PandAna.cut.analysis_cuts Namespace Reference

Functions
def	kDibMaskHelper (l)
	Nue Cuts. More...
def	kNueApplyMask (tables)
def	kNueCVNCut (tables)
def	kNueNDFiducial (tables)
def	kNueNDContain (tables)
def	kNumuBasicQuality (tables)
	Numu Cuts. More...
def	kNumuDibMaskHelper (l)
def	kNumuOptimizedContainFD (tables)
def	kNumuContainND (tables)
def	kNusNDFiducial (tables)
def	kIsFarDet (tables)
	OR'd cuts for Near and Far. More...
def	kNueContain (tables)
	nue cuts ###################### More...
def	kNuePresel (tables)
def	kNumuContain (tables)
def	kNumuPresel (tables)
def	kNusContain (tables)
	nus cuts ###################### More...
def	kNusPresel (tables)

Variables
	kIsFD = detector.kFD
	kVeto = Cut(lambda tables: tables['rec.sel.veto']['keep'] == 1)
	Basic Cuts. More...
	kHasVtx = Cut(lambda tables: tables['rec.vtx']['nelastic'] > 0)
	kHasPng = Cut(lambda tables: tables['rec.vtx.elastic.fuzzyk']['npng'] > 0)
	kNueApplyMask = Cut(kNueApplyMask)
	kNueDQ = kHasVtx&kHasPng&(kHitsPerPlane < 8)
	kNueBasicPart = kVeto&kIsFD&kNueDQ&kNueApplyMask
tuple	kNuePresel = (kNueEnergy > 1)&(kNueEnergy < 4)&\
tuple	kNueProngContainment = (kDistAllTop > 63)&(kDistAllBottom > 12)&\
tuple	kNueBackwardCut = ((kDistAllBack < 200) & (kSparsenessAsymm < -0.1))|(kDistAllBack >= 200)
tuple	kNuePtPCut = (kPtP < 0.58)|((kPtP >= 0.58) & (kPtP < 0.8) & (kMaxY < 590))|((kPtP >= 0.8) & (kMaxY < 350))
	kNueCorePart = kNueProngContainment&kNueBackwardCut&kNuePtPCut&kNuePresel
	kNueCorePresel = kNueBasicPart&kNueCorePart
float	kNueCVNFHC = 0.84
float	kNueCVNRHC = 0.89
	kNueCVNCut = Cut(kNueCVNCut)
	kNueFD = kNueCVNCut&kNueCorePresel
	kNueNDFiducial = Cut(kNueNDFiducial)
	kNueNDContain = Cut(kNueNDContain)
	kNueNDFrontPlanes = Cut(lambda tables: tables['rec.sel.contain']['nplanestofront'] > 6)
tuple	kNueNDNHits = (kNHit >= 20)&(kNHit <= 200)
tuple	kNueNDEnergy = (kNueEnergy < 4.5)
tuple	kNueNDProngLength = (kLongestProng > 100)&(kLongestProng < 500)
	kNueNDPresel = kNueDQ&kNueNDFiducial&kNueNDContain&kNueNDFrontPlanes&\
	kNueNDCVNSsb = kNueNDPresel&kNueCVNCut
	kNumuBasicQuality = Cut(kNumuBasicQuality)
	kNumuQuality = kNumuBasicQuality&(kCCE < 5.)
tuple	kNumuProngsContainFD = (kDistAllTop > 60)&(kDistAllBottom > 12)&(kDistAllEast > 16)&\
	kNumuOptimizedContainFD = Cut(kNumuOptimizedContainFD)
	kNumuContainFD = kNumuProngsContainFD&kNumuOptimizedContainFD
	kNumuNoPIDFD = kNumuQuality&kNumuContainFD
	kNumuContainND = Cut(kNumuContainND)
	kNumuNCRej = Cut(lambda tables: tables['rec.sel.remid']['pid'] > 0.75)
	kNumuNoPIDND = kNumuQuality&kNumuContainND
tuple	kNusFDContain = (kDistAllTop > 100)&(kDistAllBottom > 10)&\
	Nus Cuts. More...
	kNusContPlanes = Cut(lambda tables: tables['rec.slc']['ncontplanes'] > 2)
	kNusEventQuality = kHasVtx&kHasPng&\
	kNusFDPresel = kNueApplyMask&kVeto&kNusEventQuality&kNusFDContain
tuple	kNusBackwardCut = ((kDistAllBack < 200) & (kSparsenessAsymm < -0.1))|(kDistAllBack >= 200)
tuple	kNusEnergyCut = (kNusEnergy >= 0.5)&(kNusEnergy <= 20.)
tuple	kNusSlcTimeGap = (kClosestSlcTime > -150.)&(kClosestSlcTime < 50.)
tuple	kNusSlcDist = (kClosestSlcMinTop < 100)&(kClosestSlcMinDist < 500)
tuple	kNusShwPtp = ((kMaxY > 580) & (kPtP > 0.2))|((kMaxY > 540) & (kPtP > 0.4))
tuple	kNusNoPIDFD = (kNusFDPresel & kNusBackwardCut)&(~(kNusSlcTimeGap & kNusSlcDist))&\
	kNusNDFiducial = Cut(kNusNDFiducial)
tuple	kNusNDContain = (kDistAllTop > 25)&(kDistAllBottom > 25)&\
	kNusNDPresel = kNusEventQuality&kNusNDFiducial&kNusNDContain
	kNusNoPIDND = kNusNDPresel&(kPtP <= 0.8)&kNusEnergyCut
	kNueContain = Cut(kNueContain)
	kNumuNoPIDNoCCEFD = kNumuBasicQuality&kNumuContainFD
	numu cuts ##################### kCCE isn't working yet More...
	kNumuNoPIDNoCCEND = kNumuBasicQuality&kNumuContainND
	kNumuContain = Cut(kNumuContain)
	kNumuPresel = Cut(kNumuPresel)
	kNusContain = Cut(kNusContain)
	kNusPresel = Cut(kNusPresel)
	kCosVeto = kVeto
	ORd cuts #####################. More...
	kOrContainment = kNumuContain|kNusContain|kNueContain
	kOrPreselection = kNumuPresel|kNusPresel|kNuePresel

