def plot_cb(geofil,fil,xstart,xend,ystart,yend,zs,ze,meanax,
savfil=None,perc=99):
X,Y,P = extract_cb_terms(geofil,fil,xstart,xend,ystart,yend,zs,ze,meanax)
cmax = np.nanpercentile(np.absolute(P),perc)
fig,axc = plt.subplots(1,3,sharex=True,sharey=True,figsize=(10, 3))
ti = ['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)','(j)']
lab = [ r'$(\bar{h}\hat{u})_{\tilde{x}}$',
r'$(\bar{h}\hat{v})_{\tilde{y}}$',
r'$(\bar{h}\hat{\varpi})_{\tilde{b}}$']
for i in range(P.shape[-1]):
ax = axc.ravel()[i]
im = m6plot((X,Y,P[:,:,i]),ax,vmax=cmax,vmin=-cmax,#ptype='imshow',
txt=lab[i], ylim=(-2500,0),cmap='RdBu_r',cbar=False)
xdegtokm(ax,0.5*(ystart+yend))
if i == 0:
ax.set_ylabel('z (m)')
#ax.set_ylim(-1500,0)
fig.tight_layout()
cb = fig.colorbar(im, ax=axc.ravel().tolist())
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if savfil:
plt.savefig(savfil+'.eps', dpi=300, facecolor='w', edgecolor='w',
format='eps', transparent=False, bbox_inches='tight')
else:
im = m6plot((X,Y,np.sum(P,axis=2)),vmax=cmax,vmin=-cmax,cmap='RdBu_r')
plt.show()
def plot_momx(geofil,
fil,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
savfil=None,
alreadysaved=False):
X, Y, P = extract_momx_terms(geofil, fil, xstart, xend, ystart, yend, zs,
ze, meanax, alreadysaved)
cmax = np.nanmax(np.absolute(P))
fig = plt.figure(figsize=(12, 9))
ti = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)']
lab = [
r'$\overline{(f+\zeta)v-(KE)_{\tilde{x}}}$',
r'$-\overline{\varpi u_{\tilde{b}}}$', r'$-\overline{m_{\tilde{x}}}$',
r'$\overline{X^H}$', r'$\overline{X^V}$'
]
for i in range(P.shape[-1]):
ax = plt.subplot(5, 2, i + 1)
im = m6plot((X, Y, P[:, :, i]),
ax,
vmax=cmax,
vmin=-cmax,
txt=lab[i],
ylim=(-2500, 0),
cmap='RdBu_r')
if i % 2:
ax.set_yticklabels([])
else:
ax.set_ylabel('z (m)')
if i > 2:
xdegtokm(ax, 0.5 * (ystart + yend))
else:
ax.set_xticklabels([])
if savfil:
plt.savefig(savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
im = m6plot((X, Y, np.sum(P, axis=2)), Zmax=cmax, cmap='RdBu_r')
plt.show()
def plot_nt(geofil,vgeofil,fil,fil2,xstart,xend,ystart,yend,zs,ze,meanax,
cmaxscalefactor = 1, savfil=None):
X,Y,P,lr = extract_northward_transport(geofil,vgeofil,fil,fil2,xstart,xend,ystart,yend,zs,ze,meanax)
cmax = np.nanmax(np.absolute(P))*cmaxscalefactor
fig,ax = plt.subplots(1,2,sharey=True,figsize=(4,4))#,figsize=(8, 4))
ti = [r'$\int_{-D}^{0} (vdx) dz$ $(m^3s^{-1}) \forall v>0$',
r'$\int_{-D}^{0} (vdx) dz$ $(m^3s^{-1}) \forall v<0$']
for i in range(P.shape[-1]):
im = m6plot((X,Y,P[:,:,i]),ax[i],vmax=cmax,vmin=-cmax,ptype='imshow',
cmap='RdBu_r',cbar=False)
cs = ax[i].contour(X,Y,P[:,:,i],
colors='k',levels=[-4,-3,3,4])
cs.clabel(inline=True,fmt="%.3f")
ax[i].plot(-np.nanmean(lr,axis=1),Y,lw=2,color='k')
ax[i].grid(True)
xdegtokm(ax[i],0.5*(ystart+yend))
ax[0].set_ylabel(r'y ($^{\circ}$)')
fig.tight_layout()
cb = fig.colorbar(im,ax=[ax[0],ax[1]])
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
# ax[2].plot(np.nansum(P[:,:,0],axis=1)/1e3,Y,lw=2,label='North',color='r')
# ax[2].plot(np.nansum(-P[:,:,1],axis=1)/1e3,Y,lw=2,label='South',color='b')
# ax[2].set_xlabel('EB transport ($10^3 m^3s^{-1}$)')
# #ax.text(0.1,0.05,r'$\int_{D}^{0} \int_{EB}^{} v dx dz$ ($m^3s^{-1}$)',transform=ax.transAxes)
# ax[2].grid(True)
# plt.legend(loc='best')
# ax = plt.subplot(1,4,1)
# ax.plot(np.nanmean(lr,axis=1),Y,lw=2,color='k')
# ax.set_xlabel(r'$L_R$ (km)')
# plt.ylabel(r'$y (^{\circ}$)')
# ax.grid(True)
# plt.tight_layout()
if savfil:
plt.savefig(savfil+'.eps', dpi=300, facecolor='w',
format='eps', transparent=True, bbox_inches='tight')
else:
return fig
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 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 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 plot_twamomy(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
fil3=None,
cmaxpercfactor=1,
cmaxpercfactorforep=1,
plotterms=[3, 4, 5, 6, 7, 8],
swashperc=1,
savfil=None,
savfilep=None,
alreadysaved=False):
X, Y, P, Pep, swash, em = extract_twamomy_terms(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
alreadysaved=alreadysaved,
fil3=fil3)
P = np.ma.masked_array(P, mask=np.isnan(P)).squeeze()
cmax = np.nanpercentile(P, [cmaxpercfactor, 100 - cmaxpercfactor])
cmax = np.max(np.fabs(cmax))
fig, ax = plt.subplots(np.int8(np.ceil(len(plotterms) / 2)),
2,
sharex=True,
sharey=True,
figsize=(10, 5.5))
ti = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)']
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"""$-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}v^{\prime \prime}}+\frac{1}{2}\overline{\zeta^{\prime 2}})_{\tilde{y}}$""",
r"""$-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}v^{\prime \prime}})_{\tilde{y}}$""",
#r"""$-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}\varpi ^{\prime \prime}} + \overline{\zeta^\prime m_{\tilde{y}}^\prime})_{\tilde{b}}$""",
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, p in enumerate(plotterms):
axc = ax.ravel()[i]
im = m6plot((X, Y, P[:, :, p]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
txt=lab[p],
ylim=(-2000, 0),
cmap='RdBu_r',
cbar=False)
if fil3:
cs = axc.contour(X,
Y,
swash,
np.array([swashperc]),
colors='grey',
linewidths=4)
cs = axc.contour(X,
Y,
P[:, :, p],
levels=[-2e-6, -1e-6, 1e-6, 2e-6],
colors='k',
linestyles='dashed')
cs.clabel(inline=True, fmt="%.0e")
cs1 = axc.plot(X, em[::4, :].T, 'k')
if i % 2 == 0:
axc.set_ylabel('z (m)')
if i > np.size(ax) - 3:
xdegtokm(axc, 0.5 * (ystart + yend))
fig.tight_layout()
cb = fig.colorbar(im, ax=ax.ravel().tolist())
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if savfil:
plt.savefig(savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
im = m6plot((X, Y, np.sum(P, axis=2)),
vmax=cmax,
vmin=-cmax,
ptype='imshow',
cmap='RdBu_r',
ylim=(-2500, 0))
if savfil:
plt.savefig(savfil + 'res.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
Pep = np.ma.masked_array(Pep, mask=np.isnan(Pep)).squeeze()
cmax = np.nanpercentile(Pep,
[cmaxpercfactorforep, 100 - cmaxpercfactorforep])
cmax = np.max(np.fabs(cmax))
lab = [
r'$-\frac{(\overline{huv})_{\tilde{x}}}{\overline{h}}$',
r'$\frac{\hat{v}(\overline{hu})_{\tilde{x}}}{\overline{h}}$',
r'$-\frac{(\overline{hvv})_{\tilde{y}}}{\overline{h}}$',
r'$\frac{\hat{v}(\overline{hv})_{\tilde{y}}}{\overline{h}}$',
r"""-$\frac{1}{2\overline{h}}\overline{\zeta ^{\prime 2}}_{\tilde{y}}$""",
r'$-\frac{(\overline{hv\varpi})_{\tilde{b}}}{\overline{h}}$',
r'$\frac{\hat{v}(\overline{h\varpi})_{\tilde{b}}}{\overline{h}}$',
r"""$-\frac{(\overline{\zeta^\prime m_{\tilde{y}}^\prime})_{\tilde{b}}}{\overline{h}}$"""
