def main():
"""
Code for varying bias runs: 1174-1176
"""
run_db, cal_db = "runDB.json", "calDB.json"
par = argparse.ArgumentParser(description="A/E cut for MJ60")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-db", "--writeDB", action=st, help="store results in DB")
args = vars(par.parse_args())
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db) #,tier_dir=tier_dir)
if args["run"]:
ds = DataSet(run=int(args["run"][0]), md=run_db, cal=cal_db)
# resolution(ds, args["writeDB"])
baseline_noise(ds)
def main():
run_db, cal_db = "runDB.json", "calDB.json"
par = argparse.ArgumentParser(description="calibration suite for MJ60")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
args = vars(par.parse_args())
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db)
if args["run"]:
ds = DataSet(run=int(args["run"][0]),
sub='none',
md=run_db,
cal=cal_db)
# gain_shift(ds)
# get_power_spectrum(ds)
baseline_noise(ds)
def main():
"""
Code to implement an A/E cut
"""
# global runDB
# with open("runDB.json") as f:
# runDB = json.load(f)
# global tier_dir
# tier_dir = runDB["tier_dir"]
# global meta_dir
# meta_dir = runDB["meta_dir"]
run_db, cal_db = "runDB.json", "calDB.json"
par = argparse.ArgumentParser(description="A/E cut for MJ60")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-db", "--writeDB", action=st, help="store results in DB")
args = vars(par.parse_args())
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db) #,tier_dir=tier_dir)
if args["run"]:
ds = DataSet(run=int(args["run"][0]), md=run_db, cal=cal_db)
find_cut(ds, ds_lo, args["writeDB"])
def main():
"""
perform automatic calibration of pygama DataSets.
command line options to specify the DataSet are the same as in processing.py
save results in a JSON database for access by other routines.
"""
run_db, cal_db = "runDB.json", "calDB.json"
par = argparse.ArgumentParser(description="calibration suite for MJ60")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-s", "--spec", action=st, help="print simple spectrum")
arg("-p1", "--pass1", action=st, help="run pass-1 (linear) calibration")
arg("-p2", "--pass2", action=st, help="run pass-2 (peakfit) calibration")
arg("-e",
"--etype",
nargs=1,
help="custom energy param (default is e_ftp)")
arg("-t", "--test", action=st, help="set verbose (testing) output")
arg("-db", "--writeDB", action=st, help="store results in DB")
arg("-pr", "--printDB", action=st, help="print calibration results in DB")
args = vars(par.parse_args())
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db, v=args["test"])
if args["run"]:
ds = DataSet(run=int(args["run"][0]),
sub='none',
md=run_db,
cal=cal_db,
v=args["test"])
# -- start calibration routines --
etype = args["etype"][0] if args["etype"] else "e_ftp"
if args["spec"]:
show_spectrum(ds, etype)
if args["pass1"]:
calibrate_pass1(ds, etype, args["writeDB"], args["test"])
if args["pass2"]:
calibrate_pass2(ds, args["test"])
# fit to germanium peakshape function goes here -- take from matthew's code
# if args["pass3"]:
# calibrate_pass3(ds)
if args["printDB"]:
show_calDB(cal_db)
def main(argv):
"""
Uses pygama's amazing DataSet class to process runs for different
data sets, with arbitrary configuration options defined in a JSON file.
C. Wiseman, 2019/04/09
Modified for HADES data
A.Zschocke
"""
# -- parse args --
par = argparse.ArgumentParser(description="test data processing suite")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-t0", "--tier0", action=st, help="run ProcessTier0 on list")
arg("-t1", "--tier1", action=st, help="run ProcessTier1 on list")
arg("-t", "--test", action=st, help="test mode, don't run")
arg("-n", "--nevt", nargs='?', default=np.inf, help="limit max num events")
arg("-i", "--ioff", nargs='?', default=0, help="start at index [i]")
arg("-v", "--verbose", action=st, help="set verbose output")
arg("-o", "--ovr", action=st, help="overwrite existing files")
arg("-m", "--nomp", action=sf, help="don't use multiprocessing")
arg("-s", "--sub", nargs=1, help="number of subfiles")
arg("-db", "--db", nargs=1, help="/path/to/runDB.json")
args = vars(par.parse_args())
if args["db"]:
run_db = args["db"][0] + "/runDB.json"
if args["sub"]:
sub = int(args["sub"][0])
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(sub, ds_lo, ds_hi, md=run_db, v=args["verbose"])
if args["run"]:
ds = DataSet(sub,
run=int(args["run"][0]),
md=run_db,
v=args["verbose"])
# -- start processing --
if args["tier0"] and args["sub"]:
sub = int(args["sub"][0])
tier0(ds, sub, args["ovr"], args["nevt"], args["verbose"],
args["test"])
if args["tier1"]:
tier1(ds, sub, args["ovr"], args["nevt"], args["ioff"],
args["nomp"], args["verbose"], args["test"])
def main(argv):
"""
Uses pygama's amazing DataSet class to process runs
for different data sets and arbitrary configuration options
defined in a JSON file.
"""
#datadir = os.environ["CAGEDATA"]
run_db, cal_db = f'./meta/runDB.json', f'./meta/calDB.json'
# -- parse args --
par = argparse.ArgumentParser(description="data processing suite for CAGE")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-d2r", "--daq_to_raw", action=st, help="run daq_to_raw on list")
arg("-r2d", "--raw_to_dsp", action=st, help="run raw_to_dsp on list")
arg("-t", "--test", action=st, help="test mode, don't run")
arg("-n", "--nevt", nargs='?', default=np.inf, help="limit max num events")
arg("-i", "--ioff", nargs='?', default=0, help="start at index [i]")
arg("-o", "--ovr", action=st, help="overwrite existing files")
arg('-v', '--verbose', default=2, type=int,
help="Verbosity level: 0=silent, 1=basic warnings, 2=verbose output, 3=debug. Default is 2.")
arg('-b', '--block', default=8, type=int,
help="Number of waveforms to process simultaneously. Default is 8")
arg('-g', '--group', default='',
help="Name of group in LH5 file. By default process all base groups. Supports wildcards.")
