def funcM():
figM = plt.figure(figsize=[12, 3])
gsM = gridspec.GridSpec(1, 5)
axM = list()
axM.append(figM.add_subplot(gsM[0, 0]))
axM.append(figM.add_subplot(gsM[0, 1:3]))
axM.append(figM.add_subplot(gsM[0, 3:5]))
axM = np.array(axM)
x = corrMat[indS, codeLst.index(codeM[0]), 1]
y = corrMat[indS, codeLst.index(codeM[1]), 1]
sc = axplot.scatter121(axM[0], x, y, cMat)
figM.colorbar(sc, ax=axM[0])
axM[0].set_label('scatter')
axplot.mapPoint(axM[1], lat, lon, x, s=24)
axM[1].set_label('map1')
axplot.mapPoint(axM[2], lat, lon, y, s=24)
axM[2].set_label('map2')
labelLst = ['scatter', 'map1', 'map2']
xMat = np.stack([x, lon, lon], axis=1)
yMat = np.stack([y, lat, lat], axis=1)
figP = plt.figure(figsize=[12, 3])
gsP = gridspec.GridSpec(2, 4)
axP = list()
axP.append(figP.add_subplot(gsP[0, :-1]))
axP.append(figP.add_subplot(gsP[0, -1]))
axP.append(figP.add_subplot(gsP[1, :-1]))
axP.append(figP.add_subplot(gsP[1, -1]))
axP = np.array(axP)
return figM, axM, figP, axP, xMat, yMat, labelLst
def funcM():
figM = plt.figure(figsize=[12, 3])
gsM = gridspec.GridSpec(1, 3)
axM = list()
axM.append(figM.add_subplot(gsM[0, 0]))
axM.append(figM.add_subplot(gsM[0, 1:]))
axM = np.array(axM)
sc = axplot.scatter121(axM[0], corrMat[indS, ic, 1],
corrMat[indS, ic, 2], cMat)
figM.colorbar(sc, ax=axM[0])
axplot.mapPoint(axM[1], lat, lon, corrMat[indS, ic, 1], s=24)
axM[0].set_label('scatter')
axM[1].set_label('map')
figM.show()
figP, axP = plt.subplots(2, 1)
labelLst = ['scatter', 'map']
xMat = np.stack([corrMat[indS, ic, 1], lon], axis=1)
yMat = np.stack([corrMat[indS, ic, 2], lat], axis=1)
return figM, axM, figP, axP, xMat, yMat, labelLst
for code in dictPlot.keys():
siteNoCode = dictSite[code]
siteNoP = dictPlot[code]
indS = [siteNoLst.index(siteNo) for siteNo in siteNoCode]
ic = codeLst.index(code)
dfCrd = gageII.readData(varLst=['LAT_GAGE', 'LNG_GAGE'],
siteNoLst=siteNoCode)
lat = dfCrd['LAT_GAGE'].values
lon = dfCrd['LNG_GAGE'].values
shortName = usgs.codePdf.loc[code]['shortName']
fig = plt.figure(figsize=[12, 9])
gsM = gridspec.GridSpec(4, 5)
ax = fig.add_subplot(gsM[0, 0])
sc = axplot.scatter121(ax, corrMat[indS, ic, 1], corrMat[indS, ic, 2],
pMat[indS, ic])
fig.colorbar(sc, ax=ax)
ax.set_xlabel('LSTM correlation')
ax.set_ylabel('WRTDS correlation')
ax.set_title('power of seasonal signal')
for siteNo in siteNoP:
iS = siteNoLst.index(siteNo)
circle = plt.Circle([corrMat[iS, ic, 1], corrMat[iS, ic, 2]],
0.05,
color='red',
fill=False)
ax.add_patch(circle)
ax = fig.add_subplot(gsM[0, 1:3])
axplot.mapPoint(ax, lat, lon, corrMat[indS, ic, 1], s=24, cmap='viridis')
ax.set_title('map of LSTM correlation')
ticks = [-0.5, 0, 0.5, 1]
axM = list()
for k, code in enumerate(codeLst2):
