def job_afb_analysis(T):
data = np.loadtxt('Data/radiative_corrections.tsv')
corrections = data[:, 1]
energies = np.loadtxt('Data/energies.txt')
data = np.loadtxt('Data/afb.txt')
negative = data[:, 0]
positive = data[:, 1]
results = []
for i in range(SAMPLES):
positive_boot = bootstrap.redraw_count(positive)
negative_boot = bootstrap.redraw_count(negative)
result = afb_kernel(positive_boot, negative_boot, corrections)
if result is not None:
results.append(result)
afb_corr_dist, sin_sq_dist = zip(*results)
afb_filt, sin_sq_filt = zip(*[
(x[3], y)
for x, y in zip(afb_corr_dist, sin_sq_dist)
if not np.isnan(y)
])
print('afb:', len(afb_corr_dist), len(afb_filt))
T['sin_sq_bootstrap_acceptance'] = siunitx((1 - len(sin_sq_filt) / len(sin_sq_dist)) * 100)
afb_val, afb_err = bootstrap.average_and_std_arrays(afb_corr_dist)
sin_sq_val, sin_sq_err = bootstrap.average_and_std_arrays(sin_sq_filt)
afb_val, sin_sq_val = afb_kernel(positive, negative, corrections)
sin_sq_up, sin_sq_down = bootstrap.percentile_arrays(sin_sq_filt, sin_sq_val)
print('sin_sq:', sin_sq_val, sin_sq_err, sin_sq_up, sin_sq_down)
np.savetxt('_build/xy/afb.tsv', np.column_stack([energies, afb_val, afb_err]))
T['afb_table'] = list(zip(
siunitx(energies),
siunitx(afb_val, afb_err),
))
T['sin_sq_afb'] = siunitx(sin_sq_val, sin_sq_err)
T['sin_sq_afb_asym'] = '{:.3f}^{{+{:.3f}}}_{{-{:.3f}}}'.format(sin_sq_val, sin_sq_up, sin_sq_down)
counts, bins = np.histogram(sin_sq_filt)
counts = np.array(list(counts) + [counts[-1]])
print(bins.shape, counts.shape)
np.savetxt('_build/xy/sin_sq_hist.tsv', np.column_stack([bins, counts]))
counts, bins = np.histogram([x[3] for x in afb_corr_dist])
counts = np.array(list(counts) + [counts[-1]])
print(bins.shape, counts.shape)
np.savetxt('_build/xy/afb_hist.tsv', np.column_stack([bins, counts]))
counts, bins = np.histogram(afb_filt, bins=bins)
counts = np.array(list(counts) + [counts[-1]])
print(bins.shape, counts.shape)
np.savetxt('_build/xy/afb_filt_hist.tsv', np.column_stack([bins, counts]))
def get_indium_data(T, slope_val, width):
files = glob.glob('Data/in-*.txt')
temps_val = []
temps_err = []
all_counts = []
all_tau_0_dist = []
all_tau_bar_dist = []
all_tau_f_dist = []
all_tau_t_dist = []
all_intens_0_dist = []
all_intens_t_dist = []
all_lifetime_y_dist = []
all_lifetime_popt_dist = []
all_sigma_c_dist = []
# Process lifetime curves with bootstrap.
for sample_id in range(BOOTSTRAP_SAMPLES):
print('Bootstrap sample', sample_id, 'running …')
results = []
for file_ in sorted(files):
print('Working on lifetime spectrum', file_)
if sample_id == 0:
temp_lower, temp_upper = get_temp(file_)
temp_mean = (temp_lower + temp_upper) / 2
temp_err = temp_upper - temp_mean
temps_val.append(temp_mean)
temps_err.append(temp_err)
print('Mean temperature:', temp_mean)
data = np.loadtxt(file_)
channel = data[:, 0]
time = slope_val * channel
counts = data[:, 1]
boot_counts = bootstrap.redraw_count(counts)
if sample_id == 0:
all_counts.append(counts)
x = np.linspace(np.min(time), np.max(time), 2000)
sel = (9 < time) & (time < 15)
fit_func = lambda t, mean, A_0, A_t, tau_0, tau_t, BG: \
models.lifetime_spectrum(t, mean, width, A_0, A_t, tau_0, tau_t, BG)
p0 = [10.5, 210, 190, 0.07, 0.8, 0]
popt, pconv = op.curve_fit(fit_func,
time[sel],
boot_counts[sel],
p0=p0)
mean, A_0, A_t, tau_0, tau_t, BG = popt
intens_0 = A_0 / (A_0 + A_t)
intens_t = A_t / (A_0 + A_t)
tau_bar = intens_0 * tau_0 + intens_t * tau_t
y = fit_func(x, *popt)
tau_f = 1 / (intens_0 / tau_0 - intens_t / tau_t)
sigma_c = 1 / tau_0 - 1 / tau_f
results.append([
tau_0,
tau_bar,
tau_f,
tau_t,
intens_0,
intens_t,
y,
popt,
sigma_c,
])
tau_0_list, tau_bar_list, tau_f_list, tau_t_list, intens_0_list, \
intens_t_list, lifetime_y_list, lifetime_popt_list, sigma_c_list \
= zip(*results)
all_tau_0_dist.append(tau_0_list)
all_tau_bar_dist.append(tau_bar_list)
all_tau_f_dist.append(tau_f_list)
all_tau_t_dist.append(tau_t_list)
all_intens_0_dist.append(intens_0_list)
all_intens_t_dist.append(intens_t_list)
all_lifetime_y_dist.append(lifetime_y_list)
all_lifetime_popt_dist.append(lifetime_popt_list)
all_sigma_c_dist.append(sigma_c_list)
T['temps_int'] = []
# Generate plots with lifetime curves and fits.
for temp, counts, lifetime_y_dist in zip(temps_val, all_counts,
zip(*all_lifetime_y_dist)):
print('Creating lifetime plot with temp', temp)
y_val, y_err = bootstrap.average_and_std_arrays(lifetime_y_dist)
np.savetxt(
'_build/xy/lifetime-{}K-data.tsv'.format(int(temp)),
bootstrap.pgfplots_error_band(time[0:4000], counts[0:4000],
np.sqrt(counts[0:4000])))
np.savetxt('_build/xy/lifetime-{}K-fit.tsv'.format(int(temp)),
np.column_stack([x, y_val]))
np.savetxt('_build/xy/lifetime-{}K-band.tsv'.format(int(temp)),
bootstrap.pgfplots_error_band(x, y_val, y_err))
T['temps_int'].append(int(temp))
if False:
pl.fill_between(x,
y_val - y_err,
y_val + y_err,
alpha=0.5,
color='red')
pl.plot(time, counts, color='black')
counts_smooth = scipy.ndimage.filters.gaussian_filter1d(counts, 8)
pl.plot(time, counts_smooth, color='green')
pl.plot(x, y_val, color='red')
pl.xlabel('Time / ns')
pl.ylabel('Counts')
dandify_plot()
pl.xlim((8, 20))
pl.savefig('_build/mpl-lifetime-{:04d}K.pdf'.format(int(temp)))
pl.savefig('_build/mpl-lifetime-{:04d}K.png'.format(int(temp)))
pl.yscale('log')
pl.savefig('_build/mpl-lifetime-{:04d}K-log.pdf'.format(int(temp)))
pl.savefig('_build/mpl-lifetime-{:04d}K-log.png'.format(int(temp)))
pl.clf()
T['temps_int'].sort()
