def write_text():
with open("./experimetal_settings.log", "w") as f:
f.write("# Experimental Settings\n")
f.write("# " + pm.version() + "\n")
f.write("# " + pm.CaMa_ver() + "\n")
f.write("#======================================================\n")
# Experimental Settings
f.write("# Experiment Name: " + pm.experiment() + "\n")
f.write("# Experiment Mode: " + "%d" % (pm.mode()) + "\n")
# Runoff data
f.write("# Runoff Data: " + pm.input(pm.mode()) + "\n")
# Time domain for analysis
f.write("# Start Date: %04d-%02d-%02d\n" % (pm.starttime()))
f.write("# End Date: %04d-%02d-%02d\n" % (pm.endtime()))
# Calibration
f.write("# Model Calibration: " + calibration(pm.calibrate()) + "\n")
# Assimilation Settings
f.write("# Assimilation Mode: " + assimlation_mode(pm.conflag()) +
"\n")
f.write("# Assimilation Domain: \n")
f.write("# \tWest : %5.2f\n" % (pm.assimW()))
f.write("# \tSouth: %5.2f\n" % (pm.assimS()))
f.write("# \tEest : %5.2f\n" % (pm.assimE()))
f.write("# \tNorth: %5.2f\n" % (pm.assimN()))
f.write("# Assimilation Settings: \n")
f.write("# \tPatch Size : " + patch_character(pm.patch_size()) + "\n")
f.write("# \tPatch Id : " + pm.patch_name() + " " + pm.patch_id() +
"\n")
f.write("# \tInflation Method : " + inflation_para(pm.rho()) + "\n")
f.write("# Assimilation Statistics: \n")
f.write("# \tMean : " + stat_name(pm.conflag(), pm.calibrate())[0] +
"\n")
f.write("# \tStandrad Deviation : " +
stat_name(pm.conflag(), pm.calibrate())[1] + "\n")
return 0
def SWOT_day(yyyy,mm,dd): st_year,st_month,st_date=pm.starttime() start_time=datetime.date(st_year,st_month,st_date) this_time=datetime.date(int(yyyy),int(mm),int(dd)) days=this_time-start_time days=days.days return days%21+1
def prepare_obs_old():
"""
Prepare observations as textfile
"""
# global lname, xlist, ylist, leledif, lEGM08, lEGM96, satellite
# if pm.obs_name() == "HydroWeb":
# lname, xlist, ylist, leledif, lEGM08, lEGM96, satellite = get_HydroWeb()
# print ("lname: ",len(lname))
# else:
# lname, xlist, ylist, leledif, lEGM08, lEGM96, satellite = get_HydroWeb()
# print (len(lname))
syear, smon, sday = pm.starttime()
eyear, emon, eday = pm.endtime()
start_dt = datetime.date(syear, smon, sday)
end_dt = datetime.date(eyear, emon, eday)
start = 0
last = (end_dt - start_dt).days
# print (start,last)
#-------
inputlist = []
for day in np.arange(start, last):
target_dt = start_dt + datetime.timedelta(days=day)
yyyy = '%04d' % (target_dt.year)
mm = '%02d' % (target_dt.month)
dd = '%02d' % (target_dt.day)
# print (yyyy,mm,dd) #,obs_dir
inputlist.append([yyyy, mm, dd])
# write text files parallel
p = Pool(pm.cpu_nums() * pm.para_nums())
p.map(write_txt, inputlist)
p.terminate()
# map(write_txt,inputlist)
return 0
def make_initial_infl():
nx, ny, gsize = pm.map_dimension()
parm_infl = np.ones([ny, nx], np.float32) * pm.initial_infl()
start_year, start_month, start_date = pm.starttime(
) # Start year month date
yyyy = '%04d' % (start_year)
mm = '%02d' % (start_month)
dd = '%02d' % (start_date)
parm_infl.tofile("./inflation/parm_infl" + yyyy + mm + dd + ".bin")
return 0
def cal_monthly_mean_ens(ens_num): #start_year,end_year,ens_num,months=24):
# calc monthly mean value for two years
start_year=pm.spinup_end_year()
end_year,end_month,end_date=pm.starttime()
#start_year=pm.start_year() #inputlist[0]
#end_year=pm.end_year() #inputlist[1]
#ens_num=inputlist[2]
months=24 #inputlist[3]
#threshold=inputlist[4]