# -- declare the DataSet --
args = par.parse_args()
d_args = vars(par.parse_args())
#ds = pu.get_dataset_from_cmdline(d_args, run_db, cal_db)
# -- declare the DataSet --
if d_args["ds"]:
ds_lo = int(d_args["ds"][0])
try:
ds_hi = int(d_args["ds"][1])
except:
ds_hi = None
ds = DataSet(1,ds_lo, ds_hi, md=run_db, v=d_args["verbose"])
if d_args["run"]:
ds = DataSet(1,run=int(d_args["run"][0]), md=run_db, v=d_args["verbose"])
#print(ds.runs)
#pprint(ds.paths)
# -- start processing --
if args.daq_to_raw:
daq_to_raw(ds, args.ovr, args.nevt, args.verbose, args.test)
if args.raw_to_dsp:
raw_to_dsp(ds, args.ovr, args.nevt, args.test, args.verbose, args.block,
args.group)
def window_ds():
"""
Take a single DataSet and window it so that the file only contains events
near an expected peak location.
Create some temporary in/out files s/t the originals aren't overwritten.
"""
# run = 42
# ds = DataSet(run=run, md="runDB.json")
ds_num = 3
ds = DataSet(ds_num, md="runDB.json")
# specify temporary I/O locations
p_tmp = "~/Data/cage"
f_tier1 = "~/Data/cage/cage_ds3_t1.h5"
f_tier2 = "~/Data/cage/cage_ds3_t2.h5"
# figure out the uncalibrated energy range of the K40 peak
# xlo, xhi, xpb = 0, 2e6, 2000 # show phys. spectrum (top feature is 2615 pk)
xlo, xhi, xpb = 990000, 1030000, 250 # k40 peak, ds 3
t2df = ds.get_t2df()
hE, xE = ph.get_hist(t2df["energy"], range=(xlo, xhi), dx=xpb)
plt.semilogy(xE, hE, ls='steps', lw=1, c='r')
import matplotlib.ticker as ticker
plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.4e'))
plt.locator_params(axis='x', nbins=5)
plt.xlabel("Energy (uncal.)", ha='right', x=1)
plt.ylabel("Counts", ha='right', y=1)
plt.savefig(f"./plots/cage_ds{ds_num}_winK40.pdf")
# exit()
# write a windowed tier 1 file containing only waveforms near the peak
t1df = pd.DataFrame()
for run in ds.paths:
ft1 = ds.paths[run]["t1_path"]
print(f"Scanning ds {ds_num}, run {run}\n file: {ft1}")
for chunk in pd.read_hdf(ft1, 'ORSIS3302DecoderForEnergy', chunksize=5e4):
t1df_win = chunk.loc[(chunk.energy > xlo) & (chunk.energy < xhi)]
print(t1df_win.shape)
t1df = pd.concat([t1df, t1df_win], ignore_index=True)
# -- save to HDF5 output file --
h5_opts = {
"mode":"w", # overwrite existing
"append":False,
"format":"table",
"complib":"blosc:zlib",
"complevel":1,
"data_columns":["ievt"]
}
t1df.reset_index(inplace=True)
t1df.to_hdf(f_tier1, key="df_windowed", **h5_opts)
print("wrote file:", f_tier1)
def main():
"""
tumbsi analysis suite
"""
global display
display = 1 # allow displaying intermediate distributions for control
run_db, cal_db = "runDB.json", "calDB.json"
with open(run_db) as f:
runDB = json.load(f)
global tier_dir
tier_dir = runDB["tier_dir"]
global meta_dir
meta_dir = runDB["meta_dir"]
global dep_line
dep_line = 1592.5
global dep_acc
dep_acc = 0.9
# peaks_of_interest = sorted(runDB["peaks_of_interest"], reverse=True)
peaks_of_interest = [2614.5, 1460.8, 583.2]
# take calibration parameter for the 'calibration.py' output
# with open(cal_db) as f:
# calDB = json.load(f)
par = argparse.ArgumentParser(description="calibration suite for tumbsi")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
args = vars(par.parse_args())
ecal = np.zeros(3)
eres = np.zeros(2)
# Which run number is the being analyzed
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db)
run = ds_lo
if args["run"]:
ds = DataSet(run=int(args["run"][0]), md=run_db, cal=cal_db)
print("")
print("Start Pulse Shape Anlysis")
print("")
psa(run, ds, ecal, eres, peaks_of_interest, ds, ds_lo)
def process_ds(rise_times):
"""
and determine the trapezoid parameters that minimize
the FWHM of the peak (fitting to the peakshape function).
"""
from pygama.dsp.base import Intercom
from pygama.io.tier1 import ProcessTier1
import pygama.io.decoders.digitizers as pgd
ds_num = 3
ds = DataSet(ds_num, md="runDB.json")
first_run = ds.runs[0]
# specify temporary I/O locations
out_dir = os.path.expanduser('~') + "/Data/cage"
t1_file = f"{out_dir}/cage_ds3_t1.h5"
t2_file = f"{out_dir}/cage_ds3_t2.h5"
opt_file = f"{out_dir}/cage_ds3_optimize.h5"
if os.path.exists(opt_file):
os.remove(opt_file)
# check the windowed file
tmp = pd.read_hdf(t1_file)
nevt = len(tmp)
rc_decay = 72
for i, rt in enumerate(rise_times):
# custom tier 1 processor list -- very minimal
proc_list = {
"clk" : 100e6,
"fit_bl" : {"ihi":500, "order":1},
"blsub" : {},
"trap" : [
{"wfout":"wf_etrap", "wfin":"wf_blsub",
"rise":rt, "flat":2.5, "decay":rc_decay},
{"wfout":"wf_atrap", "wfin":"wf_blsub",
"rise":0.04, "flat":0.1, "fall":2}
],
"get_max" : [{"wfin":"wf_etrap"}, {"wfin":"wf_atrap"}],
# "ftp" : {"test":1}
"ftp" : {}
}
proc = Intercom(proc_list)
dig = pgd.SIS3302Decoder
dig.decoder_name = "df_windowed"
dig.class_name = None
ProcessTier1(t1_file, proc, output_dir=out_dir, overwrite=True,
verbose=False, multiprocess=True, nevt=np.inf, ioff=0,
chunk=ds.config["chunksize"], run=first_run,
t2_file=t2_file, digitizers=[dig])
# load the temporary file and append to the main output file
df_key = f"opt_{i}"
t2df = pd.read_hdf(t2_file)
t2df.to_hdf(opt_file, df_key)
def get_spectra():
ds = DataSet(runlist=[143, 144, 145], md='./runDB.json', tier_dir=tier_dir)
t2df = ds.get_t2df()
xlo, xhi, xpb = 0, 10000, 10
xP, hP = get_hist(t2df["trap_max"], xlo, xhi, xpb)
plt.plot(xP,
hP,
ls='steps',
lw=1.5,
c='m',
label="pygama trap_max, {} cts".format(sum(hP)))
plt.xlabel("Energy (uncal)", ha='right', x=1)
plt.ylabel("Counts", ha='right', y=1)
plt.legend()
plt.tight_layout()
plt.show()
def main(argv):
"""
Uses pygama's amazing DataSet class to process runs for different
data sets, with arbitrary configuration options defined in a JSON file.
