print "Doing diurnal plot for month "

#Do Diurnal Plots for all 12 months

#create an X axis series for all 24 hours

t = np.arange(1, 25, 1)

item_list=['Fre_Con','Fc_ustar','GPP_Con','Fc_Con','Fc_Lasslop']

Plottemp = PlottingDF[item_list]

#figure(1)

pl.figure(1, figsize=(16, 12), dpi=80, facecolor='w', edgecolor='k')

pl.subplot(321)

pl.title('Diurnal partitioning month = 1')

try:

xdata1a=Plottemp[(PlottingDF.index.month==1)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==1)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==1)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==1)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(322)

pl.title('Diurnal partitioning month = 3')

try:

xdata1a=Plottemp[(PlottingDF.index.month==3)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==3)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==3)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==3)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(323)

pl.title('Diurnal partitioning month = 5')

try:

xdata1a=Plottemp[(PlottingDF.index.month==5)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==5)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==5)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==5)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(324)

pl.title('Diurnal partitioning month = 7')

try:

xdata1a=Plottemp[(PlottingDF.index.month==7)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==7)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==7)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==7)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(325)

pl.title('Diurnal partitioning month = 9')

try:

xdata1a=Plottemp[(PlottingDF.index.month==9)][item].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1a=Plottemp[(PlottingDF.index.month==9)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==9)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==9)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==9)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(326)

pl.title('Diurnal partitioning month = 11')

try:

xdata1a=Plottemp[(PlottingDF.index.month==11)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==11)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==11)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==11)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

figure(1)

pl.suptitle('Carbon partitioning ensemble diurnal average at '+Site_ID)

pl.subplots_adjust(top=0.85)

pl.tight_layout()

pl.savefig(mypathforResults+'/ANN ensemble diurnal average at '+Site_ID)

#pl.show()

pl.close()

print "Doing diurnal Fre plot for month "

#Do Diurnal Plots for all 12 months

#create an X axis series for all 24 hours

t = np.arange(1, 25, 1)

item_list=['Fre_Con','Fc_ustar','Fre_Lasslop']

Plottemp = PlottingDF[item_list]

#figure(1)

pl.figure(1, figsize=(16, 12), dpi=80, facecolor='w', edgecolor='k')

pl.subplot(321)

pl.title('Diurnal partitioning month = 1')

try:

xdata1a=Plottemp[(PlottingDF.index.month==1)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==1)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==1)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==1)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(322)

pl.title('Diurnal partitioning month = 3')

try:

xdata1a=Plottemp[(PlottingDF.index.month==3)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==3)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==3)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==3)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(323)

pl.title('Diurnal partitioning month = 5')

try:

xdata1a=Plottemp[(PlottingDF.index.month==5)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==5)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==5)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==5)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(324)

pl.title('Diurnal partitioning month = 7')

try:

xdata1a=Plottemp[(PlottingDF.index.month==7)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==7)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==7)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==7)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(325)

pl.title('Diurnal partitioning month = 9')

try:

xdata1a=Plottemp[(PlottingDF.index.month==9)][item].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1a=Plottemp[(PlottingDF.index.month==9)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==9)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==9)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==9)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.subplot(326)

pl.title('Diurnal partitioning month = 11')

try:

xdata1a=Plottemp[(PlottingDF.index.month==11)][item_list[0]].groupby([lambda x: x.hour]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[(PlottingDF.index.month==11)][item_list[1]].groupby([lambda x: x.hour]).mean()

plotxdata1b=True

except:

plotxdata1b=False

try:

xdata1c=Plottemp[(PlottingDF.index.month==11)][item_list[2]].groupby([lambda x: x.hour]).mean()

plotxdata1c=True

except:

plotxdata1c=False

try:

xdata1d=Plottemp[(PlottingDF.index.month==11)][item_list[3]].groupby([lambda x: x.hour]).mean()

plotxdata1d=True

except:

plotxdata1d=False

if plotxdata1a==True:

pl.plot(t,xdata1a,'r',label=str(item_list[0]))

if plotxdata1b==True:

pl.plot(t,xdata1b,'b',label=str(item_list[1]))

if plotxdata1c==True:

pl.plot(t,xdata1c,'k',label=str(item_list[2]))

if plotxdata1d==True:

pl.plot(t,xdata1d,'g',label=str(item_list[3]))

pl.ylabel('Flux')

pl.legend(shadow=True, fancybox=True,loc='best')

figure(1)

pl.suptitle('Fre ensemble diurnal average at '+Site_ID)

pl.subplots_adjust(top=0.85)

pl.tight_layout()

pl.savefig(mypathforResults+'/Fre ensemble diurnal average at '+Site_ID)

