def IRENE(argv=sys.argv):
"""
IRENE DRIVER
"""
CFP = configure(argv)
fpp = Irene(run_number=CFP['RUN_NUMBER'])
files_in = glob(CFP['FILE_IN'])
files_in.sort()
fpp.set_input_files(files_in)
fpp.set_pmap_store(CFP['FILE_OUT'],
compression=CFP['COMPRESSION'])
fpp.set_print(nprint=CFP['NPRINT'])
fpp.set_BLR(n_baseline=CFP['NBASELINE'],
thr_trigger=CFP['THR_TRIGGER']*units.adc)
fpp.set_MAU(n_MAU=CFP['NMAU'], thr_MAU=CFP['THR_MAU']*units.adc)
fpp.set_CSUM(thr_csum=CFP['THR_CSUM']*units.pes)
fpp.set_S1(tmin=CFP['S1_TMIN']*units.mus, tmax=CFP['S1_TMAX']*units.mus,
stride=CFP['S1_STRIDE'], lmin=CFP['S1_LMIN'],
lmax=CFP['S1_LMAX'])
fpp.set_S2(tmin=CFP['S2_TMIN']*units.mus, tmax=CFP['S2_TMAX']*units.mus,
stride=CFP['S2_STRIDE'], lmin=CFP['S2_LMIN'],
lmax=CFP['S2_LMAX'])
fpp.set_SiPM(thr_zs=CFP['THR_ZS']*units.pes,
thr_sipm_s2=CFP['THR_SIPM_S2']*units.pes)
t0 = time()
nevts = CFP['NEVENTS'] if not CFP['RUN_ALL'] else -1
nevt = fpp.run(nmax=nevts, store_pmaps=True)
t1 = time()
dt = t1 - t0
print("run {} evts in {} s, time/event = {}".format(nevt, dt, dt/nevt))
def ANASTASIA(argv=sys.argv):
"""
ANASTASIA driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print(__doc__)
# Increate thresholds by 1% for safety
PMT_NOISE_CUT_RAW = CFP["PMT_NOISE_CUT_RAW"] * 1.01
PMT_NOISE_CUT_BLR = CFP["PMT_NOISE_CUT_BLR"] * 1.01
SIPM_ZS_METHOD = CFP["SIPM_ZS_METHOD"]
SIPM_NOISE_CUT = CFP["SIPM_NOISE_CUT"]
COMPRESSION = CFP["COMPRESSION"]
with tb.open_file(CFP["FILE_IN"], "r+",
filters=tbl.filters(CFP["COMPRESSION"])) as h5in:
pmtblr = h5in.root.RD.pmtblr
pmtcwf = h5in.root.RD.pmtcwf
sipmrwf = h5in.root.RD.sipmrwf
pmtdf = DB.DataPMT()
sipmdf = DB.DataSiPM()
NEVT, NPMT, PMTWL = pmtcwf.shape
NEVT, NSIPM, SIPMWL = sipmrwf.shape
print_configuration({"# PMT": NPMT, "PMT WL": PMTWL,
"# SiPM": NSIPM, "SIPM WL": SIPMWL,
"# events in DST": NEVT})
# Create instance of the noise sampler and compute noise thresholds
sipms_noise_sampler_ = SiPMsNoiseSampler(SIPMWL)
if SIPM_ZS_METHOD == "FRACTION":
sipms_thresholds_ = sipms_noise_sampler_.ComputeThresholds(
SIPM_NOISE_CUT, sipmdf['adc_to_pes'])
else:
sipms_thresholds_ = np.ones(NSIPM) * SIPM_NOISE_CUT
if "/ZS" not in h5in:
h5in.create_group(h5in.root, "ZS")
if "/ZS/PMT" in h5in:
h5in.remove_node("/ZS", "PMT")
if "/ZS/BLR" in h5in:
h5in.remove_node("/ZS", "BLR")
if "/ZS/SiPM" in h5in:
h5in.remove_node("/ZS", "SiPM")
# Notice the Int16, not Float32! bad for compression
pmt_zs_ = h5in.create_earray(h5in.root.ZS, "PMT",
atom=tb.Int16Atom(),
shape=(0, NPMT, PMTWL),
expectedrows=NEVT,
filters=tbl.filters(COMPRESSION))
blr_zs_ = h5in.create_earray(h5in.root.ZS, "BLR",
atom=tb.Int16Atom(),
shape=(0, NPMT, PMTWL),
expectedrows=NEVT,
filters=tbl.filters(COMPRESSION))
sipm_zs_ = h5in.create_earray(h5in.root.ZS, "SiPM",
atom=tb.Int16Atom(),
shape=(0, NSIPM, SIPMWL),
expectedrows=NEVT,
filters=tbl.filters(COMPRESSION))
adc_to_pes = abs(1.0/pmtdf["adc_to_pes"].reshape(NPMT, 1))
t0 = time()
for i in define_event_loop(CFP, NEVT):
sumpmt = np.sum(pmtcwf[i] * adc_to_pes, axis=0)
selection = np.tile(sumpmt > PMT_NOISE_CUT_RAW, (NPMT, 1))
pmtzs = np.where(selection, pmtcwf[i], 0)
blr = wfm.subtract_baseline(FE.CEILING - pmtblr[i])
sumpmt = np.sum(blr * adc_to_pes, axis=0)
selection = np.tile(sumpmt > PMT_NOISE_CUT_BLR, (NPMT, 1))
blrzs = np.where(selection, blr, 0)
pmt_zs_.append(pmtzs[np.newaxis])
blr_zs_.append(blrzs[np.newaxis])
sipmzs = sipmrwf[i]
if "/MC" not in h5in:
sipmzs = wfm.subtract_baseline(sipmzs, 200)
sipmzs = wfm.noise_suppression(sipmzs, sipms_thresholds_)
sipm_zs_.append(sipmzs[np.newaxis])
t1 = time()
dt = t1-t0
print("ANASTASIA has run over {} events in {} seconds".format(i+1, dt))
print("Leaving ANASTASIA. Safe travels!")
