smefit package

Subpackages

Submodules

smefit.basis_rotation module

Implement the corrections table basis rotation

smefit.basis_rotation.rotate_to_fit_basis(lin_dict, quad_dict, rotation_matrix_path)[source]

Rotate to fitting basis

Parameters:

lin_dict (dict) – theory dictionary with linear operator corrections in the table basis
quad_dict (dict) – theory dictionary with quadratic operator corrections in the table basis, emptry if quadratic corrections are not used
rotation_matrix (pandas.DataFrame) – rotation matrix from tables basis to fitting basis

Returns:

lin_dict_fit_basis (dict) – theory dictionary with linear operator corrections in the fit basis
quad_dict_fit_basis (dict) – theory dictionary with quadratic operator corrections in the fit basis, emptry if quadratic corrections are not used

smefit.chi2 module

Module for the computation of chi-squared values.

class smefit.chi2.Scanner(run_card, n_replica)[source]

Bases: object

Class to compute and plot the idividual \(\chi^2\) scan.

Parameters:

run_card (dict) – run card dictionary
n_replica (int) – number of replica to use

compute_bounds()[source]

Compute individual bounds solving.

..math::: chi^2`- 2 = 0

compute_scan()[source]: Compute the individual \(\chi^2\) scan for each replica and coefficient.

plot_scan()[source]: Plot and save the \(\chi^2\) scan for each coefficient.

regularized_chi2_func(coeff, xs, use_replica)[source]

Individual \(\chi^2\) wrappper over series of values.

Parameters:

coeff (smefit.coefficient.Coefficient) – coefficient to switch on.
xs (numpy.array) – coeffient values.
use_replica (bool) – if True compute the \(\chi^2\) on MC replicas.
Returns
-------- – individual reduced \(\chi^2\) for each x value.

smefit.chi2.compute_chi2(dataset, coefficients_values, use_quad, use_multiplicative_prescription, use_replica=False)[source]

Compute the \(\chi^2\).

Parameters:

dataset (DataTuple) – dataset tuple
coefficients_values (numpy.ndarray) – EFT coefficients values
use_multiplicative_prescription (bool) – if True add the EFT contribution as a key factor
use_quad (bool) – if True include also HO corrections
rgemat (numpy.ndarray) – solution matrix of the RGE

Returns:

chi2_total – \(\chi^2\) value

Return type:

float

smefit.coefficients module

class smefit.coefficients.Coefficient(name, minimum, maximum, value=None, constrain=False)[source]

Bases: object

Coefficient object

Parameters:

name (str) – name of the operator corresponding to the Wilson coefficient
minimum (float) – minimum value
maximum (float) – maximum value
value (float, optional) – best value. If None set to random between minimum and maximum
constrain (dict, bool) –
- if False, the parameter is free, default option
- if True, the parameter is fixed to the given value
- if dict the parameter is fixed to a function of other coefficients

static build_additive_factor_dict(constrain)[source]

Build the dictionary for each additive factor appearing in the constrain:

Parameters:: constrain (dict) – dict object with the form {‘c1’: a1, ‘c2’: [a2,n2], …}
Returns:: factor_dict – dict object with the form {‘c1’: [a1,1], ‘c2’: [a2,n2], …}
Return type:: dict

update_constrain(inv_rotation)[source]

Update the constrain when a new basis is chosen. Only linear constrain are supported.

Parameters:: inv_rotation (pd.DataFrame) – rotation matrix from the original basis to the new_basis

class smefit.coefficients.CoefficientManager(input_array)[source]

Bases: object

Coefficient objcts manager

Parameters:: input_array (np.ndarray or list) – list of smefit.coefficients.Coefficient instances

property free_parameters: Returns the table containing only free parameters

classmethod from_dict(coefficient_config)[source]

Create a coefficientManager from a dictionary

Parameters:: coefficient_config (dict) – coefficients configuration dictionary
Returns:: coefficient_manager – instance of the class
Return type:: smefit.coefficients.CoefficientManager

property maximum

property minimum

property name

set_constraints()[source]: Sets constraints between coefficients according to the coefficient.constrain information:

set_free_parameters(value)[source]: Set the values of the free parmaters

property size

update_constrain(inv_rotation)[source]

Update the constraints according to rotation matrix. Only linear constrain are supported.

