def get_cape(filename, flag, coords=None):
fil = hdf.File(filename, 'r')
height = getvar(fil, 'z_coords')
if height.size < 3:
height = getvar(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5',
'z_coords')
dz = getdz(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5'
)
else:
dz = getdz(fil)
tempk = getvar(fil, 'tempk')
q = getvar(fil, 'vapor')
press = getvar(fil, 'press')
fil.close()
# except:
# print 'error reading variables'
if type(coords) is tuple:
c0, c1 = coords
capevar, cinvar = capecalc(height, tempk[:, c0, c1], q[:, c0, c1],
press[:, c0, c1], flag)
else:
capevar = np.zeros((tempk.shape[1], tempk.shape[2]))
cinvar = np.zeros_like(capevar)
for c0 in range(capevar.shape[0]):
for c1 in range(capevar.shape[1]):
capevar[c0, c1], cinvar[c0,
c1] = capecalc(height, tempk[:, c0,
c1],
q[:, c0, c1],
press[:, c0, c1], flag)
return capevar, cinvar
def get8kmflux(xdir):
print xdir
files = sorted(
glob.glob('/nobackup/rstorer/convperts/revu/' + xdir + '/' + xdir +
'*h5'))
nt = len(files)
z = getvar(files[0], 'z_coords')
zarg = np.argmin(np.abs(z - 8000))
values5 = np.zeros(nt)
values2 = np.zeros(nt)
for t, fil in enumerate(files):
cond = getvar(fil, 'tracer002')[zarg, :, :] * 100. * 100. * 100.
w = getvar(fil, 'w')
massflux = cond * w[zarg, :, :]
colmax = np.max(w, 0)
w2 = np.where(colmax > 2)
w5 = np.where(colmax > 5)
if np.max(w) > 5:
updraft5 = massflux[w5[0], w5[1]]
up5 = np.sum(updraft5) * 250 * 250
values5[t] = up5
if np.max(w) > 2:
updraft2 = massflux[w2[0], w2[1]]
up2 = np.sum(updraft2) * 250 * 250
values2[t] = up2
return values2, values5
def get3levels(filename,varname):
hfile = hdf.File(filename,'r')
press = getvar(hfile,'press')
tempk = getvar(hfile,'tempk')
rho = (press*100.)/ (287.*tempk)
vartoavg = getvar(hfile,varname)
dz = getdz(hfile)
hfile.close()
vall = vartoavg * rho * dz[:,None,None]
mass = rho * dz[:,None,None]
vtop = np.mean(np.mean(vall,1),1)
vbot = np.mean(np.mean(mass,1),1)
mpress = np.mean(np.mean(press,1),1)
lows = np.where(mpress > 850.)
mids = np.logical_and(mpress < 850, mpress > 500)
highs = np.where(mpress < 500)
lowsat = np.sum(vtop[lows]) / np.sum(vbot[lows])
midsat = np.sum(vtop[mids]) / np.sum(vbot[mids])
highsat = np.sum(vtop[highs]) / np.sum(vbot[highs])
allsat = np.sum(vtop) / np.sum(vbot)
return lowsat, midsat, highsat, allsat
def getrh(fil):
heights = getvar(fil, 'z_coords')[1:]
tempk = np.mean(np.mean(getvar(fil, 'tempk')[1:, :, :], 1), 1)
press = np.mean(np.mean(getvar(fil, 'press')[1:, :, :], 1), 1)
vapor = np.mean(np.mean(getvar(fil, 'vapor')[1:, :, :], 1), 1)
rho = (press * 100.) / (287. * tempk)
dz = getdz(fil)[:-1]
es = 611.2 * np.exp(17.67 * (tempk - 273.15) / (tempk - 29.65))
rvs = 622 * es / ((press * 100.) - es)
vsat = rvs * rho * dz
vact = vapor * rho * dz
lows = np.where(press > 850.)
