ne0 = size(ocs.dx0[0, :])
xne0 = arange(ne0)
nst = ocs.cur_step
xstep = arange(ocs.cur_step)
dimnames = ['nx', 'ne', 'ne0', 'nst']
dimvars = [xnx, xne, xne0, xstep]
dimtypes = ['i', 'i', 'i', 'i']
print ocs.ne, ocs.nx, ocs.cur_step
print shape(ocs.xtm)
print shape(ocs.dx)
# out put to the restart file
xtm_info = ofb.geos_varinfo('sum_xtm', 'f', ['ne', 'ne'], ocs.xtm)
x_info = ofb.geos_varinfo('x', 'f', ['nx'], ocs.mean_x)
x0_info = ofb.geos_varinfo('x0', 'f', ['nx'], ocs.mean_x0)
dx_info = ofb.geos_varinfo('dx', 'f', ['nx', 'ne'], ocs.dx)
dx0_info = ofb.geos_varinfo('dx0', 'f', ['nx', 'ne0'], ocs.dx0)
doy_info = ofb.geos_varinfo('doy', 'i', ['nst'], ocs.co2.doy[0:nst])
yyyy_info = ofb.geos_varinfo(
'yyyy', 'i', ['nst'], ocs.co2.yyyy[0:nst]) # year for emission period
# x_info=ofb.geos_varinfo('x', 'f', ['nx'], ocs.mean_x)
# ofb.ncf_write_by_varinfo(resflnm, dimnames, dimtypes, dimvars, [x_info, dx_info, xtm_info])
ofb.ncf_write_by_varinfo(resflnm, dimnames, dimtypes, dimvars, [dx_info, \
dx0_info, xtm_info, \
x_info, x0_info, \
yyyy_info, doy_info])
def data_collect(start_step, nst, geos_datapath, obs_datapath, viewmode_list, \
err_scale=1.0, do_debug=False, do_dump=True, dumpflnm=None):
""" doys in date to be read in read in
"""
fen = open(geos_datapath + '/' + 'ens_pos.dat', 'r')
line = fen.readline()
line = fen.readline()
terms = line.split()
step = int(terms[1])
nstep = int(terms[3])
line = fen.readline()
lines = fen.readlines()
fen.close()
em_st = list()
em_end = list()
yyyy_st = list()
yyyy_end = list()
doy_st = list()
doy_end = list()
co2flnm = list()
sel_x_idx = range(1, 4)
for line in lines:
line = line.strip()
if (len(line) < 1):
break
terms = line.split()
print terms
em_st.append(int(terms[0]))
em_end.append(int(terms[1]))
yyyy_st.append(int(terms[2]))
yyyy_end.append(int(terms[3]))
doy_st.append(int(terms[4]))
doy_end.append(int(terms[5]))
co2flnm.append(terms[6])
yyyy = yyyy_st[start_step]
doy0 = doy_st[start_step]
doy1 = doy0 + nst * step - 1 # 096 0406
# set up the start days
st_days = list()
for iday in range(doy0, doy1 + 1, step):
sday = r'%4.4dD%3.3d' % (yyyy, iday)
st_days.append(sday)
print st_days
do_debug = False
iplot = 0
state_v = stv_c.state_vector(st_days,
do_debug=False,
datapath=geos_datapath)
nx, ne = state_v.nx, state_v.ne
print 'nx, ne', nx, ne
nreg = (nx) / size(st_days)
x = array(state_v.stv)
data_count = list()
istep = 0
doys = arange(doy0, doy1)
iday = 0
nusd_obs = 0
fclim = open('clim_co2.dat', 'r')
lines = fclim.readlines()
fclim.close()
aprior = list()
apr_pres = list()
for iline in lines:
terms = iline.split()
aprior.append(float(terms[1]))
apr_pres.append(float(terms[0]))
aprior = array(aprior)
apr_pres = array(apr_pres)
apr_pres = log10(apr_pres)
# starting to collect the observations and y
istep = 0