]
fig, ax = plt.subplots(np.int8(np.ceil(Pep.shape[-1] / 2)),
2,
sharex=True,
sharey=True,
figsize=(12, 9))
for i in range(Pep.shape[-1]):
axc = ax.ravel()[i]
im = m6plot((X, Y, Pep[:, :, i]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
txt=lab[i],
cmap='RdBu_r',
ylim=(-2500, 0),
cbar=False)
if fil3:
cs = axc.contour(X,
Y,
swash,
np.array([swashperc]),
colors='grey',
linewidths=4)
cs = axc.contour(X,
Y,
P[:, :, p],
levels=[-2e-6, -1e-6, 1e-6, 2e-6],
colors='k')
cs.clabel(inline=True, fmt="%.0e")
if i % 2 == 0:
axc.set_ylabel('z (m)')
if i > np.size(ax) - 3:
xdegtokm(axc, 0.5 * (ystart + yend))
fig.tight_layout()
cb = fig.colorbar(im, ax=ax.ravel().tolist())
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if savfilep:
plt.savefig(savfilep + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
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 plot_twamomy(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
cmaxscalefactor=1,
cmaxscalefactorforep=1,
savfil=None,
savfilep=None,
alreadysaved=False):
X, Y, P, Pep = extract_twamomy_terms(geofil, vgeofil, fil, fil2, xstart,
xend, ystart, yend, zs, ze, meanax,
alreadysaved)
cmax = np.nanmax(np.absolute(P)) * cmaxscalefactor
fig = plt.figure(figsize=(12, 9))
ti = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)']
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"""$-\frac{1}{\overline{h}}(\widehat{u^{\prime \prime}v^{\prime \prime}})_{\tilde{x}}$""",
r"""$-\frac{1}{\overline{h}}(\widehat{v^{\prime \prime}v^{\prime \prime}}+\frac{1}{2}\overline{\zeta^{\prime 2}})_{\tilde{y}}$""",
r"""$-\frac{1}{\overline{h}}(\widehat{v^{\prime \prime}\varpi ^{\prime \prime}} + \overline{\zeta^\prime m_{\tilde{y}}^\prime})_{\tilde{b}}$""",
r'$\widehat{Y^H}$', r'$\widehat{Y^V}$'
]
for i in range(P.shape[-1]):
ax = plt.subplot(5, 2, i + 1)
im = m6plot((X, Y, P[:, :, i]),
ax,
vmax=cmax,
vmin=-cmax,
txt=lab[i],
ylim=(-2500, 0),
cmap='RdBu_r')
if i % 2:
ax.set_yticklabels([])
else:
ax.set_ylabel('z (m)')
if i > 7:
xdegtokm(ax, 0.5 * (ystart + yend))
else:
ax.set_xticklabels([])
if savfil:
plt.savefig(savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
if savfil:
im = m6plot((X, Y, np.sum(P, axis=2)),
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r',
ylim=(-2500, 0))
plt.savefig(savfil + 'res.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
lab = [
r'$-\frac{(\overline{huv})_{\tilde{x}}}{\overline{h}}$',
r'$\frac{\hat{v}(\overline{hu})_{\tilde{x}}}{\overline{h}}$',
r'$-\frac{(\overline{hvv})_{\tilde{y}}}{\overline{h}}$',
r'$\frac{\hat{v}(\overline{hv})_{\tilde{y}}}{\overline{h}}$',
r"""-$\frac{1}{2\overline{h}}\overline{\zeta ^{\prime 2}}_{\tilde{y}}$""",
r'$-\frac{(\overline{hv\varpi})_{\tilde{b}}}{\overline{h}}$',
r'$\frac{\hat{v}(\overline{h\varpi})_{\tilde{b}}}{\overline{h}}$',
r"""$-\frac{(\overline{\zeta^\prime m_{\tilde{y}}^\prime})_{\tilde{b}}}{\overline{h}}$"""
]
cmax = np.nanmax(np.absolute(Pep)) * cmaxscalefactorforep
plt.figure(figsize=(12, 8))
for i in range(Pep.shape[-1]):
ax = plt.subplot(4, 2, i + 1)
im = m6plot((X, Y, Pep[:, :, i]),
ax,
vmax=cmax,
vmin=-cmax,
txt=lab[i],
cmap='RdBu_r',
ylim=(-2500, 0))
if i % 2:
ax.set_yticklabels([])
else:
plt.ylabel('z (m)')
if i > 5:
xdegtokm(ax, 0.5 * (ystart + yend))
else:
ax.set_xticklabels([])
