##Preamble
from __future__ import division,print_function
import pandas as pds

rc('figure',figsize=(12,4))
rc('legend',loc='best')
rc('font',size = 14.0)
rc('font', family='serif')
rc('text', usetex=False)
rc('lines', markersize=10)

import os
from os.path import expanduser

%load_ext autoreload
%autoreload 2

import scipy.optimize as opt
from scipy import stats
import uncertainties as unc

import ProcessingCTRData as pc

print("Complete")

#Global Properties

aloc = '/home/mbrown/Desktop/doipaper/figures/' #files saved here!

wrapdict = {'wrapped':'r','unwrapped':'b','partialwrap':cm.gist_rainbow(0.4)}
errwrapdict = {'wrapped':'r','unwrapped':'b','partialwrap':'g'}
savefigures = True

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^"}  

df = df[df.length == 30]

df = df[df.specialkey == 'extended']

pp = lambda aval : '{:1.2f}'.format(aval)
for config, grp in df.groupby('configuration'):
        
    for key, grp in grp.groupby('SampleB'):
        print(config,key)
        ax.errorbar(grp.DOI,grp.CTR,yerr=grp.CTRerr,fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        m,b = polyfit(grp.DOI,grp.CTR,1,w=grp.CTRerr)
        
        #ax.hlines(b,0,30,color='k',linestyles='dashed')
        
        print("covariance",pp(m))
        print("intercept",pp(b))
        m,c,b = polyfit(grp.DOI,grp.CTR,2,w=grp.CTRerr)
        print("covariance",pp(m))
        print("Gradient",pp(c),"intercept",pp(b))
        
        
        
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Coincidence Time Resolution (ps)")
    ax.set_xlim(-2,32)
    
    ax.set_ylim(160,300)

    
ax.legend(ncol=2)
fig.tight_layout()

show()    

df = pds.read_csv('alldata.csv')

rc('text', usetex=True)
rc('font', family='serif')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^"}  

df = df[df.length == 30]
df = df[df.specialkey == 'extended']

for config, grp in df.groupby('configuration'):
    
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.numofsamples,yerr=sqrt(grp.numofsamples),fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        
    ax.set_xlim(-2,32)

step = lambda x, xmin, xmax : 1/(xmax-xmin)*((x>=xmin) & (x<=xmax))

x = linspace(-5,40,500)
xs = linspace(-5,5)
y = convolve(step(x,0,30),stats.norm(scale=1/2.355).pdf(xs),mode='same')
y*=3000/max(y)

ax.plot(x,y,'k--')
ax.set_xlabel("Depth of Interaction (mm)")
ax.set_ylabel("Number of $\gamma\gamma$ events detected")
    
ax.legend(ncol=2)
fig.tight_layout()

show()
rc('text', usetex=False)

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^"}  

df = df[df.length == 30]

df = df[df.specialkey == 'extended']

for config, grp in df.groupby('configuration'):
        
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.location,yerr=grp.locationerr,fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
    
    vals = [unc.ufloat(a,b) for a,b in zip(grp.location, grp.locationerr)]
    print(ptp(vals),"ps")
        
    linear = lambda xdata, m,c: m*xdata+c
    
    for reducelim in [30,25,20,15]:
        fgrp = grp[grp.DOI < reducelim]
        (m,c), err = opt.curve_fit(linear,array(fgrp['DOI']),array(fgrp['location']),sigma=fgrp['locationerr'])
        merr,cerr = err.diagonal()
        
        #X = linspace(0,30)
        #Y = linear(X,m,c)
        #ax.plot(X,Y,'k--')
        
        factor = 3E-1
        print(config,"<",reducelim,"n", unc.ufloat(m,merr)*factor)
    
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Delay Peak Centroid (ps)")
    ax.set_xlim(-2,32)
    
    ax.set_ylim(-150,100)

    
ax.legend(ncol=2)
fig.tight_layout()

show()
    

