Functions | Variables
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.

52  mask = l[0]
53 
54  fp = l[1]
55  fpmin = fp
56  fpmax = fp
57 
58  lp = l[2]
59  lpmin = lp
60  lpmax = lp
61 
62  for i in range(fp, 14, 1):
63  if mask[13-i] == '0':
64  break
65  else:
66  fpmax = i
67 
68  for i in range(fp, -1, -1):
69  if mask[13-i] == '0':
70  break
71  else:
72  fpmin = i
73 
74  for i in range(lp, 14, 1):
75  if mask[13-i] == '0':
76  break
77  else:
78  lpmax = i
79 
80  for i in range(lp, -1, -1):
81  if mask[13-i] == '0':
82  break
83  else:
84  lpmin = i
85  return (fpmin==lpmin) & (fpmax==lpmax) & (lpmax-fpmin+1>=4)
86 
def kDibMaskHelper(l)
Nue Cuts.
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().

333 def kIsFarDet(tables):
334  query = tables._files.query
335  if not type(query) is list: query = [query]
336  return 'fardet' in query[0]
337 
def kIsFarDet(tables)
OR&#39;d cuts for Near and Far.
def PandAna.cut.analysis_cuts.kNueApplyMask (   tables)

Definition at line 87 of file analysis_cuts.py.

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

87 def kNueApplyMask(tables):
88  mask = tables['rec.hdr']['dibmask']
89  fp = tables['rec.slc']['firstplane']//64
90  lp = tables['rec.slc']['lastplane']//64
91  df = mask.apply(lambda x: bin(x)[2:].zfill(14))
92  df = pd.concat([df,fp,lp],axis=1)
93  df = df.apply(kDibMaskHelper, axis=1)
94  if df.empty: return pd.Series()
95  else: return df
float bin[41]
Definition: plottest35.C:14
def PandAna.cut.analysis_cuts.kNueContain (   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.

123 def kNueCVNCut(tables):
124  df = kCVNe(tables)
125  dfRHC = df[kRHC(tables)==1] >= kNueCVNRHC
126  dfFHC = df[kRHC(tables)!=1] >= kNueCVNFHC
127 
128  return pd.concat([dfRHC, dfFHC])
const Var kCVNe
PID
Definition: Vars.cxx:35
def PandAna.cut.analysis_cuts.kNueNDContain (   tables)

Definition at line 145 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNueNDContain.

145 def kNueNDContain(tables):
146  df = tables['rec.vtx.elastic.fuzzyk.png.shwlid']
147  df_trans = df[['start.y','stop.y', 'start.x', 'stop.x']]
148  df_long = df[['start.z', 'stop.z']]
149 
150  return ((df_trans.min(axis=1) > -170) & (df_trans.max(axis=1) < 170) & \
151  (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.

134 def kNueNDFiducial(tables):
135  check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0
136  df = tables['rec.vtx.elastic'][check]
137  return (df['vtx.x'] > -100) & \
138  (df['vtx.x'] < 160) & \
139  (df['vtx.y'] > -160) & \
140  (df['vtx.y'] < 100) & \
141  (df['vtx.z'] > 150) & \
142  (df['vtx.z'] < 900)
def PandAna.cut.analysis_cuts.kNuePresel (   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.

175 def kNumuBasicQuality(tables):
176  df_numutrkcce=tables['rec.energy.numu']['trkccE']
177  df_remid=tables['rec.sel.remid']['pid']
178  df_nhit=tables['rec.slc']['nhit']
179  df_ncontplanes=tables['rec.slc']['ncontplanes']
180  df_cosmicntracks=tables['rec.trk.cosmic']['ntracks']
181  return(df_numutrkcce > 0) &\
182  (df_remid > 0) &\
183  (df_nhit > 20) &\
184  (df_ncontplanes > 4) &\
185  (df_cosmicntracks > 0)
def PandAna.cut.analysis_cuts.kNumuContain (   tables)
def PandAna.cut.analysis_cuts.kNumuContainND (   tables)

Definition at line 239 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuContainND.

