($^{\circ}$C)',
xlabel=r'x ($^{\circ}$)',
ylabel='z (m)',
bvnorm=True,
blevs=15)
if savfil:
plt.savefig(savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
else:
return data
def plot_twamomy_pym6(initializer, **kwargs):
budgetlist = extract_twamomy_terms_pym6(initializer)
terms = kwargs.get('terms', None)
if terms:
budgetlist = [budgetlist[i] for i in terms]
with mfdset(initializer.fil) as fh, mfdset(initializer.fil2) as fh2:
e = (gv('e', budgetlist[0].dom, 'hi', fh2, fh,
plot_loc='vl').yep().read_array(extend_kwargs={
'method': 'mirror'
}).move_to('vi'))
plot_kwargs = dict(cmap='RdBu_r')
plotter_kwargs = dict(xtokm=True,
zcoord=True,
z=initializer.z,
e=e,
isop_mean=True)
ax, fig = Plotter.budget_plot(budgetlist,
initializer.meanax,
plot_kwargs=plot_kwargs,
plotter_kwargs=plotter_kwargs,
individual_cbars=False,
**kwargs)
swash = kwargs.get('swash', True)
plotter_kwargs.pop('ax')
if swash:
initializer_for_swash = Domain.Initializer(geofil=initializer.geofil,
vgeofil=initializer.vgeofil,
wlon=-0.5,
elon=0,
slat=10,
nlat=11)
domain_for_swash0 = Domain.Domain(initializer_for_swash)
with mfdset(initializer.fil3) as fh:
islayerdeep = (gv('islayerdeep',
budgetlist[0].dom,
'ql',
fh,
plot_loc='vl').xsm().read_array(tmean=False,
extend_kwargs={
'method':
'mirror'
}).move_to('vl'))
islayerdeep0 = (gv('islayerdeep',
domain_for_swash0,
'ql',
fh,
plot_loc='vl').xsm().read_array(
tmean=False,
extend_kwargs={
'method': 'mirror'
}).move_to('vl'))
swashperc = (-islayerdeep / islayerdeep0.values[-1, 0, 0, 0] +
1) * 100
plot_kwargs = kwargs.get('swash_plot_kwargs',
dict(colors='grey', linewidths=4))
for axc in ax.ravel():
axc, _ = swashperc.plot('nanmean',
initializer.meanax,
only_contour=True,
clevs=np.array([1]),
annotate='None',
ax=axc,
plot_kwargs=plot_kwargs,
**plotter_kwargs)
return ax, fig
def extract_uv(geofil,
fil,
fil2,
fil3,
xstart,
xend,
ystart,
yend,
zs,
ze,
savfil=None,
utwamaxscalefactor=1,
vtwamaxscalefactor=1):
fh = mfdset(fil)
fh2 = mfdset(fil2)
zi = rdp1.getdims(fh)[2][0]
fhgeo = dset(geofil)
(xs, xe), (ys, ye), dimutwa = rdp1.getlatlonindx(
fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xq',
yhyq='yh')
em = fh.variables['e'][0:, zs:ze, ys:ye, xs:xe]
elm = 0.5 * (em[:, 0:-1, :, :] + em[:, 1:, :, :])
elm = np.ma.apply_over_axes(np.mean, elm, (0, 2))
dycu = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[2][1]
uh = fh.variables['uh'][0:, zs:ze, ys:ye, xs:xe]
h_cu = fh2.variables['h_Cu'][0:, zs:ze, ys:ye, xs:xe]
utwa = uh / h_cu / dycu
fig = plt.figure()
ax = fig.add_subplot(221)
X = dimutwa[3]
X = np.meshgrid(X, dimutwa[1])[0]
Y = elm.squeeze()
utwa = np.ma.apply_over_axes(np.nanmean, utwa, (0, 2))
utwa = utwa.squeeze()
cmax = np.nanmax(np.absolute(utwa)) * utwamaxscalefactor
im = m6plot(
(X, Y, utwa),
ax=ax,
ylabel='z (m)',
txt=r'$\hat{u}$',
vmin=-cmax,
vmax=cmax,
cmap='RdBu_r',
ylim=(-3000, 0))
ax.set_xticklabels([])
(xs, xe), (ys, ye), dimvtwa = rdp1.getlatlonindx(
fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xh',
yhyq='yq')
em = fh.variables['e'][0:, zs:ze, ys:ye, xs:xe]
elm = 0.5 * (em[:, 0:-1, :, :] + em[:, 1:, :, :])
elm = np.ma.apply_over_axes(np.mean, elm, (0, 2))
dxcv = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[2][2]
vh = fh.variables['vh'][0:, zs:ze, ys:ye, xs:xe]
h_cv = fh2.variables['h_Cv'][0:, zs:ze, ys:ye, xs:xe]
vtwa = vh / h_cv / dxcv
ax = fig.add_subplot(222)
X = dimvtwa[3]
X = np.meshgrid(X, dimvtwa[1])[0]
Y = elm.squeeze()
vtwa = np.ma.apply_over_axes(np.nanmean, vtwa, (0, 2))
vtwa = vtwa.squeeze()
cmax = np.nanmax(np.absolute(vtwa)) * vtwamaxscalefactor
im = m6plot(
(X, Y, vtwa),
ax=ax,
txt=r'$\hat{v}$',
vmin=-cmax,
vmax=cmax,
cmap='RdBu_r',
ylim=(-3000, 0))
ax.set_xticklabels([])
ax.set_yticklabels([])
fh2.close()
fh.close()
fhgeo.close()
fh3 = mfdset(fil3)
(xs, xe), (ys, ye), dimu = rdp1.getlatlonindx(
fh3,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xq',
yhyq='yh',
zlzi='zlremap')
u = fh3.variables['u'][0:, zs:ze, ys:ye, xs:xe]
ax = fig.add_subplot(223)
X = dimu[3]
Y = dimu[1]
u = np.ma.apply_over_axes(np.nanmean, u, (0, 2))
u = u.squeeze()
cmax = np.nanmax(np.absolute(u))
im = m6plot(
(X, -Y, u),
ax=ax,
txt='u',
ylabel='z (m)',
vmin=-cmax,
vmax=cmax,
cmap='RdBu_r')
xdegtokm(ax, 0.5 * (ystart + yend))
(xs, xe), (ys, ye), dimv = rdp1.getlatlonindx(
fh3,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xh',
yhyq='yq',
zlzi='zlremap')
v = fh3.variables['v'][0:, zs:ze, ys:ye, xs:xe]
ax = fig.add_subplot(224)
X = dimv[3]
Y = dimv[1]
v = np.ma.apply_over_axes(np.nanmean, v, (0, 2))
v = v.squeeze()
cmax = np.nanmax(np.absolute(v))
im = m6plot(
(X, -Y, v), ax=ax, txt='v', vmin=-cmax, vmax=cmax, cmap='RdBu_r')
xdegtokm(ax, 0.5 * (ystart + yend))
ax.set_yticklabels([])
fh3.close()
if savfil:
plt.savefig(
savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
def extract_twamomy_terms(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
alreadysaved=False,
xyasindices=False,
calledfrompv=False):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fhvgeo = dset(vgeofil)
db = -fhvgeo.variables['g'][:]
dbi = np.append(db, 0)
fhvgeo.close()
fhgeo = dset(geofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
zi = rdp1.getdims(fh)[2][0]
dbl = np.diff(zi) * 9.8 / 1031
if xyasindices:
(xs, xe), (ys, ye) = (xstart, xend), (ystart, yend)
_, _, dimv = rdp1.getdimsbyindx(fh,
xs,
xe,
ys,
ye,
zs=zs,
ze=ze,
ts=0,
te=None,
xhxq='xh',
yhyq='yq',
zlzi='zl')
else:
(xs, xe), (ys, ye), dimv = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
yhyq='yq')
D, (ah, aq) = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[0:2]
Dforgetutwaforxdiff = rdp1.getgeombyindx(fhgeo, xs - 1, xe, ys, ye)[0]
Dforgetutwaforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1,
ye + 1)[0]
Dforgethvforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1, ye)[0]
dxt, dyt = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye + 1)[2][6:8]
dxcv, dycv = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[2][2:4]
dxcvforxdiff = rdp1.getgeombyindx(fhgeo, xs - 1, xe, ys, ye)[2][2:3]
dxcvforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1,
ye + 1)[2][3:4]
dxbu = rdp1.getgeombyindx(fhgeo, xs - 1, xe, ys, ye)[2][4]
nt_const = dimv[0].size
t0 = time.time()
em = fh.variables['e'][0:, zs:ze, ys:ye, xs:xe]
elm = 0.5 * (em[:, 0:-1, :, :] + em[:, 1:, :, :])
vh = fh2.variables['vh'][0:, zs:ze, ys:ye, xs:xe]
h_cv = fh.variables['h_Cv'][0:, zs:ze, ys:ye, xs:xe]
h_vm = h_cv
vtwa = vh / h_cv / dxcv
vhforxdiff = fh2.variables['vh'][0:, zs:ze, ys:ye, xs - 1:xe]
h_cvforxdiff = fh.variables['h_Cv'][0:, zs:ze, ys:ye, xs - 1:xe]
vtwaforxdiff = vhforxdiff / h_cvforxdiff / dxcvforxdiff
vtwaforxdiff = np.concatenate(
(vtwaforxdiff, vtwaforxdiff[:, :, :, -1:]), axis=3)
vhforydiff = fh2.variables['vh'][0:, zs:ze, ys - 1:ye + 1, xs:xe]
h_cvforydiff = fh.variables['h_Cv'][0:, zs:ze, ys - 1:ye + 1, xs:xe]
vtwaforydiff = vhforydiff / h_cvforydiff / dxcvforydiff
vtwax = np.diff(vtwaforxdiff, axis=3) / dxbu
vtwax = 0.5 * (vtwax[:, :, :, 0:-1] + vtwax[:, :, :, 1:])
vtway = np.diff(vtwaforydiff, axis=2) / dyt
vtway = 0.5 * (vtway[:, :, 0:-1, :] + vtway[:, :, 1:, :])
hvmy = np.diff(vhforydiff / dxcvforydiff, axis=2) / dyt
hvmy = 0.5 * (hvmy[:, :, :-1, :] + hvmy[:, :, 1:, :])
hum = fh.variables['hu_Cv'][0:, zs:ze, ys:ye, xs:xe]
humforxdiff = fh.variables['hu_Cv'][0:, zs:ze, ys:ye, xs - 1:xe]
humforxdiff = np.concatenate((humforxdiff, -humforxdiff[:, :, :, -1:]),
axis=3)
humx = np.diff(humforxdiff, axis=3) / dxbu
humx = 0.5 * (humx[:, :, :, :-1] + humx[:, :, :, 1:])
huvxphvvym = fh.variables['twa_huvxpt'][
0:, zs:ze, ys:ye, xs:xe] + fh.variables['twa_hvvymt'][0:, zs:ze,
ys:ye, xs:xe]
hvv = fh.variables['hvv_Cv'][0:, zs:ze, ys - 1:ye + 1, xs:xe]
hvvym = np.diff(hvv, axis=2) / dyt
hvvym = 0.5 * (hvvym[:, :, :-1, :] + hvvym[:, :, 1:, :])
huvxm = huvxphvvym - hvvym
vtwaforvdiff = np.concatenate((vtwa[:, [0], :, :], vtwa), axis=1)
vtwab = np.diff(vtwaforvdiff, axis=1) / db[:, np.newaxis, np.newaxis]
vtwab = np.concatenate(
(vtwab,
np.zeros([vtwab.shape[0], 1, vtwab.shape[2], vtwab.shape[3]])),
axis=1)
vtwab = 0.5 * (vtwab[:, 0:-1, :, :] + vtwab[:, 1:, :, :])
hwb_v = fh.variables['hwb_Cv'][0:, zs:ze, ys:ye, xs:xe]
hwm_v = fh.variables['hw_Cv'][0:, zs:ze, ys:ye, xs:xe]
esq = fh.variables['esq_Cv'][0:, zs:ze, ys - 1:ye + 1, xs:xe]
ecv = fh.variables['e_Cv'][0:, zs:ze, ys - 1:ye + 1, xs:xe]
edlsqm = (esq - ecv**2)
edlsqmy = np.diff(edlsqm, axis=2) / dyt
edlsqmy = 0.5 * (edlsqmy[:, :, :-1, :] + edlsqmy[:, :, 1:, :])
epfv = fh.variables['epfv'][0:, zs:ze, ys:ye, xs:xe]
ecv = fh.variables['e_Cv'][0:, zs:ze, ys:ye, xs:xe]
pfvm = fh.variables['pfv_masked'][0:, zs:ze, ys:ye, xs:xe]
edpfvdm = epfv - pfvm * ecv
edpfvdmb = np.diff(edpfvdm, axis=1)
edpfvdmb = np.concatenate(
(edpfvdmb[:, :1, :, :], edpfvdmb, edpfvdmb[:, -1:, :, :]), axis=1)
edpfvdmb = 0.5 * (edpfvdmb[:, :-1, :, :] + edpfvdmb[:, 1:, :, :])
hmfum = fh.variables['twa_hmfu'][0:1, zs:ze, ys:ye, xs:xe]
hvwbm = fh.variables['twa_hvwb'][0:1, zs:ze, ys:ye, xs:xe]
hdiffvm = fh.variables['twa_hdiffv'][0:1, zs:ze, ys:ye, xs:xe]
hdvdtviscm = fh.variables['twa_hdvdtvisc'][0:1, zs:ze, ys:ye, xs:xe]
fh2.close()
fh.close()
advx = hum * vtwax / h_vm
advy = vtwa * vtway
advb = hwm_v * vtwab / h_vm
cor = hmfum / h_vm
pfvm = pfvm
xdivep1 = -huvxm / h_vm
xdivep2 = -advx
xdivep3 = -vtwa * humx / h_vm
xdivep = (xdivep1 + xdivep2 + xdivep3)
ydivep1 = -hvvym / h_vm
ydivep2 = -advy
ydivep3 = -vtwa * hvmy / h_vm
ydivep4 = 0.5 * edlsqmy * dbl[:, np.newaxis, np.newaxis] / h_vm
ydivep = (ydivep1 + ydivep2 + ydivep3 + ydivep4)
bdivep1 = -hvwbm / h_vm
bdivep2 = -advb
bdivep3 = -vtwa * hwb_v / h_vm
bdivep4 = edpfvdmb / h_vm
bdivep = (bdivep1 + bdivep2 + bdivep3 + bdivep4)