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^"}  

df = df[df.length == 30]
df = df[df.specialkey == 'extended']

for config, grp in df.groupby('configuration'):
        
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.ERright,yerr=grp.ERrighterr,fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        
    
        
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Energy Resolution (%)")
    ax.set_xlim(-2,32)
    
ax.legend(ncol=2)

DOI = linspace(2,30,1000)
Y = lambda xdata, m, c,A : c+A/(xdata-m)**2

fdf = df[df.configuration == 'unwrapped']
#param, err = opt.curve_fit(Y,fdf.DOI,fdf.ERright,sigma=fdf.ERrighterr)
#for p,e in zip(param,err.diagonal()):
#    val = unc.ufloat(p,e)
#    print(val)
#ax.plot(DOI,Y(DOI,*param),'k--')

fig.tight_layout()

show()    

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^"}  

df = df[df.length == 30]
df = df[df.specialkey == 'extended']

for config, grp in df.groupby('configuration'):
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.RPloc,yerr=grp.RPlocerr,fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        
        
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Right Photopeak Centroid (Arb.)")
    ax.set_xlim(-2,32)
    ax.set_ylim(0.3,0.65)
    
ax.legend(ncol=2)
fig.tight_layout()
show()    

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()


df = df[df.length == 20]
df = df[df.SampleB != '2396']
df = df[df.specialkey == 'normal']
df = df[df.CTR < 300]

crystaldict = {"20A":"*","20B":"^"}  

for config, grp in df.groupby('configuration'):
    

    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.CTR,yerr=grp.CTRerr, fmt='.', marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        print(config,key,grp.CTR.mean(),grp.CTRerr.mean())
        
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Coincidence Time Resolution (ps)")
    ax.set_xlim(-2,20)
    
    ax.set_ylim(160,280)

    
leg = ax.legend(ncol=3)
fig.tight_layout()
show()    

df = pds.read_csv('alldata.csv')
rc('text', usetex=True)
fig, ax = subplots()
ax.grid()

df = df[df.length == 20]
df = df[df.SampleB != '2396']
df = df[df.specialkey == 'normal']

for config, grp in df.groupby('configuration'):
    
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.numofsamples,yerr=sqrt(grp.numofsamples),fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        
    ax.set_xlim(-2,20)
    ax.set_ylim(0,7000)

step = lambda x, xmin, xmax : 1/(xmax-xmin)*((x>=xmin) & (x<=xmax))

x = linspace(-5,40,500)
xs = linspace(-5,5)
y = convolve(step(x,-0.5,19.5),stats.norm(scale=2/2.355).pdf(xs),mode='same')
y*=3000/max(y)

ax.plot(x,y,'k--')
ax.set_xlabel("Depth of Interaction (mm)")
ax.set_ylabel("Number of $\gamma\gamma$ events detected")
    
ax.legend(ncol=3)
fig.tight_layout()

show()
rc('text', usetex=False)

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()


df = df[df.length == 20]

df = df[df.SampleB != '2396']

df = df[df.specialkey == 'normal']

for config, grp in df.groupby('configuration'):
        
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.location,yerr=grp.locationerr,fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        vals = [unc.ufloat(a,b) for a,b in zip(grp.location, grp.locationerr)]
        print(ptp(vals),"ps")
        
    for reducelim in [20,15,10,8]:
        fgrp = grp[grp.DOI < reducelim]
        (m,c), err = opt.curve_fit(linear,array(fgrp['DOI']),array(fgrp['location']),sigma=fgrp['locationerr'])
        merr,cerr = err.diagonal()
        
        #X = linspace(0,30)
        #Y = linear(X,m,c)
        #ax.plot(X,Y,'k--')
        
        factor = 3E-1
        print(config,"<",reducelim,"n", unc.ufloat(m,merr)*factor)
    
    
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Delay Peak Centroid (ps)")
    ax.set_xlim(-2,20)
    
    ax.set_ylim(100,300)

    
ax.legend(ncol=3)
fig.tight_layout()

show()    

