"""
plot_osoto_sipm_pde 

python script to access the halld_sipm data base (created by Yi) and plot SiPM properties
The values are then histogramed.
"""
import os
import subprocess
import numpy
from matplotlib import pyplot as plt
from datetime import datetime, date, time, timedelta
from matplotlib.backends.backend_pdf import PdfPages

Vover = 1.2
variation ="scaled"

# create list of file names
filenames = []
runs = [0]
figs = []

# create text file with data from osoto_sipm_dpe table.

nfiles = 0
filenames.append("sipmfiles/osoto_sipm_V"+str(int(10*Vover))+".dat")
command =["mysql","-hhallddb.jlab.org","-uhalld","-pGlueX_00","halld_sipm","-e","select * from osoto_sipm_pde where voltage_over="+str(Vover)+";"]
# command =["mysql","-hhallddb.jlab.org","-uhalld","-pGlueX_00","halld_sipm","-e","select * from osoto_sipm_pde where voltage_over="+str(Vover)+" and remark like 'UTFSM' limit 10;"]
print "command=",command
fout = open(filenames[nfiles],"w")
subprocess.call(command,stdout=fout)
fout.close()

# create text file with data from sipm_spec

nfiles = 1
filenames.append("sipmfiles/sipm_spec.dat")
command =["mysql","-hhallddb.jlab.org","-uhalld","-pGlueX_00","halld_sipm","-e","select * from sipm_spec;"]
print "command=",command
fout = open(filenames[nfiles],"w")
subprocess.call(command,stdout=fout)
fout.close()

# extract information from Hamamatsu specifications

fin = open(filenames[1],"r")
snH_in = []
DRhamamatsu_in = []
Vdict = {}

entries = 0
for line in fin:
    entries += 1
    linew = line.split()
    if entries == 1:
        for token in linew:
            print "First line dump, num tokens=",len(linew)," token=",token
        continue   # discard firt entry
    snH_in.append(float(linew[0]))
    DRhamamatsu_in.append(float(linew[4])/16)   # conver from array rate to ave cell rate
    Vdict[float(linew[0])] = float(linew[1])

entries -= 1
print "\nNumber of entries=",entries," for ",fin
print "Number of entries for spec file",len(DRhamamatsu_in)
fin.close()

# convert to numpy arrays for plotting
snH = numpy.array(snH_in)
DR25 = numpy.array(DRhamamatsu_in)

# now read file and plot
fin = open(filenames[0],"r")
sn_in = []
temp_in = []
voltage_over_in = []
voltage_in = []
voltage_Ham_br_in = []
pde_in5 = []
pde_in7 = []
pde_in15 = []
pde_in20 = []

DR_in5 = []
DR_in7 = []
DR_in15 = []
DR_in20 = []

gain_in5 = []
gain_in7 = []
gain_in15 = []
gain_in20 = []

xtalk_in5 = []
xtalk_in7 = []
xtalk_in15 = []
xtalk_in20 = []

entries = 0
nplots = 0

for line in fin:
    entries += 1
    linew = line.split()
    if entries == 1:
        for token in linew:
            print "First line dump, num tokens=",len(linew)," token=",token
        continue   # discard firt entry
    sn_in.append(float(linew[0]))
    if linew[1] == 'NULL':
        temp_in.apped(0.)
        voltage_over_in.append(0.)
        voltage_in.append(0.)
        voltage_Ham_br.append(0.)
    else:
        temp_in.append(float(linew[1]))
        voltage_over_in.append(float(linew[4]))
        voltage_in.append(float(linew[9]))
        if linew[10] == 'NULL':
           voltage_Ham_br_in.append(0.)
        else:
           voltage_Ham_br_in.append(float(linew[10]))

    # slice data on temperature

    # pdefactor12 = 1
    pdefactor12 = 0.294/0.231   # scale factor for UTFSM from paper Eq 1.2/0.9
    if linew[13] == 'NULL':
        if float(linew[1]) == 5:
            pde_in5.append(0.)
        elif float(linew[1]) == 7:
            pde_in7.append(0.)
        elif float(linew[1]) == 15:
            pde_in15.append(0.)
        elif float(linew[1]) == 20:
            pde_in20.append(0.)
    else:
        if float(linew[1]) == 5:
            pde_in5.append(float(linew[13])*pdefactor12)
        elif float(linew[1]) == 7:
            pde_in7.append(float(linew[13])*pdefactor12)
        elif float(linew[1]) == 15:
            pde_in15.append(float(linew[13]))
        elif float(linew[1]) == 20:
            pde_in20.append(float(linew[13])*pdefactor12)

