#!/usr/bin/env python
import pandas.rpy.common as com
import pandas as pd	
import numpy as np
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
nlme = importr('nlme')
lme4 = importr('lme4')
base = com.importr('base')
stats = com.importr('stats')
xtable = importr('xtable')
stargazer = importr('stargazer')
texreg = importr('texreg')

#formulae strings:
formulae = [
    'RT~1',
    'RT~cat1',
    'RT~cat1+cat2',
    ]

randoms = [
    '~1|ID'
    ]

for idx, formula in enumerate(formulae):
    formulae[idx] = robjects.Formula(formula + ' + (1|ID)')  #  make a formula    obect
    #formulae[idx] = robjects.Formula(formula)  #  make a formula    obect
    
for idx, random in enumerate(randoms):
    randoms[idx] = robjects.Formula(random)  #  make a formula    obect

mydata = pd.DataFrame.from_csv('http://chymera.eu/data/test/r_data.csv', parse_dates=False, index_col=False)
#dfr = com.convert_to_r_dataframe(mydata, True)  # convert from pandas to R and make string columns factors

mydata['ID'] = mydata['ID'].astype('S8')
dfr = com.convert_to_r_dataframe(mydata, True)  # convert from pandas to R and make string columns factors
print (base.levels(dfr.rx2('ID')))


res = []
for formula in formulae:
    print 'Starting :'+str(formula)[:-1]
    #lme_res = nlme.lme(fixed=formula, data=dfr, random=randoms[0], method='ML')
    lme_res = lme4.lmer(formula=formula, data=dfr, REML='false')
    #~ lme_res = base.summary(lme_res)
    res.append(lme_res)
    print 'Completed!'

res_sum = base.summary(res[0])
print res_sum

latex = texreg.extract(res_sum)

latex = texreg.extract.lme4(res_sum)

latex = texreg.extract.extract.lme4(res_sum)

latex = texreg.texreg.extract.lme4(res_sum)

comparison = stats.anova(*res[:2])
# comparison = base.summary(cmparison)