def validate_reco_method(basis_parameters,
verification_parameters,
base_equations=combination_utils.full_scan_terms,
name_suffix='',
title_suffix=''):
reweight_vector = get_amplitude_function(basis_parameters,
as_scalar=False,
base_equations=base_equations)
#var_edges = numpy.linspace(200, 1200, 31)
var_edges = numpy.linspace(200, 2000, 55)
#var_edges = numpy.arange(0, 2050, 50)
data_files = fileio_utils.read_coupling_file()
base_events_list = fileio_utils.get_events(basis_parameters, data_files)
verification_events_list = fileio_utils.get_events(verification_parameters,
data_files)
base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in base_events_list
]
base_weights, base_errors = numpy.array(list(zip(*base_histograms)))
for verification_events, coupling_parameters in zip(
verification_events_list, verification_parameters):
verification_weights, verification_errors = fileio_utils.retrieve_reco_weights(
var_edges, verification_events)
combined_weights, combined_errors = reco_reweight(
reweight_vector, coupling_parameters, base_weights, base_errors)
plot_histogram('reco_mHH' + name_suffix,
'NNT-Based Linear Combination:\n$m_{HH}$' +
title_suffix,
var_edges,
coupling_parameters,
combined_weights,
combined_errors,
verification_weights,
verification_errors,
xlabel='Reconstructed $m_{HH}$ (GeV)')
def view_reco_method(basis_parameters, view_params):
reweight_vector = get_amplitude_function(basis_parameters, as_scalar=False)
var_edges = numpy.linspace(200, 1200, 31)
data_files = fileio_utils.read_coupling_file()
base_events_list = fileio_utils.get_events(basis_parameters, data_files)
base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in base_events_list
]
base_weights, base_errors = numpy.array(list(zip(*base_histograms)))
for coupling_parameters in view_params:
print(coupling_parameters)
combined_weights, combined_errors = reco_reweight(
reweight_vector, coupling_parameters, base_weights, base_errors)
plot_histogram('preview_reco_mHH_new',
'NNT-Based Linear Combination:\n$m_{HH}$',
var_edges,
coupling_parameters,
combined_weights,
combined_errors,
xlabel='Reconstructed $m_{HH}$ (GeV)')
def compare12_reco_method(basis_parameters,
k2v_basis_parameters,
kl_basis_parameters,
verification_parameters,
base_equations=combination_utils.full_scan_terms,
name_suffix='',
title_suffix=''):
reweight_vector = get_amplitude_function(basis_parameters,
as_scalar=False,
base_equations=base_equations)
k2v_reweight_vector = get_amplitude_function(
k2v_basis_parameters,
as_scalar=False,
base_equations=combination_utils.k2v_scan_terms)
kl_reweight_vector = get_amplitude_function(
kl_basis_parameters,
as_scalar=False,
base_equations=combination_utils.kl_scan_terms)
#var_edges = numpy.linspace(200, 1200, 31)
var_edges = numpy.linspace(200, 2000, 55)
#var_edges = numpy.arange(0, 2050, 50)
data_files = fileio_utils.read_coupling_file()
base_events_list = fileio_utils.get_events(basis_parameters, data_files)
k2v_base_events_list = fileio_utils.get_events(k2v_basis_parameters,
data_files)
kl_base_events_list = fileio_utils.get_events(kl_basis_parameters,
data_files)
verification_events_list = fileio_utils.get_events(verification_parameters,
data_files)
base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in base_events_list
]
base_weights, base_errors = numpy.array(list(zip(*base_histograms)))
k2v_base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in k2v_base_events_list
]
k2v_base_weights, k2v_base_errors = numpy.array(
list(zip(*k2v_base_histograms)))
kl_base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in kl_base_events_list
]
kl_base_weights, kl_base_errors = numpy.array(
list(zip(*kl_base_histograms)))