Function Documentation

def PandAna.cut.analysis_cuts.kDibMaskHelper

(

l

)

Nue Cuts.

Definition at line 31 of file analysis_cuts.py.

References PandAna.Demos.demo1.range.

def kDibMaskHelper(l):
    mask = l[0]

    fp = l[1]
    fpmin = fp
    fpmax = fp

    lp = l[2]
    lpmin = lp
    lpmax = lp

    for i in range(fp, 14, 1):
        if mask[13-i] == '0':
            break
        else:
            fpmax = i

    for i in range(fp, -1, -1):
        if mask[13-i] == '0':
            break
        else:
            fpmin = i

    for i in range(lp, 14, 1):
        if mask[13-i] == '0':
            break
        else:
            lpmax = i

    for i in range(lp, -1, -1):
        if mask[13-i] == '0':
            break
        else:
            lpmin = i
    return (fpmin==lpmin) & (fpmax==lpmax) & (lpmax-fpmin+1>=4)

def PandAna.cut.analysis_cuts.kIsFarDet

(

tables

)

OR'd cuts for Near and Far.

FD check. Hacky but it works with standard file name conventions

Definition at line 321 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNueContain(), PandAna.cut.analysis_cuts.kNuePresel(), PandAna.cut.analysis_cuts.kNumuContain(), PandAna.cut.analysis_cuts.kNumuPresel(), PandAna.cut.analysis_cuts.kNusContain(), and PandAna.cut.analysis_cuts.kNusPresel().

def kIsFarDet(tables):
    query = tables._files.query
    if not type(query) is list: query = [query]
    return 'fardet' in query[0]

def PandAna.cut.analysis_cuts.kNueApplyMask

(

tables

)

Definition at line 67 of file analysis_cuts.py.