mids = np.logical_and(press < 850, press > 500)
highs = np.where(press < 500)
lowsat = np.sum(
vact[lows]) #100. * (np.sum(vact[lows]) / np.sum(vsat[lows]))
midsat = np.sum(
vact[mids]) #100. * (np.sum(vact[mids]) / np.sum(vsat[mids]))
highsat = np.sum(
vact[highs]) #100. * (np.sum(vact[highs]) / np.sum(vsat[highs]))
allsat = np.sum(vact) #100. * (np.sum(vact) / np.sum(vsat))
return lowsat, midsat, highsat, allsat
def calcd(fil,var,x=None):
nnames = { 'pristine':'pris_concen_kg',
'cloud':'cloud_concen_mg',
'aggregates':'agg_concen_kg',
'snow':'snow_concen_kg',
'drizzle':'drizzle_concen_mg',
'hail':'hail_concen_kg',
'rain':'rain_concen_kg',
'graupel':'graup_concen_kg'
}
mass = getvar(fil,var)/1000.
num = getvar(fil,nnames[var])
if var == 'cloud' or var == 'drizzle':
num=num*1000.*1000.
coef, ex, nu = getdefault(var)
if x is not None:
coef=x
with np.errstate(invalid='ignore',divide='ignore'):
d=(mass/(num*coef))**(1./ex)
d[num==0]=0.0
return d
def getrh(filename):
hfile = hdf.File(filename, 'r')
press = getvar(hfile, 'press')
tempk = getvar(hfile, 'tempk')
relhum = getvar(hfile, 'relhum')
rho = (press * 100.) / (287. * tempk)
vapor = getvar(hfile, 'vapor')
sat = satmixratio(tempk, press) * 1000.
dz = getdz(hfile)
hfile.close()
vsatall = sat * rho * dz[:, None, None]
vactall = vapor * rho * dz[:, None, None]
vsat = np.mean(np.mean(vsatall, 1), 1)
vact = np.mean(np.mean(vactall, 1), 1)
mpress = np.mean(np.mean(press, 1), 1)
lows = np.where(mpress > 850.)
mids = np.logical_and(mpress < 850, mpress > 500)
highs = np.where(mpress < 500)
lowsat = 100. * (np.sum(vact[lows]) / np.sum(vsat[lows]))
midsat = 100. * (np.sum(vact[mids]) / np.sum(vsat[mids]))
highsat = 100. * (np.sum(vact[highs]) / np.sum(vsat[highs]))
return lowsat, midsat, highsat
def gettimeprofs(xdir):
print xdir
files = sorted(
glob.glob('/nobackup/rstorer/convperts/revu/' + xdir + '/' + xdir +
'*h5'))
nt = len(files)
z = getvar(files[0], 'z_coords')
zarg = np.argmin(np.abs(z - 8000))
nz = len(z)
outvar2 = np.zeros((nt, nz))
outvar5 = np.zeros((nt, nz))
tmpprof = np.zeros((nt, nz))
for t, fil in enumerate(files):
cond = getvar(fil, 'tracer002') * 100. * 100. * 100.
w = getvar(fil, 'w')
massflux = cond * w
colmax = np.max(w, 0)
w2 = np.where(colmax > 2)
w5 = np.where(colmax > 5)
if np.max(w) > 5:
updraft5 = massflux[:, w5[0], w5[1]]
up5 = np.mean(updraft5, 1)
outvar5[t, :] = up5
updraft2 = massflux[:, w2[0], w2[1]]
up2 = np.mean(updraft2, 1)
outvar2[t, :] = up2
ttm = massflux[:, w5[0], w5[1]]
tmpprof[t, :] = np.mean(ttm, 1)
return outvar2, outvar5
def getbudgetvars(fil1, dt):
cond = getvar(fil1, 'total_cond') / 1000.
rho = (getvar(fil1, 'press') * 100.) / (getvar(fil1, 'tempk') * 287.)
