]
# ntn variables
dataName = 'sbWT'
caseLst = list()
wqData = waterQuality.DataModelWQ(dataName)
codeLst = varNtnUsgsLst
label = 'ntnSq'
for code in codeLst:
varX = ['00060'] + gridMET.varLst + \
[varNtnLst[varNtnUsgsLst.index(code)], 'distNTN']
varY = [code]
varYC = None
subsetLst = ['{}-Y{}'.format(code, x) for x in [1, 2]]
# wrap up
# for subset in subsetLst:
subset = subsetLst[0]
saveName = '{}-{}-{}-{}'.format(dataName, code, label, subset)
caseName = basins.wrapMaster(dataName=dataName,
trainName=subset,
batchSize=[None, 100],
outName=saveName,
varX=varX,
varY=varY,
varYC=varYC)
caseLst.append(caseName)
cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/cmdTrain.py -M {}'
for caseName in caseLst:
slurm.submitJobGPU(caseName, cmdP.format(caseName), nH=24)
from hydroDL.master import slurm
from hydroDL import kPath
from hydroDL.app import waterQuality
from hydroDL.data import gageII, usgs, gridMET
from hydroDL.master import basins
import pandas as pd
import numpy as np
import os
import time
caseLst = list()
dataName = 'basinAll'
subsetLst = ['Y8090', 'Y0010']
for subset in subsetLst:
saveName = '{}-{}-opt1'.format(dataName, subset)
caseName = basins.wrapMaster(dataName=dataName, trainName=subset, saveEpoch=50,
batchSize=[None, 1000], outName=saveName)
caseLst.append(caseName)
saveName = '{}-{}-opt2'.format(dataName, subset)
caseName = basins.wrapMaster(dataName=dataName, trainName=subset, saveEpoch=50,
batchSize=[None, 1000], varY=None,
varX=usgs.varQ+gridMET.varLst, outName=saveName)
caseLst.append(caseName)
cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/cmdTrain.py -M {}'
for caseName in caseLst:
slurm.submitJobGPU(caseName, cmdP.format(caseName), nH=48, nM=64)
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)
from hydroDL.master import slurm
from hydroDL import kPath
import os
# slurm.submitJobGPU('modelA','python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/modelA.py',nH=20)
# slurm.submitJobGPU('modelC','python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/modelC.py',nH=20)
# wrap up data
# codePath = os.path.join(kPath.dirCode, 'app',
# 'waterQual', 'model', 'wrapData.py')
# jobName = 'wrapUpData'
# cmdLine = 'python {}'.format(codePath)
# slurm.submitJob(jobName, cmdLine, nH=1, nM=64)
# TRAIN MODEL
# slurm.submitJobGPU('basinRef','python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/trainModel1.py',nH=24)
# slurm.submitJobGPU(
# 'basinAll', 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/trainModel2.py', nH=48, nM=64)
cmdP = 'python /home/users/kuaifang/GitHUB/geolearn/app/waterQual/model/runCmd.py -D {} -O {}'
nameP = '{}-opt{}'
dLst = ['HBN', 'HBN-30d']
optLst = [1, 2, 3, 4]
for d in dLst:
for opt in optLst:
print(cmdP.format(d, opt))
slurm.submitJobGPU(nameP.format(d, opt), cmdP.format(d, opt), nH=8)
