def diomira_cwf_blr(ffile, n_evt=10, plot=False):
""" 1) Reads the RWF and BLR waveforms in the RWF file produced by the previous cell
2) Computes CWF files passing a blr algorithm.
3) Returns the differences (in area) between CWF and BLR
"""
e40rwf= tb.open_file(ffile,'r+')
pmtrwf = e40rwf.root.RD.pmtrwf
pmtblr = e40rwf.root.RD.pmtblr
DataPMT = load_db.DataPMT(0)
coeff_c = DataPMT.coeff_c.values.astype(np.double)
coeff_blr = DataPMT.coeff_blr.values.astype(np.double)
DIFF = []
for event in range(n_evt):
if plot == True:
mpl.plot_waveforms(pmtrwf[event], zoom=True, window_size=600)
plt.show()
# input('Raw waveforms: return to continue')
mpl.plot_waveforms(pmtblr[event], zoom=True, window_size=600)
plt.show()
# input('BLR waveforms: return to continue')
BLR = pmtblr[event]
CWF = blr.deconv_pmt(pmtrwf[event], coeff_c, coeff_blr,
n_baseline=28000, thr_trigger=5)
if plot == True:
mpl.plot_blr_cwf(pmtblr[event], CWF, zoom=True, window_size=600)
plt.show()
#input('BLR vs CWF waveforms: return to continue')
for i, _ in enumerate(CWF):
diff = abs(np.sum(BLR[i][5000:5100]) - np.sum(CWF[i][5000:5100]))
diff = 100. * diff/np.sum(BLR[i])
DIFF.append(diff)
return np.array(DIFF)
def load_sensors(file_names : List[str],
db_file : str ,
run_no : int ) -> Generator[Tuple, None, None]:
pmt_ids = DB.DataPMT (db_file, run_no).SensorID
sipm_ids = DB.DataSiPM(db_file, run_no).SensorID
for file_name in file_names:
(all_evt ,
pmt_binwid ,
sipm_binwid,
all_wf ) = load_mcsensor_response_df(file_name, db_file, run_no)
with tb.open_file(file_name, 'r') as h5in:
mc_info = tbl.get_mc_info(h5in)
timestamps = event_timestamp(h5in)
for evt in all_evt:
pmt_wfs = all_wf.loc[evt].loc[ pmt_ids]
sipm_wfs = all_wf.loc[evt].loc[sipm_ids]
yield dict(evt = evt ,
mc = mc_info ,
timestamp = timestamps(),
pmt_binwid = pmt_binwid ,
sipm_binwid = sipm_binwid ,
pmt_wfs = pmt_wfs ,
sipm_wfs = sipm_wfs )
def __init__(self):
super(NEW_meta, self).__init__()
# Load the database objects
self._det_geo = load_db.DetectorGeo()
self._pmt_data = load_db.DataPMT()
self._sipm_data = load_db.DataSiPM()
self.skim_det_info(self._det_geo)
def __init__(self):
super(io_manager, self).__init__()
# Load the database objects
self._det_geo = load_db.DetectorGeo()
self._pmt_data = load_db.DataPMT()
self._sipm_data = load_db.DataSiPM()
self._events = []
self._entry = 0
self._max_entry = 0
self._run = 5
def relative_coordinates(detector_db: str, run_number: int) -> Callable:
pmt_xyphi = DB.DataPMT(detector_db, run_number)[['X', 'Y']]
pmt_xyphi['phi'] = np.arctan2(pmt_xyphi.loc[:, 'Y'], pmt_xyphi.loc[:, 'X'])