# figure(1)
# show()
# figure(1)
sel_doys = [-1, -8, -15]
day_cnt = 0
for doy in doys:
yyyy, mm, dd = tm.doy_to_time_array(doy, yyyy)
print 'year mm dd', yyyy, mm, dd
y = list()
iend = 0
full_doy = r'%4.4dD%3.3d' % (yyyy, doy)
# check whether it is necessary to include surface flux (stv) during cerntain days
for st_dd in st_days[:]:
if (full_doy < st_dd):
print full_doy, st_dd
break
else:
iend = iend + 1
real_nx = iend * nreg
real_ne = real_nx + 1
# generate x
# the ensemble members need to be included
sel_eid = list()
iend = 0
print em_st
while True:
print em_st[iend], real_ne
if (real_ne < em_st[iend]):
break
else:
sel_eid.append(iend)
if (real_ne <= em_end[iend]):
break
else:
iend = iend + 1
# generate model obs
sdate = r'%4.4dD%3.3d' % (yyyy, doy)
ncflnm = obs_datapath + "oco" + "." + sdate + ".nc"
if (doy == doys[0]):
sdate0 = sdate
dumpext = r"%2.2d" % nst
dumpext = "." + sdate0 + "_N" + dumpext
viewmode = viewmode_list[doy - 1]
ncflnm = obs_datapath + "/oco_" + viewmode + "." + sdate + ".nc"
print ncflnm
std_od, std_cflag = ofb.ncf_read(ncflnm, ['od', 'cloud'])
iy = 0
print 'sel_eid', sel_eid
for eid in sel_eid:
est = em_st[eid]
eend = em_end[eid]
print 'sttt days', st_days[eid]
# if (day_cnt==0):
# the new code needs the right starting time for ctm file
ctm_date = st_days[eid]
ctm_yyyy, ctm_doy = int(ctm_date[0:4]), int(ctm_date[5:8])
ctm_yyyy, ctm_mm, ctm_dd = tm.doy_to_time_array(ctm_doy, ctm_yyyy)
ctm_date0 = r'%4.4d%2.2d%2.2d' % (ctm_yyyy, ctm_mm, ctm_dd)
# r'%4.4d%2.2d%2.2d' % (yyyy, mm, dd)
print '=' * 20 + 'read in data and calculate xco2' + '=' * 20
obs=obo.obs_operator(yyyy, mm, dd, est, eend, \
aprior=aprior, apr_pres=apr_pres, \
viewmode=viewmode,\
datapath=geos_datapath,
ctm_date0=ctm_date0)
ytop = min([eend, real_ne])
for em in range(est, ytop + 1):
if (em == est):
if (istep > 300):
print 'em', em, est
obs.get_obs_prof(em, std_od=std_od, std_cf=std_cflag)
else:
obs.get_obs_prof(em, idx=used_idx, do_update=False)
if (iy == 0):
# em_id is different from em. em_id is the 'real id' in the whole ensemble set
em_id = obs.em_id[em - 1]
print 'em_id', em_id, em - 1
xgp0 = obs.obs_xgp[em_id]
xgp = obs.obs_xgp[em_id]
print size(xgp), size(std_od), size(std_cflag)
obs_err = obs.obs_err
obs_err = array(obs_err)
obs_err = obs_err
# print 'shape obs+err', shape(obs_err)
rnd_obs_err = array(obs_err)
for iobs in range(size(obs_err)):
err_val = obs_err[iobs]
rnd_err = rnd.normal(scale=err_val)
rnd_obs_err[iobs] = rnd_err
# err_scale=1.0
if (do_debug):
subplot(2, 1, 1)
plot(obs_err)
plot(rnd_obs_err)
subplot(2, 1, 2)
hist(rnd_obs_err)
hist(obs_err)
show()
rnd_obs_err = err_scale * rnd_obs_err
cflag = obs.obs_cflag
otime = obs.obs_time
olat = obs.obs_lat
olon = obs.obs_lon
od = obs.obs_od
lwi = obs.obs_lwi
