nLay = len(LAYERLIST)
BGC_CLASS4_NIT_RMS_LAYER_BASIN = np.ones((nSUB, nLay), dtype=np.float32) * np.NaN
BGC_CLASS4_PHOS_RMS_LAYER_BASIN = np.ones((nSUB, nLay), dtype=np.float32) * np.NaN
BGC_CLASS4_CHL_RMS_LAYER_BASIN = np.ones((nSUB, nLay), dtype=np.float32) * np.NaN
BGC_CLASS4_O2_RMS_LAYER_BASIN = np.ones((nSUB, nLay), dtype=np.float32) * np.NaN
VARLIST = ["N1p", "N3n", "O2o"] #'chl']
for modelvarname in VARLIST:
OUTPUT = np.ones((nSUB, nLay), dtype=np.float32) * np.NaN
for isub, sub in enumerate(SUBlist):
Profilelist = N.Selector(NUTRVARS[modelvarname], T_INT, sub)
nP = len(Profilelist)
Matchup_basin = M.getMatchups(Profilelist, nav_lev, modelvarname, read_adjusted=True)
for ilayer, layer in enumerate(LAYERLIST):
Mlayer = Matchup_basin.subset(layer)
OUTPUT[isub, ilayer] = Mlayer.RMSE()
if modelvarname == "N1p":
BGC_CLASS4_PHOS_RMS_LAYER_BASIN = OUTPUT.copy()
if modelvarname == "N3n":
BGC_CLASS4_NIT_RMS_LAYER_BASIN = OUTPUT.copy()
if modelvarname == "O2o":
BGC_CLASS4_O2_RMS_LAYER_BASIN = OUTPUT.copy()
import pickle
LIST = [
BGC_CLASS4_PHOS_RMS_LAYER_BASIN,
BGC_CLASS4_NIT_RMS_LAYER_BASIN,
for i in SubProfilelist_1:
Pres, Profile, Qc = i.read(
'CHLA',
read_adjusted[0]) #Profile.shape,Profile.size, np.mean(Profile)
if (Profile.size != 0): Goodlist.append(i)
# AllProfiles matrix: 0 Model, 1 Ref, 2 Misfit,3 Lat,4 Lon,5 ID FLOAT
# NOTE: the number of profile per float is len(Goodlist)
print "number of Goodlist Profiles:", len(Goodlist)
#AllProfiles = np.zeros((len(Goodlist),dimnewpress,6),np.float64)
OneProfile = np.zeros((1, dimnewpress, 6), np.float64)
# PROFILES FOR FLOAT AND MODEL
for ip, pp in enumerate(Goodlist):
#print ip,pp.time
singlefloatmatchup = M.getMatchups([pp], nav_lev, VARLIST[0])
s200 = singlefloatmatchup.subset(layer) #values NOT interpolated
#vertical interpolation at the grid defined in NewPress (1meter)
s200intmodel = np.interp(NewPres, s200.Depth,
s200.Model) #MODEL (201 values)
s200intobs_noqc = np.interp(NewPres, s200.Depth,
s200.Ref) #ARGO (201 values)
s200intobs = qualitycheck.test(s200intobs_noqc, wmo, pp.lat, pp.lon)
# TEMPORARY MATRIX WITH ALL THE MATCHUP
if (ip == 0):
ind = 0
count = 1
ix0, jy0 = TheMask.convert_lon_lat_to_indices(pp.lon, pp.lat)
AllProfiles = OneProfile
AllProfiles[ind, :, 0] = s200intmodel #ONLY MODEL
RMSE = np.zeros((nVar,nSub,nDepth), np.float32)
NPOINTS = np.zeros((nVar,nSub,nDepth), np.int32)
TI =R.time_interval
M = Matchup_Manager(TI,INPUTDIR,BASEDIR)
for ivar, var in enumerate(VARLIST):
print var
for isub, sub in enumerate(OGS.NRT3):
Profilelist = bio_float.FloatSelector(FLOATVARS[var], TI, sub)
nProfiles = len(Profilelist)
print sub.name, nProfiles
Matchup_object_list=[]
for ip in range(nProfiles):
floatmatchup = M.getMatchups([Profilelist[ip]], TheMask.zlevels, var, read_adjusted=read_adjusted[ivar])
Matchup_object_list.append(floatmatchup)
for ilayer, layer in enumerate(LAYERLIST):
MODEL_LAYER_MEAN = [] # one value for each suitable profile in (subbasin, layer)
REF_LAYER_MEAN = []
for floatmatchup in Matchup_object_list:
m_layer = floatmatchup.subset(layer)
#print ilayer, m_layer.number()
if m_layer.number() > 0:
REF_LAYER_MEAN.append(m_layer.Ref.mean())
MODEL_LAYER_MEAN.append(m_layer.Model.mean())
NPOINTS[ivar,isub,ilayer] = len(MODEL_LAYER_MEAN)
if len(MODEL_LAYER_MEAN) > 0:
M_LAYER = matchup(np.array(MODEL_LAYER_MEAN), np.array(REF_LAYER_MEAN))