w = getvar(fil1, 'w')
pcp = getvar(fil1, 'precip3d') * -1
dz = getdz(fil1)
massflux = cond * rho * w
return massflux, w, pcp
def modelvars(filename, arg):
fil = hdf.File(filename, 'r')
vapdep = (getvar(fil, 'vapicet')[arg:, :, :] +
getvar(fil, 'vapliqt')[arg:, :, :]) * 2
temp = getvar(fil, 'tempk')[arg:, 10, 10]
w = getvar(fil, 'w')[arg:, :, :]
fil.close()
return vapdep, w, temp
def gettavg(fil,bottom,top):
tempk = np.mean(np.mean(getvar(fil,'tempk')[1:,:,:],1),1)
dz = getdz(fil)[:-1]
press = np.mean(np.mean(getvar(fil,'press')[1:,:,:],1),1)
layer = np.logical_and(press < bottom, press > top)
vact = tempk*dz
tavg = np.sum(vact[layer])/ np.sum(dz[layer])
return tavg
def getprofs(xdir):
print xdir
budfiles = sorted(
glob.glob('/nobackup/rstorer/h5files/mature-' + xdir +
'-refbudgetvars-90s-smoothed*'))
condfiles = sorted(
glob.glob('/nobackup/rstorer/h5files/mature-' + xdir +
'-condensate-smoo*'))
rhofiles = sorted(
glob.glob('/nobackup/rstorer/h5files/mature-' + xdir + '-rho-smoo*'))
nt = len(budfiles)
profsc = []
profsz = []
profln = []
profactz = []
profactc = []
for i in range(nt):
delz = getvar(budfiles[i], 'ref2') - getvar(budfiles[i], 'ref')
onec = 10 * np.log10(
getvar(condfiles[i], 'q') * getvar(rhofiles[i], 'q'))
onec[np.isnan(onec)] = -40
onec[onec < -40] = -40.
onez = getvar(budfiles[i], 'ref')
delc = (10 * np.log10(
getvar(condfiles[i + 3], 'q') * getvar(rhofiles[i + 3], 'q')) -
10 *
np.log10(getvar(condfiles[i], 'q') * getvar(rhofiles[i], 'q')))
delc[delc > 40] = 40.
#dlnc = delc/onec
#dlnc[np.isnan(dlnc)]=0.
#dlnc[onec<0.00001]=0.
delc[np.isnan(delc)] = 0.
dlnc = delc
w = getvar(budfiles[i], 'w')
maxw = np.max(w, 0)
conv = np.where(w > 1)
pz = delz[conv[0], conv[1], conv[2]]
pc = delc[conv[0], conv[1], conv[2]]
pl = dlnc[conv[0], conv[1], conv[2]]
pac = onec[conv[0], conv[1], conv[2]]
paz = onez[conv[0], conv[1], conv[2]]
profactz.extend(paz)
profactc.extend(pac)
profln.extend(pl)
profsc.extend(pc)
profsz.extend(pz)
scout = np.asarray(profsc)
lnout = np.asarray(profln[:])
szout = np.asarray(profsz[:])
azout = np.asarray(profactz[:])
acout = np.asarray(profactc[:])
print scout.min(), lnout.min(), szout.min(), azout.min(), acout.min()
return scout, szout, lnout, azout, acout
def makeplots(fil1, fil2, mlist, dt, num):
outnum = str(num)
if num < 10:
outnum = '0' + outnum
diff, vertflux, micro, w = getbudgetvars(fil1, fil2, mlist, dt)
vert = np.diff(vertflux, axis=0)
colmax = np.max(w, 0)
w1 = np.where(colmax > 5)
diff1 = diff[:, w1[0], w1[1]]
vert1 = vert[:, w1[0], w1[1]]
flux = vertflux[:, w1[0], w1[1]]
micro1 = micro[:, w1[0], w1[1]]
height = getvar(fil1, 'z_coords')
mdiff1 = np.mean(diff1, 1)
mvert1 = np.mean(vert1, 1)
mmicro1 = np.mean(micro1, 1)
diffprof = np.mean(np.mean(diff, 1), 1)
vertprof = np.mean(np.mean(vert, 1), 1)
micprof = np.mean(np.mean(micro, 1), 1)
intdiff = np.zeros_like(mdiff1)
intmicro = np.zeros_like(mmicro1)
intflux = np.mean(flux, 1)
for z in range(82):
intdiff[z] = np.sum(mdiff1[z:])
intmicro[z] = np.sum(mmicro1[z:])
return intdiff, intmicro, intflux
def calcadvflux(fil):
u = getvar(fil, 'u')
v = getvar(fil, 'v')
q = getvar(fil, 'total_cond') / 1000.