lwi = lwi.astype(int)
# used_idx=where(logical_and(cflag==0, od<=0.3, lwi<>1))
osza = obs.obs_sza
sel1 = logical_and(cflag == 0, od <= 0.3)
sel2 = logical_and(lwi <> 1, lwi <> 2)
# used_idx=where(sel1)
# used_idx=where(logical_and(sel1, sel2))
# used_idx=where(cflag==0)
used_idx = where(logical_and(obs_err < 4.0, sel1))
used_idx = squeeze(used_idx)
print 'used_idx', len(used_idx)
xgp = xgp[used_idx]
xgp0 = xgp0[used_idx]
obs_err = 1.0e-6 * obs_err[used_idx]
rnd_obs_err = 1.0e-6 * rnd_obs_err[used_idx]
imax = argmax(rnd_obs_err)
print 'max(obs err), max(rnd_obs_err)', 1.0e6 * obs_err[
imax], 1.0e6 * rnd_obs_err[imax]
olat = olat[used_idx]
olon = olon[used_idx]
lwi = lwi[used_idx]
otime = otime[used_idx]
od = od[used_idx]
# r=array(varData)
# r=r*r
else: # just others for ensemble
em_id = obs.em_id[em - 1]
# print 'em_id', em_id, em-1
xgp = obs.obs_xgp[em_id]
if (em == est):
print em, est
print len(used_idx)
xgp = xgp[used_idx]
if (istep > 300):
print 'type xgp', type(xgp)
print 'len-xgp', len(xgp)
if (em == 2):
print 'xgp', xgp[0:6]
y.append(array(xgp))
if (do_debug):
print ii, ne, shape(xgp)
if (do_debug and ii == 4):
# figure(1)
# plot(xgp[0:300],'r')
# plot(xgp0[0:300], 'b')
z = xgp - xgp0
print 'max dev', max(z)
iy = iy + 1
for same_y in range(real_ne, ne):
y.append(array(xgp0)) # filled with the same value
# show()
y = array(y)
y = transpose(y)
if (istep > 300):
print type(y)
print len(y)
ix = y[0]
print type(ix)
# read in the data
obs_f = NetCDFFile(ncflnm)
yobs = obs_f.variables['xco2']
yobs_err = obs_f.variables['err']
yobs = array(yobs)
# yobs_err=array(yobs_err)
yobs_err = array(yobs_err)
# print 'shape yobs', shape(yobs)
yobs = squeeze(yobs)
yobs_err = squeeze(yobs_err)
obs_f.close()
yobs = yobs[used_idx]
yobs_err = yobs_err[used_idx]
print 'shape y & yobs', shape(y), shape(yobs)
if (istep == 0):
all_y = array(y)
all_yobs = array(yobs)
all_yobs_err = array(yobs_err)
all_rnd_err = array(rnd_obs_err)
all_lat = array(olat)
all_lon = array(olon)
all_lwi = array(lwi)
all_time = array(otime)
all_od = array(od)
else:
all_y = concatenate((all_y, y))
all_yobs = concatenate((all_yobs, yobs))
all_yobs_err = concatenate((all_yobs_err, yobs_err))
all_rnd_err = concatenate((all_rnd_err, rnd_obs_err))
all_lat = concatenate((all_lat, olat))
all_lon = concatenate((all_lon, olon))
all_lwi = concatenate((all_lwi, lwi))
all_time = concatenate((all_time, otime))
all_od = concatenate((all_od, od))
print 'istep & shape ', istep, shape(all_y), shape(all_yobs), shape(
all_yobs_err), shape(all_rnd_err)
istep = istep + 1
data_count.append(size(yobs))
day_cnt = day_cnt + 1
if (day_cnt == gcdf.temporal_resolution):
day_cnt = 0
data_count = array(data_count)
factor = 1.0e6
all_y = factor * all_y
all_yobs = factor * all_yobs
all_rnd_err = factor * all_rnd_err
