from hydroDL.data import dbBasin
from hydroDL.master import basinFull
dataNameLst = ['bsWN5', 'bsDN5', 'brWN5', 'brDN5']
dataName = 'bsWN5'
dm = dbBasin.DataModelFull(dataName)
varX = dm.varF + ['runoff']
varY = usgs.newC
varXC = dm.varG
varYC = None
sd = '1982-01-01'
ed = '2009-12-31'
outName = '{}-B10'.format(dataName)
dictP = basinFull.wrapMaster(outName=outName,
dataName=dataName,
varX=varX,
varY=varY,
varXC=varXC,
varYC=varYC,
sd=sd,
ed=ed,
nEpoch=100,
batchSize=[365, 100])
basinFull.trainModel(outName)
from hydroDL import kPath
from hydroDL.data import usgs, gageII, gridMET, ntn
from hydroDL.master import slurm
from hydroDL.master import basinFull
varX = gridMET.varLst
varY = ['runoff'] + usgs.newC
varXC = gageII.lstWaterQuality
varYC = None
dataName = 'sbY30N5'
sd = '1979-01-01'
ed = '2010-01-01'
outName = '{}-B10'.format(dataName)
master = basinFull.wrapMaster(outName=outName,
dataName=dataName,
varX=varX,
varY=varY,
varXC=varXC,
varYC=varYC,
sd=sd,
ed=ed)
cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/hydroDL/master/cmd/basinFull.py -M {}'
slurm.submitJobGPU(outName, cmdP.format(outName), nH=24, nM=32)
varYC = None
mtdYC = dbBasin.io.extractVarMtd(varYC)
sd = '1982-01-01'
ed = '2009-12-31'
rho = 365
trainSet = 'rmR20'
testSet = 'pkR20'
outName = '{}-{}-t{}-{}'.format(dataName, label, rho, trainSet)
dictP = basinFull.wrapMaster(outName=outName,
dataName=dataName,
trainSet=trainSet,
varX=varX,
varY=varY,
varXC=varXC,
varYC=varYC,
nEpoch=100,
batchSize=[rho, 200],
nIterEp=20,
mtdX=mtdX,
mtdY=mtdY,
mtdXC=mtdXC,
mtdYC=mtdYC)
basinFull.trainModel(outName)
yP, ycP = basinFull.testModel(outName, DF=DF, testSet='all', reTest=True)
yO = DF.extractT(codeSel)
indT1, indT2, indS, mask = DF.readSubset(testSet)
mask = np.repeat(mask, len(codeSel), axis=2)
nEpoch=500,
saveEpoch=100,
resumeEpoch=0,
optNaN=[1, 1, 0, 0],
overwrite=True,
modelName='CudnnLSTM',
crit='RmseLoss',
optim='AdaDelta',
varX=gridMET.varLst,
varXC=gageII.lstWaterQuality,
varY=['00060'],
varYC=None,
sd='1979-01-01',
ed='2010-01-01',
subset='all',
borrowStat=None)
caseName = basinFull.wrapMaster(outName=outName,
dataName=dataName,
varX=varX,
varY=varY,
varXC=varXC,
varYC=varYC,
sd=sd,
ed=ed,
subset=subset,
borrowStat=globalName)
mm = defaultMaster.copy()
out = mm.update(master)
freq=freq)
yrIn = np.arange(1985, 2020, 5).tolist()
tt = dbBasin.func.pickByYear(DF.t, yrIn, pick=False)
DF.createSubset('B10', ed='2009-12-31')
DF.createSubset('B10', sd='2010-01-01')
codeSel = ['00915', '00925', '00930', '00935', '00940', '00945', '00955']
label = 'FPR2QC'
varX = dbBasin.label2var(label.split('2')[0])
varY = codeSel
varXC = gageII.varLst
varYC = None
sd = '1982-01-01'
ed = '2009-12-31'
rho = 365
outName = '{}-{}-t{}-B10'.format(dataName, label, rho)
dictP = basinFull.wrapMaster(outName=outName,
dataName=dataName,
trainSet='B10',
varX=varX,
varY=varY,
varXC=varXC,
varYC=varYC,
nEpoch=100,
batchSize=[rho, 200],
nIterEp=20)
basinFull.trainModel(outName)
yP, ycP = basinFull.testModel(outName, DF=DF, testSet='A10')