if savfilep:
plt.savefig(savfilep + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
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_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
def plot_uv(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
minperc=[1, 1, 0],
xyasindices=False):
X, Y, P = getuv(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
xyasindices=False)
P1 = P[3:]
P = P[:3]
fig, ax = plt.subplots(len(P), 1, sharex=True, sharey=True, figsize=(4, 6))
ti = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)']
lab = [r'$\widehat{u}$', r'$\widehat{v}$', r'$w^{\#}$']
for i in range(len(P)):
P[i] = np.ma.masked_array(P[i], mask=np.isnan(P[i]))
P[i] = P[i].squeeze()
cmax = np.nanpercentile(P[i], [minperc[i], 100 - minperc[i]])
cmax = np.max(np.fabs(cmax))
axc = ax.ravel()[i]
im = m6plot((X[i], Y[i], P[i]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
txt=lab[i],
ylim=(-2500, 0),
cmap='RdBu_r',
cbar=False)
cb = fig.colorbar(im, ax=axc)
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if i == len(P) - 1:
xdegtokm(axc, 0.5 * (ystart + yend))
axc.set_ylabel('z (m)')
fig1, ax = plt.subplots(len(P1),
1,
sharex=True,
sharey=True,
figsize=(4, 4))
ti = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)']
lab = [r'$\overline{u}$', r'$\overline{v}$', r'$w^{\#}$']
for i in range(len(P1)):
P1[i] = np.ma.masked_array(P1[i], mask=np.isnan(P[i]))
P1[i] = P1[i].squeeze()
cmax = np.nanpercentile(P1[i], [minperc[i], 100 - minperc[i]])
cmax = np.max(np.fabs(cmax))
axc = ax.ravel()[i]
im = m6plot((X[i], Y[i], P1[i]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
txt=lab[i],
ylim=(-2500, 0),
cmap='RdBu_r',
cbar=False)
cb = fig.colorbar(im, ax=axc)
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if i == len(P1) - 1:
xdegtokm(axc, 0.5 * (ystart + yend))
axc.set_ylabel('z (m)')
return fig, fig1
def plot_twapv(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
fil3=None,
plotterms=[0, 1, 10, 11, 12, 13],
swashperc=1,
cmaxpercfactor=1,
cmaxpercfactorpvhash=15,
cmaxpercfactorPnew=15,
savfil=None,
savfilep=None,
alreadysaved=False):
X, Y, P, pvhash, Pnew, swash = extract_twapv_terms(
geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
alreadysaved=alreadysaved,
fil3=fil3)
cmax = np.nanpercentile(P, [cmaxpercfactor, 100 - cmaxpercfactor])
cmax = np.max(np.fabs(cmax))
fig, ax = plt.subplots(np.int8(np.ceil(P.shape[-1] / 2)),
2,
sharex=True,
sharey=True,
figsize=(12, 9))
ti = [
'(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)',
'(k)', '(l)', '(m)', '(n)', '(o)', '(p)', '(q)', '(r)'
]
labx = [
r'$(\hat{u}\hat{u}_{\tilde{x}})_{\tilde{y}}$',
r'$(\hat{v}\hat{u}_{\tilde{y}})_{\tilde{y}}$',
r'$(\hat{\varpi}\hat{u}_{\tilde{b}})_{\tilde{y}}$',
r'$(-f\hat{v})_{\tilde{y}}$',
r'$(\overline{m_{\tilde{x}}})_{\tilde{y}}$',
r"""$(\frac{1}{\overline{h}}(\overline{h}\widehat{u^{\prime \prime}u^{\prime \prime}}+\frac{1}{2}\overline{\zeta^{\prime 2}})_{\tilde{x}})_{\tilde{y}}$""",
r"""$(\frac{1}{\overline{h}}(\overline{h}\widehat{u^{\prime \prime}v^{\prime \prime}})_{\tilde{y}}$""",
r"""$(\frac{1}{\overline{h}}(\overline{h}\widehat{u^{\prime \prime}\varpi^{\prime \prime}} + \overline{\zeta^{\prime}m_{\tilde{x}}^{\prime}})_{\tilde{b}})_{\tilde{y}}$""",
r'$(-\widehat{X^H})_{\tilde{y}}$', r'$(-\widehat{X^V})_{\tilde{y}}$'