p = getvar(fil, 'press')
t = getvar(fil, 'tempk')
rho = (p * 100.) / (t * 287.)
umean = np.mean(np.mean(u, 1), 1)
vmean = np.mean(np.mean(v, 1), 1)
uprime = u - umean[:, None, None]
vprime = v - vmean[:, None, None]
uflux = rho * q * uprime
vflux = rho * q * vprime
uadv = rho * q * umean[:, None, None]
vadv = rho * q * vmean[:, None, None]
return uflux, vflux, uadv, vadv
def plot2d2panel(xdir, var1, var2, rh0, rh1):
height = getvar(
'/nobackup/rstorer/convperts/revu/' + xdir + '/' + xdir +
'-revu-001.h5', 'z_coords') / 1000.
xs = np.arange(var1.shape[1]) * 5
m = np.argmin(np.abs(height - 5))
var1[:m, :] = np.min(var1[m:, :])
var2[:m, :] = np.min(var2[m:, :])
plt.subplot(1, 2, 1)
plt.contourf(xs,
height,
var1,
levels=np.linspace(var1.min(), var1.max(), 20))
plt.title('0-2 km')
plt.xlabel(str(rh0))
plt.ylabel('Height (km)')
plt.ylim(5, 18)
plt.colorbar()
plt.subplot(1, 2, 2)
plt.contourf(xs,
height,
var2,
levels=np.linspace(var2.min(), var2.max(), 20))
plt.title('2-4 km')
plt.xlabel(str(rh1))
plt.yticks([])
plt.ylim(5, 18)
plt.colorbar()
plt.suptitle(xdir)
plt.savefig('surfacetracers2panel-' + xdir + '.png')
plt.clf()
def budgetplot(maindir, xdir, dx, dy, dt):
files = sorted(glob.glob(maindir + xdir + '/*.h5'))
nt = len(files)
updraft = np.zeros(nt)
vapflux = np.zeros(nt)
tracflux = np.zeros(nt)
totalmass = np.zeros(nt)
for t, filename in enumerate(files):
print t
f = hdf.File(filename, 'r')
w = getvar(f, 'w')
q = getvar(f, 'total_cond') / 1000.
vap = getvar(f, 'vapor') / 1000.
trac = getvar(f, 'tracer002') * (100.**3)
height = getvar(f, 'z_coords')
rho = getrho(f)
dz = getdz(f)
f.close()
nz, nx, ny = q.shape
azz = np.argmin(np.absolute(height - 8000))
bzz = nz - 1
advw = rho * w * q
advv = rho * w * vap
advt = w * trac
updraft[t] = dx * dy * dt * (np.sum(advw[azz, :, :]) -
np.sum(advw[bzz, :, :]))
vapflux[t] = dx * dy * dt * (np.sum(advv[azz, :, :]) -
np.sum(advv[bzz, :, :]))
tracflux[t] = dx * dy * dt * (np.sum(advt[azz, :, :]) -
np.sum(advt[bzz, :, :]))
totalmass[t] = np.sum(q[azz:bzz, :, :] * rho[azz:bzz, :, :] *
dz[azz:bzz, None, None]) * dx * dy
for t, filename in enumerate(files):
print t
# advw = rho * w * q
# advv = rho * w * vap
# advt = w * trac
# updraft[t] = dx * dy * dt * (np.sum(advw[azz,:,:]) - np.sum(advw[bzz,:,:]))
# vapflux[t] = dx * dy * dt * (np.sum(advv[azz,:,:]) - np.sum(advv[bzz,:,:]))
# tracflux[t] = dx * dy * dt * (np.sum(advt[azz,:,:]) - np.sum(advt[bzz,:,:]))
# totalmass[t] = np.sum(q[azz:bzz,:,:]*rho[azz:bzz,:,:] * dz[azz:bzz,None,None]) * dx * dy
return updraft, vapflux, tracflux, totalmass
def getcloudtop(fil):
q=getvar(fil,'total_cond')
c=np.where(q>.01)
q[c]=100.
