dataBox = list()
caseLst = ['Predict'] + [str(nd) + 'd Forcast' for nd in fLst]
for k in range(len(keyLst)):
key = keyLst[k]
temp = list()
data = statP[key]
temp.append(data)
print(key, np.nanmedian(statP[key]))
print(key, np.nanmedian(statF[key]))
for i in range(len(fLst)):
data = statLstF[i][key]
temp.append(data)
# print(key, np.nanmedian(data))
dataBox.append(temp)
plt.tight_layout()
fig = plot.plotBoxFig(dataBox, keyLst, caseLst, sharey=False, figsize=[8, 3])
fig.show()
fig.savefig(os.path.join(saveDir, 'box_forecast'))
# plot maps forecast_days
keyLst = ['RMSE', 'Corr']
cRangeLst = [[0, 0.05], [0.7, 1]]
[lat, lon] = df.getGeo()
fig, axes = plt.subplots(len(fLst), len(keyLst), figsize=[8, 6])
for i in range(len(keyLst)):
key = keyLst[i]
cRange = cRangeLst[i]
for j in range(len(fLst)):
data = statLstF[j][key]
titleStr = key + ' of {}d Forecast'.format(fLst[j])
grid, uy, ux = utils.grid.array2grid(data, lat=lat, lon=lon)
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
data = statDictLst[k][statStr]
data = data[~np.isnan(data)]
temp.append(data)
dataBox.append(temp)
# plt.style.use('classic')
plt.rcParams['font.size'] = 16
plt.rcParams['font.family'] = 'Times New Roman'
# plt.rcParams['mathtext.fontset'] = 'cm'
# # fig = plt.figure(figsize=(8, 5), tight_layout=True)
fig = plot.plotBoxFig(dataBox,
keyLst, ['LSTM', 'DA-1', 'DA-3', 'DA-7'],
sharey=False,
title='Dataset: HUC-' + str(HUCid))
fig.patch.set_facecolor('white')
fig.show()
# plt.savefig(pathCamels['Out'] + '/' + save_path + "/boxstat.png", dpi=500)
# # plot time series
# plt.rcParams['font.size'] = 13
# plt.rcParams['font.family'] = 'Times New Roman'
# t = utils.time.tRange2Array(tRange)
# npub = len(subset)
# fig, axes = plt.subplots(npub, 1, figsize=(16, 14))
# for k in range(npub):
# # iGrid = np.random.randint(0, 671)
# # iGrid = np.random.randint(0, len(subset))
# iGrid = k
# plot box - latency
# if 'post' in doLst:
caseLst = ['Predict'] + ['Nowcast ' + str(nd) + 'd latency' for nd in dLst]
statLst1 = [stat.statError(x, obs) for x in predLst]
keyLst = list(statLst1[0].keys())
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statLst1)):
data = statLst1[k][statStr]
data = data[~np.isnan(data)]
temp.append(data)
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox, keyLst, caseLst, sharey=False)
fig.show()
fig.savefig(os.path.join(saveDir, 'box_latency'))
# figure out how many days observation lead
maskObs = 1 * ~np.isnan(obs.squeeze())
maskDay = np.zeros(maskObs.shape).astype(int)
ngrid, nt = maskObs.shape
for j in range(ngrid):
temp = 0
for i in range(nt):
maskDay[j, i] = temp
if maskObs[j, i] == 1:
temp = 1
else:
if temp != 0:
fig.show()
indLst = [
cropRate[:, 0] > 2, cropRate[:, 4] > 2, cropRate[:, 22] > 2,
cropRate[:, 23] > 2
]
dataBox = list()
for iC in range(len(indLst)):
tempLst = list()
for k in range(4):
data = statPLst[k]['RMSE'][indLst[iC]] - statFLst[k]['RMSE'][
indLst[iC]]
tempLst.append(data)
print(key, k, np.nanmedian(data))
dataBox.append(tempLst)
fig = plot.plotBoxFig(dataBox, sharey=True, figsize=[8, 3])
plt.tight_layout()
fig.show()
# plot time series
indLst = [1023]