C. Wiseman, 2019/04/09
"""
run_db = './runDB.json'
# -- parse args --
par = argparse.ArgumentParser(description="test data processing suite")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-t0", "--daq_to_raw", action=st, help="run ProcessRaw on list")
arg("-t1", "--raw_to_dsp", action=st, help="run RunDSP on list")
arg("-t", "--test", action=st, help="test mode, don't run")
arg("-n", "--nevt", nargs='?', default=np.inf, help="limit max num events")
arg("-i", "--ioff", nargs='?', default=0, help="start at index [i]")
arg("-v", "--verbose", action=st, help="set verbose output")
arg("-o", "--ovr", action=st, help="overwrite existing files")
arg("-m", "--nomp", action=sf, help="don't use multiprocessing")
args = vars(par.parse_args())
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, v=args["verbose"])
if args["run"]:
ds = DataSet(run=int(args["run"][0]), md=run_db, v=args["verbose"])
# -- start processing --
if args["daq_to_raw"]:
daq_to_raw(ds, args["ovr"], args["nevt"], args["verbose"],
args["test"])
if args["raw_to_dsp"]:
raw_to_dsp(ds, args["ovr"], args["nevt"], args["ioff"],
args["nomp"], args["verbose"], args["test"])
def tier2_AoverE():
"""
show the A/E distribution.
"""
run = 42
ds = DataSet(run=run, md="runDB.json")
t2df = ds.get_t2df()
aoe = t2df["current_max"] / t2df["e_ftp"]
# # 1d
# xlo, xhi, xpb = -2000, 2000, 10
# h, x = ph.get_hist(aoe, range=(xlo, xhi), dx=xpb)
# plt.semilogy(x, h, ls='steps', lw=1, c='r', label=f'run {run}')
# plt.xlabel("A/E (uncal.)", ha='right', x=1)
# plt.ylabel("Counts", ha='right', y=1)
# plt.grid(linestyle=':')
# plt.legend()
# # plt.show()
# plt.cla()
# 2d vs E
xlo, xhi, xpb = 0, 6000, 5
# ylo, yhi, ypb = 0.6, 1.2, 0.001
ylo, yhi, ypb = 0, 0.1, 0.001
nbx, nby = int((xhi - xlo) / xpb), int((yhi - ylo) / ypb)
from matplotlib.colors import LogNorm
plt.hist2d(t2df["e_ftp"],
aoe,
bins=(nbx, nby),
range=((xlo, xhi), (ylo, yhi)),
norm=LogNorm(),
cmap='jet')
# cb = plt.colorbar()
# cb.set_label("Counts", ha='right', y=1)
plt.xlabel("e_ftp (uncal.)", ha='right', x=1)
plt.ylabel("A/E", ha='right', y=1)
# plt.grid(which='both', linestyle=':')
plt.grid()
plt.savefig(f"./plots/cage_run{run}_AE.png", dpi=200)
def get_dataset_from_cmdline(args, run_db, cal_db):
"""
make it easier to call this from argparse:
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
"""
from pygama import DataSet
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db, v=args["verbose"])
if args["run"]:
ds = DataSet(run=int(args["run"][0]), md=run_db, cal=cal_db,
v=args["verbose"])
return ds
def tier2_spec():
"""
show a few examples of energy spectra (onboard E and offline E)
"""
run = 42
ds = DataSet(run=run, md="runDB.json")
t2df = ds.get_t2df()
# print(t2df.columns)
# onboard E
ene = "energy"
# xlo, xhi, xpb = 0, 20e6, 5000 # show muon peak (full dyn. range)
xlo, xhi, xpb = 0, 2e6, 2000 # show phys. spectrum (top feature is 2615 pk)
# # trap_max E
# ene = "etrap_max"
# xlo, xhi, xpb = 0, 50000, 100 # muon peak
# xlo, xhi, xpb = 0, 6000, 10 # gamma spectrum
# # fixed time pickoff E
# ene = "e_ftp"
# # xlo, xhi, xpb = 0, 50000, 100 # muon peak
# xlo, xhi, xpb = 0, 6000, 10 # gamma spectrum
# get histogram
hE, xE = ph.get_hist(t2df[ene], range=(xlo, xhi), dx=xpb)
# make the plot
plt.semilogy(xE, hE, ls='steps', lw=1, c='r', label=f'run {run}')
plt.xlabel("Energy (uncal.)", ha='right', x=1)
plt.ylabel("Counts", ha='right', y=1)
# show a couple formatting tricks
import matplotlib.ticker as ticker
plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1e'))
plt.locator_params(axis='x', nbins=5)
plt.grid(linestyle=':')
plt.legend()
# plt.show()
plt.savefig(f"./plots/cage_run{run}_{ene}.pdf")
def main():
"""
mj60 waveform viewer
"""
run_db, cal_db = "runDB.json", "calDB.json"
par = argparse.ArgumentParser(description="waveform viewer for mj60")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-db", "--writeDB", action=st, help="store results in DB")
args = vars(par.parse_args())
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi,
md=run_db, cal = cal_db) #,tier_dir=tier_dir)
if args["run"]:
ds = DataSet(run=int(args["run"][0]),
md=run_db, cal=cal_db)
# Which run number is the being analyzed
# run = 249
# run = 214
# run = 204
# run = 278
# working on analysis for the AvsE cut in mj60
# t1df, t2df = chunker(run)
# cutwf, t2cut = cutter(t1df, t2df, run)
# histograms(cutwf, t2cut, run)
# histograms(ds)
drift_correction(ds)
def main():
"""
perform automatic calibration of pygama DataSets.
command line options to specify the DataSet are the same as in processing.py
save results in a JSON database for access by other routines.
