--amend

Author: andrzejnovak

src/cabinetry/fit/results_containers.py

@@ -1,6 +1,6 @@
 """Provides containers for inference results."""
 
-from typing import Dict, List, NamedTuple, Tuple
+from typing import Dict, List, NamedTuple, Optional, Tuple
 
 import numpy as np
 
@@ -24,7 +24,7 @@ class FitResults(NamedTuple):
     bestfit: np.ndarray
     uncertainty: np.ndarray
     labels: List[str]
-    types: List[str]
+    types: List[Optional[str]]
     corr_mat: np.ndarray
     best_twice_nll: float
     goodness_of_fit: float = -1

src/cabinetry/model_utils.py

@@ -519,7 +519,7 @@ def _filter_channels(
 
 def _labels_modifiers(
     model: pyhf.pdf.Model,
-) -> Tuple[List[str], List[str]]:
+) -> Tuple[List[str], List[Optional[str]]]:
     """ """
     labels = model.config.par_names()
     _mod_dict = dict(model.config.modifiers)

src/cabinetry/visualize/__init__.py

@@ -442,7 +442,7 @@ def pulls(
     *,
     figure_folder: Union[str, pathlib.Path] = "figures",
     exclude: Optional[Union[str, List[str], Tuple[str, ...]]] = None,
-    exclude_by_type: Optional[Union[str, List[str]]] = None,
+    exclude_by_type: Optional[List[str]] = None,
     close_figure: bool = True,
     save_figure: bool = True,
 ) -> mpl.figure.Figure:

tests/test_model_utils.py

@@ -396,7 +396,7 @@ def test_match_fit_results(mock_pars, mock_uncs):
         np.asarray([1.0, 2.0, 3.0]),
         np.asarray([0.1, 0.2, 0.3]),
         ["par_a", "par_b", "par_c"],
-        ["normfactor", "normfactor", "normfactor"],
+        [None, None, None],
         np.asarray([[1.0, 0.2, 0.5], [0.2, 1.0, 0.1], [0.5, 0.1, 1.0]]),
         5.0,
         0.1,