def get_data(station, tag, syear=2000, eyear=2020, egm08=0.0, egm96=0.0):
time = -9999.0
data = -9999.0
if tag == "HydroWeb":
time, data = hweb.HydroWeb_WSE(station,
syear=syear,
eyear=eyear,
egm08=egm08,
egm96=egm96)
elif tag == "HydroSat":
time, data = hsat.HydroSat_WSE(station,
syear=syear,
eyear=eyear,
egm08=egm08,
egm96=egm96)
elif tag == "ICESat":
time, data = isat.ICESat_WSE(station,
syear=syear,
eyear=eyear,
egm08=egm08,
egm96=egm96)
elif tag == "CGLS":
time, data = cgls.CGLS_WSE(station,
syear=syear,
eyear=eyear,
egm08=egm08,
egm96=egm96)
elif tag == "GRRATS":
time, data = grt.GRRATS_WSE(station,
syear=syear,
eyear=eyear,
egm08=egm08,
egm96=egm96)
return time, data
def get_data(station, tag):
time = -9999.0
data = -9999.0
if tag == "HydroWeb":
time, data = hweb.HydroWeb_WSE(station, syear=1992)
elif tag == "HydroSat":
time, data = hsat.HydroSat_WSE(station, syear=1992)
elif tag == "ICESat":
time, data = isat.ICESat_WSE(station, syear=1992)
elif tag == "CGLS":
time, data = cgls.CGLS_WSE(station, syear=1992)
elif tag == "GRRATS":
time, data = grt.GRRATS_WSE(station, syear=1992)
return time, data
def make_fig(point):
plt.close()
labels = ["HydroWeb", "corrupted", "assimilated"]
# print org[:,point]
# print "----------------"
# print np.mean(asm[:,:,point],axis=1)
# for i in np.arange(start,last):
# print opn[i,:,point]
# print asm[i,:,point]
fig, ax1 = plt.subplots()
#ax1.plot(np.arange(start,last),org[:,point],label="true",color="black",linewidth=0.7,zorder=101,marker = "o",markevery=swt[point])
time, org = hweb.HydroWeb_WSE(pname[point], syear, eyear)
# alti=hweb.altimetry(pname[point]) - EGM08[point] + EGM96[point]
data = np.array(org) - np.array(EGM08[point]) + np.array(
EGM96[point]) #- alti
#====================================
if conflag == 1:
data0 = data
elif conflag == 2:
data0 = data - np.mean(
data
) #(data-mean_obs[ylist[point],xlist[point]])+mean_sfcelv[ylist[point],xlist[point]]
elif conflag == 3:
data0 = (data - np.mean(data)) / (
np.std(data) + 1e-20
) #((data-mean_obs[ylist[point],xlist[point]])/(std_obs[ylist[point],xlist[point]]+1.0e-20))*std_sfcelv[ylist[point],xlist[point]]+mean_sfcelv[ylist[point],xlist[point]]
#====================================
lines = [
ax1.plot(time,
data0,
label="obs",
marker="o",
color="#34495e",
linewidth=0.0,
zorder=101)[0]
]
# ax1.plot(np.arange(start,last),org[:,point],label="true",color="black",linewidth=0.7,zorder=101)
# ax1.plot(np.arange(start,last),m_sf[:,point],label="mean sfcelv",color="black",linewidth=0.7,linestyle="--",zorder=107)
# plt.plot(np.arange(start,last),org[:,point],label="true",color="black",linewidth=0.7)
for num in np.arange(0, int(pm.ens_mem())):
if conflag == 1:
opn0 = opn[:, num, point]
asm0 = asm[:, num, point]
elif conflag == 2:
opn0 = opn[:, num, point] - mean_obss[num, ylist[point],
xlist[point]]
asm0 = asm[:, num, point] - mean_obss[num, ylist[point],
xlist[point]]
elif conflag == 3:
opn0 = (opn[:, num, point] -
mean_obss[num, ylist[point], xlist[point]]) / (
std_obss[num, ylist[point], xlist[point]] + 1e-20)
asm0 = (asm[:, num, point] -
mean_obss[num, ylist[point], xlist[point]]) / (
std_obss[num, ylist[point], xlist[point]] + 1e-20)
#------------------------
ax1.plot(np.arange(start, last),
opn0,
label="corrupted",
color="#4dc7ec",
linewidth=0.2,
alpha=0.5,
zorder=102)
ax1.plot(np.arange(start, last),
asm0,
label="assimilated",
color="#ff8021",
linewidth=0.3,
alpha=0.5,
zorder=103)
# ax1.plot(np.arange(start,last),em_sf[:,num,point],label="mean sfcelv",color="blue",linewidth=0.3,linestyle="--",alpha=0.5,zorder=103)
# plt.plot(np.arange(start,last),opn[:,num,point],label="corrupted",color="blue",linewidth=0.3,alpha=0.5)
# plt.plot(np.arange(start,last),asm[:,num,point],label="assimilated",color="red",linewidth=0.3,alpha=0.5)
if conflag == 1:
opn1 = np.mean(opn[:, :, point], axis=1)
asm1 = np.mean(asm[:, :, point], axis=1)
elif conflag == 2:
opn1 = np.mean(opn[:, :, point], axis=1) - np.mean(
mean_obss[:, ylist[point], xlist[point]], axis=0)
asm1 = np.mean(asm[:, :, point], axis=1) - np.mean(
mean_obss[:, ylist[point], xlist[point]], axis=0)