inds = q.shape[0]-1-np.argmax(q[::-1,:,:],0)
inds[np.max(q,0)<.01]=0
return inds
def gettracer(dirname):
files = sorted(glob.glob('/nobackup/rstorer/convperts/revu/'+dirname+'/*h5'))
outdir = '/nobackup/rstorer/plots/'
nt = len(files)
t2 = getvar(files[0],'tracer002')[:,100,100]
dz = getdz(files[0])
x = t2*dz*100*100*100
return np.sum(x)
def getcapeparcel(filename, flag):
try:
fil = hdf.File(filename, 'r')
height = getvar(fil, 'z_coords')
if height.size < 3:
height = getvar(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5',
'z_coords')
dz = getdz(
'/nobackup/rstorer/convperts/revu/aug11-control/aug11-control-revu-001.h5'
)
else:
dz = getdz(fil)
tempk = getvar(fil, 'tempk')[:, 10, 10]
q = getvar(fil, 'vapor')[:, 10, 10]
press = getvar(fil, 'press')[:, 10, 10]
fil.close()
except:
print 'error reading variables'
if flag == 'sfc':
zlcl, lcl, tlcl = find_lcl_sfc(height, tempk, q, press)
elif flag == 'mu':
zlcl, lcl, tlcl, zstart = find_lcl_mostunstable(
height, tempk, q, press)
else:
zlcl, lcl, tlcl = find_lcl_ml100(height, tempk, q, press)
parcel = moistadiabat(height, press, zlcl, tlcl, tempk, q)
parceltv = parcel * (1 + (.61 * satmixratio(parcel, press)))
parceltv[:zlcl + 1] = parcel[:zlcl + 1] * (1 + (.61 * q[0]))
Tv = tempk * (1 + (.61 * (q / 1000.)))
posarea = np.where(parceltv > Tv)
intvar = (parceltv - Tv) * (G / Tv)
lfc = np.min(np.asarray(posarea))
el = np.max(np.asarray(posarea))
if lfc < zlcl:
lfc = zlcl
intvarpos = intvar[lfc:el + 1]
zpos = height[lfc:el + 1]
cape = np.trapz(intvarpos, dx=np.diff(zpos))
return cape, parceltv - Tv
def getprofs(xdir, varname):
files = sorted(
glob.glob('/nobackup/rstorer/convperts/revu/' + xdir + '/trac*h5'))
nt = len(files)
varout = np.zeros((82, nt))
for i, fil in enumerate(files):
t = getvar(fil, varname) * 100 * 100 * 100
p = np.sum(np.sum(t, 1), 1)
varout[:, i] = p
return varout
def gettimeseries(xdir, varname):
files = sorted(
glob.glob('/nobackup/rstorer/convperts/revu/' + xdir + '/*h5'))
nt = len(files)
outmax = np.zeros(nt)
outmean = np.zeros(nt)
for x, fil in enumerate(files):
v = getvar(fil, varname)
outmax[x] = np.max(v)
outmean[x] = np.mean(v)
return outmax, outmean
def getrh(fil, bottom, top):
heights = getvar(fil, 'z_coords')[1:]
tempk = np.mean(np.mean(getvar(fil, 'tempk')[1:, :, :], 1), 1)
press = np.mean(np.mean(getvar(fil, 'press')[1:, :, :], 1), 1)
vapor = np.mean(np.mean(getvar(fil, 'vapor')[1:, :, :], 1), 1)
rho = (press * 100.) / (287. * tempk)
dz = getdz(fil)[:-1]
es = 611.2 * np.exp(17.67 * (tempk - 273.15) / (tempk - 29.65))
rvs = 622 * es / ((press * 100.) - es)
vsat = rvs * rho * dz
vact = vapor * rho * dz
layer = np.logical_and(press < bottom, press > top)
columnvapor = np.sum(vact[layer])
columnrh = 100. * (np.sum(vact[layer]) / np.sum(vsat[layer]))
return columnvapor, columnrh
def calc_diam(fil, varname, cname, cfmas, pwmas, unit):
mfile = hdf.File(fil, 'r')
mass = getvar(mfile, varname) / 1000.
