def __init__( self,
Source = 'CMCC-CESM',
dT = 1,
ff = 1,
blat = 70,
res = 200 ):
# Class attributes
self.Source = Source
self.dT = dT
self.ff = ff
# Dependants
if self.ff == 1: self.fstr = 'fwrd'
if self.ff == -1: self.fstr = 'back'
if self.dT == 1: self.dailymean = True
if self.dT == 0.25: self.dailymean = False
# U,V and W DataServers
if Source == 'ERAInt':
self.uds = reDataServer(Field='U',LevType='plev',Source=Source)
self.wds = reDataServer(Field='W',LevType='plev',Source=Source)
self.vds = reDataServer(Field='V',LevType='plev',Source=Source)
self.PVds = reDataServer(Field='pv',LevType='plev',Source=Source)
elif Source == 'NCEP1':
self.uds = reDataServer(Field='uplev',LevType='plev',Source=Source)
self.wds = reDataServer(Field='omega',LevType='plev',Source=Source)
self.vds = reDataServer(Field='vplev',LevType='plev',Source=Source)
#self.qdsh = reDataServer(Field='shum',LevType='plev',Source=Source)
elif Source[0:3] == 'CAM':
self.uds = reDataServer(Field='U',Source=Source)
self.wds = reDataServer(Field='W',Source=Source)
self.vds = reDataServer(Field='V',Source=Source)
#self.qdsh = reDataServer(Field='q',Source=Source)
else:
self.wds = cmipDataServer(Field='wap',LevType='plev',Source=Source,ExpType='rcp85',DataFreq='day')
self.uds = cmipDataServer(Field='ua' ,LevType='plev',Source=Source,ExpType='rcp85',DataFreq='day')
self.vds = cmipDataServer(Field='va' ,LevType='plev',Source=Source,ExpType='rcp85',DataFreq='day')
#self.qdsh = cmipDataServer(Field='hus',LevType='plev',Source=Source,ExpType='historical',DataFreq='day')
#self.qdsr = cmipDataServer(Field='hus',LevType='plev',Source=Source,ExpType='rcp85',DataFreq='day')
#self.qdss = [self.qdsh,self.qdsr]
# LambertProjector
self.res = res
self.proj = LambertProjector(boundinglat=blat,resolution=self.res)
self.res = self.proj.res
print self.res
self.x,self.y = self.proj.x[0,:],self.proj.y[:,0]
self.nx,self.ny = len(self.x),len(self.y)
# Mass of girdbox on each level
self.dP = []
dP = np.diff(self.uds.lev)/2.
self.dP.append(dP[0])
for i in range(len(dP)-1):
self.dP.append(dP[i]+dP[i+1])
self.dP.append(dP[-1])
self.dP = np.array(self.dP)
self.dM = 100.*self.dP/9.80665
class Reynolds:
def __init__(self, Season='DJF', case='vq'):
# Attributes
self.Season = Season
self.case = case
if self.case == 'vq':
self.Fieldf, self.orr, self.units, self.sf, self.Ef = [
'q', 'hus'
], 'moisture', 'kg s$^{-1}$ m$^{-1}$', 1, 1e03
if self.case == 'vT':
self.Fieldf, self.orr, self.units, self.sf, self.Ef = [
'T', 'ta'
], 'temperature', '10$^{3}$ K kg s$^{-1}$ m$^{-1}$', 1e-03, 334e03
self.proj = LambertProjector(boundinglat=25, resolution=200.)
# Target grid
self.lats = np.arange(0, 90 + 1, 1)
self.lons = np.arange(0, 360, 1)
self.levs = np.arange(0, 1000 + 50, 50)
self.dlev = self.levs[-1] - self.levs[0]
def makeFiles(self,Years,Months,Ndays,dt,blat0=70,area_integration=True):
# Make directory for saving files if it does not exist
os.system('mkdir -p /mnt/climstorage/cian/Tracks/%s/%s/%s' % (self.Source,self.fstr,blat0))
# Initialisation of particles (100 points north of 80N or 360 points along 70N)
if area_integration:
proj_tmp = LambertProjector(boundinglat=80,resolution=200.)