Parameters:: inv_rotation (pd.DataFrame) – rotation matrix from the original basis to the new_basis

property value

smefit.compute_theory module

Module for the generation of theory predictions

smefit.compute_theory.flatten(quad_mat, axis=0)[source]

Delete lower triangular part of a quadratic matrix and flatten it into an array

Parameters:

quad_mat (numpy.ndarray) – tensor to flatten
axis (int) – axis along which the triangular part is selected

smefit.compute_theory.make_predictions(dataset, coefficients_values, use_quad, use_multiplicative_prescription)[source]

Generate the corrected theory predictions for dataset given a set of SMEFT coefficients.

Parameters:

dataset (DataTuple) – dataset tuple
coefficients_values (numpy.ndarray) – EFT coefficients values
use_quad (bool) – if True include also HO corrections
use_multiplicative_prescription (bool) – if True add the EFT contribution as a k-factor

Returns:

corrected_theory – SM + EFT theory predictions

Return type:

numpy.ndarray

smefit.covmat module

Module containing computation of covariance matrix. Based on https://github.com/NNPDF/nnpdf/tree/master/validphys2/src/validphys/covmats_utils.py https://github.com/NNPDF/nnpdf/tree/master/validphys2/src/validphys/covmats.py

smefit.covmat.construct_covmat(stat_errors: array, sys_errors: DataFrame)[source]

Basic function to construct a covariance matrix (covmat), given the statistical error and a dataframe of systematics. Errors with name UNCORR or THEORYUNCORR are added in quadrature with the statistical error to the diagonal of the covmat. Other systematics are treated as correlated; their covmat contribution is found by multiplying them by their transpose.

Parameters:

stat_errors (numpy.ndarray) – a 1-D array of statistical uncertainties
sys_errors (pandas.DataFrame) – a dataframe with shape (N_data * N_sys) and systematic name as the column headers. The uncertainties should be in the same units as the data.

Returns:

cov_mat – Covariance matrix

Return type:

numpy.ndarray

Notes

This function doesn’t contain any logic to ignore certain contributions to the covmat, if you wanted to not include a particular systematic/set of systematics i.e all uncertainties with MULT errors, then filter those out of sys_errors before passing that to this function.

smefit.covmat.covmat_from_systematics(stat_errors: list, sys_errors: list)[source]

Given two lists containing the statistic and systematic errors, construct the full covariance matrix.

This is similar to construct_covmat() except that special corr systematics are concatenated across all datasets before being multiplied by their transpose to give off block-diagonal contributions. The other systematics contribute to the block diagonal in the same way as construct_covmat().

Parameters:

stat_errors (list[(stat_errors: np.array)]) – list of stat_errors for each dataset.
sys_errors (list[(sys_errors: pd.DataFrame)]) – list of sys_errors for each dataset.

Returns:

cov_mat – Numpy array which is N_dat x N_dat (where N_dat is the number of data points) containing uncertainty and correlation information.

Return type:

np.array

smefit.fit_manager module

class smefit.fit_manager.FitManager(path, name, label=None)[source]

Bases: object

Class to collect all the fit information, load the results, compute best theory predictions.

path

path to fit location

Type:: pathlib.Path

name

fit name

Type:: srt

label

fit label if any otherwise guess it from the name

Type:: str, optional

config

configuration dictionary

Type:: dict

has_posterior

True if the fi contains the full posterrio distribution, False if only cl bounds are stored (external fits for benchmark)

Type:: bool

results

fit results, they need to be loaded by load_results

Type:: pandas.DataFrame

Parameters:

path (pathlib.Path) – path to fit location
name (srt) – fit name
label (str, optional) – fit label if any otherwise guess it from the name

property coefficients: coefficient manager

load_configuration()[source]

Load configuration yaml card.

Returns:: configuration card
Return type:: dict

load_datasets()[source]: Load all datasets.

load_results()[source]: Load posterior distribution of a fit. If the fit is produced by and external source it loads the results. Results are stored in a class attribute

property n_replica: Number of replicas

property smeft_predictions

Compute SMEFT predictions for each replica.

Returns:: SMEFT predictions for each replica
Return type:: np.ndarray

property smeft_predictions_best_fit

Compute SMEFT predictions for the best fit point.

Returns:: SMEFT predictions for the best fit
Return type:: np.ndarray

smefit.loader module

class smefit.loader.DataTuple(Commondata, SMTheory, OperatorsNames, LinearCorrections, QuadraticCorrections, ExpNames, NdataExp, InvCovMat, ThCovMat, Luminosity, Replica)

Bases: tuple

Commondata: Alias for field number 0

ExpNames: Alias for field number 5

InvCovMat: Alias for field number 7

LinearCorrections: Alias for field number 3

Luminosity: Alias for field number 9

NdataExp: Alias for field number 6

OperatorsNames: Alias for field number 2

QuadraticCorrections: Alias for field number 4

Replica: Alias for field number 10

SMTheory: Alias for field number 1

ThCovMat: Alias for field number 8

class smefit.loader.Loader(setname, operators_to_keep, order, use_quad, use_theory_covmat, use_multiplicative_prescription, rot_to_fit_basis, cutoff_scale)[source]

Bases: object

Class to check, load commondata and corresponding theory predictions.

