def get_darks(fname,
protoevent,
roi_start=9,
ref_cm=89.5,
dev_cm=None,
roi_width=180,
nchannels=2):
"""dev_cm should be the same as in the normal cut, so it gets calculated
in the same way"""
print fname
st, wd = roi_start, roi_width
my_dtype = return_dtype(2)
protoevent = array(protoevent, dtype=uint16)
if dev_cm is None:
spom, spam, dev_cm = find_start(fname, 340, roi_width, nchannels)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, 2))
specdata = [
sum((event + protoevent)[st:st + wd]) for event in gen if
abs(center_of_mass(-(event + protoevent)[st:st + wd])[0] -
ref_cm) < dev_cm
]
return histogram(specdata, bins=2048)
def get_spec(fname, roi_start, roi_width=180, nchannels=2, force_start=False,
**kwargs):
"""return a sipm spectrum using the cm method as a cut.
roi_start is the star of the region of interest, + roi_width channels
ref_cm is the reference center of mass. if None then mean(cm) of all events
will be calculated.
dev_cm is the allowed deviation from ref_cm. if None then std(cm)
of all events will be calculated.
nchannels is the number of DRS channels with data. either 1 or 2"""
st, wd = roi_start, roi_width
my_dtype = return_dtype(nchannels)
if not force_start:
st, ref_cm, dev_cm = find_start(fname, roi_start, roi_width, nchannels,
**kwargs)
else:
cmsarr = cms_(fname, roi_start, roi_width, nchannels)
cmhist = histogram(cmsarr, bins=512)
ref_cm = cmhist[1][argmax(cmhist[0])]
dev_cm = dev_cm_(cmsarr)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
specdata = [sum(event[st:st + wd]) for event in gen
if abs(center_of_mass(- event[st:st + wd])[0] - ref_cm)
< dev_cm]
return histogram(specdata, bins=2048)
def get_spec(fname,
roi_start,
roi_width=180,
nchannels=2,
force_start=False,
**kwargs):
"""return a sipm spectrum using the cm method as a cut.
roi_start is the star of the region of interest, + roi_width channels
ref_cm is the reference center of mass. if None then mean(cm) of all events
will be calculated.
dev_cm is the allowed deviation from ref_cm. if None then std(cm)
of all events will be calculated.
nchannels is the number of DRS channels with data. either 1 or 2"""
st, wd = roi_start, roi_width
my_dtype = return_dtype(nchannels)
if not force_start:
st, ref_cm, dev_cm = find_start(fname, roi_start, roi_width, nchannels,
**kwargs)
else:
cmsarr = cms_(fname, roi_start, roi_width, nchannels)
cmhist = histogram(cmsarr, bins=512)
ref_cm = cmhist[1][argmax(cmhist[0])]
dev_cm = dev_cm_(cmsarr)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
specdata = [
sum(event[st:st + wd]) for event in gen
if abs(center_of_mass(-event[st:st + wd])[0] - ref_cm) < dev_cm
]
return histogram(specdata, bins=2048)
def cms_(fname, roi_start, roi_width, nchannels=2):
st, wd = roi_start, roi_width
my_dtype = return_dtype(nchannels)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
cms = [center_of_mass(-event[st:st + wd])[0] for event in gen]
return cms
def cms_(fname, roi_start, roi_width, nchannels=2):
st, wd = roi_start, roi_width
my_dtype = return_dtype(nchannels)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
cms = [center_of_mass(-event[st:st + wd])[0] for event in gen]
return cms
def peaks(filename, int_limits, nchannels=2):
print 'getting peak spectrum'
my_dtype = return_dtype(nchannels)
with open(filename, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
peaks = [sum(event[int_limits[0]:int_limits[1]]) for event in gen]
return peaks
def cms_dark(fname, roi_start, roi_width, protoevent):
st, wd = roi_start, roi_width
my_dtype = return_dtype(2)
protoevent = array(protoevent, dtype=uint16)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, 2))
cms = [center_of_mass(-(event + protoevent)[st:st + wd])[0]
for event in gen]
return cms
def peaks(filename, int_limits, nchannels=2):
print 'getting peak spectrum'
my_dtype = return_dtype(nchannels)
with open(filename, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
peaks = [sum(event[int_limits[0]:int_limits[1]]) for event in gen]
