def pvbudload(dirname):
import numpy as np
import fortranfile
from pylab import *
import pickle
import os
#ion()
from makefields import getrspo,getinitPsi,gethb
from operators import delsq,gradx,grady
from readjet2h import readjet2h
vardict=readjet2h(dirname)
locals().update(vardict)
[L,beta_dim,f0_dim,h1]=pickle.load(open('/data/ilebras/twolayer-jets-climate/pickles/dimize.p','r'))
U=beta_dim*L**2/beta1
gprime=f0_dim**2*L**2/h1/fr1
h2=f0_dim**2*L**2/gprime/fr2
f0_nd=f0_dim*L/U
#define x and y (m)
x=arange(xmin,xmax,(xmax-xmin)/n)*L
y=arange(ymin,ymax,(ymax-ymin)/m)*L
dx=(xmax-xmin)/real(n-1)
dy=(ymax-ymin)/real(m-1)
dx2=dx**2
t4dx=1.0/(8.0*dx)
t4dy=1.0/(8.0*dy)
betay_vec=beta1*y/L
betay=tile(betay_vec,(n,1))
predir='/data/ilebras/twolayer-jets-climate/'
rundir=predir+'testing/'+dirname+'/'
# --------load fields-------
from loadfuncs import load1
#mean pv fluxes in layer 1
mf1=load1('mf1.dat',rundir,n,m)
vm1=load1('vm1.dat',rundir,n,m)
um1=load1('um1.dat',rundir,n,m)
mfi=load1('mfi.dat',rundir,n,m)
#mean pv fluxes in layer 2
mf2=load1('mf2.dat',rundir,n,m)
vm2=load1('vm2.dat',rundir,n,m)
um2=load1('um2.dat',rundir,n,m)
betav1=beta1*vm1
mthi1=fr1*mfi
mpvf1=mf1+betav1+mthi1
# topographic term
hb=gethb(cm,cz,ymax,movedw,hw,hbmax,a2,n,m)
hby=grady(hb,t4dy,n,m)
topo=vm2*hby
# bottom drag
mz2=load1('z2m.dat',rundir,n,m)
bdrag=mz2/rdecayw
betav2=beta2*vm2
mthi2=-fr2*mfi
mpvf2=mf2+betav2+mthi2+topo+bdrag
#eddy pv fluxes in layer 1
ef1=load1('ef1.dat',rundir,n,m)
efi=load1('efi.dat',rundir,n,m)
#eddy pv fluxes in layer 2
ef2=load1('ef2.dat',rundir,n,m)
ef2[abs(ef2)>10**13]=0.0
ef2[isnan(ef2)]=0.0
ethi1=fr1*efi
epvf1=ef1+ethi1
ethi2=-fr2*efi
epvf2=ef2+ethi2
#viscosity and sponge drag
mz1=load1('z1m.dat',rundir,n,m)
visc1=-akap*delsq(mz1,dx2,n,m)
visc2=-akap*delsq(mz2,dx2,n,m)
#sponge drag
rspo=getrspo(nspo,mspo,rdecay,rdecayo,n,m,dt)
[p1init,p2init]=getinitPsi(m,n,y/L,gs1,g1,dw1,d1,as2,a2,movegs,movedw,nspo,mspo)
z1init=delsq(p1init,dx2,n,m)
z2init=delsq(p2init,dx2,n,m)
p1xinit=gradx(p1init,t4dx,n,m)
p1yinit=grady(p1init,t4dy,n,m)
p2xinit=gradx(p2init,t4dx,n,m)
p2yinit=grady(p2init,t4dy,n,m)
rspox=gradx(rspo,t4dx,n,m)
rspoy=grady(rspo,t4dy,n,m)
sponge1_del=rspo*(mz1-z1init)
sponge1_cross=rspox*(vm1-p1xinit)-rspoy*(um1+p1yinit)
sponge1=sponge1_del+sponge1_cross
sponge2_del=rspo*(mz2-z2init)
sponge2_cross=rspox*(vm2-p2xinit)-rspoy*(um2+p2yinit)
sponge2=sponge2_del+sponge2_cross
resid1=mpvf1+epvf1+visc1+sponge1
resid2=mpvf2+epvf2+visc2+sponge2
#return p1init,p2init,z1init,z2init,p1xinit,p1yinit,p2xinit,p2yinit,hb,hby
pickle.dump([mf1,betav1,mthi1,mpvf1,ef1,ethi1,epvf1,visc1,sponge1,resid1,mf2,betav2,mthi2,mpvf2,ef2,ethi2,epvf2,visc2,sponge2,topo,bdrag,resid2,hb,hby], open( predir+'pickles/pvbud/'+dirname+'_pvbud' , 'w'))
def pvfluxload(dirname):
import numpy as np
import fortranfile