def DOROTHEA(argv=sys.argv):
"""
DOROTHEA driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print(__doc__)
COMPRESSION = CFP["COMPRESSION"]
# open the input file
with tb.open_file(CFP["FILE_IN"], "r") as h5in:
# access the PMT ZS data in file
pmtzs_ = h5in.root.ZS.PMT
blrzs_ = h5in.root.ZS.BLR
sipmzs_ = h5in.root.ZS.SiPM
NEVT, NPMT, PMTWL = pmtzs_.shape
NEVT, NSIPM, SIPMWL = sipmzs_.shape
print_configuration({"# PMT": NPMT, "PMT WL": PMTWL,
"# SiPM": NSIPM, "SIPM WL": SIPMWL,
"# events in DST": NEVT})
pmtdf = DB.DataPMT()
sipmdf = DB.DataSiPM()
pmt_to_pes = abs(1.0 / pmtdf.adc_to_pes.reshape(NPMT, 1))
sipm_to_pes = abs(1.0 / sipmdf.adc_to_pes.reshape(NSIPM, 1))
# open the output file
with tb.open_file(CFP["FILE_OUT"], "w",
filters=tbl.filters(COMPRESSION)) as h5out:
# create groups and copy MC data to the new file
if "/MC" in h5in:
mcgroup = h5out.create_group(h5out.root, "MC")
twfgroup = h5out.create_group(h5out.root, "TWF")
h5in.root.MC.MCTracks.copy(newparent=mcgroup)
h5in.root.MC.FEE.copy(newparent=mcgroup)
h5in.root.TWF.PMT.copy(newparent=twfgroup)
h5in.root.TWF.SiPM.copy(newparent=twfgroup)
if "/Run" in h5in:
rungroup = h5out.create_group(h5out.root, "Run")
h5in.root.Run.runInfo.copy(newparent=rungroup)
h5in.root.Run.events.copy(newparent=rungroup)
pmapsgroup = h5out.create_group(h5out.root, "PMAPS")
# create a table to store pmaps (rebined, linked, zs wfs)
pmaps_ = h5out.create_table(pmapsgroup, "PMaps", PMAP,
"Store for PMaps",
tbl.filters(COMPRESSION))
pmaps_blr_ = h5out.create_table(pmapsgroup, "PMapsBLR", PMAP,
"Store for PMaps made with BLR",
tbl.filters(COMPRESSION))
# add index in event column
pmaps_.cols.event.create_index()
pmaps_blr_.cols.event.create_index()
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVT):
pmtwf = np.sum(pmtzs_[i] * pmt_to_pes, axis=0)
blrwf = np.sum(blrzs_[i] * pmt_to_pes, axis=0)
sipmwfs = sipmzs_[i] * sipm_to_pes
pmap = build_pmap(pmtwf, sipmwfs)
classify_peaks(pmap, **CFP)
tbl.store_pmap(pmap, pmaps_, i)
pmap_blr = build_pmap(blrwf, sipmwfs)
classify_peaks(pmap_blr, **CFP)
tbl.store_pmap(pmap_blr, pmaps_blr_, i)
t1 = time()
dt = t1 - t0
print("DOROTHEA has run over {} events in {} seconds".format(i+1,
dt))
print("Leaving DOROTHEA. Safe travels!")