References bin, and PandAna.cut.analysis_cuts.kNueApplyMask.

def kNueApplyMask(tables):
    mask = tables['rec.hdr']['dibmask']
    fp = tables['rec.slc']['firstplane']//64
    lp = tables['rec.slc']['lastplane']//64
    df = mask.apply(lambda x: bin(x)[2:].zfill(14))
    df = pd.concat([df,fp,lp],axis=1)
    return df.apply(kDibMaskHelper, axis=1)

def PandAna.cut.analysis_cuts.kNueContain

(

tables

)

nue cuts ######################

Definition at line 327 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kIsFarDet(), PandAna.cut.analysis_cuts.kNueBasicPart, PandAna.cut.analysis_cuts.kNueContain, PandAna.cut.analysis_cuts.kNueNDContain, and PandAna.cut.analysis_cuts.kNueProngContainment.

def kNueContain(tables):
    if kIsFarDet(tables):
        return kNueProngContainment(tables) & kNueBasicPart(tables)
    else:
        return kNueNDContain(tables)

def PandAna.cut.analysis_cuts.kNueCVNCut

(

tables

)

Definition at line 101 of file analysis_cuts.py.

References ana.kCVNe, and PandAna.cut.analysis_cuts.kNueCVNCut.

def kNueCVNCut(tables):
    df = kCVNe(tables)
    dfRHC = df[kRHC(tables)==1] >= kNueCVNRHC
    dfFHC = df[kRHC(tables)!=1] >= kNueCVNFHC

    return pd.concat([dfRHC, dfFHC])

def PandAna.cut.analysis_cuts.kNueNDContain

(

tables

)

Definition at line 123 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNueNDContain.

def kNueNDContain(tables):
    df = tables['rec.vtx.elastic.fuzzyk.png.shwlid']
    df_trans = df[['start.y','stop.y', 'start.x', 'stop.x']]
    df_long = df[['start.z', 'stop.z']]

    return ((df_trans.min(axis=1) > -170) & (df_trans.max(axis=1) < 170) & \
            (df_long.min(axis=1) > 100) & (df_long.max(axis=1) < 1225)).groupby(level=KL).agg(np.all)

def PandAna.cut.analysis_cuts.kNueNDFiducial

(

tables

)

Definition at line 112 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNueNDFiducial.

def kNueNDFiducial(tables):
    check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0 
    df = tables['rec.vtx.elastic'][check]
    return (df['vtx.x'] > -100) & \
        (df['vtx.x'] < 160) & \
        (df['vtx.y'] > -160) & \
        (df['vtx.y'] < 100) & \
        (df['vtx.z'] > 150) & \
        (df['vtx.z'] < 900)

def PandAna.cut.analysis_cuts.kNuePresel

(

tables

)

Definition at line 335 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kIsFarDet(), PandAna.cut.analysis_cuts.kNueCorePresel, PandAna.cut.analysis_cuts.kNueNDPresel, and PandAna.cut.analysis_cuts.kNuePresel.

def kNuePresel(tables):
    if kIsFarDet(tables):
        return kNueCorePresel(tables)
    else:
        return kNueNDPresel(tables)

def PandAna.cut.analysis_cuts.kNumuBasicQuality

(

tables

)

Numu Cuts.

Definition at line 153 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuBasicQuality.

def kNumuBasicQuality(tables):
    df_numutrkcce=tables['rec.energy.numu']['trkccE']
    df_remid=tables['rec.sel.remid']['pid']
    df_nhit=tables['rec.slc']['nhit']
    df_ncontplanes=tables['rec.slc']['ncontplanes']
    df_cosmicntracks=tables['rec.trk.cosmic']['ntracks']
    return(df_numutrkcce > 0) &\
               (df_remid > 0) &\
               (df_nhit > 20) &\
               (df_ncontplanes > 4) &\
               (df_cosmicntracks > 0)

def PandAna.cut.analysis_cuts.kNumuContain

(

tables

)

Definition at line 347 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kIsFarDet(), PandAna.cut.analysis_cuts.kNumuContain, PandAna.cut.analysis_cuts.kNumuContainFD, and PandAna.cut.analysis_cuts.kNumuContainND.

