def run_freq_step_scan(config):
# compute number of iterations for frequency step size scan
n_freq_step = round(
(config['upper_freq_step_size'] - config['lower_freq_step_size']) /
config['freq_step_size_increment']) + 1
# append results in output text file
config['append_results'] = True
# loop over the t0 parameters
for idx_freq in range(0, n_freq_step):
# set freq step size
config['freq_step_size'] = round(
config['lower_freq_step_size'] +
idx_freq * config['freq_step_size_increment'], 3)
# check that the number of frequency bins is an integer factor of the freq step size
freq_window = int(config['upper_freq'] - config['lower_freq'])
if not ((freq_window / config['freq_step_size']).is_integer()):
continue
# disable saving plots
config['print_plot'] = False
# instantiate fast rotation class
fr = fastrotation.FastRotation(config)
# class method returns numpy arrays of the fast rotation signal
opt_tS, opt_tM, n_positron_hits, bin_center, bin_content = fr.produce()
save_default_tM = config['tM']
save_default_tS = config['tS']
config['tS'] = round(opt_tS, 6)
config['tM'] = round(opt_tM, 6)
# instantiate t0 optimization class
t0 = t0optimization.Optimize_t0(config, bin_center, bin_content)
# iterative optimization of t0 (2 iterations are usually enough)
opt_t0, chi2, noise, fit_boundary1, fit_boundary2 = t0.run_t0_optimization(
)
if (config['fix_t0']):
opt_t0 = config['fixed_t0_value']
# re-enable saving plots
config['print_plot'] = True
# produce results
results = fourier.Fourier(config, bin_center, bin_content, opt_t0,
noise, fit_boundary1, fit_boundary2,
n_positron_hits)
results.run()
del fr, t0, results
config['tM'] = save_default_tM
config['tS'] = save_default_tS
def run_background_removal_threshold_scan(config):
# compute number of iterations for frequency step size scan
n_step = round((config['upper_background_removal_threshold'] -
config['lower_background_removal_threshold']) /
config['background_removal_threshold_step_size']) + 1
# append results in output text file
config['append_results'] = True
config['remove_background'] = True
# disable saving plots
config['print_plot'] = False
# instantiate fast rotation class
fr = fastrotation.FastRotation(config)
# class method returns numpy arrays of the fast rotation signal
opt_tS, opt_tM, n_positron_hits, bin_center, bin_content = fr.produce()
save_default_tM = config['tM']
save_default_tS = config['tS']
config['tS'] = round(opt_tS, 6)
config['tM'] = round(opt_tM, 6)
# instantiate t0 optimization class
t0 = t0optimization.Optimize_t0(config, bin_center, bin_content)
# iterative optimization of t0 (2 iterations are usually enough)
opt_t0, chi2, noise, fit_boundary1, fit_boundary2 = t0.run_t0_optimization(
)
if (config['fix_t0']):
opt_t0 = config['fixed_t0_value']
# re-enable saving plots
config['print_plot'] = True
# loop over the t0 parameters
for idx_noise in range(0, n_step):
# set noise threshold
config['background_removal_threshold'] = round(
config['lower_background_removal_threshold'] +
idx_noise * config['background_removal_threshold_step_size'], 3)
# produce results
results = fourier.Fourier(config, bin_center, bin_content, opt_t0,
noise, fit_boundary1, fit_boundary2,
n_positron_hits)
results.run()
del results
del fr, t0
def run_stat_fluc(config):
# append results in output text file
config['append_results'] = True
# disable saving plots
config['print_plot'] = False
for i in range(config['n_stat_fluctuation']):
# instantiate fast rotation class
fr = fastrotation.FastRotation(config)
# class method returns numpy arrays of the fast rotation signal
opt_tS, opt_tM, n_positron_hits, bin_center, bin_content = fr.produce()
save_default_tM = config['tM']
save_default_tS = config['tS']
config['tS'] = round(opt_tS, 6)
config['tM'] = round(opt_tM, 6)
# instantiate t0 optimization class
t0 = t0optimization.Optimize_t0(config, bin_center, bin_content)
# iterative optimization of t0 (2 iterations are usually enough)
opt_t0, chi2, noise, fit_boundary1, fit_boundary2 = t0.run_t0_optimization(
)
if (config['fix_t0']):
opt_t0 = config['fixed_t0_value']
config['print_plot'] = True
results = fourier.Fourier(config, bin_center, bin_content, opt_t0,
noise, fit_boundary1, fit_boundary2,
n_positron_hits)
results.run()
config['print_plot'] = False
del fr, t0, results
config['tM'] = save_default_tM
config['tS'] = save_default_tS
def run_default(config):
# instantiate fast rotation class
fr = fastrotation.FastRotation(config)
# class method returns numpy arrays of the fast rotation signal
opt_tS, opt_tM, n_positron_hits, bin_center, bin_content = fr.produce()
config['tS'] = round(opt_tS, 6)
config['tM'] = round(opt_tM, 6)
# instantiate t0 optimization class
t0 = t0optimization.Optimize_t0(config, bin_center, bin_content)
# iterative optimization of t0 (2 iterations are usually enough)
opt_t0, chi2, noise, fit_boundary1, fit_boundary2 = t0.run_t0_optimization(
)
if (config['fix_t0']):
opt_t0 = config['fixed_t0_value']
results = fourier.Fourier(config, bin_center, bin_content, opt_t0, noise,
fit_boundary1, fit_boundary2, n_positron_hits)
results.run()