j, i = utils.index2d(k, 5, 4)
axM.append(
figM.add_subplot(gsM[(j) * 10:(j + 1) * 10, i * 10:(i + 1) * 10]))
for k, code in enumerate(codeLst2):
j, i = utils.index2d(k, 5, 4)
ax = axM[k]
ic = codeLst.index(code)
x = corrMat[:, ic, 1]
y = corrMat[:, ic, 2]
c = cMat[:, ic]
# c = np.argsort(countMat2[:, ind])
sc = axplot.scatter121(ax, x, y, c, vR=cR, size=15)
rmse, corr = utils.stat.calErr(x, y)
titleStr = '{} {} {:.2f}'.format(code, usgs.codePdf.loc[code]['shortName'],
corr)
_ = ax.set_xlim([ticks[0], ticks[-1]])
_ = ax.set_ylim([ticks[0], ticks[-1]])
_ = ax.set_yticks(ticks)
_ = ax.set_xticks(ticks)
axplot.titleInner(ax, titleStr)
ax.set_label(code)
# print(i, j)
if i != 0:
_ = ax.set_yticklabels([])
if j != 4:
_ = ax.set_xticklabels([])
# _ = ax.set_aspect('equal')
code, usgs.codePdf.loc[code]['shortName'])
axplot.titleInner(ax, titleStr)
fig.show()
dfG = gageII.readData(siteNoLst=siteNoLst)
dfG = gageII.updateRegion(dfG)
dfG = gageII.updateCode(dfG)
cMat=
fig, axes = plt.subplots(nfy, nfx)
for k, code in enumerate(codeLst2):
j, i = utils.index2d(k, nfy, nfx)
ax = axes[j, i]
ic = codeLst.index(code)
x = rMat[:, ic, 1]
y = rMat[:, ic, 0]
axplot.scatter121(ax, x, y, cvR=[0, 1])
titleStr = '{} {} '.format(
code, usgs.codePdf.loc[code]['shortName'])
axplot.titleInner(ax, titleStr)
fig.show()
# Cart
dfA = pd.DataFrame(index=range(10), columns=codeLst)
dfV = pd.DataFrame(index=range(10), columns=codeLst)
for code in codeLst:
ic = codeLst.index(code)
matAll = rMat[]
[mat], indS = utils.rmNan([matAll])
siteNoCode = [siteNoLst[ind] for ind in indS]
dfGC = dfG.loc[siteNoCode]
x = qMat
y = rMat[:, ic, 0]
axplot.plot121(ax, x, y, vR=[0, 1])
titleStr = '{} {} '.format(code, usgs.codePdf.loc[code]['shortName'])
axplot.titleInner(ax, titleStr)
fig.show()
cMat = qMat
fig, axes = plt.subplots(nfy, nfx)
for k, code in enumerate(codeLst2):
j, i = utils.index2d(k, nfy, nfx)
ax = axes[j, i]
ic = codeLst.index(code)
x = rMat[:, ic, 1]
y = rMat[:, ic, 0]
axplot.scatter121(ax, x, y, qMat, vR=[0, 0.5])
titleStr = '{} {} '.format(code, usgs.codePdf.loc[code]['shortName'])
axplot.titleInner(ax, titleStr)
fig.show()
indC = [codeLst.index(code) for code in codeLst2]
labelLst = [
'{} {}'.format(code, usgs.codePdf.loc[code]['shortName'])
for code in codeLst2
]
xMat = rMat[:, indC, 1]
yMat = rMat[:, indC, 0]
nXY = [nfx, nfy]
figM, axM = figplot.scatter121Batch(xMat,
yMat,
qMat,
importlib.reload(axplot)
figM, figP = figplot.clickMap(funcMap, funcPoint)
# 121 plot
# tempLst = ['09163500', '05465500', '02175000', '09171100']
tempLst = ['10343500', '05465500', '02175000', '09171100']
code = '00915'
fig, ax = plt.subplots(1, 1, figsize=[8, 6])
ic = codeLst.index(code)
x = corrMat[:, ic, 0]
