##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")

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"]))


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

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

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"]))


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

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

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

df = df[df.length == 30]
#df = df[df.CTR < 300]
    
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)
    

ptpchange = ptp(df.groupby(['SampleB','threshold']).CTR)

df = df.groupby(['SampleB','threshold']).aggregate(ProperMean)
df['ptpchange'] = ptpchange
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=['ptpchange',"ERleft","ERright","ggevents","location","CTR","chisquared"]))