def get_xtalk(filename, int_limits, thrs_1=None):
if thrs_1 is not None:
dark, peak, sub = get_spectra(filename, int_limits, thrs_1)
else:
peak = peaks(filename, int_limits)
temp_hist = histogram(peak, bins=2048)
cutoff = 2027 - getFitData(temp_hist[0][::-1], 3)[4]
thrs = temp_hist[1][cutoff] / 180
dark = darks(filename, thrs)
mx = max(max(dark), max(peak))
mn = min(min(dark), min(peak))
dark_hist = histogram(dark, bins=2048, range=(mn, mx))
peak_hist = histogram(peak, bins=2048, range=(mn, mx))
sub = peak_hist[0] - dark_hist[0]
guess = get_guess(sub)
cutoff = get_cutoff(sub, get_cutoff(sub, guess))
nm = get_n_mean(sub, cutoff)
pg0 = 1 - exp(- nm)
pg1_p = 1 - exp(- nm) - nm * exp(- nm)
cutoff_2 = get_cutoff(sub, get_cutoff(sub, get_guess(sub, n=3, m=33)))
print cutoff_2
pg1 = sum(sub[:cutoff_2]) / float(sum(sub))
xtalk = (pg1 - pg1_p) / pg0
print xtalk
return xtalk, sub
def get_xtalk(filename, int_limits, thrs_1=None):
if thrs_1 is not None:
dark, peak, sub = get_spectra(filename, int_limits, thrs_1)
else:
peak = peaks(filename, int_limits)
temp_hist = histogram(peak, bins=2048)
cutoff = 2027 - getFitData(temp_hist[0][::-1], 3)[4]
thrs = temp_hist[1][cutoff] / 180
dark = darks(filename, thrs)
mx = max(max(dark), max(peak))
mn = min(min(dark), min(peak))
dark_hist = histogram(dark, bins=2048, range=(mn, mx))
peak_hist = histogram(peak, bins=2048, range=(mn, mx))
sub = peak_hist[0] - dark_hist[0]
guess = get_guess(sub)
cutoff = get_cutoff(sub, get_cutoff(sub, guess))
nm = get_n_mean(sub, cutoff)
pg0 = 1 - exp(-nm)
pg1_p = 1 - exp(-nm) - nm * exp(-nm)
cutoff_2 = get_cutoff(sub, get_cutoff(sub, get_guess(sub, n=3, m=33)))
print cutoff_2
pg1 = sum(sub[:cutoff_2]) / float(sum(sub))
xtalk = (pg1 - pg1_p) / pg0
print xtalk
return xtalk, sub
def xtalk_single_spec(hist):
nm = get_n_mean(hist, get_cutoff(hist, get_guess(hist)))
pg1_p = 1 - exp(-nm) - nm * exp(-nm)
pg0 = 1 - exp(-nm)
cutoff = get_cutoff(hist, get_cutoff(hist, get_guess(hist, n=3, m=33)))
pg1 = sum(hist[:cutoff]) / float(sum(hist))
xtalk = (pg1 - pg1_p) / pg0
print xtalk
return xtalk
def xtalk_single_spec(hist):
nm = get_n_mean(hist, get_cutoff(hist, get_guess(hist)))
pg1_p = 1 - exp(- nm) - nm * exp(- nm)
pg0 = 1 - exp(- nm)
cutoff = get_cutoff(hist, get_cutoff(hist, get_guess(hist, n=3, m=33)))
pg1 = sum(hist[:cutoff]) / float(sum(hist))
xtalk = (pg1 - pg1_p) / pg0
print xtalk
return xtalk
def xtalksspec_err(hist):
nm, nmerr, spm = get_nmean_errors(hist, get_cutoff(hist, get_guess(hist)))
pg1_p = 1 - exp(-nm) - nm * exp(-nm)
pg1_perr = exp(-nm) * nm * nmerr
pg0 = 1 - exp(-nm)
pg0err = nmerr
cutoff = get_cutoff(hist, get_cutoff(hist, get_guess(hist, n=3, m=33)))
pg1 = sum(hist[:cutoff]) / float(sum(hist))
pg1err = sqrt(sum(hist[:cutoff])) / float(sum(hist))
xtalk = (pg1 - pg1_p) / pg0
err = sqrt((1 / pg0 * pg1err)**2 + (1 / pg0 * pg1_perr)**2 +
(xtalk / pg0 * pg0err)**2)
print xtalk
return xtalk, err
def xtalksspec_err(hist):
nm, nmerr, spm = get_nmean_errors(hist, get_cutoff(hist, get_guess(hist)))
pg1_p = 1 - exp(- nm) - nm * exp(- nm)
pg1_perr = exp(- nm) * nm * nmerr
pg0 = 1 - exp(- nm)
pg0err = nmerr
cutoff = get_cutoff(hist, get_cutoff(hist, get_guess(hist, n=3, m=33)))
pg1 = sum(hist[:cutoff]) / float(sum(hist))
pg1err = sqrt(sum(hist[:cutoff])) / float(sum(hist))
xtalk = (pg1 - pg1_p) / pg0
err = sqrt((1 / pg0 * pg1err)**2 + (1 / pg0 * pg1_perr)**2 +
(xtalk / pg0 * pg0err)**2)
print xtalk
return xtalk, err
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 get_line(intd, mind, n=None, **kwargs):
int_hist = histogram(intd, bins=2048)
if n is None:
int_mn = get_n_mean(int_hist[0], get_cutoff(int_hist[0],
get_guess(int_hist[0])))
n = decide_n(int_mn)
temp = odr_gauss(n, int_hist[0][::-1])
x1, x2 = coord_from_fit(int_hist, temp, **kwargs)
min_hist = histogram(mind, bins=2048)
temp = odr_gauss(n, min_hist[0][::-1])
y1, y2 = coord_from_fit(min_hist, temp, **kwargs)
a = (y1 - y2) / (x1 - x2)
b = y1 - a*x1
print 'b = ' + str(b)
print 'a = ' + str(a)
return a, b
def get_line(intd, mind, n=None, **kwargs):
int_hist = histogram(intd, bins=2048)
if n is None:
int_mn = get_n_mean(int_hist[0],
get_cutoff(int_hist[0], get_guess(int_hist[0])))
n = decide_n(int_mn)
temp = odr_gauss(n, int_hist[0][::-1])
x1, x2 = coord_from_fit(int_hist, temp, **kwargs)
min_hist = histogram(mind, bins=2048)
temp = odr_gauss(n, min_hist[0][::-1])
y1, y2 = coord_from_fit(min_hist, temp, **kwargs)
a = (y1 - y2) / (x1 - x2)
b = y1 - a * x1
print 'b = ' + str(b)
print 'a = ' + str(a)
return a, b
