from hydroDL import kPath, utils
import importlib
import time
import numpy as np
from hydroDL.data import usgs, gageII, gridMET, ntn, transform
caseLst = ['0801', '0802', '0803', '0804', '0805', '0902', '0903', '0904']
dataName = 'Q90ref'
dm = dbBasin.DataModelFull(dataName)
outName = '{}-B10'.format(dataName)
yP, ycP = basinFull.testModel(
outName, DM=dm, batchSize=20, testSet='all')
yO, ycO = basinFull.getObs(outName, 'all', DM=dm)
indT = np.where(dm.t == np.datetime64('2010-01-01'))[0][0]
nash0 = utils.stat.calNash(yP[indT:, :, 0], yO[indT:, :, 0])
rmse0 = utils.stat.calRmse(yP[indT:, :, 0], yO[indT:, :, 0])
corr0 = utils.stat.calCorr(yP[indT:, :, 0], yO[indT:, :, 0])
bias0 = utils.stat.calBias(yP[indT:, :, 0], yO[indT:, :, 0])
nashLst = list()
rmseLst = list()
corrLst = list()
biasLst = list()
for case in caseLst:
testSet = 'EcoB'+case
nashLstTemp = list()
import time
import numpy as np
from hydroDL.data import usgs, gageII, gridMET, ntn, transform
import matplotlib
dataName = 'Q90ref'
dm = dbBasin.DataModelFull(dataName)
indT = np.where(dm.t == np.datetime64('2010-01-01'))[0][0]
# global model
nashLst1 = list()
rmseLst1 = list()
corrLst1 = list()
outName = '{}-B10'.format(dataName)
yP, ycP = basinFull.testModel(outName, DM=dm, batchSize=20, testSet='all')
yO, ycO = basinFull.getObs(outName, 'all', DM=dm)
corr1 = utils.stat.calCorr(yP[:indT, :, 0], yO[:indT, :, 0])
corr2 = utils.stat.calCorr(yP[indT:, :, 0], yO[indT:, :, 0])
# tsMap
siteNoLst = dm.siteNoLst
dfCrd = gageII.readData(varLst=['LAT_GAGE', 'LNG_GAGE'], siteNoLst=siteNoLst)
lat = dfCrd['LAT_GAGE'].values
lon = dfCrd['LNG_GAGE'].values
def funcMap():
figM, axM = plt.subplots(1, 1, figsize=(12, 4))
axplot.mapPoint(axM, lat, lon, corr2, vRange=[0.5, 1], s=16)
axM.set_title('corr LSTM streamflow')
figP = plt.figure(figsize=[16, 6])