def psdnmyz_2():
    # load TWO csv to be sent to be pseudonymz
    # metrics_df=pd.read_csv('/home/arasan/testrep/psmd/jureca/TOTAL_METRICS_Skel_header.csv')
    seg_df = pd.read_csv('/home/arasan/testrep/psmd/jureca/psmd_seg_vols.csv')
    # add rnadom id column to both df
    # below line is a disaster
    # metrics_df['RNDNAME'] = metrics_df['NAME'].apply(lambda x: gocept.pseudonymize.integer(x, 'secret'))
    # seg_df['RNDNAME'] = seg_df['NAME'].apply(lambda x: gocept.pseudonymize.integer(x, 'secret'))
    #    a=np.random.randint(100000,999999,metrics_df.NAME.values.size)
    #    metrics_df['RNDNAME']=a
    #    print 'after rqndom id has been added'
    #    flagg=True
    #    while(flagg):
    #        try:
    #            print pd.concat(g for _, g in metrics_df.groupby("RNDNAME") if len(g) > 1)
    #        except ValueError:
    #            print 'NO DUPLICAtes'
    #            metrics_df.to_csv('/home/arasan/testrep/psmd/jureca/TOTAL_rnd_temp.csv')
    #            flagg=False
    #        else:
    #            print 'DUPES'
    #            metrics_df=metrics_df.drop('RNDNAME', axis=1)
    #            a=np.random.randint(100000,999999,metrics_df.NAME.values.size)
    #            metrics_df['RNDNAME']=a
    # load double chekced randomeized df 1) above try catch 2) using np unique
    metrnd = pd.read_csv('/home/arasan/testrep/psmd/jureca/TOTAL_rnd_temp.csv')
    seg_df['SNO'] = seg_df.index + 1
    metrnd['SNO'] = seg_df.index + 1
    # add RNDAME column to seg_df
    seg_df['RNDNAME'] = metrnd.RNDNAME.values
    # rename columns NANME to ID and RNDNAME to NAME
    seg_df = seg_df.rename(index=str, columns={"NAME": "ID"})
    seg_df = seg_df.rename(index=str, columns={"RNDNAME": "NAME"})
    metrnd = metrnd.rename(index=str, columns={"NAME": "ID"})
    metrnd = metrnd.rename(index=str, columns={"RNDNAME": "NAME"})
    # dump map out with 3 columns ID,NAME,SNO
    mapdf = metrnd[['ID', 'NAME', 'SNO']]
    mapdf.to_csv('/home/arasan/testrep/psmd/jureca/bordeaux_packet2/psdnmyz_map.csv', index=False)
    # drop ID and SNO
    seg_df = seg_df.drop(['ID', 'SNO'], axis=1)
    metrnd = metrnd.drop(['ID', 'SNO'], axis=1)
    # move NAME column to first position
    metrnd = metrnd[['NAME', 'mean_skel_MD_LH_RH', 'sd_skel_MD_LH_RH', 'Pw90S_skel_MD_LH_RH', 'mean_skel_FA_LH_RH',
                     'sd_skel_FA_LH_RH', 'mean_skel_AD_LH_RH', 'sd_skel_AD_LH_RH', 'mean_skel_RD_LH_RH',
                     'sd_skel_RD_LH_RH']]
    seg_df = seg_df[['NAME', 'AGE', 'SEX', 'GMV', 'WMV', 'CSFV', 'ICV']]
    # if pd.concat(g for _, g in metrics_df.groupby("RNDNAME") if len(g) > 1).RNDNAME.values.size:
    #    print 'NOT OK'
    # else:
    #    print 'OK'
    metrnd.to_csv('/home/arasan/testrep/psmd/jureca/bordeaux_packet2/TOTAL_METRICS_Skel_header.csv', index=False)
    seg_df.to_csv('/home/arasan/testrep/psmd/jureca/bordeaux_packet2/psmd_seg_vols.csv', index=False)