strLst = ['east Texas']
tBar = [utils.time.t2dt(20160401)]
t = df.getT()
[lat, lon] = df.getGeo()
prcp = df.getDataTs('APCP_FORA')
tsNameLst = ['obs', 'prj', 'fore']
for k in range(len(indLst)):
fig, axes = plt.subplots(2, 1, figsize=[16, 6])
ind = indLst[k]
titleStr = 'typical {} pixel, lat {:.3}, lon {:.3}'.format(
testName = subsetLst[kc - 1]
if k != kc:
outName = 'ecoRegion{:02d}{:02d}_v2f1_Forcing'.format(kc, k)
else:
outName = 'ecoRegion{:02d}_v2f1_Forcing'.format(kc)
out = os.path.join(pathSMAP['Out_L3_NA'], 'ecoRegion', outName)
df, yp, yt = master.test(out, tRange=tRange, subset=testName)
temp = stat.statError(yp[:, :, 0], yt[:, :, 0])
tempLst.append(temp)
if k == kc:
statRefLst.append(temp)
statLst.append(tempLst)
# %% plot box
keyLst = stat.keyLst
ecoLst = ['{:02d}'.format(x) for x in range(1, 18)]
caseLst = ['{:02d}'.format(x) for x in [7, 8, 13]]
for k in range(len(caseLst)):
dataBox = list()
key = 'RMSE'
for ii in range(len(ecoLst)):
temp = list()
temp.append(statLst[k][ii][key] - statRefLst[k][key])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox, ecoLst, caseLst, title=key, figsize=(12, 4))
plt.subplots_adjust(wspace=0, hspace=0)
fig.show()
saveFile = os.path.join(saveFolder, 'ecoRegionComb_box_' + str(caseLst[k]))
fig.savefig(saveFile)
if 'post' in doLst:
# stat
ypLst = [ypLstmLst, ypAnnLst]
statDictLst = list()
for i in range(0, len(ypLst)):
tempLst = list()
for j in range(0, len(ypLst[i])):
tempLst.append(stat.statError(ypLst[i][j], yT))
statDictLst.append(tempLst)
keyLst = list(tempLst[0].keys())
# plot box
dataBox = list()
caseLst1 = keyLst
caseLst2 = ['LSTM', 'LSTM-DA']
for iS in range(len(keyLst)):
statStr = keyLst[iS]
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
temp.append(statDictLst[k][statStr])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox,
caseLst1,
caseLst2,
sharey=True,
title=statStr)
fig.show()
outName = 'Global_v4f1_y1' + '_' + case2
out = os.path.join(pathSMAP['Out_L3_Global'], outName)
df, yp, yt = master.test(out,
tRange=yrLst,
subset=testName,
epoch=500)
temp = stat.statError(yp[:, :, 0], yt[:, :, 0])
tempLst.append(temp)
statLst.append(tempLst)
# plot box
keyLst = stat.keyLst
caseLst = list()
for case1 in caseLst1:
for case2 in caseLst2:
caseLst.append(case1 + ' ' + case2)
for k in range(len(keyLst)):
dataBox = list()
key = keyLst[k]
for ss in statLst:
temp = list()
for s in ss:
temp.append(s[key])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox, contLst, caseLst, title=key)
fig.show()
# fig = plot.plotBoxFig(dataBox, keyLst, caseLst, sharey=False)
# fig.show()
# fig.savefig(os.path.join(saveDir, 'box_forecast'))
stat.statError(yp1, yt2),
stat.statError(yp2, yt2),
stat.statError(yp3, yt2)
]
keyLst = list(statDictLst[0].keys())
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
data = statDictLst[k][statStr]
data = data[~np.isnan(data)]
temp.append(data)
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox, keyLst, ['LSTM', 'DA-1', 'DA-7'], sharey=False)
fig.show()
# plot time series
t = utils.time.tRange2Array([20050101, 20150101])
fig, axes = plt.subplots(5, 1, figsize=(12, 8))
