for v in myvars:
ndays_total = 0
output = []
n_years = myyear_end[index]-myyear_start[index]+1
npy=1
for y in range(myyear_start[index],myyear_end[index]+1):
if (mypft[index] <= 0 or 'PFT' in v):
fname = rundir+case+'.'+options.model_name+'.h0.'+str(10000+y)[1:]+'-01-01-00000.nc'
myindex = max(0,mypft[index])
hol_add = 1
else:
fname = rundir+case+'.'+options.model_name+'.h1.'+str(10000+y)[1:]+'-01-01-00000.nc'
myindex = mypft[index]
hol_add = 17
if (os.path.exists(fname)):
mydata = nffun.getvar(fname,v)
if (len(mydata) < 10):
npy = 1
elif (len(mydata) >= 365): #does not currently allow hourly
npy = 365
else:
print(fname)
mydata = np.zeros([npy,34], np.float)+np.NaN
#get output and average over days/years
n_days = myday_end[index]-myday_start[index]+1
ndays_total = ndays_total + n_days
#get number of timesteps per output file
if (npy == 365):
for d in range(myday_start[index]-1,myday_end[index]):
if ('US-SPR' in case):
def calc_costfucntion(constraints, thisjob, runroot, case):
#Calculate cost function value (SSE) given the data constraints in the provided directory
rundir = runroot+'/UQ/'+case+'/g'+str(100000+thisjob)[1:]+'/'
sse = 0
os.system('rm '+rundir+case+'.clm2*_constraint.nc')
myoutput = open(rundir+'myoutput_sse.txt','w')
for filename in os.listdir(constraints):
if (not os.path.isdir(filename)):
myinput = open(constraints+'/'+filename,'r')
myvarname = filename.split('.')[0] #model variable is filename
#code to deal with special variables and/or aggregation
#-------------
lnum = 0 #line number
year = 0
for s in myinput:
if (lnum == 0):
header = s.split()
elif (len(header) == len(s.split())):
hnum = 0
PFT=-1 #default: don't use PFT-specific info
# if specified, use h1 file (PFT-specific)
doy=-1 #default: annual average
month=-1 #default: don't use monthly data
depth=-1
unc = -999
for h in header:
if (h.lower() == 'year'):
year_last = year
year = int(s.split()[hnum])
if (h.lower() == 'doy'):
doy = int(s.split()[hnum])
if (h.lower() == 'month'):
month = int(s.split()[hnum])
if (h.lower() == 'pft'):
PFT = int(s.split()[hnum])
if (h.lower() == 'value'):
value = float(s.split()[hnum])
if (h.lower() == 'depth'):
depth = float(s.split()[hnum])
if ('unc' in h.lower()):
unc = float(s.split()[hnum])
hnum = hnum+1
#get the relevant variable/dataset
#Assumes annual file with daily output
if (PFT == -1):
myfile = rundir+case+'.clm2.h0.'+str(year)+'-01-01-00000_constraint.nc'
myfileo = rundir+case+'.clm2.h0.'+str(year)+'-01-01-00000.nc'
else:
myfile = rundir+case+'.clm2.h1.'+str(year)+'-01-01-00000_constraint.nc'
myfileo = rundir+case+'.clm2.h1.'+str(year)+'-01-01-00000.nc'
#post processing of model output with nco to match constraining variables
if (not os.path.isfile(myfile)):
os.system('cp '+myfileo+' '+myfile)
if ('h1' in myfile and ('STEMC' in myvarname or 'AGBIOMASS' in myvarname)):
os.system('ncap2 -3 -s "STEMC=DEADSTEMC+LIVESTEMC" '+myfile+' '+myfile+'.tmp')
os.system('mv '+myfile+'.tmp '+myfile)
os.system('ncap2 -3 -s "AGBIOMASS=DEADSTEMC+LIVESTEMC+LEAFC" '+myfile+' '+myfile+'.tmp')
os.system('mv '+myfile+'.tmp '+myfile)
if ('h0' in myfile and 'WTHT' in myvarname):
#Water table height relative to hollow bottoms (7.5cm below hollow gridcell mean)
os.system('ncap2 -3 -s "WTHT=(ZWT*-1+0.225)*1000 " '+myfile+' '+myfile+'.tmp')
os.system('mv '+myfile+'.tmp '+myfile)
myvals = nffun.getvar(myfile, myvarname)
if (doy > 0 and value > -900):
#Daily constraint
if (myvarname == 'WTHT'):
unc = 50.0 #no uncertainty given for water table height.