return peaks
def cms_dark(fname, roi_start, roi_width, protoevent):
st, wd = roi_start, roi_width
my_dtype = return_dtype(2)
protoevent = array(protoevent, dtype=uint16)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, 2))
cms = [
center_of_mass(-(event + protoevent)[st:st + wd])[0]
for event in gen
]
return cms
def find_threshold(filename, int_limits, nchannels=2):
b, a = iirfilter(1, 0.05, btype='lowpass')
my_dtype = return_dtype(nchannels)
with open(filename, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
peaks = [min(event[int_limits[0]:int_limits[1]])for event
in (filtfilt(b, a, eventt) for eventt in gen)]
hist = histogram(peaks, bins=2048)
idx = get_cutoff(hist[0], get_guess(hist[0]))
threshold = hist[1][idx]
print threshold
return threshold
def find_threshold(filename, int_limits, nchannels=2):
b, a = iirfilter(1, 0.05, btype='lowpass')
my_dtype = return_dtype(nchannels)
with open(filename, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
peaks = [min(event[int_limits[0]:int_limits[1]])for event
in (filtfilt(b, a, eventt) for eventt in gen)]
hist = histogram(peaks, bins=2048)
idx = get_cutoff(hist[0], get_guess(hist[0]))
threshold = hist[1][idx]
print threshold
return threshold
def darks(filename, thrs_1, thrs_2=None, rng=(40, 800), nchannels=2):
print 'getting dark count spectum'
if thrs_2 is None:
thrs_2 = thrs_1
my_dtype = return_dtype(nchannels)
with open(filename, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
traces = []
for event in gen:
traces.append(trigger(thrs_1, event, rng))
return [sm for sm in (fsum(trace) for trace in traces) if sm > 0]
def darks(filename, thrs_1, thrs_2=None, rng=(40, 800), nchannels=2):
print 'getting dark count spectum'
if thrs_2 is None:
thrs_2 = thrs_1
my_dtype = return_dtype(nchannels)
with open(filename, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, nchannels))
traces = []
for event in gen:
traces.append(trigger(thrs_1, event, rng))
return [sm for sm in (fsum(trace) for trace in traces) if sm > 0]
def get_darks(fname, protoevent, roi_start=9, ref_cm=89.5, dev_cm=None,
roi_width=180, nchannels=2):
"""dev_cm should be the same as in the normal cut, so it gets calculated
in the same way"""
print fname
st, wd = roi_start, roi_width
my_dtype = return_dtype(2)
protoevent = array(protoevent, dtype=uint16)
if dev_cm is None:
spom, spam, dev_cm = find_start(fname, 340, roi_width, nchannels)
with open(fname, 'r') as f:
gen = (fromstring(event, my_dtype)[0][5]
for event in event_generator(f, 2))
specdata = [sum((event + protoevent)[st:st + wd]) for event in gen
if abs(center_of_mass(-
(event + protoevent)[st:st + wd])[0] - ref_cm)
< dev_cm]
return histogram(specdata, bins=2048)
def get_dark_counts(filelist, w_start, w_len):
my_dtype = return_dtype(2)
darks = []
for fn in filelist:
with open(fn, 'r') as fl:
sipm_data = (fromstring(event, my_dtype)[0][5]
for event in event_generator(fl, 2))
sipm_max = [sum(event[w_start - w_len:w_start])
for event in sipm_data]
sipm_spec = histogram(sipm_max, bins=2048)[0]
guess = get_guess(sipm_spec)
cutoff = get_cutoff(sipm_spec, get_cutoff(sipm_spec, guess))
mn = get_n_mean(sipm_spec, cutoff)
if mn < 1:
darks.append(mn)
"""dkcts = median(darks)
dkcts_err = max(abs(median(darks) - percentile(darks, i))
for i in (80, 20))"""
dkcts = mean(darks)
dkcts_err = std(darks)
print dkcts, dkcts_err
return dkcts, dkcts_err
def get_dark_counts(filelist, w_start, w_len):
my_dtype = return_dtype(2)
darks = []
for fn in filelist:
with open(fn, 'r') as fl:
sipm_data = (fromstring(event, my_dtype)[0][5]
for event in event_generator(fl, 2))
sipm_max = [
sum(event[w_start - w_len:w_start]) for event in sipm_data
]
sipm_spec = histogram(sipm_max, bins=2048)[0]
guess = get_guess(sipm_spec)
cutoff = get_cutoff(sipm_spec, get_cutoff(sipm_spec, guess))
mn = get_n_mean(sipm_spec, cutoff)
if mn < 1:
darks.append(mn)
"""dkcts = median(darks)
dkcts_err = max(abs(median(darks) - percentile(darks, i))
for i in (80, 20))"""
dkcts = mean(darks)