all_yobs_err = factor * all_yobs_err
if (do_dump):
if (dumpflnm == None):
ncdump = geos_datapath + '/' + 'obs' + dumpext + ".nc"
else:
ncdump = geos_datapath + '/' + dumpflnm + "_" + dumpext + ".nc"
xnx = arange(nx)
xne = arange(ne)
xny = arange(size(all_y[:, 0]))
dimTypes = ['i', 'i', 'i', 'i']
dimVars = [xnx, xne, xny, doys]
dimNames = ['nx', 'ne', 'ny', 'doys']
x_info = ofb.geos_varinfo('x', 'f', ['nx', 'ne'], x)
y_info = ofb.geos_varinfo('y', 'f', ['ny', 'ne'], all_y)
yobs_info = ofb.geos_varinfo('obs', 'f', ['ny'], all_yobs)
yobs_err_info = ofb.geos_varinfo('err', 'f', ['ny'], all_yobs_err)
rnd_err_info = ofb.geos_varinfo('rnd_err', 'f', ['ny'], all_rnd_err)
count_info = ofb.geos_varinfo('daily_count', 'i', ['doys'], data_count)
lat_info = ofb.geos_varinfo('lat', 'f', ['ny'], all_lat)
lon_info = ofb.geos_varinfo('lon', 'f', ['ny'], all_lon)
time_info = ofb.geos_varinfo('time', 'f', ['ny'], all_time)
od_info = ofb.geos_varinfo('od', 'f', ['ny'], all_od)
lwi_info = ofb.geos_varinfo('lwi', 'i', ['ny'], all_lwi)
ofb.ncf_write_by_varinfo(ncdump, dimNames, dimTypes, dimVars, \
[x_info, y_info, yobs_info, yobs_err_info, rnd_err_info, lat_info, lon_info, time_info, od_info, lwi_info, count_info])
return x, all_y, all_yobs, all_yobs_err, all_rnd_err, data_count, doys, \
all_lat, all_lon, all_time, all_lwi, all_od
mod_vals[iweek, 0:nstn, 0:ntr] = new_y0[0:nstn, 0:ntr]
# output obs and model values
nobs, nstn, ntr = shape(mod_vals)
dimnames = ['nobs', 'nstn', 'ntr']
xobs = arange(nobs)
xstn = arange(nstn)
xntr = arange(ntr)
dimvars = [xobs, xstn, xntr]
dimtypes = ['i', 'i', 'i']
olon_info = ofb.geos_varinfo('lon',
'f', ['nstn'],
olon,
varattr={
"unit": "degree",
"desc": "longitude"
})
otime_info = ofb.geos_varinfo('time',
'f', ['nstn'],
otime,
varattr={
"unit": "none",
"desc": "time idx"
})
odoy_info = ofb.geos_varinfo('odoy',
'f', ['nstn'],
odoy,
varattr={
"unit": "day",
def __init__(self, cur_yyyy, doy_st, doy_end, rerun_st=1, rerun_end=2):
self.rerun_est = rerun_st
self.rerun_eend = rerun_end
self.rerun_datapath = gcdf.data_path
self.cur_yyyy = cur_yyyy
viewmode_nadir = ['nadir'] * 16
viewmode_glint = ['glint'] * 16
if (gcdf.view_mode == 'nadir'):
viewmode_list = viewmode_nadir + viewmode_nadir
elif (gcdf.view_mode == 'glint'):
viewmode_list = viewmode_glint + viewmode_glint
else:
viewmode_list = viewmode_nadir + viewmode_glint
viewmode_list = viewmode_list * 12
viewmode_code = list()
for idoy in range(doy_st, doy_end + 1):
yyyy, mm, dd = tm.doy_to_time_array(idoy, self.cur_yyyy)
sdate = r'%4.4dD%3.3d' % (yyyy, idoy)
viewmode = viewmode_list[idoy - 1]
ncflnm = "./" + gcdf.view_type + "_" + viewmode + "." + sdate + ".nc"
org_ncflnm = gcdf.obs_path + "/" + gcdf.view_type + "_" + viewmode + "." + sdate + ".nc"
print ncflnm
