Functions | Variables
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.

32  mask = l[0]
33 
34  fp = l[1]
35  fpmin = fp
36  fpmax = fp
37 
38  lp = l[2]
39  lpmin = lp
40  lpmax = lp
41 
42  for i in range(fp, 14, 1):
43  if mask[13-i] == '0':
44  break
45  else:
46  fpmax = i
47 
48  for i in range(fp, -1, -1):
49  if mask[13-i] == '0':
50  break
51  else:
52  fpmin = i
53 
54  for i in range(lp, 14, 1):
55  if mask[13-i] == '0':
56  break
57  else:
58  lpmax = i
59 
60  for i in range(lp, -1, -1):
61  if mask[13-i] == '0':
62  break
63  else:
64  lpmin = i
65  return (fpmin==lpmin) & (fpmax==lpmax) & (lpmax-fpmin+1>=4)
66 
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 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().

321 def kIsFarDet(tables):
322  query = tables._files.query
323  if not type(query) is list: query = [query]
324  return 'fardet' in query[0]
325 
::xsd::cxx::tree::type type
Definition: Database.h:110
def kIsFarDet(tables)
OR&#39;d cuts for Near and Far.
def PandAna.cut.analysis_cuts.kNueApplyMask (   tables)

Definition at line 67 of file analysis_cuts.py.

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

67 def kNueApplyMask(tables):
68  mask = tables['rec.hdr']['dibmask']
69  fp = tables['rec.slc']['firstplane']//64
70  lp = tables['rec.slc']['lastplane']//64
71  df = mask.apply(lambda x: bin(x)[2:].zfill(14))
72  df = pd.concat([df,fp,lp],axis=1)
73  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 101 of file analysis_cuts.py.

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

101 def kNueCVNCut(tables):
102  df = kCVNe(tables)
103  dfRHC = df[kRHC(tables)==1] >= kNueCVNRHC
104  dfFHC = df[kRHC(tables)!=1] >= kNueCVNFHC
105 
106  return pd.concat([dfRHC, dfFHC])
const Var kCVNe
PID
Definition: Vars.cxx:35
def PandAna.cut.analysis_cuts.kNueNDContain (   tables)

Definition at line 123 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNueNDContain.

123 def kNueNDContain(tables):
124  df = tables['rec.vtx.elastic.fuzzyk.png.shwlid']
125  df_trans = df[['start.y','stop.y', 'start.x', 'stop.x']]
126  df_long = df[['start.z', 'stop.z']]
127 
128  return ((df_trans.min(axis=1) > -170) & (df_trans.max(axis=1) < 170) & \
129  (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.

112 def kNueNDFiducial(tables):
113  check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0
114  df = tables['rec.vtx.elastic'][check]
115  return (df['vtx.x'] > -100) & \
116  (df['vtx.x'] < 160) & \
117  (df['vtx.y'] > -160) & \
118  (df['vtx.y'] < 100) & \
119  (df['vtx.z'] > 150) & \
120  (df['vtx.z'] < 900)
def PandAna.cut.analysis_cuts.kNuePresel (   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.

153 def kNumuBasicQuality(tables):
154  df_numutrkcce=tables['rec.energy.numu']['trkccE']
155  df_remid=tables['rec.sel.remid']['pid']
156  df_nhit=tables['rec.slc']['nhit']
157  df_ncontplanes=tables['rec.slc']['ncontplanes']
158  df_cosmicntracks=tables['rec.trk.cosmic']['ntracks']
159  return(df_numutrkcce > 0) &\
160  (df_remid > 0) &\
161  (df_nhit > 20) &\
162  (df_ncontplanes > 4) &\
163  (df_cosmicntracks > 0)
def PandAna.cut.analysis_cuts.kNumuContain (   tables)
def PandAna.cut.analysis_cuts.kNumuContainND (   tables)

Definition at line 217 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNumuContainND.