def DOROTHEA(argv=sys.argv):
"""
DOROTHEA driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print(__doc__)
COMPRESSION = CFP["COMPRESSION"]
# open the input file
with tb.open_file(CFP["FILE_IN"], "r") as h5in:
# access the PMT ZS data in file
pmtzs_ = h5in.root.ZS.PMT
blrzs_ = h5in.root.ZS.BLR
sipmzs_ = h5in.root.ZS.SiPM
NEVT, NPMT, PMTWL = pmtzs_.shape
NEVT, NSIPM, SIPMWL = sipmzs_.shape
print_configuration({
"# PMT": NPMT,
"PMT WL": PMTWL,
"# SiPM": NSIPM,
"SIPM WL": SIPMWL,
"# events in DST": NEVT
})
pmtdf = DB.DataPMT()
sipmdf = DB.DataSiPM()
pmt_to_pes = abs(1.0 / pmtdf.adc_to_pes.reshape(NPMT, 1))
sipm_to_pes = abs(1.0 / sipmdf.adc_to_pes.reshape(NSIPM, 1))
# open the output file
with tb.open_file(CFP["FILE_OUT"],
"w",
filters=tbl.filters(COMPRESSION)) as h5out:
# create groups and copy MC data to the new file
if "/MC" in h5in:
mcgroup = h5out.create_group(h5out.root, "MC")
twfgroup = h5out.create_group(h5out.root, "TWF")
h5in.root.MC.MCTracks.copy(newparent=mcgroup)
h5in.root.MC.FEE.copy(newparent=mcgroup)
h5in.root.TWF.PMT.copy(newparent=twfgroup)
h5in.root.TWF.SiPM.copy(newparent=twfgroup)
if "/Run" in h5in:
rungroup = h5out.create_group(h5out.root, "Run")
h5in.root.Run.runInfo.copy(newparent=rungroup)
h5in.root.Run.events.copy(newparent=rungroup)
pmapsgroup = h5out.create_group(h5out.root, "PMAPS")
# create a table to store pmaps (rebined, linked, zs wfs)
pmaps_ = h5out.create_table(pmapsgroup, "PMaps", PMAP,
"Store for PMaps",
tbl.filters(COMPRESSION))
pmaps_blr_ = h5out.create_table(pmapsgroup, "PMapsBLR", PMAP,
"Store for PMaps made with BLR",
tbl.filters(COMPRESSION))
# add index in event column
pmaps_.cols.event.create_index()
pmaps_blr_.cols.event.create_index()
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVT):
pmtwf = np.sum(pmtzs_[i] * pmt_to_pes, axis=0)
blrwf = np.sum(blrzs_[i] * pmt_to_pes, axis=0)
sipmwfs = sipmzs_[i] * sipm_to_pes
pmap = build_pmap(pmtwf, sipmwfs)
classify_peaks(pmap, **CFP)
tbl.store_pmap(pmap, pmaps_, i)
pmap_blr = build_pmap(blrwf, sipmwfs)
classify_peaks(pmap_blr, **CFP)
tbl.store_pmap(pmap_blr, pmaps_blr_, i)
t1 = time()
dt = t1 - t0
print("DOROTHEA has run over {} events in {} seconds".format(
i + 1, dt))
print("Leaving DOROTHEA. Safe travels!")
def ISIDORA(argv=sys.argv):
CFP = configure(argv)
if CFP["INFO"]:
print("""
ISIDORA:
1. Reads an Nh5 file produced by DIOMIRA, which stores the
raw waveforms (RWF) for the PMTs and SiPMs waveforms, as well as
data on geometry, sensors and MC. The RDWF of the PMTs
show negative swing due to the HPF of the EP FEE electronics
2. Performs DBLR on the PMT RWF and produces corrected waveforms (CWF).
3. Adds the CWF and ancilliary info to the DST
4. Computes the energy of the PMTs per each event and writes to DST
""")
N_BASELINE = CFP["N_BASELINE"]
THR_TRIGGER = CFP["THR_TRIGGER"]
ACUM_DISCHARGE_LENGTH = CFP["ACUM_DISCHARGE_LENGTH"]
ACUM_TAU = CFP["ACUM_TAU"]
ACUM_COMPRESS = CFP["ACUM_COMPRESS"]
COMPRESSION = CFP["COMPRESSION"]
# open the input file in mode append
with tb.open_file(CFP["FILE_IN"], "a",
filters=tbl.filters(COMPRESSION)) as h5in:
# access the PMT raw data in file
pmtrd_ = h5in.root.RD.pmtrwf # PMT raw data must exist
NEVENTS_DST, NPMT, PMTWL = pmtrd_.shape
print_configuration({"# PMT": NPMT, "PMT WL": PMTWL,
"# events in DST": NEVENTS_DST})
# create an extensible array to store the CWF waveforms
# if it exists remove and create again
if "/RD/pmtcwf" in h5in:
h5in.remove_node("/RD", "pmtcwf")
pmtcwf = h5in.create_earray(h5in.root.RD, "pmtcwf",
atom=tb.Int16Atom(),
shape=(0, NPMT, PMTWL),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
# if "/RD/pmtacum" in h5in:
# h5in.remove_node("/RD", "pmtacum")
#
# pmtacum = h5in.create_earray(h5in.root.RD, "pmtacum",
# atom=tb.Int16Atom(),
# shape=(0, NPMT, PMTWL),
# expectedrows=NEVENTS_DST)
if "/Deconvolution" not in h5in:
h5in.create_group(h5in.root, "Deconvolution")
if "/Deconvolution/Parameters" in h5in:
h5in.remove_node("/Deconvolution", "Parameters")
if "/Deconvolution/BL" in h5in:
h5in.remove_node("/Deconvolution", "BL")
deconv_table = h5in.create_table(h5in.root.Deconvolution,
"Parameters",
DECONV_PARAM,
"Deconvolution parameters",
tbl.filters("NOCOMPR"))
tbl.store_deconv_table(deconv_table, CFP)
bl_array = h5in.create_earray(h5in.root.Deconvolution, "BL",
atom=tb.Int16Atom(),
shape=(0, NPMT, 3),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVENTS_DST):
data = DBLR(pmtrd_[i],
n_baseline=N_BASELINE,
thr_trigger=THR_TRIGGER,
discharge_length=ACUM_DISCHARGE_LENGTH,
acum_tau=ACUM_TAU,
acum_compress=ACUM_COMPRESS)
signal_r, bl_data = data[0], data[2:]
# append to pmtcwf
pmtcwf.append(signal_r.reshape(1, NPMT, PMTWL))
bl_array.append(np.array(bl_data).T[np.newaxis])
# append to pmtacum
# pmtacum.append(acum.reshape(1, NPMT, PMTWL))
t1 = time()
dt = t1 - t0
pmtcwf.flush()
bl_array.flush()
# pmtacum.flush()
print("ISIDORA has run over {} events in {} seconds".format(i+1, dt))
print("Leaving ISIDORA. Safe travels!")
def DIOMIRA(argv=sys.argv):
"""
Diomira driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print("""
DIOMIRA:
1. Reads a MCRD file produced by art/centella, which stores MCRD
waveforms for PMTs (bins of 1 ns) and SiPMs (bins of 1 mus)
2. Simulates the response of the energy plane and outputs both RWF
and TWF
3. Simulates the response of the tracking plane in the SiPMs and
outputs SiPM RWF
4. Add a table describing the FEE parameters used for simulation
5. Copies the tables on geometry, detector data and MC
""")
# open the input file
with tables.open_file(CFP["FILE_IN"], "r") as h5in:
# access the PMT raw data in file
pmtrd_ = h5in.root.pmtrd
sipmrd_ = h5in.root.sipmrd
NEVENTS_DST, NPMT, PMTWL = pmtrd_.shape
PMTWL_FEE = int(PMTWL / FE.t_sample)
NEVENTS_DST, NSIPM, SIPMWL = sipmrd_.shape
print_configuration({
"# PMT": NPMT,
"PMT WL": PMTWL,
"PMT WL (FEE)": PMTWL_FEE,
"# SiPM": NSIPM,
"SIPM WL": SIPMWL,
"# events in DST": NEVENTS_DST
})
# access the geometry and the sensors metadata info
mctrk_t = h5in.root.MC.MCTracks
sipmdf = DB.DataSiPM()
# Create instance of the noise sampler
noise_sampler_ = SiPMsNoiseSampler(SIPMWL, True)
sipms_thresholds_ = CFP["NOISE_CUT"] * np.array(sipmdf["adc_to_pes"])
COMPRESSION = CFP["COMPRESSION"]
# open the output file
with tables.open_file(CFP["FILE_OUT"],
"w",
filters=tbl.filters(COMPRESSION)) as h5out:
# create a group to store sensors IDs
sgroup = h5out.create_group(h5out.root, "Sensors")
# copy DataSiPM & DataPMT table
h5in.root.Sensors.DataSiPM.copy(newparent=sgroup)
h5in.root.Sensors.DataPMT.copy(newparent=sgroup)
# create a group to store MC data
mcgroup = h5out.create_group(h5out.root, "MC")
# copy the mctrk table
mctrk_t.copy(newparent=mcgroup)
# create a table to store Energy plane FEE, hang it from MC group
fee_table = h5out.create_table(mcgroup, "FEE", FEE,
"EP-FEE parameters",
tbl.filters("NOCOMPR"))
# create a group to store True waveform data
twfgroup = h5out.create_group(h5out.root, "TWF")
# create a table to store true waveform (zs, rebinned)
pmt_twf_table = h5out.create_table(twfgroup, "PMT", SENSOR_WF,
"Store for PMTs TWF",
tbl.filters(COMPRESSION))
sipm_twf_table = h5out.create_table(twfgroup, "SiPM", SENSOR_WF,
"Store for SiPM TWF",
tbl.filters(COMPRESSION))
# and index in event column
pmt_twf_table.cols.event.create_index()
sipm_twf_table.cols.event.create_index()
# fill FEE table
tbl.store_FEE_table(fee_table)
# create a group to store RawData
h5out.create_group(h5out.root, "RD")
# create an extensible array to store the RWF waveforms
pmtrwf = h5out.create_earray(h5out.root.RD,
"pmtrwf",
atom=tables.Int16Atom(),
shape=(0, NPMT, PMTWL_FEE),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
pmtblr = h5out.create_earray(h5out.root.RD,
"pmtblr",
atom=tables.Int16Atom(),
shape=(0, NPMT, PMTWL_FEE),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
sipmrwf = h5out.create_earray(h5out.root.RD,
"sipmrwf",
atom=tables.Int16Atom(),
shape=(0, NSIPM, SIPMWL),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVENTS_DST):
# supress zeros in MCRD and rebin the ZS function in 1 mus bins
rebin = int(units.mus / units.ns)
trueSiPM = wfm.zero_suppression(sipmrd_[i], 0.)
