##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)
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
from uncertainties import unumpy

import ProcessingCTRData as pc

print("Complete")

LeftData = {'Threshold (V)': (1.64, 1.56), 'Bias (V)' : 72.6, 'Overvoltage (V)' : 2.2}
RightData = {'Threshold (V)': (1.64, 1.56), 'Bias (V)' : 72.7, 'Overvoltage (V)' : 2.2}   

Data = {'Left Detector':LeftData, 'Right Detector':RightData}

df = pds.DataFrame(Data)
df = df.T

def tidythreshold(astr):
    a, b = astr
    return "{0} , {1}".format(a,b)

df['Threshold (V)'] = df['Threshold (V)'].apply(tidythreshold)

print(df.to_latex())


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

df['configuration'] = 'wrapped'
df["person"] = df.uniquename.apply(lambda astr : astr.split('_')[-2])
df['num'] = df.uniquename.apply(lambda astr : int(astr.split('_')[-1]))

df = df[((df.person == 'MW') & (df.num == 1)) | 
        (df.length == 5) & (df.person == 'SW') & ((df.num == 4) | (df.num==25)) |
        (df.length == 10) & (df.person == 'SW') & (df.num == 1) |
        (df.length == 15) & (df.person == 'SW') & (df.num == 1) |
        (df.length == 20) & (df.person == 'SW') & ((df.num == 1) | (df.num==9)) |
        (df.length == 30) & (df.person == 'SW') & ((df.num == 1) | (df.num==9)) ]

## Combine data back together

for col in df.columns:
    if col.endswith('err'):
        df[col[:-3]] = df.apply(lambda srs : unc.ufloat(srs[col[:-3]],srs[col]),axis=1)
        df = df.drop(col,1)
    
df['ggevents'] = unumpy.uarray(list(df.numofsamples),list(sqrt(df.numofsamples)))

##
Person = {'SW' : 'Wrap 2','MW' : ' Wrap 1'}
df['Wrapped By'] = df.person.apply(lambda astr : Person[astr])


df = df.sort(['length','CTR', 'person'], ascending=[True,False,True])

for col in df.columns:
    if col in ['length', 'ggevents']:
        ff = "{0:.0f}"
    elif col in ['ERleft','ERright']:
        ff = "{0:.2f}"
    else:
        ff = "{0:.1f}"
    try:
        df[col] = [ff.format(val).replace("+/-","$\pm$") for val in df[col]]
    except ValueError:
        continue
        
            
print(df.to_latex(float_format=lambda x: '%10.1f' % x, index=False,columns=['Wrapped By','length',"ERleft","ERright","ggevents","location","CTR","chisquared"]))

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

df['num'] = df.uniquename.apply(lambda astr : int(astr.split('_')[-1]))

df = df[df.num == 1]

#initial DOI measurement removed to reduce confusion - value should be valid!
df = df[df.uniquename.apply(lambda astr : astr.find("6th") < 0)] 

## Combine data back together

for col in df.columns:
    if col.endswith('err'):
        df[col[:-3]] = df.apply(lambda srs : unc.ufloat(srs[col[:-3]],srs[col]),axis=1)
        df = df.drop(col,1)
    
df['ggevents'] = unumpy.uarray(list(df.numofsamples),list(sqrt(df.numofsamples)))

##

expr = {0: "Standard", 1: "DOI"}
df['DOI'] = df.uniquename.apply(lambda astr : expr[astr.find("DOI")>0])

df['configuration'] = 'wrapped'
df = df.sort('length')

for col in df.columns:
    if col in ['length', 'ggevents']:
        ff = "{0:.0f}"
    else:
        ff = "{0:.1f}"
    try:
        df[col] = [ff.format(val).replace("+/-","$\pm$") for val in df[col]]
    except ValueError:
        continue

print(df.to_latex(float_format=lambda x: '%10.1f' % x, index=False,columns=["uniquename","ERleft","ERright","ggevents","location","TimeResolution","chisquared"]))
#print(pc.GenerateLaTeXTable(df,sortby=['length'],cols=["configuration","DOI","energyresolutionLeft","energyresolution","ggevents","loc","ctr","chisquared"]))

df = pds.read_csv('alldata.csv',index_col=[0,1])