varnames=['time', 'obs_lvl', 'lon', 'lat', 'cloud', 'nclear', 'xco2', 'xco2_ap', \
'err', 'rnd_err', \
'od', 'lwi', 'sza', 'obs_pres', 'obs_ak', 'obs_apr']
time, obs_lvl, lon, lat, cloud, nclear, xco2, xco2_ap, \
err, rnd_err, \
od, lwi, sza, obs_pres, obs_ak, obs_apr=\
ofb.ncf_read(org_ncflnm, varnames)
nobs = size(lat)
self.olat = array(lat)
self.olon = array(lon)
self.otime = array(time)
self.obs_ak = array(obs_ak)
self.obs_apr = array(obs_apr)
self.obs_xgp0 = array(xco2_ap)
self.obs_pres = array(obs_pres)
hm0 = self.read_new_mean_y(yyyy, mm, dd)
hm0 = squeeze(hm0)
if (max(hm0) > 1.0):
hm0 = 1.0e-6 * hm0
dimnames = ['time', 'obs_lvl']
dimtypes = ['f', 'i']
dimvars = [time, obs_lvl]
lon_info = ofb.geos_varinfo('lon', 'f', ['time'], lon)
lat_info = ofb.geos_varinfo('lat', 'f', ['time'], lat)
cloud_info = ofb.geos_varinfo('cloud', 'f', ['time'], cloud)
nclear_info = ofb.geos_varinfo('nclear', 'f', ['time'], nclear)
xco2_info = ofb.geos_varinfo('xco2', 'f', ['time'], hm0)
xco2_ap_info = ofb.geos_varinfo('xco2_ap', 'f', ['time'], xco2_ap)
err_info = ofb.geos_varinfo('err', 'f', ['time'], err)
rnd_err_info = ofb.geos_varinfo('rnd_err', 'f', ['time'], rnd_err)
od_info = ofb.geos_varinfo('od', 'f', ['time'], od)
lwi_info = ofb.geos_varinfo('lwi', 'f', ['time'], lwi)
sza_info = ofb.geos_varinfo('sza', 'f', ['time'], sza)
obs_pres_info = ofb.geos_varinfo('obs_pres', 'f',
['time', 'obs_lvl'], obs_pres)
obs_ak_info = ofb.geos_varinfo('obs_ak', 'f', ['time', 'obs_lvl'],
obs_ak)
obs_apr_info = ofb.geos_varinfo('obs_apr', 'f',
['time', 'obs_lvl'], obs_apr)
ofb.ncf_write_by_varinfo(ncflnm, dimnames, dimtypes, dimvars, [lon_info, lat_info, cloud_info, \
nclear_info, xco2_info, xco2_ap_info, \
err_info, rnd_err_info, \
od_info, lwi_info, sza_info, obs_pres_info, obs_ak_info, obs_apr_info])
def data_collect(yyyy, doy, \
nst,\
step,\
em_st,\
em_end,\
geos_datapath, \
obs_datapath,\
viewmode_list, \
not_first_period=False,\
err_scale=1.0, \
do_debug=False,\
do_dump=True, \
dumpflnm=None):
""" doys in date to be read in read in
"""
doy0=doy
doy1=doy0 +nst*step # 096 0406
# set up the start days
doys=arange(doy0, doy1)
iday=0
nusd_obs=0
fclim=open('clim_co2.dat', 'r')
lines=fclim.readlines()
fclim.close()
aprior=list()
apr_pres=list()
for iline in lines:
terms=iline.split()
aprior.append(float(terms[1]))
apr_pres.append(float(terms[0]))
aprior=array(aprior)
apr_pres=array(apr_pres)
apr_pres=log10(apr_pres)
# starting to collect the observations and y
#
# figure(1)
# show()
# figure(1)
sel_doys=[-1, -8, -15]
int_step=0
all_days=size(em_st)
all_data_list=arange(all_data)
int_step=0
out_step=step
data_count=list()
print doy0, doy1, doys
# tt=raw_input()
for doy in doys:
yyyy, mm,dd=tm.doy_to_time_array(doy, yyyy)
print 'year mm dd', yyyy, mm, dd