def get_relative_coords(x: np.ndarray, y: np.ndarray) -> Tuple:
rel_r = np.sqrt((x - pmt_xyphi.X)**2 + (y - pmt_xyphi.Y)**2)
rel_phi = np.abs(np.arctan2(y, x) - pmt_xyphi.phi)
rel_phi[rel_phi > np.pi] = 2 * np.pi - rel_phi
return rel_r, rel_phi
return get_relative_coords
def main():
""" version of fft_tests.py stripped down and adapted for MC wf """
## Filter definition
fSample = 40E6
freqLPF = 100E3
freqLPFd = 2 * freqLPF / fSample
b, a = signal.butter(1, freqLPFd, 'low', analog=False)
##
DataPMT = load_db.DataPMT(4821)
defsDone = False
with load_input(sys.argv[1]) as file0:
npmt = len(DataPMT.ChannelID)
if not defsDone:
wf_len = get_wf_len(file0)
meanAbs = [
np.zeros(int(wf_len / 2 + 1), np.float64) for i in range(npmt)
]
frq_plot = np.empty(int(wf_len / 2 + 1))
defsDone = True
for i in range(npmt):
for ievt in range(len(file0.root.RD.pmtrwf)):
sg = file0.root.RD.pmtrwf[ievt][i]
sg = sg - np.mean(sg)
## Filter wf
sg = signal.lfilter(b, a, sg)
ft = np.fft.rfft(sg)
if i == 0 and ievt == 0:
frq_plot = np.fft.rfftfreq(len(sg), d=25E-9)
meanAbs[i] += np.abs(ft)
meanAbs[i] /= len(file0.root.RD.pmtrwf)
fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(20, 6))
for i, (ax, row) in enumerate(zip(axes.flatten(), meanAbs)):
ax.plot(frq_plot, row)
ax.set_title('Mean FFT magnitude, Eid ' + str(DataPMT.ChannelID[i]) +
', Sensor ' + DataPMT.PmtID[i])
ax.set_xlim(300, 25000)
plt.tight_layout()
fig.show()
fig.savefig('avFFTMag_simOsc2.png')
input('happy with plots?')
def load_sensors(file_names: List[str], db_file: str,
run_no: int) -> Generator:
"""
Loads the nexus MC sensor information into
a pandas DataFrame using the IC function
load_mcsensor_response_df.
Returns info event by event in as a
generator in the structure expected by
the dataflow.
file_names : List of strings
List of input file names to be read
db_file : string
Name of detector database to be used
run_no : int
Run number for database
"""
pmt_ids = DB.DataPMT(db_file, run_no).SensorID
sipm_ids = DB.DataSiPM(db_file, run_no).SensorID
for file_name in file_names:
(all_evt, pmt_binwid, sipm_binwid,
all_wf) = load_mcsensor_response_df(file_name, db_file, run_no)
with tb.open_file(file_name, 'r') as h5in:
mc_info = tbl.get_mc_info(h5in)
timestamps = event_timestamp(h5in)
for evt in all_evt:
pmt_wfs = all_wf.loc[evt].loc[pmt_ids]
sipm_wfs = all_wf.loc[evt].loc[sipm_ids]
yield dict(evt=evt,
mc=mc_info,
timestamp=timestamps(),
pmt_binwid=pmt_binwid,
sipm_binwid=sipm_binwid,
pmt_wfs=pmt_wfs,
sipm_wfs=sipm_wfs)
def plot_cwf_blr(ffile, event, pmt, xi=4900, xf=5200):
"""Draw CWF and BLR waveform togethers for selected event and pmt %.