Y1twa = hdiffvm / h_vm
Y2twa = hdvdtviscm / h_vm
terms = np.ma.concatenate(
(-advx[:, :, :, :, np.newaxis], -advy[:, :, :, :, np.newaxis],
-advb[:, :, :, :, np.newaxis], cor[:, :, :, :, np.newaxis],
pfvm[:, :, :, :, np.newaxis], -xdivep[:, :, :, :, np.newaxis],
-ydivep[:, :, :, :, np.newaxis], -bdivep[:, :, :, :, np.newaxis],
Y1twa[:, :, :, :, np.newaxis], Y2twa[:, :, :, :, np.newaxis]),
axis=4)
termsep = np.ma.concatenate((
-xdivep1[:, :, :, :, np.newaxis], -xdivep3[:, :, :, :, np.newaxis],
-ydivep1[:, :, :, :, np.newaxis], -ydivep3[:, :, :, :, np.newaxis],
-ydivep4[:, :, :, :, np.newaxis], -bdivep1[:, :, :, :, np.newaxis],
-bdivep3[:, :, :, :, np.newaxis],
-bdivep4[:, :, :, :, np.newaxis]),
axis=4)
terms[np.isinf(terms)] = np.nan
termsep[np.isinf(termsep)] = np.nan
termsm = np.ma.apply_over_axes(np.nanmean, terms, meanax)
termsepm = np.ma.apply_over_axes(np.nanmean, termsep, meanax)
X = dimv[keepax[1]]
Y = dimv[keepax[0]]
if 1 in keepax:
em = np.ma.apply_over_axes(np.mean, em, meanax)
elm = np.ma.apply_over_axes(np.mean, elm, meanax)
Y = elm.squeeze()
X = np.meshgrid(X, dimv[1])[0]
P = termsm.squeeze()
P = np.ma.filled(P.astype(float), np.nan)
Pep = termsepm.squeeze()
Pep = np.ma.filled(Pep.astype(float), np.nan)
X = np.ma.filled(X.astype(float), np.nan)
Y = np.ma.filled(Y.astype(float), np.nan)
if not calledfrompv:
np.savez('twamomy_complete_terms', X=X, Y=Y, P=P, Pep=Pep)
else:
npzfile = np.load('twamomy_complete_terms.npz')
X = npzfile['X']
Y = npzfile['Y']
P = npzfile['P']
Pep = npzfile['Pep']
return (X, Y, P, Pep)
def get_pveddy_coeffs(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs=0,
ze=None,
z=np.linspace(-3000, 0, 100),
percx=0,
percy=0):
fhgeo = dset(geofil)
fhvgeo = dset(vgeofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
db = fhvgeo.variables['g'][:]
fhvgeo.close()
dbi = np.append(db, 0)
zi = fh.variables['zi'][:]
zl = fh.variables['zl'][:]
dbl = np.diff(zi) * 9.8 / 1031
dt = fh.variables['average_DT'][:]
dt = dt[:, np.newaxis, np.newaxis, np.newaxis]
sl, dimq = rdp1.getslice(fh,
xstart,
xend,
ystart,
yend,
xhxq='xq',
yhyq='yq')
xs = sl[3].start
xe = sl[3].stop
ys = sl[2].start
ye = sl[2].stop
zs = sl[1].start
ze = sl[1].stop
slmx = np.s_[:, :, sl[2], (sl[3].start - 1):sl[3].stop]
slpx = np.s_[:, :, sl[2], (sl[3].start + 1):sl[3].stop]
slmxpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1),
(sl[3].start - 1):sl[3].stop]
slpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1), sl[3]]
sl2d = sl[2:]
slmx2d = slmx[2:]
slpx2d = slpx[2:]
dxbu = fhgeo.variables['dxBu'][sl2d]
dxcv = fhgeo.variables['dxCv'][sl2d]
e = (fh2.variables['e'][slpy] * dt).sum(axis=0, keepdims=True) / dt.sum(
axis=0, keepdims=True)
e = np.concatenate((e, e[:, :, :, -1:]), axis=3)
eatq = 0.25 * (e[:, :, :-1, :-1] + e[:, :, 1:, 1:] + e[:, :, 1:, :-1] +
e[:, :, :-1, 1:])
(_, _, _, q, _, _) = pv2.extract_twapv_terms(geofil,
vgeofil,
fil,
fil2,
xs - 1,
xe,
ys,
ye,
zs,
ze,
meanax=(0, ),
fil3=None,
calledfromgeteddycoeffs=True)
(_, _, _, _, qbud,
_) = pv2.extract_twapv_terms(geofil,
vgeofil,
fil,
fil2,
xs,
xe,
ys,
ye,
zs,
ze,
meanax=(0, ),
fil3=None,
calledfromgeteddycoeffs=True)
fd = qbud[:, :, :, :, 12]
q[np.isnan(q)] = 0
qx = np.diff(q, axis=3) / dxcv
qx = np.concatenate((qx, qx[:, :, :, -1:]), axis=3)
qxx = np.diff(qx, axis=3) / dxbu
fdaz = getvaratzc(fd.astype(np.float32), z.astype(np.float32),
eatq.astype(np.float32))
qxxaz = getvaratzc(qxx.astype(np.float32), z.astype(np.float32),
eatq.astype(np.float32))
qxxazm = np.apply_over_axes(np.nanmean, qxxaz, (0, 2))
fdazm = np.apply_over_axes(np.nanmean, fdaz, (0, 2))
fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 3))
cmax = np.fabs(np.nanpercentile(qxxazm, (1, 99))).max()
im = ax[0].pcolormesh(dimq[3],
z,
qxxazm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
cb = fig.colorbar(im, ax=ax[0])
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
cmax = np.fabs(np.nanpercentile(fdazm, (1, 99))).max()
im = ax[1].pcolormesh(dimq[3],
z,
fdazm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
cb = fig.colorbar(im, ax=ax[1])
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
for axc in ax:
xdegtokm(axc, 0.5 * (ystart + yend))
axc.set_xlabel('x (km)')
axc.grid()
ax[0].set_ylabel('z (m)')
fig2, ax = plt.subplots(1, 1, sharey=True)
ax.linfit(qxx, fd, percx=percx, percy=percy)
ax.set_xlabel('qxx')
ax.set_ylabel('grad fd')
ax.grid()
return fig, fig2
def gethatuv(filename,i):
fh = mfdset(filename)
frhatu = fh.variables['frhatu'][i,:,:,:]
frhatv = fh.variables['frhatv'][i,:,:,:]
fh.close()
return (frhatu,frhatv)
def getPVRV(filename,i):
fh = mfdset(filename)
PV = fh.variables['PV'][i,:,:,:]
RV = fh.variables['RV'][i,:,:,:]
fh.close()
return (PV,RV)
def extract_twamomy_terms(geofil,
fil,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
alreadysaved=False):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fh = mfdset(fil)
(xs, xe), (ys, ye), dimv = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
yhyq='yq')
# _,(vys,vye),_ = rdp1.getlatlonindx(fh,wlon=xstart,elon=xend,
# slat=ystart, nlat=yend,zs=zs,ze=ze,yhyq='yq')
# (xs,xe),(ys,ye),_ = rdp1.getlatlonindx(fh,wlon=xstart,elon=xend,
# slat=ystart, nlat=yend,zs=zs,ze=ze)
D, (ah, aq) = rdp1.getgeombyindx(geofil, xs, xe, ys, ye)[0:2]
nt_const = dimv[0].size
e = fh.variables['e'][0:, zs:ze, ys:ye, xs:xe]
hmfu = fh.variables['twa_hmfu'][0:, zs:ze, ys:ye, xs:xe]
hpfv = fh.variables['twa_hpfv'][0:, zs:ze, ys:ye, xs:xe]
huvxpt = fh.variables['twa_huvxpt'][0:, zs:ze, ys:ye, xs:xe]
hvvymt = fh.variables['twa_hvvymt'][0:, zs:ze, ys:ye, xs:xe]
hvwb = fh.variables['twa_hvwb'][0:, zs:ze, ys:ye, xs:xe]
hdvdtvisc = fh.variables['twa_hdvdtvisc'][0:, zs:ze, ys:ye, xs:xe]
hdiffv = fh.variables['twa_hdiffv'][0:, zs:ze, ys:ye, xs:xe]
fh.close()
terms = np.ma.concatenate(
(hmfu[:, :, :, :, np.newaxis], hpfv[:, :, :, :, np.newaxis],
hvwb[:, :, :, :, np.newaxis], huvxpt[:, :, :, :, np.newaxis],
hvvymt[:, :, :, :, np.newaxis], hdvdtvisc[:, :, :, :, np.newaxis],
hdiffv[:, :, :, :, np.newaxis]),
axis=4)
termsm = np.ma.apply_over_axes(np.nanmean, terms, meanax)
X = dimv[keepax[1]]
Y = dimv[keepax[0]]
if 1 in keepax:
el = 0.5 * (e[:, 0:-1, :, :] + e[:, 1:, :, :])
e = np.ma.apply_over_axes(np.mean, e, meanax)
el = np.ma.apply_over_axes(np.mean, el, meanax)
Y = el.squeeze()
X = np.meshgrid(X, dimv[1])[0]
P = termsm.squeeze()
P = np.ma.filled(P.astype(float), np.nan)
X = np.ma.filled(X.astype(float), np.nan)
Y = np.ma.filled(Y.astype(float), np.nan)
np.savez('twamomy_terms', X=X, Y=Y, P=P)
else:
npzfile = np.load('twamomy_terms.npz')
X = npzfile['X']
Y = npzfile['Y']
P = npzfile['P']
return (X, Y, P)
def plot_forcing(fil, fil2=None):
y = np.linspace(10, 60, 100)
sflat = 20
nflat = 40
T = np.zeros(y.shape)
Tnorth = 10
Tsouth = 20
T[y >= sflat] = (Tsouth - Tnorth) / (sflat - nflat) * (y[y >= sflat] -
nflat) + Tnorth
T[y < sflat] = Tsouth
T[y > nflat] = Tnorth
rho0 = 1031
drhodt = -0.2
rho = rho0 + drhodt * T
gs = gridspec.GridSpec(2, 3, width_ratios=[1, 4, 1], height_ratios=[1, 4])
fig = plt.figure(figsize=(9, 4))
#ax = fig.add_subplot(121)
ax = plt.subplot(gs[1])
ax.plot(y, T, 'k', lw=1)
# ax.set_ylabel('T ($^{\circ}$C)')
ax.set_xlabel('y ($^{\circ}$N)')
ax.set_ylim(8, 22)
ax.grid(True)
ax.xaxis.tick_top()
ax.xaxis.set_label_position("top")
ax2 = ax.twinx()
ax2.set_ylabel(r"""$\rho (kgm^{-3})$""")
ax2.set_ylim(ax.get_ylim())
yticks = ax.get_yticks()
ax2.set_yticks(yticks[::2])
yticks = rho0 + drhodt * yticks[::2]
ax2.set_yticklabels(['{}'.format(i) for i in yticks])
z = np.linspace(0, 3000)
dabyss = 1500
Tabyss = 5
Tz = (Tabyss - Tsouth) / dabyss * z + Tsouth
Tz[z > dabyss] = Tabyss
#ax = fig.add_subplot(122)
ax = plt.subplot(gs[3])
ax.plot(Tz, z, 'k', lw=1)
ax.set_xlabel('T ($^{\circ}$C)')
ax.set_ylabel('z (m)')
ax.set_xlim(4, 21)
ax.grid(True)
ax.invert_yaxis()
# ax.yaxis.tick_right()
# ax.yaxis.set_label_position("right")
# ax2 = ax.twiny()
# ax2.set_xlabel(r"""$\rho (kgm^{-3})$""")
# ax2.set_xlim(ax.get_xlim())
# xticks = ax.get_xticks()
# ax2.set_xticks(xticks[::2])
# xticks = rho0 + drhodt*xticks[::2]
# ax2.set_xticklabels(['{}'.format(i) for i in xticks])
with mfdset(fil) as fh, mfdset(fil2) as fh2:
geofil = 'ocean_geometry.nc'
vgeofil = 'Vertical_coordinate.nc'
domain = Domain.Domain2(geofil,
vgeofil,
-0.5,
0,
10,
60,
ls=0,
le=None)
e = gv('e', domain, 'hi', fh).read_array()
ax = plt.subplot(gs[4])
ax.plot(domain.lath[e.plot_slice[2, 0]:e.plot_slice[2, 1]],
np.mean(e.values, axis=(0, 3)).T[:, ::2],
'k',
lw=1)
ax.plot(domain.lath[e.plot_slice[2, 0]:e.plot_slice[2, 1]],
np.mean(e.values, axis=(0, 3)).T[:, -1],
'k',
lw=1)
ax.set_yticklabels('')
ax.set_xlabel('y ($^{\circ}$N)')
ax.axvline(x=38.5, color='k')
ax.grid()
swash = gv('islayerdeep', domain, 'ql', fh2,
plot_loc='hl').xsm().ysm().read_array(
extend_kwargs=dict(method='mirror'),
tmean=False).move_to('ul').move_to('hl')
swash0 = fh2.variables['islayerdeep'][-1, 0, 0, 320]
swash = (-swash + swash0) * (100 / swash0)
z = np.linspace(-3000, 0)
swash = swash.toz(z, e=e)
ax.contour(swash.dom.lath[e.plot_slice[2, 0]:e.plot_slice[2, 1]],
z,
np.nanmean(swash.values, axis=(0, 3)),
levels=[1],
colors='r')
domain = Domain.Domain2(geofil,
vgeofil,
-0.5,
0,
38,
39,
ls=0,
le=None)
ax = plt.subplot(gs[5])
e = gv('e', domain, 'hi', fh).read_array()
ax.plot(domain.lonh[e.plot_slice[3, 0]:e.plot_slice[3, 1]],
np.mean(e.values, axis=(0, 2)).T[:, ::2],
'k',
lw=1)
ax.plot(domain.lonh[e.plot_slice[3, 0]:e.plot_slice[3, 1]],
np.mean(e.values, axis=(0, 2)).T[:, -1],
'k',
lw=1)
ax.set_yticklabels('')
ax.set_xlabel('x ($^{\circ}$)')
swash = gv('islayerdeep', domain, 'ql', fh2,
plot_loc='hl').xsm().ysm().read_array(