y = corrMat[:, ic, 1]
c = corrMat[:, ic, 2]
out = axplot.scatter121(ax, x, y, c)
# for siteNo in tempLst:
# indS = siteNoLst.index(siteNo)
# circle = plt.Circle([x[indS], y[indS]], 0.05,
# color='black', fill=False)
# ax.add_patch(circle)
ax.set_xlabel('LSTM correlation')
ax.set_ylabel('WRTDS correlation')
ax.set_title('Performance on Calcium')
fig.colorbar(out, ax=ax)
fig.show()
figM, axM = plt.subplots(1, 2, figsize=(12, 4))
indS = [siteNoLst.index(siteNo) for siteNo in dictSite[code]]
siteNoLstCode = dictSite[code]
dfCrd = gageII.readData(
fileGlim = os.path.join(kPath.dirData, 'USGS', 'GLiM', 'tab_1KM')
tabGlim = pd.read_csv(fileGlim, dtype={'siteNo': str}).set_index('siteNo')
matV = np.argmax(tabGlim.values, axis=1)
labelLst = [
'{} {}'.format(code, usgs.codePdf.loc[code]['shortName'])
for code in codeLst2
]
icLst = [codeLst.index(code) for code in codeLst2]
figM, axM = figplot.scatter121Batch(rMat[:, icLst, 1],
rMat[:, icLst, 0],
matV,
labelLst, [nfx, nfy],
optCb=1,
ticks=[0, 0.5, 1])
figM.show()
temp = ['00930', '00940']
ic1 = codeLst.index(temp[0])
ic2 = codeLst.index(temp[1])
nameLst = [usgs.codePdf.loc[code]['shortName'] for code in temp]
fig, axes = plt.subplots(1, 2)
axplot.scatter121(axes[0], rMat[:, ic1, 0], rMat[:, ic2, 0], qMat, vR=[0, .6])
axes[0].set_xlabel('Linearity of {}'.format(nameLst[0]))
axes[0].set_ylabel('Linearity of {}'.format(nameLst[1]))
axplot.scatter121(axes[1], rMat[:, ic1, 1], rMat[:, ic2, 1], qMat, vR=[0, .6])
axes[1].set_xlabel('Seasonality of {}'.format(nameLst[0]))
axes[1].set_ylabel('Seasonality of {}'.format(nameLst[1]))
fig.show()
v3 = dictObs[siteNo][code].iloc[ind2].values
vv1, vv2, vv3 = utils.rmNan([v1, v2, v3], returnInd=False)
rmse1, corr1 = utils.stat.calErr(vv1, vv2)
rmse2, corr2 = utils.stat.calErr(vv1, vv3)
rmse3, corr3 = utils.stat.calErr(vv2, vv3)
corrMat[indS, ic, 0] = corr1
corrMat[indS, ic, 1] = corr2
corrMat[indS, ic, 2] = corr3
# load basin attributes
regionLst = [
'ECO2_BAS_DOM', 'NUTR_BAS_DOM', 'HLR_BAS_DOM_100M', 'PNV_BAS_DOM'
]
dfG = gageII.readData(siteNoLst=siteNoLst)
fileT = os.path.join(gageII.dirTab, 'lookupPNV.csv')
tabT = pd.read_csv(fileT).set_index('PNV_CODE')
for code in range(1, 63):
siteNoTemp = dfG[dfG['PNV_BAS_DOM'] == code].index
dfG.at[siteNoTemp, 'PNV_BAS_DOM2'] = tabT.loc[code]['PNV_CLASS_CODE']
dfG = gageII.updateCode(dfG)
var = 'PLANTNLCD06'
codePlot = ['00618', '00660']
fig, ax = plt.subplots(1, 1)
c = dfG[var].values
x = corrMat[:, codeLst.index(codePlot[0]), 1]
y = corrMat[:, codeLst.index(codePlot[1]), 1]
sc = axplot.scatter121(ax, x, y, c=c, vR=[0, 1])
fig.colorbar(sc, ax=ax)
fig.show()
cVar = 'Estimated Travel time'
cR = [np.nanmin(cMat), np.nanmax(cMat)]