def kNumuContain(tables):
    if kIsFarDet(tables):
        return kNumuContainFD(tables)
    else:
        return kNumuContainND(tables)
        

def PandAna.cut.analysis_cuts.kNumuContainND

(

tables

)

Definition at line 217 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuContainND.

def kNumuContainND(tables):
    check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0 
    shw_df = tables['rec.vtx.elastic.fuzzyk.png.shwlid'][check]
    shw_df_trans = shw_df[['start.y','stop.y', 'start.x', 'stop.x']]
    shw_df_long = shw_df[['start.z', 'stop.z']]
    no_shw = (tables['rec.vtx.elastic.fuzzyk']['nshwlid'] == 0)

    shw_contain = ((shw_df_trans.min(axis=1) >= -180.) & (shw_df_trans.max(axis=1) <= 180.) & \
             (shw_df_long.min(axis=1) >= 20.) & (shw_df_long.max(axis=1) <= 1525.)).groupby(level=KL).agg(np.all)
    shw_contain = (shw_contain | no_shw)

    trk_df = tables['rec.trk.kalman.tracks'][['start.z', 'stop.z', 'rec.trk.kalman.tracks_idx']]
    kalman_contain = False
    if not trk_df.empty:
      trk_df = trk_df.apply(lambda x: (x['rec.trk.kalman.tracks_idx'] == 0) | ((x['start.z'] <= 1275) & (x['stop.z'] <= 1275)), axis = 1)
      kalman_contain = trk_df.groupby(level=KL).agg(np.all)

    df_ntracks = tables['rec.trk.kalman']['ntracks']
    df_remid = tables['rec.trk.kalman']['idxremid']
    df_firstplane = tables['rec.slc']['firstplane']
    df_lastplane = tables['rec.slc']['lastplane']
    
    first_trk = tables['rec.trk.kalman.tracks']['rec.trk.kalman.tracks_idx'] == 0
    df_startz = tables['rec.trk.kalman.tracks'][first_trk]['start.z']
    df_stopz  = tables['rec.trk.kalman.tracks'][first_trk]['stop.z']
    
    df_containkalposttrans = tables['rec.sel.contain']['kalyposattrans']
    df_containkalfwdcellnd = tables['rec.sel.contain']['kalfwdcellnd']
    df_containkalbakcellnd = tables['rec.sel.contain']['kalbakcellnd']
    
    return (df_ntracks > df_remid) &\
           (df_firstplane > 1) &\
           (df_lastplane < 212) &\
           (df_containkalfwdcellnd > 5) &\
           (df_containkalbakcellnd > 10) &\
           (df_startz < 1100 ) & (( df_containkalposttrans < 55) | (df_stopz < 1275) ) &\
           shw_contain &\
           kalman_contain

def PandAna.cut.analysis_cuts.kNumuDibMaskHelper

(

l

)

Definition at line 173 of file analysis_cuts.py.

References PandAna.Demos.demo1.range.

def kNumuDibMaskHelper(l):
    mask = l[0]
    
    fd = l[1]//64
    ld = l[2]//64

    dmin = 0
    dmax = 13

    for i in range(fd, 14, 1):
        if mask[13-i] == '0':
            break
        else:
            dmax = i

    for i in range(fd, -1, -1):
        if mask[13-i] == '0':
            break
        else:
            dmin = i

    return ((l[1]-64*dmin) > 1) & ((64*(dmax+1)-l[2]-1) > 1) 

def PandAna.cut.analysis_cuts.kNumuOptimizedContainFD

(

tables

)

Definition at line 196 of file analysis_cuts.py.