if (PFT > 0):
#PFT-specific constraints (daily)
if (myvarname == 'AGBIOMASS' and PFT == 3):
#Both tree types - Larch and spruce, hummock+hollow
model_val = (myvals[doy,PFT-1]*0.25+myvals[doy,PFT]*0.25)*0.75 \
+(myvals[doy,PFT+16]*0.25+myvals[doy,PFT+17]*0.25)*0.25
else:
#use hummock+hollow
model_val = myvals[doy,PFT-1]*0.25*0.75 + myvals[doy,PFT+16]*0.25*0.25
if (unc < 0):
unc = value*0.25 #default uncertainty set to 25%
sse = sse + ((model_val-value) /unc)**2
elif (depth > 0):
#depth-specific constraint in cm (relative to hollow)
layers = [0,1.8,4.5,9.1,16.6,28.9,49.3,82.9,138.3,229.6,343.3]
for l in range(0,10):
if (depth >= layers[l] and depth < layers[l+1]):
thislayer = l
model_val = myvals[doy,thislayer,1] #Hollow
sse = sse + ((model_val-value) / unc )**2
else:
#Column-level constraint (daily)
#Water table, column-level (no PFT info), use hummock only
model_val = myvals[doy,0]
sse = sse + ((model_val-value) / unc )**2
elif (value > -900):
#Monthly or annual constraint
daysm=[0,31,59,90,120,151,181,212,243,273,304,334,365]
if (month > 0):
lbound = daysm[month-1]
ubound = daysm[month]
else:
lbound = 0
ubound = 365
#model_val = sum(myvals[0:365,PFT-1]*0.25*0.75)*24*3600 + \
# sum(myvals[0:365,PFT+16]*0.25*0.25)*24*3600
if (PFT <= 0):
model_val = sum(myvals[lbound:ubound,0]) / (ubound - lbound)
else:
model_val = sum(myvals[lbound:ubound,PFT-1]) / (ubound - lbound)
sse = sse + ((model_val-value) / unc )**2
myoutput.write(str(myvarname)+' '+str(year)+' '+str(month)+' '+str(PFT)+' '+ \
str(model_val)+' '+str(value)+' '+str(unc)+' '+str(sse)+'\n')
lnum = lnum+1
myoutput.close()
return sse
os.system(' mv ' + ens_dir + '/' + f + '.tmp ' + ens_dir + '/' + f)
pnum = 0
CNP_parms = ['ks_sorption', 'r_desorp', 'r_weather', 'r_adsorp', 'k_s1_biochem', 'smax', 'k_s3_biochem', \
'r_occlude', 'k_s4_biochem', 'k_s2_biochem']
fates_seed_zeroed = [False, False]
for p in parm_names:
if ('INI' in p):
if ('BGC' in casename):
scalevars = ['soil3c_vr', 'soil3n_vr', 'soil3p_vr']
else:
scalevars = ['soil4c_vr', 'soil4n_vr', 'soil4p_vr']
sumvars = ['totsomc', 'totsomp', 'totcolc', 'totcoln', 'totcolp']
for v in scalevars:
myvar = nffun.getvar(finidat_file_new, v)
myvar = parm_values[pnum] * myvar
ierr = nffun.putvar(finidat_file_new, v, myvar)
elif (p == 'lai'):
myfile = surffile
param = nffun.getvar(myfile, 'MONTHLY_LAI')
param[:, :, :, :] = parm_values[pnum]
ierr = nffun.putvar(myfile, 'MONTHLY_LAI', param)
elif (p != 'co2'):
if (p in CNP_parms):
myfile = CNPfile
elif ('fates' in p):
myfile = fates_paramfile
else:
myfile = pftfile
param = nffun.getvar(myfile, p)
#get corresponding 0.5x0.5 and 1.9x2.5 degree grid cells
if (options.res == 'hcru_hcru'):
longxy = (numpy.cumsum(numpy.ones([721])) - 1) * 0.5
latixy = (numpy.cumsum(numpy.ones([361])) - 1) * 0.5 - 90.0
elif (options.res == 'f19_f19'):
longxy = (numpy.cumsum(numpy.ones([145])) - 1) * 2.5 - 1.25
latixy_centers = (numpy.cumsum(numpy.ones([96])) - 1) * (180.0 / 95) - 90.0