    # DRfactor12 = 1
    # DRfactor12 = 0.049/0.036   # scale factor to UTFSM eq soto 2013
    DRfactor12 = 2.3   # scale factor to UTFSM eq soto 2014
    if linew[11] == 'NULL':
        if float(linew[1]) == 5:
            DR_in5.append(0.)
        elif float(linew[1]) == 7:
            DR_in7.append(0.)
        elif float(linew[1]) == 15:
            DR_in15.append(0.)
        elif float(linew[1]) == 20:
            DR_in20.append(0.)
    else:
        if float(linew[1]) == 5:
            DR_in5.append(float(linew[11])*DRfactor12)
        elif float(linew[1]) == 7:
            DR_in7.append(float(linew[11])*DRfactor12)
        elif float(linew[1]) == 15:
            DR_in15.append(float(linew[11]))
        elif float(linew[1]) == 20:
            DR_in20.append(float(linew[11])*DRfactor12)

    # gainfactor12 = 1
    gainfactor12 = 6.12/4.59   # scale factor for UTFSM Eq
    if linew[15] == 'NULL':
        if float(linew[1]) == 5:
            gain_in5.append(0.)
        elif float(linew[1]) == 7:
            gain_in7.append(0.)
        elif float(linew[1]) == 15:
            gain_in15.append(0.)
        elif float(linew[1]) == 20:
            gain_in20.append(0.)
    else:
        if float(linew[1]) == 5:
            gain_in5.append(float(linew[15])*gainfactor12)
        elif float(linew[1]) == 7:
            gain_in7.append(float(linew[15])*gainfactor12)
        elif float(linew[1]) == 15:
            gain_in15.append(float(linew[15]))  
        elif float(linew[1]) == 20:
            gain_in20.append(float(linew[15])*gainfactor12)

    # xtalkfactor12 = 1
    xtalkfactor12 = 0.237/0.130   # scale factor for UTFSM paper Eq
    if linew[17] == 'NULL':
        if float(linew[1]) == 5:
            xtalk_in5.append(0.)
        elif float(linew[1]) == 7:
            xtalk_in7.append(0.)
        elif float(linew[1]) == 15:
            xtalk_in15.append(0.)
        elif float(linew[1]) == 20:
            xtalk_in20.append(0.)
    else:
        if float(linew[1]) == 5:
            xtalk_in5.append(float(linew[17])*xtalkfactor12)
        elif float(linew[1]) == 7:
            xtalk_in7.append(float(linew[17])*xtalkfactor12)
        elif float(linew[1]) == 15:
            xtalk_in15.append(float(linew[17]))  
        elif float(linew[1]) == 20:
            xtalk_in20.append(float(linew[17])*xtalkfactor12)
            

entries -= 1
print "\nNumber of entries=",entries," for ",fin
print "Number of entries at 5 deg=",len(pde_in5)
print "Number of entries at 7 deg=",len(pde_in7)
print "Number of entries at 15 deg=",len(pde_in15)
print "Number of entries at 20 deg=",len(pde_in20)
print "Total 5 deg + 15 deg=",len(pde_in5)+len(pde_in15),"\n"
# print "pde=",pde_in
fin.close()

Vbr_diff_in = []

# check consistency between spec and orlando data bases
for (ndx,serial) in enumerate(sn_in):
    Vbr_calc = voltage_in[ndx] - voltage_over_in[ndx] + 0.0565*(25-temp_in[ndx])
    if temp_in[ndx]==5 or temp_in[ndx]==7 or  temp_in[ndx]==15 or temp_in[ndx]==20:
        Vbr_diff_in.append(Vbr_calc - (Vdict[serial]-0.9))
    else:
        Vbr_diff_in.append(-1.5)
    # print "serial number=",serial," Temp=",temp_in[ndx]," Vover=",voltage_over_in[ndx]," Vbr_dict=",Vdict[serial]-0.9," Vbr_calc=",Vbr_calc," voltage=",voltage_in[ndx]," Vbr Hamamatsu=",voltage_Ham_br_in[ndx]
    

# convert to numpy arrays for plotting
sn = numpy.array(sn_in)
temp = numpy.array(temp_in)
pde5 = numpy.array(pde_in5)
pde7 = numpy.array(pde_in7)
pde15 = numpy.array(pde_in15)
pde20 = numpy.array(pde_in20)

DR5 = numpy.array(DR_in5)
DR7 = numpy.array(DR_in7)
DR15 = numpy.array(DR_in15)
DR20 = numpy.array(DR_in20)

gain5 = numpy.array(gain_in5)
gain7 = numpy.array(gain_in7)
gain15 = numpy.array(gain_in15)
gain20 = numpy.array(gain_in20)

xtalk5 = numpy.array(xtalk_in5)
xtalk7 = numpy.array(xtalk_in7)
xtalk15 = numpy.array(xtalk_in15)
xtalk20 = numpy.array(xtalk_in20)

Vbr_diff = numpy.array(Vbr_diff_in)

numpy.set_printoptions(threshold='nan')
# print "Length 5=",len(DR5),"DR5=", DR5,"\n\n"
# print "Length 15=",len(DR15),"DR15=", DR15,"\n\n"
    
fig = plt.figure(1)
figs.append(fig)

nbins = 100
nsub = 1
xmin = 0
xmax = 5000
plt.subplot(2,2,nsub)
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(sn,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='black',log=False)
plt.ylabel("Entries")
plt.xlabel("Serial Number")
plt.title("Vover="+str(Vover)+" V, "+variation)
plt.axis([xmin,xmax,0,10000])
plt.autoscale(enable=True,axis='y')

nbins = 30
nsub = 2
plt.subplot(2,2,nsub)
xmin = 0
xmax = 30
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(temp,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',log=False)
plt.ylabel("Entries")
plt.xlabel("Temperature (degC)")
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.1*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 3
plt.subplot(2,2,nsub)
xmin = 0.1
xmax = 0.4
binwidth = (xmax-xmin)/float(nbins)
# compute averages
aveUTFSM = numpy.average(pde5)
aveJLab = numpy.average(pde15)
print "PDE aveUTFSM=", aveUTFSM, "aveJLab=",aveJLab
n, bins, patches = plt.hist(pde5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.3f}'.format(aveUTFSM),log=False)
# n, bins, patches = plt.hist(pde7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='black',log=False)
n, bins, patches = plt.hist(pde15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab>   ='+'{:.3f}'.format(aveJLab),log=False)
# n, bins, patches = plt.hist(pde20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='green',log=False)
plt.ylabel("Entries")
plt.xlabel("PDE")
plt.axis([xmin,xmax,0,500])
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 4
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 5
binwidth = (xmax-xmin)/float(nbins)
# compute averages
DRave5 = numpy.average(DR5)
DRave7 = numpy.average(DR7)
DRave15 = numpy.average(DR15)
DRave20 = numpy.average(DR20)
DRave25 = numpy.average(DR25)
print "DR ave5=", DRave5, "ave7=",DRave7, "ave15=",DRave15, "ave20=",DRave20," ave25=",DRave25
print "DR JLab max=",numpy.amax(DR15)," minimum=",numpy.amin(DR15)
n, bins, patches = plt.hist(DR5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=5>='+'{:.3f}'.format(DRave5),log=True)
n, bins, patches = plt.hist(DR7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='black',label='<T=7>='+'{:.3f}'.format(DRave7),log=False)
n, bins, patches = plt.hist(DR15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<T=15>='+'{:.3f}'.format(DRave15),log=True)
n, bins, patches = plt.hist(DR20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='green',label='<T=20>='+'{:.3f}'.format(DRave20),log=False)
n, bins, patches = plt.hist(DR25,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='orange',label='<T=25>='+'{:.3f}'.format(DRave25),log=False)
plt.ylabel("Entries")
plt.xlabel("Dark Rate (MHz)")
plt.axis([xmin,xmax,1,10000])
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
# plt.autoscale(enable=True,axis='y')

plt.tight_layout()
fig = plt.figure(2)
figs.append(fig)

nbins = 100
nsub = 1
plt.subplot(2,2,nsub)
xmin = -1.5
xmax = 1.5
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(Vbr_diff,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',log=False)
plt.ylabel("Entries")
plt.xlabel("Vbr Calc - Hamamatsu")
plt.title("Vover="+str(Vover)+" V, "+variation)
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.1*ymax])
# plt.autoscale(enable=True,axis='y')


plt.tight_layout()
fig = plt.figure(3)
figs.append(fig)

nbins = 100
nsub = 1
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 10
binwidth = (xmax-xmin)/float(nbins)
# compute averages
gainave5 = numpy.average(gain5)
gainave7 = numpy.average(gain7)
gainave15 = numpy.average(gain15)
gainave20 = numpy.average(gain20)
# gainave25 = numpy.average(gain25)
print "gain ave5=", gainave5, "ave7=",gainave7, "ave15=",gainave15, "ave20=",gainave20
# print "gain ave25=",gainave25
print "gain JLab max=",numpy.amax(gain15)," minimum=",numpy.amin(gain15)
n, bins, patches = plt.hist(gain5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=5>='+'{:.2f}'.format(gainave5),log=False)
plt.ylabel("Entries")
plt.xlabel("Gain (x10^5)")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 2
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 10
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(gain7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=7>='+'{:.2f}'.format(gainave7),log=False)
plt.ylabel("Entries")
plt.xlabel("Gain (x10^5)")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 3
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 10
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(gain15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<T=15>='+'{:.2f}'.format(gainave15),log=False)
plt.ylabel("Entries")
plt.xlabel("Gain (x10^5)")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 4
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 10
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(gain20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=20>='+'{:.2f}'.format(gainave20),log=False)
plt.ylabel("Entries")
plt.xlabel("Gain (x10^5)")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')



plt.tight_layout()
fig = plt.figure(4)
figs.append(fig)

nbins = 50
nsub = 1
# plt.subplot(2,2,nsub)
xmin = 0.
xmax = 3
binwidth = (xmax-xmin)/float(nbins)
print "DR JLab max=",numpy.amax(DR15)," minimum=",numpy.amin(DR15)
n, bins, patches = plt.hist(DR5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=5>='+'{:.3f}'.format(DRave5),log=True)
n, bins, patches = plt.hist(DR7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='black',label='<T=7>='+'{:.3f}'.format(DRave7),log=False)
n, bins, patches = plt.hist(DR15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<T=15>='+'{:.3f}'.format(DRave15),log=True)
n, bins, patches = plt.hist(DR20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='green',label='<T=20>='+'{:.3f}'.format(DRave20),log=False)
n, bins, patches = plt.hist(DR25,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='orange',label='<T=25>='+'{:.3f}'.format(DRave25),log=False)
plt.ylabel("Entries")
plt.xlabel("Dark Rate (MHz)")
plt.axis([xmin,xmax,1,10000])
plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
# plt.autoscale(enable=True,axis='y')


plt.tight_layout()
fig = plt.figure(5)
figs.append(fig)


nbins = 100
nsub = 1
# plt.subplot(2,2,nsub)
xmin = 0.
xmax = 10
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(gain15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab>='+'{:.2f}'.format(gainave15),log=False)
n, bins, patches = plt.hist(gain20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.2f}'.format(gainave20),log=False)
plt.ylabel("Entries")
plt.xlabel("Gain (x10^5)")
plt.axis([xmin,xmax,1,600])
plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
# plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


plt.tight_layout()
fig = plt.figure(6)
figs.append(fig)

nbins = 100
nsub = 1
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 0.5
binwidth = (xmax-xmin)/float(nbins)
# compute averages
xtalkave5 = numpy.average(xtalk5)
xtalkave7 = numpy.average(xtalk7)
xtalkave15 = numpy.average(xtalk15)
xtalkave20 = numpy.average(xtalk20)
# xtalkave25 = numpy.average(xtalk25)
print "xtalk ave5=", xtalkave5, "ave7=",xtalkave7, "ave15=",xtalkave15, "ave20=",xtalkave20
# print "xtalk ave25=",xtalkave25
print "xtalk JLab max=",numpy.amax(xtalk15)," minimum=",numpy.amin(xtalk15)
n, bins, patches = plt.hist(xtalk5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=5>='+'{:.3f}'.format(xtalkave5),log=False)
plt.ylabel("Entries")
plt.xlabel("Cross Talk")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 2
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 0.5
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(xtalk7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=7>='+'{:.3f}'.format(xtalkave7),log=False)
plt.ylabel("Entries")
plt.xlabel("Cross Talk")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 3
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 0.5
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(xtalk15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<T=15>='+'{:.3f}'.format(xtalkave15),log=False)
plt.ylabel("Entries")
plt.xlabel("Cross Talk")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 4
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 0.5
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(xtalk20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<T=20>='+'{:.3f}'.format(xtalkave20),log=False)
plt.ylabel("Entries")
plt.xlabel("Cross Talk")
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
print "ymin, ymax=",ymin,ymax
plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')


plt.tight_layout()
fig = plt.figure(7)
figs.append(fig)

nbins = 100
nsub = 1
# plt.subplot(2,2,nsub)
xmin = 0.
xmax = 0.5
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(xtalk15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab>='+'{:.2f}'.format(xtalkave15),log=False)
n, bins, patches = plt.hist(xtalk20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.2f}'.format(xtalkave20),log=False)
plt.ylabel("Entries")
plt.xlabel("Cross Talk")
plt.axis([xmin,xmax,1,600])
plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
# plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')

plt.tight_layout()
fig = plt.figure(8)
figs.append(fig)

params = {'legend.fontsize': 8,
          'legend.linewidth': 2}
plt.rcParams.update(params)


nbins = 100
nsub = 1
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 10
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(gain15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab>='+'{:.2f}'.format(gainave15),log=False)
n, bins, patches = plt.hist(gain20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.2f}'.format(gainave20),log=False)
plt.ylabel("Entries")
plt.xlabel("Gain (x10^5)")
plt.axis([xmin,xmax,1,600])
plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
# plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')

nbins = 100
nsub = 2
plt.subplot(2,2,nsub)
xmin = 0.1
xmax = 0.4
binwidth = (xmax-xmin)/float(nbins)
# compute averages
pdeave20= numpy.average(pde20)
pdeave15 = numpy.average(pde15)
print "PDE aveUTFSM=", aveUTFSM, "aveJLab=",aveJLab
# n, bins, patches = plt.hist(pde5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.3f}'.format(pdeave5),log=False)
# n, bins, patches = plt.hist(pde7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='black',log=False)
n, bins, patches = plt.hist(pde15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab>   ='+'{:.3f}'.format(pdeave15),log=False)
n, bins, patches = plt.hist(pde20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.3f}'.format(pdeave20),log=False)
plt.ylabel("Entries")
plt.xlabel("PDE")
plt.axis([xmin,xmax,0,500])
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
# plt.autoscale(enable=True,axis='y')


nbins = 50
nsub = 3
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 3
binwidth = (xmax-xmin)/float(nbins)
print "DR JLab max=",numpy.amax(DR15)," minimum=",numpy.amin(DR15)
n, bins, patches = plt.hist(DR5,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='green',label='<UTFSM> (T=5)='+'{:.3f}'.format(DRave5),log=True)
n, bins, patches = plt.hist(DR7,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='black',label='<UTFSM>(T=7)='+'{:.3f}'.format(DRave7),log=False)
n, bins, patches = plt.hist(DR15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab> (T=15)='+'{:.3f}'.format(DRave15),log=True)
n, bins, patches = plt.hist(DR20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM> (T=20)='+'{:.3f}'.format(DRave20),log=False)
n, bins, patches = plt.hist(DR25,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='orange',label='<Hamamatsu> (T=25)='+'{:.3f}'.format(DRave25),log=False)
plt.ylabel("Entries")
plt.xlabel("Dark Rate (MHz)")
plt.axis([xmin,xmax,1,100000])
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
# plt.autoscale(enable=True,axis='y')


nbins = 100
nsub = 4
plt.subplot(2,2,nsub)
xmin = 0.
xmax = 0.5
binwidth = (xmax-xmin)/float(nbins)
n, bins, patches = plt.hist(xtalk15,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='red',label='<JLab>='+'{:.2f}'.format(xtalkave15),log=False)
n, bins, patches = plt.hist(xtalk20,bins=numpy.arange(xmin,xmax,binwidth),histtype='step',color='blue',label='<UTFSM>='+'{:.2f}'.format(xtalkave20),log=False)
plt.ylabel("Entries")
plt.xlabel("Cross Talk")
plt.axis([xmin,xmax,1,600])
# plt.title("Vover="+str(Vover)+" V, "+variation)
plt.legend()
ymin, ymax = plt.ylim()
# plt.axis([xmin,xmax,0,1.5*ymax])
# plt.autoscale(enable=True,axis='y')

plt.tight_layout()
# plt.show()
pdfname = "plot_osoto_sipm_pde_V"+str(int(10*Vover))+".pdf"
print "PDF filename=",pdfname
with PdfPages(pdfname) as pdf:
    for fig in figs:
        pdf.savefig(fig)