def run_scan(datafn,
reffn,
treen,
ref_range=[-0.3, 1],
n_points=250,
n_bins=32):
"""
Run a scan using n_points in ref_range
"""
datah, refh = get_histos(datafn, reffn, treen, n_bins=n_bins)
ref_scan_points = np.linspace(ref_range[0], ref_range[1], n_points)
fit_results = []
for lth_ref in ref_scan_points:
fit_rlt, _ = do_fit(datah,
refh,
chic1_limits=False,
fix_ref=lth_ref,
fit_range=False,
run_quiet=True,
fix_norm=False)
fit_results.append(extract_par_from_result(fit_rlt))
return fit_results
def get_ratio_hist(dataf, reff, treen, n_bins, bin_thresh=0):
"""Get the 'cleaned up' ratio hist"""
from run_ratio_histo_fit import get_histos, divide, set_bins_to_zero
logging.debug('Creating histogram from datafile \'{}\' and '
'reffile \'{}\' using {} bins'.format(dataf, reff, n_bins))
datah, refh = get_histos(dataf, reff, treen, n_bins)
set_bins_to_zero(datah, bin_thresh, True)
set_bins_to_zero(refh, bin_thresh, True)
return divide(datah, refh)
def main(datafn,
reffn,
outbase,
treen,
n_bins,
bin_thresh=0,
scan_plot=True,
use_center=False):
# first run a scan to check how stable the normalization is
# mean_norm = calc_mean_norm(datafn, reffn, treen, n_bins)
mean_norm = get_free_norm(datafn, reffn, treen, n_bins)
fit_func = r.TF1(
'fit_func',
'[0] * (3 + [1]) / (3 + [1] + [2]) * (1 + ([1] + [2]) * x[0]*x[0]) / (1 + [1] * x[0]*x[0])',
-1, 1)
datah, refh = get_histos(datafn, reffn, treen, n_bins)
# for (i,b) in enumerate(datah):
# print('{}, data = {}, ref = {}'.format(i, b, refh.GetBinContent(i))
set_bins_to_zero(datah, bin_thresh, True)
set_bins_to_zero(refh, bin_thresh, True)
ratioh = divide(datah, refh)
ratio_fit = CosthRatioFit(ratioh,
fit_func,
fix_params=[(0, mean_norm)],
use_center=use_center)
fit_results = ratio_fit.get_results(error_levels)
plotbase = '_'.join([outbase, str(n_bins)])
make_fit_plot(ratioh, fit_results[0], fit_func, plotbase)
if scan_plot:
x_ran, y_ran = get_plotting_range(fit_results[-1])
scan_fit_result = make_paed_plot(ratioh,
fit_func,
error_levels,
fit_results,
'.'.join([plotbase, 'pdf']),
n_grid=250,
x_ran=x_ran,
y_ran=y_ran)
fit_results += scan_fit_result
for result in fit_results:
print_result(result)
cfile_name = '_'.join([plotbase, 'contours']) + '.root'
write_result_file(cfile_name, fit_results)
def run_one_fit(datafn, reffn, treen, n_bins):
"""Run fit for one data and reference file"""
import ROOT as r
norm = get_free_norm(datafn, reffn, treen, n_bins)
if norm is None:
return None
fit_func = r.TF1('fit_func',
'[0] * (3 + [1]) / (3 + [1] + [2]) * (1 + ([1] + [2]) * x[0]*x[0]) / (1 + [1] * x[0]*x[0])',
-1, 1)
datah, refh = get_histos(datafn, reffn, treen, n_bins)
ratioh = divide(datah, refh)
ratio_fit = CosthRatioFit(ratioh, fit_func, fix_params=[(0, norm)])
return ratio_fit.get_results(error_levels)