lon0s,lat0s = proj_tmp.lon.reshape(-1),proj_tmp.lat.reshape(-1)
else:
lon0s,lat0s = np.arange(360),[blat0 for kk in range(360)]
steps = self.ff*np.arange(24.*Ndays/dt + 1)*dt
x0s,y0s = self.proj.m(lon0s,lat0s)
p0s = [800 for kk in range(len(x0s))]
# Loop trhough years and months
for Year in Years:
for Month in Months:
#try:
fname = '/mnt/climstorage/cian/Tracks/%s/%s/%s/%s_%s_%02d.nc' % (self.Source,self.fstr,blat0,self.fstr,Year,Month)
print fname
if not os.path.isfile(fname):
print 'Computing %s trajectories for %s %s ...' % (self.fstr,Year,Month)
self.makeMonth(Year,Month,steps,lon0s,x0s,y0s,p0s,Ndays,dt,fname)
else:
print 'File %s already exists' % (fname)
def density0(self,year0=1981,N=23):
# Attributes
proj = LambertProjector(boundinglat=40,resolution=390.)
projt = LambertProjector(boundinglat=80,resolution=200.)
xlim_,ylim0 = proj.m(0,50)
xlim_,ylim1 = proj.m(180,50)
xlim0,ylim_ = proj.m(-90,50)
xlim1,ylim_ = proj.m(90,50)
print proj.res
# Axes
xpole,ypole = proj.m(0,90)
xyR2 = (proj.x-xpole)**2 + (proj.y-ypole)**2
xyR2 = xyR2/1e13
x,y = proj.x[0,:],proj.y[:,0]
# Masking of trakectories
LatRange = [80,85]
LonRange = [-30,90]
# LonRange = [90,-150]
# LonRange = [-150,-30]
bounds = self.boxBounds(proj.m,LatRange,LonRange,35)
if LonRange[1] < LonRange[0]:
inds = np.where((projt.lon.reshape(-1)<LonRange[1])|(projt.lon.reshape(-1)>LonRange[0])\
&(projt.lat.reshape(-1)<LatRange[1])&(projt.lat.reshape(-1)>LatRange[0])==True)[0]
else:
inds = np.where((projt.lon.reshape(-1)<LonRange[1])&(projt.lon.reshape(-1)>LonRange[0])\
&(projt.lat.reshape(-1)<LatRange[1])&(projt.lat.reshape(-1)>LatRange[0])==True)[0]
# Data holders
years1 = range(year0,year0+N/2,1)
years2 = range(year0+N/2,year0+N,1)
print years1,len(years1)
print years2,len(years2)
#years1 = range(1981,1998+1,1)
#years2 = range(1999,2016+1,1)
N1,N2 = np.array([np.zeros((proj.nx,proj.ny)) for i in years1]),np.array([np.zeros((proj.nx,proj.ny)) for i in years2])
for t in range(len(years1)):
year = years1[t]
print year
s = self.getDataSnaps(Year=year,Season='DJF')
lat,lon,p = s[:,inds,:28,0],s[:,inds,:28,1],s[:,inds,:28,2]
xs,ys = proj.m(lon,lat)
xs,ys = xs.reshape(-1),ys.reshape(-1)
for i in range(len(xs)):
xi,yi = np.argmin(abs(x-xs[i])),np.argmin(abs(y-ys[i]))
N1[t,yi,xi] = N1[t,yi,xi] + 1
for t in range(len(years2)):
year = years2[t]
print year
s = self.getDataSnaps(Year=year,Season='DJF')
lat,lon,p = s[:,inds,:28,0],s[:,inds,:28,1],s[:,inds,:28,2]
xs,ys = proj.m(lon,lat)
xs,ys = xs.reshape(-1),ys.reshape(-1)
for i in range(len(xs)):
xi,yi = np.argmin(abs(x-xs[i])),np.argmin(abs(y-ys[i]))
N2[t,yi,xi] = N2[t,yi,xi] + 1
# Full time array for trend
N0 = np.append(N1,N2,axis=0)
Ntrend,pval = self.getTrend(years1+years2,N0)
# Time series
projlat1 = np.tile(proj.lat[np.newaxis,:,:],(len(years1),1,1))
projlon1 = np.tile(proj.lon[np.newaxis,:,:],(len(years1),1,1))
projlat2 = np.tile(proj.lat[np.newaxis,:,:],(len(years2),1,1))
projlon2 = np.tile(proj.lon[np.newaxis,:,:],(len(years2),1,1))
nn1 = np.ma.masked_where(projlat1>85,N1)
nn1 = np.ma.masked_where(projlat1<70,nn1)
nn1 = np.ma.masked_where(projlon1<20,nn1)
nn1 = np.ma.masked_where(projlon1>90,nn1)
nn2 = np.ma.masked_where(projlat2>85,N2)
nn2 = np.ma.masked_where(projlat2<70,nn2)
nn2 = np.ma.masked_where(projlon2<20,nn2)
nn2 = np.ma.masked_where(projlon2>90,nn2)
nn1 = nn1.sum(axis=1).sum(axis=1)/10000.