for verification_events, coupling_parameters in zip(
verification_events_list, verification_parameters):
k2v, kl, kv = coupling_parameters
if coupling_parameters == (1, 1, 1): continue
if k2v != 1 and kl != 1: continue
if kv != 1: continue
alt_combined_weights, alt_combined_errors = None, None
if k2v != 1 and kl == 1:
alt_combined_weights, alt_combined_errors = reco_reweight(
k2v_reweight_vector, coupling_parameters, k2v_base_weights,
k2v_base_errors)
if k2v == 1 and kl != 1:
alt_combined_weights, alt_combined_errors = reco_reweight(
kl_reweight_vector, coupling_parameters, kl_base_weights,
kl_base_errors)
verification_weights, verification_errors = fileio_utils.retrieve_reco_weights(
var_edges, verification_events)
combined_weights, combined_errors = reco_reweight(
reweight_vector, coupling_parameters, base_weights, base_errors)
plot_histogram(
'reco_mHH_1-2D_compare' + name_suffix,
'NNT-Based Linear Combination:\n$m_{HH}$' + title_suffix,
var_edges,
coupling_parameters,
combined_weights,
combined_errors,
verification_weights,
verification_errors,
alt_linearly_combined_weights=alt_combined_weights,
alt_linearly_combined_errors=alt_combined_errors,
generated_label='3D Combination',
xlabel='Reconstructed $m_{HH}$ (GeV)',
)
def compare_bases_reco_method(basis_parameters_list,
verification_parameters,
base_equations=combination_utils.full_scan_terms,
name_suffix='',
title_suffix='',
labels=('', ''),
is_verification=True,
truth_level=False,
truth_data_files=None):
#var_edges = numpy.linspace(200, 1200, 31)
#var_edges = numpy.arange(0, 2050, 50)
var_edges = numpy.linspace(200, 2000, 55)
basis_tuple_list = []
for basis_parameters in basis_parameters_list:
reweight_vector = get_amplitude_function(basis_parameters,
as_scalar=False,
base_equations=base_equations)
if truth_level:
data_files = fileio_utils.read_coupling_file(
coupling_file='basis_files/truth_LHE_couplings_extended.dat')
basis_files = [
truth_data_files[coupling] for coupling in basis_parameters
]
truth_weights, truth_errors = fileio_utils.extract_lhe_truth_data(
basis_files, var_edges)
basis_tuple_list.append(
(truth_weights, truth_errors, reweight_vector))
else:
data_files = fileio_utils.read_coupling_file()
base_events_list = fileio_utils.get_events(basis_parameters,
data_files)
base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in base_events_list
]
base_weights, base_errors = numpy.array(list(
zip(*base_histograms)))
basis_tuple_list.append(
(base_weights, base_errors, reweight_vector))
testpoint_list = verification_parameters
if is_verification:
if truth_level:
verification_files = [
data_files[key] for key in verification_parameters
]
truth_verification_weights, truth_verification_errors = fileio_utils.extract_lhe_truth_data(
verification_files, var_edges)
testpoint_list = zip(verification_parameters,
truth_verification_weights,
truth_verification_errors)
else:
testpoint_list = []
verification_events_list = fileio_utils.get_events(
verification_parameters, data_files)
for events, param in zip(verification_events_list,
verification_parameters):
verification_weights, verification_errors = fileio_utils.retrieve_reco_weights(
var_edges, events)
testpoint_list.append(
(param, verification_weights, verification_errors))
for testpoint in testpoint_list:
verification_weights, verification_errors = None, None
if is_verification:
coupling_parameters, verification_weights, verification_errors = testpoint
else:
coupling_parameters = testpoint
combined_tuples = []
for base_weights, base_errors, reweight_vector in basis_tuple_list:
combined_tuples.append(
reco_reweight(reweight_vector, coupling_parameters,
base_weights, base_errors))
if truth_level:
name = 'truth_mHH_compare' + name_suffix
title = 'Truth LHE-Based Linear Combination:\nTruth $m_{HH}$' + title_suffix
xlabel = 'Truth $m_{HH}$ (GeV)'
else:
name = 'reco_mHH_compare' + name_suffix
title = 'NNT-Based Linear Combination:\n$m_{HH}$' + title_suffix
xlabel = 'Reconstructed $m_{HH}$ (GeV)'
plot_histogram(
name,
title,
var_edges,
coupling_parameters,
combined_tuples[0][0],
combined_tuples[0][1],
verification_weights,
verification_errors,
alt_linearly_combined_weights=combined_tuples[1][0],
alt_linearly_combined_errors=combined_tuples[1][1],
generated_label=labels[0],
alt_label=labels[1],
xlabel=xlabel,
)
def compare1D3S9S_reco_method(k2v_3S_basis_parameters, k2v_9S_basis_tuple):
vmin, vmax = 1e-5, 5
generate_1D9S_pojection_scans(k2v_9S_basis_tuple, vmin, vmax)
#var_edges = numpy.linspace(200, 1200, 31)
alt_var_edges = numpy.linspace(200, 1200, 31)
var_edges = numpy.linspace(200, 2000, 55)
#var_edges = numpy.arange(0, 2050, 50)
num_kappa_bins = 10
k2v_vals = numpy.linspace(-2, 4, num_kappa_bins + 1)
k2v_vals_alt = numpy.linspace(-2, 4, 100 + 1)
data_files = fileio_utils.read_coupling_file()
k2v_3S_reweight_vector = get_amplitude_function(
k2v_3S_basis_parameters,
as_scalar=False,
base_equations=combination_utils.k2v_scan_terms)
k2v_3S_base_events_list = fileio_utils.get_events(k2v_3S_basis_parameters,
data_files)
k2v_3S_base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in k2v_3S_base_events_list
]
k2v_3S_base_weights, k2v_3S_base_errors = numpy.array(
list(zip(*k2v_3S_base_histograms)))
k2v_3S_base_histograms_alt = [
fileio_utils.retrieve_reco_weights(alt_var_edges, base_events)
for base_events in k2v_3S_base_events_list
]
k2v_3S_base_weights_alt, k2v_3S_base_errors_alt = numpy.array(
list(zip(*k2v_3S_base_histograms_alt)))
draw_1D_mhh_heatmap(k2v_3S_basis_parameters,
k2v_3S_base_weights_alt,
alt_var_edges,
k2v_vals_alt,
1,
1,
base_equations=combination_utils.k2v_scan_terms,
which_coupling='k2v',
filename='projectionscan_k2v_multicompare',
title_suffix='Using Single Basis',
vrange=(vmin, vmax))
multibasis_list = []
for k2v_list in k2v_9S_basis_tuple[1]:
basis_parameters = [(k2v, 1, 1) for k2v in k2v_list]
base_events_list = fileio_utils.get_events(basis_parameters,
data_files)
base_histograms = [
fileio_utils.retrieve_reco_weights(var_edges, base_events)
for base_events in base_events_list
]
weights, errors = numpy.array(list(zip(*base_histograms)))
reweight_vector_function = combination_utils.get_amplitude_function(
basis_parameters,
as_scalar=False,
base_equations=combination_utils.k2v_scan_terms)
multibasis_list.append((weights, errors, reweight_vector_function))
index_bounds = k2v_9S_basis_tuple[0]
for k2v in k2v_vals:
coupling_parameters = [k2v, 1, 1]
k2v_combined_weights, k2v_combined_errors = reco_reweight(
k2v_3S_reweight_vector, coupling_parameters, k2v_3S_base_weights,
k2v_3S_base_errors)
multibasis_index = None
if k2v <= index_bounds[0]: multibasis_index = 0
elif k2v <= index_bounds[1]: multibasis_index = 1
else: multibasis_index = 2
multibasis_weights, multibasis_errors, multibasis_reweight_vector_function = multibasis_list[
multibasis_index]
multicombined_weights, multicombined_errors = reco_reweight(
multibasis_reweight_vector_function, coupling_parameters,
multibasis_weights, multibasis_errors)
view_linear_combination.plot_histogram(
'preview_reco_mHH_multibasis',
'NNT-Based Linear Combination:\n$m_{HH}$',
var_edges,
coupling_parameters,
k2v_combined_weights,
k2v_combined_errors,
alt_linearly_combined_weights=multicombined_weights,
alt_linearly_combined_errors=multicombined_errors,
alt_label='3-Basis Set',
generated_label='1-Basis Equation',
xlabel='Reconstructed $m_{HH}$ (GeV)',
)