y=list()
iend=0
full_doy=r'%4.4dD%3.3d' % (yyyy, doy)
sdate=r'%4.4dD%3.3d' % (yyyy, doy)
# check whether it is necessary to include surface flux (stv) during cerntain days
iy=0
if (int_step==0):
dumpext=r"%2.2d" % nst
dumpext="."+sdate+"_N"+dumpext
viewmode=viewmode_list[doy-1]
ncflnm_obs=obs_datapath+"/oco_"+viewmode+"."+sdate+".nc"
print ncflnm_obs
std_od, std_cflag=ofb.ncf_read(ncflnm_obs, ['od', 'cloud'])
for eid in all_data_list:
est=em_st[eid]
eend=em_end[eid]
sdate=r'%4.4dD%3.3d' % (yyyy, doy)
ncflnm=obs_datapath+"oco"+"."+sdate+".nc"
# print 'sel_eid', sel_eid
obs=obo.obs_operator(yyyy, mm, dd, est, eend, \
aprior=aprior, apr_pres=apr_pres, \
viewmode=viewmode,\
datapath=geos_datapath)
ytop=eend
for em in range(est,ytop+1):
if (em==est):
obs.get_obs_prof(em, std_od=std_od, std_cf=std_cflag)
else:
# print 'iy', iy, em, est
obs.get_obs_prof(em, idx=used_idx, do_update=False)
if (iy==0):
# em_id is different from em. em_id is the 'real id' in the whole ensemble set
em_id=obs.em_id[em-1]
print 'em_id', em_id, em-1
xgp0=obs.obs_xgp[em_id]
xgp=obs.obs_xgp[em_id]
print size(xgp), size(std_od), size(std_cflag)
obs_err=obs.obs_err
obs_err=array(obs_err)
obs_err=obs_err
# print 'shape obs+err', shape(obs_err)
rnd_obs_err=array(obs_err)
rnd_err=rnd.normal(scale=obs_err)
rnd_obs_err[iobs]=rnd_err
# err_scale=1.0
if (do_debug):
subplot(2,1,1)
plot(obs_err)
plot(rnd_obs_err)
subplot(2,1,2)
hist(rnd_obs_err)
hist(obs_err)
show()
rnd_obs_err=err_scale*rnd_obs_err
cflag=obs.obs_cflag
otime=obs.obs_time
olat=obs.obs_lat
olon=obs.obs_lon
od=obs.obs_od
lwi=obs.obs_lwi
lwi=lwi.astype(int)
# used_idx=where(logical_and(cflag==0, od<=0.3, lwi<>1))
osza=obs.obs_sza
sel1=logical_and(cflag==0, od<=0.3)
sel2=logical_and(lwi<>1, lwi<>2)
# used_idx=where(sel1)
# used_idx=where(logical_and(sel1, sel2))
# used_idx=where(cflag==0)
used_idx=where(logical_and(obs_err<4.0, sel1))
used_idx=squeeze(used_idx)
print 'used_idx', len(used_idx)
xgp=xgp[used_idx]
xgp0=xgp0[used_idx]
obs_err=1.0e-6*obs_err[used_idx]
rnd_obs_err=1.0e-6*rnd_obs_err[used_idx]
imax=argmax(rnd_obs_err)
print 'max(obs err), max(rnd_obs_err)', 1.0e6*obs_err[imax], 1.0e6*rnd_obs_err[imax]
olat=olat[used_idx]
olon=olon[used_idx]
lwi=lwi[used_idx]
otime=otime[used_idx]
od=od[used_idx]
# r=array(varData)
# r=r*r
else: # just others for ensemble
em_id=obs.em_id[em-1]
# print 'em_id', em_id, em-1
xgp=obs.obs_xgp[em_id]
if (em==est):
print em, est
print len(used_idx)
xgp=xgp[used_idx]
if (istep>300):
print 'type xgp',type(xgp)
print 'len-xgp', len(xgp)
if (em==2):
print 'xgp', xgp[0:6]
y.append(array(xgp))
if (do_debug):
print ii, ne, shape(xgp)
if (do_debug and ii==4):
# figure(1)
# plot(xgp[0:300],'r')
# plot(xgp0[0:300], 'b')
z=xgp-xgp0
print 'max dev', max(z)
iy=iy+1
# show()
y=array(y)