References bin, and PandAna.cut.analysis_cuts.kNumuOptimizedContainFD.

def kNumuOptimizedContainFD(tables):
    mask = tables['rec.hdr']['dibmask']
    fp = tables['rec.slc']['firstplane']
    lp = tables['rec.slc']['lastplane']
    df = mask.apply(lambda x: bin(x)[2:].zfill(14))
    df = pd.concat([df,fp,lp],axis=1)
    df = df.apply(kNumuDibMaskHelper, axis=1, result_type='reduce')

    df_containkalfwdcell = tables['rec.sel.contain']['kalfwdcell'] > 6
    df_containkalbakcell = tables['rec.sel.contain']['kalbakcell'] > 6
    df_containcosfwdcell = tables['rec.sel.contain']['cosfwdcell'] > 0 
    df_containcosbakcell = tables['rec.sel.contain']['cosbakcell'] > 7

    return df & df_containkalfwdcell & df_containkalbakcell & \
        df_containcosfwdcell & df_containkalbakcell

def PandAna.cut.analysis_cuts.kNumuPresel

(

tables

)

Definition at line 355 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kIsFarDet(), PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEFD, PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEND, and PandAna.cut.analysis_cuts.kNumuPresel.

def kNumuPresel(tables):
    if kIsFarDet(tables):
        return kNumuNoPIDNoCCEFD(tables)
    else:
        return kNumuNoPIDNoCCEND(tables)

def PandAna.cut.analysis_cuts.kNusContain

(

tables

)

nus cuts ######################

Definition at line 364 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kIsFarDet(), PandAna.cut.analysis_cuts.kNusContain, PandAna.cut.analysis_cuts.kNusFDContain, and PandAna.cut.analysis_cuts.kNusNDContain.

def kNusContain(tables):
    if kIsFarDet(tables):
        return kNusFDContain(tables)
    else:
        return kNusNDContain(tables)

def PandAna.cut.analysis_cuts.kNusNDFiducial

(

tables

)

Definition at line 297 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNusNDFiducial.

def kNusNDFiducial(tables):
    check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0 
    df = tables['rec.vtx.elastic'][check]
    return (df['vtx.x'] > -100) & \
        (df['vtx.x'] < 100) & \
        (df['vtx.y'] > -100) & \
        (df['vtx.y'] < 100) & \
        (df['vtx.z'] > 150) & \
        (df['vtx.z'] < 1000)

def PandAna.cut.analysis_cuts.kNusPresel

(

tables

)

Definition at line 371 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kIsFarDet(), PandAna.cut.analysis_cuts.kNusFDPresel, PandAna.cut.analysis_cuts.kNusNDPresel, and PandAna.cut.analysis_cuts.kNusPresel.

def kNusPresel(tables):
    if kIsFarDet(tables):
        return kNusFDPresel(tables)
    else:
        return kNusNDPresel(tables)

Variable Documentation

PandAna.cut.analysis_cuts.kCosVeto = kVeto

ORd cuts #####################.

Definition at line 379 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kHasPng = Cut(lambda tables: tables['rec.vtx.elastic.fuzzyk']['npng'] > 0)

Definition at line 21 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kHasVtx = Cut(lambda tables: tables['rec.vtx']['nelastic'] > 0)

Definition at line 20 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kIsFD = detector.kFD

Definition at line 8 of file analysis_cuts.py.

Referenced by make_numu_decaf(), reduce_nue_2018(), reduce_numu_ana2017(), reduce_numu_ana2017_prod(), reduce_numu_ana2018(), reduce_numu_ana2018_cosmics(), reduce_prod5_nue(), and reduce_prod5_numu().

PandAna.cut.analysis_cuts.kNueApplyMask = Cut(kNueApplyMask)

Definition at line 74 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNueApplyMask().

tuple PandAna.cut.analysis_cuts.kNueBackwardCut = ((kDistAllBack < 200) & (kSparsenessAsymm < -0.1))|(kDistAllBack >= 200)

Definition at line 89 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueBasicPart = kVeto&kIsFD&kNueDQ&kNueApplyMask

Definition at line 78 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNueContain().

PandAna.cut.analysis_cuts.kNueContain = Cut(kNueContain)

Definition at line 333 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNueContain().

PandAna.cut.analysis_cuts.kNueCorePart = kNueProngContainment&kNueBackwardCut&kNuePtPCut&kNuePresel

Definition at line 93 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueCorePresel = kNueBasicPart&kNueCorePart

Definition at line 95 of file analysis_cuts.py.

Referenced by PandAna.reco_validation.prod5_pid_validation.kNuePresel(), and PandAna.cut.analysis_cuts.kNuePresel().