tmean=False).move_to('ul').move_to('hl')
swash0 = fh2.variables['islayerdeep'][-1, 0, 0, 320]
swash = (-swash + swash0) * (100 / swash0)
z = np.linspace(-3000, 0)
swash = swash.toz(z, e=e)
ax.contour(swash.dom.lonh[swash.plot_slice[3, 0]:swash.plot_slice[3,
1]],
z,
np.nanmean(swash.values, axis=(0, 2)),
levels=[1],
colors='r')
xdegtokm(ax, (38 + 39) / 2)
ax.grid()
#swash.plot('nanmean',(0,2),zcoord=True,e=e,z=np.linspace(-3000,0),cbar=True,ax=ax)
return fig
def extract_twamomy_terms(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
fil3=None,
alreadysaved=False,
xyasindices=False,
calledfrompv=False,
z=np.linspace(-3000, 0, 100),
htol=1e-3):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fhvgeo = dset(vgeofil)
db = -fhvgeo.variables['g'][:]
dbi = np.append(db, 0)
fhvgeo.close()
fhgeo = dset(geofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
zi = rdp1.getdims(fh)[2][0]
dbl = np.diff(zi) * 9.8 / 1031
if xyasindices:
(xs, xe), (ys, ye) = (xstart, xend), (ystart, yend)
_, _, dimv = rdp1.getdimsbyindx(fh,
xs,
xe,
ys,
ye,
zs=zs,
ze=ze,
ts=0,
te=None,
xhxq='xh',
yhyq='yq',
zlzi='zl')
else:
(xs, xe), (ys, ye), dimv = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
yhyq='yq')
sl = np.s_[:, :, ys:ye, xs:xe]
slmx = np.s_[:, :, ys:ye, xs - 1:xe]
D, (ah, aq) = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[0:2]
Dforgetutwaforxdiff = rdp1.getgeombyindx(fhgeo, xs - 1, xe, ys, ye)[0]
Dforgetutwaforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1,
ye + 1)[0]
Dforgethvforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1, ye)[0]
dxt, dyt = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye + 1)[2][6:8]
dxcv, dycv = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[2][2:4]
dxcvforxdiff, dycvforxdiff = rdp1.getgeombyindx(
fhgeo, xs - 1, xe, ys, ye)[2][2:4]
dxcvforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1,
ye + 1)[2][3:4]
dxbu, dybu = rdp1.getgeombyindx(fhgeo, xs - 1, xe, ys, ye)[2][4:6]
nt_const = dimv[0].size
t0 = time.time()
dt = fh.variables['average_DT'][:]
dt = dt[:, np.newaxis, np.newaxis, np.newaxis]
if fil3:
fh3 = mfdset(fil3)
slmxtn = np.s_[-1:, :, ys:ye, xs - 1:xe]
# islayerdeep0 = fh3.variables['islayerdeep'][:,0,0,0].sum()
# islayerdeep = (fh3.variables['islayerdeep'][slmx].filled(np.nan)).sum(axis=0,
# keepdims=True)
islayerdeep0 = fh3.variables['islayerdeep'][-1:, 0, 0, 0]
islayerdeep = (fh3.variables['islayerdeep'][slmxtn].filled(np.nan))
islayerdeep[:, :, :, -1:] = islayerdeep[:, :, :, -2:-1]
swash = (islayerdeep0 - islayerdeep) / islayerdeep0 * 100
swash = 0.5 * (swash[:, :, :, :-1] + swash[:, :, :, 1:])
fh3.close()
else:
swash = None
em = (fh2.variables['e'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
elm = 0.5 * (em[:, 0:-1, :, :] + em[:, 1:, :, :])
vh = (fh.variables['vh_masked'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
h_cv = (fh.variables['h_Cv'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
h_cv[h_cv < htol] = np.nan
h_vm = h_cv
vtwa = vh / h_cv / dxcv
vhforxdiff = (fh.variables['vh_masked'][0:, zs:ze, ys:ye, xs - 1:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
h_cvforxdiff = (fh.variables['h_Cv'][0:, zs:ze, ys:ye, xs - 1:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
h_cvforxdiff[h_cvforxdiff < htol] = np.nan
vtwaforxdiff = vhforxdiff / h_cvforxdiff #/dxcvforxdiff
vtwaforxdiff = np.concatenate(
(vtwaforxdiff, vtwaforxdiff[:, :, :, -1:]), axis=3)
vhforydiff = (
fh.variables['vh_masked'][0:, zs:ze, ys - 1:ye + 1, xs:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
h_cvforydiff = (fh.variables['h_Cv'][0:, zs:ze, ys - 1:ye + 1, xs:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
h_cvforydiff[h_cvforydiff < htol] = np.nan
vtwaforydiff = vhforydiff / h_cvforydiff #/dxcvforydiff
vtwax = np.diff(vtwaforxdiff, axis=3) / dxbu / dybu
vtwax = 0.5 * (vtwax[:, :, :, 0:-1] + vtwax[:, :, :, 1:])
vtway = np.diff(vtwaforydiff, axis=2) / dyt / dxt
vtway = 0.5 * (vtway[:, :, 0:-1, :] + vtway[:, :, 1:, :])
hvmy = np.diff(vhforydiff, axis=2) / dyt / dxt
hvmy = 0.5 * (hvmy[:, :, :-1, :] + hvmy[:, :, 1:, :])
uh = (fh.variables['uh_masked'][0:, zs:ze, ys:ye + 1, xs - 1:xe] *
dt).filled(0).sum(axis=0, keepdims=True) / np.sum(dt)
hum = 0.25 * (uh[:, :, :-1, :-1] + uh[:, :, :-1, 1:] +
uh[:, :, 1:, :-1] + uh[:, :, 1:, 1:]) / dycv
humx = np.diff(np.nan_to_num(uh), axis=3) / dxt / dyt
humx = 0.5 * (humx[:, :, :-1, :] + humx[:, :, 1:, :])
huvxphvvym = (
fh.variables['twa_huvxpt'][0:, zs:ze, ys:ye, xs:xe] * dt +
fh.variables['twa_hvvymt'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
hvv = (fh.variables['hvv_Cv'][0:, zs:ze, ys - 1:ye + 1, xs:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
hvvym = np.diff(hvv, axis=2) / dxt / dyt
hvvym = 0.5 * (hvvym[:, :, :-1, :] + hvvym[:, :, 1:, :])
# hvv = (fh.variables['hvv_T'][0:,zs:ze,ys:ye+1,xs:xe]*dt).sum(axis=0,keepdims=True)*dxt/np.sum(dt)
# hvvym = np.diff(hvv,axis=2)/dycv/dxcv
huvxm = -(huvxphvvym + hvvym)
# hvv = (fh.variables['hvv_Cv'][0:,zs:ze,ys-1:ye+1,xs:xe]*dt).sum(axis=0,keepdims=True)/np.sum(dt)
# hvvym = np.diff(hvv,axis=2)/dxt/dyt
# hvvym = 0.5*(hvvym[:,:,:-1,:] + hvvym[:,:,1:,:])
# huv = (fh.variables['huv_Bu'][0:,zs:ze,ys:ye,xs-1:xe].filled(0)*dt).sum(axis=0,keepdims=True)/np.sum(dt)
# huvxm = np.diff(huv,axis=3)/dxcv
vtwaforvdiff = np.concatenate((vtwa[:, [0], :, :], vtwa), axis=1)
vtwab = np.diff(vtwaforvdiff, axis=1) / db[:, np.newaxis, np.newaxis]
vtwab = np.concatenate(
(vtwab,
np.zeros([vtwab.shape[0], 1, vtwab.shape[2], vtwab.shape[3]])),
axis=1)
vtwab = 0.5 * (vtwab[:, 0:-1, :, :] + vtwab[:, 1:, :, :])
hwb = (fh2.variables['wd'][0:, zs:ze, ys:ye + 1, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
hwb = np.diff(hwb, axis=1)
hwb_v = 0.5 * (hwb[:, :, :-1, :] + hwb[:, :, 1:, :])
hwb = (fh2.variables['wd'][0:, zs:ze, ys:ye + 1, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
hwm = hwb_v * dbl[:, np.newaxis, np.newaxis]
hwm = 0.5 * (hwb[:, :-1] + hwb[:, 1:]) * dbl[:, np.newaxis, np.newaxis]
hwm_v = 0.5 * (hwm[:, :, :-1, :] + hwm[:, :, 1:, :])
#hwb_v = fh.variables['hwb_Cv'][0:,zs:ze,ys:ye,xs:xe]
#hwm_v = fh.variables['hw_Cv'][0:,zs:ze,ys:ye,xs:xe]
esq = (fh.variables['esq'][0:, zs:ze, ys:ye + 1, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
emforydiff = (fh2.variables['e'][0:, zs:ze, ys:ye + 1, xs:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
elmforydiff = 0.5 * (emforydiff[:, 0:-1, :, :] +
emforydiff[:, 1:, :, :])
edlsqm = (esq - elmforydiff**2)
edlsqmy = np.diff(edlsqm, axis=2) / dycv
hpfv = (fh.variables['twa_hpfv'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
pfvm = (fh2.variables['PFv'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
edpfvdmb = -hpfv + h_cv * pfvm - 0.5 * edlsqmy * dbl[:, np.newaxis,
np.newaxis]
hmfum = (fh.variables['twa_hmfu'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
hvwbm = (fh.variables['twa_hvwb'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
hdiffvm = (fh.variables['twa_hdiffv'][0:, zs:ze, ys:ye, xs:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
hdvdtviscm = (fh.variables['twa_hdvdtvisc'][0:, zs:ze, ys:ye, xs:xe] *
dt).sum(axis=0, keepdims=True) / np.sum(dt)
fh2.close()
fh.close()
advx = hum * vtwax / h_vm
advy = vtwa * vtway
advb = hwm_v * vtwab / h_vm
cor = hmfum / h_vm
pfvm = pfvm
xdivep1 = -huvxm / h_vm
xdivep2 = advx
xdivep3 = vtwa * humx / h_vm
xdivep = (xdivep1 + xdivep2 + xdivep3)
ydivep1 = -hvvym / h_vm
ydivep2 = advy
ydivep3 = vtwa * hvmy / h_vm
ydivep4 = -0.5 * edlsqmy * dbl[:, np.newaxis, np.newaxis] / h_vm
ydivep = (ydivep1 + ydivep2 + ydivep3 + ydivep4)
bdivep1 = hvwbm / h_vm
bdivep2 = advb
bdivep3 = vtwa * hwb_v / h_vm
bdivep4 = -edpfvdmb / h_vm
bdivep = (bdivep1 + bdivep2 + bdivep3 + bdivep4)
Y1twa = hdiffvm / h_vm
Y2twa = hdvdtviscm / h_vm
terms = np.concatenate(
(-advx[:, :, :, :, np.newaxis], -advy[:, :, :, :, np.newaxis],
-advb[:, :, :, :, np.newaxis], cor[:, :, :, :, np.newaxis],
pfvm[:, :, :, :, np.newaxis], xdivep[:, :, :, :, np.newaxis],
ydivep[:, :, :, :, np.newaxis], bdivep[:, :, :, :, np.newaxis],
Y1twa[:, :, :, :, np.newaxis], Y2twa[:, :, :, :, np.newaxis]),
axis=4)
termsep = np.concatenate(
(xdivep1[:, :, :, :, np.newaxis], xdivep3[:, :, :, :, np.newaxis],
ydivep1[:, :, :, :, np.newaxis], ydivep3[:, :, :, :, np.newaxis],
ydivep4[:, :, :, :, np.newaxis], bdivep1[:, :, :, :, np.newaxis],
bdivep3[:, :, :, :, np.newaxis], bdivep4[:, :, :, :, np.newaxis]),
axis=4)
termsm = np.nanmean(terms, meanax, keepdims=True)
termsepm = np.nanmean(termsep, meanax, keepdims=True)
X = dimv[keepax[1]]
Y = dimv[keepax[0]]
if 1 in keepax and not calledfrompv:
em = np.nanmean(em, axis=meanax, keepdims=True)
elm = np.nanmean(elm, axis=meanax, keepdims=True)
#z = np.linspace(-3000,0,100)
Y = z
P = getvaratzc5(termsm.astype(np.float32), z.astype(np.float32),
em.astype(np.float32))
Pep = getvaratzc5(termsepm.astype(np.float32),
z.astype(np.float32), em.astype(np.float32))
if fil3:
swash = np.nanmean(swash, meanax, keepdims=True)
swash = getvaratzc(swash.astype(np.float32),
z.astype(np.float32),
em.astype(np.float32)).squeeze()
else:
P = termsm.squeeze()
Pep = termsepm.squeeze()
if not calledfrompv:
np.savez('twamomy_complete_terms', X=X, Y=Y, P=P, Pep=Pep)
else:
npzfile = np.load('twamomy_complete_terms.npz')
X = npzfile['X']
Y = npzfile['Y']
P = npzfile['P']
Pep = npzfile['Pep']
return (X, Y, P, Pep, swash, em.squeeze())
def get_deddy_coeffs_fromflx(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs=0,
ze=None,
zlim=(-1500, 0),
percx=0,
percy=0,
nsqdep=False,
fil3=None,
swashperc=1):
fhgeo = dset(geofil)
fhvgeo = dset(vgeofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
db = fhvgeo.variables['g'][:]
fhvgeo.close()
dbi = np.append(db, 0)