239 def kNumuContainND(tables):
240  df_ncellsfromedge = tables['rec.slc']['ncellsfromedge']
241 
242  df_ntracks = tables['rec.trk.kalman']['ntracks']
243  df_remid = tables['rec.trk.kalman']['idxremid']
244  df_firstplane = tables['rec.slc']['firstplane']
245  df_lastplane = tables['rec.slc']['lastplane']
246 
247  first_trk = tables['rec.trk.kalman.tracks']['rec.trk.kalman.tracks_idx'] == 0
248  df_startz = tables['rec.trk.kalman.tracks'][first_trk]['start.z']
249  df_stopz = tables['rec.trk.kalman.tracks'][first_trk]['stop.z']
250 
251  df_containkalposttrans = tables['rec.sel.contain']['kalyposattrans']
252  df_containkalfwdcellnd = tables['rec.sel.contain']['kalfwdcellnd']
253  df_containkalbakcellnd = tables['rec.sel.contain']['kalbakcellnd']
254 
255  df_ndhadcalcatE = tables['rec.energy.numu']['ndhadcalcatE']
256  df_ndhadcaltranE = tables['rec.energy.numu']['ndhadcaltranE']
257 
258  return (df_ntracks > df_remid) &\
259  (df_ncellsfromedge > 1) &\
260  (df_firstplane > 1) &\
261  (df_lastplane < 212) &\
262  (df_startz < 1150 ) &\
263  ((df_stopz < 1275) | ( df_containkalposttrans < 55)) &\
264  (df_ndhadcalcatE + df_ndhadcaltranE < .03) &\
265  (df_containkalfwdcellnd > 4) &\
266  (df_containkalbakcellnd > 8)
267 
def PandAna.cut.analysis_cuts.kNumuDibMaskHelper (   l)

Definition at line 195 of file analysis_cuts.py.

References PandAna.Demos.demo1.range.

196  mask = l[0]
197 
198  fd = l[1]//64
199  ld = l[2]//64
200 
201  dmin = 0
202  dmax = 13
203 
204  for i in range(fd, 14, 1):
205  if mask[13-i] == '0':
206  break
207  else:
208  dmax = i
209 
210  for i in range(fd, -1, -1):
211  if mask[13-i] == '0':
212  break
213  else:
214  dmin = i
215 
216  return ((l[1]-64*dmin) > 1) & ((64*(dmax+1)-l[2]-1) > 1)
217 
def PandAna.cut.analysis_cuts.kNumuOptimizedContainFD (   tables)

Definition at line 218 of file analysis_cuts.py.

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

219  mask = tables['rec.hdr']['dibmask']
220  fp = tables['rec.slc']['firstplane']
221  lp = tables['rec.slc']['lastplane']
222  df = mask.apply(lambda x: bin(x)[2:].zfill(14))
223  df = pd.concat([df,fp,lp],axis=1)
224  df = df.apply(kNumuDibMaskHelper, axis=1, result_type='reduce')
225 
226  df_containkalfwdcell = tables['rec.sel.contain']['kalfwdcell'] > 6
227  df_containkalbakcell = tables['rec.sel.contain']['kalbakcell'] > 6
228  df_containcosfwdcell = tables['rec.sel.contain']['cosfwdcell'] > 0
229  df_containcosbakcell = tables['rec.sel.contain']['cosbakcell'] > 7
230 
231  return df & df_containkalfwdcell & df_containkalbakcell & \
232  df_containcosfwdcell & df_containkalbakcell
float bin[41]
Definition: plottest35.C:14
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.

367 def kNumuPresel(tables):
368  if kIsFarDet(tables):
369  return kNumuNoPIDNoCCEFD(tables)
370  else:
371  return kNumuNoPIDNoCCEND(tables)
372 
kNumuNoPIDNoCCEFD
numu cuts ##################### kCCE isn&#39;t working yet
def kIsFarDet(tables)
OR&#39;d cuts for Near and Far.
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.

376 def kNusContain(tables):
377  if kIsFarDet(tables):
378  return kNusFDContain(tables)
379  else:
380  return kNusNDContain(tables)
tuple kNusFDContain
Nus Cuts.
def kIsFarDet(tables)
OR&#39;d cuts for Near and Far.
def PandAna.cut.analysis_cuts.kNusNDFiducial (   tables)

Definition at line 309 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNusNDFiducial.

309 def kNusNDFiducial(tables):
310  check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0
311  df = tables['rec.vtx.elastic'][check]
312  return (df['vtx.x'] > -100) & \
313  (df['vtx.x'] < 100) & \
314  (df['vtx.y'] > -100) & \
315  (df['vtx.y'] < 100) & \
316  (df['vtx.z'] > 150) & \
317  (df['vtx.z'] < 1000)
def PandAna.cut.analysis_cuts.kNusPresel (   tables)
def PandAna.cut.analysis_cuts.kRemoveCosmicCVNOverlaps (   tables)

Definition at line 36 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kRemoveCosmicCVNOverlaps.

37  df = tables['rec.slc']
38  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.

31  df = tables['rec.slc']
32  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
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
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)
PandAna.cut.analysis_cuts.kNueNDCVNSsb = kNueNDPresel&kNueCVNCut
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&\
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)&\
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)
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().