PandAna.cut.analysis_cuts Namespace Reference

Functions
def	kRemoveDAQTimingEdges (tables)
	Timing Cuts. More...
def	kRemoveCosmicCVNOverlaps (tables)
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.elastic']['IsValid'] == 1)
	kHasPng = Cut(lambda tables: tables['rec.vtx.elastic.fuzzyk']['npng'] > 0)
	kRemoveDAQTimingEdges = Cut(kRemoveDAQTimingEdges)
	kRemoveCosmicCVNOverlaps = Cut(kRemoveCosmicCVNOverlaps)
	kTimingCuts = kRemoveDAQTimingEdges&kRemoveCosmicCVNOverlaps
	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 51 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 333 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 87 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)
    df = df.apply(kDibMaskHelper, axis=1)
    if df.empty: return pd.Series()
    else: return df

def PandAna.cut.analysis_cuts.kNueContain

(

tables

)

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

Definition at line 339 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 123 of file analysis_cuts.py.

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

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 145 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 134 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 347 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 175 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 359 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 239 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuContainND.

def kNumuContainND(tables):
    df_ncellsfromedge = tables['rec.slc']['ncellsfromedge']

    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']

    df_ndhadcalcatE = tables['rec.energy.numu']['ndhadcalcatE']
    df_ndhadcaltranE = tables['rec.energy.numu']['ndhadcaltranE']
    
    return (df_ntracks > df_remid) &\
           (df_ncellsfromedge > 1) &\
           (df_firstplane > 1) &\
           (df_lastplane < 212) &\
           (df_startz < 1150 ) &\
           ((df_stopz < 1275) | ( df_containkalposttrans < 55)) &\
           (df_ndhadcalcatE + df_ndhadcaltranE < .03) &\
           (df_containkalfwdcellnd > 4) &\
           (df_containkalbakcellnd > 8)

def PandAna.cut.analysis_cuts.kNumuDibMaskHelper

(

l

)

Definition at line 195 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 218 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 367 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 376 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 309 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 383 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)

def PandAna.cut.analysis_cuts.kRemoveCosmicCVNOverlaps

(

tables

)

Definition at line 36 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kRemoveCosmicCVNOverlaps.

def kRemoveCosmicCVNOverlaps(tables):
    df = tables['rec.slc']
    return ( ((df['meantime'] - 216000.0)/1000.0) % 15 ) > 1.0

def PandAna.cut.analysis_cuts.kRemoveDAQTimingEdges

(

tables

)

Timing Cuts.

Definition at line 30 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kRemoveDAQTimingEdges.

def kRemoveDAQTimingEdges(tables):
    df = tables['rec.slc']
    return (df['meantime'] > 25000.0) & (df['meantime'] < 475000.0)

Variable Documentation

PandAna.cut.analysis_cuts.kCosVeto = kVeto

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

Definition at line 391 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.elastic']['IsValid'] == 1)

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 96 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 111 of file analysis_cuts.py.

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

Definition at line 100 of file analysis_cuts.py.

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

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

Definition at line 345 of file analysis_cuts.py.

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

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

Definition at line 115 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueCorePresel = kNueBasicPart&kNueCorePart

Definition at line 117 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 129 of file analysis_cuts.py.

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

float PandAna.cut.analysis_cuts.kNueCVNFHC = 0.84

Definition at line 120 of file analysis_cuts.py.

float PandAna.cut.analysis_cuts.kNueCVNRHC = 0.89

Definition at line 121 of file analysis_cuts.py.

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

Definition at line 98 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueFD = kNueCVNCut&kNueCorePresel

Definition at line 132 of file analysis_cuts.py.

Referenced by genie_preds_make().

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

Definition at line 152 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 165 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 158 of file analysis_cuts.py.

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

Definition at line 143 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 154 of file analysis_cuts.py.

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

Definition at line 156 of file analysis_cuts.py.

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

Definition at line 162 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 160 of file analysis_cuts.py.

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

Definition at line 103 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 107 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 113 of file analysis_cuts.py.

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

Definition at line 186 of file analysis_cuts.py.

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

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

Definition at line 365 of file analysis_cuts.py.

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

PandAna.cut.analysis_cuts.kNumuContainFD = kNumuProngsContainFD&kNumuOptimizedContainFD

Definition at line 235 of file analysis_cuts.py.

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

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

Definition at line 268 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 270 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDFD = kNumuQuality&kNumuContainFD

Definition at line 237 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDND = kNumuQuality&kNumuContainND

Definition at line 272 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEFD = kNumuBasicQuality&kNumuContainFD

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

Definition at line 357 of file analysis_cuts.py.

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

PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEND = kNumuBasicQuality&kNumuContainND

Definition at line 358 of file analysis_cuts.py.

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

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

Definition at line 233 of file analysis_cuts.py.

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

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

Definition at line 373 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 192 of file analysis_cuts.py.

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

Definition at line 188 of file analysis_cuts.py.

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

Definition at line 294 of file analysis_cuts.py.

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

Definition at line 381 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 287 of file analysis_cuts.py.

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

Definition at line 296 of file analysis_cuts.py.

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

Definition at line 289 of file analysis_cuts.py.

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

Nus Cuts.

Definition at line 283 of file analysis_cuts.py.

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

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

Definition at line 292 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 320 of file analysis_cuts.py.

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

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

Definition at line 318 of file analysis_cuts.py.

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

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

Definition at line 324 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 303 of file analysis_cuts.py.

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

Definition at line 325 of file analysis_cuts.py.

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

Definition at line 388 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 300 of file analysis_cuts.py.

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

Definition at line 299 of file analysis_cuts.py.

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

Definition at line 298 of file analysis_cuts.py.

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

Definition at line 392 of file analysis_cuts.py.

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

Definition at line 393 of file analysis_cuts.py.

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

Definition at line 39 of file analysis_cuts.py.

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

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

Definition at line 33 of file analysis_cuts.py.

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

PandAna.cut.analysis_cuts.kTimingCuts = kRemoveDAQTimingEdges&kRemoveCosmicCVNOverlaps

Definition at line 41 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().