# Plot the lifetimes.
taus_0_val, taus_0_err = bootstrap.average_and_std_arrays(all_tau_0_dist)
taus_t_val, taus_t_err = bootstrap.average_and_std_arrays(all_tau_t_dist)
taus_f_val, taus_f_err = bootstrap.average_and_std_arrays(all_tau_f_dist)
taus_bar_val, taus_bar_err = bootstrap.average_and_std_arrays(
all_tau_bar_dist)
pl.errorbar(temps_val,
taus_0_val,
xerr=temps_err,
yerr=taus_0_err,
label=r'$\tau_0$',
linestyle='none',
marker='+')
pl.errorbar(temps_val,
taus_bar_val,
xerr=temps_err,
yerr=taus_bar_err,
label=r'$\bar\tau$',
linestyle='none',
marker='+')
pl.errorbar(temps_val,
taus_t_val,
xerr=temps_err,
yerr=taus_t_err,
label=r'$\tau_\mathrm{t}$',
linestyle='none',
marker='+')
pl.errorbar(temps_val,
taus_f_val,
xerr=temps_err,
yerr=taus_f_err,
label=r'$\tau_\mathrm{f}$',
linestyle='none',
marker='+')
pl.xlabel('T / K')
pl.ylabel(r'$\tau$ / ns')
dandify_plot()
pl.savefig('_build/mpl-tau_0-tau_t.pdf')
pl.savefig('_build/mpl-tau_0-tau_t.png')
pl.clf()
np.savetxt('_build/xy/tau_0.tsv',
np.column_stack([temps_val, taus_0_val, taus_0_err]))
np.savetxt('_build/xy/tau_t.tsv',
np.column_stack([temps_val, taus_t_val, taus_t_err]))
np.savetxt('_build/xy/tau_f.tsv',
np.column_stack([temps_val, taus_f_val, taus_f_err]))
np.savetxt('_build/xy/tau_bar.tsv',
np.column_stack([temps_val, taus_bar_val, taus_bar_err]))
T['taus_table'] = list(
zip(
siunitx(temps_val, temps_err),
siunitx(taus_0_val, taus_0_err),
siunitx(taus_t_val, taus_t_err),
siunitx(taus_f_val, taus_f_err),
siunitx(taus_bar_val, taus_bar_err),
))
# Plot relative intensities.
all_intens_0_val, all_intens_0_err = bootstrap.average_and_std_arrays(
all_intens_0_dist)
all_intens_t_val, all_intens_t_err = bootstrap.average_and_std_arrays(
all_intens_t_dist)
pl.errorbar(temps_val,
all_intens_0_val,
xerr=temps_err,
yerr=all_intens_0_err,
label=r'$A_0$',
linestyle='none',
marker='+')
pl.errorbar(temps_val,
all_intens_t_val,
xerr=temps_err,
yerr=all_intens_t_err,
label=r'$A_\mathrm{t}$',
linestyle='none',
marker='+')
pl.xlabel('T / K')
pl.ylabel(r'Relative Intensity')
dandify_plot()
pl.savefig('_build/mpl-intensities.pdf')
pl.savefig('_build/mpl-intensities.png')
pl.clf()
np.savetxt(
'_build/xy/intensities-0.tsv',
np.column_stack([temps_val, all_intens_0_val, all_intens_0_err]))
np.savetxt(
'_build/xy/intensities-t.tsv',
np.column_stack([temps_val, all_intens_t_val, all_intens_t_err]))
T['intensities_table'] = list(
zip(
siunitx(temps_val, temps_err),
siunitx(all_intens_0_val, all_intens_0_err),
siunitx(all_intens_t_val, all_intens_t_err),
))
inv_temps = 1 / np.array(temps_val)
results = []
x = np.linspace(np.min(inv_temps), np.max(inv_temps), 1000)
kelvin_to_eV = 8.621738e-5
for all_sigma_c in all_sigma_c_dist:
p0 = [11, 240]
print('inv_temps:', inv_temps)
print('all_sigma_c:', all_sigma_c)
for leave_out in range(len(all_sigma_c)):
inv_temps_jack = np.delete(inv_temps, leave_out)
all_sigma_c_jack = np.delete(all_sigma_c, leave_out)
popt, pconv = op.curve_fit(exp_decay,
inv_temps_jack,
all_sigma_c_jack,
p0=p0)
y = exp_decay(x, *popt)
results.append([
popt,
popt[1] * kelvin_to_eV,
y,
])
popt_dist, Ht_eV_dist, arr_y_dist = zip(*results)
popt_val, popt_err = bootstrap.average_and_std_arrays(popt_dist)
print('popt:', siunitx(popt_val, popt_err))
Ht_eV_val, Ht_eV_err = bootstrap.average_and_std_arrays(Ht_eV_dist)
arr_y_val, arr_y_err = bootstrap.average_and_std_arrays(arr_y_dist)
sigma_c_val, sigma_c_err = bootstrap.average_and_std_arrays(
all_sigma_c_dist)
pl.fill_between(x,
arr_y_val - arr_y_err,
arr_y_val + arr_y_err,
alpha=0.5,
color='red')
pl.plot(x, arr_y_val, color='red')
pl.errorbar(inv_temps,
sigma_c_val,
yerr=sigma_c_err,
marker='+',
linestyle='none',
color='black')
pl.xlabel(r'$1 / T$')
pl.ylabel(r'$\sigma C_t(T)$')
pl.savefig('_build/mpl-arrhenius.pdf')
pl.savefig('_build/mpl-arrhenius.png')
pl.clf()
np.savetxt('_build/xy/arrhenius-data.tsv',
np.column_stack([inv_temps, sigma_c_val, sigma_c_err]))
np.savetxt('_build/xy/arrhenius-fit.tsv', np.column_stack([x, arr_y_val]))
np.savetxt('_build/xy/arrhenius-band.tsv',
bootstrap.pgfplots_error_band(x, arr_y_val, arr_y_err))
T['arrhenius_table'] = list(
zip(
siunitx(inv_temps),
siunitx(sigma_c_val, sigma_c_err),
))
print('Ht_eV:', siunitx(Ht_eV_val, Ht_eV_err))
T['Ht_eV'] = siunitx(Ht_eV_val, Ht_eV_err)
pl.errorbar(temps_val,
taus_bar_val,
xerr=temps_err,
yerr=taus_bar_err,
label=r'$\bar\tau$',
linestyle='none',
marker='+')
dandify_plot()
pl.xlabel('T / K')
pl.ylabel(r'$\bar\tau$ / ns')
pl.savefig('_build/mpl-s_curve.pdf')
pl.savefig('_build/mpl-s_curve.png')
pl.clf()
np.savetxt('_build/xy/s_curve.tsv',
np.column_stack([temps_val, taus_bar_val, taus_bar_err]))
def bootstrap_driver(T):
# Load all the input data from the files.
lum_data = np.loadtxt('Data/luminosity.txt')
lum_val = lum_data[:, 0]
lum_err = lum_data[:, 3]
radiative_hadrons = np.loadtxt('Data/radiative-hadrons.tsv')
radiative_leptons = np.loadtxt('Data/radiative-leptons.tsv')
raw_matrix = np.loadtxt('Data/matrix.txt').T
mc_sizes = np.loadtxt('Data/monte-carlo-sizes.txt')
filtered = np.loadtxt('Data/filtered.txt')