zi = fh.variables['zi'][:]
zl = fh.variables['zl'][:]
dbl = np.diff(zi) * 9.8 / 1031
dt = fh.variables['average_DT'][:]
dt = dt[:, np.newaxis, np.newaxis, np.newaxis]
z = np.linspace(zlim[0], zlim[1], 100)
sl, dimv = rdp1.getslice(fh, xstart, xend, ystart, yend, yhyq='yq')
sl2d = sl[2:]
dycv = fhgeo.variables['dyCv'][sl2d]
slpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1), sl[3]]
f = fhgeo.variables['f'][sl2d]
e = (fh2.variables['e'][slpy] * dt).sum(axis=0,
keepdims=True) / dt.sum(axis=0)
eatv = 0.5 * (e[:, :, :-1, :] + e[:, :, 1:, :])
v, h = pv.getvtwa(fhgeo, fh, fh2, sl)
v = v[:, :, :, :-1]
h = h[:, :, :, :-1]
sig = h / dbl[:, np.newaxis, np.newaxis]
hi = 0.5 * (h[:, :-1] + h[:, 1:])
sigi = hi / dbi[1:-1, np.newaxis, np.newaxis]
vb = -np.diff(v, axis=1) / dbi[1:-1, np.newaxis, np.newaxis]
#vb = np.concatenate((vb[:,:1,:,:],vb,vb[:,-1:,:,:]),axis=1)
vi = np.concatenate((v[:, :1, :, :], v, -v[:, -1:, :, :]), axis=1)
vi = 0.5 * (vi[:, :-1, :, :] + vi[:, 1:, :, :])
vib = -np.diff(vi, axis=1) / dbl[:, np.newaxis, np.newaxis]
if nsqdep:
fsqvb = f**2 * vb * sigi
else:
fsqvb = f**2 * vb
fsqvbaz = getvaratzc(fsqvb.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
esq = (fh.variables['esq'][slpy] * dt).sum(axis=0,
keepdims=True) / np.sum(dt)
elmforydiff = 0.5 * (e[:, 0:-1, :, :] + e[:, 1:, :, :])
edlsqm = (esq - elmforydiff**2)
edlsqmy = np.diff(edlsqm, axis=2) / dycv
hpfv = (fh.variables['twa_hpfv'][sl] * dt).sum(axis=0,
keepdims=True) / np.sum(dt)
pfvm = (fh2.variables['PFv'][sl] * dt).sum(axis=0,
keepdims=True) / np.sum(dt)
edpfvdmb = -(-hpfv + h * pfvm -
0.5 * edlsqmy * dbl[:, np.newaxis, np.newaxis])
fh.close()
fh2.close()
fhgeo.close()
zdpfvd = sint.cumtrapz(edpfvdmb, dx=-dbl[0], axis=1)
# zdpfvd = np.concatenate((np.zeros(zdpfvd[:,:1,:,:].shape),
# zdpfvd,
# np.zeros(zdpfvd[:,:1,:,:].shape)),axis=1)
# zdpfvd = 0.5*(zdpfvd[:,:-1,:,:]+zdpfvd[:,1:,:,:])
zdpfvdaz = getvaratzc(zdpfvd.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
fsqvbazm = np.apply_over_axes(np.nanmean, fsqvbaz, (0, 2))
zdpfvdazm = np.apply_over_axes(np.nanmean, zdpfvdaz, (0, 2))
fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 3))
cmax = np.fabs(np.percentile(zdpfvdazm, (1, 99))).max()
im = ax[0].pcolormesh(dimv[3],
z,
zdpfvdazm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[0])
cmax = np.fabs(np.percentile(fsqvbazm, (1, 99))).max()
im = ax[1].pcolormesh(dimv[3],
z,
fsqvbazm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[1])
if fil3:
fh3 = mfdset(fil3)
slmxtn = np.s_[-1:, sl[1], sl[2], (sl[3].start - 1):sl[3].stop]
islayerdeep0 = fh3.variables['islayerdeep'][-1:, 0, 0, 0]
islayerdeep = fh3.variables['islayerdeep'][slmxtn]
if np.ma.is_masked(islayerdeep):
islayerdeep = islayerdeep.filled(np.nan)
islayerdeep[:, :, :, -1:] = islayerdeep[:, :, :, -2:-1]
swash = (islayerdeep0 - islayerdeep) / islayerdeep0 * 100
swash = 0.5 * (swash[:, :, :, :-1] + swash[:, :, :, 1:])
fh3.close()
swash = getvaratzc(swash.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
swash = np.apply_over_axes(np.nanmean, swash, (0, 2))
em = np.apply_over_axes(np.nanmean, e, (0, 2))
xx, zz = np.meshgrid(dimv[3], zi)
for axc in ax:
axc.contour(dimv[3],
z,
swash.squeeze(),
np.array([swashperc]),
colors='k')
axc.contour(xx, em.squeeze(), zz, ls='k--')
axc.set_ylim(zlim[0], zlim[1])
fig2, ax = plt.subplots(1, 1, sharey=True, figsize=(12, 3))
ax.linfit(fsqvbaz, zdpfvdaz, percx=percx, percy=percy)
ax.set_xlabel(r'$f^2 v_b$')
ax.set_ylabel(r'$\zeta^{\prime} m_y^{\prime}$')
return fig, fig2
def extract_pvterms(geofil,
fil,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
savfil=None,
alreadysaved=False):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fh = mfdset(fil)
(xs, xe), (ys, ye), dimq = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xq',
yhyq='yq')
_, _, dimqp1 = rdp1.getdimsbyindx(fh,
xs,
xe,
ys - 1,
ye,
zs=zs,
ze=ze,
xhxq='xq',
yhyq='yq')
D, (ah, aq), (dxcu, dycu, dxcv, dycv, dxbu, dybu, dxt,
dyt) = rdp1.getgeombyindx(geofil, xs, xe, ys, ye)[0:3]
D_u = rdp1.getgeombyindx(geofil, xs, xe, ys - 1, ye)[0]
D_v = rdp1.getgeombyindx(geofil, xs - 1, xe, ys, ye)[0]
nt = dimq[0].size
omega = 2 * np.pi / 24 / 3600 + 2 * np.pi / 24 / 3600 / 365
R = 6378000
f_q = 2 * omega * np.sin(dimq[2] * np.pi / 180)
beta = 2 * omega * np.cos(dimq[2][None, :, None] * np.pi / 180) / R
t0 = time.time()
frhatu = fh.variables['frhatu'][0:1, zs:ze, ys - 1:ye, xs:xe]
frhatv = fh.variables['frhatv'][0:1, zs:ze, ys:ye, xs - 1:xe]
h_u = frhatu * D_u[np.newaxis, np.newaxis, :, :]
h_v = frhatv * D_v[np.newaxis, np.newaxis, :, :]
h_q = 0.25 * (h_u[:, :, 0:-1, :] + h_u[:, :, 1:, :] +
h_v[:, :, :, 0:-1] + h_v[:, :, :, 1:])
h = fh.variables['h'][0:1, zs:ze, ys:ye, xs:xe]
u = fh.variables['u'][0:1, zs:ze, ys:ye, xs - 1:xe]
v = fh.variables['v'][0:1, zs:ze, ys - 1:ye, xs:xe]
uh = fh.variables['uh'][0:1, zs:ze, ys:ye, xs - 1:xe]
vh = fh.variables['vh'][0:1, zs:ze, ys - 1:ye, xs:xe]
wd = fh.variables['wd'][0:1, zs:ze, ys:ye, xs:xe]
cau = fh.variables['CAu'][0:1, zs:ze, ys:ye + 1, xs:xe]
gkeu = fh.variables['gKEu'][0:1, zs:ze, ys:ye + 1, xs:xe]
rvxv = fh.variables['rvxv'][0:1, zs:ze, ys:ye + 1, xs:xe]
pfu = fh.variables['PFu'][0:1, zs:ze, ys:ye + 1, xs:xe]
dudtvisc = fh.variables['du_dt_visc'][0:1, zs:ze, ys:ye + 1, xs:xe]
diffu = fh.variables['diffu'][0:1, zs:ze, ys:ye + 1, xs:xe]
cav = fh.variables['CAv'][0:1, zs:ze, ys:ye, xs:xe + 1]
gkev = fh.variables['gKEv'][0:1, zs:ze, ys:ye, xs:xe + 1]
rvxu = fh.variables['rvxu'][0:1, zs:ze, ys:ye, xs:xe + 1]
pfv = fh.variables['PFv'][0:1, zs:ze, ys:ye, xs:xe + 1]
diffv = fh.variables['diffv'][0:1, zs:ze, ys:ye, xs:xe + 1]
dvdtvisc = fh.variables['dv_dt_visc'][0:1, zs:ze, ys:ye, xs:xe + 1]
uadv = -gkeu - rvxv
uadv = np.ma.filled(uadv.astype(float), 0)
uadvym = h_q * (np.diff(uadv, axis=2) / dybu) / nt
vadv = -gkev - rvxu
vadv = np.ma.filled(vadv.astype(float), 0)
vadvxm = h_q * (diffx_bound(vadv, dxbu)) / nt
fv = cau - gkeu - rvxv
fv = np.ma.filled(fv.astype(float), 0)
fvym = h_q * (np.diff(fv, axis=2) / dybu) / nt
fu = -cav + gkev + rvxu
fu = np.ma.filled(fu.astype(float), 0)
fuxm = h_q * (diffx_bound(fu, dxbu)) / nt
Y1 = diffv
Y1 = np.ma.filled(Y1.astype(float), 0)
Y1xm = h_q * (diffx_bound(Y1, dxbu)) / nt
Y2 = dvdtvisc
Y2 = np.ma.filled(Y2.astype(float), 0)
Y2xm = h_q * (diffx_bound(Y2, dxbu)) / nt
# hbetavm = (h_v*beta*v)/nt
u = np.ma.filled(u.astype(float), 0)
ux = np.diff(u, axis=3) / dxt
v = np.ma.filled(v.astype(float), 0)
vy = np.diff(v, axis=2) / dyt
uh = np.ma.filled(uh.astype(float), 0)
uh_x = np.diff(uh, axis=3) / ah
uhxm = f_q[np.newaxis, np.newaxis, :,
np.newaxis] * (uh_x - h * ux) / nt
vh = np.ma.filled(vh.astype(float), 0)
vh_y = np.diff(vh, axis=2) / ah
vhym = f_q[np.newaxis, np.newaxis, :,
np.newaxis] * (vh_y - h * vy) / nt
fdiapycm = f_q[np.newaxis, np.newaxis, :, np.newaxis] * np.diff(
wd, axis=1) / nt
if 1 in keepax:
em = fh.variables['e'][0:1, zs:ze, ys:ye, xs:xe] / nt
for i in range(1, nt):
frhatu = fh.variables['frhatu'][i:i + 1, zs:ze, ys - 1:ye, xs:xe]
frhatv = fh.variables['frhatv'][i:i + 1, zs:ze, ys:ye, xs - 1:xe]
h_u = frhatu * D_u[np.newaxis, np.newaxis, :, :]
h_v = frhatv * D_v[np.newaxis, np.newaxis, :, :]
h_q = 0.25 * (h_u[:, :, 0:-1, :] + h_u[:, :, 1:, :] +
h_v[:, :, :, 0:-1] + h_v[:, :, :, 1:])
h = fh.variables['h'][i:i + 1, zs:ze, ys:ye, xs:xe]
u = fh.variables['u'][i:i + 1, zs:ze, ys:ye, xs - 1:xe]
v = fh.variables['v'][i:i + 1, zs:ze, ys - 1:ye, xs:xe]
uh = fh.variables['uh'][i:i + 1, zs:ze, ys:ye, xs - 1:xe]
vh = fh.variables['vh'][i:i + 1, zs:ze, ys - 1:ye, xs:xe]
wd = fh.variables['wd'][i:i + 1, zs:ze, ys:ye, xs:xe]
cau = fh.variables['CAu'][i:i + 1, zs:ze, ys:ye + 1, xs:xe]
gkeu = fh.variables['gKEu'][i:i + 1, zs:ze, ys:ye + 1, xs:xe]
rvxv = fh.variables['rvxv'][i:i + 1, zs:ze, ys:ye + 1, xs:xe]
pfu = fh.variables['PFu'][i:i + 1, zs:ze, ys:ye + 1, xs:xe]
dudtvisc = fh.variables['du_dt_visc'][i:i + 1, zs:ze, ys:ye + 1,
xs:xe]
diffu = fh.variables['diffu'][i:i + 1, zs:ze, ys:ye + 1, xs:xe]
cav = fh.variables['CAv'][i:i + 1, zs:ze, ys:ye, xs:xe + 1]
gkev = fh.variables['gKEv'][i:i + 1, zs:ze, ys:ye, xs:xe + 1]
rvxu = fh.variables['rvxu'][i:i + 1, zs:ze, ys:ye, xs:xe + 1]
pfv = fh.variables['PFv'][i:i + 1, zs:ze, ys:ye, xs:xe + 1]
diffv = fh.variables['diffv'][i:i + 1, zs:ze, ys:ye, xs:xe + 1]
dvdtvisc = fh.variables['dv_dt_visc'][i:i + 1, zs:ze, ys:ye,
xs:xe + 1]
uadv = -gkeu - rvxv
uadv = np.ma.filled(uadv.astype(float), 0)
uadvym += h_q * (np.diff(uadv, axis=2) / dybu) / nt
vadv = -gkev - rvxu
vadv = np.ma.filled(vadv.astype(float), 0)
vadvxm += h_q * (diffx_bound(vadv, dxbu)) / nt
fv = cau - gkeu - rvxv
fv = np.ma.filled(fv.astype(float), 0)
fvym += h_q * (np.diff(fv, axis=2) / dybu) / nt
fu = -cav + gkev + rvxu
fu = np.ma.filled(fu.astype(float), 0)
fuxm += h_q * (diffx_bound(fu, dxbu)) / nt
Y1 = diffv
Y1 = np.ma.filled(Y1.astype(float), 0)
Y1xm += h_q * (diffx_bound(Y1, dxbu)) / nt
Y2 = dvdtvisc
Y2 = np.ma.filled(Y2.astype(float), 0)
Y2xm += h_q * (diffx_bound(Y2, dxbu)) / nt
# hbetavm += (h_v*beta*v)/nt
u = np.ma.filled(u.astype(float), 0)
ux = np.diff(u, axis=3) / dxt
v = np.ma.filled(v.astype(float), 0)
vy = np.diff(v, axis=2) / dyt
uh = np.ma.filled(uh.astype(float), 0)
uh_x = np.diff(uh, axis=3) / ah
uhxm += f_q[np.newaxis, np.newaxis, :,
np.newaxis] * (uh_x - h * ux) / nt
vh = np.ma.filled(vh.astype(float), 0)
vh_y = np.diff(vh, axis=2) / ah
vhym += f_q[np.newaxis, np.newaxis, :,
np.newaxis] * (vh_y - h * vy) / nt
fdiapycm += f_q[np.newaxis, np.newaxis, :, np.newaxis] * np.diff(
wd, axis=1) / nt
if 1 in keepax:
em += fh.variables['e'][i:i + 1, zs:ze, ys:ye, xs:xe] / nt
sys.stdout.write('\r' + str(int((i + 1) / nt * 100)) + '% done...')