runname=pm.runname(pm.mode())
if runname=="E2O":
nx=1440
ny=720
threshold=0.1
else:
nx=360
ny=180
threshold=1.0e-8
#os.system("rm -Rf ./CaMa_in/ELSE_GPCC/mean_month/*")
mkdir("./CaMa_in/"+runname+"/mean_month")
#os.system("rm -Rf ./CaMa_in/ELSE_KIM2009/mean_month/*")
#threshold=0.1
start_dt=datetime.date(start_year,1,1)
end_dt=datetime.date(end_year,12,31)
ens_char="%03d"%(ens_num)
for month in np.arange(months):
ynow=int(start_year+int(month/12))
ychar="%04d"%(ynow)
mchar="%02d"%((month%12)+1)
#print ychar, mchar
roff_mon=np.zeros([ny,nx],np.float32)#.reshape([180,360])
count=np.zeros([ny,nx],np.float32)#.reshape([180,360])
for day in np.arange(month*30,(month+1)*30):
day_num=day-month*30
running_dt=start_dt+datetime.timedelta(days=day)
yyyy='%04d' % (running_dt.year)
mm='%02d' % (running_dt.month)
dd='%02d' % (running_dt.day)
roff=np.fromfile(pm.DA_dir()+"/inp/"+runname+"/Roff/Roff__"+str(yyyy)+str(mm)+str(dd)+ens_char+".one",np.float32).reshape([ny,nx])
roff_mon=roff_mon+roff*(roff>threshold)
count=count+(roff>threshold)
roff_mean=roff_mon/(count+1e-20)
roff_mean=roff_mean.astype(np.float32)
roff_mean=roff_mean+threshold
roff_mean.tofile("./CaMa_in/"+runname+"/mean_month/mean_"+ychar+mchar+ens_char+".bin")
nextxy = np.fromfile(nextxy, np.int32).reshape(2, ny, nx) rivwth = np.fromfile(rivwth, np.float32).reshape(ny, nx) rivhgt = np.fromfile(rivhgt, np.float32).reshape(ny, nx) rivlen = np.fromfile(rivlen, np.float32).reshape(ny, nx) elevtn = np.fromfile(elevtn, np.float32).reshape(ny, nx) lonlat = np.fromfile(lonlat, np.float32).reshape(2, ny, nx) uparea = np.fromfile(uparea, np.float32).reshape(ny, nx) # #higher resolution data # catmxy = pm.CaMa_dir()+"/map/"+pm.mapname()+"/1min/1min.catmxy.bin" # catmxy = np.fromfile(catmxy,np.int16).reshape(2,ny*60,nx*60) #---- rivnum = "../dat/rivnum_" + pm.mapname() + ".bin" rivnum = np.fromfile(rivnum, np.int32).reshape(ny, nx) rivermap = ((nextxy[0] > 0) * (rivnum == 1)) * 1.0 #---- syear, smonth, sdate = pm.starttime() #2004#1991 2004,1,1 # 2003,1,1 # eyear, emonth, edate = pm.endtime() #2005,1,1 # 2005,1,1 # #month=1 #date=1 start_dt = datetime.date(syear, smonth, sdate) end_dt = datetime.date(eyear, emonth, edate) size = 60 start = 0 last = (end_dt - start_dt).days N = int(last) green2 = "greenyellow" green = "green" #------- pname = []
#def sendslack(msg):
# slack=Slacker('xoxb-316041665447-hNXDYiTz9PZMITg0gfRGzyrC')
# message="` "+os.path.basename(__file__)+"` : "+msg
# slack.chat.post_message('#camasim',message);
# return 0
#---
#--
#slink("/hydro/covariance/CaMa_simulation/params.py", "params.py")
#slink("/hydro/SWOTDA/img_code/read_grdc.py","read_grdc.py")
import params as pm
import read_grdc as grdc
#--
mk_dir(pm.out_dir() + "/figures")
mk_dir(pm.out_dir() + "/figures/timeseries")
#--read outflow netCDF4--
tag = "%04d-%04d" % (pm.starttime()[0], pm.endtime()[0])
# sfcelv
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name() + "/sfcelv" + tag + ".nc"
nc_sfcelv = xr.open_dataset(fname)
# removed trend line
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/rmdtrnd" + tag + ".nc"
nc_rmdtrend = xr.open_dataset(fname)
# removed seasonality
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/rmdsesn" + tag + ".nc"
nc_rmdseson = xr.open_dataset(fname)
# standardized
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/standardized" + tag + ".nc"
nc_standz = xr.open_dataset(fname)