# Some output into the template.
T['luminosities_table'] = list(zip(siunitx(energies), siunitx(lum_val, lum_err)))
T['radiative_cs_table'] = list(zip(
siunitx(energies),
siunitx(radiative_hadrons),
siunitx(radiative_leptons),
))
# Container for the results of each bootstrap run.
results = []
for r in range(SAMPLES):
# Draw new numbers for the matrix.
boot_matrix = bootstrap.redraw_count(raw_matrix)
# Draw new luminosities.
boot_lum_val = np.array([
random.gauss(val, err)
for val, err
in zip(lum_val, lum_err)])
# Draw new filtered readings.
boot_readings = bootstrap.redraw_count(filtered)
# Run the analysis on the resampled data and save the results.
results.append(bootstrap_kernel(mc_sizes, boot_matrix, boot_readings,
boot_lum_val, radiative_hadrons,
radiative_leptons))
# The `results` is a list which contains one entry per bootstrap run. This
# is not particularly helpful as the different interesting quantities are
# only on the second index on the list. The first index of the `results`
# list is the bootstrap run index. Therefore we use the `zip(*x)` trick to
# exchange the two indices. The result will be a list of quantities which
# are themselves lists of the bootstrap samples. Then using Python tuple
# assignments, we can split that (now) outer list into different
# quantities. Each of the new variables created here is a list of R
# bootstrap samples.
x_dist, masses_dist, widths_dist, cross_sections_dist, y_dist, corr_dist, \
matrix_dist, inverted_dist, readings_dist, peaks_dist, brs_dist, \
width_electron_dist, width_flavors_dist, missing_width_dist, \
width_lepton_dist, neutrino_families_dist, popts_dist, \
mean_mass_dist, mean_width_dist \
= zip(*results)
# We only need one of the lists of the x-values as they are all the same.
# So take the first and throw the others out.
x = x_dist[0]
# The masses and the widths that are given back from the `bootstrap_kernel`
# are a list of four elements (electrons, muons, tauons, hadrons) each. The
# variable `masses_dist` contains R copies of this four-list, one copy for
# each bootstrap sample. We now average along the bootstrap dimension, that
# is the outermost dimension. For each of the four masses, we take the
# average along the R copies. This will give us four masses and four
# masses-errors.
masses_val, masses_err = bootstrap.average_and_std_arrays(masses_dist)
widths_val, widths_err = bootstrap.average_and_std_arrays(widths_dist)
peaks_val, peaks_err = bootstrap.average_and_std_arrays(peaks_dist)
brs_val, brs_err = bootstrap.average_and_std_arrays(brs_dist)
T['brs'] = siunitx(brs_val[0:3], brs_err[0:3])
# Format masses and widths for the template.
T['lorentz_fits_table'] = list(zip(
display_names,
siunitx(masses_val, masses_err),
siunitx(widths_val, widths_err),
siunitx(peaks_val, peaks_err),
))
width_electron_val, width_electron_err = bootstrap.average_and_std_arrays(width_electron_dist)
width_flavors_val, width_flavors_err = bootstrap.average_and_std_arrays(width_flavors_dist)
T['width_electron_mev'] = siunitx(width_electron_val*1000, width_electron_err*1000)
T['width_flavors_mev'] = siunitx(width_flavors_val*1000, width_flavors_err*1000)
missing_width_val, missing_width_err = bootstrap.average_and_std_arrays(missing_width_dist)
width_lepton_val, width_lepton_err = bootstrap.average_and_std_arrays(width_lepton_dist)
neutrino_families_val, neutrino_families_err = bootstrap.average_and_std_arrays(neutrino_families_dist)
T['missing_width_mev'] = siunitx(missing_width_val*1000, missing_width_err*1000)
T['width_lepton_mev'] = siunitx(width_lepton_val*1000, width_lepton_err*1000)
T['neutrino_families'] = siunitx(neutrino_families_val, neutrino_families_err)
# Format original counts for the template.
val, err = bootstrap.average_and_std_arrays(readings_dist)
T['counts_table'] = []
for i in range(7):
T['counts_table'].append([siunitx(energies[i])] + siunitx(val[i, :], err[i, :], allowed_hang=10))
# Format corrected counts for the template.
val, err = bootstrap.average_and_std_arrays(corr_dist)
T['corrected_counts_table'] = []
for i in range(7):
T['corrected_counts_table'].append([siunitx(energies[i])] + siunitx(val[i, :], err[i, :], allowed_hang=10))
# Format matrix for the template.
matrix_val, matrix_err = bootstrap.average_and_std_arrays(matrix_dist)
T['matrix'] = []
for i in range(4):
T['matrix'].append([display_names[i]] + siunitx(matrix_val[i, :]*100, matrix_err[i, :]*100, allowed_hang=10))
# Format inverted matrix for the template.
inverted_val, inverted_err = bootstrap.average_and_std_arrays(inverted_dist)
T['inverted'] = []
for i in range(4):
T['inverted'].append([display_names[i]] +
list(map(number_padding,
siunitx(inverted_val[i, :], inverted_err[i, :], allowed_hang=10))))
# Format cross sections for the template.
cs_val, cs_err = bootstrap.average_and_std_arrays(cross_sections_dist)
T['cross_sections_table'] = []
for i in range(7):
T['cross_sections_table'].append([siunitx(energies[i])] + siunitx(cs_val[:, i], cs_err[:, i]))
# Build error band for pgfplots.
y_list_val, y_list_err = bootstrap.average_and_std_arrays(y_dist)
for i, name in zip(itertools.count(), names):
# Extract the y-values for the given decay type.
y_val = y_list_val[i, :]
y_err = y_list_err[i, :]