sys.stdout.flush()
fh.close()
print('Time taken for reading: {}'.format(time.time() - t0))
terms = np.ma.concatenate(
(uadvym[:, :, :, :, np.newaxis], vadvxm[:, :, :, :, np.newaxis],
Y1xm[:, :, :, :, np.newaxis], Y2xm[:, :, :, :, np.newaxis],
fdiapycm[:, :, :, :, np.newaxis], uhxm[:, :, :, :, np.newaxis],
vhym[:, :, :, :, np.newaxis]),
axis=4)
termsm = np.ma.apply_over_axes(np.nanmean, terms, meanax)
X = dimq[keepax[1]]
Y = dimq[keepax[0]]
if 1 in keepax:
elm = 0.5 * (em[:, 0:-1, :, :] + em[:, 1:, :, :])
em = np.ma.apply_over_axes(np.mean, em, meanax)
elm = np.ma.apply_over_axes(np.mean, elm, meanax)
Y = elm.squeeze()
X = np.meshgrid(X, dimq[1])[0]
P = termsm.squeeze()
P = np.ma.filled(P.astype(float), np.nan)
X = np.ma.filled(X.astype(float), np.nan)
Y = np.ma.filled(Y.astype(float), np.nan)
np.savez('pv_terms', X=X, Y=Y, P=P)
else:
npzfile = np.load('pv_terms.npz')
X = npzfile['X']
Y = npzfile['Y']
P = npzfile['P']
return (X, Y, P)
def get_heddy_coeffs(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs=0,
ze=None,
z=np.linspace(-3000, 0, 100),
percx=0,
percy=0):
fhgeo = dset(geofil)
fhvgeo = dset(vgeofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
db = fhvgeo.variables['g'][:]
fhvgeo.close()
dbi = np.append(db, 0)
zi = fh.variables['zi'][:]
zl = fh.variables['zl'][:]
dbl = np.diff(zi) * 9.8 / 1031
dt = fh.variables['average_DT'][:]
dt = dt[:, np.newaxis, np.newaxis, np.newaxis]
sl, dimv = rdp1.getslice(fh, xstart, xend, ystart, yend, yhyq='yq')
slmx = np.s_[:, :, sl[2], (sl[3].start - 1):sl[3].stop]
slpx = np.s_[:, :, sl[2], (sl[3].start + 1):sl[3].stop]
slmxpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1),
(sl[3].start - 1):sl[3].stop]
slpy = np.s_[:, :, sl[2].start:(sl[2].stop + 1), sl[3]]
sl2d = sl[2:]
slmx2d = slmx[2:]
slpx2d = slpx[2:]
dxbu = fhgeo.variables['dxBu'][slmx2d]
dxcv = fhgeo.variables['dxCv'][sl2d]
v, _ = pv.getvtwa(fhgeo, fh, fh2, slmx)
vx = np.diff(v, axis=3) / dxbu
vxx = np.diff(vx, axis=3) / dxcv
e = (fh2.variables['e'][slpy] * dt).sum(axis=0, keepdims=True) / dt.sum(
axis=0, keepdims=True)
eatv = 0.5 * (e[:, :, :-1, :] + e[:, :, 1:, :])
vxx = getvaratzc(vxx.astype(np.float32), z.astype(np.float32),
eatv.astype(np.float32))
e = fh2.variables['e'][slmxpy]
eatvmx = 0.5 * (e[:, :, :-1, :] + e[:, :, 1:, :])
vaz = getvaratzc(v.astype(np.float32), z.astype(np.float32),
eatvmx.astype(np.float32))
vazx = np.diff(vaz, axis=3) / dxbu
vazxx = np.diff(vazx, axis=3) / dxcv
x, y, P, _, _ = py.extract_twamomy_terms(geofil, vgeofil, fil, fil2,
xstart, xend, ystart, yend, zs,
ze, (0, ))
uv = P[:, :, :, :, 5]
vvalid = vaz[:, :, :, 1:-1]
vazm = np.apply_over_axes(np.nanmean, vvalid, (0, 2))
vxxm = np.apply_over_axes(np.nanmean, vxx, (0, 2))
vazxxm = np.apply_over_axes(np.nanmean, vazxx, (0, 2))
uvm = np.apply_over_axes(np.nanmean, uv, (0, 2))
fig, ax = plt.subplots(1, 4, sharex=True, sharey=True, figsize=(12, 3))
cmax = np.fabs(np.percentile(vxxm, (1, 99))).max()
im = ax[0].pcolormesh(dimv[3],
z,
vxxm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[0])
cmax = np.fabs(np.percentile(vazxxm, (1, 99))).max()
im = ax[1].pcolormesh(dimv[3],
z,
vazxxm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[1])
cmax = np.fabs(np.percentile(vvalid, (0.5, 99.5))).max()
im = ax[2].pcolormesh(dimv[3],
z,
vazm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
fig.colorbar(im, ax=ax[2])
cmax = np.fabs(np.percentile(uvm, (0.5, 99.5))).max()
im = ax[3].pcolormesh(dimv[3],
z,
uvm.squeeze(),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r')
cb = fig.colorbar(im, ax=ax[3])
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
for axc in ax:
xdegtokm(axc, 0.5 * (ystart + yend))
axc.set_xlabel('x (km)')
axc.grid()
ax[0].set_ylabel('z (m)')
fig2, ax = plt.subplots(1, 2, sharey=True)
ax[0].linfit(vvalid, uv, percx=percx, percy=percy)
ax[0].set_xlabel('v (m/s)')
ax[0].set_ylabel('RS div (m/s2)')
ax[0].grid()
ax[1].linfit(vxx, uv, percx=percx, percy=percy)
ax[1].set_xlabel('vxx (1/ms)')
ax[1].grid()
return fig, fig2
def extract_ep_terms_pym6(initializer):
domain = Domain.Domain(initializer)
plot_loc = 'vl'
with mfdset(initializer.fil) as fh, mfdset(initializer.fil2) as fh2:
#initializer.z = np.linspace(-500, 0, 20)
e = (gv('e', domain, 'hi', fh2, fh, plot_loc=plot_loc)
.yep().read_array(
extend_kwargs={'method': 'mirror'}).move_to('vi'))
h = (gv('h_Cv', domain, plot_loc, fh2, fh, plot_loc=plot_loc)
.read_array(filled=0))
vr = (gv('vh',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc,
divisor='h_Cv').read_array(
divide_by_dx=True, filled=0))
hum = (gv('uh', domain, 'ul', fh2, fh, plot_loc=plot_loc).xsm().yep()
.read_array(
divide_by_dy=True,
filled=0,
extend_kwargs={'method': 'vorticity'})
.move_to('hl').move_to(plot_loc))
huvm = (gv('huv_Bu', domain, 'ql', fh2, fh, plot_loc=plot_loc).xsm()
.read_array(
filled=0,
extend_kwargs={'method': 'vorticity'}).move_to(plot_loc))
uv_e_flux = (huvm - vr * hum) / h
uv_e_flux = uv_e_flux.mean(axis=initializer.meanax).toz(initializer.z,
e=e)
ex = (gv('e', domain, 'hi', fh2, fh, plot_loc=plot_loc)
.xsm().xep().yep().read_array(extend_kwargs={'method': 'mirror'})
.ddx(3).move_to('hi').move_to('vi').move_to('vl'))
ex = ex.mean(axis=initializer.meanax).toz(initializer.z, e=e)
e1 = (gv('e', domain, 'hi', fh2, fh, plot_loc=plot_loc)
.yep().read_array(extend_kwargs={'method': 'mirror'})
.move_to('hl'))
esq = (gv('esq', domain, 'hl', fh2, fh, plot_loc=plot_loc)
.yep().read_array(extend_kwargs={'method': 'mirror'}))
edlsqm = esq - e1 * e1
edlsqmy = edlsqm.ddx(2)
hpfv = (gv('twa_hpfv', domain, plot_loc, fh2, fh, plot_loc=plot_loc)
.read_array(filled=0))
pfvm = (gv('PFv', domain, plot_loc, fh2, fh, plot_loc=plot_loc)
.read_array(filled=0))
edpfvdmb = -hpfv + h * pfvm - edlsqmy * domain.db * 0.5
edpfvdmb1 = copy.copy(edpfvdmb)
edpfvdmb1 = edpfvdmb1.mean(axis=initializer.meanax).toz(initializer.z,
e=e)
edpfvdmb = edpfvdmb / domain.db
edpfvdm = edpfvdmb.vert_integral()
edpfvdm = edpfvdm.mean(axis=initializer.meanax).toz(initializer.z, e=e)
flux_list = [uv_e_flux, edpfvdm]
lab = [
r"""$\widehat{u^{\prime \prime}v^{\prime \prime}}(\hat{i} + \bar{\zeta_{\tilde{x}}}\hat{k})$""",
r"""$\overline{\zeta^\prime m_{\tilde{y}}^\prime} \hat{k}$""",
r"""$\widehat{u^{\prime \prime}v^{\prime \prime}}(\hat{i} + \bar{\zeta_{\tilde{x}}}\hat{k}) +\overline{\zeta^\prime m_{\tilde{y}}^\prime} \hat{k}$"""
]
dz = np.diff(initializer.z)[0]
dx = np.radians(
np.diff(domain.lonh[ex._plot_slice[3, 0]:ex._plot_slice[3, 1]])[0]
) * 6378000 * np.cos(
np.radians(0.5 * (initializer.slat + initializer.nlat)))
fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(10, 5))
units = 'xy'
angles = 'xy'
q1 = ax[0].quiver(
domain.lonh[ex._plot_slice[3, 0]:ex._plot_slice[3, 1]],
initializer.z[::2],
uv_e_flux.values.squeeze()[::2] / dx * np.sqrt(dx**2 + dz**2),
uv_e_flux.values.squeeze()[::2] * ex.values.squeeze()[::2] / dz *
np.sqrt(dx**2 + dz**2),
units=units)
# angles=angles)
tx = ax[0].text(0.05, 0.1, lab[0], transform=ax[0].transAxes)
tx.set_fontsize(15)
tx.set_bbox(dict(facecolor='white', alpha=1, edgecolor='white'))
ax[0].quiverkey(
q1, 0.75, 1.05, 1e-2, r'$1e-2$', labelpos='E', coordinates='axes')
xdegtokm(ax[0], 0.5 * (initializer.slat + initializer.nlat))
ax[0].set_ylabel('z (m)')
q2 = ax[1].quiver(
domain.lonh[ex._plot_slice[3, 0]:ex._plot_slice[3, 1]],
initializer.z[::2],
np.zeros(edpfvdm.values.squeeze()[::2].shape),
edpfvdm.values.squeeze()[::2] / dx * np.sqrt(dx**2 + dz**2),
units=units)
# angles=angles)
tx = ax[1].text(0.05, 0.1, lab[1], transform=ax[1].transAxes)
tx.set_fontsize(15)
tx.set_bbox(dict(facecolor='white', alpha=1, edgecolor='white'))
ax[1].quiverkey(
q2, 0.75, 1.05, 1e-3, r'$1e-3$', labelpos='E', coordinates='axes')
xdegtokm(ax[1], 0.5 * (initializer.slat + initializer.nlat))
q3 = ax[2].quiver(
domain.lonh[ex._plot_slice[3, 0]:ex._plot_slice[3, 1]],
initializer.z[::2],
np.zeros(edpfvdm.values.squeeze()[::2].shape) +
uv_e_flux.values.squeeze()[::2] / dx * np.sqrt(dx**2 + dz**2),
(edpfvdm.values.squeeze()[::2] + uv_e_flux.values.squeeze()[::2] *
ex.values.squeeze()[::2]) / dz * np.sqrt(dx**2 + dz**2),
units=units)
# angles=angles)
tx = ax[2].text(0.05, 0.1, lab[2], transform=ax[2].transAxes)
tx.set_fontsize(15)
tx.set_bbox(dict(facecolor='white', alpha=1, edgecolor='white'))
ax[2].quiverkey(
q3, 0.75, 1.05, 1e-2, r'$1e-2$', labelpos='E', coordinates='axes')
xdegtokm(ax[2], 0.5 * (initializer.slat + initializer.nlat))
return fig
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as dset
from netCDF4 import MFDataset as mfdset
import readParams as rdp
geofil = 'ocean_geometry.nc'
fil = 'output__002?_01.nc'
D, (ah,aq), (dxcu,dycu,dxcv,dycv,dxbu,dybu,dxt,dyt), f = rdp.getgeom(geofil)
(xh,yh), (xq,yq), (zi,zl), time = rdp.getdims(fil)
fh = mfdset(fil)
e = fh.variables['e'][:,:,:,:]
h = fh.variables['h'][:,:,:,:]
fh.close()
e1 = np.swapaxes(e,0,1).reshape((e.shape[1],-1))
z = np.linspace(-np.max(D),-1,num=50)
rho = np.zeros((z.size,e1.shape[1]))
#rho[:] = np.NaN
for i in range(z.size):
t1 = e1[0:-1,:] - z[i]
t2 = e1[1:,:] - z[i]
tt = t1*t2
indices = np.nonzero(tt<=0)
rho[i,indices[1]]=zl[indices[0]]
rho = rho.reshape((z.size,e.shape[0],e.shape[2],e.shape[3])).swapaxes(0,1)
def extract_twamomy_terms_pym6(initializer):
domain = Domain.Domain(initializer)
plot_loc = 'vl'
with mfdset(initializer.fil) as fh, mfdset(initializer.fil2) as fh2:
h = (gv('h_Cv', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0))
vr = (gv('vh',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc,
divisor='h_Cv').read_array(divide_by_dx=True, filled=0))
vrx = (gv('vh',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc,
divisor='h_Cv').xsm().xep().read_array(
extend_kwargs={
'method': 'vorticity'
},
divide_by_dx=True,
filled=0).ddx(3).move_to(plot_loc))
vry = (gv('vh',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc,
divisor='h_Cv').ysm().yep().read_array(
extend_kwargs={
'method': 'symmetric'
},
divide_by_dx=True,
filled=0).ddx(2).move_to(plot_loc))
humx = (gv('uh', domain, 'ul', fh2, fh,
plot_loc=plot_loc).xsm().yep().read_array(
extend_kwargs={
'method': 'vorticity'
}, filled=0).ddx(3, div_by_area=True).move_to(plot_loc))
hum = (gv('uh', domain, 'ul', fh2, fh,
plot_loc=plot_loc).xsm().yep().read_array(
divide_by_dy=True,
filled=0,
extend_kwargs={
'method': 'vorticity'
}).move_to('hl').move_to(plot_loc))
hvmy = (gv('vh', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).ysm().yep().read_array(
filled=0, extend_kwargs={
'method': 'symmetric'
}).ddx(2, div_by_area=True).move_to(plot_loc))
huvxphvvym = (
(gv('twa_huvxpt', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0)) +
(gv('twa_hvvymt', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0)))
hvvym = (gv('hvv_Cv', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).ysm().yep().read_array(
extend_kwargs={
'method': 'symmetric'
}, filled=0).ddx(2,
div_by_area=True).move_to(plot_loc))
huvxm = -(huvxphvvym + hvvym)
vrb = (gv('vh',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc,
divisor='h_Cv').lsm().lep().read_array(
extend_kwargs={
'method': 'mirror'
},
divide_by_dx=True,
filled=0).ddx(1).move_to(plot_loc))
hwb = (gv('wd', domain, 'hi', fh2, fh,
plot_loc=plot_loc).yep().read_array(
filled=0, extend_kwargs={
'method': 'vorticity'
}).o1diff(1).move_to(plot_loc))
hwm = (gv('wd', domain, 'hi', fh2, fh,
plot_loc=plot_loc).yep().read_array(
filled=0, extend_kwargs={
'method': 'vorticity'
}).move_to('hl').move_to(plot_loc)) * domain.db
esq = (gv('esq', domain, 'hl', fh2, fh,
plot_loc=plot_loc).yep().read_array(
extend_kwargs={'method': 'mirror'}))
e = (gv('e', domain, 'hi', fh2, fh,
plot_loc=plot_loc).yep().read_array(extend_kwargs={
'method': 'mirror'
}).move_to('hl'))
edlsqm = esq - e * e
edlsqmy = edlsqm.ddx(2)
hpfv = (gv('twa_hpfv', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0))
pfvm = (gv('PFv', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0))
edpfvdmb = -hpfv + h * pfvm - edlsqmy * domain.db * 0.5
hmfum = (gv('twa_hmfu', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0))
hvwbm = (gv('twa_hvwb', domain, plot_loc, fh2, fh,
plot_loc=plot_loc).read_array(filled=0))
hdiffvm = (gv('twa_hdiffv',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc).read_array(filled=0))
hdvdtviscm = (gv('twa_hdvdtvisc',
domain,
plot_loc,
fh2,
fh,
plot_loc=plot_loc).read_array(filled=0))
advx = hum * vrx / h
advy = vr * vry
advb = hwm * vrb / h
cor = hmfum / h
pfvm = pfvm
xdivep1 = -huvxm / h
xdivep2 = advx
xdivep3 = vr * humx / h
xdivep = (xdivep1 + xdivep2 + xdivep3)
ydivep1 = -hvvym / h
ydivep2 = advy
ydivep3 = vr * hvmy / h
ydivep4 = -edlsqmy / 2 * domain.db / h
ydivep = (ydivep1 + ydivep2 + ydivep3 + ydivep4)
bdivep1 = hvwbm / h
bdivep2 = advb
bdivep3 = vr * hwb / h
bdivep4 = -edpfvdmb / h
bdivep = (bdivep1 + bdivep2 + bdivep3 + bdivep4)
Y1twa = hdiffvm / h
Y2twa = hdvdtviscm / h
budgetlist = [
-advx, -advy, -advb, cor, pfvm, xdivep, ydivep1 + ydivep2 + ydivep3,
ydivep4, bdivep1 + bdivep2 + bdivep3, bdivep4, Y1twa, Y2twa
]
ti = [
'(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)',
'(k)', '(l)'
]
lab = [
r'$-\hat{u}\hat{v}_{\tilde{x}}$', r'$-\hat{v}\hat{v}_{\tilde{y}}$',
r'$-\hat{\varpi}\hat{v}_{\tilde{b}}$', r'$-f\hat{u}$',
r'$-\overline{m_{\tilde{y}}}$',
r"""$-\frac{1}{\overline{h}}(\overline{h}\widehat{u^{\prime \prime}v^{\prime \prime}})_{\tilde{x}}$""",
r"""$-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}v^{\prime \prime}})_{\tilde{y}}$""",
r"""$-\frac{1}{2\overline{\zeta_{\tilde{b}}}}(\overline{\zeta^{\prime 2}})_{\tilde{y}}$""",
r"""$-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}\varpi ^{\prime \prime}})_{\tilde{b}}$""",
r"""$-\frac{1}{\overline{\zeta_{\tilde{b}}}}(\overline{\zeta^\prime m_{\tilde{y}}^\prime})_{\tilde{b}}$""",
r'$\widehat{Y^H}$', r'$\widehat{Y^V}$'
]
for i, var in enumerate(budgetlist):
var.name = lab[i]
return budgetlist
def extract_twamomx_terms(geofil,fil,xstart,xend,ystart,yend,zs,ze,meanax,
alreadysaved=False):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i,)
fh = mfdset(fil)
(xs,xe),(ys,ye),dimu = rdp1.getlatlonindx(fh,wlon=xstart,elon=xend,
slat=ystart, nlat=yend,zs=zs,ze=ze,xhxq='xq')
# _,(vys,vye),_ = rdp1.getlatlonindx(fh,wlon=xstart,elon=xend,
# slat=ystart, nlat=yend,zs=zs,ze=ze,yhyq='yq')
# (xs,xe),(ys,ye),_ = rdp1.getlatlonindx(fh,wlon=xstart,elon=xend,
# slat=ystart, nlat=yend,zs=zs,ze=ze)
D, (ah,aq) = rdp1.getgeombyindx(geofil,xs,xe,ys,ye)[0:2]
nt_const = dimu[0].size
t0 = time.time()
print('Reading data using loop...')
