def diagnostics(self, var, t):
""" should provide at least 'maxspeed' (for cfl determination) """
if t == 0.:
# since the flow is prescribed, compute it only at t=0
u = var.get('u')
v = var.get('v')
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
cst = self.mpitools.local_to_global([(maxu, 'max'), (ke, 'sum')])
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = cst[1] / self.area
self.diags['enstrophy'] = 0.
trac = var.get('tracer')
z, z2 = fd.computesumandnorm(self.msk, trac, self.nh)
cst = self.mpitools.local_to_global([(z, 'sum'), (z2, 'sum')])
z = cst[0] / self.area
z2 = cst[1] / self.area
self.diags['mean'] = z
self.diags['rms'] = np.sqrt(z2)
def diagnostics(self, var, t):
""" Integral diagnostics for the QG model
should provide at least 'maxspeed' (for cfl determination) """
u = var.get('u')
v = var.get('v')
trac = var.get('pv')
psi = var.get('psi')
fo.cornertocell(psi, self.ope.work)
psim, psi2 = fd.computesumandnorm(self.msk, self.ope.work, self.nh)
ape = 0.5 * psi2 / self.Rd**2
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
z, z2 = fd.computesumandnorm(self.msk, trac, self.nh)
cst = self.mpitools.local_to_global([(maxu, 'max'),
(ke, 'sum'),
(z, 'sum'),
(z2, 'sum'),
(ape, 'sum')])
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = cst[1] / self.area
self.diags['pv'] = cst[2] / self.area
self.diags['pv2'] = 0.5*cst[3] / self.area
self.diags['ape'] = cst[4] / self.area
self.diags['energy'] = (cst[1]+cst[4]) / self.area
def diagnostics(self, var, t):
""" should provide at least 'maxspeed' (for cfl determination) """
nh = self.nh
u = var.get('u')
v = var.get('v')
vort = var.get('vorticity')
buoy = var.get('buoyancy')
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
z, z2 = fd.computesumandnorm(self.msk, vort, self.nh)
b, b2 = fd.computesumandnorm(self.msk, buoy, self.nh)
# potential energy (minus sign because buoyancy is minus density)
pe = -self.gravity * fd.computesum(self.msk, buoy * self.yr, nh)
cst = self.mpitools.local_to_global([(maxu, 'max'), (ke, 'sum'),
(z, 'sum'), (z2, 'sum'),
(pe, 'sum'), (b, 'sum'),
(b2, 'sum')])
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = cst[1] / self.area
self.diags['pe'] = cst[4] / self.area
self.diags['energy'] = (cst[1] + cst[4]) / self.area
self.diags['vorticity'] = cst[2] / self.area
self.diags['enstrophy'] = 0.5 * cst[3] / self.area
self.diags['buoyancy'] = cst[5] / self.area
self.diags['brms'] = np.sqrt(cst[6] / self.area -
(cst[5] / self.area)**2)
def diagnostics(self, var, t):
""" should provide at least 'maxspeed' (for cfl determination) """
nh = self.nh
u = var.get('u')
v = var.get('v')
vort = var.get('vorticity')
buoy = var.get('buoyancy')
V = var.get('V')
qE = var.get('qE')
qEneg = qE.copy()
qEneg[qE > 0] = 0.
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
z, z2 = fd.computesumandnorm(self.msk, vort, self.nh)
b, b2 = fd.computesumandnorm(self.msk, buoy, self.nh)
vm, v2 = fd.computesumandnorm(self.msk, V, self.nh)
q, q2 = fd.computesumandnorm(self.msk, qE, self.nh)
qn, qn2 = fd.computesumandnorm(self.msk, qEneg, self.nh)
# potential energy (minus sign because buoyancy is minus density)
pe = fd.computesum(self.msk, buoy * self.yr, nh)
pe = -self.gravity * pe
cst = self.mpitools.local_to_global([(maxu, 'max'), (ke, 'sum'),
(z, 'sum'), (z2, 'sum'),
(pe, 'sum'), (b, 'sum'),
(b2, 'sum'), (q, 'sum'),
(q2, 'sum'), (qn, 'sum'),
(qn2, 'sum'), (v2, 'sum')])
a = self.area
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = (cst[1]) / a
self.diags['keV'] = (0.5 * cst[11]) / a
self.diags['pe'] = cst[4] / a
self.diags[
'energy'] = self.diags['ke'] + self.diags['pe'] + self.diags['keV']
self.diags['vorticity'] = cst[2] / a
self.diags['enstrophy'] = 0.5 * cst[3] / a
bm = cst[5] / a
self.diags['buoyancy'] = bm
self.diags['brms'] = sqrt(cst[6] / a - bm**2)
pvm = cst[7] / a
pvneg_mean = cst[9] / a
self.diags['pv_mean'] = pvm
self.diags['pv_std'] = sqrt(cst[8] / a - pvm**2)
self.diags['pvneg_mean'] = pvneg_mean
self.diags['pvneg_std'] = sqrt(cst[10] / a - pvneg_mean**2)
def integrals(self, state):
u = state.get('u')
v = state.get('v')
vort = state.get('vorticity')
# u2,umax = computenormmaxu(self.msk,u,self.nh)
# v2,vmax = computenormmaxv(self.msk,v,self.nh)
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
# ke = computekewithpsi(self.msk,psi,vort,self.nh)
z, z2 = fd.computesumandnorm(self.msk, vort, self.nh)
# u2 = u2 / self.area
# v2 = v2 / self.area
ke = ke / self.area
z = z / self.area
z2 = z2 / self.area
if self.mpi == -1: # TODO
cst = np.zeros((6,))
cst[0] = u2
cst[1] = v2
cst[2] = z
cst[3] = z2
cst[4] = umax
cst[5] = vmax
cst_glo = np.array(MPI.COMM_WORLD.allgather(cst))
u2, v2, z, z2, umax, vmax = (
0., 0., 0., 0., 0., 0.)
