linewidth=0,
zorder=102)
m.drawmeridians(np.arange(west, east + 0.1, 5),
labels=[0, 0, 0, 1],
fontsize=10,
linewidth=0,
zorder=102)
#--
box = "%f %f %f %f" % (west, east, north, south)
os.system("./bin/txt_vector " + box + " " + pm.CaMa_dir() + " " +
pm.mapname() + " > KGEAItmp1.txt")
#map(vec_par,np.arange(1,10+1,1))
map(vec_par, np.arange(2, 10 + 1, 1))
#--
for point in np.arange(pnum):
org = grdc.grdc_dis(staid[point], syear, eyear - 1)
org = np.array(org)
if np.sum(ma.masked_where(org != -99.9, org)) == 0.0:
print("no obs", np.sum(ma.masked_where(org != -99.9, org)))
continue
KGEasm = KGE(np.mean(asm[:, :, point], axis=1), org)
KGEopn = KGE(np.mean(opn[:, :, point], axis=1), org)
if KGEopn == 1.00 or KGEopn == np.nan:
print(KGEopn, staid[point], pname[point])
continue
KGEAI = (KGEasm - KGEopn) / (1.0 - KGEopn + 1.0e-20)
#KGEAI=KGEasm
ix = xlist[point]
iy = ylist[point]
if rivermap[iy, ix] != 1.0:
continue
def make_fig(point):
plt.close()
#labels=["GRDC","corrupted","assimilated"]
labels=["GRDC","simulated","assimilated"]
#
#print org[:,point]
#for i in np.arange(start,last):
#print opn[i,:,point]
#print asm[i,:,point]
# plt.plot(np.arange(start,last),org[:,point],label="true",color=colors["true"],linewidth=0.7)
#
# for num in np.arange(0,pm.ens_mem()):
# plt.plot(np.arange(start,last),opn[:,num,point],label="corrupted",color=colors["corrupted"],linewidth=0.3,alpha=0.5)
# plt.plot(np.arange(start,last),asm[:,num,point],label="assimilated",color=colors["assimilated"],linewidth=0.3,alpha=0.5)
#
# plt.ylim(ymin=0)
fig, ax1 = plt.subplots()
org=grdc.grdc_dis(staid[point],syear,eyear-1)
org=np.array(org)
lines=[ax1.plot(np.arange(start,last),ma.masked_less(org,0.0),label="GRDC",color="#34495e",linewidth=3.0,zorder=101)[0]] #,marker = "o",markevery=swt[point])
# ax1.plot(np.arange(start,last),hgt[:,point],label="true",color="gray",linewidth=0.7,linestyle="--",zorder=101)
# plt.plot(np.arange(start,last),org[:,point],label="true",color="black",linewidth=0.7)
for num in np.arange(0,pm.ens_mem()):
ax1.plot(np.arange(start,last),opn[:,num,point],label="corrupted",color="blue",linewidth=0.1,alpha=0.1,zorder=102)
ax1.plot(np.arange(start,last),asm[:,num,point],label="assimilated",color="red",linewidth=0.1,alpha=0.1,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)
# draw mean of ensembles
lines.append(ax1.plot(np.arange(start,last),np.mean(ma.masked_less(opn[:,:,point],0.0),axis=1),label="corrupted",color="#4dc7ec",linewidth=1.0,alpha=1,zorder=104)[0])
lines.append(ax1.plot(np.arange(start,last),np.mean(ma.masked_less(asm[:,:,point],0.0),axis=1),label="assimilated",color="#ff8021",linewidth=1.0,alpha=1,zorder=106)[0])
# plt.ylim(ymin=)
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('discharge (m$^3$/s)', color='k')
ax1.set_xlim(xmin=0,xmax=last+1)
ax1.tick_params('y', colors='k')
# scentific notaion
ax1.ticklabel_format(style="sci",axis="y",scilimits=(0,0))
ax1.yaxis.major.formatter._useMathText=True
#
#xxlist=np.linspace(0,N,(eyear-syear)+1)
#xlab=np.arange(syear,eyear+1,1)
#xxlab=[calendar.month_name[i][:3] for i in range(1,13)]
if eyear-syear > 8:
dtt=2
dt=int(math.ceil(((eyear-syear)+1)/5.0))
elif eyear-syear > 10:
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)
# Nash-Sutcllf calcuation
NS1=NS(np.mean(asm[:,:,point],axis=1),org)
NS2=NS(np.mean(opn[:,:,point],axis=1),org)
KGE1=KGE(np.mean(asm[:,:,point],axis=1),org)
KGE2=KGE(np.mean(opn[:,:,point],axis=1),org)
COR1=correlation(np.mean(asm[:,:,point],axis=1),org)
COR2=correlation(np.mean(opn[:,:,point],axis=1),org)
RSE1=RMSE(np.mean(asm[:,:,point],axis=1),org)
RSE2=RMSE(np.mean(opn[:,:,point],axis=1),org)
#1-((np.sum((org[:ed,point]-org_Q)**2))/(np.sum((org_Q-np.mean(org_Q))**2)))
#print point,NS1,NS2
Nash1="NS (assim):%4.2f"%(NS1)
Nash2="NS (open):%4.2f"%(NS2)
kgeh1="KGE(assim):%4.2f"%(KGE1)
kgeh2="KGE(open):%4.2f"%(KGE2)
corr1="$r$(assim):%4.2f"%(COR1)
corr2="$r$(open):%4.2f"%(COR2)
rmse1="RMSE: %4.2f"%(RSE1)
rmse2="RMSE: %4.2f"%(RSE2)
# #
# ax1.text(0.02,0.95,Nash1,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.85,Nash2,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.42,0.95,kgeh1,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.42,0.85,kgeh2,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.75,corr1,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.55,corr2,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
#
ax1.text(0.02,0.95,Nash1,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.02,0.90,Nash2,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.02,0.80,kgeh1,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.02,0.75,kgeh2,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.42,0.95,corr1,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.42,0.90,corr2,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.42,0.80,rmse1,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
ax1.text(0.42,0.75,rmse2,ha="left",va="center",transform=ax1.transAxes,fontsize=8)
# # twin axis
# ax2 = ax1.twinx()
# #aiv = stat.AI(asm[:,:,point],opn[:,:,point],org[:,point])
# #aivn= stat.AI_new(asm[:,:,point],opn[:,:,point],org[:,point])
# aivn,error = stat.AI_new(asm[:,:,point],opn[:,:,point],org[:,point])
# #print 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))
# # RMSE
# RootMSE=stat.RMSE(asm[:,:,point],org[:,point])
# # rRMSE
# rRootMSE=stat.rRMSE(asm[:,:,point],org[:,point])
# # NRMSE
# NRootMSE=stat.NRMSE(asm[:,:,point],org[:,point])
# # VE
# VolEff=stat.VE(asm[:,:,point],org[:,point])
# # NSE
# NSEc,NSa,NSc=stat.NSE(asm[:,:,point],opn[:,:,point],org[:,point])
# # PDRI PTRI
# PDRI,PTRI=stat.PRI(asm[:,:,point],opn[:,:,point],org[:,point])
# #---
# EnsSprd=stat.EnsSpr(asm[:,:,point],opn[:,:,point],org[:,point])
# EnsSpr_mean=np.mean(ma.masked_less_equal(EnsSprd,0.0))
# #---
# AssimQlt=stat.AQ(PDRI,PTRI,ai_mean,EnsSpr_mean)
# #---
# mai = "meanAI:%1.2f"%(ai_mean)
# pB = "pBIAS:%1.1f%%"%(pBias)
# rms = "RMSE:%8.2f"%(RootMSE)
# rrms= "rRMSE:%3.2f"%(rRootMSE)
# nrms= "NRMSE:%3.2f"%(NRootMSE)
# veff= "VE:%3.2f"%(VolEff)
# nsec= "NSE:%3.2f"%(NSEc)
# pdr = "PDRI:%3.2f"%(PDRI)
# ptr = "PTRI:%3.2f"%(PTRI)
# ESrd= "EnsSpr:%3.2f"%(EnsSpr_mean)
# AssQ= "AQ:%3.2f"%(AssimQlt)
# NSac= "NSEa:%3.2f, NSEc:%3.2f"%(NSa,NSc)
# print mai , pB#, "pB_c", pB_c, "%"
# #pB = pB + r'{1.1f}\%'.format(pBias)
# ax1.text(0.02,0.95,mai,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.85,nsec,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.75,pB,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.65,rms,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.55,rrms,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.45,nrms,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.35,veff,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.25,pdr,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.15,ptr,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# #ax1.text(0.02,0.05,AssQ,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.02,0.05,NSac,ha="left",va="center",transform=ax1.transAxes,fontsize=10)
#---
# pBA_p=np.sum(ma.masked_greater((np.mean(asm[:,:,point],axis=1)-org[:,point]),0.0).filled(0.0))
# pBA_n=np.sum(ma.masked_less((np.mean(asm[:,:,point],axis=1)-org[:,point]),0.0).filled(0.0))
# #---
# pBC_p=np.sum(ma.masked_greater((np.mean(opn[:,:,point],axis=1)-org[:,point]),0.0).filled(0.0))
# pBC_n=np.sum(ma.masked_less((np.mean(opn[:,:,point],axis=1)-org[:,point]),0.0).filled(0.0))
# ax1.text(0.80,0.95,"pBa_p:%1.1f%%"%(pBA_p),ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.80,0.85,"pBa_n:%1.1f%%"%(pBA_n),ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.80,0.75,"pBc_p:%1.1f%%"%(pBC_p),ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.80,0.65,"pBc_n:%1.1f%%"%(pBC_n),ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.80,0.55,"pBa_p/pBa_n:%1.1f"%(abs(pBA_p/pBA_n+1.0e-20)),ha="left",va="center",transform=ax1.transAxes,fontsize=10)
# ax1.text(0.80,0.45,"pBc_p/pBc_n:%1.1f"%(abs(pBC_p/pBC_n+1.0e-20)),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.plot(np.arange(start,last),ma.masked_where(error<=0.1,aivn),color=green,marker = "o",markeredgecolor =green, alpha=1,linewidth=0.5,markevery=swt[point],markersize=3,zorder=100)#,label="AI")
# ax2.plot(np.arange(start,last),ma.masked_where(error>0.1,aivn),color=green2,marker = "o",markeredgecolor =green2, alpha=1,linewidth=0.5,markevery=swt[point],markersize=3,zorder=100)#,label="AI")
# ax2.set_ylabel('AI', color='green')
# ax2.tick_params('y', colors='green')
# ax2.set_ylim(ymin=0.,ymax=1.)
# ax2.set_xlim(xmin=0,xmax=last+1)
# print swt[point]
plt.legend(lines,labels,ncol=1,loc='upper right') #, bbox_to_anchor=(1.0, 1.0),transform=ax1.transAxes)
station_loc_list=pname[point].split("/")
station_name="-".join(station_loc_list)
print ('save',river[point] , station_name)
plt.savefig(assim_out+"/figures/disgraph/"+river[point]+"-"+station_name+".png",dpi=500)
return 0