u = fh.variables['u'][0:1,zs:ze,ys:ye,xs:xe]
nt = np.ones(u.shape)*nt_const
frhatu = fh.variables['frhatu'][0:1,zs:ze,ys:ye,xs:xe]
h_u = frhatu*D[np.newaxis,np.newaxis,:,:]
h_u = np.ma.masked_array(h_u,mask=(h_u<=1e-3).astype(int))
nt[h_u<=1e-3] -= 1
cau = fh.variables['CAu'][0:1,zs:ze,ys:ye,xs:xe]
gkeu = fh.variables['gKEu'][0:1,zs:ze,ys:ye,xs:xe]
rvxv = fh.variables['rvxv'][0:1,zs:ze,ys:ye,xs:xe]
pfu = fh.variables['PFu'][0:1,zs:ze,ys:ye,xs:xe]
hagum = h_u*(cau - gkeu - rvxv + pfu)
hagum = hagum.filled(0)
hdudtviscm = h_u*fh.variables['du_dt_visc'][0:1,zs:ze,ys:ye,xs:xe]
hdudtviscm = hdudtviscm.filled(0)
hdiffum = h_u*fh.variables['diffu'][0:1,zs:ze,ys:ye,xs:xe]
hdiffum = hdiffum.filled(0)
dudtdia = fh.variables['dudt_dia'][0:1,zs:ze,ys:ye,xs:xe]
wd = fh.variables['wd'][0:1,zs:ze,ys:ye,xs:xe]
wd = np.diff(wd,axis=1)
wd = np.concatenate((wd,wd[:,:,:,-1:]),axis=3)
huwbm = (u*(wd[:,:,:,0:-1]+wd[:,:,:,1:])/2 - h_u*dudtdia)
huwbm = huwbm.filled(0)
uh = fh.variables['uh'][0:1,zs:ze,ys:ye,xs-1:xe]
uh = np.ma.filled(uh.astype(float), 0)
uhx = np.diff(uh,axis = 3)/ah
uhx = np.concatenate((uhx,uhx[:,:,:,-1:]),axis=3)
huuxpTm = (u*(uhx[:,:,:,0:-1]+uhx[:,:,:,1:])/2 - h_u*gkeu)
huuxpTm = huuxpTm.filled(0)
vh = fh.variables['vh'][0:1,zs:ze,ys-1:ye,xs:xe]
vh = np.ma.filled(vh.astype(float), 0)
vhy = np.diff(vh,axis = 2)/ah
vhy = np.concatenate((vhy,vhy[:,:,:,-1:]),axis=3)
huvymTm = (u*(vhy[:,:,:,0:-1]+vhy[:,:,:,1:])/2 - h_u*rvxv)
huvymTm = huvymTm.filled(0)
if 1 in keepax:
em = fh.variables['e'][0:1,zs:ze,ys:ye,xs:xe]/nt_const
for i in range(1,nt_const):
u = fh.variables['u'][i:i+1,zs:ze,ys:ye,xs:xe]
frhatu = fh.variables['frhatu'][i:i+1,zs:ze,ys:ye,xs:xe]
h_u = frhatu*D[np.newaxis,np.newaxis,:,:]
h_u = np.ma.masked_array(h_u,mask=(h_u<=1e-3).astype(int))
nt[h_u<=1e-3] -= 1
cau = fh.variables['CAu'][i:i+1,zs:ze,ys:ye,xs:xe]
gkeu = fh.variables['gKEu'][i:i+1,zs:ze,ys:ye,xs:xe]
rvxv = fh.variables['rvxv'][i:i+1,zs:ze,ys:ye,xs:xe]
pfu = fh.variables['PFu'][i:i+1,zs:ze,ys:ye,xs:xe]
hagum += (h_u*(cau - gkeu - rvxv + pfu)).filled(0)
hdudtviscm += (h_u*fh.variables['du_dt_visc'][i:i+1,zs:ze,
ys:ye,xs:xe]).filled(0)
hdiffum += (h_u*fh.variables['diffu'][i:i+1,zs:ze,
ys:ye,xs:xe]).filled(0)
dudtdia = fh.variables['dudt_dia'][i:i+1,zs:ze,ys:ye,xs:xe]
wd = fh.variables['wd'][i:i+1,zs:ze,ys:ye,xs:xe]
wd = np.diff(wd,axis=1)
wd = np.concatenate((wd,wd[:,:,:,-1:]),axis=3)
huwbm += ((u*(wd[:,:,:,0:-1]+wd[:,:,:,1:])/2 -
h_u*dudtdia)).filled(0)
uh = fh.variables['uh'][i:i+1,zs:ze,ys:ye,xs-1:xe]
uh = np.ma.filled(uh.astype(float), 0)
uhx = np.diff(uh,axis = 3)/ah
uhx = np.concatenate((uhx,uhx[:,:,:,-1:]),axis=3)
huuxpTm += ((u*(uhx[:,:,:,0:-1]+uhx[:,:,:,1:])/2 -
h_u*gkeu)).filled(0)
vh = fh.variables['vh'][i:i+1,zs:ze,ys-1:ye,xs:xe]
vh = np.ma.filled(vh.astype(float), 0)
vhy = np.diff(vh,axis = 2)/ah
vhy = np.concatenate((vhy,vhy[:,:,:,-1:]),axis=3)
huvymTm += ((u*(vhy[:,:,:,0:-1]+vhy[:,:,:,1:])/2 -
h_u*rvxv)).filled(0)
if 1 in keepax:
em += fh.variables['e'][i:i+1,zs:ze,ys:ye,xs:xe]/nt_const
sys.stdout.write('\r'+str(int((i+1)/nt_const*100))+'% done...')
sys.stdout.flush()
fh.close()
print('Time taken for data reading: {}s'.format(time.time()-t0))
terms = np.ma.concatenate(( hagum[:,:,:,:,np.newaxis],
-huwbm[:,:,:,:,np.newaxis],
-huuxpTm[:,:,:,:,np.newaxis],
-huvymTm[:,:,:,:,np.newaxis],
hdudtviscm[:,:,:,:,np.newaxis],
hdiffum[:,:,:,:,np.newaxis]),
axis=4)/nt[:,:,:,:,np.newaxis]
termsm = np.ma.apply_over_axes(np.nanmean, terms, meanax)
X = dimu[keepax[1]]
Y = dimu[keepax[0]]
if 1 in keepax:
elm = 0.5*(em[:,0:-1,:,:]+em[:,1:,:,:])
em = np.ma.apply_over_axes(np.mean, em, meanax)
elm = np.ma.apply_over_axes(np.mean, elm, meanax)
Y = elm.squeeze()
X = np.meshgrid(X,dimu[1])[0]
P = termsm.squeeze()
P = np.ma.filled(P.astype(float), np.nan)
X = np.ma.filled(X.astype(float), np.nan)
Y = np.ma.filled(Y.astype(float), np.nan)
np.savez('twamomx_terms', X=X,Y=Y,P=P)
else:
npzfile = np.load('twamomx_terms.npz')
X = npzfile['X']
Y = npzfile['Y']
P = npzfile['P']
return (X,Y,P)
def extract_twapv_terms(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
fil3=None,
calledfromgeteddycoeffs=False,
alreadysaved=False):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fhgeo = dset(geofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
zi = rdp1.getdims(fh)[2][0]
dbl = -np.diff(zi) * 9.8 / 1031
if not calledfromgeteddycoeffs:
(xs, xe), (ys, ye), dimq = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xq',
yhyq='yq')
else:
(xs, xe), (ys, ye), dimq = rdp1.getdimsbyindx(fh,
xstart,
xend,
ystart,
yend,
zs=zs,
ze=ze,
ts=0,
te=None,
xhxq='xq',
yhyq='yq')
sl = np.s_[:, zs:ze, ys:ye, xs:xe]
nt_const = dimq[0].size
pvhash, hq = getpv(fhgeo, fh, fh2, xs, xe, ys, ye)
slpy = np.s_[:, zs:ze, ys:ye + 1, xs:xe]
dxcu = fhgeo.variables['dxCu'][slpy[2:]]
dycv = fhgeo.variables['dyCv'][sl[2:]]
dycv = np.concatenate((dycv, dycv[:, -1:]), axis=1)
dxbu = fhgeo.variables['dxBu'][sl[2:]]
dybu = fhgeo.variables['dyBu'][sl[2:]]
aq = fhgeo.variables['Aq'][sl[2:]]
f = fhgeo.variables['f'][sl[2:]]
if fil3:
fh3 = mfdset(fil3)
sltn = np.s_[-1:, zs:ze, ys:ye, xs:xe]
islayerdeep0 = fh3.variables['islayerdeep'][-1:, 0, 0, 0]
islayerdeep = (fh3.variables['islayerdeep'][sltn].filled(np.nan))
swash = (islayerdeep0 - islayerdeep) / islayerdeep0 * 100
fh3.close()
else:
swash = None
xmom = extract_twamomx_terms(geofil,
vgeofil,
fil,
fil2,
xs,
xe,
ys,
ye + 1,
zs,
ze, (0, ),
alreadysaved=False,
xyasindices=True,
calledfrompv=True)[2]
ymom = extract_twamomy_terms(geofil,
vgeofil,
fil,
fil2,
xs,
xe,
ys,
ye,
zs,
ze, (0, ),
alreadysaved=False,
xyasindices=True,
calledfrompv=True)[2]
xmom = xmom[np.newaxis, :, :, :, :]
ymom = ymom[np.newaxis, :, :, :, :]
ymom = np.concatenate((ymom, ymom[:, :, :, -1:]), axis=3)
bxppvflx = np.sum(xmom[:, :, :, :, [0, 1, 3, 4]], axis=4)
pvhash1, _ = getpv(fhgeo, fh, fh2, xs, xe, ys - 1, ye + 1)
sl1 = np.s_[:, zs:ze, ys - 1:ye + 1, xs:xe]
vtwa1, h_cv1 = getvtwa(fhgeo, fh, fh2, sl1)
vtwa1 = 0.5 * (vtwa1[:, :, :, :-1] + vtwa1[:, :, :, 1:])
pvhashvtwa = pvhash1 * vtwa1
sl1 = np.s_[:, zs:ze, ys:ye + 1, xs:xe]
h_cu1 = fh.variables['h_Cu'][sl1].filled(np.nan).mean(axis=0,
keepdims=True)
h_cu1 = np.ma.masked_array(h_cu1, mask=(h_cu1 < 1e-3))
pvflxx = h_cu1 * (pvhashvtwa[:, :, :-1, :] +
pvhashvtwa[:, :, 1:, :]) / 2
byppvflx = np.sum(ymom[:, :, :, :, [0, 1, 3, 4]], axis=4)
pvhash1, _ = getpv(fhgeo, fh, fh2, xs - 1, xe, ys, ye)
sl1 = np.s_[:, zs:ze, ys:ye + 1, xs - 1:xe]
utwa1, h_cu1 = getutwa(fhgeo, fh, fh2, sl1)
utwa1 = 0.5 * (utwa1[:, :, :-1, :] + utwa1[:, :, 1:, :])
pvhashutwa = pvhash1 * utwa1
pvhashutwa[:, :, :, -1] = 0.0
sl1 = np.s_[:, zs:ze, ys:ye, xs:xe]
h_cv1 = fh.variables['h_Cv'][sl1].mean(axis=0, keepdims=True)
h_cv1 = np.ma.masked_array(h_cv1, mask=(h_cv1 < 1e-3))
pvflxy = h_cv1 * (pvhashutwa[:, :, :, :-1] +
pvhashutwa[:, :, :, 1:]) / 2
pvflxy = np.concatenate((pvflxy, pvflxy[:, :, :, -1:]), axis=3)
bx = bxppvflx - pvflxx
by = byppvflx + pvflxy
xmom1 = xmom[:, :, :, :, [2, 5, 6, 7, 8, 9]]
xmom1 = np.concatenate(
(+pvflxx[:, :, :, :, np.newaxis], xmom1, bx[:, :, :, :,
np.newaxis]),
axis=4)
ymom1 = ymom[:, :, :, :, [2, 5, 6, 7, 8, 9]]
ymom1 = np.concatenate(
(-pvflxy[:, :, :, :, np.newaxis], ymom1, by[:, :, :, :,
np.newaxis]),
axis=4)
#pv = (-np.diff(xmom*dxcu[:,:,np.newaxis],axis=2) + np.diff(ymom*dycv[:,:,np.newaxis],axis=3))/aq[:,:,np.newaxis]
pv = -np.diff(xmom, axis=2) / dybu[:, :, np.newaxis] + np.diff(
ymom, axis=3) / dxbu[:, :, np.newaxis]
pv1x = -np.diff(xmom1 * dxcu[:, :, np.newaxis],
axis=2) / aq[:, :, np.newaxis]
pv1y = np.diff(ymom1 * dycv[:, :, np.newaxis], axis=3) / aq[:, :,
np.newaxis]
slyp = np.s_[:, :, ys:ye + 1, xs:xe]
ah1 = fhgeo.variables['Ah'][slyp[2:]]
slxmyp = np.s_[:, :, ys:ye + 1, xs - 1:xe]
uh = fh2.variables['uh'][slxmyp].filled(0).mean(axis=0, keepdims=True)
uhx = np.diff(uh, axis=3) / ah1
uhx = np.concatenate((uhx, uhx[:, :, :, -1:]), axis=3)
uhx = 0.25 * (uhx[:, :, :-1, :-1] + uhx[:, :, :-1, 1:] +
uhx[:, :, 1:, :-1] + uhx[:, :, 1:, 1:])
pv1y[:, :, :, :, 0] += pvhash * uhx
slymp = np.s_[:, :, ys - 1:ye + 1, xs:xe]
vh = fh2.variables['vh'][slymp].mean(axis=0, keepdims=True)
vhy = np.diff(vh, axis=2) / ah1
vhy = np.concatenate((vhy, vhy[:, :, :, -1:]), axis=3)
vhy = 0.25 * (vhy[:, :, :-1, :-1] + vhy[:, :, :-1, 1:] +
vhy[:, :, 1:, :-1] + vhy[:, :, 1:, 1:])
pv1x[:, :, :, :, 0] += pvhash * vhy
wd = fh2.variables['wd'][slyp].mean(axis=0, keepdims=True)
wdb = np.diff(wd, axis=1)
wdb = np.concatenate((wdb, wdb[:, :, :, -1:]), axis=3)
wdb = 0.25 * (wdb[:, :, :-1, :-1] + wdb[:, :, :-1, 1:] +
wdb[:, :, 1:, :-1] + wdb[:, :, 1:, 1:])
pv3 = pvhash * wdb
pv3 = pv3[:, :, :, :, np.newaxis]
#hq[hq<1] = np.nan
pvnew = np.concatenate(
(pv1y[:, :, :, :, :1], pv1x[:, :, :, :, :1], pv3,
pv1x[:, :, :, :, 1:-1], pv1y[:, :, :, :, 1:-1],
pv1x[:, :, :, :, -1:] + pv1y[:, :, :, :, -1:]),
axis=4) / hq[:, :, :, :, np.newaxis]
pv = np.ma.filled(pv.astype(np.float64), np.nan)
pvnew = np.ma.filled(pvnew.astype(np.float64), np.nan)
pvhash = np.ma.filled(pvhash.astype(np.float64), np.nan)
pv = np.nanmean(pv, meanax, keepdims=True)