from pylab import *
import pickle
import os
# ion()
# %matplotlib inline
from makefields import getrspo,getinitPsi,gethb
from operators import delsq,gradx,grady
from readjet2h import readjet2h
vardict=readjet2h(dirname)
locals().update(vardict)
[L,beta_dim,f0_dim,h1]=pickle.load(open('/data/ilebras/twolayer-jets-climate/pickles/dimize.p','r'))
U=beta_dim*L**2/beta1
gprime=f0_dim**2*L**2/h1/fr1
h2=f0_dim**2*L**2/gprime/fr2
f0_nd=f0_dim*L/U
#define x and y (m)
x=arange(xmin,xmax,(xmax-xmin)/n)*L
y=arange(ymin,ymax,(ymax-ymin)/m)*L
dx=(xmax-xmin)/real(n-1)
dy=(ymax-ymin)/real(m-1)
dx2=dx**2
t4dx=1.0/(8.0*dx)
t4dy=1.0/(8.0*dy)
betay_vec=beta1*y/L
betay=tile(betay_vec,(n,1))
predir='/data/ilebras/twolayer-jets-climate/'
rundir=predir+'testing/'+dirname+'/'
# --------load fields-------
from loadfuncs import load1
#mean pv fluxes
um1=load1('um1.dat',rundir,n,m)
um2=load1('um2.dat',rundir,n,m)
vm1=load1('vm1.dat',rundir,n,m)
vm2=load1('vm2.dat',rundir,n,m)
zm1=load1('z1m.dat',rundir,n,m)
zm2=load1('z2m.dat',rundir,n,m)
qm1=load1('qm1.dat',rundir,n,m)
qm2=load1('qm2.dat',rundir,n,m)
pm1=load1('pm1.dat',rundir,n,m)
pm2=load1('pm2.dat',rundir,n,m)
hb=gethb(cm,cz,ymax,movedw,hw,hbmax,a2,n,m)
# qm1=zm1+betay+fr1*(pm2-pm1)#+f0_nd
qm2_check=zm2+betay+fr2*(pm1-pm2)+hb#+f0_nd
# figure()
# plot(qm2.T);
# plot(qm2_check.T);
# figure()
# subplot(211)
# plot(um2.T)
# subplot(212)
# plot(vm2.T)
uqm1=um1*qm1
uqm2=um2*qm2
vqm1=vm1*qm1
vqm2=vm2*qm2
# #calculate divergence
# duqm1x=gradx(uqm1,t4dx,n,m)
# duqm2x=gradx(uqm2,t4dx,n,m)
# dvqm1y=grady(vqm1,t4dx,n,m)
# dvqm2y=grady(vqm2,t4dx,n,m)
# divuq1m=duqm1x+dvqm1y
# divuq2m=duqm2x+dvqm2y
#eddy pv fluxes
uz1=-(load1('uz1.dat',rundir,n,m)+2*um1*zm1)
uz2=-(load1('uz2.dat',rundir,n,m)+2*um2*zm2)
vz1=load1('vz1.dat',rundir,n,m)
vz2=load1('vz2.dat',rundir,n,m)
#need to correct for a missing minus sign in stats2_pvbud.F
u1p1=-(load1('u1p1.da',rundir,n,m)+2*um1*pm1)
u1p2=-(load1('u1p2.da',rundir,n,m)+2*um1*pm2)
u2p1=-(load1('u2p1.da',rundir,n,m)+2*um2*pm1)
u2p2=-(load1('u2p2.da',rundir,n,m)+2*um2*pm2)
v1p1=load1('v1p1.da',rundir,n,m)
v1p2=load1('v1p2.da',rundir,n,m)
v2p1=load1('v2p1.da',rundir,n,m)
v2p2=load1('v2p2.da',rundir,n,m)
uthi1=fr1*(u1p2-u1p1)
uthi2=fr2*(u2p1-u2p2)
vthi1=fr1*(v1p2-v1p1)
vthi2=fr2*(v2p1-v2p2)
uqb1=uz1+uthi1
uqb2=uz2+uthi2
vqb1=vz1+vthi1
vqb2=vz2+vthi2
dqy1=grady(qm1,t4dx,n,m)
dqy2=grady(qm2,t4dx,n,m)
uz={}
uz['1']=uz1
uz['2']=uz2
vz={}
vz['1']=vz1
vz['2']=vz2
ub={}
ub['1']=uqb1
ub['2']=uqb2
vb={}
vb['1']=vqb1
vb['2']=vqb2
up={}
up['11']=u1p1
up['12']=u1p2
up['21']=u2p1
up['22']=u2p2
vp={}
vp['11']=v1p1
vp['12']=v1p2
vp['21']=v2p1
vp['22']=v2p2
uqm={}
uqm['1']=uqm1
uqm['2']=uqm2
vqm={}
vqm['1']=vqm1
vqm['2']=vqm2
pickle.dump([uz,vz,ub,vb,up,vp,uqm,vqm,dqy1,dqy2],open(predir+'pickles/pvfluxmv/'+dirname+'_pvflmv.p', 'w'))
