a2areane = lupscale_prep[7].T
#
pergrid = 0
missflag = 0
a2upscale = myfunc_fsub.upscale_fast(a2org.T\
, a1xw_corres_fort, a1xe_corres_fort\
, a1ys_corres_fort, a1yn_corres_fort\
, a2areasw.T, a2arease.T, a2areanw.T, a2areane.T\
, len(a1lon_upscale), len(a1lat_upscale)\
, pergrid, missflag, miss_out\
).T
#-- Interpolation --------
#
#
a2fin = cf.biIntp( a1lat_upscale, a1lon_upscale, a2upscale, a1lat_fin, a1lon_fin, miss = miss_out)[0]
#
a2fin.tofile( oname )
print tempname
##-- delete temp file ----------------
os.remove(tempname)
#------------------------------------
a1lat_org = nc.variables["lat"][:]
a1lon_org = nc.variables["lon"][:]
data = nc.variables["%s"%(var)][:]
#*** Regrid ********
upflag = cmip_para.ret_upflag(model)
if upflag == True:
pergrid = 0 # per area (e.g. mm/m2), others (e.g, K, kg/kg, mm/s)
#pergrid = 1 # per grid (e.g. km2/grid, population/grid)
missflag = 1
ny_org = len(a1lat_org)
nx_org = len(a1lon_org)
data_one = myfunc_fsub.upscale( data.T\
, a1lon_org, a1lat_org\
, a1lon_one, a1lat_one\
, pergrid, missflag, miss\
, nx_org, ny_org\
, nx_one, ny_one).T
#-- downscale: Interpolation ----
elif upflag == False:
data_one = cf.biIntp(a1lat_org, a1lon_org, data, a1lat_one, a1lon_one)[0]
#-------
data_one = array(data_one, float32)
########
oname = odir + "/%s.sa.one"%(ohead)
print oname
########
f = open(oname, "wb")
f.write(data_one)
f.close()
if aphromask == "TRUE":
da2in[var] = ma.masked_where(a2aphromask ==miss_out, da2in[var]).filled(miss_out)
#-- for global --
a2num_glob = a2num_glob + ma.masked_where(da2in[var]==miss_out, a2one_glob).filled(0.0)
a2sum_glob = a2sum_glob + ma.masked_equal(da2in[var], miss_out).filled(0.0)
#---------------
diname["life"] = didir["life"] + "/life.%04d%02d%02d%02d.sa.one"%(year, mon, day, hour)
diname["pgrad"] = didir["pgrad"] + "/pgrad.%04d%02d%02d%02d.sa.one"%(year, mon, day, hour)
#---------------
da2in["pgrad"] = fromfile(diname["pgrad"],float32).reshape(ny_org, nx_org)
#da2in["life"] = fromfile(diname["life"], int32).reshape(ny_org, nx_org)
#--- original --> fine grid ---
a2in_fin = cf.biIntp(a1lat_org, a1lon_org, da2in[var], a1lat_fin, a1lon_fin, miss=miss_out)[0]
#--- cyclone center data -------------
da2in["center"] = ma.masked_less(da2in["pgrad"], 0.0).filled(0.0)
da2in["center"] = ma.masked_greater(da2in["center"], 0.0).filled(1.0)
#-------------------------------------
for iy_org in ly_org:
for ix_org in lx_org:
#-------------
if thgrad_min <= da2in["pgrad"][iy_org, ix_org] < thgrad_max:
i = i+1
print iy_org, ix_org
#---- project center position original --> fine grid
lat_org = a1lat_org[iy_org]
lon_org = a1lon_org[ix_org]
iy_fin, ix_fin = latlon2yx(lat_org, lon_org, lat_fin_first, lon_fin_first, dlat_fin, dlon_fin)
if var in lvar_2d:
args = "wgrib %s | grep %s | wgrib %s -i -nh -o %s"%(gribname, var, gribname, tempname)
elif var in lvar_700mb:
args = "wgrib %s | grep %s | grep '700 mb' | wgrib %s -i -nh -o %s"%(gribname, var, gribname, tempname)
#print args
