validphys.tests package

Subpackages

Submodules

validphys.tests.conftest module

validphys.tests.test_alpha_s_bundle_pdf module

validphys.tests.test_arclengths module

validphys.tests.test_calcutils module

validphys.tests.test_closuretest module

test_closuretest.py

contains some unit tests for closure test estimators

class validphys.tests.test_closuretest.TestResult(central_value, rawdata=None)[source]

Bases: object

class for testing base level estimators which expect a results object

validphys.tests.test_closuretest.test_bias_function()[source]
validphys.tests.test_closuretest.test_variance_function()[source]

validphys.tests.test_commondataparser module

validphys.tests.test_core module

validphys.tests.test_covmatreg module

validphys.tests.test_covmats module

validphys.tests.test_cuts module

validphys.tests.test_datafiles module

validphys.tests.test_effexponents module

validphys.tests.test_filter_rules module

validphys.tests.test_fitdata module

validphys.tests.test_fitveto module

validphys.tests.test_loader module

validphys.tests.test_mc2hessian module

validphys.tests.test_metaexps module

test_metaexps

Test that the experiments key defined in the commondata meta data, which is subsequently used for grouping makes sense.

validphys.tests.test_metaexps.test_no_systematic_overlaps()[source]

Take every available dataset and check that there are no overlapping systematics when the grouping is by metadata experiments.

This is important because we make the assumption that the total covariance matrix is block diagonal in metadata experiment, and it is therefore used as an optimisation in several places.

validphys.tests.test_multiclosure module

validphys.tests.test_overfit_metric module

validphys.tests.test_plots module

validphys.tests.test_postfit module

validphys.tests.test_pseudodata module

validphys.tests.test_pyfkdata module

validphys.tests.test_pythonmakereplica module

validphys.tests.test_regressions module

validphys.tests.test_results module

Tests for functions in the validphys.results file.

validphys.tests.test_results.test_groups_central_values_no_table(data_internal_cuts_config)[source]

Check if the output of groups_central_values_no_table agrees with the replica 0 value calculated by calling group_result_table_no_table. group_result_table_no_table also computes the predictions for all other replicas.

validphys.tests.test_sumrules module

validphys.tests.test_tableloader module

validphys.tests.test_tableloader.test_extrasum_slice()[source]
validphys.tests.test_tableloader.test_min_combination()[source]

validphys.tests.test_theorydbutils module

validphys.tests.test_totalchi2 module

test_totalchi2.py

test that the action which calculates the total chi2 produces sensible results for both MC and hessian pdfs

validphys.tests.test_totalchi2.test_abs_chi2_data(single_data_internal_cuts_config)[source]

Test abs_chi2_data with a normal dataset

validphys.tests.test_totalchi2.test_abs_chi2_data_singlepoint(single_data_single_point_internal_cuts_config)[source]

Test abs_chi2_data with the corner case of a single datapoint dataset

validphys.tests.test_totalchi2.test_hessian_total_chi2(hessian_data_internal_cuts_config)[source]

testing total chi2 for hessian pdf

In particular check that the sum across experiments is handled correctly

and that calculating the total chi2 from the flat list of datasets gives the same answer as using total_chi2_data

validphys.tests.test_totalchi2.test_mc_total_chi2(data_internal_cuts_config)[source]

Testing total chi2 for mc pdf

In particular check that the sum across experiments is handled correctly

and that calculating the total chi2 from the flat list of datasets gives the same answer as using total_chi2_data

validphys.tests.test_utils module

validphys.tests.test_vplistscript module

test_vplistscript.py

Module for testing vp-list. The output of which is dynamic and so we just check that the script runs and gives some output

validphys.tests.test_vplistscript.test_listdatasets()[source]

Checks listing datasets returns output

validphys.tests.test_vplistscript.test_listfits()[source]

Checks listing fits returns output

validphys.tests.test_vplistscript.test_listpdfs()[source]

Checks listing pdfs returns output

validphys.tests.test_vplistscript.test_listtheories()[source]

Checks listing theories returns output

validphys.tests.test_vplistscript.test_local()[source]

Check local flag

validphys.tests.test_vplistscript.test_remote()[source]

Test remote flag on both datasets (which should return empty string) and pdfs which returns output

validphys.tests.test_weights module

test_weights.py

validphys.tests.test_weights.test_chi2_arithmetic(weighted_data_witht0_internal_cuts_config)[source]
validphys.tests.test_weights.test_disable_weights(weighted_data_witht0_internal_cuts_config)[source]
validphys.tests.test_weights.test_python_weights(weighted_data_witht0_config)[source]

Test python implementation of weighted covmats and that use_weights_in_covmat is working

validphys.tests.test_weights.test_weights_have_same_commondata(weighted_data_witht0_config)[source]

Module contents