# Store the data for pgfplots.
np.savetxt('_build/xy/cross_section-{}s.tsv'.format(name),
np.column_stack([energies, cs_val[i, :], cs_err[i, :]]))
np.savetxt('_build/xy/cross_section-{}s-band.tsv'.format(name),
bootstrap.pgfplots_error_band(x, y_val, y_err))
np.savetxt('_build/xy/cross_section-{}s-fit.tsv'.format(name),
np.column_stack((x, y_val)))
popts_val, popts_err = bootstrap.average_and_std_arrays(popts_dist)
T['chi_sq'] = []
T['chi_sq_red'] = []
T['p'] = []
for i in range(4):
residuals = cs_val[i, :] - propagator(energies, *popts_val[i, :])
chi_sq = np.sum((residuals / cs_err[i, :])**2)
dof = len(residuals) - 1 - len(popts_val[i, :])
p = 1 - scipy.stats.chi2.cdf(chi_sq, dof)
print('chi_sq', chi_sq, chi_sq/dof, p)
T['chi_sq'].append(siunitx(chi_sq))
T['chi_sq_red'].append(siunitx(chi_sq/dof))
T['p'].append(siunitx(p))
T['confidence_table'] = list(zip(
display_names,
T['chi_sq'],
T['chi_sq_red'],
T['p'],
))
mean_mass_val, mean_mass_err = bootstrap.average_and_std_arrays(mean_mass_dist)
mean_width_val, mean_width_err = bootstrap.average_and_std_arrays(mean_width_dist)
T['mean_mass'] = siunitx(mean_mass_val, mean_mass_err)
T['mean_width'] = siunitx(mean_width_val, mean_width_err)
def get_acryl_data(T, slope_val, width):
data = np.loadtxt('Data/longlong.txt')
channel = data[:, 0]
time = slope_val * channel
counts = data[:, 1]
x = np.linspace(np.min(time), np.max(time), 500)
fit_func = lambda t, mean, A_0, A_t, tau_0, tau_t, BG: \
np.log(models.lifetime_spectrum(t, mean, width, A_0, A_t, tau_0, tau_t, BG))
results = []
sel1 = (10.92 < time) & (time < 11.58)
sel2 = (13.11 < time) & (time < 22)
sels = [sel1, sel2]
x1 = np.linspace(np.min(time[sel1]), np.max(time[sel1]), 10)
x2 = np.linspace(np.min(time[sel2]), np.max(time[sel2]), 10)
for sample_id in range(BOOTSTRAP_SAMPLES):
print('Bootstrap sample', sample_id, 'running …')
boot_counts = bootstrap.redraw_count(counts)
lin_lifetimes = []
lin_results = []
for sel_lin, x_lin in zip(sels, [x1, x2]):
popt_lin, pconv_lin = op.curve_fit(exp_decay,
time[sel_lin],
boot_counts[sel_lin],
p0=[1e5, 0.3])
y_lin = exp_decay(x_lin, *popt_lin)
lin_results.append(y_lin)
lin_results.append(popt_lin)
lin_results.append(1 / popt_lin[1])
lin_lifetimes.append(1 / popt_lin[1])
sel = (10 < time) & (time < 50) & (boot_counts > 0)
p0 = [10.5, 13e3, 34e2] + lin_lifetimes + [2]
popt, pconv = op.curve_fit(fit_func,
time[sel],
np.log(boot_counts[sel]),
p0=p0)
mean, A_0, A_t, tau_0, tau_t, BG = popt
intens_0 = A_0 / (A_0 + A_t)
intens_t = A_t / (A_0 + A_t)
tau_bar = intens_0 * tau_0 + intens_t * tau_t
y = np.exp(fit_func(x, *popt))
tau_f = 1 / (intens_0 / tau_0 - intens_t / tau_t)
sigma_c = 1 / tau_0 - 1 / tau_f
results.append([
tau_0,
tau_bar,
tau_f,
tau_t,
intens_0,
intens_t,
y,
popt,
sigma_c,
] + lin_results)
tau_0_dist, tau_bar_dist, tau_f_dist, tau_t_dist, intens_0_dist, \
intens_t_dist, lifetime_y_dist, lifetime_popt_dist, sigma_c_dist, \
y_lin1_dist, popt_lin1_dist, tau_lin1_dist, \
y_lin2_dist, popt_lin2_dist, tau_lin2_dist, \
= zip(*results)
tau_0_val, tau_0_err = bootstrap.average_and_std_arrays(tau_0_dist)
tau_t_val, tau_t_err = bootstrap.average_and_std_arrays(tau_t_dist)
tau_f_val, tau_f_err = bootstrap.average_and_std_arrays(tau_f_dist)
tau_bar_val, tau_bar_err = bootstrap.average_and_std_arrays(tau_bar_dist)
popt_val, popt_err = bootstrap.average_and_std_arrays(lifetime_popt_dist)
y_val, y_err = bootstrap.average_and_std_arrays(lifetime_y_dist)
popt_lin1_val, popt_lin1_err = bootstrap.average_and_std_arrays(
popt_lin1_dist)
y_lin1_val, y_lin1_err = bootstrap.average_and_std_arrays(y_lin1_dist)
tau_lin1_val, tau_lin1_err = bootstrap.average_and_std_arrays(
tau_lin1_dist)
popt_lin2_val, popt_lin2_err = bootstrap.average_and_std_arrays(
popt_lin2_dist)
y_lin2_val, y_lin2_err = bootstrap.average_and_std_arrays(y_lin2_dist)
tau_lin2_val, tau_lin2_err = bootstrap.average_and_std_arrays(
tau_lin2_dist)
print('tau_0', siunitx(tau_0_val, tau_0_err))
print('tau_t', siunitx(tau_t_val, tau_t_err))
print('tau_f', siunitx(tau_f_val, tau_f_err))
print('tau_bar', siunitx(tau_bar_val, tau_bar_err))
T['acryl_tau_0'] = siunitx(tau_0_val, tau_0_err)
T['acryl_tau_t'] = siunitx(tau_t_val, tau_t_err)
T['acryl_tau_f'] = siunitx(tau_f_val, tau_f_err)
T['acryl_tau_bar'] = siunitx(tau_bar_val, tau_bar_err)
print('popt', siunitx(popt_val, popt_err))
print('popt_lin1', siunitx(popt_lin1_val, popt_lin1_err))
print('popt_lin2', siunitx(popt_lin2_val, popt_lin2_err))
print('tau_lin1', siunitx(tau_lin1_val, tau_lin1_err))
print('tau_lin2', siunitx(tau_lin2_val, tau_lin2_err))
T['acryl_tau_0_lin'] = siunitx(tau_lin1_val, tau_lin1_err)
T['acryl_tau_t_lin'] = siunitx(tau_lin2_val, tau_lin2_err)
print(x.shape)
print(y_lin1_val.shape)
pl.plot(time, counts, color='black', alpha=0.3)
counts_smooth = scipy.ndimage.filters.gaussian_filter1d(counts, 8)
pl.plot(time, counts_smooth, color='green')
pl.fill_between(x, y_val - y_err, y_val + y_err, alpha=0.5, color='red')
pl.plot(x, y_val, color='red')
pl.xlabel('Time / ns')
pl.ylabel('Counts')
dandify_plot()
#pl.xlim((8, 20))
pl.ylim((0.1, np.max(counts) * 1.1))
pl.savefig('_build/mpl-lifetime-acryl.pdf')
pl.savefig('_build/mpl-lifetime-acryl.png')
pl.yscale('log')
pl.fill_between(x1,
y_lin1_val - y_lin1_err,
y_lin1_val + y_lin1_err,
alpha=0.5,
color='blue')
pl.fill_between(x2,
y_lin2_val - y_lin2_err,
y_lin2_val + y_lin2_err,
alpha=0.5,
color='blue')
pl.plot(x1, y_lin1_val, color='blue', alpha=0.5)
pl.plot(x2, y_lin2_val, color='blue', alpha=0.5)
dandify_plot()
pl.savefig('_build/mpl-lifetime-acryl-log.pdf')
pl.savefig('_build/mpl-lifetime-acryl-log.png')
#pl.show()
pl.clf()
np.savetxt('_build/xy/acryl-lifetime-data.tsv',
np.column_stack([time, counts]))
np.savetxt('_build/xy/acryl-lifetime-smoothed.tsv',
np.column_stack([time, counts_smooth]))
np.savetxt('_build/xy/acryl-lifetime-fit.tsv', np.column_stack([x, y_val]))
np.savetxt('_build/xy/acryl-lifetime-band.tsv',
bootstrap.pgfplots_error_band(x, y_val, y_err))
np.savetxt('_build/xy/acryl-lifetime-fit-lin1.tsv',
np.column_stack([x1, y_lin1_val]))
np.savetxt('_build/xy/acryl-lifetime-band-lin1.tsv',
bootstrap.pgfplots_error_band(x1, y_lin1_val, y_lin1_err))
np.savetxt('_build/xy/acryl-lifetime-fit-lin2.tsv',
np.column_stack([x2, y_lin2_val]))
np.savetxt('_build/xy/acryl-lifetime-band-lin2.tsv',
bootstrap.pgfplots_error_band(x2, y_lin2_val, y_lin2_err))
def get_indium_data(T, slope_val, width):
files = glob.glob('Data/in-*.txt')
temps_val = []
temps_err = []
all_counts = []
all_tau_0_dist = []
all_tau_bar_dist = []
all_tau_f_dist = []
all_tau_t_dist = []
all_intens_0_dist = []
all_intens_t_dist = []
all_lifetime_y_dist = []
all_lifetime_popt_dist = []
all_sigma_c_dist = []