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

df = df[df.length == 20]
df = df[df.SampleB != '2396']
df = df[df.specialkey == 'normal']

for config, grp in df.groupby('configuration'):
        
    for key, grp in grp.groupby('SampleB'):
        ax.errorbar(grp.DOI,grp.ERright,yerr=grp.ERrighterr,fmt='.',marker=crystaldict[key],color=wrapdict[config],ecolor=errwrapdict[config],label=key+' '+config)
        
        
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Energy Resolution (%)")
    ax.set_xlim(-2,20)
    ax.set_ylim(10,20)
    
ax.legend(ncol=3)
fig.tight_layout()

show()

data = "1.640	1.560	1.640	1.560	72.4	72.7	5 1.640	1.560	1.700	1.500	72.4	72.7	20 1.640	1.560	1.900	1.300	72.4	72.7	20 1.640	1.560	2.100	1.100	72.4	72.7	20"
data = array([row.split('\t') for row in data.split(' ')],dtype=float)
Voltages = (data[:,2]-data[:,3])*1e3
Voltages

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^","2396":"."}  
wrapdict = {'wrapped':'g','unwrapped':'r','partialwrap':'b'}


fdf = df[(df.specialkey == 'normal') & (df.SampleB == '2396') & (df.configuration == 'wrapped')]
firstvalue = fdf.location.min()
#ax.errorbar(fdf.DOI,fdf.location/firstvalue,yerr=fdf.locationerr/firstvalue,fmt='.',label=str(80)+'mV')

df = df[df.length == 30]

#df = df[df.SampleB == '2396']


df = df[df.specialkey == 'threshold']

df['threshold'] = df.uniquename.apply(lambda astr : Voltages[int(astr.split('_')[-1])])
                                      
for config, grp in df.groupby('configuration'):
    
    linear = lambda xdata, m,c: m*xdata+c
    
    Y = array(grp[grp.threshold < 100].DOI)
    
    for key, grp in grp.groupby('threshold'):
        #if key < 100: #skips 5 minute measurement
        #    continue
        #print(list(grp.DOI))
        firstvalue = grp.location.min()
        
        (m,c), err = opt.curve_fit(linear,array(grp['DOI']),array(grp['location']),sigma=grp['locationerr'])
        merr,cerr = err.diagonal()
        
        factor = 3E-1
        print(str(int(key+0.6))+'mV',"<",reducelim,"n", unc.ufloat(m,merr)*factor)    
        
        ax.errorbar(grp.DOI,grp.location,yerr=grp.locationerr,fmt='.',label=str(int(key+0.6))+'mV')
 
        
    
    
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_ylabel("Delay Peak Centroid (ps)")
    ax.set_xlim(-1,21)
    
    ax.set_ylim(100,1200)

    
ax.legend(ncol=4)
fig.tight_layout()

show()

df = pds.read_csv('alldata.csv')

fig, ax = subplots()
ax.grid()

crystaldict = {"30A":"*","30B":"^","2396":"."}  
wrapdict = {'wrapped':'g','unwrapped':'r','partialwrap':'b'}

df = df[df.length == 30]

#df = df[df.SampleB == '2396']

df = df[df.specialkey == 'threshold']

df['threshold'] = df.uniquename.apply(lambda astr : Voltages[int(astr.split('_')[-1])])
                                      
for config, grp in df.groupby('configuration'):
    
    linear = lambda xdata, m,c: m*xdata+c
    
    
    
    for key, grp in grp.groupby('threshold'):
        if key < 100:
            continue
        ax.errorbar(grp.DOI,grp.numofsamples,yerr=sqrt(grp.numofsamples),fmt='.',label=str(int(key+0.6))+'mV')
    
    ax.set_xlabel("Depth of Interaction (mm)")
    ax.set_xlim(-1,21)
    
    #ax.set_ylim(0.9,2.1)

    
ax.legend(ncol=3)
fig.tight_layout()

show()