ptemp = subprocess.call(args, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#-- Interpolation and Flipud --------
a2org = flipud(fromfile(tempname, float32).reshape(ny_org, nx_org))
#
if var in lvar_nonegative:
a2org = ma.masked_less(a2org, 0.0).filled(miss_out)
#
a2fin = cf.biIntp( a1lat_org, a1lon_org, a2org, a1lat_fin, a1lon_fin, miss = miss_out)[0]
#-- change unit [kg m-2] -> [kg m-2 s-1] ---
if var in lvar_prec:
coef = 1.0/(60.0*60.0*24.0)
a2fin = ma.masked_equal(a2fin , miss_out) * coef
a2fin = a2fin.filled(0.0)
#
a2fin.tofile( oname )
print oname
##-- delete temp file ----------------
os.remove(tempname)
#------------------------------------
if upflag == True:
pergrid = 0 # per area (e.g. mm/m2), others (e.g, K, kg/kg, mm/s)
#pergrid = 1 # per grid (e.g. km2/grid, population/grid)
missflag = 1
ny_org = len(a1lat_org)
nx_org = len(a1lon_org)
data_one = myfunc_fsub.upscale( data.T\
, a1lon_org, a1lat_org\
, a1lon_one, a1lat_one\
, pergrid, missflag, miss\
, nx_org, ny_org\
, nx_one, ny_one).T
#-- downscale: Interpolation --
elif upflag == False:
data_one = cf.biIntp(a1lat_org, a1lon_org, data, a1lat_one, a1lon_one, miss=miss)[0]
#---
data_one = array(data_one, float32)
########
oname = odir + "/%s.%04dhPa.%s.%s.sa.one"%(var, lev, ens, stime)
########
f = open(oname, "wb")
f.write(data_one)
f.close()
print oname
#-------------
##----------------
## make pa for 6hrLev
##----------------
#if dattype in ["6hrLev"]:
print "Directory was made."
lon = arange(118, 150, 0.0125)
lat = arange(20, 47.888, 0.0083) #before regridding
LON = arange(123.024, 147.976, 0.05)
LAT = arange(24.024, 46.024, 0.05) #after regridding
#Precipitation conversion kg/m^2 => kg/m^2/s [0.0125x0.0083...]=>[0.05x0.05]
INFILE1 = DATADIR + 'rdr_' + str(YEAR) + str(MON) + str(DAY) + str(
TIME) + '.bin'
prcp = fromfile(INFILE1, 'float32').reshape(24, 3360, 2560)
#array=zeros((505,481))
#intarray=array.reshape(1,505,-1)
#t_prcp=concatenate([intarray,prcp],axis=0)
P = prcp[:] / 3600
PRCP = biIntp(lat, lon, P, LAT, LON)
print str(YEAR) + str(MON) + str(DAY)
print PRCP.shape
PRCPf = PRCP.flatten()
print PRCPf.shape
PRCPf.astype('float32').tofile(OUTDIR + 'rdr_' + str(YEAR) + str(MON) +
str(DAY) + str(TIME) + '.bin')
a2up = myfunc_fsub.upscale(a2in.T\
, a1lon_org, a1lat_org\
, a1lon_upscale, a1lat_upscale\
, pergrid, missflag, miss\
, nx_org, ny_org\
, nx_upscale, ny_upscale).T
#----
else:
model = "org"
a2up = a2in
a1lat_upscale = a1lat_org
a1lon_upscale = a1lon_org
#--- downscale -------
a2one = cf.biIntp(a1lat_upscale,
a1lon_upscale,
a2up,
a1lat_one,
a1lon_one,
miss=miss)[0]
#-------------------------------
da2mean[mon] = da2mean[mon] + a2one
da2mean[0] = da2mean[0] + a2one
#------------------------------
#for mon in range(0, 12+1):
for mon in [0] + lmon:
da2mean[mon] = da2mean[mon] / dtimes[mon]
soname = odir + "/%s.%s.0000%02d0000.sa.one" % (stype, var, mon)
da2mean[mon].tofile(soname)
print soname
#------------------------------
