chaimain /
asgardpy
| Conditions | 5 |
| Total Lines | 77 |
| Code Lines | 47 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
| 1 | import pytest |
||
| 4 | @pytest.mark.test_data |
||
| 5 | def test_preferred_model(base_config_1d): |
||
| 6 | """ |
||
| 7 | Testing the script code of checking the preferred spectral model. |
||
| 8 | """ |
||
| 9 | |||
| 10 | import numpy as np |
||
| 11 | |||
| 12 | from asgardpy.analysis import AsgardpyAnalysis |
||
| 13 | from asgardpy.stats import ( |
||
| 14 | check_model_preference_aic, |
||
| 15 | check_model_preference_lrt, |
||
| 16 | copy_target_config, |
||
| 17 | fetch_all_analysis_fit_info, |
||
| 18 | get_model_config_files, |
||
| 19 | tabulate_best_fit_stats, |
||
| 20 | write_output_config_yaml, |
||
| 21 | ) |
||
| 22 | |||
| 23 | select_model_tags = ["lp", "bpl2", "ecpl", "pl", "eclp"] |
||
| 24 | spec_model_temp_files = [] |
||
| 25 | spec_model_temp_files = get_model_config_files(select_model_tags) |
||
| 26 | |||
| 27 | main_analysis_list = {} |
||
| 28 | spec_models_list = [] |
||
| 29 | |||
| 30 | for temp in spec_model_temp_files: |
||
| 31 | temp_model = AsgardpyAnalysis(base_config_1d) |
||
| 32 | temp_model.config.fit_params.fit_range.min = "100 GeV" |
||
| 33 | |||
| 34 | temp_model.config.target.models_file = temp |
||
| 35 | |||
| 36 | temp_model_2 = AsgardpyAnalysis(temp_model.config) |
||
| 37 | |||
| 38 | copy_target_config(temp_model, temp_model_2) |
||
| 39 | |||
| 40 | spec_tag = temp.name.split(".")[0].split("_")[-1] |
||
| 41 | spec_models_list.append(spec_tag) |
||
| 42 | main_analysis_list[spec_tag] = {} |
||
| 43 | |||
| 44 | main_analysis_list[spec_tag]["Analysis"] = temp_model_2 |
||
| 45 | |||
| 46 | spec_models_list = np.array(spec_models_list) |
||
| 47 | |||
| 48 | # Run Analysis Steps till Fit |
||
| 49 | for tag in spec_models_list: |
||
| 50 | main_analysis_list[tag]["Analysis"].run(["datasets-1d", "fit"]) |
||
| 51 | |||
| 52 | fit_success_list, stat_list, dof_list, pref_over_pl_chi2_list = fetch_all_analysis_fit_info( |
||
| 53 | main_analysis_list, spec_models_list |
||
| 54 | ) |
||
| 55 | |||
| 56 | # If any spectral model has at least 5 sigmas preference over PL |
||
| 57 | best_sp_idx_lrt = np.nonzero(pref_over_pl_chi2_list == np.nanmax(pref_over_pl_chi2_list))[0] |
||
| 58 | for idx in best_sp_idx_lrt: |
||
| 59 | if pref_over_pl_chi2_list[idx] > 5: |
||
| 60 | lrt_best_model = spec_models_list[idx] |
||
| 61 | |||
| 62 | list_rel_p = check_model_preference_aic(stat_list, dof_list) |
||
| 63 | |||
| 64 | best_sp_idx_aic = np.nonzero(list_rel_p == np.nanmax(list_rel_p))[0] |
||
| 65 | |||
| 66 | aic_best_model = select_model_tags[best_sp_idx_aic[0]] |
||
| 67 | |||
| 68 | stats_table = tabulate_best_fit_stats(spec_models_list, fit_success_list, main_analysis_list, list_rel_p) |
||
| 69 | |||
| 70 | assert lrt_best_model == "lp" |
||
|
1 ignored issue
–
show
|
|||
| 71 | assert aic_best_model == "lp" |
||
| 72 | assert len(stats_table.colnames) == 11 |
||
| 73 | |||
| 74 | tag = spec_models_list[fit_success_list][0] |
||
| 75 | write_output_config_yaml(main_analysis_list[tag]["Analysis"].final_model[0]) |
||
| 76 | |||
| 77 | # Check for bad comparisons, same dof |
||
| 78 | p_val_0, g_sig_0, dof_0 = check_model_preference_lrt(4.4, 2.2, 2, 2) |
||
| 79 | |||
| 80 | assert np.isnan(p_val_0) |
||
| 81 |
Loading history...