--amend

tests/fit/test_fit.py

@@ -463,8 +463,8 @@ def test_ranking(mock_fit, example_spec):
     bestfit = np.asarray([0.9, 1.0])
     uncertainty = np.asarray([0.02, 0.1])
     labels = ["staterror", "mu"]
-    labels = ["staterror", "normfactor"]
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 0.0)
+    types = ["staterror", "normfactor"]
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 0.0)
     model, data = model_utils.model_and_data(example_spec)
     ranking_results = fit.ranking(model, data, fit_results=fit_results)
 

tests/fit/test_fit_results_containers.py

@@ -9,9 +9,12 @@ def test_FitResults():
     bestfit = np.asarray([1.0])
     uncertainty = np.asarray([0.1])
     labels = ["par_a"]
+    types = [None]
     corr_mat = np.asarray([[1.0]])
     best_twice_nll = 2.0
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, corr_mat, best_twice_nll)
+    fit_results = fit.FitResults(
+        bestfit, uncertainty, labels, types, corr_mat, best_twice_nll
+    )
     assert np.allclose(fit_results.bestfit, bestfit)
     assert np.allclose(fit_results.uncertainty, uncertainty)
     assert fit_results.labels == labels
@@ -25,16 +28,25 @@ def test_RankingResults():
     bestfit = np.asarray([1.0])
     uncertainty = np.asarray([0.1])
     labels = ["par_a"]
+    types = [None]
     prefit_up = np.asarray([0.3])
     prefit_down = np.asarray([-0.3])
     postfit_up = np.asarray([0.2])
     postfit_down = np.asarray([-0.2])
     ranking_results = fit.RankingResults(
-        bestfit, uncertainty, labels, prefit_up, prefit_down, postfit_up, postfit_down
+        bestfit,
+        uncertainty,
+        labels,
+        types,
+        prefit_up,
+        prefit_down,
+        postfit_up,
+        postfit_down,
     )
     assert np.allclose(ranking_results.bestfit, bestfit)
     assert np.allclose(ranking_results.uncertainty, uncertainty)
     assert ranking_results.labels == labels
+    assert ranking_results.types == types
     assert np.allclose(ranking_results.prefit_up, prefit_up)
     assert np.allclose(ranking_results.prefit_down, prefit_down)
     assert np.allclose(ranking_results.postfit_up, postfit_up)
@@ -100,7 +112,8 @@ def test_print_results(caplog):
     bestfit = np.asarray([1.0, 2.0])
     uncertainty = np.asarray([0.1, 0.3])
     labels = ["param_A", "param_B"]
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 0.0)
+    types = [None, None]
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 0.0)
 
     fit.print_results(fit_results)
     assert "param_A =  1.0000 +/- 0.1000" in [rec.message for rec in caplog.records]

tests/visualize/test_visualize.py

@@ -360,10 +360,11 @@ def test_correlation_matrix(mock_draw):
     corr_mat = np.asarray([[1.0, 0.2, 0.1], [0.2, 1.0, 0.1], [0.1, 0.1, 1.0]])
     corr_mat_pruned = np.asarray([[1.0, 0.2], [0.2, 1.0]])
     labels = ["a", "b", "c"]
+    types = [None, None, None]
     labels_pruned = ["a", "b"]
     folder_path = "tmp"
     figure_path = pathlib.Path(folder_path) / "correlation_matrix.pdf"
-    fit_results = fit.FitResults(np.empty(0), np.empty(0), labels, corr_mat, 1.0)
+    fit_results = fit.FitResults(np.empty(0), np.empty(0), labels, types, corr_mat, 1.0)
 
     # pruning with threshold
     fig = visualize.correlation_matrix(
@@ -382,7 +383,7 @@ def test_correlation_matrix(mock_draw):
     # close figure, do not save
     corr_mat_fixed = np.asarray([[1.0, 0.2, 0.0], [0.2, 1.0, 0.0], [0.0, 0.0, 0.0]])
     fit_results_fixed = fit.FitResults(
-        np.empty(0), np.empty(0), labels, corr_mat_fixed, 1.0
+        np.empty(0), np.empty(0), labels, types, corr_mat_fixed, 1.0
     )
     _ = visualize.correlation_matrix(
         fit_results_fixed,
@@ -407,9 +408,10 @@ def test_pulls(mock_draw):
     bestfit = np.asarray([0.8, 1.0, 1.05, 1.1])
     uncertainty = np.asarray([0.9, 1.0, 0.03, 0.7])
     labels = ["a", "b", "staterror_region[0]", "c"]
+    types = [None, None, None, None]
     exclude = ["a"]
     folder_path = "tmp"
-    fit_results = fit.FitResults(bestfit, uncertainty, labels, np.empty(0), 1.0)
+    fit_results = fit.FitResults(bestfit, uncertainty, labels, types, np.empty(0), 1.0)
 
     filtered_bestfit = np.asarray([1.0, 1.1])
     filtered_uncertainty = np.asarray([1.0, 0.7])