# dict_map applies a function to the dictionary values
truePMT = cf.dict_map(
lambda df: wfm.rebin_df(df, rebin),
wfm.zero_suppression(pmtrd_[i],
0.,
to_mus=int(units.ns / units.ms)))
# store in table
tbl.store_wf_table(i, pmt_twf_table, truePMT)
tbl.store_wf_table(i, sipm_twf_table, trueSiPM)
# simulate PMT response and return an array with RWF;BLR
# convert to float, append to EVector
dataPMT, blrPMT = simulate_pmt_response(i, pmtrd_)
pmtrwf.append(dataPMT.astype(int).reshape(1, NPMT, PMTWL_FEE))
pmtblr.append(blrPMT.astype(int).reshape(1, NPMT, PMTWL_FEE))
# simulate SiPM response and return an array with RWF
# convert to float, zero suppress and dump to table
dataSiPM = simulate_sipm_response(i, sipmrd_, noise_sampler_)
dataSiPM = wfm.to_adc(dataSiPM, sipmdf)
dataSiPM = wfm.noise_suppression(dataSiPM, sipms_thresholds_)
sipmrwf.append(dataSiPM.astype(int).reshape(1, NSIPM, SIPMWL))
pmtrwf.flush()
sipmrwf.flush()
pmtblr.flush()
t1 = time()
dt = t1 - t0
print("DIOMIRA has run over {} events in {} seconds".format(
i + 1, dt))
print("Leaving Diomira. Safe travels!")
def ISIDORA(argv=sys.argv):
CFP = configure(argv)
if CFP["INFO"]:
print("""
ISIDORA:
1. Reads an Nh5 file produced by DIOMIRA, which stores the
raw waveforms (RWF) for the PMTs and SiPMs waveforms, as well as
data on geometry, sensors and MC. The RDWF of the PMTs
show negative swing due to the HPF of the EP FEE electronics
2. Performs DBLR on the PMT RWF and produces corrected waveforms (CWF).
3. Adds the CWF and ancilliary info to the DST
4. Computes the energy of the PMTs per each event and writes to DST
""")
N_BASELINE = CFP["N_BASELINE"]
THR_TRIGGER = CFP["THR_TRIGGER"]
ACUM_DISCHARGE_LENGTH = CFP["ACUM_DISCHARGE_LENGTH"]
ACUM_TAU = CFP["ACUM_TAU"]
ACUM_COMPRESS = CFP["ACUM_COMPRESS"]
# open the input file in mode append
with tb.open_file(CFP["FILE_IN"], "a") as h5in:
# access the PMT raw data in file
pmtrd_ = h5in.root.RD.pmtrwf # PMT raw data must exist
NEVENTS_DST, NPMT, PMTWL = pmtrd_.shape
logger.info("nof PMTs = {} WF side = {} ".format(NPMT, PMTWL))
logger.info("nof events in input DST = {} ".format(NEVENTS_DST))
# create an extensible array to store the CWF waveforms
# if it exists remove and create again
if "/RD/pmtcwf" in h5in:
h5in.remove_node("/RD", "pmtcwf")
pmtcwf = h5in.create_earray(h5in.root.RD,
"pmtcwf",
atom=tb.Int16Atom(),
shape=(0, NPMT, PMTWL),
expectedrows=NEVENTS_DST)
# if "/RD/pmtacum" in h5in:
# h5in.remove_node("/RD", "pmtacum")
#
# pmtacum = h5in.create_earray(h5in.root.RD, "pmtacum",
# atom=tb.Int16Atom(),
# shape=(0, NPMT, PMTWL),
# expectedrows=NEVENTS_DST)
if "/Deconvolution" not in h5in:
h5in.create_group(h5in.root, "Deconvolution")
if "/Deconvolution/Parameters" in h5in:
h5in.remove_node("/Deconvolution", "Parameters")
deconv_table = h5in.create_table(h5in.root.Deconvolution, "Parameters",
DECONV_PARAM,
"Deconvolution parameters",
tbl.filters("NOCOMPR"))
tbl.store_deconv_table(deconv_table, CFP)
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVENTS_DST):
signal_r, acum, baseline, baseline_end, \
noise_rms = DBLR(pmtrd_[i],
n_baseline=N_BASELINE,
thr_trigger=THR_TRIGGER,
acum_discharge_length=ACUM_DISCHARGE_LENGTH,
acum_tau=ACUM_TAU,
acum_compress=ACUM_COMPRESS)
# append to pmtcwf
pmtcwf.append(signal_r.reshape(1, NPMT, PMTWL))
# append to pmtacum
#pmtacum.append(acum.reshape(1, NPMT, PMTWL))
t1 = time()
dt = t1 - t0
pmtcwf.flush()
#pmtacum.flush()
print("ISIDORA has run over {} events in {} seconds".format(i + 1, dt))
print("Leaving ISIDORA. Safe travels!")
