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')