Merge branch 'master' into maly-issue-491

Author: MoAly98

.pre-commit-config.yaml

@@ -3,16 +3,16 @@ repos:
     rev: 25.1.0
     hooks:
     -   id: black
-# -   repo: https://github.com/pre-commit/mirrors-mypy
-#     rev: v1.14.1
-#     hooks:
-#     -   id: mypy
-#         name: mypy with Python 3.12
-#         files: src/cabinetry
-#         additional_dependencies: ["numpy>=1.22", "boost-histogram>=1.0.1", "click>=8", "types-tabulate", "types-PyYAML", "hist>=2.3.0"]
-#         args: ["--python-version=3.12"]
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.15.0
+    hooks:
+    -   id: mypy
+        name: mypy with Python 3.12
+        files: src/cabinetry
+        additional_dependencies: ["numpy>=1.22", "boost-histogram>=1.0.1", "click>=8", "types-tabulate", "types-PyYAML", "hist>=2.3.0"]
+        args: ["--python-version=3.12"]
 -   repo: https://github.com/pycqa/flake8
-    rev: 7.1.1
+    rev: 7.1.2
     hooks:
     -   id: flake8
         additional_dependencies: [flake8-bugbear, flake8-import-order, flake8-print]

src/cabinetry/fit/__init__.py

@@ -2,7 +2,7 @@
 
 from collections import defaultdict
 import logging
-from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, cast, Dict, List, Literal, Optional, Tuple, Union
 
 import iminuit
 import numpy as np
@@ -1105,7 +1105,7 @@ def scan(
     for i_par, par_value in enumerate(scan_values):
         log.debug(f"performing fit with {par_name} = {par_value:.3f}")
         init_pars_scan = init_pars.copy()
-        init_pars_scan[par_index] = par_value
+        init_pars_scan[par_index] = cast(float, par_value)
         scan_fit_results = _fit_model(
             model,
             data,

src/cabinetry/model_utils.py

@@ -3,7 +3,17 @@
 from collections import defaultdict
 import json
 import logging
-from typing import Any, DefaultDict, Dict, List, NamedTuple, Optional, Tuple, Union
+from typing import (
+    Any,
+    cast,
+    DefaultDict,
+    Dict,
+    List,
+    NamedTuple,
+    Optional,
+    Tuple,
+    Union,
+)
 
 import numpy as np
 import pyhf
@@ -746,7 +756,7 @@ def _parameters_maximizing_constraint_term(
     Returns:
         List[float]: parameters maximizing the model constraint term
     """
-    best_pars = []  # parameters maximizing constraint term
+    best_pars: List[float] = []  # parameters maximizing constraint term
     i_aux = 0  # current position in auxiliary data list
     i_poisson = 0  # current position in list of Poisson rescale factors
 
@@ -771,8 +781,12 @@ def _parameters_maximizing_constraint_term(
             else:
                 rescale_factors = [1.0] * n_params  # no rescaling by default
 
-            best_pars += list(
-                np.asarray(aux_data[i_aux : i_aux + n_params]) / rescale_factors
+            # manually cast, possible cause https://github.com/numpy/numpy/issues/27944
+            best_pars += cast(
+                List[float],
+                (
+                    np.asarray(aux_data[i_aux : i_aux + n_params]) / rescale_factors
+                ).tolist(),
             )
             i_aux += n_params
 

src/cabinetry/workspace.py

@@ -3,7 +3,7 @@
 import json
 import logging
 import pathlib
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, cast, Dict, List, Optional, Tuple, Union
 
 import pyhf
 
@@ -189,8 +189,9 @@ def normplusshape_modifiers(
         else:
             norm_effect_up = histogram_up.normalize_to_yield(histogram_nominal)
             norm_effect_down = histogram_down.normalize_to_yield(histogram_nominal)
-            histo_yield_up = histogram_up.yields.tolist()
-            histo_yield_down = histogram_down.yields.tolist()
+            # manually cast due to https://github.com/numpy/numpy/issues/27944
+            histo_yield_up = cast(List[float], histogram_up.yields.tolist())
+            histo_yield_down = cast(List[float], histogram_down.yields.tolist())
 
         log.debug(
             f"normalization impact of systematic {systematic['Name']} on sample "
@@ -508,9 +509,12 @@ def _symmetrized_templates_and_norm(
     # normalize the variation to the same yield as nominal
     norm_effect_var = variation.normalize_to_yield(reference)
     norm_effect_sym = 2 - norm_effect_var
-    histo_yield_var = variation.yields.tolist()
+    # manually cast due to https://github.com/numpy/numpy/issues/27944
+    histo_yield_var = cast(List[float], variation.yields.tolist())
     # need another histogram that corresponds to the symmetrized variation,
     # which is 2*nominal - variation
-    histo_yield_sym = (2 * reference.yields - variation.yields).tolist()
+    histo_yield_sym = cast(
+        List[float], (2 * reference.yields - variation.yields).tolist()
+    )
 
     return histo_yield_var, histo_yield_sym, norm_effect_var, norm_effect_sym

tests/fit/test_fit.py

@@ -376,12 +376,14 @@ def test__goodness_of_fit(
     assert np.allclose(p_val, 0.91926079)
     caplog.clear()
 
-    # same setup but using custom auxdata
+    # same setup but using custom auxdata and custom fixed parameters
     model, _ = model_utils.model_and_data(example_spec_multibin)
     data = [35, 8, 10] + [0.9, 1.1, 0.8]  # custom aux
-    p_val = fit._goodness_of_fit(model, data, 9.964913)
+    fix_pars = [False, False, False, True]  # custom fixed
+    p_val = fit._goodness_of_fit(model, data, 9.964913, fix_pars=fix_pars)
     assert mock_pars.call_count == 2
     assert np.allclose(mock_pars.call_args[0][1], [0.9, 1.1, 0.8])  # aux picked up
+    assert mock_count.call_args[1] == {"fix_pars": fix_pars}  # fixed pars picked up
     assert np.allclose(p_val, 0.91926079)  # same result as before
 
     # no auxdata and zero degrees of freedom in chi2 test
@@ -501,9 +503,9 @@ def test_fit(mock_fit, mock_print, mock_gof):
     assert fit_results.bestfit == [1.0]
 
     # goodness-of-fit test
-    fit_results_gof = fit.fit(model, data, goodness_of_fit=True)
+    fit_results_gof = fit.fit(model, data, goodness_of_fit=True, fix_pars=fix_pars)
     assert mock_gof.call_args[0] == (model, data, 2.0)
-    assert mock_gof.call_args[1] == {"fix_pars": None}
+    assert mock_gof.call_args[1] == {"fix_pars": fix_pars}
     assert fit_results_gof.goodness_of_fit == 0.1