"""
e40rwf= tb.open_file(ffile,'r+')
pmtrwf = e40rwf.root.RD.pmtrwf
pmtblr = e40rwf.root.RD.pmtblr
DataPMT = load_db.DataPMT(0)
coeff_c = DataPMT.coeff_c.values.astype(np.double)
coeff_blr = DataPMT.coeff_blr.values.astype(np.double)
CWF = blr.deconv_pmt(pmtrwf[event], coeff_c, coeff_blr,
n_baseline=28000, thr_trigger=5)
plt.plot(pmtblr[event,pmt][xi:xf])
plt.plot(CWF[pmt][xi:xf])
plt.show()
def s2_peak():
times = np.arange(20) * units.mus
bin_widths = np.full_like(times, units.mus)
pmt_ids = DB.DataPMT('new', 6400).SensorID.values
sipm_ids = DB.DataSiPM('new', 6400).index.values
np.random.seed(22)
pmts = PMTResponses(pmt_ids,
np.random.uniform(0, 100, (len(pmt_ids), len(times))))
sipms = SiPMResponses(
sipm_ids, np.random.uniform(0, 10, (len(sipm_ids), len(times))))
## Values for comparison in tests
chan_slice = slice(3, 9)
raw_arr = [
[
7.51759755, 5.60509619, 6.42950506, 9.20429423, 9.70312128,
2.02362046
],
[
7.05972334, 2.22846472, 5.46749176, 7.31111882, 3.40960906,
4.56294121
],
[
6.70472842, 3.59272265, 5.16309049, 1.56536978, 9.81538459,
6.82850322
],
[
4.15689334, 3.41990664, 5.79051761, 0.81210519, 1.0304272,
0.40297561
],
[4.73786661, 0.6207543, 1.6485871, 2.52693314, 8.91532864, 1.52840296],
[
5.92994762, 3.13080909, 2.56228467, 3.18354286, 1.55118873,
0.60703854
],
[
3.51052921, 6.40861592, 4.52017217, 1.08859183, 5.93003437,
7.38501235
],
[
8.57725314, 5.32329684, 4.3160815, 6.77629621, 7.49288644,
9.25124645
],
[8.0475847, 6.4163836, 3.62396899, 5.04696816, 8.86944591, 0.08356193],
[3.0936211, 3.95499815, 5.96360799, 0.6605126, 3.86466816, 9.6719478],
[
1.96541611, 5.36489548, 3.70399066, 1.12084339, 7.96734339,
3.24741959
],
[6.14385189, 1.83113282, 2.0741162, 4.83746891, 3.2362641, 0.17227037],
[
1.67604863, 5.59126381, 2.09007632, 5.46945561, 9.71342408,
1.6268169
],
[
9.61221953, 5.27643349, 2.7304223, 7.30143289, 2.37011787,
7.83264795
],
[
9.90983548, 5.79429284, 3.23532663, 9.17294685, 5.97553658,
4.4452637
],
[
0.35683435, 4.30303963, 1.90266857, 4.17023551, 4.06186979,
2.7965879
],
[0.83376644, 9.7300586, 6.05654181, 5.09992868, 8.97882656, 4.6280693],
[
8.48222031, 0.12398542, 7.06665329, 7.90802805, 8.97576213,
1.98215824
],
[0.45055814, 9.90187098, 0.7664873, 4.74533329, 8.9772245, 6.48808184],
[
0.88080893, 5.19986295, 6.27365681, 4.46247949, 4.42179112,
0.3344096
]
]
sn_arr = [[
2.55346379, 2.1727307, 2.35093766, 2.86042287, 2.97285019, 1.15426079
], [
2.46390195, 1.23075187, 2.14164701, 2.51344824, 1.6329798, 1.92597048
], [
2.3922998, 1.6673905, 2.07136149, 0.94995374, 2.99152438, 2.43278461
], [
1.80446599, 1.61757723, 2.21397598, 0.57976386, 0.73221374, 0.33431018
], [
1.95255757, 0.48024565, 1.00356077, 1.31780419, 2.83845215, 0.95183676
], [
2.22861903, 1.5311806, 1.34814435, 1.52928755, 0.98082474, 0.47157412
], [
1.62612892, 2.34657869, 1.91522882, 0.72757474, 2.26079876, 2.54277753
], [
2.75012934, 2.10862141, 1.86320656, 2.40689549, 2.57871696, 2.88240738
], [
2.65355845, 2.34820047, 1.67626069, 2.02740696, 2.83043378, 0.07848485
], [
1.50175408, 1.76784557, 2.25184422, 0.49088564, 1.76128254, 2.95377289
], [
1.11351605, 2.11819761, 1.69879401, 0.74379173, 2.66806122, 1.56651489
], [
2.27489977, 1.07879322, 1.17346325, 1.97695394, 1.58172378, 0.15582134
], [
0.99700377, 2.16962471, 1.17948397, 2.1258517, 2.97456874, 0.99432725
], [
2.93003466, 2.09778509, 1.40429925, 2.51155634, 1.30074808, 2.62807274
], [
