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 331 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().

331 def kIsFarDet(tables):
332  query = tables._files.query
333  if not type(query) is list: query = [query]
334  return 'fardet' in query[0]
335 
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  return df.apply(kDibMaskHelper, axis=1)
float bin[41]
Definition: plottest35.C:14
def PandAna.cut.analysis_cuts.kNueContain (   tables)
def PandAna.cut.analysis_cuts.kNueCVNCut (   tables)

Definition at line 121 of file analysis_cuts.py.

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

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

Definition at line 143 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNueNDContain.

143 def kNueNDContain(tables):
144  df = tables['rec.vtx.elastic.fuzzyk.png.shwlid']
145  df_trans = df[['start.y','stop.y', 'start.x', 'stop.x']]
146  df_long = df[['start.z', 'stop.z']]
147 
148  return ((df_trans.min(axis=1) > -170) & (df_trans.max(axis=1) < 170) & \
149  (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 132 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNueNDFiducial.

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

Numu Cuts.

Definition at line 173 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuBasicQuality.

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

Definition at line 237 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuContainND.

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

Definition at line 193 of file analysis_cuts.py.

References PandAna.Demos.demo1.range.

194  mask = l[0]
195 
196  fd = l[1]//64
197  ld = l[2]//64
198 
199  dmin = 0
200  dmax = 13
201 
202  for i in range(fd, 14, 1):
203  if mask[13-i] == '0':
204  break
205  else:
206  dmax = i
207 
208  for i in range(fd, -1, -1):
209  if mask[13-i] == '0':
210  break
211  else:
212  dmin = i
213 
214  return ((l[1]-64*dmin) > 1) & ((64*(dmax+1)-l[2]-1) > 1)
215 
def PandAna.cut.analysis_cuts.kNumuOptimizedContainFD (   tables)

Definition at line 216 of file analysis_cuts.py.

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

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

Definition at line 365 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.

365 def kNumuPresel(tables):
366  if kIsFarDet(tables):
367  return kNumuNoPIDNoCCEFD(tables)
368  else:
369  return kNumuNoPIDNoCCEND(tables)
370 
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 374 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.

374 def kNusContain(tables):
375  if kIsFarDet(tables):
376  return kNusFDContain(tables)
377  else:
378  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 307 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNusNDFiducial.

307 def kNusNDFiducial(tables):
308  check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0
309  df = tables['rec.vtx.elastic'][check]
310  return (df['vtx.x'] > -100) & \
311  (df['vtx.x'] < 100) & \
312  (df['vtx.y'] > -100) & \
313  (df['vtx.y'] < 100) & \
314  (df['vtx.z'] > 150) & \
315  (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 389 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 94 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 109 of file analysis_cuts.py.

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

Definition at line 98 of file analysis_cuts.py.

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

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

Definition at line 343 of file analysis_cuts.py.

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

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

Definition at line 113 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueCorePresel = kNueBasicPart&kNueCorePart
PandAna.cut.analysis_cuts.kNueCVNCut = Cut(kNueCVNCut)

Definition at line 127 of file analysis_cuts.py.

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

float PandAna.cut.analysis_cuts.kNueCVNFHC = 0.84

Definition at line 118 of file analysis_cuts.py.

float PandAna.cut.analysis_cuts.kNueCVNRHC = 0.89

Definition at line 119 of file analysis_cuts.py.

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

Definition at line 96 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNueFD = kNueCVNCut&kNueCorePresel

Definition at line 130 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 156 of file analysis_cuts.py.

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

Definition at line 141 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 152 of file analysis_cuts.py.

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

Definition at line 154 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 158 of file analysis_cuts.py.

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

Definition at line 101 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 111 of file analysis_cuts.py.

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

Definition at line 184 of file analysis_cuts.py.

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

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

Definition at line 363 of file analysis_cuts.py.

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

PandAna.cut.analysis_cuts.kNumuContainFD = kNumuProngsContainFD&kNumuOptimizedContainFD

Definition at line 233 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 268 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDFD = kNumuQuality&kNumuContainFD

Definition at line 235 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDND = kNumuQuality&kNumuContainND

Definition at line 270 of file analysis_cuts.py.

PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEFD = kNumuBasicQuality&kNumuContainFD

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

Definition at line 355 of file analysis_cuts.py.

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

PandAna.cut.analysis_cuts.kNumuNoPIDNoCCEND = kNumuBasicQuality&kNumuContainND

Definition at line 356 of file analysis_cuts.py.

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

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

Definition at line 231 of file analysis_cuts.py.

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

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

Definition at line 371 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 190 of file analysis_cuts.py.

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

Definition at line 186 of file analysis_cuts.py.

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

Definition at line 292 of file analysis_cuts.py.

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

Definition at line 379 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 285 of file analysis_cuts.py.

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

Definition at line 294 of file analysis_cuts.py.

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

Definition at line 287 of file analysis_cuts.py.

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

Nus Cuts.

Definition at line 281 of file analysis_cuts.py.

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

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

Definition at line 290 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 318 of file analysis_cuts.py.

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

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

Definition at line 316 of file analysis_cuts.py.

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

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

Definition at line 322 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 301 of file analysis_cuts.py.

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

Definition at line 323 of file analysis_cuts.py.

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

Definition at line 386 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 298 of file analysis_cuts.py.

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

Definition at line 297 of file analysis_cuts.py.

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

Definition at line 296 of file analysis_cuts.py.

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

Definition at line 390 of file analysis_cuts.py.

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

Definition at line 391 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().