chaimain /
asgardpy
| Conditions | 5 |
| Total Lines | 59 |
| Code Lines | 34 |
| 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 numpy as np |
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| 14 | def test_preferred_model(base_config_1d): |
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| 15 | """ |
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| 16 | Testing the script code of checking the preferred spectral model. |
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| 17 | """ |
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| 18 | select_model_tags = ["lp", "bpl2", "ecpl", "pl", "eclp"] |
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| 19 | spec_model_temp_files = [] |
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| 20 | spec_model_temp_files = get_model_config_files(select_model_tags) |
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| 21 | |||
| 22 | main_analysis_list = {} |
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| 23 | spec_models_list = [] |
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| 24 | |||
| 25 | for temp in spec_model_temp_files: |
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| 26 | temp_model = AsgardpyAnalysis(base_config_1d) |
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| 27 | temp_model.config.fit_params.fit_range.min = "100 GeV" |
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| 28 | |||
| 29 | temp_model.config.target.models_file = temp |
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| 30 | |||
| 31 | temp_model_2 = AsgardpyAnalysis(temp_model.config) |
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| 32 | |||
| 33 | copy_target_config(temp_model, temp_model_2) |
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| 34 | |||
| 35 | spec_tag = temp.name.split(".")[0].split("_")[-1] |
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| 36 | spec_models_list.append(spec_tag) |
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| 37 | main_analysis_list[spec_tag] = {} |
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| 38 | |||
| 39 | main_analysis_list[spec_tag]["Analysis"] = temp_model_2 |
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| 40 | |||
| 41 | spec_models_list = np.array(spec_models_list) |
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| 42 | |||
| 43 | # Run Analysis Steps till Fit |
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| 44 | for tag in spec_models_list: |
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| 45 | main_analysis_list[tag]["Analysis"].run(["datasets-1d", "fit"]) |
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| 46 | |||
| 47 | fit_success_list, stat_list, dof_list, pref_over_pl_chi2_list = fetch_all_analysis_fit_info( |
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| 48 | main_analysis_list, spec_models_list |
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| 49 | ) |
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| 50 | |||
| 51 | # If any spectral model has at least 5 sigmas preference over PL |
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| 52 | best_sp_idx_lrt = np.nonzero(pref_over_pl_chi2_list == np.nanmax(pref_over_pl_chi2_list))[0] |
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| 53 | for idx in best_sp_idx_lrt: |
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| 54 | if pref_over_pl_chi2_list[idx] > 5: |
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| 55 | lrt_best_model = spec_models_list[idx] |
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| 56 | |||
| 57 | list_rel_p = check_model_preference_aic(stat_list, dof_list) |
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| 58 | |||
| 59 | best_sp_idx_aic = np.nonzero(list_rel_p == np.nanmax(list_rel_p))[0] |
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| 60 | |||
| 61 | aic_best_model = select_model_tags[best_sp_idx_aic[0]] |
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| 62 | |||
| 63 | stats_table = tabulate_best_fit_stats(spec_models_list, fit_success_list, main_analysis_list, list_rel_p) |
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| 64 | |||
| 65 | assert lrt_best_model == "lp" |
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| 66 | assert aic_best_model == "lp" |
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| 67 | assert len(stats_table.colnames) == 11 |
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| 68 | |||
| 69 | # Check for bad comparisons, same dof |
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| 70 | p_val_0, g_sig_0, dof_0 = check_model_preference_lrt(4.4, 2.2, 2, 2) |
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| 71 | |||
| 72 | assert np.isnan(p_val_0) |
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| 73 |