2.97983427, 2.21480833, 1.56288192, 2.85500604, 2.27064999, 1.89628347
], [
0.29069255, 1.859838, 1.10727576, 1.80798637, 1.81437105, 1.42590517
], [
0.59196924, 2.96349975, 2.27193322, 2.03998311, 2.84951285, 1.94222486
], [
2.73304224, 0.11442983, 2.48042035, 2.62755231, 2.84898002, 1.13831482
], [0.35613537, 2.99207933, 0.56870727, 1.95439588, 2.8492343, 2.36312065],
[
0.61808354, 2.07996798, 2.3182313, 1.8836441, 1.90782557,
0.28421464
]]
expected_array = {
SiPMCharge.raw: raw_arr,
SiPMCharge.signal_to_noise: sn_arr
}
expected_single = {
SiPMCharge.raw:
[99.6473, 93.8179, 81.3852, 92.4639, 125.2603, 75.8990],
SiPMCharge.signal_to_noise:
[9.6651, 9.3927, 8.7087, 9.3396, 10.9941, 8.4203]
}
return (S2(times, bin_widths, pmts,
sipms), chan_slice, expected_single, expected_array)
def pmt_ids():
return DB.DataPMT('new', 6400).SensorID.values
def __init__(
self,
detector: str = 'new', # detector name in data-base
run_number: int = -1, # run number -1 for MC
krmap_filename: str = '', # KrMap file name to generate PMT-PSF
psfsipm_filename:
str = '', # PSF-SiPM file name to generate SiPM-PSF
**kargs):
self.ws = 60.0 * units.eV
self.wi = 22.4 * units.eV
self.fano_factor = 0.15
self.conde_policarpo_factor = 0.10
self.drift_velocity = 1.00 * units.mm / units.mus
self.lifetime = 7.00 * units.ms
self.transverse_diffusion = 1.00 * units.mm / units.cm**0.5
self.longitudinal_diffusion = 0.30 * units.mm / units.cm**0.5
self.voxel_sizes = np.array(
(2.00 * units.mm, 2.00 * units.mm, 0.2 * units.mm), dtype=float)
self.el_gain = 1e3
self.wf_buffer_time = 1300 * units.mus
self.wf_pmt_bin_time = 25 * units.ns
self.wf_sipm_bin_time = 1 * units.mus
self.EP_z = 632 * units.mm
self.EL_dz = 5 * units.mm
self.TP_z = -self.EL_dz
self.EL_dtime = self.EL_dz / self.drift_velocity
self.EL_pmt_time_sample = self.wf_pmt_bin_time
self.EL_sipm_time_sample = self.wf_sipm_bin_time
self.s1_dtime = 200 * units.ns
db_pmts = db.DataPMT(detector, run_number)
self.x_pmts = db_pmts['X'].values
self.y_pmts = db_pmts['Y'].values
self.adc_to_pes_pmts = db_pmts['adc_to_pes'].values
self.npmts = len(self.x_pmts)
db_sipms = db.DataSiPM(detector, run_number)
self.x_sipms = db_sipms['X'].values
self.y_sipms = db_sipms['Y'].values
self.adc_to_pes_sipms = db_sipms['adc_to_pes'].values
self.nsipms = len(self.x_sipms)
## TODO
nphotons = (41.5 * units.keV / self.wi) * self.el_gain * self.npmts
if (krmap_filename != ''):
print('load psf pmt from file : ', krmap_filename)
if (psfsipm_filename != ''):
print('load psf sipm from file : ', psfsipm_filename)
psf_pmt = partial(_psf, dz=self.EP_z, factor=25e3)
psf_sipm = partial(_psf, dz=self.TP_z, factor=0.15)
psf_s1 = partial(_psf, factor=1e3)
factor = 1. / nphotons
self.psf_pmt = psf_pmt if krmap_filename == '' else get_psf_pmt_from_krmap(
krmap_filename, factor)
self.psf_sipm = psf_sipm if psfsipm_filename == '' else get_psf_sipm_from_file(
psfsipm_filename, factor / 0.7e-4)
self.psf_s1 = psf_s1
self._configure(**kargs)
self._update()
def relative_pmt_response():
"""
Script which uses pmaps (will be generalised in future to check XY dependence)
to look at the relative response of the PMTs in Kr events and
compares to the results on Poisson mu from calibrations
"""
pmap_file_base = sys.argv[1]
dst_file_base = sys.argv[2]
run_number = pmap_file_base.split('/')[2][1:]
pmt_dats = DB.DataPMT(int(run_number))
s1hists = {x: [] for x in range(12)}
s2hists = {x: [] for x in range(12)}