"""
par = argparse.ArgumentParser(description="calibration suite for tumbsi")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
arg("-s", "--spec", action=st, help="print simple spectrum")
arg("-sc", "--cal", action=st, help="print calibrated spectrum")
arg("-p0",
"--pass0",
action=st,
help="run pass0 (single peak) calibration")
arg("-p1", "--pass1", action=st, help="run pass-1 (linear) calibration")
arg("-p2", "--pass2", action=st, help="run pass-2 (peakfit) calibration")
arg("-e",
"--etype",
nargs=1,
help="custom energy param (default is e_ftp)")
arg("-t", "--test", action=st, help="set verbose (testing) output")
arg("-w", "--writeDB", action=st, help="store results in DB")
arg("-pr", "--printDB", action=st, help="print calibration results in DB")
arg("-pa", "--path", nargs=1, help="Set Path to runDB.json file")
arg("-db", "--db", nargs=1, help="Path to runDB.json and calDB.json")
arg("-sub", "--sub", nargs=1, help="Number of Subfiles")
args = vars(par.parse_args())
etype = args["etype"][0] if args["etype"] else "e_ftp"
if args["db"]:
path_to_files = args["db"][0]
run_db, cal_db = path_to_files + "/runDB.json", path_to_files + "/calDB.json"
# -- declare the DataSet --
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, 1, md=run_db, cal=cal_db, v=args["test"])
if args["run"]:
run = int(args["run"][0])
ds = DataSet(1, run, md=run_db, cal=cal_db, v=args["test"])
fp = ds.paths[run]["t2_path"].split("t2")[0]
t2_file = ds.get_t2df()
if args["sub"]:
subNumber = int(args["sub"][0])
counter = 0
for p, d, files in os.walk(ds.tier2_dir):
for f in files:
if any("{}-".format(r) in f for r in [run]):
if counter < subNumber:
t2_file = t2_file.append(pd.read_hdf(fp + f))
counter += 1
print("Whaat")
if args["spec"]:
his = t2_file.hist("e_ftp", bins=2000)
plt.yscale('log')
plt.savefig(path_to_files + 'plots/Raw.png',
bbox_inches='tight',
transperent=True)
plt.show()
if args["pass0"]:
calibrate_pass0(ds, t2_file, etype, args["writeDB"])
if args["pass1"]:
calibrate_pass1(ds, t2_file, etype, args["writeDB"], args["test"])
if args["pass2"]:
calibrate_pass2(ds, t2_file, run, cal_db, run_db, args["writeDB"])
if args["printDB"]:
show_calDB(cal_db)
if args["cal"]:
show_calspectrum(ds, t2_file, cal_db, etype, run, args["pass1"],
args["pass2"])
def tier1_wfs():
"""
show some waveforms, with an example of a data cleaning cut.
"""
run = 42
iwf_max = 100000 # tier 1 files can be a lot to load into memory
ds = DataSet(run=run, md="runDB.json")
ft1 = ds.paths[run]["t1_path"]
t1df = pd.read_hdf(ft1,
"ORSIS3302DecoderForEnergy",
where=f"ievt < {iwf_max}")
t1df.reset_index(inplace=True) # required step -- fix pygama "append" bug
# get waveform dataframe
wf_cols = []
for col in t1df.columns:
if isinstance(col, int):
wf_cols.append(col)
wfs = t1df[wf_cols]
# apply a cut based on the t1 columns
# idx = t1df.index[(t1df.energy > 1.5e6)&(t1df.energy < 2e6)]
# apply a cut based on the t2 columns
ft2 = ds.paths[run]['t2_path']
t2df = pd.read_hdf(ft2, where=f"ievt < {iwf_max}")
t2df.reset_index(inplace=True)
# t2df['AoE'] = t2df.current_max / t2df.e_ftp # scipy method
t2df['AoE'] = t2df.atrap_max / t2df.e_ftp # trapezoid method
idx = t2df.index[(t2df.AoE < 0.7)
& (t2df.e_ftp > 1000) & (t2df.e_ftp < 10000)
& (t2df.index < iwf_max)]
wfs = wfs.loc[idx]
wf_idxs = wfs.index.values # kinda like a TEntryList
# make sure the cut output makes sense
cols = [
'ievt', 'timestamp', 'energy', 'e_ftp', 'atrap_max', 'current_max',
't0', 't_ftp', 'AoE', 'tslope_pz', 'tail_tau'
]
print(t2df.loc[idx][cols].head())
print(t1df.loc[idx].head())
print(wfs.head())
# iterate over the waveform block
iwf = -1
while True:
if iwf != -1:
inp = input()
if inp == "q": exit()
if inp == "p": iwf -= 2
iwf += 1
iwf_cut = wf_idxs[iwf]
# get waveform and dsp values
wf = wfs.iloc[iwf]
dsp = t2df.iloc[iwf_cut]
ene = dsp.e_ftp
aoe = dsp.AoE
ts = np.arange(len(wf))
