Esempio n. 1
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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)
Esempio n. 2
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#!/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)
Esempio n. 3
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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)
Esempio n. 4
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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)
Esempio n. 5
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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():