subset='CONUSv4f1',
tRange=ty2)
x = df.getDataTs(dbCsv.varForcing, doNorm=True, rmNan=True)
c = df.getDataConst(dbCsv.varConst, doNorm=True, rmNan=True)
nx = x.shape[-1] + c.shape[-1]
yT = df.getDataTs('SMAP_AM', doNorm=False, rmNan=False)
yT = yT[:, :, 0]
# test
ypLstmLst = list()
ypAnnLst = list()
modelName = 'LSTM'
model = train.loadModel(outFolder, 100, modelName=modelName)
yp = train.testModel(model, x, c, batchSize=100).squeeze()
ypLstmLst.append(
dbCsv.transNorm(yp, rootDB=rootDB, fieldName='SMAP_AM', fromRaw=False))
for k in dLst:
sd = utils.time.t2dt(ty2[0]) - dt.timedelta(days=k)
ed = utils.time.t2dt(ty2[1]) - dt.timedelta(days=k)
df2 = hydroDL.data.dbCsv.DataframeCsv(rootDB=rootDB,
subset='CONUSv4f1',
tRange=[sd, ed])
obs = df2.getDataTs('SMAP_AM', doNorm=True, rmNan=False)
modelName = 'LSTM-DA-' + str(k)
model = train.loadModel(outFolder, nEpoch, modelName=modelName)
yP = train.testModel(model, (x, obs), c, batchSize=100).squeeze()
ypLstmLst.append(
dbCsv.transNorm(yP,
rootDB=rootDB,
fieldName='SMAP_AM',
x = df.getData(varT=dbCsv.varForcing,
varC=dbCsv.varConst,
doNorm=True,
rmNan=True)
y = df.getData(varT='SMAP_AM', doNorm=True, rmNan=False)
nx = x.shape[-1]
ny = 1
model = rnn.CnnCondLstm(nx=nx, ny=ny, ct=365, hiddenSize=64, cnnSize=32, opt=3)
lossFun = crit.RmseLoss()
model = train.trainModel(model,
x,
y,
lossFun,
xc=c,
nEpoch=nEpoch,
miniBatch=[100, 30])
yOut = train.testModelCnnCond(model, x, y)
# yOut = train.testModel(model, x)
yP = dbCsv.transNorm(yOut[:, :, 0],
rootDB=rootDB,
fieldName='SMAP_AM',
fromRaw=False)
yT = dbCsv.transNorm(y[:, model.ct:, 0],
rootDB=rootDB,
fieldName='SMAP_AM',
fromRaw=False)
statDict = post.statError(yP, yT)
statDict['RMSE'].mean()