nn2 = nn2.sum(axis=1).sum(axis=1)/10000.
"""
pl.plot(years1,nn1,'b',linewidth=1.5)
pl.plot([years1[-1],years2[0]],[nn1[-1],nn2[0]],'r',linewidth=1.5)
pl.plot(years2,nn2,'r',linewidth=1.5)
pl.ylabel('Number density [10$^{4}$]')
pl.xlabel('Year')
pl.grid()
pl.savefig('figs/dshed_timeseries.pdf',format='pdf')
pl.show()
"""
# Format data
N1,N2 = N1.sum(axis=0),N2.sum(axis=0)
N1,N2 = N1[1:-1,1:-1]/(120.*len(years1)),N2[1:-1,1:-1]/(120.*len(years2))
Ntrend = Ntrend[1:-1,1:-1]/120.
pval = pval[1:-1,1:-1]
x,y = x[1:-1],y[1:-1]
N1,xx,yy = self.interp2d(N1,x,y,6,kind='cubic')
N2,xx,yy = self.interp2d(N2,x,y,6,kind='cubic')
#Ntrend,xx,yy = self.interp2d(Ntrend,x,y,6,kind='cubic')
#pval,xx,yy = self.interp2d(pval,x,y,6,kind='cubic')
# Plot
cseqa,cseqf = np.arange(-3,3+0.5,0.5),np.arange(0,80+5,5)
pl.figure(1)
cf = pl.contourf(xx,yy,N2-N1,cseqa,cmap=pl.cm.RdBu_r,extend='both')
cbar = pl.colorbar(cf)
cbar.set_label(r'Number density {400$\times$400 km$^{2}$ day}$^{-1}$')
drawCoastlinesNoRivers(proj.m)
proj.m.drawparallels([70,80],latmax=90)
pl.xlim(xlim0,xlim1)
pl.ylim(ylim0,ylim1)
#pl.xlim(x[1],x[-2])
#pl.ylim(y[1],y[-2])
pl.title('%s to %s minus %s to %s' % (years2[0],years2[-1],years1[0],years1[-1]))
pl.savefig('figs/dshed.pdf',format='pdf')
pl.clf()
pl.figure(2)
cf = pl.contourf(xx,yy,N1,cseqf,cmap=pl.cm.OrRd,extend='max')
cbar = pl.colorbar(cf)
cbar.set_label(r'Number density {400$\times$400 km$^{2}$ day}$^{-1}$')
drawCoastlinesNoRivers(proj.m)
proj.m.drawparallels([70,80],latmax=90)
pl.xlim(x[1],x[-2])
pl.ylim(y[1],y[-2])
pl.title('%s to %s' % (years1[0],years1[-1]))
pl.savefig('figs/shed.%s-%s.pdf' % (years1[0],years1[-1]),format='pdf')
pl.clf()
pl.figure(3)
cf = pl.contourf(xx,yy,N2,cseqf,cmap=pl.cm.OrRd,extend='max')
cbar = pl.colorbar(cf)
cbar.set_label(r'Number density {400$\times$400 km$^{2}$ day}$^{-1}$')
drawCoastlinesNoRivers(proj.m)
proj.m.drawparallels([70,80],latmax=90)
pl.xlim(x[1],x[-2])
pl.ylim(y[1],y[-2])
pl.title('%s to %s' % (years2[0],years2[-1]))