# nice horizontal print of a pd.Series
print(iwf, iwf_cut)
print(wf.to_frame().T)
print(t2df.iloc[iwf_cut][cols].to_frame().T)
plt.cla()
plt.plot(ts,
wf,
"-b",
alpha=0.9,
label=f'e: {ene:.1f}, a/e: {aoe:.1f}')
# savitzky-golay smoothed
# wfsg = signal.savgol_filter(wf, 47, 2)
wfsg = signal.savgol_filter(wf, 47, 1)
plt.plot(ts, wfsg, "-r", label='savitzky-golay filter')
plt.xlabel("clock ticks", ha='right', x=1)
plt.ylabel("ADC", ha='right', y=1)
plt.legend()
plt.tight_layout()
plt.show(block=False)
plt.pause(0.01)
import json
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from pygama import DataSet
with open("runDB.json") as f:
runDB = json.load(f)
tier_dir = runDB["tier_dir"]
ds0 = DataSet(runlist=[554], md='./runDB.json', tier_dir=tier_dir)
t2df_0 = ds0.get_t2df()
ds1 = DataSet(runlist=[555], md='./runDB.json', tier_dir=tier_dir)
t2df_1 = ds1.get_t2df()
ds2 = DataSet(runlist=[556], md='./runDB.json', tier_dir=tier_dir)
t2df_2 = ds2.get_t2df()
e_0 = t2df_0["energy"]
e_1 = t2df_1["energy"]
e_2 = t2df_2["energy"]
e_full = [0, 3.3e6]
e_pks = [1.2e6, 2.6e6]
e_K = [1.3e6, 1.36e6]
e_T = [2.35e6, 2.42e6]
h_0, edg_0 = np.histogram(e_0, bins=5000, range=e_full)
x_0 = (edg_0[:-1] + edg_0[1:]) / 2
# h_0_K,edg_0_K = np.histogram(e_0, bin=500, range=e_K)
def histograms(run):
ds = DataSet(runlist=[run], md='./runDB.json', tier_dir=tier_dir)
t2 = ds.get_t2df()
t2df = os.path.expandvars('{}/Spectrum_{}.hdf5'.format(meta_dir, run))
t2df = pd.read_hdf(t2df, key="df")
# n = "tslope_savgol"
# n = "current_max"
# n = "tslope_pz"
n = "tail_tau"
# n = "tail_amp"
e = "e_cal"
x = t2df[e]
# y = t2df[n]
y = t2df[n] / x
plt.clf()
# H, xedges, yedges = np.histogram2d(t2df["tail_tau"], t2df["e_ftp"], bins=[2000,200], range=[[0, 6600], [0, 5]])
plt.hist2d(x,
y,
bins=[1000, 200],
range=[[0, 200], [0, .001]],
norm=LogNorm(),
cmap='jet')
# plt.hist2d(x, y, bins=[1000,1000], norm=LogNorm())
# plt.scatter(H[0],H[1])
# f = plt.figure(figsize=(20,5))
# p1 = f.add_subplot(111, title='Test', xlabel='Energy (keV)', ylabel=n)
# h1,xedg1,yedg1 = np.histogram2d(x, y, bins=[1000,200], range=[[0,2000],[0,100]])
# h1 = h1.T
# # hMin, hMax = np.amin(h1), np.amax(h1)
# # im1 = p1.imshow(h1,cmap='jet',vmin=hMin,vmax=hMax, aspect='auto') #norm=LogNorm())
# im1 = p1.imshow(h1,cmap='jet', origin='lower', aspect='auto', norm=LogNorm(), extent=[xedg1[0], xedg1[-1], yedg1[0], yedg1[-1]])
# cb1 = f.colorbar(im1, ax=p1)#, fraction=0.037, pad=0.04)
cbar = plt.colorbar()
# plt.xscale('symlog')
# plt.yscale('symlog')
plt.title("Run {}".format(run))
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel(n, ha='right', y=1)
# cbar.ax.set_ylabel('Counts')
# plt.ylabel("tslope_savgol", ha='right', y=1)
# plt.ylabel("A/E_ftp", ha='right', y=1)
# plt.tight_layout()
# # plt.savefig('./plots/meeting_plots/run{}_{}_vs_{}.png'.format(run, n, e))
# plt.show()
# xlo, xhi, xpb = 0, 10000, 10
# xP, hP = get_hist(t2df["trap_max"], xlo, xhi, xpb)
#
# plt.plot(xP, hP, ls='steps', lw=1.5, c='m',
# label="pygama trap_max, {} cts".format(sum(hP)))
# plt.xlabel("Energy (uncal)", ha='right', x=1)
# plt.ylabel("Counts", ha='right', y=1)
# plt.legend()
plt.tight_layout()
plt.show()
testDB = json.load(f)
print("-- Top-level information -- ")
for key in testDB:
if not isinstance(testDB[key], dict):
print(key, ":", testDB[key])
print("-- Data set definitions -- ")
pprint(testDB["ds"])
try:
xrun = int(sys.argv[1])
except:
print("You have to give a run number as argument!")
exit(0)
ds = DataSet(run=xrun, md=db_file, v=True) # can also use a list of run number
# print some of the DataSet attributes
print("raw dir : ", ds.raw_dir)
print("tier1 dir : ", ds.tier1_dir)
print("t1 file prefix :", ds.t1pre)
print("t2 file prefix :", ds.t2pre)
print("current run list :", ds.runs)
print("current file paths :")
pprint(ds.paths)
print("IF YOUR t0_path IS EMPTY, CROSS-CHECK $DATADIR AND FILE NAMING")
"""
Show waveforms from the Tier 1 file.