for k in range(self.npx*self.npy):
u2 += cst_glo[k][0]
v2 += cst_glo[k][1]
z += cst_glo[k][2]
z2 += cst_glo[k][3]
umax = max(umax, cst_glo[k][4])
vmax = max(vmax, cst_glo[k][5])
# todo: add potential energy for the Boussinesq model
self.ke = ke # 0.5*(u2+v2)
self.vorticity = z
self.enstrophy = 0.5*z2
# self.umax = umax
# self.vmax = vmax
self.maxspeed = maxu # max([umax,vmax])
def diagnostics(self, var, t):
""" Integral diagnostics for the QG model
should provide at least 'maxspeed' (for cfl determination) """
u = var.get('u')
v = var.get('v')
trac = var.get('pv')
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
z, z2 = fd.computesumandnorm(self.msk, trac, self.nh)
cst = self.mpitools.local_to_global([(maxu, 'max'),
(ke, 'sum'),
(z, 'sum'),
(z2, 'sum')])
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = cst[1] / self.area
self.diags['pv'] = cst[2] / self.area
self.diags['pv2'] = cst[3] / self.area
def diagnostics(self, var, t):
""" should provide at least 'maxspeed' (for cfl determination) """
self.timers.tic('diag')
u = var.get('u')
v = var.get('v')
trac = var.get('vorticity')
psi = var.get('psi')
source = self.var.get('source')
xr = self.xr
yr = self.yr
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
# ke = fd.computekewithpsi(self.msk, trac, psi, self.nh)
z, z2 = fd.computesumandnorm(self.msk, trac, self.nh)
px = fd.computedotprod(self.msk, trac, xr, self.nh)
py = fd.computedotprod(self.msk, trac, yr, self.nh)
angmom = fd.computesum(self.msk, psi, self.nh)
sce = fd.computedotprod(self.msk, trac, source, self.nh)
cst = self.mpitools.local_to_global([(maxu, 'max'), (ke, 'sum'),
(z, 'sum'), (z2, 'sum'),
(px, 'sum'), (py, 'sum'),
(angmom, 'sum'), (sce, 'sum')])
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = cst[1] / self.area
self.diags['vorticity'] = cst[2] / self.area
self.diags['enstrophy'] = 0.5 * cst[3] / self.area
self.diags['px'] = cst[4] / self.area
self.diags['py'] = cst[5] / self.area
self.diags['angmom'] = cst[6] / self.area
self.diags['source'] = cst[7] / self.area
self.timers.toc('diag')
def diagnostics(self, var, t):
"""Integral diagnostics for the SQG model
should provide at least 'maxspeed' (for cfl determination)
WARNING: the diag for KE and APE are wrong. A SQG flow is 3D
the integration should be carried over in the Fourier space
The total PV should be conserved (in the absence of external
forcing) and the enstrophy should be conserved as long as the
flow is not developping filaments (grid-scale enstrophy). The
numerical dissipation wipes out Filaments which yields
enstrophy dissipation.
"""
u = var.get('u')
v = var.get('v')
trac = var.get('pv')
psi = var.get('psi')
#fo.cornertocell(psi, self.ope.work)
#psim, psi2 = fd.computesumandnorm(self.msk, self.ope.work, self.nh)
#ape = 0.5 * psi2 / self.Rd**2
ke, maxu = fd.computekemaxu(self.msk, u, v, self.nh)
z, z2 = fd.computesumandnorm(self.msk, trac, self.nh)
cst = self.mpitools.local_to_global([(maxu, 'max'), (ke, 'sum'),
(z, 'sum'), (z2, 'sum')])
self.diags['maxspeed'] = cst[0]
self.diags['ke'] = cst[1] / self.area
self.diags['pv'] = cst[2] / self.area
self.diags['pv2'] = 0.5 * cst[3] / self.area