def make_fig(point):
plt.close()
#print org[:,point]
fig=plt.figure()#figsize=(8.27,11.69))
G = gridspec.GridSpec(1,1)#4,2)
ax= fig.add_subplot(G[0,0])
#fig, ax = plt.subplots()
#org_Q=grdc.grdc_Q(pname[point],start_dt,last_dt)
#org_Q=np.array(org_Q)
org_Q=grdc_Q(staid[point],start_dt,last_dt)
#print org, org_Q
ed=np.shape(org_Q)[0]
#print ed , np.shape(org[:,point])
org=nc.outflw[:,ylist[point],xlist[point]]
print ed , np.shape(org)
ax.plot(np.arange(start,last),org,label="CaMa-Flood",color="blue",linewidth=0.7,zorder=102)
if ed == 0:
print "no GRDC data"
#return 0
else:
ax.plot(np.arange(start,last),ma.masked_less_equal(org_Q,0.0),label="GRDC",color="black",linewidth=0.7,zorder=101)
NS1=NS(org,org_Q)
#NS2=1-((np.sum((org[:ed,point]-org_Q)**2))/(np.sum((org_Q-np.mean(org_Q))**2)))
#print point,NS1,NS2
Nash="NS:%4.2f"%(NS1)
ax.text(0.02,0.95,Nash,ha="left",va="center",transform=ax.transAxes,fontsize=10)
plt.legend(loc=1,ncol=1,prop={"size":8})
# Make the y-axis label, ticks and tick labels match the line color.
ax.set_ylabel('$discharge$ (m$^3$/s)', color='k')
ax.set_xlim(xmin=0,xmax=ed+1)
ax.set_ylim(ymin=0)#,xmax=ed+1)
ax.tick_params('y', colors='k')
days=np.arange(start_dt.year,last_dt.year+1,5)
xxlist=np.linspace(1,N,len(days))
ax.set_xticks(xxlist)
ax.set_xticklabels(days,fontsize=8)
#ax1.set_yticklabels(fontsize=11)
ax.set_xlabel('$year$', color='k')
# scentific notaion
ax.ticklabel_format(style="sci",axis="y",scilimits=(0,0))
plt.tight_layout(pad=0.2,w_pad=0.05,h_pad=0.05)
plt.savefig(pm.out_dir()+"/figures/disgraph/"+river[point]+"/"+pname[point]+"_disgraph_GRDC.png",dpi=500)
#plt.show()
return 0
def stanz_fig(point):
plt.close()
#print org[:,point]
# standardized data
sdz=nc_standz.standardize[:,ylist[point],xlist[point]]
#fig=plt.figure()
fig=plt.figure(figsize=(8.27,4))#11.69/3.0))
G = gridspec.GridSpec(1,1)
# standadized data
ax3 = fig.add_subplot(G[0,0])
# print np.log(org[:,point])
mu = np.mean(sdz.values)
sigma=np.std(sdz.values)
#ks=stats.kstest(sdz[:,point],"norm",stats.norm.fit(sdz[:,point]))
#ks=stats.kstest(sdz[:,point],"norm",args=(mu,sigma))
#ks=stats.normaltest(sdz[:,point].T)
ks=stats.shapiro(sdz)
bin1=100#int(abs(np.amax(sdz[:,point])-np.amin(sdz[:,point])))
print mu, sigma, bin1
#print np.amax(sdz[:,point]),np.amin(sdz[:,point])
n, bins, patches = ax3.hist(sdz, bin1, normed=1, facecolor='green', alpha=0.75)#-np.mean(sdz[:,point]
# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = ax3.plot(bins, y, 'r--', linewidth=1)
#ms="mean:%6.2f\nstd:%6.2f\nks:%6.4f\np-val:%0.2E"%(mu,sigma,ks[0],ks[1])
ms="mean:%6.2f\nstd:%6.2f"%(mu,sigma)
print ms
ax3.text(0.02,0.8,ms,ha="left",va="center",fontsize=10,transform=ax3.transAxes,color="black")
# Make the y-axis label, ticks and tick labels match the line color.
ax3.set_ylabel('$frequency$', color='k')
ax3.set_xlabel('$standardized$ $data$', color='k')
#ax3.set_xlim(xmin=0,xmax=la+1)
#ax3.set_ylim(ymin=0)#,xmax=ed+1)
ax3.tick_params('y', colors='k')
plt.savefig(pm.out_dir()+"/figures/hist_sfcelv/"+river[point]+"/"+pname[point]+"_hist_standz.png",pad_inches=0.02,dpi=800)
return 0
def make_fig(point):
plt.close()
#print np.shape(org[:,point])
#print np.shape(fft[:,point])
fig = plt.figure(figsize=(wdt, hgt))
#fig=plt.figure()
G = gridspec.GridSpec(4, 1)
# standardized data
sdz = nc_standz.standardize[:, ylist[point], xlist[point]]
# actuall curve
org = nc_sfcelv.sfcelv[:, ylist[point], xlist[point]]
# seasonality removed
fft = nc_rmdseson.rmdsesn[:, ylist[point], xlist[point]]
# trend line
tdl = nc_rmdtrend.rmdtrnd[:, ylist[point], xlist[point]]
# original data
ax1 = fig.add_subplot(G[3, 0])
ax1.plot(np.arange(start, last),
sdz,
label="standardize",
color="black",
linestyle="-",
linewidth=0.7,
zorder=101)
#ax1.plot(np.arange(start,last),org-fft,label="fft",color="green",linewidth=0.7,zorder=101)
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('standardized\n data', color='k')
ax1.set_xlabel('$year$', color='k')
#ax1.get_yaxis().set_ticklabels([])
ax1.set_xlim(xmin=0, xmax=last + 1)
ax1.tick_params('y', colors='k')
ax1.set_xticks(xxlist)
ax1.set_xticklabels(days, fontsize=8)
#--trend line
ax2 = fig.add_subplot(G[1, 0])
ax2.plot(np.arange(start, last),
tdl,
label="trend line",
color="grey",
linewidth=0.7,
zorder=103)
# sesonality
#ax2.plot(np.arange(start,last),yif,label="fft",color="r",linewidth=0.7,zorder=102)
ax2.plot(np.arange(start, last),
tdl - fft,
label="seasonality",
color="red",
linewidth=0.7,
zorder=101)
# Make the y-axis label, ticks and tick labels match the line color.
ax2.set_ylabel('WSE ($m$)', color='k')
ax2.set_xlabel('$year$)', color='k')
#ax2.get_yaxis().set_ticklabels([])
ax2.set_xlim(xmin=0, xmax=last + 1)
ax2.tick_params('y', colors='k')
ax2.set_xticks(xxlist)
ax2.set_xticklabels(days, fontsize=8)
#########################
#--senality removed data
ax3 = fig.add_subplot(G[2, 0])
ax3.plot(np.arange(start, last),
fft,
label="seasonality removed",
color="xkcd:ocean",
linewidth=0.7,
zorder=102)
# ax3.scatter(np.arange(start,last),fft[:,point],marker="*",color="r")
# actual curve
#ax3.plot(np.arange(start,last),org-tdl,label="true",color="gray",linewidth=0.7,zorder=101)
#ax3.plot(xf[1:N//2],1.0/N * np.abs(yf[0:N//2])[1:],color="blue",linewidth=0.7,zorder=101)
#ax3.axhline((1.0/N) * yfmax * coeff,color="g",linestyle="--",linewidth=0.7,zorder=100)
#ax3.plot(xf[1:],psd,color="r",linewidth=0.7,zorder=101)
# Make the y-axis label, ticks and tick labels match the line color.
ax3.set_ylabel('$m$', color='k')
ax3.set_xlabel('$year$', color='k')
ax3.set_xlim(xmin=0, xmax=last + 1)
#ax3.set_ylim(ymin=0)
#ax2.tick_params('y', colors='k')
ax3.set_xticks(xxlist)
ax3.set_xticklabels(days, fontsize=8)
#--CaMa-Flood WSE
ax4 = fig.add_subplot(G[0, 0])
# actual curve
ax4.plot(np.arange(start, last),
org,
label="WSE",
color="b",
linewidth=0.7,
zorder=102)
# ax3.scatter(np.arange(start,last),fft[:,point],marker="*",color="r")
ax4.plot(np.arange(start, last),
org - tdl,
label="trend line",
color="g",
linewidth=0.7,
zorder=101)
# actual curve
#ax4.plot(np.arange(start,last),org-tdl,label="trend line",color="gray",linewidth=0.7,zorder=101)
#ax4.plot(xf[1:N//2],1.0/N * np.abs(yf[0:N//2])[1:],color="blue",linewidth=0.7,zorder=101)
#ax4.axhline((1.0/N) * yfmax * coeff,color="g",linestyle="--",linewidth=0.7,zorder=100)
#ax4.plot(xf[1:],psd,color="r",linewidth=0.7,zorder=101)
# Make the y-axis label, ticks and tick labels match the line color.
ax4.set_ylabel('$WSE$ ($m$)', color='k')
ax4.set_xlabel('$year$', color='k')
ax4.set_xlim(xmin=0, xmax=last + 1)
#ax4.set_ylim(ymin=0)
#ax4.tick_params('y', colors='k')
ax4.set_xticks(xxlist)
ax4.set_xticklabels(days, fontsize=8)
#------------------
#xff1.tofile("../img/fft_sfcelv/"+river[point]+"/"+pname[point]+"_fft_freq.bin")
plt.savefig(pm.out_dir() + "/figures/timeseries/" + river[point] + "/" +
pname[point] + "_sfcelv.png",
dpi=500)
print "/figures/timeseries/" + river[point] + "/" + pname[
point] + "_sfcelv.png"
#plt.show()
return 0
#---
#def sendslack(msg):
# slack=Slacker('xoxb-316041665447-hNXDYiTz9PZMITg0gfRGzyrC')
# message="` "+os.path.basename(__file__)+"` : "+msg
# slack.chat.post_message('#camasim',message);
# return 0
#---
#--
#slink("/hydro/covariance/CaMa_simulation/params.py", "params.py")
#slink("/hydro/SWOTDA/img_code/read_grdc.py","read_grdc.py")
import params as pm
import read_grdc as grdc
#--
mk_dir(pm.out_dir() + "/figures")
mk_dir(pm.out_dir() + "/figures/timeseries")
#--read outflow netCDF4--
tag = "%04d-%04d" % (pm.starttime()[0], pm.endtime()[0])
# sfcelv
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name() + "/sfcelv" + tag + ".nc"
nc_sfcelv = xr.open_dataset(fname)
# removed trend line
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/rmdtrnd" + tag + ".nc"
nc_rmdtrend = xr.open_dataset(fname)
# removed seasonality
fname = pm.out_dir() + "/CaMa_out/" + pm.input_name(
) + "/rmdsesn" + tag + ".nc"
nc_rmdseson = xr.open_dataset(fname)
# standardized
nextxy = pm.CaMa_dir()+"/map/"+pm.map_name()+"/nextxy.bin"
rivwth = pm.CaMa_dir()+"/map/"+pm.map_name()+"/rivwth.bin"
rivhgt = pm.CaMa_dir()+"/map/"+pm.map_name()+"/rivhgt.bin"
rivlen = pm.CaMa_dir()+"/map/"+pm.map_name()+"/rivlen.bin"
elevtn = pm.CaMa_dir()+"/map/"+pm.map_name()+"/elevtn.bin"
uparea = pm.CaMa_dir()+"/map/"+pm.map_name()+"/uparea.bin"
lonlat = pm.CaMa_dir()+"/map/"+pm.map_name()+"/lonlat.bin"
nextxy = np.fromfile(nextxy,np.int32).reshape(2,ny,nx)
rivwth = np.fromfile(rivwth,np.float32).reshape(ny,nx)
rivhgt = np.fromfile(rivhgt,np.float32).reshape(ny,nx)
rivlen = np.fromfile(rivlen,np.float32).reshape(ny,nx)
elevtn = np.fromfile(elevtn,np.float32).reshape(ny,nx)
uparea = np.fromfile(uparea,np.float32).reshape(ny,nx)
lonlat = np.fromfile(lonlat,np.float32).reshape(2,ny,nx)
#----
rivnum = pm.out_dir()+"/dat/rivnum_"+pm.map_name()[-5::]+".bin"
rivnum = np.fromfile(rivnum,np.int32).reshape(nYY,nXX)
#----
nextx = nextxy[0]#ma.masked_where(rivwth<=500.0,nextxy[0]).filled(0)
nexty = nextxy[1]#ma.masked_where(rivwth<=500.0,nextxy[1]).filled(0)
#--------------
#fname = "./congo_nextxy.bin"
#c_nextx= np.fromfile(fname,np.int32).reshape([2,nyYYnXX])[0]
#fname = pm.CaMa_dir()+"/map/glb_15min/outclm.bin"
#trueforo = np.fromfile(fname,np.float32).reshape([2,nYY,nXX])
#dis=(trueforo[0]>500.)*1
#dis=dis*((nextx>0)*1)
#--major rivers and Ids
rivid={}
fname=pm.out_dir()+"/dat/river30_id.txt"
f = open(fname,"r")
line = filter(None, re.split(" ",line))
num = line[0]
river = line[1]
station = line[3]
d_info = line[2]
print station, num
grdc_id[station]=num
return grdc_id
#----
#slink("/hydro/covariance/CaMa_simulation/params.py", "params.py")
#slink("../CaMa_simulation/params.py", "params.py")
#slink("../read_grdc.py","read_grdc.py")
import params as pm
import read_grdc as grdc
#--
mk_dir(pm.out_dir()+"/figures")
mk_dir(pm.out_dir()"/figures/disgraph")
#--read outflow netCDF4--
tag="%04d-%04d"%(pm.starttime()[0],pm.endtime()[0])
fname=pm.out_dir()+"/CaMa_out/"+pm.input_name()+"/outflw"+tag+".nc"
nc=xr.open_dataset(fname)
print pm.patch_start()
syear=pm.starttime()[0]
smonth=pm.starttime()[1]
sdate=pm.starttime()[2]
start_dt=datetime.date(syear,smonth,sdate)
size=60
start=0
#last_dt=datetime.date(int(argvs[1]),int(argvs[2]),int(argvs[3]))
def semivari_fig(point):
lon = xlist[point] + 1
lat = ylist[point] + 1
#--
up, dn = updn[lon, lat]
print(pname[point], up, dn)
#--
llat = -(lat - 1) * 0.25 + 90.
llon = (lon - 1) * 0.25 - 180.
lllat, urlat, lllon, urlon = max(llat - 50, -90.), min(
llat + 50, 90.), max(llon - 50, -180.), min(llon + 50, 180.)
#--
al = np.zeros([720, 1440], np.float32)
#--
cs = cm.viridis(np.linspace(0, 1, up + 2))
xfig = int(math.ceil(up / 2.0)) + 1
print(lon, lat)
#print xfig, cs[0]
pathname = pm.out_dir() + "/figures/semivar/%s/%s" % (river[point],
pname[point])
#--
if up + dn == 0:
#continue
return 0
#--
if dn > 0:
print("downstream", dn)
#--
lix = []
liy = []
ldis = []
lgamma = []
lstd = []
#--
fname = "%s/semivar/%04d%04d/dn%05d.svg" % (pm.out_dir(), lon, lat, 0)
f = open(fname, "r")
lines = f.readlines()
f.close()
#--
for line in lines[1::]:
line = filter(None, re.split(" ", line))
#print line
ix = int(line[0])
iy = int(line[1])
dis = float(line[2])
gamma = float(line[3])
std = float(line[4])
#--
print(dis, gamma)
#--
lix.append(ix)
liy.append(iy)
ldis.append(dis)
lgamma.append(gamma)
lstd.append(std)
#---
#print ldis,lgamma
#--
mk_dir(pathname)
mk_svfig(lgamma, ldis, pathname, 0)
#====================================
# upstream
if up > 0:
for iup in np.arange(1, up):
print("upstream", iup)
#--
lix = []
liy = []
ldis = []
lgamma = []
lstd = []
#--
fname = "%s/semivar/%04d%04d/up%05d.svg" % (pm.out_dir(), lon, lat,
iup)
f = open(fname, "r")
lines = f.readlines()
f.close()
#--
for line in lines[1::]:
line = filter(None, re.split(" ", line))
ix = int(line[0])
iy = int(line[1])
dis = float(line[2])
gamma = float(line[3])
std = float(line[4])
#--
print(dis, gamma)
#--
lix.append(ix)
liy.append(iy)
ldis.append(dis)
lgamma.append(gamma)
lstd.append(std)
#--
#print ldis,lgamma
#--
mk_svfig(lgamma, ldis, pathname, iup)
return 0
# high=0
# for ix in lx:
# if ix==1:
# low+=1
# elif ix==1440:
# high+=1
# if low==0 and high==0:
# return lx,ly
# elif low<high
#--
#slink("/hydro/covariance/CaMa_simulation/params.py", "params.py")
#slink("/hydro/covariance/img_code/read_grdc.py","read_grdc.py")
import params as pm
import read_grdc as grdc
#--------------
mk_dir(pm.out_dir() + "/figures")
mk_dir(pm.out_dir() + "/figures/semivar")
#--------------
###nextxy = pm.CaMa_dir()+"/map/glb_15min/nextxy.bin"
###rivwth = pm.CaMa_dir()+"/map/glb_15min/rivwth.bin"
###rivhgt = pm.CaMa_dir()+"/map/glb_15min/rivhgt.bin"
###rivlen = pm.CaMa_dir()+"/map/glb_15min/rivlen.bin"
###elevtn = pm.CaMa_dir()+"/map/glb_15min/elevtn.bin"
###nextxy = np.fromfile(nextxy,np.int32).reshape(2,720,1440)
###rivwth = np.fromfile(rivwth,np.float32).reshape(720,1440)
###rivhgt = np.fromfile(rivhgt,np.float32).reshape(720,1440)
###rivlen = np.fromfile(rivlen,np.float32).reshape(720,1440)
###elevtn = np.fromfile(elevtn,np.float32).reshape(720,1440)
####----
####----
###nextx = nextxy[0]#ma.masked_where(rivwth<=500.0,nextxy[0]).filled(0)
def make_fig(point):
plt.close()
#print org[:,point]
# standardized data
sdz=nc_standz.standardize[:,ylist[point],xlist[point]]
# water surface elevation
org=nc_sfcelv.sfcelv[:,ylist[point],xlist[point]]
# seasonality removed
fft=nc_rmdseson.rmdsesn[:,ylist[point],xlist[point]]
# trend line
tdl=nc_rmdtrend.rmdtrnd[:,ylist[point],xlist[point]]
#fig=plt.figure()
fig=plt.figure(figsize=(8.27,11.69))
G = gridspec.GridSpec(3,1)
ax1 = fig.add_subplot(G[0,0])
mu = np.mean(org.values)
sigma=np.std(org.values)
bin1=100#int(abs(np.amax(org[:,point])-np.amin(org[:,point])))
print mu, sigma, bin1#, type(mu)
#print np.amax(org[:,point]),np.amin(org[:,point])
n, bins, patches = ax1.hist(org, bin1, normed=1, facecolor='green', alpha=0.75)#-np.mean(org[:,point]
# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = ax1.plot(bins, y, 'r--', linewidth=1)
ms="mean:%6.2f\nstd:%6.2f"%(mu,sigma)
ax1.text(0.02,0.9,ms,ha="left",va="center",fontsize=8,transform=ax1.transAxes,color="black")
# Make the y-axis label, ticks and tick labels match the line color.
ax1.set_ylabel('$frequency$', color='k')
ax1.set_xlabel('WSE ($m$)', color='k')
#ax1.set_xlim(xmin=0,xmax=la+1)
#ax1.set_ylim(ymin=0)#,xmax=ed+1)
ax1.tick_params('y', colors='k')
#------------------------
# seasonality removed data
ax2 = fig.add_subplot(G[1,0])
# print np.log(org[:,point])
mu = np.mean(fft.values)
sigma=np.std(fft.values)
bin1=100#int(abs(np.amax(org[:,point])-np.amin(org[:,point])))
print mu, sigma, bin1
#print np.amax(org[:,point]),np.amin(org[:,point])
n, bins, patches = ax2.hist(fft, bin1, normed=1, facecolor='green', alpha=0.75)#-np.mean(org[:,point]
# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = ax2.plot(bins, y, 'r--', linewidth=1)
ms="mean:%6.2f\nstd:%6.2f"%(mu,sigma)
ax2.text(0.02,0.9,ms,ha="left",va="center",fontsize=8,transform=ax2.transAxes,color="black")
# Make the y-axis label, ticks and tick labels match the line color.
ax2.set_ylabel('$frequency$', color='k')
ax2.set_xlabel('WSE - TD -SEASON', color='k')
#ax2.set_xlim(xmin=0,xmax=la+1)
#ax2.set_ylim(ymin=0)#,xmax=ed+1)
ax2.tick_params('y', colors='k')
#------------------------
# standadized data
ax3 = fig.add_subplot(G[2,0])
# print np.log(org[:,point])
mu = np.mean(sdz.values)
sigma=np.std(sdz.values)
#ks=stats.kstest(sdz[:,point],"norm",stats.norm.fit(sdz[:,point]))
#ks=stats.kstest(sdz[:,point],"norm",args=(mu,sigma))
#ks=stats.normaltest(sdz[:,point].T)
ks=stats.shapiro(sdz.values)
bin1=100#int(abs(np.amax(sdz[:,point])-np.amin(sdz[:,point])))
print mu, sigma, bin1
#print np.amax(sdz[:,point]),np.amin(sdz[:,point])
n, bins, patches = ax3.hist(sdz, bin1, normed=1, facecolor='green', alpha=0.75)#-np.mean(sdz[:,point]
# add a 'best fit' line
y = mlab.normpdf( bins, mu, sigma)
l = ax3.plot(bins, y, 'r--', linewidth=1)
ms="mean:%6.2f\nstd:%6.2f\nks:%6.4f\np-val:%0.2E"%(mu,sigma,ks[0],ks[1])
print ms
ax3.text(0.02,0.7,ms,ha="left",va="center",fontsize=8,transform=ax3.transAxes,color="black")
# Make the y-axis label, ticks and tick labels match the line color.
ax3.set_ylabel('$frequency$', color='k')
ax3.set_xlabel('$standardized$ $data$', color='k')
#ax3.set_xlim(xmin=0,xmax=la+1)
#ax3.set_ylim(ymin=0)#,xmax=ed+1)
ax3.tick_params('y', colors='k')
#ax.text(0.02,0.9,Nash,ha="left",va="center",transform=ax.transAxes,fontsize=8)
#plt.legend(loc=1)
plt.savefig(pm.out_dir()+"/figures/hist_sfcelv/"+river[point]+"/"+pname[point]+"_hist_sfcelv.png",dpi=500)
#plt.show()
return 0
#----
#----
def mk_dir(sdir):
try:
os.makedirs(sdir)
except:
pass
#---
#--
#slink("/hydro/covariance/CaMa_simulation/params.py", "params.py")
#slink("/hydro/SWOTDA/img_code/read_grdc.py","read_grdc.py")
import params as pm
import read_grdc as grdc
#--
#--
mk_dir(pm.out_dir()+"/figures")
mk_dir(pm.out_dir()+"/figures/hist_sfcelv")
#--read outflow netCDF4--
tag="%04d-%04d"%(pm.starttime()[0],pm.endtime()[0])
# sfcelv
fname=pm.out_dir()+"/CaMa_out/"+pm.input_name()+"/sfcelv"+tag+".nc"
nc_sfcelv=xr.open_dataset(fname)
# removed trend line
fname=pm.out_dir()+"/CaMa_out/"+pm.input_name()+"/rmdtrnd"+tag+".nc"
nc_rmdtrend=xr.open_dataset(fname)
# removed seasonality
fname=pm.out_dir()+"/CaMa_out/"+pm.input_name()+"/rmdsesn"+tag+".nc"
nc_rmdseson=xr.open_dataset(fname)
# standardized
fname=pm.out_dir()+"/CaMa_out/"+pm.input_name()+"/standardized"+tag+".nc"
nc_standz=xr.open_dataset(fname)