PandAna.cut.analysis_cuts.kNueCVNCut = Cut(kNueCVNCut)

Definition at line 107 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNueCVNCut().

float PandAna.cut.analysis_cuts.kNueCVNFHC = 0.84

Definition at line 98 of file analysis_cuts.py.

float PandAna.cut.analysis_cuts.kNueCVNRHC = 0.89

Definition at line 99 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueDQ = kHasVtx&kHasPng&(kHitsPerPlane < 8)

Definition at line 76 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueFD = kNueCVNCut&kNueCorePresel

Definition at line 110 of file analysis_cuts.py.

Referenced by genie_preds_make().

PandAna.cut.analysis_cuts.kNueNDContain = Cut(kNueNDContain)

Definition at line 130 of file analysis_cuts.py.

Referenced by PandAna.reco_validation.prod5_pid_validation.kNueContain(), PandAna.cut.analysis_cuts.kNueContain(), and PandAna.cut.analysis_cuts.kNueNDContain().

PandAna.cut.analysis_cuts.kNueNDCVNSsb = kNueNDPresel&kNueCVNCut

Definition at line 143 of file analysis_cuts.py.

Referenced by futureSig_getNueNumuSyst(), nue_data_mc_validation(), and xsec_extrap_plots().

tuple PandAna.cut.analysis_cuts.kNueNDEnergy = (kNueEnergy < 4.5)

Definition at line 136 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueNDFiducial = Cut(kNueNDFiducial)

Definition at line 121 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNueNDFiducial().

PandAna.cut.analysis_cuts.kNueNDFrontPlanes = Cut(lambda tables: tables['rec.sel.contain']['nplanestofront'] > 6)

Definition at line 132 of file analysis_cuts.py.

tuple PandAna.cut.analysis_cuts.kNueNDNHits = (kNHit >= 20)&(kNHit <= 200)

Definition at line 134 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueNDPresel = kNueDQ&kNueNDFiducial&kNueNDContain&kNueNDFrontPlanes&\

Definition at line 140 of file analysis_cuts.py.

Referenced by PandAna.reco_validation.prod5_pid_validation.kNuePresel(), and PandAna.cut.analysis_cuts.kNuePresel().

tuple PandAna.cut.analysis_cuts.kNueNDProngLength = (kLongestProng > 100)&(kLongestProng < 500)

Definition at line 138 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNuePresel = (kNueEnergy > 1)&(kNueEnergy < 4)&\

Definition at line 81 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNuePresel().

tuple PandAna.cut.analysis_cuts.kNueProngContainment = (kDistAllTop > 63)&(kDistAllBottom > 12)&\

Definition at line 85 of file analysis_cuts.py.

Referenced by PandAna.reco_validation.prod5_pid_validation.kNueContain(), and PandAna.cut.analysis_cuts.kNueContain().

tuple PandAna.cut.analysis_cuts.kNuePtPCut = (kPtP < 0.58)|((kPtP >= 0.58) & (kPtP < 0.8) & (kMaxY < 590))|((kPtP >= 0.8) & (kMaxY < 350))

Definition at line 91 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuBasicQuality = Cut(kNumuBasicQuality)

Definition at line 164 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuBasicQuality().

PandAna.cut.analysis_cuts.kNumuContain = Cut(kNumuContain)

Definition at line 353 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuContain().

PandAna.cut.analysis_cuts.kNumuContainFD = kNumuProngsContainFD&kNumuOptimizedContainFD

Definition at line 213 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuContain().

PandAna.cut.analysis_cuts.kNumuContainND = Cut(kNumuContainND)

Definition at line 256 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuContain(), and PandAna.cut.analysis_cuts.kNumuContainND().

PandAna.cut.analysis_cuts.kNumuNCRej = Cut(lambda tables: tables['rec.sel.remid']['pid'] > 0.75)

Definition at line 258 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDFD = kNumuQuality&kNumuContainFD

Definition at line 215 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDND = kNumuQuality&kNumuContainND

Definition at line 260 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEFD = kNumuBasicQuality&kNumuContainFD

numu cuts ##################### kCCE isn't working yet

Definition at line 345 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuPresel().

PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEND = kNumuBasicQuality&kNumuContainND

Definition at line 346 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuPresel().

PandAna.cut.analysis_cuts.kNumuOptimizedContainFD = Cut(kNumuOptimizedContainFD)

Definition at line 211 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuOptimizedContainFD().

PandAna.cut.analysis_cuts.kNumuPresel = Cut(kNumuPresel)

Definition at line 361 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNumuPresel().

tuple PandAna.cut.analysis_cuts.kNumuProngsContainFD = (kDistAllTop > 60)&(kDistAllBottom > 12)&(kDistAllEast > 16)&\

Definition at line 170 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuQuality = kNumuBasicQuality&(kCCE < 5.)

Definition at line 166 of file analysis_cuts.py.

tuple PandAna.cut.analysis_cuts.kNusBackwardCut = ((kDistAllBack < 200) & (kSparsenessAsymm < -0.1))|(kDistAllBack >= 200)

Definition at line 282 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNusContain = Cut(kNusContain)

Definition at line 369 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusContain().

PandAna.cut.analysis_cuts.kNusContPlanes = Cut(lambda tables: tables['rec.slc']['ncontplanes'] > 2)

Definition at line 275 of file analysis_cuts.py.

tuple PandAna.cut.analysis_cuts.kNusEnergyCut = (kNusEnergy >= 0.5)&(kNusEnergy <= 20.)

Definition at line 284 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNusEventQuality = kHasVtx&kHasPng&\

Definition at line 277 of file analysis_cuts.py.

tuple PandAna.cut.analysis_cuts.kNusFDContain = (kDistAllTop > 100)&(kDistAllBottom > 10)&\

Nus Cuts.

Definition at line 271 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusContain().

PandAna.cut.analysis_cuts.kNusFDPresel = kNueApplyMask&kVeto&kNusEventQuality&kNusFDContain

Definition at line 280 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusPresel().

tuple PandAna.cut.analysis_cuts.kNusNDContain = (kDistAllTop > 25)&(kDistAllBottom > 25)&\

Definition at line 308 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusContain().

PandAna.cut.analysis_cuts.kNusNDFiducial = Cut(kNusNDFiducial)

Definition at line 306 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusNDFiducial().

PandAna.cut.analysis_cuts.kNusNDPresel = kNusEventQuality&kNusNDFiducial&kNusNDContain

Definition at line 312 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusPresel().

tuple PandAna.cut.analysis_cuts.kNusNoPIDFD = (kNusFDPresel & kNusBackwardCut)&(~(kNusSlcTimeGap & kNusSlcDist))&\

Definition at line 291 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNusNoPIDND = kNusNDPresel&(kPtP <= 0.8)&kNusEnergyCut

Definition at line 313 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNusPresel = Cut(kNusPresel)

Definition at line 376 of file analysis_cuts.py.

Referenced by PandAna.cut.analysis_cuts.kNusPresel().

tuple PandAna.cut.analysis_cuts.kNusShwPtp = ((kMaxY > 580) & (kPtP > 0.2))|((kMaxY > 540) & (kPtP > 0.4))

Definition at line 288 of file analysis_cuts.py.

tuple PandAna.cut.analysis_cuts.kNusSlcDist = (kClosestSlcMinTop < 100)&(kClosestSlcMinDist < 500)

Definition at line 287 of file analysis_cuts.py.

tuple PandAna.cut.analysis_cuts.kNusSlcTimeGap = (kClosestSlcTime > -150.)&(kClosestSlcTime < 50.)

Definition at line 286 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kOrContainment = kNumuContain|kNusContain|kNueContain

Definition at line 380 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kOrPreselection = kNumuPresel|kNusPresel|kNuePresel

Definition at line 381 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kVeto = Cut(lambda tables: tables['rec.sel.veto']['keep'] == 1)

Basic Cuts.

Definition at line 17 of file analysis_cuts.py.

Referenced by prod4_pid(), and reduce_nue_2018().