cR = [np.nanpercentile(cMat, 10), np.nanpercentile(cMat, 90)]
code = '00660'
cVar = 'DEVOPENNLCD06'
th = 4.755000114
cMat = dfG[cVar].values
# attr vs diff
cMatLog = np.log(cMat + 1)
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
ic = codeLst.index(code)
sc = axplot.scatter121(axes[0],
corrMat[:, ic, 1],
corrMat[:, ic, 2],
cMatLog,
size=30)
axes[0].set_xlabel('Corr LSTM')
axes[0].set_ylabel('Corr WRTDS')
fig.colorbar(sc, ax=axes[0])
x = cMat
y = corrMat[:, ic, 1]**2 - corrMat[:, ic, 2]**2
axes[1].plot(x, y, '*')
axes[1].plot([np.nanmin(x), np.nanmax(x)], [0, 0], 'k-')
axes[1].set_ylim([-0.5, 0.5])
axes[1].set_xlabel(cVar)
axes[1].set_ylabel('Rsq LSTM - Rsq WRTDS')
fig.suptitle('affect of {} on {} {}'.format(
cVar, code, usgs.codePdf.loc[code]['shortName']))
fig.show()
j, i = utils.index2d(k, nfy, nfx)
axM.append(
figM.add_subplot(gsM[(j) * 10:(j + 1) * 10, i * 10:(i + 1) * 10]))
for k, code in enumerate(codeLst2):
j, i = utils.index2d(k, nfy, nfx)
ax = axM[k]
ic = codeLst.index(code)
x = corrMat[:, ic, 1]
y = corrMat[:, ic, 2]
if cMat.ndim > 1:
c = cMat[:, ic]
else:
c = cMat
# c = np.argsort(countMat2[:, ind])
sc = axplot.scatter121(ax, x, y, c, vR=cR)
rmse, corr = utils.stat.calErr(x, y)
titleStr = '{} {} {:.2f}'.format(code, usgs.codePdf.loc[code]['shortName'],
corr)
_ = ax.set_xlim([ticks[0], ticks[-1]])
_ = ax.set_ylim([ticks[0], ticks[-1]])
_ = ax.set_yticks(ticks)
_ = ax.set_xticks(ticks)
axplot.titleInner(ax, titleStr)
ax.set_label(code)
# print(i, j)
if i != 0:
_ = ax.set_yticklabels([])
if j != 4:
_ = ax.set_xticklabels([])
# _ = ax.set_aspect('equal')
yMat = rMat[:, indC, 0]
cMat = bMat2[:, indC]
nXY = [nfx, nfy]
figM, axM = figplot.scatter121Batch(
xMat, yMat, cMat, labelLst, nXY, optCb=1,
ticks=[0, 0.5, 1], s=20, cmap='jet')
figM.show()
###############
ic1 = codeLst.index('00915')
ic2 = codeLst.index('00955')
fig, axes = plt.subplots(1, 2)
# axplot.plot121(axes[0], rMat[:, ic1, 0], rMat[:, ic2, 0], vR=[0, 1])
# axplot.plot121(axes[1], rMat[:, ic1, 1], rMat[:, ic2, 1], vR=[0, 1])
axplot.scatter121(axes[0], rMat[:, ic1, 0], rMat[:, ic2, 0], qMat, vR=[0, .5])
axplot.scatter121(axes[1], rMat[:, ic1, 1], rMat[:, ic2, 1], qMat, vR=[0, .5])
fig.show()
temp = ['00915', '00955']
ic1 = codeLst.index(temp[0])
ic2 = codeLst.index(temp[1])
nameLst = [usgs.codePdf.loc[code]['shortName'] for code in temp]
fig, axes = plt.subplots(1, 2)
sc1 = axplot.scatter121(axes[0], rMat[:, ic1, 1],
rMat[:, ic1, 0], qMat, vR=[0, 0.5])
axes[0].set_xlabel('Seasonality of {}'.format(nameLst[0]))
axes[0].set_ylabel('Linearity of {}'.format(nameLst[0]))
sc2 = axplot.scatter121(axes[1], rMat[:, ic2, 1],
rMat[:, ic2, 0], qMat, vR=[0, 0.5])
axes[1].set_xlabel('Seasonality of {}'.format(nameLst[1]))