# iLst = [54, 219, 298, 325, 408]
for k in range(5):
iGrid = np.random.randint(0, 671)
# iGrid = iLst[k]
yPlot = list()
for y in yLst:
yPlot.append(y[iGrid, :])
if k == 0:
plot.plotTS(
t,
plot.plotMap(grid, ax=axes[1], lat=uy, lon=ux, cRange=[0, 10])
fig.show()
indLst = [cropRate[:, 0] > 20, cropRate[:, 22] > 5, cropRate[:, 23] > 10]
labMonth = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Agu', 'Sep', 'Oct',
'Nov', 'Dec'
]
labCrop = ['Corn', 'Spring wheat', 'Winter wheat']
dataBox = list()
for k in range(12):
tempLst = list()
for iC in range(len(indLst)):
data = statPLst[k]['RMSE'][indLst[iC]] - statFLst[k]['RMSE'][
indLst[iC]]
if len(data[~np.isnan(data)]) < 20:
data = None
tempLst.append(data)
dataBox.append(tempLst)
fig = plot.plotBoxFig(dataBox,
label1=labMonth,
label2=labCrop,
sharey=True,
figsize=[8, 3],
colorLst='rgb')
plt.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0)
plt.ylim(-0.02, 0.04)
fig.show()
fig.savefig(os.path.join(saveDir, 'box_month'))
'Nov', 'Dec'
]
labCrop = ['Corn', 'Spring wheat', 'Winter wheat', 'Rice']
dataBox = list()
for k in range(12):
tempLst = list()
for iC in range(len(indLst)):
data = statPLst[k]['RMSE'][indLst[iC]] - statFLst[k]['RMSE'][
indLst[iC]]
if len(data[~np.isnan(data)]) < 20:
data = None
tempLst.append(data)
dataBox.append(tempLst)
fig = plot.plotBoxFig(dataBox,
label1=labMonth,
label2=None,
sharey=True,
figsize=[8, 3],
colorLst='rgbk')
plt.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0)
plt.ylim(-0.02, 0.04)
fig.show()
fig.savefig(os.path.join(saveDir, 'box_crop.eps'))
fig.savefig(os.path.join(saveDir, 'box_crop.png'))
fig = plot.plotBoxFig(dataBox[10:11],
label1=labMonth[10:11],
label2=labCrop,
sharey=True,
figsize=[8, 3],
colorLst='rgbk',
matplotlib.rcParams.update({'lines.markersize': 12})
matplotlib.rcParams.update({'legend.fontsize': 11})
keyLst = stat.keyLst
keyLegLst = ['Bias', 'RMSE', 'ubRMSE', 'R']
dataBox = list()
caseLst = ['Project'] + [str(nd) + 'd Forecast' for nd in fLst]
for k in range(len(keyLst)):
key = keyLst[k]
temp = list()
data = statP[key]
temp.append(data)
for i in range(len(fLst)):
data = statLstF[i][key]
temp.append(data)
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox, keyLegLst, sharey=False, figsize=[8, 4])
plt.suptitle('Error metrics of projection and forecast model')
plt.tight_layout()
plt.subplots_adjust(top=0.85, right=0.95)
fig.show()
fig.savefig(os.path.join(saveDir, 'box_forecast_RK.eps'))
fig.savefig(os.path.join(saveDir, 'box_forecast_RK.png'))
[np.nanmean(dataBox[1][1]), np.nanmean(
dataBox[1][2]), np.nanmean(dataBox[1][3])]
# fig = plot.plotBoxFig(
# dataBox, keyLst, caseLst, sharey=False, figsize=[8, 3], legOnly=True)
# # plt.suptitle('Error matrices of project and forecast model')
# plt.tight_layout()
# fig.show()
outName = subsetPattern.format(case) + '_Forcing'
out = os.path.join(pathSMAP['Out_L3_NA'], 'ecoRegionL3', outName)
df, yp, yt = master.test(out, tRange=tRange, subset=testName)
errLst.append(stat.statError(yp[:, :, 0], yt[:, :, 0]))
# plot box
keyLst = stat.keyLst
dataBox = list()
for key in keyLst:
temp = list()
for err in errLst:
temp.append(err[key])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox,
keyLst,
caseLst,
title=caseLst[0],
figsize=(12, 4),
sharey=False)
# plt.subplots_adjust(wspace=0, hspace=0)
fig.show()
saveFile = os.path.join(saveFolder, 'case_' + caseLst[0])
fig.savefig(saveFile)
# plot maps
cLst = 'rbkgcmy'
nCase = len(caseLst)
fig, ax = plt.subplots(figsize=(8, 4))
data = np.tile(np.array(to_rgb('lightgrey')), (latC.shape[0], 1))
for k in range(1, nCase):
case = caseLst[k]
subsetLst = [subsetPattern.format(x) for x in case.split('+')]
import matplotlib
matplotlib.rcParams.update({'font.size': 14})
matplotlib.rcParams.update({'lines.linewidth': 2})
matplotlib.rcParams.update({'lines.markersize': 6})
indLst = [cropRate[:, 0] > 30, cropRate[:, 22] > 5, cropRate[:, 23] > 10]
labMonth = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Agu', 'Sep', 'Oct',
'Nov', 'Dec'
]
labCrop = ['Corn', 'Spring wheat', 'Winter wheat']
cLst = 'rgb'
dataBox = list()
for iC in range(len(indLst)):
dataBox = list()
for k in range(12):
data = statPLst[k]['RMSE'][indLst[iC]] - statFLst[k]['RMSE'][
indLst[iC]]
if len(data[~np.isnan(data)]) < 20:
data = []
dataBox.append(data)
fig = plot.plotBoxFig(dataBox,
label1=labMonth,
label2=[labCrop[iC]],
sharey=True,
figsize=[8, 3],
colorLst=cLst[iC])
plt.subplots_adjust(wspace=0, hspace=0)
plt.ylim(-0.02, 0.04)
fig.show()
ypLst.append(
dbCsv.transNorm(yP,
rootDB=rootDB,
fieldName='SMAP_AM',
fromRaw=False))
if 'post' in doLst:
statDictLst = list()
for k in range(0, len(ypLst)):
statDictLst.append(stat.statError(ypLst[k], yT))
keyLst = ['RMSE', 'ubRMSE', 'Bias', 'Corr']
caseLst = ['LSTM']
for k in dLst:
caseLst.append('DA-' + str(k))
# plot box
dataBox = list()
cmap = plt.cm.jet
cLst = cmap(np.linspace(0, 1, len(caseLst)))
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
temp.append(statDictLst[k][statStr])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox,
keyLst,
caseLst,
sharey=False,
colorLst=cLst)
fig.show()
out = os.path.join(pathSMAP['Out_L3_NA'], 'ecoRegionCase', outName)
df, yp, yt = master.test(out, tRange=tRange, subset=testName)
errLst.append(stat.statError(yp[:, :, 0], yt[:, :, 0]))
# plot box
cLst = 'ygbr'
keyLst = ['RMSE', 'Corr']
dataBox = list()
for key in keyLst:
temp = list()
for err in errLst:
temp.append(err[key])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox,
' ',
figsize=(8, 6),
colorLst=cLst,
sharey=False)
# plt.subplots_adjust(wspace=0, hspace=0)
plt.tight_layout()
fig.show()
saveFile = os.path.join(saveFolder, 'case_{}_box'.format(case))
fig.savefig(saveFile)
# plot box
cLst = 'ygbr'
keyLst = ['Corr', 'RMSE']
dataBox = list()
for key in keyLst:
temp = list()
for err in errLst:
keyLst = ['Bias', 'RMSE', 'NSE']
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
data = statDictLst[k][statStr]
data = data[~np.isnan(data)]
temp.append(data)
dataBox.append(temp)
# plt.style.use('classic')
plt.rcParams['font.size'] = 16
plt.rcParams['font.family'] = 'Times New Roman'
# plt.rcParams['mathtext.fontset'] = 'cm'
# # fig = plt.figure(figsize=(8, 5), tight_layout=True)
fig = plot.plotBoxFig(dataBox, keyLst, sharey=False)
fig.patch.set_facecolor('white')