# Process lifetime curves with bootstrap.
for sample_id in range(BOOTSTRAP_SAMPLES):
print('Bootstrap sample', sample_id, 'running …')
results = []
for file_ in sorted(files):
print('Working on lifetime spectrum', file_)
if sample_id == 0:
temp_lower, temp_upper = get_temp(file_)
temp_mean = (temp_lower + temp_upper)/2
temp_err = temp_upper - temp_mean
temps_val.append(temp_mean)
temps_err.append(temp_err)
print('Mean temperature:', temp_mean)
data = np.loadtxt(file_)
channel = data[:, 0]
time = slope_val * channel
counts = data[:, 1]
boot_counts = bootstrap.redraw_count(counts)
if sample_id == 0:
all_counts.append(counts)
x = np.linspace(np.min(time), np.max(time), 2000)
sel = (9 < time) & (time < 15)
fit_func = lambda t, mean, A_0, A_t, tau_0, tau_t, BG: \
models.lifetime_spectrum(t, mean, width, A_0, A_t, tau_0, tau_t, BG)
p0 = [10.5, 210, 190, 0.07, 0.8, 0]
popt, pconv = op.curve_fit(fit_func, time[sel], boot_counts[sel], p0=p0)
mean, A_0, A_t, tau_0, tau_t, BG = popt
intens_0 = A_0 / (A_0 + A_t)
intens_t = A_t / (A_0 + A_t)
tau_bar = intens_0 * tau_0 + intens_t * tau_t
y = fit_func(x, *popt)
tau_f = 1 / (intens_0 / tau_0 - intens_t / tau_t)
sigma_c = 1 / tau_0 - 1 / tau_f
results.append([
tau_0,
tau_bar,
tau_f,
tau_t,
intens_0,
intens_t,
y,
popt,
sigma_c,
])
tau_0_list, tau_bar_list, tau_f_list, tau_t_list, intens_0_list, \
intens_t_list, lifetime_y_list, lifetime_popt_list, sigma_c_list \
= zip(*results)
all_tau_0_dist.append(tau_0_list)
all_tau_bar_dist.append(tau_bar_list)
all_tau_f_dist.append(tau_f_list)
all_tau_t_dist.append(tau_t_list)
all_intens_0_dist.append(intens_0_list)
all_intens_t_dist.append(intens_t_list)
all_lifetime_y_dist.append(lifetime_y_list)
all_lifetime_popt_dist.append(lifetime_popt_list)
all_sigma_c_dist.append(sigma_c_list)
T['temps_int'] = []
# Generate plots with lifetime curves and fits.
for temp, counts, lifetime_y_dist in zip(temps_val, all_counts, zip(*all_lifetime_y_dist)):
print('Creating lifetime plot with temp', temp)
y_val, y_err = bootstrap.average_and_std_arrays(lifetime_y_dist)
np.savetxt('_build/xy/lifetime-{}K-data.tsv'.format(int(temp)),
bootstrap.pgfplots_error_band(time[0:4000], counts[0:4000], np.sqrt(counts[0:4000])))
np.savetxt('_build/xy/lifetime-{}K-fit.tsv'.format(int(temp)),
np.column_stack([x, y_val]))
np.savetxt('_build/xy/lifetime-{}K-band.tsv'.format(int(temp)),
bootstrap.pgfplots_error_band(x, y_val, y_err))
T['temps_int'].append(int(temp))
if False:
pl.fill_between(x, y_val - y_err, y_val + y_err, alpha=0.5, color='red')
pl.plot(time, counts, color='black')
counts_smooth = scipy.ndimage.filters.gaussian_filter1d(counts, 8)
pl.plot(time, counts_smooth, color='green')
pl.plot(x, y_val, color='red')
pl.xlabel('Time / ns')
pl.ylabel('Counts')
dandify_plot()
pl.xlim((8, 20))
pl.savefig('_build/mpl-lifetime-{:04d}K.pdf'.format(int(temp)))
pl.savefig('_build/mpl-lifetime-{:04d}K.png'.format(int(temp)))
pl.yscale('log')
pl.savefig('_build/mpl-lifetime-{:04d}K-log.pdf'.format(int(temp)))
pl.savefig('_build/mpl-lifetime-{:04d}K-log.png'.format(int(temp)))
pl.clf()
T['temps_int'].sort()
# Plot the lifetimes.
taus_0_val, taus_0_err = bootstrap.average_and_std_arrays(all_tau_0_dist)
taus_t_val, taus_t_err = bootstrap.average_and_std_arrays(all_tau_t_dist)
taus_f_val, taus_f_err = bootstrap.average_and_std_arrays(all_tau_f_dist)
taus_bar_val, taus_bar_err = bootstrap.average_and_std_arrays(all_tau_bar_dist)
pl.errorbar(temps_val, taus_0_val, xerr=temps_err, yerr=taus_0_err,
label=r'$\tau_0$', linestyle='none', marker='+')
pl.errorbar(temps_val, taus_bar_val, xerr=temps_err, yerr=taus_bar_err,
label=r'$\bar\tau$', linestyle='none', marker='+')
pl.errorbar(temps_val, taus_t_val, xerr=temps_err, yerr=taus_t_err,
label=r'$\tau_\mathrm{t}$', linestyle='none', marker='+')
pl.errorbar(temps_val, taus_f_val, xerr=temps_err, yerr=taus_f_err,
label=r'$\tau_\mathrm{f}$', linestyle='none', marker='+')
pl.xlabel('T / K')
pl.ylabel(r'$\tau$ / ns')
dandify_plot()
pl.savefig('_build/mpl-tau_0-tau_t.pdf')
pl.savefig('_build/mpl-tau_0-tau_t.png')
pl.clf()
np.savetxt('_build/xy/tau_0.tsv',
np.column_stack([temps_val, taus_0_val, taus_0_err]))
np.savetxt('_build/xy/tau_t.tsv',
np.column_stack([temps_val, taus_t_val, taus_t_err]))
np.savetxt('_build/xy/tau_f.tsv',
np.column_stack([temps_val, taus_f_val, taus_f_err]))
np.savetxt('_build/xy/tau_bar.tsv',
np.column_stack([temps_val, taus_bar_val, taus_bar_err]))
T['taus_table'] = list(zip(
siunitx(temps_val, temps_err),
siunitx(taus_0_val, taus_0_err),
siunitx(taus_t_val, taus_t_err),
siunitx(taus_f_val, taus_f_err),
siunitx(taus_bar_val, taus_bar_err),
))