s1sumh = []
s2sumh = []
hitPMTdist = {x: [] for x in range(12)}
hitPMTZpos = {x: [] for x in range(12)}
pmap_sorter = sorter_func(pmap_file_base)
pmap_file_list = sorted(glob(pmap_file_base + '*.h5'), key=pmap_sorter)
dst_sorter = sorter_func(dst_file_base)
dst_file_list = sorted(glob(dst_file_base + '*.h5'), key=dst_sorter)
## dst_frame = load_dsts(dst_file_list, 'DST', 'Events')
dst_frame = load_dsts(dst_file_list, 'RECO', 'Events')
dst_evt_list = dst_frame['event'].unique()
#for fn in iglob(pmap_file_base + '*.h5'):
for fn in pmap_file_list:
## This version just using pmt databases
s1df, s2df, _, s1pmtdf, s2pmtdf = load_pmaps_as_df(fn)
common_evts = np.intersect1d(s1pmtdf['event'].unique(), dst_evt_list)
for evt in common_evts:
#for evt in s1pmtdf['event'].unique():
#evt = dst_evt_iter[0]
s1evt = s1pmtdf[s1pmtdf['event'] == evt]
s2evt = s2pmtdf[s2pmtdf['event'] == evt]
s1sevt = s1df[s1df['event'] == evt]
s2sevt = s2df[s2df['event'] == evt]
hit_evt = dst_frame[dst_frame['event'] == evt]
## if hit_evt['nS2'].iloc[0] == 1 and len(s2evt['peak'].unique()) == 1 and len(s1evt['peak'].unique()) == 1:
if hit_evt['npeak'].nunique() == 1 and len(
s2evt['peak'].unique()) == 1 and len(
s1evt['peak'].unique()) == 1:
## Not well defined for multi-S2 events
hit_x = hit_evt['X'].iloc[0]
hit_y = hit_evt['Y'].iloc[0]
hit_z = hit_evt['Z'].iloc[0]
for peak in s1evt['peak'].unique():
s1peak = s1evt[s1evt['peak'] == peak]
s1sumh.append(s1sevt[s1sevt['peak'] == peak]['ene'].sum())
pmt1Q = s1peak[s1peak['npmt'] == 1]['ene'].sum()
for pmt in s1peak['npmt'].unique():
hitPMTdist[pmt].append(
np.sqrt(
np.power(
hit_x - pmt_dats[pmt_dats['SensorID'] ==
pmt].X.values, 2) +
np.power(
hit_y - pmt_dats[pmt_dats['SensorID'] ==
pmt].Y.values, 2)))
hitPMTZpos[pmt].append(hit_z)
if pmt != 1:
s1hists[pmt].append(
s1peak[s1peak['npmt'] == pmt]['ene'].sum() /
pmt1Q)
else:
s1hists[pmt].append(pmt1Q)
for peak in s2evt['peak'].unique():
s2peak = s2evt[s2evt['peak'] == peak]
s2sumh.append(s2sevt[s2sevt['peak'] == peak]['ene'].sum())
if s2sumh[-1] > 4000: # and s2sumh[-1] < 12000:
## pmt1Q = s2peak[s2peak['npmt'] == 1]['ene'].values[5:-5]
pmt1Q = s2peak[s2peak['npmt'] == 1]['ene'].sum()
for pmt in s2peak['npmt'].unique():
if pmt != 1:
## s2hists[pmt].append(s2peak[s2peak['npmt'] == pmt]['ene'].values[5:-5]/pmt1Q)
s2hists[pmt].append(
s2peak[s2peak['npmt'] == pmt]['ene'].sum()
/ pmt1Q)
else:
s2hists[pmt].append(pmt1Q)
#dst_evt_iter.iternext()
## Make the plots
s1sumh = np.array(s1sumh)
s2sumh = np.array(s2sumh)
figs0, axes0 = plt.subplots(nrows=1, ncols=2)
axes0[0].hist(s1sumh)
axes0[0].set_title('PMT sum S1 distribution')
axes0[1].hist(s2sumh)
axes0[1].set_title('PMT sum S2 distribution')
plt.tight_layout()
figs0.show()
figs0.savefig('SumChargescharge_R' + run_number + '.png')
figs1, axess1 = plt.subplots(nrows=3, ncols=4, figsize=(20, 6))
s1pmt1 = np.array(s1hists[1])
s1bins = np.arange(-2, 4, 0.1)
s2bins = np.arange(0.4, 1.1, 0.005)
s1select = (s1sumh > 2) & (s1sumh < 150)
for (key, val), ax in zip(s1hists.items(), axess1.flatten()):
if key == 1:
ax.hist(np.array(val)[s1select], bins=100)
#ax.scatter(s1sumh[s1select], np.array(val)[s1select])
## ax.scatter(np.array(hitPMTdist[key])[s1select], np.array(val)[s1select])
#ax.scatter(np.array(hitPMTZpos[key])[s1select], np.array(val)[s1select])
ax.set_title('PMT 1 S1 charge')
#ax.set_xlabel('integrated charge in PMT sum (pe)')
#ax.set_xlabel('z pos.')