pvnew = np.nanmean(pvnew, meanax, keepdims=True)
pvhash = np.nanmean(pvhash, meanax, keepdims=True)
X = dimq[keepax[1]]
Y = dimq[keepax[0]]
if 1 in keepax and not calledfromgeteddycoeffs:
dt = fh.variables['average_DT'][:]
dt = dt[:, np.newaxis, np.newaxis, np.newaxis]
em = (fh2.variables['e'][0:, zs:ze, ys:ye, xs:xe] * dt).sum(
axis=0, keepdims=True) / np.sum(dt)
em = np.nanmean(em, meanax, keepdims=True)
z = np.linspace(-3000, 0, 100)
Y = z
P = getvaratzc5(pv.astype(np.float32), z.astype(np.float32),
em.astype(np.float32)).squeeze()
Pnew = getvaratzc5(pvnew.astype(np.float32), z.astype(np.float32),
em.astype(np.float32)).squeeze()
pvhash = getvaratzc(pvhash.astype(np.float32),
z.astype(np.float32),
em.astype(np.float32)).squeeze()
if fil3:
swash = np.nanmean(swash, meanax, keepdims=True)
swash = getvaratzc(swash.astype(np.float32),
z.astype(np.float32),
em.astype(np.float32)).squeeze()
else:
P = pv
Pnew = pvnew
fhgeo.close()
fh.close()
fh2.close()
return (X, Y, P, pvhash, Pnew, swash)
def extract_twamomx_terms(geofil,
vgeofil,
fil,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
alreadysaved=False,
xyasindices=False,
calledfrompv=False):
if not alreadysaved:
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fhvgeo = dset(vgeofil)
db = -fhvgeo.variables['g'][:]
dbi = np.append(db, 0)
fhvgeo.close()
fhgeo = dset(geofil)
fh = mfdset(fil)
zi = rdp1.getdims(fh)[2][0]
dbl = -np.diff(zi) * 9.8 / 1031
if xyasindices:
(xs, xe), (ys, ye) = (xstart, xend), (ystart, yend)
_, _, dimu = rdp1.getdimsbyindx(fh,
xs,
xe,
ys,
ye,
zs=zs,
ze=ze,
ts=0,
te=None,
xhxq='xq',
yhyq='yh',
zlzi='zl')
else:
(xs, xe), (ys, ye), dimu = rdp1.getlatlonindx(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze,
xhxq='xq')
D, (ah, aq) = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[0:2]
Dforgetutwaforxdiff = rdp1.getgeombyindx(fhgeo, xs - 1, xe, ys, ye)[0]
Dforgetutwaforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1,
ye + 1)[0]
Dforgethvforydiff = rdp1.getgeombyindx(fhgeo, xs, xe, ys - 1, ye)[0]
dxt, dyt = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[2][6:8]
dxcu = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye)[2][0]
dybu = rdp1.getgeombyindx(fhgeo, xs, xe, ys, ye + 1)[2][5]
nt_const = dimu[0].size
t0 = time.time()
print('Reading data using loop...')
h = fh.variables['h'][0:1, zs:ze, ys:ye, xs:xe]
u = fh.variables['u'][0:1, zs:ze, ys:ye, xs:xe]
nt = np.ones(u.shape) * nt_const
frhatu = fh.variables['frhatu'][0:1, zs:ze, ys:ye, xs:xe]
h_u = frhatu * D[np.newaxis, np.newaxis, :, :]
h_u = np.ma.masked_array(h_u, mask=(h <= 1e-3).astype(int))
nt[h <= 1e-3] -= 1
hum = (h_u * u).filled(0)
huum = (h_u * u * u).filled(0)
h_um = h_u.filled(0)
humforxdiff, h_umforxdiff, huumforxdiff = getutwaforxdiff(
fh, fhgeo, Dforgetutwaforxdiff, 0, xs - 1, xe, ys, ye, zs, ze)
humforydiff, h_umforydiff = getutwaforydiff(fh, fhgeo,
Dforgetutwaforydiff, 0, xs,
xe, ys - 1, ye + 1, zs, ze)
cau = fh.variables['CAu'][0:1, zs:ze, ys:ye, xs:xe]
gkeu = fh.variables['gKEu'][0:1, zs:ze, ys:ye, xs:xe]
rvxv = fh.variables['rvxv'][0:1, zs:ze, ys:ye, xs:xe]
hfvm = h_u * (cau - gkeu - rvxv).filled(0)
pfu = fh.variables['PFu'][0:1, zs:ze, ys:ye, xs:xe]
pfu = np.ma.masked_array(pfu, mask=(h <= 1e-3).astype(int))
pfum = pfu.filled(0)
hdudtviscm = (
h_u *
fh.variables['du_dt_visc'][0:1, zs:ze, ys:ye, xs:xe]).filled(0)
hdiffum = (h_u *
fh.variables['diffu'][0:1, zs:ze, ys:ye, xs:xe]).filled(0)
dudtdia = fh.variables['dudt_dia'][0:1, zs:ze, ys:ye, xs:xe]
wd = fh.variables['wd'][0:1, zs:ze, ys:ye, xs:xe]
wd = np.diff(wd, axis=1)
wd = np.concatenate((wd, wd[:, :, :, -1:]), axis=3)
wdm = 0.5 * (wd[:, :, :, :-1] + wd[:, :, :, 1:])
wdm = np.ma.masked_array(wdm, mask=(h <= 1e-3).astype(int))
wdm = wdm.filled(0)
huwbm = (u * (wd[:, :, :, 0:-1] + wd[:, :, :, 1:]) / 2 -
h_u * dudtdia).filled(0)
uh = fh.variables['uh'][0:1, zs:ze, ys:ye, xs - 1:xe]
uh = np.ma.filled(uh.astype(float), 0)
uhx = np.diff(uh, axis=3) / ah
uhx = np.concatenate((uhx, uhx[:, :, :, -1:]), axis=3)
huuxpTm = (u * (uhx[:, :, :, 0:-1] + uhx[:, :, :, 1:]) / 2 -
h_u * gkeu).filled(0)
vh = fh.variables['vh'][0:1, zs:ze, ys - 1:ye, xs:xe]
vh = np.ma.filled(vh.astype(float), 0)
vhy = np.diff(vh, axis=2) / ah
vhy = np.concatenate((vhy, vhy[:, :, :, -1:]), axis=3)
huvymTm = (u * (vhy[:, :, :, 0:-1] + vhy[:, :, :, 1:]) / 2 -
h_u * rvxv).filled(0)
v = fh.variables['v'][0:1, zs:ze, ys - 1:ye, xs:xe]
v = np.concatenate((v, -v[:, :, :, [-1]]), axis=3)
v = 0.25 * (v[:, :, 0:-1, 0:-1] + v[:, :, 1:, 0:-1] +
v[:, :, 0:-1, 1:] + v[:, :, 1:, 1:])
hvm = (h_u * v).filled(0)
hvmforydiff = gethvforydiff(fh, fhgeo, Dforgethvforydiff, 0, xs, xe,
ys - 1, ye, zs, ze)
wd = fh.variables['wd'][0:1, zs:ze, ys:ye, xs:xe]
wd = np.concatenate((wd, -wd[:, :, :, -1:]), axis=3)
hw = wd * dbi[:, np.newaxis, np.newaxis]
hw = 0.5 * (hw[:, 0:-1, :, :] + hw[:, 1:, :, :])
hwm_u = 0.5 * (hw[:, :, :, 0:-1] + hw[:, :, :, 1:])
hwm_u = np.ma.masked_array(hwm_u, mask=(h <= 1e-3).astype(int))
hwm_u = hwm_u.filled(0)
e = fh.variables['e'][0:1, zs:ze, ys:ye, xs:xe]
em = e / nt_const
el = 0.5 * (e[:, :-1, :, :] + e[:, 1:, :, :])
el = np.ma.masked_array(el, mask=(h <= 1e-3).astype(int))
elm = el.filled(0)
esq = el**2
esq = np.ma.masked_array(esq, mask=(h <= 1e-3).astype(int))
esqm = esq.filled(0)
el = np.concatenate((el, el[:, :, :, -1:]), axis=3)
el = 0.5 * (el[:, :, :, :-1] + el[:, :, :, 1:])
elmatu = el.filled(0)
epfum = (pfu * el).filled(0)
for i in range(1, nt_const):
h = fh.variables['h'][i:i + 1, zs:ze, ys:ye, xs:xe]
u = fh.variables['u'][i:i + 1, zs:ze, ys:ye, xs:xe]
frhatu = fh.variables['frhatu'][i:i + 1, zs:ze, ys:ye, xs:xe]
h_u = frhatu * D[np.newaxis, np.newaxis, :, :]
h_u = np.ma.masked_array(h_u, mask=(h <= 1e-3).astype(int))
nt[h <= 1e-3] -= 1
hum += (h_u * u).filled(0)
h_um += h_u.filled(0)
huforxdiff, h_uforxdiff, huuforxdiff = getutwaforxdiff(
fh, fhgeo, Dforgetutwaforxdiff, i, xs - 1, xe, ys, ye, zs, ze)
huforydiff, h_uforydiff = getutwaforydiff(fh, fhgeo,
Dforgetutwaforydiff, i,
xs, xe, ys - 1, ye + 1,
zs, ze)
humforxdiff += huforxdiff
h_umforxdiff += h_uforxdiff
huumforxdiff += huuforxdiff
humforydiff += huforydiff
h_umforydiff += h_uforydiff
cau = fh.variables['CAu'][i:i + 1, zs:ze, ys:ye, xs:xe]
gkeu = fh.variables['gKEu'][i:i + 1, zs:ze, ys:ye, xs:xe]
rvxv = fh.variables['rvxv'][i:i + 1, zs:ze, ys:ye, xs:xe]
hfv = h_u * (cau - gkeu - rvxv)
hfvm += hfv.filled(0)
pfu = fh.variables['PFu'][i:i + 1, zs:ze, ys:ye, xs:xe]
pfu = np.ma.masked_array(pfu, mask=(h <= 1e-3).astype(int))
pfum += pfu.filled(0)
hdudtvisc = h_u * fh.variables['du_dt_visc'][i:i + 1, zs:ze, ys:ye,
xs:xe]
hdudtviscm += hdudtvisc.filled(0)
hdiffu = h_u * fh.variables['diffu'][i:i + 1, zs:ze, ys:ye, xs:xe]
hdiffum += hdiffu.filled(0)
dudtdia = fh.variables['dudt_dia'][i:i + 1, zs:ze, ys:ye, xs:xe]
wd = fh.variables['wd'][i:i + 1, zs:ze, ys:ye, xs:xe]
wd = np.diff(wd, axis=1)
wd = np.concatenate((wd, wd[:, :, :, -1:]), axis=3)
huwb = (u * (wd[:, :, :, 0:-1] + wd[:, :, :, 1:]) / 2 -
h_u * dudtdia)
huwb = np.ma.masked_array(huwb, mask=(h_u <= 1e-3).astype(int))
huwbm += huwb.filled(0)
wd = 0.5 * (wd[:, :, :, :-1] + wd[:, :, :, 1:])
wd = np.ma.masked_array(wd, mask=(h <= 1e-3).astype(int))
wd = wd.filled(0)
wdm += wd
uh = fh.variables['uh'][i:i + 1, zs:ze, ys:ye, xs - 1:xe]
uh = np.ma.filled(uh.astype(float), 0)
uhx = np.diff(uh, axis=3) / ah
uhx = np.concatenate((uhx, uhx[:, :, :, -1:]), axis=3)
huuxpT = (u * (uhx[:, :, :, 0:-1] + uhx[:, :, :, 1:]) / 2 -
h_u * gkeu)
huuxpT = np.ma.masked_array(huuxpT, mask=(h_u <= 1e-3).astype(int))
huuxpTm += huuxpT.filled(0)
vh = fh.variables['vh'][i:i + 1, zs:ze, ys - 1:ye, xs:xe]
vh = np.ma.filled(vh.astype(float), 0)
vhy = np.diff(vh, axis=2) / ah
vhy = np.concatenate((vhy, vhy[:, :, :, -1:]), axis=3)
huvymT = (u * (vhy[:, :, :, 0:-1] + vhy[:, :, :, 1:]) / 2 -
h_u * rvxv)
huvymT = np.ma.masked_array(huvymT, mask=(h_u <= 1e-3).astype(int))
huvymTm += huvymT.filled(0)
v = fh.variables['v'][i:i + 1, zs:ze, ys - 1:ye, xs:xe]
v = np.concatenate((v, -v[:, :, :, [-1]]), axis=3)
v = 0.25 * (v[:, :, 0:-1, 0:-1] + v[:, :, 1:, 0:-1] +
v[:, :, 0:-1, 1:] + v[:, :, 1:, 1:])
hv = h_u * v
hvm += hv.filled(0)
hvmforydiff += gethvforydiff(fh, fhgeo, Dforgethvforydiff, i, xs,
xe, ys - 1, ye, zs, ze)
wd = fh.variables['wd'][i:i + 1, zs:ze, ys:ye, xs:xe]
wd = np.concatenate((wd, -wd[:, :, :, -1:]), axis=3)
hw = wd * dbi[:, np.newaxis, np.newaxis]
hw = 0.5 * (hw[:, 0:-1, :, :] + hw[:, 1:, :, :])
hw_u = 0.5 * (hw[:, :, :, 0:-1] + hw[:, :, :, 1:])
hw_u = np.ma.masked_array(hw_u, mask=(h <= 1e-3).astype(int))
hw_u = hw_u.filled(0)
hwm_u += hw_u
e = fh.variables['e'][i:i + 1, zs:ze, ys:ye, xs:xe]
em += e / nt_const
el = 0.5 * (e[:, :-1, :, :] + e[:, 1:, :, :])
el = np.ma.masked_array(el, mask=(h <= 1e-3).astype(int))
elm += el.filled(0)
esq = el**2
esqm += esq.filled(0)
el = np.concatenate((el, el[:, :, :, -1:]), axis=3)
el = 0.5 * (el[:, :, :, :-1] + el[:, :, :, 1:])
elmatu += el.filled(0)
epfum += (pfu * el).filled(0)
sys.stdout.write('\r' + str(int((i + 1) / nt_const * 100)) +
'% done...')