]
laby = [
r'$(-\hat{u}\hat{v}_{\tilde{x}})_{\tilde{x}}$',
r'$(-\hat{v}\hat{v}_{\tilde{y}})_{\tilde{x}}$',
r'$(-\hat{\varpi}\hat{v}_{\tilde{b}})_{\tilde{x}}$',
r'$(-f\hat{u})_{\tilde{x}}$',
r'$(-\overline{m_{\tilde{y}}})_{\tilde{x}}$',
r"""$(-\frac{1}{\overline{h}}(\overline{h}\widehat{u^{\prime \prime}v^{\prime \prime}})_{\tilde{x}})_{\tilde{x}}$""",
r"""$(-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}v^{\prime \prime}}+\frac{1}{2}\overline{\zeta^{\prime 2}})_{\tilde{y}})_{\tilde{x}}$""",
r"""$(-\frac{1}{\overline{h}}(\overline{h}\widehat{v^{\prime \prime}\varpi^{\prime \prime}} + \overline{\zeta^{\prime}m_{\tilde{y}}^{\prime}})_{\tilde{b}})_{\tilde{x}}$""",
r'$(\widehat{Y^H})_{\tilde{x}}$', r'$(\widehat{Y^V})_{\tilde{x}}$'
]
for i in range(P.shape[-1]):
axc = ax.ravel()[i]
im = m6plot((X, Y, P[:, :, i]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
txt=labx[i] + ' + ' + laby[i],
ylim=(-2500, 0),
cmap='RdBu_r',
cbar=False)
if fil3:
cs = axc.contour(X, Y, swash, np.array([swashperc]), colors='k')
if i % 2 == 0:
axc.set_ylabel('z (m)')
if i > np.size(ax) - 3:
xdegtokm(axc, 0.5 * (ystart + yend))
fig.tight_layout()
cb = fig.colorbar(im, ax=ax.ravel().tolist())
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if savfil:
plt.savefig(savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
im = m6plot((X, Y, np.sum(P, axis=2)),
vmax=cmax,
vmin=-cmax,
ptype='imshow',
cmap='RdBu_r',
ylim=(-2500, 0))
if savfil:
plt.savefig(savfil + 'res.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
fig, ax = plt.subplots(np.int8(np.ceil(len(plotterms) / 2)),
2,
sharex=True,
sharey=True,
figsize=(12, 7))
cmaxpvhash = np.nanpercentile(
pvhash, [cmaxpercfactorpvhash, 100 - cmaxpercfactorpvhash])
cmaxpvhash = np.max(np.fabs(cmaxpvhash))
cmax = np.nanpercentile(Pnew,
[cmaxpercfactorPnew, 100 - cmaxpercfactorPnew])
cmax = np.max(np.fabs(cmax))
lab = [
r"$-\hat{u}\Pi^{\#}_{\tilde{x}}$",
r"$-\hat{v}\Pi^{\#}_{\tilde{y}}$",
r"$\Pi^{\#}(\bar{h} \hat{\varpi})_{\tilde{b}}$",
r"$\frac{(\hat{\varpi}\hat{u}_{\tilde{b}})_{\tilde{y}}}{\bar{h}}$",
r"""$\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{u^{\prime \prime}u^{\prime \prime}}+\frac{1}{2}\overline{\zeta^{\prime 2}})_{\tilde{x}})_{\tilde{y}}$""",
r"""$\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{u^{\prime \prime}v^{\prime \prime}})_{\tilde{y}})_{\tilde{y}}$""",
r"""$\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{u^{\prime \prime}\varpi^{\prime \prime}} + \overline{\zeta^{\prime}m_{\tilde{x}}^{\prime}})_{\tilde{b}})_{\tilde{y}}$""",
r'$-\frac{1}{\bar{h}}(\widehat{X^H})_{\tilde{y}}$',
r'$-\frac{1}{\bar{h}}(\widehat{X^V})_{\tilde{y}}$',
r'$-\frac{(\hat{\varpi}\hat{v}_{\tilde{b}})_{\tilde{x}}}{\bar{h}}$',
r"""$-\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{u^{\prime \prime}v^{\prime \prime}})_{\tilde{x}})_{\tilde{x}}$""",
#r"""$-\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{v^{\prime \prime}v^{\prime \prime}}+\frac{1}{2}\overline{\zeta^{\prime 2}})_{\tilde{y}})_{\tilde{x}}$""",
r"""$-\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{v^{\prime \prime}v^{\prime \prime}})_{\tilde{y}})_{\tilde{x}}$""",
#r"""$-\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\bar{h}\widehat{v^{\prime \prime}\varpi^{\prime \prime}} + \overline{\zeta^{\prime}m_{\tilde{y}}^{\prime}})_{\tilde{b}})_{\tilde{x}}$""",
