def getOffset():
""" we're looking for the difference in the offsets between HG and LG waveforms. """
from ROOT import GATDataSet, TChain
ds, cIdx, run = 1, 41, 13387
det = dsi.DetInfo()
pMons = det.getPMon(ds)
# skim = TChain("skimTree")
# skim.Add("/global/homes/w/wisecg/project/cal/skim/skimDS1_run13387_low.root")
gds = GATDataSet(run)
gat = gds.GetGatifiedChain(False)
n = gat.Draw("Entry$:tOffset:channel", "trapENFCal < 250", "goff")
iEnt, tOff, chan = gat.GetV1(), gat.GetV2(), gat.GetV3()
iEnt = np.asarray([int(iEnt[i]) for i in range(n)])
tOff = np.asarray([tOff[i] for i in range(n)])
chan = np.asarray([chan[i] for i in range(n)])
chList = sorted(list(set(chan)))
chList = [ch for ch in chList if ch % 2 == 0]
chDiff = {ch: [] for ch in chList}
chNoPair = {ch: [] for ch in chList}
eList = sorted(list(
set(iEnt))) # we do this b/c the list isn't insanely huge
for iE in eList:
idx = np.where(iEnt == iE)
tOffTmp = tOff[idx]
chTmp = chan[idx]
for i, ch in enumerate(chTmp):
if ch % 2 == 1: continue
iM = np.where(chTmp == ch + 1)
if len(iM[0]) == 0:
chNoPair[ch].append(tOffTmp[i])
continue
diff = tOffTmp[i] - tOffTmp[iM] # HG - LG
chDiff[ch].append(tOffTmp[i] - tOffTmp[iM])
for ch in chList:
chDiff[ch] = np.asarray(chDiff[ch])
np.savez("../data/tOff-%d.npz" % run, chDiff, chNoPair)
#!/usr/bin/env python
import sys, os
sys.argv.append("-b")
import tinydb as db
import numpy as np
import waveLibs as wl
import dsi
bkg = dsi.BkgInfo()
cal = dsi.CalInfo()
det = dsi.DetInfo()
from ROOT import TFile, TTree, MGTWaveform
# switches
fLimit = None # set to None to run over everything
skipDS6Cal = True
verbose = True
testMode = False
def main(argv):
""" NOTE: don't use globs when getting files.
Manually make sure everything is here.
Can submit these commands as separate batch jobs:
./check-files.py -all
./check-files.py -c -all
"""
global checkCal
checkCal = False
if checkCal: print("Skip DS6 cal?", skipDS6Cal)
def plotOffset():
ds, run = 1, 13387
det = dsi.DetInfo()
f = np.load("../data/tOff-%d.npz" % run)
chDiff = f['arr_0'].item()
chNoPair = f['arr_1'].item()
tLo, tHi, tpb = -5000, 500, 10
xTot, hTot = wl.GetHisto([], tLo, tHi, tpb)
# remember, diffs are HG - LG
# ==== 1. plot tOffset_HG - tOffset_LG ====
cmap = plt.cm.get_cmap('jet', len(chDiff) + 1)
for i, ch in enumerate(chDiff):
# print(ch, len(chDiff[ch]))
x, hDiff = wl.GetHisto(chDiff[ch], tLo, tHi, tpb)
hTot = np.add(hTot, hDiff)
cpd = det.getChanCPD(ds, ch)
plt.semilogy(x,
hDiff,
ls='steps',
lw=2,
c=cmap(i),
alpha=0.5,
label="C%sP%sD%s" % (cpd[0], cpd[1], cpd[2]))
p = 99.9
tmp = np.cumsum(hTot) / np.sum(hTot) * 100
idx = np.where(tmp > p)
x99 = xTot[idx][0]
plt.plot(xTot, hTot, "k", ls='steps', label="Total, run %d" % run)
plt.axvline(x99, c='r', lw=5, label="99.9%% value: %d" % x99)
plt.legend(loc=2, ncol=3, fontsize=12)
plt.xlabel("HG-LG tOffset (10ns)", ha='right', x=1)
plt.ylabel("Counts", ha='right', y=1)
plt.tight_layout()
# plt.show()
plt.savefig("../plots/tOffset-run%d.pdf" % run)
# ==== 2. plot tOffset_HG of any HG hits w/o a paired LG hit ====
plt.close()
tLo, tHi, tpb = 0, 10000, 50
xTot, hTot = wl.GetHisto([], tLo, tHi, tpb)
for i, ch in enumerate(chNoPair):
cpd = det.getChanCPD(ds, ch)
if cpd in ['173', '112']: continue
print(ch, cpd)
x, hNoPair = wl.GetHisto(chNoPair[ch], tLo, tHi, tpb)
hTot = np.add(hTot, hNoPair)
plt.semilogy(x,
hNoPair,
ls='steps',
lw=2,
c=cmap(i),