pl.savefig('figs/shed.%s-%s.pdf' % (years2[0],years2[-1]),format='pdf')
pl.clf()
pl.figure(4)
mx,my = np.ma.masked_where(pval>0.1,proj.x[1:-1,1:-1]),np.ma.masked_where(pval>0.1,proj.y[1:-1,1:-1])
cf = pl.contourf(x,y,Ntrend,cseqa,cmap=pl.cm.RdBu_r,extend='both')
cbar = pl.colorbar(cf)
cbar.set_label(r'Number density {400$\times$400 km$^{2}$ day}$^{-1}$ decade$^{-1}$')
pl.plot(mx,my,'k.',alpha=0.3,markersize=4)
drawCoastlinesNoRivers(proj.m)
proj.m.drawparallels([70,80],latmax=90)
for xi,yi in bounds:
pl.plot(xi,yi,'b',linewidth=1.5,alpha=0.6)
pl.xlim(x[1],x[-2])
pl.ylim(y[1],y[-2])
pl.title('Trend %s to %s' % (years1[0],years2[-1]))
pl.savefig('figs/shed_trend.%s.%syears.%s-%sN.%s-%sE.pdf' % (year0,N,LatRange[0],LatRange[1],LonRange[0],LonRange[1]),format='pdf')
pl.clf()
def trackField2(self,Field,LevType,b1,b2,blat,LonRange):
# Dates
dates = unpick('../WarmArctic/dates.warm.1000.p')
days = np.arange(-5,0+0.25,0.25)
vq,Dates = unpick('newfluxfiles/70/ERAInt/ERAInt.moist.1979-2016.30-1000hPa.70N.Annual.p')
# Flux axes
fluxlons = np.array(range(0,180+1,1) + range(-179,0,1))
inds = np.arange(-4,4+1,1)
#inds = np.arange(0,0+1,1)
# DataServers
ds = reDataServer(Field=Field,LevType=LevType)
proj = LambertProjector(boundinglat=45,resolution=200.)
# Get data
datelist = ds.getDateList(Year=2000,Month=1)
#datelist = [ds.getDate(ds.getHours(*dates[8]) + k*24) for k in days]
for date in datelist:
s = proj(ds.snapshot(*date),ds.lon,ds.lat)
f = interpolate.interp2d(proj.x, proj.y, s, kind='linear')
F = self.snapshot(*date)
lat,lon,p = F[:,:,0],F[:,:,1],F[:,:,2]
x,y = proj.m(lon,lat)
cseq = np.arange(0,20+1,1)
f70,nn0 = np.zeros(360),np.zeros(360)
pl.figure(1)
cf = pl.contourf(proj.x,proj.y,s,cseq,cmap=pl.cm.RdBu_r,extend='both')
cbar = pl.colorbar()
cbar.set_label('%s [%s]' % (ds.long_name,ds.units))
pl.contour(proj.x,proj.y,s,levels=[-20],colors='k',linewidths=1.5)
for i in range(len(x)):
if (b1 < f(x[i][0],y[i][0]) < b2) and (blat<lat[i,0]):
P,xii,dx = self.interpolateTrackToLat(lon[i],lat[i],70)
pl.plot(x[i][0],y[i][0],'k.')