# Plot relative intensities.
all_intens_0_val, all_intens_0_err = bootstrap.average_and_std_arrays(all_intens_0_dist)
all_intens_t_val, all_intens_t_err = bootstrap.average_and_std_arrays(all_intens_t_dist)
pl.errorbar(temps_val, all_intens_0_val, xerr=temps_err, yerr=all_intens_0_err,
label=r'$A_0$', linestyle='none', marker='+')
pl.errorbar(temps_val, all_intens_t_val, xerr=temps_err, yerr=all_intens_t_err,
label=r'$A_\mathrm{t}$', linestyle='none', marker='+')
pl.xlabel('T / K')
pl.ylabel(r'Relative Intensity')
dandify_plot()
pl.savefig('_build/mpl-intensities.pdf')
pl.savefig('_build/mpl-intensities.png')
pl.clf()
np.savetxt('_build/xy/intensities-0.tsv',
np.column_stack([temps_val, all_intens_0_val, all_intens_0_err]))
np.savetxt('_build/xy/intensities-t.tsv',
np.column_stack([temps_val, all_intens_t_val, all_intens_t_err]))
T['intensities_table'] = list(zip(
siunitx(temps_val, temps_err),
siunitx(all_intens_0_val, all_intens_0_err),
siunitx(all_intens_t_val, all_intens_t_err),
))
inv_temps = 1 / np.array(temps_val)
results = []
x = np.linspace(np.min(inv_temps), np.max(inv_temps), 1000)
kelvin_to_eV = 8.621738e-5
for all_sigma_c in all_sigma_c_dist:
p0 = [11, 240]
print('inv_temps:', inv_temps)
print('all_sigma_c:', all_sigma_c)
for leave_out in range(len(all_sigma_c)):
inv_temps_jack = np.delete(inv_temps, leave_out)
all_sigma_c_jack = np.delete(all_sigma_c, leave_out)
popt, pconv = op.curve_fit(exp_decay, inv_temps_jack, all_sigma_c_jack, p0=p0)
y = exp_decay(x, *popt)
results.append([
popt,
popt[1] * kelvin_to_eV,
y,
])
popt_dist, Ht_eV_dist, arr_y_dist = zip(*results)
popt_val, popt_err = bootstrap.average_and_std_arrays(popt_dist)
print('popt:', siunitx(popt_val, popt_err))
Ht_eV_val, Ht_eV_err = bootstrap.average_and_std_arrays(Ht_eV_dist)
arr_y_val, arr_y_err = bootstrap.average_and_std_arrays(arr_y_dist)
sigma_c_val, sigma_c_err = bootstrap.average_and_std_arrays(all_sigma_c_dist)
pl.fill_between(x, arr_y_val - arr_y_err, arr_y_val + arr_y_err, alpha=0.5, color='red')
pl.plot(x, arr_y_val, color='red')
pl.errorbar(inv_temps, sigma_c_val, yerr=sigma_c_err, marker='+', linestyle='none', color='black')
pl.xlabel(r'$1 / T$')
pl.ylabel(r'$\sigma C_t(T)$')
pl.savefig('_build/mpl-arrhenius.pdf')
pl.savefig('_build/mpl-arrhenius.png')
pl.clf()
np.savetxt('_build/xy/arrhenius-data.tsv',
np.column_stack([inv_temps, sigma_c_val, sigma_c_err]))
np.savetxt('_build/xy/arrhenius-fit.tsv',
np.column_stack([x, arr_y_val]))
np.savetxt('_build/xy/arrhenius-band.tsv',
bootstrap.pgfplots_error_band(x, arr_y_val, arr_y_err))
T['arrhenius_table'] = list(zip(
siunitx(inv_temps),
siunitx(sigma_c_val, sigma_c_err),
))
print('Ht_eV:', siunitx(Ht_eV_val, Ht_eV_err))
T['Ht_eV'] = siunitx(Ht_eV_val, Ht_eV_err)
pl.errorbar(temps_val, taus_bar_val, xerr=temps_err, yerr=taus_bar_err,
label=r'$\bar\tau$', linestyle='none', marker='+')
dandify_plot()
pl.xlabel('T / K')
pl.ylabel(r'$\bar\tau$ / ns')
pl.savefig('_build/mpl-s_curve.pdf')
pl.savefig('_build/mpl-s_curve.png')
pl.clf()
np.savetxt('_build/xy/s_curve.tsv',
np.column_stack([temps_val, taus_bar_val, taus_bar_err]))
def get_acryl_data(T, slope_val, width):
data = np.loadtxt('Data/longlong.txt')
channel = data[:, 0]
time = slope_val * channel
counts = data[:, 1]
x = np.linspace(np.min(time), np.max(time), 500)
fit_func = lambda t, mean, A_0, A_t, tau_0, tau_t, BG: \
np.log(models.lifetime_spectrum(t, mean, width, A_0, A_t, tau_0, tau_t, BG))
results = []
sel1 = (10.92 < time) & (time < 11.58)
sel2 = (13.11 < time) & (time < 22)
sels = [sel1, sel2]
x1 = np.linspace(np.min(time[sel1]), np.max(time[sel1]), 10)
x2 = np.linspace(np.min(time[sel2]), np.max(time[sel2]), 10)
for sample_id in range(BOOTSTRAP_SAMPLES):
print('Bootstrap sample', sample_id, 'running …')
boot_counts = bootstrap.redraw_count(counts)
lin_lifetimes = []
lin_results = []
for sel_lin, x_lin in zip(sels, [x1, x2]):
popt_lin, pconv_lin = op.curve_fit(exp_decay, time[sel_lin], boot_counts[sel_lin], p0=[1e5, 0.3])
y_lin = exp_decay(x_lin, *popt_lin)
lin_results.append(y_lin)
lin_results.append(popt_lin)
lin_results.append(1/popt_lin[1])
lin_lifetimes.append(1/popt_lin[1])
sel = (10 < time) & (time < 50) & (boot_counts > 0)
p0 = [10.5, 13e3, 34e2] + lin_lifetimes + [2]
popt, pconv = op.curve_fit(fit_func, time[sel], np.log(boot_counts[sel]), p0=p0)
mean, A_0, A_t, tau_0, tau_t, BG = popt
intens_0 = A_0 / (A_0 + A_t)
intens_t = A_t / (A_0 + A_t)
tau_bar = intens_0 * tau_0 + intens_t * tau_t
y = np.exp(fit_func(x, *popt))
tau_f = 1 / (intens_0 / tau_0 - intens_t / tau_t)
sigma_c = 1 / tau_0 - 1 / tau_f
results.append([
tau_0,
tau_bar,
tau_f,
tau_t,
intens_0,
intens_t,
y,
popt,
sigma_c,
] + lin_results)
tau_0_dist, tau_bar_dist, tau_f_dist, tau_t_dist, intens_0_dist, \
intens_t_dist, lifetime_y_dist, lifetime_popt_dist, sigma_c_dist, \
y_lin1_dist, popt_lin1_dist, tau_lin1_dist, \
y_lin2_dist, popt_lin2_dist, tau_lin2_dist, \
= zip(*results)
tau_0_val, tau_0_err = bootstrap.average_and_std_arrays(tau_0_dist)
tau_t_val, tau_t_err = bootstrap.average_and_std_arrays(tau_t_dist)
tau_f_val, tau_f_err = bootstrap.average_and_std_arrays(tau_f_dist)
tau_bar_val, tau_bar_err = bootstrap.average_and_std_arrays(tau_bar_dist)
popt_val, popt_err = bootstrap.average_and_std_arrays(lifetime_popt_dist)
y_val, y_err = bootstrap.average_and_std_arrays(lifetime_y_dist)
