def get_best_k_from_global_correlation(regularisation_settings):
'''
returns optimal_k, k_values, tau_values, rho_values
- optimal_k: k-value with lowest rho
- minimal_rho: lowest rho value
- k_values: all scanned k-values
- tau_values: tau values for all scanned k-values
- rho_values: rho values for all scanned k-values
'''
h_truth, h_response, h_measured, h_data = regularisation_settings.get_histograms(
)
n_toy = regularisation_settings.n_toy
# initialise variables
optimal_k = 0
minimal_rho = 9999
n_bins = h_data.nbins()
k_values = []
tau_values = []
rho_values = []
# first calculate one set to get the matrices
# tau = 0 is equal to k = nbins
unfolding = Unfolding(
h_truth,
h_measured,
h_response,
method='RooUnfoldSvd',
tau=0., # no regularisation
k_value=-1,
)
unfolding.unfold(h_data)
# get unfolding object
svd_unfold = unfolding.Impl()
# get covariance matrix
cov = svd_unfold.get_data_covariance_matrix(h_data)
# cache functions and save time in the loop
SetTau = svd_unfold.SetTau
GetCovMatrix = svd_unfold.GetUnfoldCovMatrix
GetRho = svd_unfold.get_global_correlation
kToTau = svd_unfold.kToTau
add_k = k_values.append
add_tau = tau_values.append
add_rho = rho_values.append
# now lets loop over all possible k-values
for k in range(2, n_bins + 1):
tau_from_k = kToTau(k)
SetTau(tau_from_k)
cov_matrix = GetCovMatrix(cov, n_toy, 1)
rho = GetRho(cov_matrix, h_data)
add_k(k)
add_tau(tau_from_k)
add_rho(rho)
if rho < minimal_rho:
optimal_k = k
minimal_rho = rho
return optimal_k, minimal_rho, k_values, tau_values, rho_values
def get_best_tau_from_global_correlation(regularisation_settings):
'''
returns optimal_tau, tau_values, rho_values
- optimal_tau: k-value with lowest rho
- minimal_rho: lowest rho value
- tau_values: all scanned tau values
- rho_values: rho values for all scanned tau-values
'''
h_truth, h_response, h_measured, h_data = regularisation_settings.get_histograms(
)
n_toy = regularisation_settings.n_toy
number_of_iterations = regularisation_settings.n_tau_scan_points
tau_min = 0.1
tau_max = 1000
optimal_tau = 0
minimal_rho = 9999
tau_values = []
rho_values = []
# first calculate one set to get the matrices
# tau = 0 is equal to k = nbins
unfolding = Unfolding(
h_truth,
h_measured,
h_response,
method='RooUnfoldSvd',
tau=0., # no regularisation
k_value=-1,
)
unfolding.unfold(h_data)
# get unfolding object
svd_unfold = unfolding.Impl()
# get covariance matrix
cov = svd_unfold.get_data_covariance_matrix(h_data)
# cache functions and save time in the loop
SetTau = svd_unfold.SetTau
GetCovMatrix = svd_unfold.GetUnfoldCovMatrix
GetRho = svd_unfold.get_global_correlation
add_tau = tau_values.append
add_rho = rho_values.append
# now lets loop over all tau-values in range
for current_tau in get_tau_range(tau_min, tau_max, number_of_iterations):
SetTau(current_tau)
cov_matrix = GetCovMatrix(cov, n_toy, 1)
current_rho = GetRho(cov_matrix, h_data)
add_tau(current_tau)
add_rho(current_rho)
if current_rho < minimal_rho:
minimal_rho = current_rho
optimal_tau = current_tau
print 'Best tau for', regularisation_settings.channel, ':', optimal_tau
return optimal_tau, minimal_rho, tau_values, rho_values
def get_tau_from_global_correlation( h_truth, h_measured, h_response, h_data = None ):
global used_k
# this gives 9.97e-05 with TUnfold
tau_0 = 1
tau_max = 1000
number_of_iterations = int(100)
n_toy = int(1000)
# tau_step = ( tau_max - tau_0 ) / number_of_iterations
optimal_tau = 0
minimal_rho = 9999
# bias_scale = 0.
unfolding = Unfolding( h_truth,
h_measured,
h_response,
method = 'RooUnfoldSvd',
tau = tau_0,
k_value = -1, )
data = None
if h_data:
data = h_data
else: # closure test
data = h_measured
unfolding.unfold( data )
# get unfolding object
tau_svd_unfold = unfolding.Impl()
# get covariance matrix
cov = tau_svd_unfold.get_data_covariance_matrix(data)
# cache functions and save time in the loop
SetTau = tau_svd_unfold.SetTau
GetCovMatrix = tau_svd_unfold.GetUnfoldCovMatrix
GetRho = tau_svd_unfold.get_global_correlation
n_bins = h_data.nbins()
print 'k to tau'
to_return = None
for k in range(2, n_bins + 1):
tau_from_k = tau_svd_unfold.kToTau(k)
SetTau( tau_from_k )
cov_matrix = GetCovMatrix(cov, n_toy, 1)
rho = GetRho(cov_matrix, data)
if k == used_k:
to_return = (tau_from_k, rho)
print 'k =', k, ', tau = ', tau_from_k, ', rho = ', rho, '<-- currently used'
else:
print 'k =', k, ', tau = ', tau_from_k, ', rho = ', rho
#print 'used k (=%d) to tau' % used_k
tau_from_k = tau_svd_unfold.kToTau(used_k)
#SetTau( tau_from_k )
#cov_matrix = GetCovMatrix(cov, 10, 1)
#rho_from_tau_from_k = GetRho(cov_matrix, data)
#print "tau from k", tau_from_k
#print 'rho for tau from used k', rho_from_tau_from_k
# create lists
tau_values = []
rho_values = []
add_tau = tau_values.append
add_rho = rho_values.append
# for current_tau in drange(tau_0, tau_max, tau_step):
for current_tau in get_tau_range( tau_0, tau_max, number_of_iterations ):
SetTau( current_tau )
cov_matrix = GetCovMatrix(cov, n_toy, 1)
current_rho = GetRho(cov_matrix, data)
add_tau( current_tau )
add_rho( current_rho )
if current_rho < minimal_rho:
minimal_rho = current_rho
optimal_tau = current_tau
del unfolding
print 'optimal tau = %.2f' % optimal_tau
return optimal_tau, minimal_rho, tau_values, rho_values, to_return