pl.plot(x[i],y[i],'k',alpha=0.3)
if xii != -999 and (LonRange[0] < P[0] <= LonRange[1]):
ixi = Dates.index(ds.getDate(ds.getHours(*date) - xii*6))
lonx = np.argmin((fluxlons - P[0])**2)
loninds = lonx+inds
for il in range(len(loninds)):
if loninds[il]<0: loninds[il] = loninds[il]+360
if loninds[il]>=360: loninds[il] = loninds[il]-360
f70[loninds] += vq[ixi,loninds] + dx*(vq[ixi+1,loninds]-vq[ixi,loninds])
nn0[loninds] += 1
xx,yy = proj.m(P[0],P[1])
pl.plot(xx,yy,'r.',alpha=0.6)
f70,nn0 = np.ma.masked_where(nn0==0,f70),np.ma.masked_where(nn0==0,nn0)
f70 = f70/nn0
weighted = (f70*nn0).sum()/(nn0.sum())
proj.m.drawcoastlines()
proj.m.drawparallels([70,80],latmax=90)
pl.title('%s' % (str(date)))
fig,ax1 = pl.subplots(num=2)
ax2 = ax1.twinx()
ax1.plot(range(360),f70,'k')
ax1.plot([0,359],[0,0],'k--')
ax2.plot(range(360),nn0,'r')
#ax1.set_ylim(-100,600)
ax1.set_ylabel('Mean meridional moisture flux')
ax2.set_ylabel('Sample number')
pl.xlabel('Longitude')
pl.xlim(0,359)
pl.title('%s %s' % (str(date),weighted))
pl.show()
def trackField1(self,Field,LevType,b1,b2,YearRange=(1981,2016),blat=85,plot=True,LonRange=(30,90)):
fname = 'tracked/%s_vq70_%s-%s_%s-%skgm2.%s.p' % (Field,YearRange[0],YearRange[1],b1,b2,blat)
print fname
if not os.path.isfile(fname):
# Dates
dates = unpick('../WarmArctic/dates.warm.1000.p')
vq,Dates = unpick('newfluxfiles/70/ERAInt/ERAInt.moist.1979-2016.30-1000hPa.70N.Annual.p')
fluxlons = np.array(range(0,180+1,1) + range(-179,0,1))
inds = np.arange(-4,4+1,1)
#inds = np.arange(0,0+1,1)
# DataServers
ds = reDataServer(Field=Field,LevType=LevType)
dsv = reDataServer(Field='V',LevType='plev',LatRange=(70,70))
dsq = reDataServer(Field='q',LevType='plev',LatRange=(70,70))
proj = LambertProjector(boundinglat=80,resolution=200.)
# Get data
Years = range(YearRange[0],YearRange[1]+1,1)
f70,nn0 = np.zeros(360),np.zeros(360)
vplev = np.zeros((16,360))
nnplev = np.zeros((16,360))
pps,llons = [],[]
datelist = [ds.getDateList(Year=year,Season='DJF') for year in Years]
datelist = [datelist[i][j] for i in range(len(datelist)) for j in range(len(datelist[i]))]
#datelist = ds.getDateList(2001,Season='NDJFM')
Flux,S,PP = [],[],[]
for date in datelist:
print date
s = proj(ds.snapshot(*date),ds.lon,ds.lat)
F = self.snapshot(*date)
lat,lon,p = F[:,:,0],F[:,:,1],F[:,:,2]
x,y = proj.m(lon,lat)
for i in range(len(x)):
ix,iy = np.argmin((proj.x[0,:]-x[i][0])**2),np.argmin((proj.y[:,0]-y[i][0])**2)
if (b1 < s[iy,ix] < b2) and (blat<lat[i,0]):