fig.show()
# plt.savefig(pathCamels['Out'] + '/' + save_path + "/boxstat_within30.png", dpi=500)
# plot only NSE box
keyLst = ['NSE']
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
data = statDictLst[k][statStr]
data = data[~np.isnan(data)]
temp.append(data)
dataBox.append(temp)
# axes[1].plot(corrLst0, corrLst1, '*')
# fig.show()
# plot box
keyLst = ['RMSE', 'Corr']
# ecoLst = ['{0:0>2}'.format(x) for x in range(1, 18)]
ecoLst = 'ABDEFGHIJKLMNOPQR'
matplotlib.rcParams.update({'font.size': 16})
matplotlib.rcParams.update({'lines.linewidth': 2})
matplotlib.rcParams.update({'lines.markersize': 10})
for k in range(len(keyLst)):
dataBox = list()
key = keyLst[k]
for ss in statLst:
temp = list()
for s in ss:
temp.append(s[key])
dataBox.append(temp)
if key == 'RMSE':
fig = plot.plotBoxFig(dataBox,
ecoLst,
caseLst1,
widths=0.5,
figsize=(12, 4))
else:
fig = plot.plotBoxFig(dataBox, ecoLst, widths=0.5, figsize=(12, 4))
plt.subplots_adjust(wspace=0, hspace=0)
fig.show()
saveFile = os.path.join(saveFolder, 'ecoRegion_box_' + key)
fig.savefig(saveFile)
matplotlib.rcParams.update({'lines.markersize': 12})
matplotlib.rcParams.update({'legend.fontsize': 11})
keyLst = stat.keyLst
keyLegLst = ['Bias', 'RMSE', 'ubRMSE', 'R']
dataBox = list()
caseLst = ['Project'] + [str(nd) + 'd Forecast' for nd in fLst]
for k in range(len(keyLst)):
key = keyLst[k]
temp = list()
data = statP[key]
temp.append(data)
for i in range(len(fLst)):
data = statLstF[i][key]
temp.append(data)
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox, keyLegLst, sharey=False, figsize=[8, 4])
plt.suptitle('Error metrics of projection and forecast model')
plt.tight_layout()
plt.subplots_adjust(top=0.85, right=0.95)
fig.show()
fig.savefig(os.path.join(saveDir, 'box_forecast.eps'))
fig.savefig(os.path.join(saveDir, 'box_forecast.png'))
fig = plot.plotBoxFig(dataBox,
keyLst,
caseLst,
sharey=False,
figsize=[8, 3],
legOnly=True)
# plt.suptitle('Error matrices of project and forecast model')
plt.tight_layout()
errLst.append(err)
errLstAll.append(errLst)
# plot box
cLst = 'ygbr'
keyLst = ['RMSE', 'Corr']
for key in keyLst:
dataBox = list()
for errLst in errLstAll:
temp = list()
for err in errLst:
temp.append(err[key])
dataBox.append(temp)
fig = plot.plotBoxFig(dataBox,
caseLabLst,
colorLst=cLst,
figsize=(12, 4),
sharey=True)
plt.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0)
fig.show()
saveFile = os.path.join(saveFolder, 'sim_box_lim_{}'.format(key))
fig.savefig(saveFile)
# plot box
cLst = 'ygbr'
keyLst = ['Corr', 'RMSE']
dataBox = list()
for key in keyLst:
temp = list()
for err in errLst:
temp.append(data)
dataBox.append(temp)
# plt.style.use('classic')
plt.rcParams['font.size'] = 14
plt.rcParams['font.family'] = 'Times New Roman'
plt.rcParams["legend.columnspacing"] = 0.1
plt.rcParams["legend.handletextpad"] = 0.2
labelname = ['LSTM']
for nDay in nDayLst:
labelname.append('DA-' + str(nDay) + 'M')
xlabel = [
'Bias ($\mathregular{ft^3}$/s)', 'RMSE ($\mathregular{ft^3}$/s)', 'NSE'
]
fig = plot.plotBoxFig(dataBox,
xlabel,
labelname,
sharey=False,
figsize=(10, 5))
fig.patch.set_facecolor('white')
fig.show()