conc = getvar(mfile, cname[varname])
rpw = 1. / pwmas[varname]
if varname == 'cloud' or varname == 'drizzle':
conc = conc * 1e6
diam = (mass / (cfmas[varname] * conc))**(1 / pwmas[varname])
diam[np.isnan(diam)] = 0.
diam[mass < 1e-10] = 0
label = unit[varname]
if label == 'mm':
diam = diam * 1000.
else:
diam = diam * 1e6
print varname, diam[diam > 0].min(), diam.max(), label
mfile.close()
return diam
def makeoneplot(i,fil1,fil2,yval):
outdir = '/nobackup/rstorer/plots/budgetslices/'
cond1 = getvar(fil1, 'total_cond')/1000.
cond2 = getvar(fil2, 'total_cond')/1000.
micro2 = (getvar(fil2, 'nuccldrt') + getvar(fil2, 'nucicert') + getvar(fil2, 'vapliqt') + getvar(fil2, 'vapicet'))/1000.
diff = cond2-cond1
height = getvar(fil1, 'z_coords')
xs = np.arange(400)*.25
dz = getdz(fil1)
plt.subplot(2,1,1)
plt.contourf(xs[160:320], height, diff[:,yval,160:320])
plt.ylim(0,18000)
plt.ylabel('height')
plt.title('Condensate Difference')
plt.colorbar()
plt.subplot(2,1,2)
plt.contourf(xs[160:320], height, micro2[:,yval,160:320])
plt.ylim(0,18000)
plt.ylabel('height')
plt.title('microphysics')
plt.colorbar()
plt.savefig(outdir+'time_'+i+'_y'+str(yval)+'.png')
plt.clf()
def getprofs(xdir):
print xdir
budfiles = sorted(
glob.glob('/nobackup/rstorer/h5files/mature-' + xdir +
'-refbudgetvars-90s-smoothed*'))
condfiles = sorted(
glob.glob('/nobackup/rstorer/h5files/mature-' + xdir +
'-condensate-smoo*'))
nt = len(budfiles)
profsc = []
profsz = []
profln = []
for i in range(nt):
delz = getvar(budfiles[i], 'ref2') - getvar(budfiles[i], 'ref')
onec = getvar(condfiles[i], 'q')
delc = (getvar(condfiles[i + 3], 'q') - getvar(condfiles[i], 'q'))
dlnc = delc / onec
dlnc[np.isnan(dlnc)] = 0.
dlnc[onec < 0.00001] = 0.
w = getvar(budfiles[i], 'w')
maxw = np.max(w, 0)
conv = np.where(maxw > 1)
pz = delz[:, conv[0], conv[1]]
pc = delc[:, conv[0], conv[1]]
pl = dlnc[:, conv[0], conv[1]]
profln.append(pl)
profsc.append(pc)
profsz.append(pz)
scout = np.concatenate(profsc[:], axis=1)
lnout = np.concatenate(profln[:], axis=1)
szout = np.concatenate(profsz[:], axis=1)
return scout, szout, lnout
def makeplots(fil1, dt, num):
outnum = str(num)
if num < 10:
outnum = '0' + outnum
w = getvar(fil1, 'w')
colmax = np.max(w, 0)
w1 = np.where(colmax > 5)
wup = w[:, w1[0], w1[1]]
wout = np.mean(wup, 1)
return wout
def advprofiles(fil, u, v):
w = getvar(fil, 'w')
wmax = np.max(w[37:, :, :], 0)
advprofs = np.zeros(82)
ii = 200
jj = 200
uleft = u[:, ii - 100:ii + 100, jj - 100]
uright = u[:, ii - 100:ii + 100, jj + 100]
udiff = (uleft - uright) / 200.