#ax.set_ylabel('integrated charge in PMT1 (pe)')
ax.set_ylabel('AU)')
ax.set_xlabel('integrated charge in PMT1 (pe)')
sh_hits = np.array(hitPMTZpos[key])[s1select].shape
sh_val = np.array(val)[s1select].shape
covar = np.cov(
np.array(hitPMTZpos[key])[s1select].reshape(1, sh_hits[0]),
np.array(val)[s1select].reshape(1, sh_val[0]))[0, 1]
corr_coef = covar / (
np.std(np.array(val)[s1select], ddof=1) *
np.std(np.array(hitPMTZpos[key])[s1select], ddof=1))
print('Sensor ', key, ' correlation coefficient = ', corr_coef)
else:
vals, bins, _ = ax.hist(np.array(val)[s1select], bins=s1bins)
## ax.scatter(s1pmt1[np.abs(val) < 10], np.array(val)[np.abs(val) < 10])
#ax.scatter(s1sumh[s1select], np.array(val)[s1select])
## ax.scatter(np.array(hitPMTdist[key])[s1select & (np.abs(val) < 10)], np.array(val)[s1select & (np.abs(val) < 10)])
#ax.scatter(np.array(hitPMTZpos[key])[s1select & (np.abs(val) < 10)], np.array(val)[s1select & (np.abs(val) < 10)])
ax.set_title('PMT ' + str(key) + ' S1 relative charge')
#ax.set_xlabel('integrated charge in PMT sum (pe)')
## ax.set_xlabel('PMT-hit dist. (mm)')
#ax.set_xlabel('hit Z pos')
#ax.set_ylabel('pmt q / pmt1 q')
ax.set_ylabel('AU')
ax.set_xlabel('pmt q / pmt1 q')
## sh_hits = np.array(hitPMTZpos[key])[s1select].shape
## sh_val = np.array(val)[s1select].shape
## covar = np.cov(np.array(hitPMTZpos[key])[s1select].reshape(1, sh_hits[0]), np.array(val)[s1select].reshape(1, sh_val[0]))[0, 1]
## corr_coef = covar / (np.std(np.array(val)[s1select], ddof=1)*np.std(np.array(hitPMTZpos[key])[s1select], ddof=1))
s1select2 = s1select & (np.array(val) > 0) & (np.array(val) <= 2)
print('Sensor ', key, ' mean = ',
np.mean(np.array(val)[s1select2]))
useful_bins = np.argwhere(vals >= 100)
b1 = useful_bins[0][0]
b2 = useful_bins[-1][0]
errs = np.sqrt(vals[b1:b2])
fvals = fitf.fit(fitf.gauss,
shift_to_bin_centers(bins)[b1:b2],
vals[b1:b2],
seed=(vals.sum(), bins[vals.argmax()], 0.1),
sigma=errs)
ax.plot(
shift_to_bin_centers(bins)[b1:b2],
fvals.fn(shift_to_bin_centers(bins)[b1:b2]))
print('Fit S1 ' + str(key), fvals.values, fvals.errors, fvals.chi2)
plt.tight_layout()
figs1.show()
figs1.savefig('s1relativechargeThzoom_R' + run_number + '.png')
fitVals = {}
figs2, axess2 = plt.subplots(nrows=3, ncols=4, figsize=(20, 6))
s2pmt1 = np.array(s2hists[1])
for (key, val), ax in zip(s2hists.items(), axess2.flatten()):
if key == 1:
## ax.set_title('PMT 1 S2 charge')
ax.set_title('PMT 1 S2 charge vs s2 sum charge')
ax.set_xlabel('S2pmt sum charge (pe)')
#ax.set_xlabel('integrated charge in PMT sum (pe)')
#ax.set_xlabel('z pos')
ax.set_ylabel('integrated charge in PMT1 (pe)')
#ax.set_ylabel('AU')
ax.set_xlabel('integrated charge in PMT1 (pe)')
## ax.hist(np.array(val)[(s2sumh>4000) & (s2sumh<12000)], bins=100)
##ax.hist(np.concatenate(val), bins=100)