#pl.show()

pl.close()

ANN_label=str(item+"_NN") #Do Monthly Plots

print "Doing MOnthly plot"

#t = arange(1, 54, 1)

NN_label='Fc'

Plottemp = PlottingDF[[NN_label,item]][PlottingDF['day_night']!=1]

#Plottemp = PlottingDF[[NN_label,item]].dropna(how='any')

figure(1)

pl.title('Nightime ANN v Tower by year-month for '+item+' at '+Site_ID)

try:

xdata1a=Plottemp[item].groupby([lambda x: x.year,lambda x: x.month]).mean()

plotxdata1a=True

except:

plotxdata1a=False

try:

xdata1b=Plottemp[NN_label].groupby([lambda x: x.year,lambda x: x.month]).mean()

plotxdata1b=True

except:

plotxdata1b=False

if plotxdata1a==True:

pl.plot(xdata1a,'r',label=item)

if plotxdata1b==True:

pl.plot(xdata1b,'b',label=NN_label)

pl.ylabel('Flux')

pl.xlabel('Year - Month')

pl.legend()

pl.savefig(mypathforResults+'/ANN and Tower plots by year and month for variable '+item+' at '+Site_ID)

#pl.show()

pl.close()

ANN_label=str(item+"_NN")

graphtext1=str('slope ' + str("{0:.2f}".format(float(regress[0][0]))) +'\n' +

'intercept ' + str("{0:.2f}".format(float(regress[0][1]))) +'\n' +

'r-value ' + str("{0:.2f}".format(float(regress[0][2]))) +'\n' +

'p-value ' + str("{0:.2f}".format(float(regress[0][3]))) +'\n' +

'slope SE ' + str("{0:.2f}".format(float(regress[0][4]))) )

pl.figtext(0.7,0.6,graphtext1, bbox=dict())

pl.plot(observed, predicted, 'o', label='targets vs. outputs')

slope = regress[0][0]; intercept = regress[0][1]

x = np.linspace(min(observed),max(observed))

y = slope * x + intercept

pl.plot(x, y, linewidth = 2, label = 'regression line')

pl.legend()

pl.title('Tower vs ANN for '+item+' at ' +Site_ID)

pl.xlabel('Tower ' + '('+units+')')

pl.ylabel('ANN ' + '('+units+')')

pl.legend(shadow=True, fancybox=True,loc='best')

pl.savefig(mypathforResults+'/'+'Tower vs ANN for '+item+' at ' +Site_ID)

#pl.show()

ANN_label=str(item+"_NN")

pl.plot( targets, 'b--' )

pl.plot( output, 'k-' )

pl.legend(('targets', 'output'))

pl.xlabel('Time');

pl.title('Outputs vs. target of trained network for '+item)

pl.grid(True)

pl.legend()

pl.title('Tower vs ANN 30 min timeseries for '+item+' at ' +Site_ID)

pl.ylabel(item + '('+units+')')

pl.savefig(mypathforResults+'/'+'Tower vs ANN 30 min timeseries for '+item+' at ' +Site_ID)

#pl.show()

###########################################################################################################

## START MAIN CODE

###########################################################################################################

print "Starting GPP Calculations"

#Check for place to put results - does it exist? If not create

if not os.path.isdir(myBaseforResults):

os.mkdir(myBaseforResults)

#Then subdirectories

if not os.path.isdir(myBaseforResults+"/GPP"):

os.mkdir(myBaseforResults+"/GPP")

mypathforResults=myBaseforResults+"/GPP"

#Set columns to missing first

New_combined['Fre_Con'] = -9999

New_combined['Fc_ustar'] = -9999

New_combined['GPP_Con'] = -9999

New_combined['Fre_Lasslop'] = -9999

New_combined['Fc_Lasslop'] = -9999

#New_combined['GPP_Lasslop'] = -9999

#Create a new column of constructed GPP based on nighttime and ustar greater than ustar max

New_combined['Fre_Con']=New_combined['Fc'][New_combined['ustar']>New_combined['ustar_max']][New_combined['day_night']!=1]

#Then fill any remaining with ANN Fre

New_combined['Fre_Con'][New_combined['Fre_Con'].isnull()]=New_combined['Fre_NN']

New_combined['Fre_Lasslop']=New_combined['Fre_day']

#Then daytime make Fc_ustar equal to the obs if valid

New_combined['Fc_ustar']=New_combined['Fc'][New_combined['day_night']==1]

New_combined['Fc_Lasslop']=New_combined['Fc'][New_combined['day_night']==1]

#Then any remaining values fill with ANN Fc

New_combined['Fc_ustar'][New_combined['Fc_ustar'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

New_combined['Fc_Lasslop'][New_combined['Fc_Lasslop'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

#Simply make Fc_ustar = Fre at night

New_combined['Fc_ustar'][New_combined['day_night']!=1]=New_combined['Fre_Con']

#LAsslop nighttime use daytime derived Fre using Lasslop

New_combined['Fc_Lasslop'][New_combined['day_night']!=1]=New_combined['Fre_Lasslop']

#Create a GPP column based on Fc-Re

New_combined['GPP_Con']=New_combined['Fc_ustar']-New_combined['Fre_Con']

#New_combined['GPP_Lasslop']=New_combined['Fc_Lasslop']-New_combined['Fre_Lasslop']

#Force to zero during the night

New_combined['GPP_Con'][New_combined['day_night']!=1]=0

#New_combined['GPP_Lasslop'][New_combined['day_night']!=1]=0

ustarrange=True

if ustarrange==True:

############ Ustar threshold to max ustar over entire period #######################

#Set columns to missing first

New_combined['Fre_Con_max'] = -9999

New_combined['Fc_ustar_max'] = -9999

New_combined['GPP_Con_max'] = -9999

#Create a new column of constructed GPP based on nighttime and ustar greater than ustar max

New_combined['Fre_Con_max']=New_combined['Fc'][New_combined['ustar']>New_combined['ustar_max']][New_combined['day_night']!=1]

#Then fill any remaining with ANN Fre

New_combined['Fre_Con_max'][New_combined['Fre_Con_max'].isnull()]=New_combined['Fre_NN']

#Then daytime make Fc_ustar equal to the obs if valid

New_combined['Fc_ustar_max']=New_combined['Fc'][New_combined['day_night']==1]

#Then any remaining values fill with ANN Fc

New_combined['Fc_ustar_max'][New_combined['Fc_ustar_max'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

#Simply make Fc_ustar = Fre at night

New_combined['Fc_ustar_max'][New_combined['day_night']!=1]=New_combined['Fre_Con_max']

#Create a GPP column based on Fc-Re

New_combined['GPP_Con_max']=New_combined['Fc_ustar_max']-New_combined['Fre_Con_max']

#Force to zero during the night

New_combined['GPP_Con_max'][New_combined['day_night']!=1]=0

############ Ustar threshold to 3 month variable ustar #######################

#Set columns to missing first

New_combined['Fre_Con_var'] = -9999

New_combined['Fc_ustar_var'] = -9999

New_combined['GPP_Con_var'] = -9999

#Create a new column of constructed GPP based on nighttime and ustar greater than ustar max

New_combined['Fre_Con_var']=New_combined['Fc'][New_combined['ustar']>New_combined['ustar_threshold']][New_combined['day_night']!=1]

#Then fill any remaining with ANN Fre

New_combined['Fre_Con_var'][New_combined['Fre_Con_var'].isnull()]=New_combined['Fre_NN']

#Then daytime make Fc_ustar equal to the obs if valid

New_combined['Fc_ustar_var']=New_combined['Fc'][New_combined['day_night']==1]

#Then any remaining values fill with ANN Fc

New_combined['Fc_ustar_var'][New_combined['Fc_ustar_var'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

#Simply make Fc_ustar = Fre at night

New_combined['Fc_ustar_var'][New_combined['day_night']!=1]=New_combined['Fre_Con_var']

#Create a GPP column based on Fc-Re

New_combined['GPP_Con_var']=New_combined['Fc_ustar_var']-New_combined['Fre_Con_var']

#Force to zero during the night

New_combined['GPP_Con_var'][New_combined['day_night']!=1]=0

############ All nightime data removed #######################

#Set columns to missing first

New_combined['Fre_Con_all'] = -9999

New_combined['Fc_ustar_all'] = -9999

New_combined['GPP_Con_all'] = -9999

#Create a new column of constructed GPP based on nighttime and ustar greater than ustar max

#New_combined['Fre_Con_all']=New_combined['Fc'][New_combined['ustar']>New_combined['ustar_all']][New_combined['day_night']!=1]

#Then fill any remaining with ANN Fre

New_combined['Fre_Con_all']=New_combined['Fre_NN']

#Then daytime make Fc_ustar equal to the obs if valid

New_combined['Fc_ustar_all']=New_combined['Fc'][New_combined['day_night']==1]

#Then any remaining values fill with ANN Fc

New_combined['Fc_ustar_all'][New_combined['Fc_ustar_all'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

#Simply make Fc_ustar = Fre at night

New_combined['Fc_ustar_all'][New_combined['day_night']!=1]=New_combined['Fre_Con_all']

#Create a GPP column based on Fc-Re

New_combined['GPP_Con_all']=New_combined['Fc_ustar_all']-New_combined['Fre_Con_all']

#Force to zero during the night

New_combined['GPP_Con_all'][New_combined['day_night']!=1]=0

############ Ustar threshold to 50% of max #######################

#Set columns to missing first

New_combined['Fre_Con_50pc'] = -9999

New_combined['Fc_ustar_50pc'] = -9999

New_combined['GPP_Con_50pc'] = -9999

#Create a new column of constructed GPP based on nighttime and ustar greater than ustar max

New_combined['Fre_Con_50pc']=New_combined['Fc'][New_combined['ustar']>(New_combined['ustar_max']*0.5)][New_combined['day_night']!=1]

#Then fill any remaining with ANN Fre

New_combined['Fre_Con_50pc'][New_combined['Fre_Con_50pc'].isnull()]=New_combined['Fre_NN']

#Then daytime make Fc_ustar equal to the obs if valid

New_combined['Fc_ustar_50pc']=New_combined['Fc'][New_combined['day_night']==1]

#Then any remaining values fill with ANN Fc

New_combined['Fc_ustar_50pc'][New_combined['Fc_ustar_50pc'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

#Simply make Fc_ustar = Fre at night

New_combined['Fc_ustar_50pc'][New_combined['day_night']!=1]=New_combined['Fre_Con_50pc']

#Create a GPP column based on Fc-Re

New_combined['GPP_Con_50pc']=New_combined['Fc_ustar_50pc']-New_combined['Fre_Con_50pc']

#Force to zero during the night

New_combined['GPP_Con_50pc'][New_combined['day_night']!=1]=0

############ Ustar threshold to none #######################

#Set columns to missing first

New_combined['Fre_Con_none'] = -9999

New_combined['Fc_ustar_none'] = -9999

New_combined['GPP_Con_none'] = -9999

#Create a new column of constructed GPP based on nighttime and ustar greater than ustar max

New_combined['Fre_Con_none']=New_combined['Fc'][New_combined['day_night']!=1]

#Then fill any remaining with ANN Fre

New_combined['Fre_Con_none'][New_combined['Fre_Con_none'].isnull()]=New_combined['Fc_NN']

#Then daytime make Fc_ustar equal to the obs if valid

New_combined['Fc_ustar_none']=New_combined['Fc'][New_combined['day_night']==1]

#Then any remaining values fill with ANN Fc

New_combined['Fc_ustar_none'][New_combined['Fc_ustar_none'].isnull()]=New_combined['Fc_NN'][New_combined['day_night']==1]

#Simply make Fc_ustar = Fre at night

New_combined['Fc_ustar_none'][New_combined['day_night']!=1]=New_combined['Fre_Con_none']

#Create a GPP column based on Fc-Re

New_combined['GPP_Con_none']=New_combined['Fc_ustar_none']-New_combined['Fre_Con_none']

#Force to zero during the night

New_combined['GPP_Con_none'][New_combined['day_night']!=1]=0

################################################

# Plots

################################################

Doplots_diurnal(mypathforResults,New_combined, Site_ID)

Doplots_diurnal_Fre(mypathforResults,New_combined, Site_ID)

#####################################

# File stuff

###################################################

#Write out to CSV

#Just CO2 stuff

New_combined[['Fc','Fc_Con','Fc_ustar','Fc_Lasslop','Fre_NN','Fre_Con','Fre_Lasslop','GPP_Con','GPP_Lasslop','ustar','ustar_max','day_night']].to_csv(mypathforResults+'Carbon flux components_for_'+Site_ID+'.csv', sep=',')

# Save dataframe.

New_combined[['Fc','Fc_Con','Fc_ustar','Fc_Lasslop','Fre_NN','Fre_Con','Fre_Lasslop','GPP_Con','GPP_Lasslop','ustar','ustar_max','day_night']].save(myBaseforResults+'Carbon flux components_for_'+Site_ID+'.df')

print"Finished GPP outputs at "+ Site_ID