sys.stdout.flush()
fhgeo.close()
fh.close()
print('Time taken for data reading: {}s'.format(time.time() - t0))
utwa = hum / h_um
utwaforxdiff = humforxdiff / h_umforxdiff
utwaforydiff = humforydiff / h_umforydiff
utwaforxdiff[np.isnan(utwaforxdiff)] = 0
utwax = np.diff(utwaforxdiff, axis=3) / dxt
utwax = np.concatenate((utwax, -utwax[:, :, :, [-1]]), axis=3)
utwax = 0.5 * (utwax[:, :, :, 0:-1] + utwax[:, :, :, 1:])
utway = np.diff(utwaforydiff, axis=2) / dybu
utway = 0.5 * (utway[:, :, 0:-1, :] + utway[:, :, 1:, :])
humx = np.diff(humforxdiff, axis=3) / dxt
humx = np.concatenate((humx, -humx[:, :, :, [-1]]), axis=3)
humx = 0.5 * (humx[:, :, :, 0:-1] + humx[:, :, :, 1:])
hvmy = np.diff(hvmforydiff, axis=2) / dyt
hvmy = np.concatenate((hvmy, -hvmy[:, :, :, [-1]]), axis=3)
hvmy = 0.5 * (hvmy[:, :, :, 0:-1] + hvmy[:, :, :, 1:])
huuxphuvym = huuxpTm + huvymTm
huuxm = np.diff(huumforxdiff, axis=3) / dxt
huuxm = np.concatenate((huuxm, -huuxm[:, :, :, [-1]]), axis=3)
huuxm = 0.5 * (huuxm[:, :, :, 0:-1] + huuxm[:, :, :, 1:])
huvym = huuxphuvym - huuxm
utwaforvdiff = np.concatenate((utwa[:, [0], :, :], utwa), axis=1)
utwab = np.diff(utwaforvdiff, axis=1) / db[:, np.newaxis, np.newaxis]
utwab = np.concatenate(
(utwab,
np.zeros([utwab.shape[0], 1, utwab.shape[2], utwab.shape[3]])),
axis=1)
utwab = 0.5 * (utwab[:, 0:-1, :, :] + utwab[:, 1:, :, :])
hwb_u = wdm
edlsqm = esqm / nt - (elm / nt)**2
edlsqm = np.concatenate((edlsqm, edlsqm[:, :, :, [-1]]), axis=3)
edlsqmx = np.diff(edlsqm, axis=3) / dxcu * nt
edpfudm = (epfum / nt - elmatu / nt * pfum / nt) * nt
edpfudmb = np.diff(edpfudm, axis=1)
# edpfudmb = np.diff(edpfudm,axis=1)/db[1:,np.newaxis,np.newaxis]
edpfudmb = np.concatenate(
(edpfudmb[:, :1, :, :], edpfudmb, edpfudmb[:, -1:, :, :]), axis=1)
edpfudmb = 0.5 * (edpfudmb[:, :-1, :, :] + edpfudmb[:, 1:, :, :])
advx = utwa * utwax
advy = hvm * utway / h_um
advb = hwm_u * utwab / h_um
cor = hfvm / h_um
pfum = pfum / nt
xdivep1 = huuxm / h_um
xdivep2 = -advx
xdivep3 = -utwa * humx / h_um
xdivep4 = 0.5 * edlsqmx * dbl[:, np.newaxis, np.newaxis] / h_um
xdivep = (xdivep1 + xdivep2 + xdivep3 + xdivep4)
ydivep1 = huvym / h_um
ydivep2 = -advy
ydivep3 = -utwa * hvmy / h_um
ydivep = (ydivep1 + ydivep2 + ydivep3)
bdivep1 = huwbm / h_um
bdivep2 = -advb
bdivep3 = -utwa * hwb_u / h_um
bdivep4 = edpfudmb / h_um
bdivep = (bdivep1 + bdivep2 + bdivep3 + bdivep4)
X1twa = hdiffum / h_um
X2twa = hdudtviscm / h_um
terms = np.ma.concatenate(
(-advx[:, :, :, :, np.newaxis], -advy[:, :, :, :, np.newaxis],
-advb[:, :, :, :, np.newaxis], cor[:, :, :, :, np.newaxis],
pfum[:, :, :, :, np.newaxis], -xdivep[:, :, :, :, np.newaxis],
-ydivep[:, :, :, :, np.newaxis], -bdivep[:, :, :, :, np.newaxis],
X1twa[:, :, :, :, np.newaxis], X2twa[:, :, :, :, np.newaxis]),
axis=4)
termsep = np.ma.concatenate(
(xdivep1[:, :, :, :, np.newaxis], xdivep3[:, :, :, :, np.newaxis],
xdivep4[:, :, :, :, np.newaxis], ydivep1[:, :, :, :, np.newaxis],
ydivep3[:, :, :, :, np.newaxis], bdivep1[:, :, :, :, np.newaxis],
bdivep3[:, :, :, :, np.newaxis], bdivep4[:, :, :, :, np.newaxis]),
axis=4)
terms[np.isinf(terms)] = np.nan
termsep[np.isinf(termsep)] = np.nan
termsm = np.ma.apply_over_axes(np.nanmean, terms, meanax)
termsepm = np.ma.apply_over_axes(np.nanmean, termsep, meanax)
elm = 0.5 * (em[:, :-1, :, :] + em[:, 1:, :, :])
X = dimu[keepax[1]]
Y = dimu[keepax[0]]
if 1 in keepax:
em = np.ma.apply_over_axes(np.mean, em, meanax)
elm = np.ma.apply_over_axes(np.mean, elm, meanax)
Y = elm.squeeze()
X = np.meshgrid(X, dimu[1])[0]
P = termsm.squeeze()
P = np.ma.filled(P.astype(float), np.nan)
Pep = termsepm.squeeze()
Pep = np.ma.filled(Pep.astype(float), np.nan)
X = np.ma.filled(X.astype(float), np.nan)
Y = np.ma.filled(Y.astype(float), np.nan)
if not calledfrompv:
np.savez('twamomx_complete_terms', X=X, Y=Y, P=P, Pep=Pep)
else:
npzfile = np.load('twamomx_complete_terms.npz')
X = npzfile['X']
Y = npzfile['Y']
P = npzfile['P']
Pep = npzfile['Pep']
return (X, Y, P, Pep)
def getuv(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
xyasindices=False):
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fhvgeo = dset(vgeofil)
db = -fhvgeo.variables['g'][:]
dbi = np.append(db, 0)
fhvgeo.close()
fhgeo = dset(geofil)
fh = mfdset(fil)
fh2 = mfdset(fil2)
zi = rdp1.getdims(fh)[2][0]
dbl = np.diff(zi) * 9.8 / 1031
slh, dimh = rdp1.getslice(fh,
wlon=xstart,
elon=xend,
slat=ystart,
nlat=yend,
zs=zs,
ze=ze)
ys, ye = slh[2].start, slh[2].stop
xs, xe = slh[3].start, slh[3].stop
slhmx = np.s_[:, zs:ze, ys:ye, xs - 1:xe]
slhmy = np.s_[:, zs:ze, ys - 1:ye, xs:xe]
slhmpy = np.s_[:, zs:ze, ys - 1:ye + 1, xs:xe]
slu, dimu = rdp1.getslice(fh,
xstart,
xend,
ystart,
yend,
zs=zs,
ze=ze,
ts=0,
te=None,
xhxq='xq',
yhyq='yh',
zlzi='zl')
slv, dimv = rdp1.getslice(fh,
xstart,
xend,
ystart,
yend,
zs=zs,
ze=ze,
ts=0,
te=None,
xhxq='xh',
yhyq='yq',
zlzi='zl')
ys, ye = slv[2].start, slv[2].stop
xs, xe = slv[3].start, slv[3].stop
slvpy = np.s_[:, zs:ze, ys:ye + 1, xs:xe]
uh = getvaravg(fh2, 'uh', slu)
u = getvaravg(fh2, 'u', slu).filled(np.nan)
h_cu = getvaravg(fh, 'h_Cu', slu)
h_cu = np.ma.masked_array(h_cu, mask=(h_cu < 1e-3))
dycu = fhgeo.variables['dyCu'][slu[2:]]
utwa = uh / h_cu / dycu
vh = getvaravg(fh2, 'vh', slv)
v = getvaravg(fh2, 'v', slv)
h_cv = getvaravg(fh, 'h_Cv', slv)
h_cv = np.ma.masked_array(h_cv, mask=(h_cv < 1e-3))
dxcv = fhgeo.variables['dxCv'][slv[2:]]
vtwa = vh / dxcv / h_cv
emforxdiff = getvaravg(fh2, 'e', slhmx)
elmforxdiff = 0.5 * (emforxdiff[:, 0:-1, :, :] + emforxdiff[:, 1:, :, :])
elmforxdiff = np.concatenate((elmforxdiff, elmforxdiff[:, :, :, -1:]),
axis=3)
dxcuforxdiff = fhgeo.variables['dxCu'][slhmx[2:]]
ex = np.diff(elmforxdiff, axis=3) / dxcuforxdiff
uh = getvaravg(fh2, 'uh', slhmx)
h_cu = getvaravg(fh, 'h_Cu', slhmx)
h_cu = np.ma.masked_array(h_cu, mask=(h_cu < 1e-3))
dycu = fhgeo.variables['dyCu'][slhmx[2:]]
uzx = (uh / h_cu / dycu).filled(0) * ex
uzx = 0.5 * (uzx[:, :, :, 1:] + uzx[:, :, :, :-1])
emforydiff = getvaravg(fh2, 'e', slhmpy)
elmforydiff = 0.5 * (emforydiff[:, 0:-1, :, :] + emforydiff[:, 1:, :, :])
dycv = fhgeo.variables['dyCv'][slhmy[2:]]
ey = np.diff(elmforydiff, axis=2) / dycv
vh = getvaravg(fh2, 'vh', slhmy)
h_cv = getvaravg(fh, 'h_Cv', slhmy)
h_cv = np.ma.masked_array(h_cv, mask=(h_cv < 1e-3))
dxcv = fhgeo.variables['dxCv'][slhmy[2:]]
vtwa = vh / dxcv / h_cv
vzy = vtwa * ey
vtwa = 0.5 * (vtwa[:, :, 1:, :] + vtwa[:, :, :-1, :])
vzy = 0.5 * (vzy[:, :, 1:, :] + vzy[:, :, :-1, :])
wd = getvaravg(fh2, 'wd', slh)
hw = wd * dbi[:, np.newaxis, np.newaxis]
hw = 0.5 * (hw[:, 1:, :, :] + hw[:, :-1, :, :])
wzb = hw / dbl[:, np.newaxis, np.newaxis]
whash = uzx + vzy + wzb
terms = [utwa, vtwa, whash, u, v]
slices = [slu, slvpy, slh, slu, slvpy]
X = [
dimu[keepax[1]], dimv[keepax[1]], dimh[keepax[1]], dimu[keepax[1]],
dimv[keepax[1]]
]
Y = [
dimu[keepax[0]], dimv[keepax[0]], dimh[keepax[0]], dimu[keepax[1]],
dimv[keepax[1]]
]
termsm = []
for item in terms:
try:
item = item.filled(np.nan)
except AttributeError:
item = item
termsm.append(np.ma.apply_over_axes(np.nanmean, item, meanax))
if 1 in keepax:
Y = []
for i in range(len(terms)):
z = np.linspace(-3000, 0, 100)
em = fh2.variables['e'][slices[i]]
if i == 0 or i == 3:
em = np.concatenate((em, em[:, :, :, -1:]), axis=3)
em = 0.5 * (em[:, :, :, :-1] + em[:, :, :, 1:])
elif i == 1 or i == 4:
em = 0.5 * (em[:, :, :-1, :] + em[:, :, 1:, :])
em = np.ma.apply_over_axes(np.mean, em, meanax)
termsm[i] = getvaratzc(termsm[i].astype(np.float32),
z.astype(np.float32), em.astype(np.float32))
Y.append(z)
fh2.close()
fh.close()
fhgeo.close()
P = []
for i, item in enumerate(termsm):
P.append(item)
X[i] = np.ma.filled(X[i].astype(float), np.nan)
Y[i] = np.ma.filled(Y[i].astype(float), np.nan)
return X, Y, P