P,xii,dx = self.interpolateTrackToLat(lon[i],lat[i],70)
if xii != -999 and (LonRange[0] < P[0] <= LonRange[1]):
ixi = Dates.index(ds.getDate(ds.getHours(*date) - xii*6))
lonx = np.argmin((fluxlons - P[0])**2)
loninds = lonx+inds
for il in range(len(loninds)):
if loninds[il]<0: loninds[il] = loninds[il]+360
if loninds[il]>=360: loninds[il] = loninds[il]-360
vqslice = vq[ixi,loninds] + dx*(vq[ixi+1,loninds]-vq[ixi,loninds])
#if vqslice.mean() > 0:
#vplev[:,loninds] += (dsv.snapshot(*Dates[ixi+1])*dsq.snapshot(*Dates[ixi+1]))[:,loninds]
#nnplev[:,loninds] += 1
#pps.append(p[i,xii])
#llon0 = lon[i,xii]
#if llon0 < 0: llon0 = llon0 + 360
#llons.append(llon0)
f70[loninds] += vqslice
nn0[loninds] += 1
Flux.append(vqslice.mean())
S.append(s[iy,ix])
PP.append(p[i,:])
PM,PS = np.array(PP).mean(axis=0),np.array(PP).std(axis=0)
f70,nn0 = np.ma.masked_where(nn0==0,f70),np.ma.masked_where(nn0==0,nn0)
f70 = f70/nn0
weighted = (f70*nn0).sum()/(nn0.sum())
Ntot = nn0.sum()
#pl.figure(1)
#pl.plot(S,Flux,'k.',alpha=0.15)
#pl.figure(2)
#days = np.arange(-10,0+0.25,0.25)
#pl.plot(days,PM[::-1],'k')
#pl.ylim(1000,700)
#pl.plot(days,PM[::-1]-PS[::-1],'k--')
#pl.plot(days,PM[::-1]+PS[::-1],'k--')
#pl.ylim(1000,700)
#pl.show()
#vplev,nnplev = np.ma.masked_where(nnplev==0,vplev),np.ma.masked_where(nnplev==0,nnplev)
#vplev = vplev/nnplev
#cseq = np.arange(-0.012,0.012+0.002,0.002)
#pl.contourf(dsv.lon,dsv.lev,vplev,cseq,cmap=pl.cm.RdBu_r,extend='both')
#pl.plot(llons,pps,'k.',alpha=0.15)
#pl.colorbar()
#pl.savefig('figs/neg.test.pdf',format='pdf')
#pl.ylim(1000,30)
#pl.show()
toPick([f70,nn0,weighted,Ntot],fname)
else:
f70,nn0,weighted,Ntot = unpick(fname)
# Plot
if plot:
fig,ax1 = pl.subplots(num=2)
ax2 = ax1.twinx()
ax1.plot(range(360),f70,'k')
ax1.plot([0,359],[0,0],'k--')
ax2.plot(range(360),nn0,'r')
ax1.set_ylim(-100,600)
ax2.set_ylim(0,1200)
ax1.set_ylabel('Mean meridional moisture flux')
ax2.set_ylabel('Sample number')
pl.xlabel('Longitude')
pl.xlim(0,359)
pl.title('DJF %s to %s: %s' % (YearRange[0],YearRange[1],weighted))
pl.savefig('figs/characflux/%s_vq70_%s-%s_%s-%skgm2.pdf' % (Field,YearRange[0],YearRange[1],b1,b2), format='pdf')
pl.close()
return f70,nn0,weighted,Ntot
def crossing(self,Year=1998,Month=1):
fname = '../Tracks/back1_crossings/%s_%s_%02d.nc' % ('cross',Year,Month)
print fname
if not os.path.isfile(fname):
# Create array of all crossings of 70N, meridional moisture flux and time since initialisation
proj = LambertProjector(boundinglat=80,resolution=200.)