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

chifit = [pc.getchi(grp,plot=False) for key, grp in df.groupby(['SampleB','configuration'])]

for col in df.columns:
    if col.endswith('err'):
        df[col[:-3]] = df.apply(lambda srs : unc.ufloat(srs[col[:-3]],srs[col]),axis=1)
        df = df.drop(col,1)
    
df['ggevents'] = unumpy.uarray(list(df.numofsamples),list(sqrt(df.numofsamples)))

def ProperMean(agrp):
    if isinstance(agrp.dtype,object):
        return mean(list(agrp))
    else:
        return mean(agrp)
    
    
df = df.groupby(['SampleB','configuration']).aggregate(ProperMean)
df['chifit'] = chifit

df = df.sort('CTR',ascending=False)


for col in df.columns:
    if col in ['length', 'ggevents']:
        ff = "{0:.0f}"
    elif col in ['ERleft','ERright']:
        ff = "{0:.2f}"
    else:
        ff = "{0:.1f}"
    try:
        df[col] = [ff.format(val).replace("+/-","$\pm$") for val in df[col]]
    except ValueError:
        continue

print(df.to_latex(float_format=lambda x: '%10.1f' % x, index=True,columns=["ERleft","ERright","ggevents","location","CTR","chisquared","chifit"]))


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

df = df[df.length == 20]
df = df[df.SampleB == '2396']
df = df[df.CTR < 300]
    
grp = df.groupby(['SampleB','configuration']).mean()
params, grp['$\chi^2_\text{nofit}$'] = zip(*[pc.getchi(agrp, dist='nofit', verbose=0, plot=False, fetchparam=True) for key, agrp in df.groupby(['SampleB','configuration'])])

grp['intercept-nofit'] = [pc.tidystring(astr) for astr in params]

params, grp['$\chi^2_\text{linear}$'] = zip(*[pc.getchi(agrp, dist='linear', verbose=0, plot=False, fetchparam=True) for key, agrp in df.groupby(['SampleB','configuration'])])

m, c = zip(*params)

grp['intercept-linearfit'] = [pc.tidystring(astr) for astr in m]
grp['gradient-linearfit'] = [pc.tidystring(astr) for astr in c]
grp['ptpchange'] = pc.CalculatePTP(df)
grp['std'] = pc.CalculateSTD(df)

grp['specialkey'] = 'normal'

print(pc.GenerateLaTeXTable(grp,index=True,sortby=None,cols=["energyresolutionLeft","energyresolution","ggevents","loc","ctr","chisquared"]))
print(pc.GenerateLaTeXTable(grp,index=True,sortby=None,cols=['std','ptpchange','intercept-nofit',"$\chi^2_\text{nofit}$",'intercept-linearfit','gradient-linearfit','$\chi^2_\text{linear}$']))
 


df = pds.read_csv('alldata.csv',index_col=[0,1])

df = df[df.length == 30]
df = df[df.CTR < 300]
df = df[df.specialkey == 'normal']

chifit = [pc.getchi(grp,plot=False) for key, grp in df.groupby(['SampleB','configuration'])]

for col in df.columns:
    if col.endswith('err'):
        df[col[:-3]] = df.apply(lambda srs : unc.ufloat(srs[col[:-3]],srs[col]),axis=1)
        df = df.drop(col,1)
    
df['ggevents'] = unumpy.uarray(list(df.numofsamples),list(sqrt(df.numofsamples)))

def ProperMean(agrp):
    if isinstance(agrp.dtype,object):
        return mean(list(agrp))
    else:
        return mean(agrp)
    

df = df.groupby(['SampleB','configuration']).aggregate(ProperMean)
print(df.CTR)

df['chifit'] = chifit

fdf = list(df.CTR)
#df = df.sort('CTR',ascending=False)


for col in df.columns:
    if col in ['length', 'ggevents']:
        ff = "{0:.0f}"
    elif col in ['ERleft','ERright']:
        ff = "{0:.2f}"
    else:
        ff = "{0:.1f}"
    try:
        df[col] = [ff.format(val).replace("+/-","$\pm$") for val in df[col]]
    except ValueError:
        continue

print(df.to_latex(float_format=lambda x: '%10.1f' % x, index=True,columns=["ERleft","ERright","ggevents","location","CTR","chisquared","chifit"]))