NOTE: pygama.DataSet has a convenience function "get_t1df" but is undeveloped.
def calibrate():
"""
do a rough energy calibration
"automatic": based on finding ratios
"""
from scipy.signal import medfilt, find_peaks_cwt
from scipy.stats import linregress
pks_lit = [239, 911, 1460.820, 1764, 2614.511]
# ds = DataSet(11, md='./runDB.json', tier_dir=tier_dir)
ds = DataSet(run=204, md='./runDB.json', tier_dir=tier_dir)
t2df = ds.get_t2df()
rt = ds.get_runtime() / 3600 # hrs
ene = t2df["e_ftp"]
xlo, xhi, xpb = 0, 10000, 10 # damn, need to remove the overflow peak
nbins = int((xhi - xlo) / xpb)
hE, xE, _ = get_hist(ene, nbins, (xlo, xhi))
# xE, hE = get_hist(ene, xlo, xhi, xpb)
# -- pygama's cal routine needs some work ... --
# need to manually remove the overflow peak?
# data_peaks = get_most_prominent_peaks(ene, xlo, xhi, xpb, test=True)
# ene_peaks = get_calibration_energies("uwmjlab")
# ene_peaks = get_calibration_energies("th228")
# best_m, best_b = match_peaks(data_peaks, ene_peaks)
# ecal = best_m * t2df["trap_max"] + best_b
# -- test out a rough automatic calibration here --
npks = 15
hE_med = medfilt(hE, 21)
hE_filt = hE - hE_med
pk_width = np.arange(1, 10, 0.1)
pk_idxs = find_peaks_cwt(hE_filt, pk_width, min_snr=5)
pks_data = xE[pk_idxs]
pk_counts = hE[pk_idxs]
idx_sorted = np.argsort(pk_counts)
pk_idx_max = pk_idxs[idx_sorted[-npks:]]
pks_data = np.sort(xE[pk_idx_max])
r0 = pks_lit[4] / pks_lit[2]
# this is pretty ad hoc, should use more of the match_peaks function
found_match = False
for pk1 in pks_data:
for pk2 in pks_data:
r = pk1 / pk2
if np.fabs(r - r0) < 0.005:
print("found match to peak list:\n "
"r0 {:.3f} r {:.3f} pk1 {:.0f} pk2 {:.0f}".format(
r0, r, pk1, pk2))
found_match = True # be careful, there might be more than one
break
if found_match:
break
# # check uncalibrated spectrum
# plt.plot(xE, hE, ls='steps', lw=1, c='b')
# # plt.plot(xE, hE_filt, ls='steps', lw=1, c='b')
# # for pk in pks_data:
# # plt.axvline(pk, color='r', lw=1, alpha=0.6)
# plt.axvline(pk1, color='r', lw=1)
# plt.axvline(pk2, color='r', lw=1)
# plt.show()
# exit()
# two-point calibration
data = np.array(sorted([pk1, pk2]))
lit = np.array([pks_lit[2], pks_lit[4]])
m, b, _, _, _ = linregress(data, y=lit)
print("Paste this into runDB.json:\n ", m, b)
# err = np.sum((lit - (m * data + b))**2)
# plt.plot(data, lit, '.b', label="E = {:.2e} x + {:.2e}".format(m, b))
# xf = np.arange(data[0], data[1], 1)
# plt.plot(xf, m * xf + b, "-r")
# plt.legend()
# plt.show()
# apply calibration
ecal = m * ene + b
# # check calibrated spectrum
xlo, xhi, xpb = 0, 3000, 1
hC, xC, _ = get_hist(ecal, int((xhi - xlo) / xpb), (xlo, xhi))
hC = np.concatenate(
(hC, [0])) # FIXME: annoying - have to add an extra zero
plt.semilogy(xC,
hC / rt,
c='b',
ls='steps',
lw=1,
label="MJ60 data, {:.2f} hrs".format(rt))
plt.axvline(pks_lit[2], c='r', lw=3, alpha=0.7, label="40K, 1460.820 keV")
plt.axvline(pks_lit[4],
c='m',
lw=3,
alpha=0.7,
label="208Tl, 2614.511 keV")
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel("Counts / hr / {:.2f} keV".format(xpb), ha='right', y=1)
plt.legend()
plt.tight_layout()
# plt.show()
plt.savefig("./plots/surface_spec.pdf")
# exit()
# check low-e spectrum
plt.figure()
xlo, xhi, xpb = 0, 50, 0.1
hC, xC, _ = get_hist(ecal, int((xhi - xlo) / xpb), (xlo, xhi))
hC = np.concatenate(
(hC, [0])) # FIXME: annoying - have to add an extra zero
plt.plot(xC, hC, c='b', ls='steps', lw=1, label="Kr83 data")
plt.axvline(9.4057, color='r', lw=1.5, alpha=0.6,
label="9.4057 keV") # kr83 lines
plt.axvline(12.651, color='g', lw=1.5, alpha=0.6,
label="12.651 keV") # kr83 lines
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel("Counts / {:.2f} keV".format(xpb), ha='right', y=1)
plt.legend()
plt.tight_layout()
# plt.show()
plt.savefig("./plots/test_kr83_cal.pdf")
def resolution():
"""
fit the 208Tl 2615 keV peak and give me the resolution
test out pygama's peak fitting routines
"""
ds_num = 11
ds = DataSet(ds_num, md='./runDB.json', tier_dir=tier_dir)
t2df = ds.get_t2df()
ene = t2df["energy"].values
rt = ds.get_runtime() / 3600 # hrs
# apply calibration
cal = runDB["cal_onboard"][str(ds_num)]