217 def kNumuContainND(tables):
218  check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0
219  shw_df = tables['rec.vtx.elastic.fuzzyk.png.shwlid'][check]
220  shw_df_trans = shw_df[['start.y','stop.y', 'start.x', 'stop.x']]
221  shw_df_long = shw_df[['start.z', 'stop.z']]
222  no_shw = (tables['rec.vtx.elastic.fuzzyk']['nshwlid'] == 0)
223 
224  shw_contain = ((shw_df_trans.min(axis=1) >= -180.) & (shw_df_trans.max(axis=1) <= 180.) & \
225  (shw_df_long.min(axis=1) >= 20.) & (shw_df_long.max(axis=1) <= 1525.)).groupby(level=KL).agg(np.all)
226  shw_contain = (shw_contain | no_shw)
227 
228  trk_df = tables['rec.trk.kalman.tracks'][['start.z', 'stop.z', 'rec.trk.kalman.tracks_idx']]
229  kalman_contain = False
230  if not trk_df.empty:
231  trk_df = trk_df.apply(lambda x: (x['rec.trk.kalman.tracks_idx'] == 0) | ((x['start.z'] <= 1275) & (x['stop.z'] <= 1275)), axis = 1)
232  kalman_contain = trk_df.groupby(level=KL).agg(np.all)
233 
234  df_ntracks = tables['rec.trk.kalman']['ntracks']
235  df_remid = tables['rec.trk.kalman']['idxremid']
236  df_firstplane = tables['rec.slc']['firstplane']
237  df_lastplane = tables['rec.slc']['lastplane']
238 
239  first_trk = tables['rec.trk.kalman.tracks']['rec.trk.kalman.tracks_idx'] == 0
240  df_startz = tables['rec.trk.kalman.tracks'][first_trk]['start.z']
241  df_stopz = tables['rec.trk.kalman.tracks'][first_trk]['stop.z']
242 
243  df_containkalposttrans = tables['rec.sel.contain']['kalyposattrans']
244  df_containkalfwdcellnd = tables['rec.sel.contain']['kalfwdcellnd']
245  df_containkalbakcellnd = tables['rec.sel.contain']['kalbakcellnd']
246 
247  return (df_ntracks > df_remid) &\
248  (df_firstplane > 1) &\
249  (df_lastplane < 212) &\
250  (df_containkalfwdcellnd > 5) &\
251  (df_containkalbakcellnd > 10) &\
252  (df_startz < 1100 ) & (( df_containkalposttrans < 55) | (df_stopz < 1275) ) &\
253  shw_contain &\
254  kalman_contain
255 
def PandAna.cut.analysis_cuts.kNumuDibMaskHelper (   l)

Definition at line 173 of file analysis_cuts.py.

References PandAna.Demos.demo1.range.

174  mask = l[0]
175 
176  fd = l[1]//64
177  ld = l[2]//64
178 
179  dmin = 0
180  dmax = 13
181 
182  for i in range(fd, 14, 1):
183  if mask[13-i] == '0':
184  break
185  else:
186  dmax = i
187 
188  for i in range(fd, -1, -1):
189  if mask[13-i] == '0':
190  break
191  else:
192  dmin = i
193 
194  return ((l[1]-64*dmin) > 1) & ((64*(dmax+1)-l[2]-1) > 1)
195 
def PandAna.cut.analysis_cuts.kNumuOptimizedContainFD (   tables)

Definition at line 196 of file analysis_cuts.py.

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

197  mask = tables['rec.hdr']['dibmask']
198  fp = tables['rec.slc']['firstplane']
199  lp = tables['rec.slc']['lastplane']
200  df = mask.apply(lambda x: bin(x)[2:].zfill(14))
201  df = pd.concat([df,fp,lp],axis=1)
202  df = df.apply(kNumuDibMaskHelper, axis=1, result_type='reduce')
203 
204  df_containkalfwdcell = tables['rec.sel.contain']['kalfwdcell'] > 6
205  df_containkalbakcell = tables['rec.sel.contain']['kalbakcell'] > 6
206  df_containcosfwdcell = tables['rec.sel.contain']['cosfwdcell'] > 0
207  df_containcosbakcell = tables['rec.sel.contain']['cosbakcell'] > 7
208 
209  return df & df_containkalfwdcell & df_containkalbakcell & \
210  df_containcosfwdcell & df_containkalbakcell
float bin[41]
Definition: plottest35.C:14
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.

355 def kNumuPresel(tables):
356  if kIsFarDet(tables):
357  return kNumuNoPIDNoCCEFD(tables)
358  else:
359  return kNumuNoPIDNoCCEND(tables)
360 
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 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.

364 def kNusContain(tables):
365  if kIsFarDet(tables):
366  return kNusFDContain(tables)
367  else:
368  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 297 of file analysis_cuts.py.

References PandAna.cut.analysis_cuts.kNusNDFiducial.

297 def kNusNDFiducial(tables):
298  check = tables['rec.vtx.elastic']['rec.vtx.elastic_idx'] == 0
299  df = tables['rec.vtx.elastic'][check]
300  return (df['vtx.x'] > -100) & \
301  (df['vtx.x'] < 100) & \
302  (df['vtx.y'] > -100) & \
303  (df['vtx.y'] < 100) & \
304  (df['vtx.z'] > 150) & \
305  (df['vtx.z'] < 1000)
def PandAna.cut.analysis_cuts.kNusPresel (   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
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
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)
PandAna.cut.analysis_cuts.kNueNDCVNSsb = kNueNDPresel&kNueCVNCut
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&\
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)&\
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)
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().