def DIOMIRA(argv=sys.argv):
"""
Diomira driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print("""
DIOMIRA:
1. Reads a MCRD file produced by art/centella, which stores MCRD
waveforms for PMTs (bins of 1 ns) and SiPMs (bins of 1 mus)
2. Simulates the response of the energy plane and outputs both RWF
and TWF
3. Simulates the response of the tracking plane in the SiPMs and
outputs SiPM RWF
4. Add a table describing the FEE parameters used for simulation
5. Copies the tables on geometry, detector data and MC
""")
# open the input file
with tables.open_file(CFP["FILE_IN"], "r") as h5in:
# access the PMT raw data in file
pmtrd_ = h5in.root.pmtrd
sipmrd_ = h5in.root.sipmrd
NEVENTS_DST, NPMT, PMTWL = pmtrd_.shape
PMTWL_FEE = int(PMTWL/FE.t_sample)
NEVENTS_DST, NSIPM, SIPMWL = sipmrd_.shape
print_configuration({"# PMT": NPMT, "PMT WL": PMTWL,
"PMT WL (FEE)": PMTWL_FEE,
"# SiPM": NSIPM, "SIPM WL": SIPMWL,
"# events in DST": NEVENTS_DST})
# access the geometry and the sensors metadata info
mctrk_t = h5in.root.MC.MCTracks
sipmdf = DB.DataSiPM()
# Create instance of the noise sampler
noise_sampler_ = SiPMsNoiseSampler(SIPMWL, True)
sipms_thresholds_ = CFP["NOISE_CUT"] * np.array(sipmdf["adc_to_pes"])
COMPRESSION = CFP["COMPRESSION"]
# open the output file
with tables.open_file(CFP["FILE_OUT"], "w",
filters=tbl.filters(COMPRESSION)) as h5out:
# create a group to store sensors IDs
sgroup = h5out.create_group(h5out.root, "Sensors")
# copy DataSiPM & DataPMT table
h5in.root.Sensors.DataSiPM.copy(newparent=sgroup)
h5in.root.Sensors.DataPMT.copy(newparent=sgroup)
# create a group to store MC data
mcgroup = h5out.create_group(h5out.root, "MC")
# copy the mctrk table
mctrk_t.copy(newparent=mcgroup)
# create a table to store Energy plane FEE, hang it from MC group
fee_table = h5out.create_table(mcgroup, "FEE", FEE,
"EP-FEE parameters",
tbl.filters("NOCOMPR"))
# create a group to store True waveform data
twfgroup = h5out.create_group(h5out.root, "TWF")
# create a table to store true waveform (zs, rebinned)
pmt_twf_table = h5out.create_table(twfgroup, "PMT", SENSOR_WF,
"Store for PMTs TWF",
tbl.filters(COMPRESSION))
sipm_twf_table = h5out.create_table(twfgroup, "SiPM", SENSOR_WF,
"Store for SiPM TWF",
tbl.filters(COMPRESSION))
# and index in event column
pmt_twf_table.cols.event.create_index()
sipm_twf_table.cols.event.create_index()
# fill FEE table
tbl.store_FEE_table(fee_table)
# create a group to store RawData
h5out.create_group(h5out.root, "RD")
# create an extensible array to store the RWF waveforms
pmtrwf = h5out.create_earray(h5out.root.RD, "pmtrwf",
atom=tables.Int16Atom(),
shape=(0, NPMT, PMTWL_FEE),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
pmtblr = h5out.create_earray(h5out.root.RD, "pmtblr",
atom=tables.Int16Atom(),
shape=(0, NPMT, PMTWL_FEE),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
sipmrwf = h5out.create_earray(h5out.root.RD, "sipmrwf",
atom=tables.Int16Atom(),
shape=(0, NSIPM, SIPMWL),
expectedrows=NEVENTS_DST,
filters=tbl.filters(COMPRESSION))
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVENTS_DST):
# supress zeros in MCRD and rebin the ZS function in 1 mus bins
rebin = int(units.mus/units.ns)
trueSiPM = wfm.zero_suppression(sipmrd_[i], 0.)