y=transpose(y)
print 'y-shape', shape(y), shape(xgp)
if (istep>300):
print type(y)
print len(y)
ix=y[0]
print type(ix)
# read in the data
print ncflnm_obs
obs_f=NetCDFFile(ncflnm_obs)
yobs=obs_f.variables['xco2']
yobs_err=obs_f.variables['err']
yobs=array(yobs)
# yobs_err=array(yobs_err)
yobs_err=array(yobs_err)
# print 'shape yobs', shape(yobs)
yobs=squeeze(yobs)
yobs_err=squeeze(yobs_err)
obs_f.close()
yobs=yobs[used_idx]
yobs_err=yobs_err[used_idx]
if (int_step==0):
all_y=array(y)
all_yobs=array(yobs)
all_yobs_err=array(yobs_err)
all_rnd_err=array(rnd_obs_err)
all_lat=array(olat)
all_lon=array(olon)
all_lwi=array(lwi)
all_time=array(otime)
all_od=array(od)
state_v=stv_c.state_vector(sdate, do_debug=False, datapath=geos_datapath)
nx, ne=state_v.nx, state_v.ne
print 'nx, ne', nx,ne
nreg=(nx)/size(st_days)
x=array(state_v.stv)
else:
all_y=concatenate((all_y, y))
all_yobs=concatenate((all_yobs, yobs))
all_yobs_err=concatenate((all_yobs_err, yobs_err))
all_rnd_err=concatenate((all_rnd_err, rnd_obs_err))
all_lat=concatenate((all_lat, olat))
all_lon=concatenate((all_lon, olon))
all_lwi=concatenate((all_lwi, lwi))
all_time=concatenate((all_time, otime))
all_od=concatenate((all_od, od))
print 'shape y & yobs', shape(y), shape(yobs)
print 'istep & shape ',istep, shape(all_y), shape(all_yobs), shape(all_yobs_err), shape(all_rnd_err)
data_count.append(size(yobs))
int_step=int_step+1
print '*'*40+'int_step'+'*'*80
print int_step
if ((int_step==out_step) and do_dump):
if (dumpflnm==None):
ncdump=geos_datapath+'/'+'obs'+dumpext+"_"+sdate0+".nc"
else:
ncdump=geos_datapath+'/'+dumpflnm+"_"+dumpext+".nc"
tmp_data_count=array(data_count)
factor=1.0e6
all_y=factor*all_y
all_yobs=factor*all_yobs
all_rnd_err=factor*all_rnd_err
all_yobs_err=factor*all_yobs_err
xnx=arange(nx)
xne=arange(ne)
xny=arange(size(all_y[:,0]))
xney=arange(size(all_y[0,:]))
dimTypes=['i', 'i','i','i', 'i']
dimVars=[xnx, xne, xny, xney, doys]
dimNames=['nx', 'ne', 'ny', 'ney', 'doys']
x_info=ofb.geos_varinfo('x', 'f', ['nx', 'ne'], x)
y_info=ofb.geos_varinfo('y', 'f', ['ny', 'ney'], all_y)
yobs_info=ofb.geos_varinfo('obs', 'f', ['ny'], all_yobs)
yobs_err_info=ofb.geos_varinfo('err', 'f', ['ny'], all_yobs_err)
rnd_err_info=ofb.geos_varinfo('rnd_err', 'f', ['ny'], all_rnd_err)
count_info=ofb.geos_varinfo('daily_count', 'i', ['doys'], tmp_data_count)
lat_info=ofb.geos_varinfo('lat', 'f', ['ny'], all_lat)
lon_info=ofb.geos_varinfo('lon', 'f', ['ny'], all_lon)
time_info=ofb.geos_varinfo('time', 'f', ['ny'], all_time)
od_info=ofb.geos_varinfo('od', 'f', ['ny'], all_od)
lwi_info=ofb.geos_varinfo('lwi', 'i', ['ny'], all_lwi)
ofb.ncf_write_by_varinfo(ncdump, dimNames, dimTypes, dimVars, \
[x_info, y_info, yobs_info, yobs_err_info, \
rnd_err_info, lat_info, lon_info, time_info, od_info, lwi_info, count_info])