Parameters:

setname (str) – dataset name to load
operators_to_keep (list) – list of operators for which corrections are loaded
order ("LO", "NLO") – EFT perturbative order
use_quad (bool) – if True loads also HO corrections
use_theory_covmat (bool)
one (if True add the theory covariance matrix to the experimental)
rot_to_fit_basis (dict, None) – matrix rotation to fit basis or None

apply_cutoff_mask(cutoff_scale)[source]

Updates previously loaded theory and datasets by filtering out points with scales above the cutoff scale

Parameters:: cutoff_scale (flaot) – Value of the cutoff scale as specified in the runcard

property central_values

Central values

Returns:: central_values – experimental central values
Return type:: numpy.ndarray

commondata_path = PosixPath('.'): path to commondata folder, commondata excluded

property covmat

Experimental covariance matrix

Returns:: covmat – experimental covariance matrix of a single dataset
Return type:: numpy.ndarray

property lin_corrections

NHO corrections

Returns:: lin_corrections – dictionary with operator names and NHO correctsions
Return type:: dict

load_experimental_data()[source]

Load experimental data with corresponding uncertainties

Returns:

cental_values (numpy.ndarray) – experimental central values
covmat (numpy.ndarray) – experimental covariance matrix

static load_theory(setname, operators_to_keep, order, use_quad, use_theory_covmat, use_multiplicative_prescription, rotation_matrix=None)[source]

Load theory predictions

Parameters:

operators_to_keep (list) – list of operators to keep
order ("LO", "NLO") – EFT perturbative order
use_quad (bool) – if True returns also HO corrections
use_theory_covmat (bool) – if True add the theory covariance matrix to the experimental one
rotation_matrix (numpy.ndarray) – rotation matrix from tables basis to fitting basis

Returns:

sm (numpy.ndarray) – SM predictions
lin_dict (dict) – dictionary with NHO corrections
quad_dict (dict) – dictionary with HO corrections, empty if not use_quad
scales (list) – list of energy scales for the theory predictions

property lumi

Integrated luminosity of the dataset in fb^-1

Returns:: lumi – Integrated luminosity of the dataset in fb^-1
Return type:: float

property n_data

Number of data

Returns:: n_data – number of experimental data
Return type:: int

property quad_corrections

HO corrections

Returns:: quad_corrections – dictionary with operator names and HO correctsions
Return type:: dict

property sm_prediction

SM prediction for the dataset

Returns:: SM_predictions – best SM prediction
Return type:: numpy.ndarray

property stat_error

Statistical errors

Returns:: stat_error – statistical errors of the dataset
Return type:: np.array

property sys_error

Systematic errors

Returns:: sys_error – systematic errors of the dataset
Return type:: pd.DataFrame

property sys_error_t0

Systematic errors modified according to t0 prescription

Returns:: sys_error_t0 – t0 systematic errors of the dataset
Return type:: pd.DataFrame

property theory_covmat

Theory covariance matrix

Returns:: theory covmat – theory covariance matrix of a single dataset
Return type:: numpy.ndarray

theory_path = PosixPath('.'): path to theory folder, theory excluded. Default it assumes to be the same as commondata_path

smefit.loader.check_file(path)[source]: Check if path exists.

smefit.loader.check_missing_operators(loaded_corrections, coeff_config)[source]: Check if all the coefficient in the runcard are also present inside the theory tables.

smefit.loader.construct_corrections_matrix_linear(corrections_list, n_data_tot, sorted_keys, rgemat=None)[source]

Constructs the linear EFT corrections.

Parameters:

corrections_list (list(dict)) – list containing corrections per experiment
n_data_tot (int) – total number of experimental data points
sorted_keys (numpy.ndarray) – list of sorted operator corrections
rgemat (numpy.ndarray, optional) – solution matrix of the RGE, shape (k, l, m) with k the number of datapoints, l the number of generated coefficients and m the number of original EFT coefficients specified in the runcard.