# dict_map applies a function to the dictionary values
truePMT = cf.dict_map(lambda df: wfm.rebin_df(df, rebin),
wfm.zero_suppression(pmtrd_[i],
0., to_mus=int(units.ns/units.ms)))
# store in table
tbl.store_wf_table(i, pmt_twf_table, truePMT)
tbl.store_wf_table(i, sipm_twf_table, trueSiPM)
# simulate PMT response and return an array with RWF;BLR
# convert to float, append to EVector
dataPMT, blrPMT = simulate_pmt_response(i, pmtrd_)
pmtrwf.append(dataPMT.astype(int).reshape(1, NPMT, PMTWL_FEE))
pmtblr.append(blrPMT.astype(int).reshape(1, NPMT, PMTWL_FEE))
# simulate SiPM response and return an array with RWF
# convert to float, zero suppress and dump to table
dataSiPM = simulate_sipm_response(i, sipmrd_, noise_sampler_)
dataSiPM = wfm.to_adc(dataSiPM, sipmdf)
dataSiPM = wfm.noise_suppression(dataSiPM, sipms_thresholds_)
sipmrwf.append(dataSiPM.astype(int).reshape(1, NSIPM, SIPMWL))
pmtrwf.flush()
sipmrwf.flush()
pmtblr.flush()
t1 = time()
dt = t1 - t0
print("DIOMIRA has run over {} events in {} seconds".format(i+1,
dt))
print("Leaving Diomira. Safe travels!")
def ANASTASIA(argv=sys.argv):
"""
ANASTASIA driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print(__doc__)
# Increate thresholds by 1% for safety
PMT_NOISE_CUT_RAW = CFP["PMT_NOISE_CUT_RAW"] * 1.01
PMT_NOISE_CUT_BLR = CFP["PMT_NOISE_CUT_BLR"] * 1.01
SIPM_ZS_METHOD = CFP["SIPM_ZS_METHOD"]
SIPM_NOISE_CUT = CFP["SIPM_NOISE_CUT"]
with tb.open_file(CFP["FILE_IN"], "r+") as h5in:
pmtblr = h5in.root.RD.pmtblr
pmtcwf = h5in.root.RD.pmtcwf
sipmrwf = h5in.root.RD.sipmrwf
sipmdf = DB.DataSiPM()
NEVT, NPMT, PMTWL = pmtcwf.shape
NEVT, NSIPM, SIPMWL = sipmrwf.shape
logger.info("# events in DST = {}".format(NEVT))
logger.info("#PMTs = {} #SiPMs = {}".format(NPMT, NSIPM))
logger.info("PMT WFL = {} SiPM WFL = {}".format(PMTWL, SIPMWL))
# Create instance of the noise sampler and compute noise thresholds
sipms_noise_sampler_ = SiPMsNoiseSampler(SIPMWL)
if SIPM_ZS_METHOD == "FRACTION":
sipms_thresholds_ = sipms_noise_sampler_.ComputeThresholds(
SIPM_NOISE_CUT, sipmdf['adc_to_pes'])
else:
sipms_thresholds_ = np.ones(NSIPM) * SIPM_NOISE_CUT
if "/ZS" not in h5in:
h5in.create_group(h5in.root, "ZS")
if "/ZS/PMT" in h5in:
h5in.remove_node("/ZS", "PMT")
if "/ZS/BLR" in h5in:
h5in.remove_node("/ZS", "BLR")
if "/ZS/SiPM" in h5in:
h5in.remove_node("/ZS", "SiPM")
# Notice the Int16, not Float32! bad for compression
pmt_zs_ = h5in.create_earray(h5in.root.ZS,
"PMT",
atom=tb.Int16Atom(),
shape=(0, NPMT, PMTWL),
expectedrows=NEVT)
blr_zs_ = h5in.create_earray(h5in.root.ZS,
"BLR",
atom=tb.Int16Atom(),
shape=(0, NPMT, PMTWL),
expectedrows=NEVT)
sipm_zs_ = h5in.create_earray(h5in.root.ZS,
"SiPM",
atom=tb.Int16Atom(),
shape=(0, NSIPM, SIPMWL),
expectedrows=NEVT)
t0 = time()
for i in define_event_loop(CFP, NEVT):
pmtzs = wfm.noise_suppression(pmtcwf[i], PMT_NOISE_CUT_RAW)
blrzs = wfm.subtract_baseline(FE.CEILING - pmtblr[i])
blrzs = wfm.noise_suppression(blrzs, PMT_NOISE_CUT_BLR)
pmt_zs_.append(pmtzs[np.newaxis])
blr_zs_.append(blrzs[np.newaxis])
sipmzs = sipmrwf[i]
if "/MC" not in h5in:
sipmzs = wfm.subtract_baseline(sipmzs, 200)
sipmzs = wfm.noise_suppression(sipmzs, sipms_thresholds_)
sipm_zs_.append(sipmzs[np.newaxis])
t1 = time()
dt = t1 - t0
print("ANASTASIA has run over {} events in {} seconds".format(
i + 1, dt))
print("Leaving ANASTASIA. Safe travels!")