alpha=0.7,
label="C%sP%sD%s" % (cpd[0], cpd[1], cpd[2]))
pctTot = 99
tmp = np.cumsum(hTot) / np.sum(hTot) * 100
idx = np.where(tmp > pctTot)
xPctVal = xTot[idx][0]
plt.plot(xTot, hTot, "k", ls='steps', label="Total, run %d" % run)
plt.axvline(xPctVal,
c='r',
lw=5,
label="%d%% value: %d" % (pctTot, xPctVal))
plt.legend(loc=1, ncol=3, fontsize=10)
plt.xlabel("Unpaired tOffset_HG (10ns)", ha='right', x=1)
plt.ylabel("Counts", ha='right', y=1)
plt.tight_layout()
# plt.show()
plt.savefig("../plots/tOffset-unpaired-run%d.pdf" % run)
def printOffsets():
from ROOT import GATDataSet, TChain
ds, cIdx, run = 1, 41, 13387
det = dsi.DetInfo()
# pMons = det.getPMon(ds) # this cal run isn't getting these
# print(pMons)
# return
cpdList = det.dets["M1"]
cpdPairs = {cpd: 0 for cpd in cpdList}
cpdOrphanHG = {cpd: 0 for cpd in cpdList}
cpdOrphanLG = {cpd: 0 for cpd in cpdList}
skim = TChain("skimTree")
skim.Add(
"/global/homes/w/wisecg/project/cal/skim/skimDS1_run13387_low.root")
# n = skim.Draw("iEvent:tOffset:channel","mH==1 && tOffset > 100","goff")
n = skim.Draw("iEvent:tOffset:channel", "mH==1", "goff")
iEnt, tOff, chan = skim.GetV1(), skim.GetV2(), skim.GetV3()
iEnt = [int(iEnt[i]) for i in range(n)]
tOff = [tOff[i] for i in range(n)]
chan = [chan[i] for i in range(n)]
gds = GATDataSet(run)
gat = gds.GetGatifiedChain(False)
iNWF, hitChans, tOffs, hitEs = [], [], [], []
for iE in iEnt:
gat.GetEntry(iE)
iN = gat.channel.size()
iNWF.append(iN)
hChs = [int(gat.channel.at(j)) for j in range(iN)]
hitChans.append(hChs)
tOffs.append([int(gat.tOffset.at(j)) for j in range(iN)])
hits = [gat.trapENFCal.at(j) for j in range(iN)]
hitEs.append(wl.niceList(hits))
pairs = []
for ch in hChs:
if ch + 1 in hChs: pairs.extend([ch, ch + 1])
hgOrphans = [
ch for ch in hChs
if ch + 1 not in hChs and ch not in pairs and ch % 2 == 0
]
lgOrphans = [
ch for ch in hChs
if ch - 1 not in hChs and ch not in pairs and ch % 2 == 1
]
# print(hChs, "pairs:", pairs, "hg orph", hgOrphans, "lg orph", lgOrphans)
for ch in pairs:
if ch % 2 == 1: continue
cpd = det.getChanCPD(ds, ch)
cpdPairs[cpd] += 1
for ch in hgOrphans:
cpd = det.getChanCPD(ds, ch)
cpdOrphanHG[cpd] += 1
for ch in lgOrphans:
cpd = det.getChanCPD(ds, ch - 1)
cpdOrphanLG[cpd] += 1
# nLim = 200 if n > 200 else n
# for i in range(nLim):
# print("%d %-5d %-4d gNWF %d" % (chan[i], iEnt[i], tOff[i], iNWF[i]), hitChans[i], tOffs[i], hitEs[i])
np.savez("../data/toffset-orphans.npz", cpdPairs, cpdOrphanHG, cpdOrphanLG)
import os
import numpy as np
import tinydb as db
import pandas as pd
from statsmodels.stats import proportion
from scipy.optimize import curve_fit
from pprint import pprint
import matplotlib.pyplot as plt
plt.style.use('pltReports.mplstyle')
import dsi
import waveLibs as wl
calDB = db.TinyDB('{}/calDB-v2.json'.format(
dsi.latSWDir)) # match LAT's v2 tag
pars = db.Query()
detInfo = dsi.DetInfo()
# Skip these detectors because of low statistics
skipList = ['111', '211', '214', '221', '261', '274']
# Enriched and Natural detector lists, I pulled this from the output of the combined efficiencies -- could probably just use skipList and then check if a detector is Enr or Nat but whatever
enrDetList = [
112, 113, 114, 122, 123, 132, 133, 134, 152, 153, 154, 161, 162, 163, 164,
172, 173, 174, 231, 232, 253, 254, 262, 273
]
natDetList = [
121, 141, 142, 143, 144, 145, 151, 171, 222, 223, 241, 242, 244, 251
]
def main():