## ax.hist(np.array(val)[s2sumh>4000], bins=100)
ax.scatter(s2sumh[s2sumh > 4000], np.array(val)[s2sumh > 4000])
#ax.scatter(s2sumh[(s2sumh>4000) & (s2sumh<12000)], np.array(val)[(s2sumh>4000) & (s2sumh<12000)])
## ax.scatter(np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)], np.array(val)[(s2sumh>4000) & (s2sumh<12000)])
#ax.scatter(np.array(hitPMTZpos[key])[(s2sumh>4000) & (s2sumh<12000)], np.array(val)[(s2sumh>4000) & (s2sumh<12000)])
## sh_hits = np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)].shape
## sh_val = np.array(val)[(s2sumh>4000) & (s2sumh<12000)].shape
## covar = np.cov(np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)].reshape(1, sh_hits[0]), np.array(val)[(s2sumh>4000) & (s2sumh<12000)].reshape(1, sh_val[0]))[0, 1]
## corr_coef = covar / (np.std(np.array(val)[(s2sumh>4000) & (s2sumh<12000)], ddof=1)*np.std(np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)], ddof=1))
## print('Sensor ', key, ' correlation coefficient = ', corr_coef)
else:
## ax.set_title('PMT '+str(key)+' S2 relative charge')
ax.set_title('PMT ' + str(key) + ' S2 relative charge vs pmt sum')
ax.set_ylabel('pmt q / pmt1 q')
#ax.set_xlabel('zpos')
ax.set_xlabel('integrated charge in PMT sum (pe)')
#ax.set_xlabel('pmt q / pmt1 q')
#ax.set_ylabel('AU')
#ax.scatter(s2pmt1[np.abs(val) < 10], np.array(val)[np.abs(val) < 10])
#ax.scatter(s2sumh[(s2sumh>4000) & (s2sumh<12000)], np.array(val)[(s2sumh>4000) & (s2sumh<12000)])
## vals, bins, _ = ax.hist(np.array(val)[(s2sumh>4000) & (s2sumh<12000)], bins=s2bins)
## vals, bins, _ = ax.hist(np.concatenate(val), bins=s2bins)
## vals, bins, _ = ax.hist(np.array(val)[s2sumh>4000], bins=s2bins)
ax.scatter(s2sumh[s2sumh > 4000], np.array(val)[s2sumh > 4000])
## ax.scatter(np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)], np.array(val)[(s2sumh>4000) & (s2sumh<12000)])
#ax.scatter(np.array(hitPMTZpos[key])[(s2sumh>4000) & (s2sumh<12000)], np.array(val)[(s2sumh>4000) & (s2sumh<12000)])
sh_sum = s2sumh[s2sumh > 4000].shape
sh_vals = np.array(val)[s2sumh > 4000].shape
covar = np.cov(s2sumh[s2sumh > 4000].reshape(1, sh_sum[0]),
np.array(val)[s2sumh > 4000].reshape(1,
sh_vals[0]))[0,
1]
corr_coef = covar / (np.std(np.array(val)[s2sumh > 4000], ddof=1) *
np.std(s2sumh[s2sumh > 4000], ddof=1))
## sh_hits = np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)].shape
## sh_val = np.array(val)[(s2sumh>4000) & (s2sumh<12000)].shape
## covar = np.cov(np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)].reshape(1, sh_hits[0]), np.array(val)[(s2sumh>4000) & (s2sumh<12000)].reshape(1, sh_val[0]))[0, 1]
## corr_coef = covar / (np.std(np.array(val)[(s2sumh>4000) & (s2sumh<12000)], ddof=1)*np.std(np.array(hitPMTdist[key])[(s2sumh>4000) & (s2sumh<12000)], ddof=1))
print('Sensor ', key, ' correlation coefficient = ', corr_coef)