dkcts_err = std(darks)
print dkcts, dkcts_err
return dkcts, dkcts_err
def extract_pde(dark_counts=False,
cutoff=32675,
w_start=340,
w_len=180,
small_pmt_win=False,
print_pe=False):
filelist = [fname for fname in glob('*_*') if not '.' in fname]
wavelengths = unique(i[:-2] for i in filelist)
QE_file = '/home/jammer/diplom/calib/pmt2c.dat'
f = loadtxt(QE_file, unpack=True)
pde_array = []
wavelength_array = []
sterr_array = []
syserr_array = []
my_dtype = return_dtype(2)
if not dark_counts:
dark_counts = get_dark_counts(filelist, w_start, w_len)
for wavelength in wavelengths:
A = []
B = []
quantum_eff = f[2][where(f[0] == float(wavelength))[0][0]]
for i in range(2):
#data = read_drs_binary(wavelength + '_' +str(i+1), 2)
fname = wavelength + '_' + str(i + 1)
#create sipm spectrum
with open(fname, 'r') as fl:
sipm_data = (fromstring(event, my_dtype)[0][5]
for event in event_generator(fl, 2))
sipm_max = [
sum(event[w_start:w_start + w_len]) for event in sipm_data
]
sipm_spec = histogram(sipm_max, bins=2048)[0]
#create pmt spectrum
with open(fname, 'r') as fl:
pmt_data = (fromstring(event, my_dtype)[0][7]
for event in event_generator(fl, 2))
pmt_sum = [sum(event[600:900]) for event in pmt_data]
pmt_spec = histogram(pmt_sum, bins=2048)[0]
sipm_guess = get_guess(sipm_spec)
sipm_pe = get_nmean_errors(sipm_spec,
get_cutoff(sipm_spec, guess=sipm_guess))
pmt_pe = get_nmean_errors(pmt_spec, get_co_exp(pmt_spec, 1950))
#if pmt_pe == inf or small_pmt_win:
# pmt_pe = get_nmean_errors(pmt_spec, get_cutoff(pmt_spec,
# 1950, window=10))
if print_pe:
print 'sipm pe' + str(sipm_pe)
print 'pmt pe' + str(pmt_pe)
A.append(sipm_pe)
B.append(pmt_pe)
real_wl = correct_wavelength(float(wavelength))
pde, sterr, syserr = calc_pde_with_errors(A[0],
A[1],
B[0],
B[1],
quantum_eff,
dkcts=dark_counts)
print ' '.join((str((pde, sterr, syserr)), wavelength))
pde_array.append(pde)
wavelength_array.append(real_wl)
sterr_array.append(sterr)
syserr_array.append(syserr)
return pde_array, wavelength_array, sterr_array, syserr_array
def extract_pde(dark_counts=False, cutoff=32675, w_start=340,
w_len=180, small_pmt_win=False, print_pe=False):
filelist = [fname for fname in glob('*_*') if not '.' in fname]
wavelengths = unique(i[:-2] for i in filelist)
QE_file = '/home/jammer/diplom/calib/pmt2c.dat'
f = loadtxt(QE_file, unpack=True)
pde_array = []
wavelength_array = []
sterr_array = []
syserr_array = []
my_dtype = return_dtype(2)
if not dark_counts:
dark_counts = get_dark_counts(filelist, w_start, w_len)
for wavelength in wavelengths:
A = []
B = []
quantum_eff = f[2][where(f[0] == float(wavelength))[0][0]]
for i in range(2):
#data = read_drs_binary(wavelength + '_' +str(i+1), 2)
fname = wavelength + '_' + str(i+1)
#create sipm spectrum
with open(fname, 'r') as fl:
sipm_data = (fromstring(event, my_dtype)[0][5]
for event in event_generator(fl, 2))
sipm_max = [sum(event[w_start:w_start + w_len])
for event in sipm_data]
sipm_spec = histogram(sipm_max, bins=2048)[0]
#create pmt spectrum
with open(fname, 'r') as fl:
pmt_data = (fromstring(event, my_dtype)[0][7]
for event in event_generator(fl, 2))
pmt_sum = [sum(event[600:900]) for event in pmt_data]
pmt_spec = histogram(pmt_sum, bins=2048)[0]
sipm_guess = get_guess(sipm_spec)
sipm_pe = get_nmean_errors(sipm_spec,
get_cutoff(sipm_spec, guess=sipm_guess))
pmt_pe = get_nmean_errors(pmt_spec, get_co_exp(pmt_spec, 1950))
#if pmt_pe == inf or small_pmt_win:
# pmt_pe = get_nmean_errors(pmt_spec, get_cutoff(pmt_spec,
# 1950, window=10))
if print_pe:
print 'sipm pe' + str(sipm_pe)
print 'pmt pe' + str(pmt_pe)
A.append(sipm_pe)
B.append(pmt_pe)
real_wl = correct_wavelength(float(wavelength))
pde, sterr, syserr = calc_pde_with_errors(A[0], A[1], B[0], B[1],
quantum_eff,
dkcts=dark_counts)
print ' '.join((str((pde, sterr, syserr)), wavelength))
pde_array.append(pde)
wavelength_array.append(real_wl)
sterr_array.append(sterr)
syserr_array.append(syserr)
return pde_array, wavelength_array, sterr_array, syserr_array