latixy = numpy.zeros([97], numpy.float)
longxy[0] = 0
latixy[0] = -90
latixy[96] = 90
for i in range(1, 96):
latixy[i] = (latixy_centers[i - 1] + latixy_centers[i]) / 2.0
else:
longxy = nffun.getvar(surffile_orig, 'LONGXY')
latixy = nffun.getvar(surffile_orig, 'LATIXY')
xgrid_min = []
xgrid_max = []
ygrid_min = []
ygrid_max = []
for n in range(0, n_grids):
if (issite):
lon_bounds = [lon[n], lon[n]]
lat_bounds = [lat[n], lat[n]]
xgrid_min.append(-1)
xgrid_max.append(-1)
ygrid_min.append(-1)
ygrid_max.append(-1)
if ('ne' in options.res):
os.system(' mv '+ens_dir+'/'+f+'.tmp '+ens_dir+'/'+f)
pnum = 0
CNP_parms = ['ks_sorption', 'r_desorp', 'r_weather', 'r_adsorp', 'k_s1_biochem', 'smax', 'k_s3_biochem', \
'r_occlude', 'k_s4_biochem', 'k_s2_biochem']
fates_seed_zeroed=[False,False]
for p in parm_names:
if ('INI' in p):
if ('BGC' in casename):
scalevars = ['soil3c_vr','soil3n_vr','soil3p_vr']
else:
scalevars = ['soil4c_vr','soil4n_vr','soil4p_vr']
sumvars = ['totsomc','totsomp','totcolc','totcoln','totcolp']
for v in scalevars:
myvar = nffun.getvar(finidat_file_new, v)
myvar = parm_values[pnum] * myvar
ierr = nffun.putvar(finidat_file_new, v, myvar)
elif (p == 'lai'):
myfile = surffile
param = nffun.getvar(myfile, 'MONTHLY_LAI')
param[:,:,:,:] = parm_values[pnum]
ierr = nffun.putvar(myfile, 'MONTHLY_LAI', param)
elif (p != 'co2'):
if (p in CNP_parms):
myfile= CNPfile
elif ('fates' in p):
myfile = fates_paramfile
else:
myfile = pftfile
param = nffun.getvar(myfile,p)
var_names2d = ['CWDC_vr', 'CWDN_vr', 'CWDP_vr', 'SOIL4C_vr', 'SOIL4N_vr', 'SOIL4P_vr', 'SOIL3C_vr', \
'SOIL3N_vr', 'SOIL3P_vr']
if (options.harvest):
var_names_harvest = ['DEADSTEMC','DEADSTEMN','DEADSTEMP','LIVESTEMN','LIVESTEMC','LIVESTEMP', \
'LEAFC', 'LEAFN', 'LEAFP', 'DEADSTEMC_STORAGE', 'DEADSTEMN_STORAGE', \
'DEADSTEMP_STORAGE', 'LIVESTEMC_STORAGE', 'LIVESTEMN_STORAGE', \
'LIVESTEMP_STORAGE', 'LEAFC_STORAGE', 'LEAFN_STORAGE', 'LEAFP_STORAGE', \
'FROOTC_STORAGE', 'FROOTC', 'FROOTN_STORAGE', 'FROOTN', 'FROOTP_STORAGE', \
'FROOTP', 'LIVECROOTC', 'LIVECROOTC_STORAGE', 'LIVECROOTN', 'LIVECROOTN_STORAGE', \
'LIVECROOTP', 'LIVECROOTP_STORAGE', 'DEADCROOTC', 'DEADCROOTC_STORAGE', \
'DEADCROOTN', 'DEADCROOTN_STORAGE', 'DEADCROOTP', 'DEADCROOTP_STORAGE', 'XSMRPOOL', \
'XSMRPOOL_RECOVER']
if (options.harvest):
for v in range(0,len(var_names_harvest)):
rest_vals = nffun.getvar(fname_restart, var_names_harvest[v].lower())
#Loop through all valid values in the restart file.
n_rest = len(rest_vals)
for i in range(0,n_rest):
rest_vals[i] = rest_vals[i]*0.05
if (var_names_harvest[v] == 'LEAFC'):
rest_vals[i] = 0.33/0.03
print(rest_vals[i])
elif (var_names_harvest[v] == 'FROOTC'):
rest_vals[i] = 0.33/0.03*0.666
elif (var_names_harvest[v] == 'LEAFN'):
rest_vals[i] = 0.33/0.03/25.0
elif (var_names_harvest[v] == 'FROOTN'):
rest_vals[i] = 0.33/0.03/42.0
ierr = nffun.putvar(fname_restart, var_names_harvest[v].lower(), rest_vals)
else:
