/
fit_rise_times_pulser.py
720 lines (567 loc) · 26.5 KB
/
fit_rise_times_pulser.py
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import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime,timedelta
import scipy.integrate as integrate
from scipy.optimize import curve_fit,leastsq
import parameters
from cogent_utilities import *
from fitting_utilities import *
from lichen.plotting_utilities import *
import lichen.pdfs as pdfs
import lichen.iminuit_fitting_utilities as fitutils
import lichen.lichen as lch
import iminuit as minuit
import argparse
import math
import os
if os.path.isdir('./Plots') == False:
os.makedirs('./Plots')
pi = np.pi
first_event = 2750361.2
start_date = datetime(2009, 12, 3, 0, 0, 0, 0) #
np.random.seed(200)
yearly_mod = 2*pi/365.0
################################################################################
# Rise time fit
################################################################################
def fitfunc(data,p,parnames,params_dict):
pn = parnames
flag = p[pn.index('flag')]
pdf = None
x = data
xlo = params_dict['var_rt']['limits'][0]
xhi = params_dict['var_rt']['limits'][1]
tot_pdf = np.zeros(len(x))
#print "HERE"
#print data[data<0]
#print data[data>5.0]
############################################################################
# Log-norm structures
############################################################################
means = []
sigmas = []
nums = []
num_tot = 0.0
num_tot += p[parnames.index("fast_num")]
num_tot += p[parnames.index("slow_num")]
means.append(p[pn.index('fast_logn_mean')])
means.append(p[pn.index('slow_logn_mean')])
sigmas.append(p[pn.index('fast_logn_sigma')])
sigmas.append(p[pn.index('slow_logn_sigma')])
nums.append(p[pn.index('fast_num')]/num_tot)
nums.append(p[pn.index('slow_num')]/num_tot)
#print means,sigmas,nums
for n,m,s in zip(nums,means,sigmas):
pdf = pdfs.lognormal(x,m,s,xlo,xhi)
pdf *= n
tot_pdf += pdf
return tot_pdf
################################################################################
# Extended maximum likelihood function for minuit, normalized already.
################################################################################
def emlf(data,p,parnames,params_dict):
#print data[0]
ndata = len(data[0])
flag = p[parnames.index('flag')]
# Constrain this.
num_tot = 0.0
num_tot += p[parnames.index("fast_num")]
num_tot += p[parnames.index("slow_num")]
tot_pdf = fitfunc(data[0],p,parnames,params_dict)
likelihood_func = (-np.log(tot_pdf)).sum()
#print num_tot,ndata
ret = likelihood_func - fitutils.pois(num_tot,ndata)
return ret
################################################################################
################################################################################
# Read in the CoGeNT data
################################################################################
def main():
############################################################################
# Parse the command lines.
############################################################################
parser = argparse.ArgumentParser()
parser.add_argument('--fit', dest='fit', type=int,\
default=0, help='Which fit to perform (0,1,2)')
parser.add_argument('--verbose', dest='verbose', action='store_true',\
default=False, help='Verbose output')
parser.add_argument('--dataset', dest='dataset', type=str,\
default='nicole', help='Dataset to use in fitting. Nicoles simulated (nicole) or Juans pulser (juan)')
parser.add_argument('--batch', dest='batch', action='store_true',\
default=False, help='Run in batch mode (exit on completion).')
args = parser.parse_args()
############################################################################
#tag = 'pulser_onelognormal'
#tag = 'pulser'
#tag = 'pulser_zoomed_in'
#tag = 'pulser_simulated_Nicole'
#tag = 'pulser_simulated_Nicole_zoomed_in'
#tag = 'pulser_simulated_Nicole_onelognormal'
#tag = 'pulser_simulated_Nicole_no_fit'
tag = "risetime_determination_%s" % (args.dataset)
outfilename = "risetime_parameters_%s.py" % (tag)
outfile = open(outfilename,'w')
outfile.write("def risetime_parameters():\n\n")
'''
if args.help:
parser.print_help()
exit(-1)
'''
############################################################################
# Read in the data
############################################################################
infile_name = 'data/pulser_data_325ns.dat' # SIMULATED DATA FROM NICOLE
if args.dataset=='nicole':
infile_name = 'data/pulser_data_325ns.dat' # SIMULATED DATA FROM NICOLE
elif args.dataset=='juan':
infile_name = 'data/pulser_data.dat' # FROM JUAN, 8/2/13, manually scanned pulser runs.
tdays,energies,rise_times = get_3yr_cogent_data(infile_name,first_event=first_event,calibration=0)
print (tdays)
print (energies)
print (rise_times)
print (energies)
if args.verbose:
print_data(energies,tdays,rise_times)
data = [energies.copy(),tdays.copy(),rise_times.copy()]
print(("data before range cuts: ",len(data[0]),len(data[1]),len(data[2])))
############################################################################
# Declare the ranges.
############################################################################
ranges,subranges,nbins = parameters.fitting_parameters(args.fit)
bin_widths = np.ones(len(ranges))
for i,n,r in zip(list(range(len(nbins))),nbins,ranges):
bin_widths[i] = (r[1]-r[0])/n
# Cut events out that fall outside the range.
data = cut_events_outside_range(data,ranges)
data = cut_events_outside_subrange(data,subranges[1],data_index=1)
if args.verbose:
print_data(energies,tdays)
print(("data after range cuts: ",len(data[0]),len(data[1])))
nevents = float(len(data[0]))
'''
plt.figure()
plt.plot(energies,rise_times,'o',markersize=1.5)
plt.yscale('log')
plt.ylim(0.1,10)
plt.figure()
plt.plot(tdays,rise_times,'o',markersize=1.5)
plt.yscale('log')
plt.ylim(0.1,10)
'''
############################################################################
# Plot the data
############################################################################
############################################################################
# Look at the rise-time information.
############################################################################
# Will use this later when trying to figure out the energy dependence of
# the log-normal parameters.
# define our (line) fitting function
expfunc = lambda p, x: p[1]*np.exp(-p[0]*x) + p[2]
errfunc = lambda p, x, y, err: (y - expfunc(p, x)) / err
############################################################################
# Starting values for fits.
############################################################################
# For the data (two lognormals)
#starting_params = [-0.6,0.6,0.2*nevents, 0.1,0.8,0.8*nevents]
# For the pulser fast rise times (two lognormals)
starting_params = [-0.6,0.5,0.6*nevents, 0.5,0.8,0.4*nevents]
fit_parameters = []
fit_errors = []
fit_mnerrors = []
nevs = []
axrt = []
elo = 0.0
ehi = 1.0
eoffset = 0.5
ewidth = 0.15
estep = 0.15
#ewidth = 0.200
#estep = 0.050
expts = []
figcount = 0
for i in range(0,24):
j = i
if j%6==0:
figrt = plt.figure(figsize=(12,6),dpi=100)
axrt.append(figrt.add_subplot(2,3, i%6 + 1))
#figrt = plt.figure(figsize=(6,4),dpi=100)
#axrt.append(figrt.add_subplot(1,1,1))
data_to_fit = []
#h,xpts,ypts,xpts_err,ypts_err = lch.hist_err(data[1],bins=nbins[1],range=ranges[1],axes=ax1)
if i>=0:
elo = i*estep + eoffset
ehi = elo + ewidth
index0 = data[0]>=elo
index1 = data[0]< ehi
print((elo,ehi))
index = index0*index1
data_to_fit = data[2][index]
if len(data_to_fit)>0:
lch.hist_err(data_to_fit,bins=nbins[2],range=ranges[2],axes=axrt[j])
plt.ylim(0)
plt.xlim(ranges[2][0],ranges[2][1])
name = "Energy: %0.2f-%0.2f (keVee)" % (elo,ehi)
plt.text(0.20,0.75,name,transform=axrt[j].transAxes)
print ("=======-------- E BIN ----------===========")
print (name)
nevents = len(data_to_fit)
print(("Nevents for this fit: ",nevents))
#starting_params = [-0.6,0.6,0.2*nevents, 0.6,0.55,0.8*nevents]
# For pulser fits
#starting_params = [-0.1,0.8,0.2*nevents, 0.6,0.55,0.8*nevents]
'''
if i==0:
starting_params = [-0.6,0.6,0.2*nevents, 0.6,0.55,0.8*nevents]
'''
'''
if elo>=1.0 and elo<1.2:
starting_params = [0.1,0.2,0.3*nevents, 0.2,3.0,0.7*nevents]
'''
############################################################################
# Declare the fit parameters
############################################################################
params_dict = {}
params_dict['flag'] = {'fix':True,'start_val':args.fit}
params_dict['var_rt'] = {'fix':True,'start_val':0,'limits':(ranges[2][0],ranges[2][1])}
#params_dict['fast_logn_mean'] = {'fix':False,'start_val':0.005,'limits':(-2,2),'error':0.1}
#params_dict['fast_logn_sigma'] = {'fix':False,'start_val':0.5,'limits':(0.01,5),'error':0.1}
#params_dict['fast_num'] = {'fix':False,'start_val':0.2*nevents,'limits':(0.0,1.5*nevents),'error':0.1}
#params_dict['slow_logn_mean'] = {'fix':False,'start_val':0.5,'limits':(-2,2),'error':0.1}
#params_dict['slow_logn_sigma'] = {'fix':False,'start_val':1.0,'limits':(0.01,5),'error':0.1}
#params_dict['slow_num'] = {'fix':False,'start_val':0.8*nevents,'limits':(0.0,1.5*nevents),'error':0.1}
#starting_params = [1.0,1.2,0.6*nevents, 0.1,0.8,0.4*nevents]
# Worked for 1.0-1.25
#params_dict['fast_logn_mean'] = {'fix':False,'start_val':1.000,'limits':(-2,2),'error':0.1}
#params_dict['fast_logn_sigma'] = {'fix':False,'start_val':1.2,'limits':(0.01,5),'error':0.1}
#params_dict['fast_num'] = {'fix':False,'start_val':0.6*nevents,'limits':(0.0,1.5*nevents),'error':0.1}
#params_dict['slow_logn_mean'] = {'fix':False,'start_val':0.1,'limits':(-2,2),'error':0.1}
#params_dict['slow_logn_sigma'] = {'fix':False,'start_val':0.8,'limits':(0.01,5),'error':0.1}
#params_dict['slow_num'] = {'fix':False,'start_val':0.4*nevents,'limits':(0.0,1.5*nevents),'error':0.1}
params_dict['fast_logn_mean'] = {'fix':False,'start_val':starting_params[0],'limits':(-2,2),'error':0.01}
params_dict['fast_logn_sigma'] = {'fix':False,'start_val':starting_params[1],'limits':(0.05,30),'error':0.01}
params_dict['fast_num'] = {'fix':False,'start_val':nevents,'limits':(0.0,1.5*nevents),'error':0.01}
#params_dict['slow_logn_mean'] = {'fix':False,'start_val':starting_params[3],'limits':(-2,2),'error':0.01}
#params_dict['slow_logn_sigma'] = {'fix':False,'start_val':starting_params[4],'limits':(0.05,30),'error':0.01}
#params_dict['slow_num'] = {'fix':False,'start_val':starting_params[5],'limits':(0.0,1.5*nevents),'error':0.01}
# For the pulser fits
#params_dict['slow_logn_mean'] = {'fix':True,'start_val':0.000,'limits':(-0.002,0.002),'error':0.000001}
#params_dict['slow_logn_sigma'] = {'fix':True,'start_val':1.000,'limits':(0.9005,1.002),'error':0.000001}
#params_dict['slow_num'] = {'fix':True,'start_val':0.001,'limits':(0.0,0.002),'error':0.000001}
# float them
params_dict['slow_logn_mean'] = {'fix':False,'start_val':starting_params[3],'limits':(-2,2),'error':0.01}
params_dict['slow_logn_sigma'] = {'fix':False,'start_val':starting_params[4],'limits':(0.05,30),'error':0.01}
params_dict['slow_num'] = {'fix':False,'start_val':starting_params[5],'limits':(0.0,1.5*nevents),'error':0.01}
# To try one lognormal
#params_dict['slow_logn_mean'] = {'fix':True,'start_val':starting_params[3],'limits':(-2,2),'error':0.01}
#params_dict['slow_logn_sigma'] = {'fix':True,'start_val':starting_params[4],'limits':(0.05,30),'error':0.01}
#params_dict['slow_num'] = {'fix':True,'start_val':0.1,'limits':(0.0,1.5*nevents),'error':0.01}
# Above some value, lock down the second log normal, as the distribution is pretty well
# fit with just one log-normal.
elomax = 2.8
if args.dataset=='nicole':
elomax = 2.8
elif args.dataset=='juan':
elomax = 2.2
if elo>=elomax:
params_dict['slow_logn_mean'] = {'fix':True,'start_val':0.0,'limits':(-2,2),'error':0.01}
params_dict['slow_logn_sigma'] = {'fix':True,'start_val':1.0,'limits':(0.05,30),'error':0.01}
params_dict['slow_num'] = {'fix':True,'start_val':1,'limits':(0.0,1.5*nevents),'error':0.01}
'''
if i==0:
None
# From Nicole's simulation.
#params_dict['fast_logn_mean'] = {'fix':True,'start_val':-0.10,'limits':(-2,2),'error':0.01}
# From Juan
#params_dict['fast_logn_mean'] = {'fix':True,'start_val':-0.60,'limits':(-2,2),'error':0.01}
#params_dict['slow_logn_sigma'] = {'fix':True,'start_val':0.50,'limits':(0.05,30),'error':0.01}
'''
# Try fixing the slow sigma
#params_dict['slow_logn_sigma'] = {'fix':True,'start_val':0.52,'limits':(-2,2),'error':0.01}
#figrt.subplots_adjust(left=0.07, bottom=0.15, right=0.95, wspace=0.2, hspace=None,top=0.85)
#figrt.subplots_adjust(left=0.05, right=0.98)
#figrt.subplots_adjust(left=0.15, right=0.98,bottom=0.15)
figrt.subplots_adjust(left=0.07, right=0.98,bottom=0.10)
#plt.show()
#exit()
############################################################################
# Fit
############################################################################
if i>=0 and len(data_to_fit)>0:
params_names,kwd = fitutils.dict2kwd(params_dict)
#print data_to_fit
f = fitutils.Minuit_FCN([[data_to_fit]],params_dict,emlf)
# For maximum likelihood method.
kwd['errordef'] = 0.5
kwd['print_level'] = 0
#print kwd
m = minuit.Minuit(f,**kwd)
m.print_param()
m.migrad()
#m.hesse()
m.minos()
print ("Finished fit!!\n")
values = m.values # Dictionary
errors = m.errors # Dictionary
mnerrors = m.get_merrors()
print ("MNERRORS: ")
print (mnerrors)
fit_parameters.append(values)
fit_errors.append(errors)
fit_mnerrors.append(mnerrors)
nevs.append(len(data_to_fit))
xpts = np.linspace(ranges[2][0],ranges[2][1],1000)
tot_ypts = np.zeros(len(xpts))
ypts = pdfs.lognormal(xpts,values['fast_logn_mean'],values['fast_logn_sigma'],ranges[2][0],ranges[2][1])
y,plot = plot_pdf(xpts,ypts,bin_width=bin_widths[2],scale=values['fast_num'],fmt='r--',linewidth=2,axes=axrt[j])
tot_ypts += y
ypts = pdfs.lognormal(xpts,values['slow_logn_mean'],values['slow_logn_sigma'],ranges[2][0],ranges[2][1])
y,plot = plot_pdf(xpts,ypts,bin_width=bin_widths[2],scale=values['slow_num'],fmt='r:',linewidth=2,axes=axrt[j])
tot_ypts += y
axrt[j].plot(xpts,tot_ypts,'r',linewidth=2)
axrt[j].set_ylabel(r'Events')
axrt[j].set_xlabel(r'Rise time ($\mu$s)')
axrt[j].set_xlim(0,5.0)
'''
name = "Plots/rt_slice_%d.png" % (figcount)
if j%6==5:
plt.savefig(name)
figcount += 1
'''
#'''
if math.isnan(values['fast_logn_mean']) == False:
starting_params = [ \
values['fast_logn_mean'], \
values['fast_logn_sigma'], \
values['fast_num'], \
values['slow_logn_mean'], \
values['slow_logn_sigma'],
values['slow_num'] \
]
#'''
expts.append((ehi+elo)/2.0)
if j%6==5:
name = "Plots/rt_slice_%s_%d.png" % (tag,j/6)
plt.savefig(name)
print (fit_parameters)
print (nevs)
ypts = [[],[],[],[],[],[]]
yerr = [[],[],[],[],[],[]]
yerrlo = [[],[],[],[],[],[]]
yerrhi = [[],[],[],[],[],[]]
npts = []
if len(expts)>0:
#for i,fp,fe,n in zip(range(len(nevs)),fit_parameters,fit_errors,nevs):
for i,fp,fe,n in zip(list(range(len(nevs))),fit_parameters,fit_mnerrors,nevs):
print ("----------")
#ypts[0].append(fp['fast_logn_mean'])
#ypts[1].append(fp['fast_logn_sigma'])
#ypts[2].append(fp['fast_num'])
#ypts[3].append(fp['slow_logn_mean'])
#ypts[4].append(fp['slow_logn_sigma'])
#ypts[5].append(fp['slow_num'])
pars = ['fast_logn_mean','fast_logn_sigma','fast_num',\
'slow_logn_mean','slow_logn_sigma','slow_num']
for i,p in enumerate(pars):
if p in fe:
#if fe.has_key(p):
ypts[i].append(fp[p])
yerrlo[i].append(abs(fe[p]['lower']))
yerrhi[i].append(abs(fe[p]['upper']))
else:
ypts[i].append(0.0)
yerrlo[i].append(0.0)
yerrhi[i].append(0.0)
npts.append(n)
for i in range(len(ypts)):
ypts[i] = np.array(ypts[i])
yerrlo[i] = np.array(yerrlo[i])
yerrhi[i] = np.array(yerrhi[i])
colors = ['r','b']
labels = ['narrow','wide']
########################################################################
# Use all or some of the points in the fit.
########################################################################
index = np.arange(1,16)
if args.dataset=='nicole':
index = np.arange(1,16)
elif args.dataset=='juan':
index = np.arange(0,9)
#xp = np.linspace(min(expts),max(expts),100)
xp = np.linspace(min(expts),expts[17],100)
if args.dataset=='nicole':
xp = np.linspace(min(expts),expts[17],100)
elif args.dataset=='juan':
xp = np.linspace(min(expts),expts[8],100)
expts = np.array(expts)
fvals2 = plt.figure(figsize=(13,4),dpi=100)
yfitpts = []
for k in range(0,3):
# Some of the broad rise times are set to 0.
#index0s = ypts[3+k]!=0
#index0s = np.ones(len(ypts[3+k])).astype(bool)
index0s = np.ones(16).astype(bool)
fvals2.add_subplot(1,3,k+1)
tempypts = ypts[0+k]-ypts[3+k]
# Fractional error
tempyerrlo = np.sqrt((yerrlo[0+k])**2 + (yerrlo[3+k])**2)
tempyerrhi = np.sqrt((yerrhi[0+k])**2 + (yerrhi[3+k])**2)
if k>1:
tempypts = ypts[0+k][index0s]/ypts[3+k][index0s]
tempyerrlo = np.sqrt((yerrlo[0+k][index0s]/ypts[3+k][index0s])**2 + (yerrlo[3+k][index0s]*(ypts[0+k][index0s]/(ypts[3+k][index0s]**2)))**2)
tempyerrhi = np.sqrt((yerrhi[0+k][index0s]/ypts[3+k][index0s])**2 + (yerrhi[3+k][index0s]*(ypts[0+k][index0s]/(ypts[3+k][index0s]**2)))**2)
plt.errorbar(expts[index0s],tempypts[index0s],xerr=0.01,yerr=[tempyerrlo[index0s],tempyerrhi[index0s]],\
fmt='o',ecolor='k',mec='k',mfc='m',label='Ratio')
if k==0:
plt.ylabel(r'$\Delta \mu$')
elif k==1:
plt.ylabel(r'$\Delta \sigma$')
elif k==2:
plt.ylabel(r'# wide/# narrow')
plt.xlim(0.5,3.5)
plt.xlabel('Energy (keVee)')
########################################################################
# Fit to exponentials.
########################################################################
# Choose appropriate starting values, depending on the dataset.
pinit = [1,1,1]
if args.dataset=='nicole':
if k==0:
pinit = [1.0, 1.0, -1.2]
elif k==1:
#pinit = [1.0, -1.0, -0.5]
pinit = [-3.0,0.0015,-0.4]
elif k==2:
pinit = [-2.0, 1.0, 2.0]
elif args.dataset=='juan':
if k==0:
pinit = [1.0, 1.0, -1.2]
elif k==1:
#pinit = [1.0, -1.0, -0.5]
pinit = [-3.0,0.0015,-0.4]
elif k==2:
pinit = [-2.0, 1.0, 2.0]
out = leastsq(errfunc, pinit, args=(expts[index], tempypts[index], (tempyerrlo[index]+tempyerrhi[index])/2.0), full_output=1)
z = out[0]
zcov = out[1]
#print "Differences and ratios: %d [%f,%f,%f]" % (k,z[0],z[1],z[2])
variable = None
if (k==0):
variable = "fast_mean_rel_k"
if (k==1):
variable = "fast_sigma_rel_k"
elif (k==2):
variable = "fast_num_rel_k"
output = "\t%s = [%f,%f,%f]\n" % (variable,z[0],z[1],z[2])
print (output)
outfile.write(output)
#print "zcov: ",zcov
'''
if zcov is not None:
print "Differences and ratios: %d [%f,%f,%f]" % (k,np.sqrt(zcov[0][0]),np.sqrt(zcov[1][1]),np.sqrt(zcov[2][2]))
'''
yfitpts = expfunc(z,xp)
#print zcov
plt.plot(xp,yfitpts,'-',color='m')
fvals2.subplots_adjust(left=0.10, right=0.98,bottom=0.15,wspace=0.25,hspace=0.25)
name = 'Plots/rt_summary_%s_1.png' % (tag)
plt.savefig(name)
outfile.write("\n")
########################################################################
# Try to fit the individual distributions.
########################################################################
yfitpts = []
for i in range(0,6):
yfitpts.append(np.zeros(len(xp)))
fvals = plt.figure(figsize=(13,4),dpi=100)
for k in range(0,3):
fvals.add_subplot(1,3,k+1)
for ik in range(0,2):
nindex = k+3*ik
#print "HERERERERE"
#print ypts[nindex]
#print ypts[nindex][ypts[nindex]!=0]
print((len(yerrlo[nindex][ypts[nindex]!=0])))
print((len(yerrhi[nindex][ypts[nindex]!=0])))
plt.errorbar(expts[ypts[nindex]!=0],ypts[nindex][ypts[nindex]!=0],xerr=0.01,yerr=[yerrlo[nindex][ypts[nindex]!=0],yerrhi[nindex][ypts[nindex]!=0]],\
fmt='o',ecolor='k',mec='k',mfc=colors[ik],label=labels[ik])
#'''
# Use part of the data
#index0 = np.arange(0,3)
#index1 = np.arange(7,len(expts))
#index = np.append(index0,index1)
# Use all or some of the points
index = np.arange(1,16)
if args.dataset=='nicole':
index = np.arange(1,15)
elif args.dataset=='juan':
index = np.arange(0,7)
########################################################################
# Fit to exponentials.
########################################################################
pinit = [1,1,1]
if ik==0 and k==0:
pinit = [1.0, 1.0, -1.2]
elif ik==0 and k==1:
pinit = [4.0, 2.0, 0.0]
elif ik==0 and k==2:
pinit = [2.0, 2000.0, 300.0]
elif ik==1:
pinit = [3.0, 1.5, 0.5]
out = leastsq(errfunc, pinit, args=(expts[index], ypts[nindex][index], (yerrlo[nindex][index]+yerrhi[nindex][index])/2.0), full_output=1)
z = out[0]
zcov = out[1]
variable = None
if (k==0):
variable = "fast_mean0_k"
if (k==1):
variable = "fast_sigma0_k"
elif (k==2):
variable = "fast_num0_k"
#print "Data points: %d %d [%f,%f,%f]" % (k,ik,z[0],z[1],z[2])
if (ik==0):
output = "\t%s = [%f,%f,%f]\n" % (variable,z[0],z[1],z[2])
outfile.write(output)
print (output)
#print "Data points: %d %d [%f,%f,%f]" % (k,ik,np.sqrt(zcov[0][0]),np.sqrt(zcov[1][1]),np.sqrt(zcov[2][2]))
yfitpts[nindex] = expfunc(z,xp)
#print zcov
plt.plot(xp,yfitpts[nindex],'-',color=colors[ik])
#'''
if k==0:
plt.ylim(-1.5,1.5)
elif k==1:
plt.ylim(0,1.5)
plt.xlabel('Energy (keVee)')
if k==0:
plt.ylabel(r'Lognormal $\mu$')
elif k==1:
plt.ylabel(r'Lognormal $\sigma$')
elif k==2:
plt.ylabel(r'Number of events')
plt.legend()
#fval
'''
fvals.add_subplot(2,3,4)
plt.plot(xp,yfitpts[3]-yfitpts[0],'-',color='m')
fvals.add_subplot(2,3,5)
plt.plot(xp,yfitpts[4]-yfitpts[1],'-',color='m')
fvals.add_subplot(2,3,6)
plt.plot(xp,yfitpts[5]/yfitpts[2],'-',color='m')
'''
fvals.subplots_adjust(left=0.10, right=0.98,bottom=0.15,wspace=0.25,hspace=0.25)
name = 'Plots/rt_summary_%s_0.png' % (tag)
plt.savefig(name)
np.savetxt('rt_parameters.txt',[expts,ypts[0],ypts[1],ypts[2],ypts[3],ypts[4],ypts[5],npts])
#'''
#print "Sum ypts[5]: ",sum(ypts[5])
outfile.write("\n\treturn fast_mean_rel_k,fast_sigma_rel_k,fast_num_rel_k,fast_mean0_k,fast_sigma0_k,fast_num0_k\n")
if not args.batch:
plt.show()
#exit()
################################################################################
################################################################################
if __name__=="__main__":
main()