elif conflag == 3:
opn1 = (np.mean(opn[:, :, point], axis=1) - np.mean(
mean_obss[:, ylist[point], xlist[point]], axis=0)) / (np.mean(
std_obss[:, ylist[point], xlist[point]], axis=0) + 1e-20)
asm1 = (np.mean(asm[:, :, point], axis=1) - np.mean(
mean_obss[:, ylist[point], xlist[point]], axis=0)) / (np.mean(
std_obss[:, ylist[point], xlist[point]], axis=0) + 1e-20)
lines.append(
ax1.plot(np.arange(start, last),
opn1,
label="corrupted",
color="#4dc7ec",
linewidth=0.8,
alpha=0.8,
zorder=102)[0])
lines.append(
ax1.plot(np.arange(start, last),
asm1,
label="assimilated",
color="#ff8021",
linewidth=0.8,
alpha=0.8,
zorder=103)[0])
# ax1.plot(np.arange(start,last),np.mean(em_sf[:,:,point],axis=1),label="mean sfelv",color="blue",linewidth=0.5,linestyle="--",alpha=0.5,zorder=103)
# plt.ylim(ymin=)
meanch = "mean (long-term): %5.2f" % (np.mean(
mean_obss[:, ylist[point], xlist[point]], axis=0))
stdch = "std (long-term):%5.2f" % (np.mean(
std_obss[:, ylist[point], xlist[point]], axis=0))
omch = "mean (obs): %5.2f" % (np.mean(data))
osch = "std (obs): %5.2f" % (np.std(data))
cmch = "mean (cor): %5.2f" % (np.mean(opn[:, :, point]))
csch = "std (cor): %5.2f" % (np.std(opn[:, :, point]))
ax1.text(0.02,
0.9,
meanch,
ha="left",
va="center",
transform=ax1.transAxes,
fontsize=10)
ax1.text(0.02,
0.8,
stdch,
ha="left",
va="center",
transform=ax1.transAxes,
fontsize=10)
ax1.text(0.02,
0.7,
omch,
ha="left",
va="center",
transform=ax1.transAxes,
fontsize=10)
ax1.text(0.02,
0.6,
osch,
ha="left",
va="center",
transform=ax1.transAxes,
fontsize=10)
ax1.text(0.02,
0.5,
cmch,
ha="left",
va="center",
transform=ax1.transAxes,
fontsize=10)
ax1.text(0.02,
0.4,
csch,
ha="left",
va="center",
transform=ax1.transAxes,
fontsize=10)
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('anomaly WSE (m)', color='k')
#ax1.set_ylim(ymin=0,ymax=250.)
ax1.set_xlim(xmin=0, xmax=last + 1)
ax1.tick_params('y', colors='k')
# xxlist=np.linspace(15,N-15,int(N/30))
# xxlab=[calendar.month_name[i][:3] for i in range(1,13)]
# ax1.set_xticks(xxlist)
# ax1.set_xticklabels(xxlab,fontsize=10)
if eyear - syear > 5:
dtt = 5
dt = int(math.ceil(((eyear - syear) + 1) / 5.0))
else:
dtt = 1
dt = (eyear - syear) + 1
xxlist = np.linspace(0, N, dt, endpoint=True)
#xxlab=[calendar.month_name[i][:3] for i in range(1,13)]
xxlab = np.arange(syear, eyear + 1, dtt)
ax1.set_xticks(xxlist)
ax1.set_xticklabels(xxlab, fontsize=10)
# ax2 = ax1.twinx()
# aiv = stat.AI(asm[:,:,point],opn[:,:,point],org[:,point])
# aivn,error= stat.AI_new(asm[:,:,point],opn[:,:,point],org[:,point])#[0]
# fmax = np.finfo(np.float32).max
# aivn[aivn>fmax]=0
# aivn[aivn<-fmax]=0
# print np.mean(aivn)
# #--
# pBias,pB_c = stat.pBias(asm[:,:,point],opn[:,:,point],org[:,point])
# ai_mean = np.mean(ma.masked_less_equal(aivn,0.0)) #np.nanmean(ma.masked_less_equal(aivn,0.0))
# mai = "meanAI:%1.2f"%(ai_mean)
# pB = "pBIAS:%1.1f%%"%(pBias)
# print mai # pB, "pB_c", pB_c, "%"
# #pB = pB + r'{1.1f}\%'.format(pBias)
# ax1.text(0.02,0.9,mai,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.8,pB,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
## ax2.plot(np.arange(start,last),aiv,color="green",zorder=104,marker = "o",alpha=0.3, markevery=swt[point])
# ax2.plot(np.arange(start,last),ma.masked_less_equal(aivn,1.0e-20),color="green",zorder=104,marker = "o", alpha=0.5,linewidth=0.5,markevery=swt[point])
# ax2.set_ylabel('AI', color='green')
# ax2.tick_params('y', colors='green')
# ax2.set_ylim(ymin=0.,ymax=1.)
plt.legend(lines, labels, ncol=1, loc='upper right'
) #, bbox_to_anchor=(1.0, 1.0),transform=ax1.transAxes)
# fig.legend(lines,labels,ncol=1,loc='lower left', bbox_to_anchor=(1.0, 1.0))
print(
'save', river[point],
re.split("_", pname[point])[2] + "_" + re.split("_", pname[point])[3]
) #,ylist[point],xlist[point],mean_obs[ylist[point],xlist[point]],std_obs[ylist[point],xlist[point]]
# print (asm[:,num,point])
#plt.savefig(assim_out+"/figures/sfcelv/"+river[point]+"_"+re.split("_",pname[point])[2]+"_"+re.split("_",pname[point])[3]+".png",dpi=300)
plt.savefig(assim_out + "/figures/anomaly/" + pname[point][2::] + ".png",
dpi=300)
return 0