popt_lin1_val, popt_lin1_err = bootstrap.average_and_std_arrays(popt_lin1_dist)
y_lin1_val, y_lin1_err = bootstrap.average_and_std_arrays(y_lin1_dist)
tau_lin1_val, tau_lin1_err = bootstrap.average_and_std_arrays(tau_lin1_dist)
popt_lin2_val, popt_lin2_err = bootstrap.average_and_std_arrays(popt_lin2_dist)
y_lin2_val, y_lin2_err = bootstrap.average_and_std_arrays(y_lin2_dist)
tau_lin2_val, tau_lin2_err = bootstrap.average_and_std_arrays(tau_lin2_dist)
print('tau_0', siunitx(tau_0_val, tau_0_err))
print('tau_t', siunitx(tau_t_val, tau_t_err))
print('tau_f', siunitx(tau_f_val, tau_f_err))
print('tau_bar', siunitx(tau_bar_val, tau_bar_err))
T['acryl_tau_0'] = siunitx(tau_0_val, tau_0_err)
T['acryl_tau_t'] = siunitx(tau_t_val, tau_t_err)
T['acryl_tau_f'] = siunitx(tau_f_val, tau_f_err)
T['acryl_tau_bar'] = siunitx(tau_bar_val, tau_bar_err)
print('popt', siunitx(popt_val, popt_err))
print('popt_lin1', siunitx(popt_lin1_val, popt_lin1_err))
print('popt_lin2', siunitx(popt_lin2_val, popt_lin2_err))
print('tau_lin1', siunitx(tau_lin1_val, tau_lin1_err))
print('tau_lin2', siunitx(tau_lin2_val, tau_lin2_err))
T['acryl_tau_0_lin'] = siunitx(tau_lin1_val, tau_lin1_err)
T['acryl_tau_t_lin'] = siunitx(tau_lin2_val, tau_lin2_err)
print(x.shape)
print(y_lin1_val.shape)
pl.plot(time, counts, color='black', alpha=0.3)
counts_smooth = scipy.ndimage.filters.gaussian_filter1d(counts, 8)
pl.plot(time, counts_smooth, color='green')
pl.fill_between(x, y_val - y_err, y_val + y_err, alpha=0.5, color='red')
pl.plot(x, y_val, color='red')
pl.xlabel('Time / ns')
pl.ylabel('Counts')
dandify_plot()
#pl.xlim((8, 20))
pl.ylim((0.1, np.max(counts)*1.1))
pl.savefig('_build/mpl-lifetime-acryl.pdf')
pl.savefig('_build/mpl-lifetime-acryl.png')
pl.yscale('log')
pl.fill_between(x1, y_lin1_val - y_lin1_err, y_lin1_val + y_lin1_err, alpha=0.5, color='blue')
pl.fill_between(x2, y_lin2_val - y_lin2_err, y_lin2_val + y_lin2_err, alpha=0.5, color='blue')
pl.plot(x1, y_lin1_val, color='blue', alpha=0.5)
pl.plot(x2, y_lin2_val, color='blue', alpha=0.5)
dandify_plot()
pl.savefig('_build/mpl-lifetime-acryl-log.pdf')
pl.savefig('_build/mpl-lifetime-acryl-log.png')
#pl.show()
pl.clf()
np.savetxt('_build/xy/acryl-lifetime-data.tsv',
np.column_stack([time, counts]))
np.savetxt('_build/xy/acryl-lifetime-smoothed.tsv',
np.column_stack([time, counts_smooth]))
np.savetxt('_build/xy/acryl-lifetime-fit.tsv',
np.column_stack([x, y_val]))
np.savetxt('_build/xy/acryl-lifetime-band.tsv',
bootstrap.pgfplots_error_band(x, y_val, y_err))
np.savetxt('_build/xy/acryl-lifetime-fit-lin1.tsv',
np.column_stack([x1, y_lin1_val]))
np.savetxt('_build/xy/acryl-lifetime-band-lin1.tsv',
bootstrap.pgfplots_error_band(x1, y_lin1_val, y_lin1_err))
np.savetxt('_build/xy/acryl-lifetime-fit-lin2.tsv',
np.column_stack([x2, y_lin2_val]))
np.savetxt('_build/xy/acryl-lifetime-band-lin2.tsv',
bootstrap.pgfplots_error_band(x2, y_lin2_val, y_lin2_err))
def job_afb_analysis(T):
data = np.loadtxt('Data/radiative_corrections.tsv')
corrections = data[:, 1]
energies = np.loadtxt('Data/energies.txt')
data = np.loadtxt('Data/afb.txt')
negative = data[:, 0]
positive = data[:, 1]
results = []
for i in range(SAMPLES):
positive_boot = bootstrap.redraw_count(positive)
negative_boot = bootstrap.redraw_count(negative)
result = afb_kernel(positive_boot, negative_boot, corrections)
if result is not None:
results.append(result)
afb_corr_dist, sin_sq_dist = zip(*results)
afb_filt, sin_sq_filt = zip(*[(x[3], y)
for x, y in zip(afb_corr_dist, sin_sq_dist)
if not np.isnan(y)])
print('afb:', len(afb_corr_dist), len(afb_filt))
T['sin_sq_bootstrap_acceptance'] = siunitx(
(1 - len(sin_sq_filt) / len(sin_sq_dist)) * 100)
afb_val, afb_err = bootstrap.average_and_std_arrays(afb_corr_dist)
sin_sq_val, sin_sq_err = bootstrap.average_and_std_arrays(sin_sq_filt)
afb_val, sin_sq_val = afb_kernel(positive, negative, corrections)
sin_sq_up, sin_sq_down = bootstrap.percentile_arrays(
sin_sq_filt, sin_sq_val)
print('sin_sq:', sin_sq_val, sin_sq_err, sin_sq_up, sin_sq_down)
np.savetxt('_build/xy/afb.tsv',
np.column_stack([energies, afb_val, afb_err]))
T['afb_table'] = list(zip(
siunitx(energies),
siunitx(afb_val, afb_err),
))
T['sin_sq_afb'] = siunitx(sin_sq_val, sin_sq_err)
T['sin_sq_afb_asym'] = '{:.3f}^{{+{:.3f}}}_{{-{:.3f}}}'.format(
sin_sq_val, sin_sq_up, sin_sq_down)
counts, bins = np.histogram(sin_sq_filt)
counts = np.array(list(counts) + [counts[-1]])
print(bins.shape, counts.shape)
np.savetxt('_build/xy/sin_sq_hist.tsv', np.column_stack([bins, counts]))
counts, bins = np.histogram([x[3] for x in afb_corr_dist])
counts = np.array(list(counts) + [counts[-1]])
print(bins.shape, counts.shape)
np.savetxt('_build/xy/afb_hist.tsv', np.column_stack([bins, counts]))
counts, bins = np.histogram(afb_filt, bins=bins)
counts = np.array(list(counts) + [counts[-1]])
print(bins.shape, counts.shape)
np.savetxt('_build/xy/afb_filt_hist.tsv', np.column_stack([bins, counts]))
def bootstrap_driver(T):
# Load all the input data from the files.
lum_data = np.loadtxt('Data/luminosity.txt')
lum_val = lum_data[:, 0]
lum_err = lum_data[:, 3]
radiative_hadrons = np.loadtxt('Data/radiative-hadrons.tsv')
radiative_leptons = np.loadtxt('Data/radiative-leptons.tsv')
raw_matrix = np.loadtxt('Data/matrix.txt').T
mc_sizes = np.loadtxt('Data/monte-carlo-sizes.txt')
filtered = np.loadtxt('Data/filtered.txt')