Returns:

corr_values – matrix with correction values (n_data_tot, sorted_keys.size)

Return type:

np.ndarray

smefit.loader.construct_corrections_matrix_quadratic(corrections_list, n_data_tot, sorted_keys, rgemat=None)[source]

Constructs quadratic EFT corrections.

Parameters:

corrections_list (list(dict)) – list containing per experiment the number of datapoints and corresponding corrections
n_data_tot (int) – total number of experimental data points
sorted_keys (numpy.ndarray) – list of sorted operator corrections, shape=(n rg generated coeff,) or shape=(n original coeff,) in the absence of rgemat
rgemat (numpy.ndarray, optional) – solution matrix of the RGE, shape=(k, l, m) with k the number of datapoints, l the number of generated coefficients under the RG and m the number of original EFT coefficients specified in the runcard.

Returns:

corr_values – matrix with correction values (n_data_tot, sorted_keys.size, sorted_keys.size)

Return type:

np.ndarray

smefit.loader.get_dataset(datasets, data_name)[source]

smefit.loader.load_datasets(commondata_path, datasets, operators_to_keep, use_quad, use_theory_covmat, use_t0, use_multiplicative_prescription, default_order='LO', theory_path=None, rot_to_fit_basis=None, has_uv_couplings=False, has_external_chi2=False, rgemat=None, cutoff_scale=None)[source]

Loads experimental data, theory and SMEFT corrections into a namedtuple

Parameters:

commondata_path (str, pathlib.Path) – path to commondata folder, commondata excluded
datasets (list) – List of datasets to be loaded
operators_to_keep (list) – list of operators for which corrections are loaded
default_order (str) – Default perturbative order of the theory predictions
use_quad (bool) – if True loads also HO corrections
use_theory_covmat (bool) – if True add the theory covariance matrix to the experimental one
theory_path (str, pathlib.Path, optional) – path to theory folder, theory excluded. Default it assumes to be the same as commondata_path
rot_to_fit_basis (dict, optional) – matrix rotation to fit basis or None
has_uv_couplings (bool, optional) – True for UV fits
has_external_chi2 (bool, optional) – True in the presence of external chi2 modules
rgemat (numpy.ndarray, optional) – solution matrix of the RGE, shape=(k, l, m) with k the number of datapoints, l the number of generated coefficients under the RG and m the number of original EFT coefficients specified in the runcard.
cutoff_scale (float, optional) – kinematic cutoff scale

smefit.log module

smefit.log.print_banner()[source]

smefit.log.setup_console(logfile)[source]

smefit.runner module

class smefit.runner.Runner(run_card, single_parameter_fits, pairwise_fits, runcard_file=None)[source]

Bases: object

Container for all the possible SMEFiT run methods.

Init the root path of the package where tables, results, plot config and reports are stored.

Parameters:

run_card (dict) – run card dictionary
single_parameter_fits (bool) – True for single parameter fits
runcard_file (pathlib.Path, None) – path to runcard if already present

analytic(config)[source]: Sample the analytic linear solution.

chi2_scan(n_replica, compute_bounds)[source]

Run an individual \(\chi^2\) scan.

Parameters:

n_replica (int) – number of replicas to use. If 0 only the \(\chi^2\) experimental data will be computed.
compute_bounds (bool) – if True compute and save the \(\chi^2\) bounds.

classmethod from_file(runcard_file, replica=None)[source]

Create Runner from a runcard file

Parameters:

runcard_file (pathlib.Path, str) – path to runcard
replica (int) – replica number. Optional used only for MC

Returns:

runner – instance of class Runner

Return type:

smefit.runner.Runner

get_optimizer(optimizer)[source]: Return the seleted optimizer.

global_analysis(optimizer)[source]

Run a global fit using the selected optimizer.

Parameters:: optimizer (string) – optimizer to be used (NS, MC or A)

mc(config)[source]: Run a fit with MC.

pairwise_analysis(optimizer)[source]

Run a series of pairwise parameter fits for all the operators specified in the runcard.

Parameters:: optimizer (string) – optimizer to be used only NS is supported

rotate_to_pca()[source]: Rotate to PCA basis.

run_analysis(optimizer)[source]

Run either the global analysis or a series of single parameter fits using the selected optimizer.

Parameters:: optimizer (string) – optimizer to be used (NS, MC or A)

setup_result_folder()[source]: Create result folder and copy the runcard there

single_parameter_analysis(optimizer)[source]

Run a seried of single parameter fits for all the operators specified in the runcard.

Parameters:: optimizer (string) – optimizer to be used (NS, MC or A)

ultranest(config)[source]: Run a fit with Ultra Nest.