plt.savefig(pathCamels['Out'] + '/' + save_path + "/boxstat_0625.png", dpi=600)
# # plot time series
# plt.rcParams['font.size'] = 13
# plt.rcParams['font.family'] = 'Times New Roman'
# t = utils.time.tRange2Array(tRange)
# fig, axes = plt.subplots(5, 1, figsize=(12, 8))
# for k in range(5):
# iGrid = np.random.randint(0, 671)
# yPlot = [obs[iGrid, :]]
# for y in predLst:
xLst = ['{}d Forecast'.format(x) for x in fLst]
key = 'RMSE'
dataBox = list()
for i in range(len(statLst)):
ss = statLst[i]
RKrmse = RKrmseMat[:, i]
ind = np.where(~np.isnan(RKrmse))[0]
temp = list()
for s in ss:
temp.append(s[key][ind])
temp.append(RKrmse[ind])
dataBox.append(temp)
fig = plot.plotBoxFig(
dataBox,
xLst,
None,
sharey=True,
figsize=[6, 4],
title='RMSE of forecast models using different training set')
fig.show()
fig.savefig(os.path.join(saveDir, 'compareRK.eps'))
fig.savefig(os.path.join(saveDir, 'compareRK.png'))
importlib.reload(plot)
fig = plot.plotBoxFig(dataBox,
xLst,
caseLst,
sharey=True,
figsize=[8, 4],
legOnly=True)
fig.show()
if 'plotBox' in doLst:
for grp in grpLst:
grpCodeLst = codePdf[codePdf['group'] == grp].index.tolist()
dataBox = list()
for code in grpCodeLst:
ind = dictData['varC'].index(code)
# temp = [[npf['matRmse1'][:, ind], npf['matRmse2'][:, ind]] for npf in npfLst]
temp = [[npf['matRho1'][:, ind], npf['matRho2'][:, ind]]
for npf in npfLst]
dataBox.append([item for sublist in temp for item in sublist])
legLst = [item for sublist in [[s+'_train', s+'_test']
for s in modelLst] for item in sublist]
labLst = codePdf[codePdf['group'] == grp]['shortName'].tolist()
fig = plot.plotBoxFig(dataBox, label1=labLst,
label2=legLst, sharey=False)
fig.show()
if 'plotTsMap' in doLst:
# plot map
iCLst = [0, 11]
tempLst = [npfLst[0]['matRmse2'][:, iC] for iC in iCLst]
temp = np.sum(tempLst, axis=0)
indG = np.where(~np.isnan(temp))[0].tolist()
npf = npfLst[0]
dataLst = [npf['matRmse2'][indG, iC] for iC in iCLst]
dataNLst = [npf['matN2'][indG, iC] for iC in iCLst]
mapTitleLst = ['RMSE of ' + codePdf['shortName'][varC[iC]]
for iC in iCLst]
siteNoLstTemp = [siteNoLst[i] for i in indG]
keyLst=['Bias', 'RMSE', 'NSE']
dataBox = list()
for iS in range(len(keyLst)):
statStr = keyLst[iS]
temp = list()
for k in range(len(statDictLst)):
data = statDictLst[k][statStr]
data = data[~np.isnan(data)]
temp.append(data)
dataBox.append(temp)
# plt.style.use('classic')
plt.rcParams['font.size'] = 16
plt.rcParams['font.family'] = 'Times New Roman'
# plt.rcParams['mathtext.fontset'] = 'cm'
# # fig = plt.figure(figsize=(8, 5), tight_layout=True)
fig = plot.plotBoxFig(dataBox, keyLst, ['LSTM', 'DA-1', 'DA-3', 'DA-7'], sharey=False, title='Dataset: North Great Plain')
fig.patch.set_facecolor('white')
fig.show()
# plt.savefig(pathCamels['Out'] + '/' + save_path + "/boxstat_test.png", dpi=500)
# plot time series
plt.rcParams['font.size'] = 13
plt.rcParams['font.family'] = 'Times New Roman'
t = utils.time.tRange2Array(tRange)
fig, axes = plt.subplots(5, 1, figsize=(12, 8))
for k in range(5):
# iGrid = np.random.randint(0, 671)
iGrid = np.random.randint(0, len(subset))
yPlot = [obs[iGrid, :]]
for y in predLst:
