In [1]:

    

%run basics
%matplotlib


    

    




Using matplotlib backend: Qt4Agg

In [50]:

    

cf = qcio.load_controlfile(path="controlfiles")
l6name = qcio.get_outfilenamefromcf(cf)
ds = qcio.nc_read_series(l6name)
site_name = ds.globalattributes["site_name"]
dt = monthly_dict["DateTime"]["data"]


    

In [5]:

    

series_dict = qcrp.L6_summary_createseriesdict(cf,ds)


    

In [16]:

    

monthly_dict = qcrp.L6_summary_monthly(ds,series_dict)
print monthly_dict.keys()


    

    




['GPP_BIOS2', 'GPP_DINGO', 'Ts', 'DateTime', 'GPP_SOLO', 'NEP_SOLO', 'NEE_SOLO', 'Sws', 'Ta', 'ps', 'Fc', 'Ws', 'Fe', 'Fg', 'Fh', 'NEP_FFNET', 'Fn', 'Fsd', 'NEE_FFNET', 'Flu', 'Precip', 'Fsu', 'ET', 'Fld', 'RH', 'NEP_DINGO', 'ustar', 'Cc', 'Ah', 'GPP_FFNET', 'q', 'NEE_DINGO', 'Fre_FFNET', 'Fre_DINGO', 'Fre_SOLO']

In [58]:

    

# NEE
nee_ofqc = monthly_dict["NEE_SOLO"]["data"]
nee_dingo = monthly_dict["NEE_DINGO"]["data"]
m,b = numpy.polyfit(nee_ofqc,nee_dingo,1)
r = numpy.corrcoef(nee_ofqc,nee_dingo)
eqnstr = 'y = %.3fx + %.3f, r = %.3f'%(m,b,r[0][1])
titlestr = site_name+": NEE"
fig = plt.figure()
# time series
ax1 = plt.subplot(2,1,1)
plt.title(titlestr)
plt.plot(dt,nee_ofqc,label="OFQC")
plt.plot(dt,nee_dingo,label="DINGO")
plt.legend(loc="lower left",frameon=False,prop={'size':10})
plt.ylabel("NEE gC/month")
# XY plot
ax2 = plt.subplot(2,1,2)
plt.plot(nee_ofqc,nee_dingo,'bo')
plt.plot(nee_ofqc,m*nee_ofqc+b,'r--')
plt.xlabel("OFQC gC/month")
plt.ylabel("DINGO gC/month")
plt.text(0.5,0.9,eqnstr,transform=ax2.transAxes,horizontalalignment='center')
plt.show()


    

In [59]:

    

# GPP
gpp_ofqc = monthly_dict["GPP_SOLO"]["data"]
gpp_dingo = -1*monthly_dict["GPP_DINGO"]["data"]
gpp_bios2 = monthly_dict["GPP_BIOS2"]["data"]
m,b = numpy.polyfit(gpp_ofqc,gpp_dingo,1)
r = numpy.corrcoef(gpp_ofqc,gpp_dingo)
eqnstr = 'y = %.3fx + %.3f, r = %.3f'%(m,b,r[0][1])
titlestr = site_name+": GPP"
fig = plt.figure()
# time series
ax1 = plt.subplot(2,1,1)
plt.title(titlestr)
plt.plot(dt,gpp_ofqc,label="OFQC")
plt.plot(dt,gpp_dingo,label="DINGO")
plt.plot(dt,gpp_bios2,label="BIOS2")
plt.legend(loc="upper left",frameon=False,prop={'size':10})
plt.ylabel("GPP gC/month")
# XY plot
ax2 = plt.subplot(2,1,2)
plt.plot(gpp_ofqc,gpp_dingo,'bo')
plt.plot(gpp_ofqc,m*gpp_ofqc+b,'r--')
plt.xlabel("OFQC gC/month")
plt.ylabel("DINGO gC/month")
plt.text(0.5,0.9,eqnstr,transform=ax2.transAxes,horizontalalignment='center')
plt.show()


    

In [60]:

    

# Reco
fre_ofqc = monthly_dict["Fre_SOLO"]["data"]
fre_dingo = monthly_dict["Fre_DINGO"]["data"]
m,b = numpy.polyfit(fre_ofqc,fre_dingo,1)
r = numpy.corrcoef(fre_ofqc,fre_dingo)
eqnstr = 'y = %.3fx + %.3f, r = %.3f'%(m,b,r[0][1])
titlestr = site_name+": Fre"
fig = plt.figure()
# time series
ax1 = plt.subplot(2,1,1)
plt.title(titlestr)
plt.plot(dt,fre_ofqc,label="OFQC")
plt.plot(dt,fre_dingo,label="DINGO")
plt.legend(loc="upper left",frameon=False,prop={'size':10})
plt.ylabel("Fre gC/month")
# XY plot
ax2 = plt.subplot(2,1,2)
plt.plot(fre_ofqc,fre_dingo,'bo')
plt.plot(fre_ofqc,m*fre_ofqc+b,'r--')
plt.xlabel("OFQC gC/month")
plt.ylabel("DINGO gC/month")
plt.text(0.5,0.9,eqnstr,transform=ax2.transAxes,horizontalalignment='center')
plt.show()


    

In [57]:

    

# time series of "closure"
nee_ofqc2 = -1*(gpp_ofqc-fre_ofqc)
nee_dingo2 = -1*(gpp_dingo-fre_dingo)
fig=plt.figure()
ax1 = plt.subplot(2,1,1)
plt.plot(dt,nee_ofqc,label="Orig")
plt.plot(dt,nee_ofqc2,label="Synth")
plt.ylabel("NEE (OFQC,gC/month)")
plt.legend(loc="lower left")
ax2 = plt.subplot(2,1,2,sharex=ax1)
plt.plot(dt,nee_dingo,label="Orig")
plt.plot(dt,nee_dingo2,label="Synth")
plt.ylabel("NEE (DINGO,gC/month)")
plt.legend(loc="upper right")
plt.xlabel("Date")
plt.show()


    

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