# Some output into the template.
T['luminosities_table'] = list(
zip(siunitx(energies), siunitx(lum_val, lum_err)))
T['radiative_cs_table'] = list(
zip(
siunitx(energies),
siunitx(radiative_hadrons),
siunitx(radiative_leptons),
))
# Container for the results of each bootstrap run.
results = []
for r in range(SAMPLES):
# Draw new numbers for the matrix.
boot_matrix = bootstrap.redraw_count(raw_matrix)
# Draw new luminosities.
boot_lum_val = np.array(
[random.gauss(val, err) for val, err in zip(lum_val, lum_err)])
# Draw new filtered readings.
boot_readings = bootstrap.redraw_count(filtered)
# Run the analysis on the resampled data and save the results.
results.append(
bootstrap_kernel(mc_sizes, boot_matrix, boot_readings,
boot_lum_val, radiative_hadrons,
radiative_leptons))
# The `results` is a list which contains one entry per bootstrap run. This
# is not particularly helpful as the different interesting quantities are
# only on the second index on the list. The first index of the `results`
# list is the bootstrap run index. Therefore we use the `zip(*x)` trick to
# exchange the two indices. The result will be a list of quantities which
# are themselves lists of the bootstrap samples. Then using Python tuple
# assignments, we can split that (now) outer list into different
# quantities. Each of the new variables created here is a list of R
# bootstrap samples.
x_dist, masses_dist, widths_dist, cross_sections_dist, y_dist, corr_dist, \
matrix_dist, inverted_dist, readings_dist, peaks_dist, brs_dist, \
width_electron_dist, width_flavors_dist, missing_width_dist, \
width_lepton_dist, neutrino_families_dist, popts_dist, \
mean_mass_dist, mean_width_dist \
= zip(*results)
# We only need one of the lists of the x-values as they are all the same.
# So take the first and throw the others out.
x = x_dist[0]
# The masses and the widths that are given back from the `bootstrap_kernel`
# are a list of four elements (electrons, muons, tauons, hadrons) each. The
# variable `masses_dist` contains R copies of this four-list, one copy for
# each bootstrap sample. We now average along the bootstrap dimension, that
# is the outermost dimension. For each of the four masses, we take the
# average along the R copies. This will give us four masses and four
# masses-errors.
masses_val, masses_err = bootstrap.average_and_std_arrays(masses_dist)
widths_val, widths_err = bootstrap.average_and_std_arrays(widths_dist)
peaks_val, peaks_err = bootstrap.average_and_std_arrays(peaks_dist)
brs_val, brs_err = bootstrap.average_and_std_arrays(brs_dist)
T['brs'] = siunitx(brs_val[0:3], brs_err[0:3])
# Format masses and widths for the template.
T['lorentz_fits_table'] = list(
zip(
display_names,
siunitx(masses_val, masses_err),
siunitx(widths_val, widths_err),
siunitx(peaks_val, peaks_err),
))
width_electron_val, width_electron_err = bootstrap.average_and_std_arrays(
width_electron_dist)
width_flavors_val, width_flavors_err = bootstrap.average_and_std_arrays(
width_flavors_dist)
T['width_electron_mev'] = siunitx(width_electron_val * 1000,
width_electron_err * 1000)
T['width_flavors_mev'] = siunitx(width_flavors_val * 1000,
width_flavors_err * 1000)
missing_width_val, missing_width_err = bootstrap.average_and_std_arrays(
missing_width_dist)
width_lepton_val, width_lepton_err = bootstrap.average_and_std_arrays(
width_lepton_dist)
neutrino_families_val, neutrino_families_err = bootstrap.average_and_std_arrays(
neutrino_families_dist)
T['missing_width_mev'] = siunitx(missing_width_val * 1000,
missing_width_err * 1000)
T['width_lepton_mev'] = siunitx(width_lepton_val * 1000,
width_lepton_err * 1000)
T['neutrino_families'] = siunitx(neutrino_families_val,
neutrino_families_err)
# Format original counts for the template.
val, err = bootstrap.average_and_std_arrays(readings_dist)
T['counts_table'] = []
for i in range(7):
T['counts_table'].append(
[siunitx(energies[i])] +
siunitx(val[i, :], err[i, :], allowed_hang=10))
# Format corrected counts for the template.
val, err = bootstrap.average_and_std_arrays(corr_dist)
T['corrected_counts_table'] = []
for i in range(7):
T['corrected_counts_table'].append(
[siunitx(energies[i])] +
siunitx(val[i, :], err[i, :], allowed_hang=10))
# Format matrix for the template.
matrix_val, matrix_err = bootstrap.average_and_std_arrays(matrix_dist)
T['matrix'] = []
for i in range(4):
T['matrix'].append([display_names[i]] + siunitx(
matrix_val[i, :] * 100, matrix_err[i, :] * 100, allowed_hang=10))
# Format inverted matrix for the template.
inverted_val, inverted_err = bootstrap.average_and_std_arrays(
inverted_dist)
T['inverted'] = []
for i in range(4):
T['inverted'].append([display_names[i]] + list(
map(
number_padding,
siunitx(
inverted_val[i, :], inverted_err[i, :], allowed_hang=10))))
# Format cross sections for the template.
cs_val, cs_err = bootstrap.average_and_std_arrays(cross_sections_dist)
T['cross_sections_table'] = []
for i in range(7):
T['cross_sections_table'].append([siunitx(energies[i])] +
siunitx(cs_val[:, i], cs_err[:, i]))
# Build error band for pgfplots.
y_list_val, y_list_err = bootstrap.average_and_std_arrays(y_dist)
for i, name in zip(itertools.count(), names):
# Extract the y-values for the given decay type.
y_val = y_list_val[i, :]
y_err = y_list_err[i, :]
# Store the data for pgfplots.
np.savetxt('_build/xy/cross_section-{}s.tsv'.format(name),
np.column_stack([energies, cs_val[i, :], cs_err[i, :]]))
np.savetxt('_build/xy/cross_section-{}s-band.tsv'.format(name),
bootstrap.pgfplots_error_band(x, y_val, y_err))
np.savetxt('_build/xy/cross_section-{}s-fit.tsv'.format(name),
np.column_stack((x, y_val)))
popts_val, popts_err = bootstrap.average_and_std_arrays(popts_dist)
T['chi_sq'] = []
T['chi_sq_red'] = []
T['p'] = []
for i in range(4):
residuals = cs_val[i, :] - propagator(energies, *popts_val[i, :])
chi_sq = np.sum((residuals / cs_err[i, :])**2)
dof = len(residuals) - 1 - len(popts_val[i, :])
p = 1 - scipy.stats.chi2.cdf(chi_sq, dof)
print('chi_sq', chi_sq, chi_sq / dof, p)
T['chi_sq'].append(siunitx(chi_sq))
T['chi_sq_red'].append(siunitx(chi_sq / dof))
T['p'].append(siunitx(p))
T['confidence_table'] = list(
zip(
display_names,
T['chi_sq'],
T['chi_sq_red'],
T['p'],
))
mean_mass_val, mean_mass_err = bootstrap.average_and_std_arrays(
mean_mass_dist)
mean_width_val, mean_width_err = bootstrap.average_and_std_arrays(
mean_width_dist)
T['mean_mass'] = siunitx(mean_mass_val, mean_mass_err)
T['mean_width'] = siunitx(mean_width_val, mean_width_err)