## limit fit to region with stat error <= 10% Poisson
## useful_bins = np.argwhere(vals>=200)
## b1 = useful_bins[0][0]
## b2 = useful_bins[-1][0]
## errs = np.sqrt(vals[b1:b2])
## print('Seed check: ', (vals.sum(), bins[vals.argmax()], 0.02))
## fvals = fitf.fit(fitf.gauss, shift_to_bin_centers(bins)[b1:b2], vals[b1:b2],
## seed=(vals.sum(), bins[vals.argmax()], 0.02),
## sigma=errs, bounds=[(0, 0, 0.00001), (1e10, 2, 3)])
## ax.plot(shift_to_bin_centers(bins),
## fitf.gauss(shift_to_bin_centers(bins), *fvals.values))
## fitVals[key] = (fvals.values[1], fvals.values[2])
## print('Fit PMT '+str(key), fvals.values, fvals.errors, fvals.chi2)
plt.tight_layout()
figs2.show()
figs2.savefig('s2relativechargeThvsSum_R' + run_number + '.png')
## figcal, axcal = plt.subplots()
## axcal.errorbar(list(fitVals.keys()),
## np.fromiter((x[0] for x in fitVals.values()), np.float),
## yerr=np.fromiter((x[1] for x in fitVals.values()), np.float),
## label='Average response of PMTs to Kr relative to PMT 1')
## ## Get the calibration info for comparison.
## cal_files = [ fname for fname in sys.argv[3:] ]
## read_params = partial(spr, table_name='FIT_pmt_scaled_dark_pedestal',
## param_names=['poisson_mu'])
## ## Assumes ordering, ok?
## for i, fn in enumerate(cal_files):
## cal_run = fn.split('_')[1]
## with tb.open_file(fn) as cal_in:
## pmt1Val = 0
## pmt1Err = 0
## cVals = []
## cErrs = []
## for sens, (pars, errs) in read_params(cal_in):
## if sens != 1:
## cVals.append(pars['poisson_mu'])
## cErrs.append(errs['poisson_mu'])
## else:
## pmt1Val = pars['poisson_mu']
## pmt1Err = errs['poisson_mu']
## normVals = np.array(cVals) / pmt1Val
## normErrs = normVals * np.sqrt(np.power(np.array(cErrs)/np.array(cVals), 2) +
## np.power(pmt1Err/pmt1Val, 2))
## axcal.errorbar(list(fitVals.keys()), normVals,
## yerr=normErrs, label='Calibration '+cal_run)
## axcal.legend()
## axcal.set_xlabel('PMT sensor ID')
## axcal.set_ylabel('Response relative to that of PMT 1')
## figcal.show()
## figcal.savefig('calPoisKrRelCompStatsFILT.png')
input('plots good?')
import invisible_cities.sierpe.fee as FE # In[6]: from invisible_cities.cities.diomira import Diomira # In[7]: import invisible_cities.sierpe.blr as blr # In[8]: DataPMT = load_db.DataPMT() # ## Run example configuration # ### Configuration file # In[9]: os.environ['IC_NOTEBOOK_DIR'] # Below an example of configuration file for Diomira. The main variables to set are the input file path and name list (in the example a single file), the output path and file, print frequency, number of events to run and the noise cut (in pes) for the SiPMs. # # The noise cut is discussed later in the NB # set_input_files