r"""$-\frac{1}{\bar{h}}(\frac{1}{\bar{h}}(\overline{\zeta^{\prime}m_{\tilde{y}}^{\prime}})_{\tilde{b}})_{\tilde{x}}$""",
r'$\frac{1}{\bar{h}}(\widehat{Y^H})_{\tilde{x}}$',
r'$\frac{1}{\bar{h}}(\widehat{Y^V})_{\tilde{x}}$',
r'$B_{\tilde{x} \tilde{y}} - B_{\tilde{y} \tilde{x}}$'
]
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
for i, p in enumerate(plotterms):
axc = ax.ravel()[i]
im = m6plot((X, Y, Pnew[:, :, p]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
ylim=(-1200, 0),
txt=lab[p],
cmap='RdBu_r',
cbar=False)
im2 = axc.contour(X, Y, pvhash, np.logspace(-6, -5.5, 5), colors='k')
if fil3:
cs = axc.contour(X, Y, swash, np.array([swashperc]), colors='k')
if i % 2 == 0:
axc.set_ylabel('z (m)')
if i > np.size(ax) - 3:
xdegtokm(axc, 0.5 * (ystart + yend))
fig.tight_layout()
cb = fig.colorbar(im, ax=ax.ravel().tolist())
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if savfil:
plt.savefig(savfil + 'Pnew.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
im = m6plot((X, Y, np.sum(Pnew, axis=2)),
ptype='imshow',
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r',
ylim=(-2500, 0))
if savfil:
plt.savefig(savfil + 'Pnewres.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
cmax = np.nanpercentile(pvhash,
[cmaxpercfactorpvhash, 100 - cmaxpercfactorpvhash])
cmax = np.max(np.fabs(cmax))
im = m6plot((X, Y, pvhash),
ptype='imshow',
vmax=cmax,
vmin=-cmax,
cmap='RdBu_r',
ylim=(-2500, 0))
if fil3:
cs = axc.contour(X, Y, swash, np.array([swashperc]), colors='k')
if savfil:
plt.savefig(savfil + 'pvhash.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
def plot_buoy_budget(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
meanax,
minperc=0,
savfil=None,
z=np.linspace(-3000, 0, 100),
htol=1e-3,
fil3=None):
dims, P = extract_buoy_terms(geofil,
vgeofil,
fil,
fil2,
xstart,
xend,
ystart,
yend,
zs,
ze,
z=z,
htol=htol,
fil3=fil3)
keepax = ()
for i in range(4):
if i not in meanax:
keepax += (i, )
fig, ax = plt.subplots(int(np.ceil(P.shape[4] / 2)),
2,
sharex=True,
sharey=True,
figsize=(6, 4))
ti = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)', '(j)']
lab = [
r'$\widehat{u}b^{\#}_{x}$', r'$\widehat{v}b^{\#}_{y}$',
r'$w^{\#}b^{\#}_{z}$', r'$\widehat{\varpi}$'
]
cmax = np.nanpercentile(P.ravel(), [minperc, 100 - minperc])
cmax = np.max(np.fabs(cmax))
P = np.ma.masked_array(P, mask=np.isnan(P))
P = np.apply_over_axes(np.nanmean, P, meanax).squeeze()
X = dims[keepax[1]]
Y = dims[keepax[0]]
if 1 in keepax:
Y = z
for i in range(ax.size):
axc = ax.ravel()[i]
im = m6plot((X, Y, P[:, :, i]),
axc,
vmax=cmax,
vmin=-cmax,
ptype='imshow',
txt=lab[i],
cmap='RdBu_r',
cbar=False)
if i % 2 == 0:
axc.set_ylabel('z (m)')
if i >= np.size(ax) - 2:
xdegtokm(axc, 0.5 * (ystart + yend))
fig.tight_layout()
cb = fig.colorbar(im, ax=ax.ravel().tolist())
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
if savfil:
plt.savefig(savfil + '.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
im = m6plot((X, Y, np.sum(P, axis=2)),
vmax=cmax,
vmin=-cmax,
ptype='imshow',
cmap='RdBu_r',
ylim=(-2500, 0))
if savfil:
plt.savefig(savfil + 'res.eps',
dpi=300,
facecolor='w',
edgecolor='w',
format='eps',
transparent=False,
bbox_inches='tight')
else:
plt.show()