m, b = cal[0], cal[1]
ene = m * ene + b
# zoom in to the area around the 2615 peak
xlo, xhi, xpb = 2565, 2665, 0.5
ene2 = ene[np.where((ene > xlo) & (ene < xhi))]
xE, hE = get_hist(ene, xlo, xhi, xpb)
# set peak bounds
guess_ene = 2615
guess_sig = 5
idxpk = np.where((xE > guess_ene - guess_sig)
& (xE > guess_ene + guess_sig))
guess_area = np.sum(hE[idxpk])
# radford_peak function pars: mu, sigma, hstep, htail, tau, bg0, a
p0 = [guess_ene, guess_sig, 1E-3, 0.7, 5, 0, guess_area]
bnd = [[0.9 * guess_ene, 0.5 * guess_sig, 0, 0, 0, 0, 0],
[1.1 * guess_ene, 2 * guess_sig, 0.1, 0.75, 10, 10, 5 * guess_area]]
pars = fit_binned(radford_peak, hE, xE, p0) #, bounds=bnd)
print("mu:", pars[0], "\n", "sig", pars[1], "\n", "hstep:", pars[2], "\n",
"htail:", pars[3], "\n", "tau:", pars[4], "\n", "bg0:", pars[5],
"\n", "a:", pars[6])
plt.plot(xE,
hE,
c='b',
ls='steps',
lw=1,
label="MJ60 data, {:.2f} hrs".format(rt))
plt.plot(xE,
radford_peak(xE, *pars),
color="r",
alpha=0.7,
label=r"Radford peak, $\sigma$={:.2f} keV".format(pars[1]))
plt.axvline(2614.511,
color='r',
alpha=0.6,
lw=1,
label=r"$E_{lit}$=2614.511")
plt.axvline(pars[0],
color='g',
alpha=0.6,
lw=1,
label=r"$E_{fit}$=%.3f" % (pars[0]))
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel("Counts / {:.2f} keV".format(xpb), ha='right', y=1)
plt.legend()
plt.tight_layout()
# plt.show()
plt.savefig("./plots/kr83_resolution.pdf")
def get_multiple_spectra():
# energy (onboard)
# xlo, xhi, xpb = 0, 2000000, 1000
# xlo, xhi, xpb = 0, 500000, 1000
# xlo, xhi, xpb = 0, 50000, 100
# energy (onboard, calibrated)
xlo, xhi, xpb = 0, 40, 0.1
# trap_max
# xlo, xhi, xpb = 0, 10000, 10
# xlo, xhi, xpb = 0, 300, 0.3
# xlo, xhi, xpb = 0, 80, 0.2
# xlo, xhi, xpb = 0, 40, 0.1
# ds = DataSet(run=147, md='./runDB.json', tier_dir=tier_dir)
# get calibration
cal = runDB["cal_onboard"]["11"]
m, b = cal[0], cal[1]
ds = DataSet(10, md='./runDB.json', tier_dir=tier_dir)
rt1 = ds.get_runtime() / 3600
t2df = ds.get_t2df()
ene1 = m * t2df["energy"] + b
x, h1 = get_hist(ene1, xlo, xhi, xpb)
# x, h1 = get_hist(t2df["trap_max"], xlo, xhi, xpb)
h1 = np.divide(h1, rt1)
ds2 = DataSet(11, md='./runDB.json', tier_dir=tier_dir)
t2df2 = ds2.get_t2df()
rt2 = ds2.get_runtime() / 3600
ene2 = m * t2df2["energy"] + b
x, h2 = get_hist(ene2, xlo, xhi, xpb)
# x, h2 = get_hist(t2df2["trap_max"], xlo, xhi, xpb)
h2 = np.divide(h2, rt2)
plt.figure(figsize=(7, 5))
plt.plot(x,
h1,
ls='steps',
lw=1,
c='b',
label="bkg, {:.2f} hrs".format(rt1))
plt.plot(x,
h2,
ls='steps',
lw=1,
c='r',
label="Kr83, {:.2f} hrs".format(rt2))
plt.axvline(9.4057, color='m', lw=2, alpha=0.4,
label="9.4057 keV") # kr83 lines
plt.axvline(12.651, color='g', lw=2, alpha=0.4,
label="12.651 keV") # kr83 lines
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel("Counts / hr / {:.2f} keV".format(xpb), ha='right', y=1)
plt.legend()
plt.tight_layout()
# plt.show()
# plt.savefig("./plots/krSpec_{:.0f}_{:.0f}_onboard.pdf".format(xlo,xhi))
# plt.savefig("./plots/krSpec_{:.0f}_{:.0f}_uncal.pdf".format(xlo,xhi))
plt.savefig("./plots/krSpec_{:.0f}_{:.0f}_cal.pdf".format(xlo, xhi))
def get_spectra():
with open("runDB.json") as f:
runDB = json.load(f)
tier_dir = runDB["tier_dir"]
ds = DataSet(runlist=[555], md='./runDB.json', tier_dir=tier_dir)
t2df = ds.get_t2df()
t2df = t2df.loc[t2df.e_ftp > 500] # Low energy cut
#print(t2df.columns)
# print(t2df)
# exit()
# 4 to 36 pF variable cap
rise_time = t2df["tp90"] - t2df["tp10"]
ds2 = DataSet(runlist=[556], md='./runDB.json', tier_dir=tier_dir)
t2df_2 = ds2.get_t2df()
t2df_2 = t2df_2.loc[t2df_2.e_ftp > 500]
rise_time2 = t2df_2["tp90"] - t2df_2["tp10"]
ds3 = DataSet(runlist=[554], md='./runDB.json', tier_dir=tier_dir)
t2df_3 = ds3.get_t2df()
t2df_3 = t2df_3.loc[t2df_3.e_ftp > 500]
rise_time3 = t2df_3["tp90"] - t2df_3["tp10"]
xlo, xhi, xpb = 0., 500., 1
hP, xP, _ = get_hist(rise_time, range=(xlo, xhi), dx=xpb)
hP2, xP2, _ = get_hist(rise_time2, range=(xlo, xhi), dx=xpb)
hP3, xP3, _ = get_hist(rise_time3, range=(xlo, xhi), dx=xpb)
#Note to self: for risetime histograms, use similar to above, but replace
#first parameter with rise_time!