int_step=0
data_count=array(data_count)
print 'shape, all_y', shape(all_y)
return x, all_y, all_yobs, all_yobs_err, all_rnd_err, data_count, doys, \
all_lat, all_lon, all_time, all_lwi, all_od
all_doy.append(doy)
all_co2.append(co2)
all_pres.append(pres)
all_sp.append(squeeze(sp))
all_doy = array(all_doy)
all_co2 = array(all_co2)
all_pres = array(all_pres)
all_sp = array(all_sp)
print shape(all_co2), shape(all_sp)
lat_info=ofb.geos_varinfo('latitude', 'f', ['latitude'], rlat, \
varattr=None)
lon_info=ofb.geos_varinfo('longitude', 'f', ['longitude'], rlon, \
varattr=None)
lvl_info=ofb.geos_varinfo('level', 'f', ['level'], rz, \
varattr=None)
time_info=ofb.geos_varinfo('time', 'f', ['time'], all_doy, \
varattr={"units":"days since 2006-1-1 00:00:00"})
co2 = transpose(all_co2, (0, 3, 2, 1))
pres = transpose(all_pres, (0, 3, 2, 1))
sp = transpose(all_sp, (0, 2, 1))
print shape(co2)
y1 = data1 / ndays
sp = sp / ndays
sp = squeeze(sp)
t = t / ndays
nx, ny, nz = shape(t)
print size(lon), size(lat), size(z)
print shape(sp), shape(t)
dimnames = ['lon', 'lat', 'z']
dimvars = [lon, lat, z]
dimtypes = ['f', 'f', 'f']
outflnm = 'mean_field_orch.' + sdate + ".nc"
y0_info=ofb.geos_varinfo('CO2.0001', 'f', ['lon', 'lat', 'z'], y0, \
varattr={"unit":"ppmv", "desc":"CO2 mixing ratio"})
y1_info=ofb.geos_varinfo('CO2.0002', 'f', ['lon', 'lat','z'], y1, \
varattr={"unit":"ppmv", "desc":"CO2 mixing ratio"})
sp_info=ofb.geos_varinfo('pressure', 'f', ['lon', 'lat'], sp, \
varattr={"unit":"hPa", "desc":"surface pressure"})
t_info=ofb.geos_varinfo('t', 'f', ['lon', 'lat', 'z'], t, \
varattr={"unit":"K", "desc":"temperature"})
ofb.ncf_write_by_varinfo(outflnm, dimnames, dimtypes, dimvars,\
[sp_info,y0_info, y1_info, t_info])
sstep = r'%2.2d' % (i)
resflnm = gcdf.inv_path + "/" + "std_oco_assim_res" + "." + sstep + ".nc"
ocs.do_one_step(update_step=0, rerun_step=0, hm_saving_doy=0)
xne = arange(ocs.ne)
xnx = arange(ocs.nx)
xwhole = arange(size(ocs.whole_mean_x))
dimnames = ['nx', 'ne', 'nwhole']
dimvars = [xnx, xne, xwhole]
dimtypes = ['i', 'i', 'i']
# print ocs.ne, ocs.nx
# print shape(ocs.xtm)
# print shape(ocs.dx)
# out put to the restart file
xtm_info = ofb.geos_varinfo('sum_xtm', 'f', ['ne', 'ne'], ocs.xtm)
x_info = ofb.geos_varinfo('x', 'f', ['nx'], ocs.mean_x)
xinc_info = ofb.geos_varinfo('xinc', 'f', ['nx'], ocs.x_inc)
x0_info = ofb.geos_varinfo('x0', 'f', ['nx'], ocs.mean_x0)
whole_x0_info = ofb.geos_varinfo('whole_x0', 'f', ['nwhole'],
ocs.whole_mean_x0)
whole_x_info = ofb.geos_varinfo('whole_x', 'f', ['nwhole'],
ocs.whole_mean_x)
whole_err0_info = ofb.geos_varinfo('whole_err0', 'f', ['nwhole'],
ocs.whole_err_x0)
whole_err_info = ofb.geos_varinfo('whole_err', 'f', ['nwhole'],
ocs.whole_err_x)
print shape(ocs.whole_err_x)