diname["pr"] = didir["pr"] + "/%s_3hr_%s_%s_%s_%04d%02d%02d%02d%02d.sa.one"%("pr", model, expr, ens, year_target, mon_target, day_target, hour_target, min_target)
#-- prec load ------
a2invar_temp = fromfile(diname[var], float32).reshape(ny_org, nx_org)
da2in[var] = da2in[var] + ma.masked_equal(a2invar_temp, miss_out).reshape(ny_org, nx_org)
#-------------------
da2in[var] = da2in[var] / len(lhour_inc)
if type(da2in[var]) == ma.core.MaskedArray:
da2in[var] = da2in[var].filled(miss_out)
#--- load ------------
da2in["pgrad"] = fromfile(diname["pgrad"],float32).reshape(ny_org, nx_org)
#da2in["life"] = fromfile(diname["life"], int32).reshape(ny_org, nx_org)
#--- original --> fine grid ---
a2in_fin = cf.biIntp(a1lat_org, a1lon_org, da2in[var], a1lat_fin, a1lon_fin)[0]
#--- cyclone center data -------------
da2in["center"] = ma.masked_less(da2in["pgrad"], 0.0).filled(0.0)
da2in["center"] = ma.masked_greater(da2in["center"], 0.0).filled(1.0)
#-------------------------------------
for iy_org in ly_org:
for ix_org in lx_org:
if thgrad_min <= da2in["pgrad"][iy_org, ix_org] < thgrad_max:
i = i+1
print iy_org, ix_org
#---- project center position original --> fine grid
lat_org = a1lat_org[iy_org]
lon_org = a1lon_org[ix_org]
iy_fin, ix_fin = latlon2yx(lat_org, lon_org, lat_fin_first, lon_fin_first, dlat_fin, dlon_fin)
dumpname = odir + "/dump.txt"
print dumpname
ptemp = subprocess.call("wgrib -V %s > %s"%(gribname, dumpname), shell=True)
#-----------------
#----- Names ------------
oname_tail = gribname.split("/")[-1]
oname_tail = ".".join(oname_tail.split(".")[:-1])
tempname = odir + "/%s.temp.sa.one"%(oname_tail)
oname = odir + "/%s.sa.one"%(oname_tail)
#-- grib --> binary -----
args = "wgrib %s | wgrib %s -i -nh -o %s"%(gribname, gribname, tempname)
#args = "wgrib %s -nh -o %s"%(gribname, tempname)
ptemp = subprocess.call(args, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#-- Interpolation and Flipud --------
a2org = flipud(fromfile(tempname, float32).reshape(ny_org, nx_org))
a2fin = cf.biIntp( a1lat_org, a1lon_org, a2org, a1lat_fin, a1lon_fin,)
a2fin.tofile( oname )
print oname
#-- delete temp file ----------------
os.remove(tempname)
#------------------------------------
LON = arange(123.024, 147.976, 0.05)
LAT = arange(24.024, 46.024, 0.05) #after regridding
#Precipitation conversion kg/m^2 => kg/m^2/s [0.05x0.0625]=>[0.05x0.05]
INFILE1 = DATADIR + 'apcp_' + str(YEAR) + str(MON) + str(DAY) + str(
TIME) + '.bin'
prcp = fromfile(INFILE1, 'float32').reshape(FT, 505, 481)
#array=zeros((505,481))
#intarray=array.reshape(1,505,-1)
#t_prcp=concatenate([intarray,prcp],axis=0)
P = prcp[:] / 3600
PRCP = biIntp(lat, lon, P, LAT, LON)
print PRCP.shape
PRCPf = PRCP.flatten()
print PRCPf.shape
PRCPf.astype('float32').tofile(OUTDIR + 'prcp_' + str(YEAR) + str(MON) +
str(DAY) + str(TIME) + '.bin')
#Air Pressure conversion Pa => hPa [0.05x0.0625]=>[0.05x0.05]
INFILE2 = DATADIR + 'pres_' + str(YEAR) + str(MON) + str(DAY) + str(
TIME) + '.bin'
pres = fromfile(INFILE2, 'float32').reshape(FT + 1, 505, 481)