vbottom = v[:, ii - 100, jj - 100:jj + 100]
vtop = v[:, ii + 100, jj - 100:jj + 100]
vdiff = (vbottom - vtop) / 200.
advprofs = udiff + vdiff
return advprofs
def intsizedist(fil,var,coord=None):
nnames = { 'pristine':'pris_concen_kg',
'cloud':'cloud_concen_mg',
'aggregates':'agg_concen_kg',
'snow':'snow_concen_kg',
'drizzle':'drizzle_concen_mg',
'hail':'hail_concen_kg',
'rain':'rain_concen_kg',
'graupel':'graup_concen_kg'
}
#If no coord given, uses None, which just returns full array + a dimension
mass = np.array(getvar(fil,var)[coord]/1000.)
num = np.array(getvar(fil,nnames[var])[coord])
if var == 'cloud' or var == 'drizzle':
num=num*1000.*1000.
coef, ex, nu = getdefault(var)
m=mass.flatten()
n=num.flatten()
tval = np.zeros_like(m)
bval = np.zeros_like(m)
for i in range(len(m)):
if n[i] >0:
dvals, func = calcgammadist(m[i],n[i],coef,ex,nu)
diffr = np.zeros_like(dvals)
diffr[1:] = np.diff(dvals)/2
tval[i] = np.sum( (dvals/2.)**3 * diffr * func * 3.14 )
bval[i] = np.sum( (dvals/2.)**2 * diffr * func * 3.14 )
bot = np.reshape(bval,mass.shape)
top = np.reshape(tval,mass.shape)
return mass, num, bot,top
def budgetplot(maindir, xdir, dx, dy, dt):
files = sorted(glob.glob(maindir + xdir + '/*.h5'))
nt = len(files)
updraft = np.zeros(nt)
onlyup = np.zeros(nt)
for t, filename in enumerate(files):
print t
f = hdf.File(filename, 'r')
w = getvar(f, 'w')
height = getvar(f, 'z_coords')
rho = getrho(f)
dz = getdz(f)
f.close()
nz, nx, ny = w.shape
azz = np.argmin(np.absolute(height - 8000))
bzz = nz - 1
advw = rho * w
upw = copy.deepcopy(advw)
upw[w < 1] = 0.
updraft[t] = dx * dy * dt * (np.sum(advw[azz, :, :]) -
np.sum(advw[bzz, :, :]))
onlyup[t] = dx * dy * dt * (np.sum(upw[azz, :, :]) -
np.sum(upw[bzz, :, :]))
return updraft, onlyup
def plot2d(xdir, varname, var):
height = getvar(
'/nobackup/rstorer/convperts/revu/' + xdir + '/' + xdir +
'-revu-001.h5', 'z_coords') / 1000.
xs = np.arange(var.shape[1]) * 5
m = np.argmin(np.abs(height - 5))
var[:m, :] = np.min(var[m:, :])
plt.contourf(xs, height, var, levels=np.linspace(var.min(), var.max(), 20))
plt.title(xdir)
plt.xlabel('Time (min)')
plt.ylabel('Height (km)')
plt.ylim(5, 18)
plt.colorbar()
plt.savefig('total2dprof-' + xdir + '-' + varname + '.png')
plt.clf()
def advprofiles(fil, u, v):
w = getvar(fil, 'w')
wmax = np.max(w[37:, :, :], 0)
vals = np.where(wmax > 5)
nprofs = len(vals[0])
print nprofs
advprofs = np.zeros((82, nprofs))
for i in range(nprofs):
ii = vals[0][i]
jj = vals[1][i]
udiff = (
u[:, ii, jj - 10] - u[:, ii, jj + 10]
) / 20. #April 11, changed from +-1/2 to +-10/20 for 5km avg for advection
vdiff = (v[:, ii - 10, jj] - v[:, ii + 10, jj]) / 20.
advprofs[:, i] = udiff + vdiff
return advprofs