def DOROTHEA(argv=sys.argv):
"""
DOROTHEA driver
"""
CFP = configure(argv)
if CFP["INFO"]:
print(__doc__)
FILE_IN = CFP["FILE_IN"]
FILE_OUT = CFP["FILE_OUT"]
COMPRESSION = CFP["COMPRESSION"]
NEVENTS = CFP["NEVENTS"]
logger.info("Debug level = {}".format(CFP["VERBOSITY"]))
logger.info("Input file = {}".format(FILE_IN))
logger.info("Output file = {}".format(FILE_OUT))
logger.info("# events requested = {}".format(NEVENTS))
logger.info("Compression library/level = {}".format(COMPRESSION))
# open the input file
with tb.open_file(FILE_IN, "r") as h5in:
# access the PMT ZS data in file
pmtzs_ = h5in.root.ZS.PMT
blrzs_ = h5in.root.ZS.BLR
sipmzs_ = h5in.root.ZS.SiPM
NEVT, NPMT, PMTWL = pmtzs_.shape
NEVT, NSIPM, SIPMWL = sipmzs_.shape
logger.info("# events in DST: {}".format(NEVT))
logger.info("# PMTs = {}, # SiPMs = {} ".format(NPMT, NSIPM))
logger.info("PMT WFL = {}, SiPM WFL = {}".format(PMTWL, SIPMWL))
pmtdf = DB.DataPMT()
sipmdf = DB.DataSiPM()
pmt_to_pes = abs(1.0 / pmtdf.adc_to_pes.reshape(NPMT, 1))
sipm_to_pes = abs(1.0 / sipmdf.adc_to_pes.reshape(NSIPM, 1))
# open the output file
with tb.open_file(FILE_OUT, "w",
filters=tbl.filters(COMPRESSION)) as h5out:
# create groups and copy MC data to the new file
if "/MC" in h5in:
mcgroup = h5out.create_group(h5out.root, "MC")
twfgroup = h5out.create_group(h5out.root, "TWF")
h5in.root.MC.MCTracks.copy(newparent=mcgroup)
h5in.root.MC.FEE.copy(newparent=mcgroup)
h5in.root.TWF.PMT.copy(newparent=twfgroup)
h5in.root.TWF.SiPM.copy(newparent=twfgroup)
pmapsgroup = h5out.create_group(h5out.root, "PMAPS")
# create a table to store pmaps (rebined, linked, zs wfs)
pmaps_ = h5out.create_table(pmapsgroup, "PMaps", PMAP,
"Store for PMaps",
tbl.filters(COMPRESSION))
pmaps_blr_ = h5out.create_table(pmapsgroup, "PMapsBLR", PMAP,
"Store for PMaps made with BLR",
tbl.filters(COMPRESSION))
# add index in event column
pmaps_.cols.event.create_index()
pmaps_blr_.cols.event.create_index()
# LOOP
t0 = time()
for i in define_event_loop(CFP, NEVT):
pmtwf = np.sum(pmtzs_[i] * pmt_to_pes, axis=0)
blrwf = np.sum(blrzs_[i] * pmt_to_pes, axis=0)
sipmwfs = sipmzs_[i] * sipm_to_pes
pmap = build_pmap(pmtwf, sipmwfs)
classify_peaks(pmap, **CFP)
tbl.store_pmap(pmap, pmaps_, i)
pmap_blr = build_pmap(blrwf, sipmwfs)
classify_peaks(pmap_blr, **CFP)
tbl.store_pmap(pmap_blr, pmaps_blr_, i)
t1 = time()
dt = t1 - t0
print("DOROTHEA has run over {} events in {} seconds".format(
i + 1, dt))
print("Leaving DOROTHEA. Safe travels!")