if __name__ == '__main__':
iwid = int(sys.argv[1])
EXPNAME = 'myopic_exp0'
EXPDATE = '072715' # date that the raw data was pulled from database
BASEFOLDER = './'
# FIXME: not actually raw; processed by prep_bayesOpt.load_*
RAWFOLDER = BASEFOLDER + 'raw/' # where data pulled from database is.
PREPPEDFOLDER = BASEFOLDER + 'prepped/' # where data is after being scrubbed after pulled from db
OUTFOLDER = BASEFOLDER + 'output/' # where results will be saved
SUBSERIES_FNAME = '_'.join(['series_lsfit_regret', EXPNAME, EXPDATE]) # template for the name of the output of the analysis
# ### uncomment to source from pkl
PICKLED_DF_NAME = '_'.join(['df', EXPNAME, EXPDATE]) + '.pkl'
# df = prep_bayesOpt.load_pickled(PREPPEDFOLDER + PICKLED_DF_NAME) #FIXME: commented out bc not working yet
#FIXME: line below uses a hacked prepped dataset prepped in ipython
df = unpickle(PREPPEDFOLDER + PICKLED_DF_NAME)
# ### uncomment to source from AWS
# AWS_DB_URL = 'mysql://*****:*****@mydb.c4dh2hic3vxp.us-east-1.rds.amazonaws.com:3306/myexp'
# SQL_TABLE_NAME = 'bayesOpt_e'
# df = prep_bayesOpt.load_sql(AWS_DB_URL, SQL_TABLE_NAME)
# get this subject's id
wids = df.workerid.unique()
print wids.shape
wid = wids[iwid]
# get fits
fit_ls_to_EIregret_sub(df, wid, SUBSERIES_FNAME, OUTFOLDER)
import load_and_prep
# import prep_bayesOpt
# RUN fit_ls_eiregret_sub_runner.py FOR ALL SUBJECTS BEFORE RUNNING THIS SCRIPT!
print 'RUN fit_ls_eiregret_sub_runner.py FOR ALL SUBJECTS BEFORE RUNNING THIS SCRIPT!'
EXPNAME = 'noChoice_exp0'
EXPDATE = '072415' # date that the raw data was pulled from database
BASEFOLDER = './'
# FIXME: not actually raw; processed by prep_bayesOpt.load_*
RAWFOLDER = BASEFOLDER + 'raw/' # where data pulled from database is.
PREPPEDFOLDER = BASEFOLDER + 'prepped/' # dfs prepped after extracting from db
OUTFOLDER = BASEFOLDER + 'output/' # where results will be saved
SUBSERIES_FNAME = '_'.join(['series_lsfit_regret', EXPNAME, EXPDATE]) # template for the name of the output of the analysis
OUT_DF_NAME = '_'.join(['df_lsfit_regret', EXPNAME, EXPDATE])
PICKLED_DF_NAME = '_'.join(['df', EXPNAME, EXPDATE]) + '.pkl'
# stitch into a df
df_lsfits = stitch_pickled(OUTFOLDER+SUBSERIES_FNAME+'*.pkl')
# unpickle raw
dfprepped = unpickle(PREPPEDFOLDER + PICKLED_DF_NAME)
# merge experiment condition
merge_first(df_lsfits, dfprepped, 'LENSCALE', 'workerid')
merge_first(df_lsfits, dfprepped, 'counterbalance', 'workerid')
df_lsfits.rename(columns={'LENSCALE': 'exp_ls'}, inplace=True)
# save to csv for analysis in r
df_lsfits.to_csv(OUTFOLDER+OUT_DF_NAME+'.csv', index=False)
df_lsfits.to_pickle(OUTFOLDER+OUT_DF_NAME+'.pkl')
