drop off the additional List

Author: kratsg

src/pyhf/compat.py

@@ -19,7 +19,7 @@ def paramset_to_rootnames(paramset):
         paramset (:obj:`pyhf.paramsets.paramset`): The parameter set.
 
     Returns:
-        :obj:`List[str]` or :obj:`str`: The generated parameter names
+        :obj:`list[str]` or :obj:`str`: The generated parameter names
         (for the non-scalar/scalar case) respectively.
 
     Example:

src/pyhf/modifiers/staterror.py

@@ -1,5 +1,6 @@
+from __future__ import annotations
+
 import logging
-from typing import List
 
 import pyhf
 from pyhf import events
@@ -10,7 +11,7 @@
 log = logging.getLogger(__name__)
 
 
-def required_parset(sigmas, fixed: List[bool]):
+def required_parset(sigmas, fixed: list[bool]):
     n_parameters = len(sigmas)
     return {
         'paramset_type': 'constrained_by_normal',

src/pyhf/parameters/paramsets.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import pyhf
 
 __all__ = [
@@ -29,7 +27,7 @@ def __init__(self, **kwargs):
             )
 
     @property
-    def suggested_fixed(self) -> List[bool]:
+    def suggested_fixed(self) -> list[bool]:
         if isinstance(self._suggested_fixed, bool):
             return [self._suggested_fixed] * self.n_parameters
         return self._suggested_fixed

src/pyhf/pdf.py

@@ -2,7 +2,7 @@
 
 import copy
 import logging
-from typing import List, Union
+from typing import Union
 
 import pyhf.parameters
 import pyhf
@@ -406,7 +406,7 @@ def param_set(self, name):
         """
         return self.par_map[name]['paramset']
 
-    def suggested_fixed(self) -> List[bool]:
+    def suggested_fixed(self) -> list[bool]:
         """
         Identify the fixed parameters in the model.
 

src/pyhf/readxml.py

@@ -5,7 +5,6 @@
     IO,
     Callable,
     Iterable,
-    List,
     MutableMapping,
     MutableSequence,
     Sequence,
@@ -99,7 +98,7 @@ def extract_error(hist: uproot.behaviors.TH1.TH1) -> list[float]:
     """
 
     variance = hist.variances() if hist.weighted else hist.to_numpy()[0]
-    return cast(List[float], np.sqrt(variance).tolist())
+    return cast(list[float], np.sqrt(variance).tolist())
 
 
 def import_root_histogram(