plt.semilogy(xP[:-1] * 0.423,
hP,
ls='steps',
lw=1.5,
c='k',
label="Rise Time, Preamp 1".format(sum(hP)))
# hist = plt.hist(rise_time, bins = 1000)
plt.semilogy(xP2[:-1] * 0.423,
hP2,
ls='steps',
lw=1.5,
c='c',
label="Rise Time, Preamp 2".format(sum(hP)))
plt.semilogy(xP3[:-1] * 0.423,
hP3,
ls='steps',
lw=1.5,
c='0.5',
label="Rise Time, Preamp 0".format(sum(hP)))
plt.xlabel("Rise Time", ha='right', x=1)
plt.ylabel("Counts", ha='right', y=1)
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("Rise Time Comparison")
def main():
"""
tumbsi analysis suite
"""
global display
display = 1 # allow displaying intermediate distributions for control
run_db, cal_db = "runDB.json", "calDB.json"
with open(run_db) as f:
runDB = json.load(f)
global tier_dir
tier_dir = runDB["tier_dir"]
global meta_dir
meta_dir = runDB["meta_dir"]
global dep_line
dep_line = 1592.5
global dep_acc
dep_acc = 0.9
peaks_of_interest = sorted(runDB["peaks_of_interest"], reverse=True)
# take calibration parameter for the 'calibration.py' output
with open(cal_db) as f:
calDB = json.load(f)
par = argparse.ArgumentParser(description="calibration suite for tumbsi")
arg, st, sf = par.add_argument, "store_true", "store_false"
arg("-ds", nargs='*', action="store", help="load runs for a DS")
arg("-r", "--run", nargs=1, help="load a single run")
args = vars(par.parse_args())
ecal = np.zeros(3)
ecal[0] = calDB["cal_pass1"]["1"]["p1cal"]
if ("cal_pass2" in calDB):
ecal[1] = calDB["cal_pass2"]["1"]["p2acal"]
ecal[2] = calDB["cal_pass2"]["1"]["p2bcal"]
eres = np.zeros(2)
if ("eres_curve" in calDB):
eres[0] = calDB["eres_curve"]["1"]["acal"]
eres[1] = calDB["eres_curve"]["1"]["bcal"]
else:
print(
"You must run a calibration to get the energy resolution curve. Exit."
)
sys.exit()
# Which run number is the being analyzed
if args["ds"]:
ds_lo = int(args["ds"][0])
try:
ds_hi = int(args["ds"][1])
except:
ds_hi = None
ds = DataSet(ds_lo, ds_hi, md=run_db, cal=cal_db)
run = ds_lo
if args["run"]:
ds = DataSet(run=int(args["run"][0]), md=run_db, cal=cal_db)
print("")
print("Start Pulse Shape Anlysis")
print("")
psa(run, ds, ecal, eres, peaks_of_interest)
def process_data():
from pygama import DataSet
ds = DataSet(0, md="config.json")
ds.daq_to_raw(overwrite=True, test=False)
def psa(run, dataset, ecal, eres, peaks_of_interest):
# ds = DataSet(runlist=[191], md='./runDB.json', tier_dir=tier_dir)
ds = DataSet(ds_lo=0, md='./runDB.json', tier_dir=tier_dir)
t2 = ds.get_t2df()
# t2df = os.path.expandvars('{}/Spectrum_{}.hdf5'.format(meta_dir,run))
# t2df = pd.read_hdf(t2df, key="df")
# t2df = t2df.reset_index(drop=True)
t2 = t2.reset_index(drop=True)
print(" Energy calibration:")
cal = ecal[0] * np.asarray(t2["e_ftp"])
print(" -> 1st pass linear energy calibration done")
if (cal[1]):
cal = cal / ecal[1] - ecal[2]
print(" -> 2nd pass linear energy calibration done")
n = "current_max"
e = "e_cal"
e_over_unc = cal / np.asarray(t2["e_ftp"])
aoe0 = np.asarray(t2[n])
print(" Apply quality cuts")
Nall = len(cal)
bl0 = np.asarray(t2["bl0"])
bl1 = np.asarray(t2["bl1"])
e_over_unc = e_over_unc[(bl1 - bl0) < 2]
cal = cal[(bl1 - bl0) < 2]
aoe0 = aoe0[(bl1 - bl0) < 2]
Nqc_acc = len(cal)
print(" -> Total number of events: ", Nall)
print(" -> After quality cuts : ", Nqc_acc)
print(" -> Quality cuts rejection: ", 100 * float(Nqc_acc) / float(Nall),
"%")
aoe = aoe0 * e_over_unc / cal
print(" Compute AoE normalization curve")
aoe_norm = AoEcorrection(cal, aoe)
print(" -> parameteres (a x E + b):", aoe_norm[0], aoe_norm[1])
aoe = aoe / (aoe_norm[0] * cal + aoe_norm[1])
print(" Find the low-side A/E cut for ", 100 * dep_acc,
"% 208Tl DEP acceptance")
cut = get_aoe_cut(cal, aoe, dep_line, eres)
print(" -> cut: ", '{:1.3f}'.format(cut))
if cut == 0:
print(" -> cut not found. Exit.")
sys.exit()
print(" Compute energy spectrum after A/E cut")
cal_cut = cal[aoe >= cut]
print(" Compute survival fractions: ")
sf = np.zeros(len(peaks_of_interest))
sferr = np.zeros(len(peaks_of_interest))
for i, peak in enumerate(peaks_of_interest):
sf[i], sferr[i] = get_sf(cal, aoe, cut, peak, eres)
print(" -> ", peak, '{:2.1f}'.format(100. * sf[i]), " +/- ",
'{:2.1f}'.format(100. * sferr[i]), "%")
print(" Display hitograms")
plt.figure(2)
plt.hist2d(cal,
aoe,
bins=[2000, 400],
range=[[0, 3000], [0, 1.5]],
norm=LogNorm(),
cmap='jet')
cbar = plt.colorbar()
plt.title("Dataset {}".format(dataset))
plt.xlabel("Energy (keV)", ha='right', x=1)
plt.ylabel("A/E (a.u.)", ha='right', y=1)
cbar.ax.set_ylabel('Counts')
plt.tight_layout()
plt.savefig('./plots/aoe_versus_energy.pdf',
bbox_inches='tight',
transparent=True)
plt.show()
plt.figure(3)
hist, bins = np.histogram(cal, bins=3000, range=[0, 3000])
hist1, bins1 = np.histogram(cal_cut, bins=3000, range=[0, 3000])
plt.clf()
plt.plot(bins[1:],
hist,
color='black',
ls="steps",
linewidth=1.5,
label='all events')
plt.plot(bins1[1:],
hist1,
'-r',
ls="steps",
linewidth=1.5,
label='after A/E cut')
plt.ylabel('Counts', ha='right', y=1)
plt.xlabel('Energy (keV)', ha='right', x=1)
plt.legend(title='Calibrated Energy')
plt.yscale('log')
plt.savefig('./plots/calEnergy_spectrum_after_psa.pdf',
bbox_inches='tight',
transparent=True)
plt.show()
print("")
print(" Normal termination")
print("")