ds = reDataServer(Field='pw',LevType='surface_analysis')
vq,Dates = unpick('newfluxfiles/70/ERAInt/ERAInt.moist.1979-2016.30-1000hPa.70N.Annual.p')
F = self.getDataSnaps(Year=Year,Month=Month)
datelist = self.getDateList(Year=Year,Month=Month)
hourlist = [self.getHours(*date) for date in datelist]
fluxlons = np.array(range(0,180+1,1) + range(-179,0,1))
inds = np.arange(-4,4+1,1)
lat,lon,p = F[:,:,:,0],F[:,:,:,1],F[:,:,:,2]
lon0s = lon[0,:,0]
x,y = proj.m(lon,lat)
n0,n1,n2 = lat.shape
flux,steps = np.zeros((n0,n1)),np.zeros((n0,n1))
loncross = np.zeros((n0,n1))
latstart = np.zeros((n0,n1))
Pcross = np.zeros((n0,n1))
Field = np.zeros((n0,n1))
for t in range(len(lat)):
date = datelist[t]
snap = proj(ds.snapshot(*date),ds.lon,ds.lat)
for i in range(len(lat[t])):
P,xii,dx = self.interpolateTrackToLat(lon[t,i],lat[t,i],70)
ix,iy = np.argmin((proj.x[0,:]-x[t,i,0])**2),np.argmin((proj.y[:,0]-y[t,i,0])**2)
if xii != -999:
latst = lat[t,i,0]
field = snap[iy,ix]
lonc = P[0]
pc = p[t,i,xii] + dx*(p[t,i,xii-1] - p[t,i,xii])
stepc = xii + 1 - dx
ixi = Dates.index(self.getDate(self.getHours(*date) - xii*6))
lonx = np.argmin((fluxlons - P[0])**2)
loninds = lonx+inds
for il in range(len(loninds)):
if loninds[il]<0: loninds[il] = loninds[il]+360
if loninds[il]>=360: loninds[il] = loninds[il]-360
vqslice = vq[ixi,loninds] + dx*(vq[ixi+1,loninds]-vq[ixi,loninds])
vqslice = vqslice.mean()
else:
latst = -9999
field = -9999
lonc = -9999
pc = -9999
stepc = -9999
vqslice = -9999
latstart[t,i] = latst
Field[t,i] = field
loncross[t,i] = lonc
Pcross[t,i] = pc
steps[t,i] = stepc
flux[t,i] = vqslice
latstart = np.ma.masked_where(latstart==-9999,latstart)
Field = np.ma.masked_where(Field==-9999,Field)
loncross = np.ma.masked_where(loncross==-9999,loncross)
Pcross = np.ma.masked_where(Pcross==-9999,Pcross)
steps = np.ma.masked_where(steps==-9999,steps)
flux = np.ma.masked_where(flux==-9999,flux)
crossNetCDF(hourlist,lon0s,latstart,Field,loncross,flux,steps,fname)
else:
print 'File %s already exists!' % (fname)
# Draw the coastlines, with a thin line and same color as the continent fill.
coasts = m.drawcoastlines(zorder=1, color='white', linewidth=0)
# Exact the paths from coasts
coasts_paths = coasts.get_paths()
# In order to see which paths you want to retain or discard
# you'll need to plot them one at a time noting those that you want etc.
for ipoly in range(0, 35 + 1, 1):
if ipoly > poly_stop: continue
r = coasts_paths[ipoly]
# Convert into lon/lat vertices
polygon_vertices = [(vertex[0], vertex[1])
for (vertex,
code) in r.iter_segments(simplify=False)]
px = [polygon_vertices[i][0] for i in xrange(len(polygon_vertices))]
py = [polygon_vertices[i][1] for i in xrange(len(polygon_vertices))]
m.plot(px, py, 'k-', linewidth=linewidth, color=color, zorder=50)
#pl.title(ipoly)
#pl.show()
#pl.clf()
if __name__ == "__main__":
import sys
sys.path.insert(0, '/mnt/climstorage/cian/scripts')
from LambertProjector import *
import matplotlib.pyplot as pl
proj = LambertProjector(boundinglat=55, resolution=350.)
drawCoastlinesNoRivers(proj.m)
dUdz = dudz(U, Z)
f = (2 * (7.29e-5) * np.sin(lat * np.pi / 180))[:, np.newaxis]
gr = (0.31 * 9.81 * f * dUdz / N)
return gr, N, dUdz, f
if __name__ == "__main__":
import sys
sys.path.insert(0, '/home/cian/scripts')
from ReanalysisDataServer import *
from LambertProjector import *
import matplotlib.pyplot as pl
proj = LambertProjector(boundinglat=60, resolution=100.)
dsu = DataServer(Field='U',
LevType='plev',
LevRange=(500, 925),
LatRange=(30, 90))
dsz = DataServer(Field='Z',
LevType='plev',
LevRange=(500, 925),
LatRange=(30, 90))
dst = DataServer(Field='T',
LevType='plev',
LevRange=(500, 925),
LatRange=(30, 90))
latx = np.argmin((dst.lat - 70)**2)
for date in dsu.getDateList(Year=2016, Month=12):