Transforms cleanup

hexrd/core/fitting/calibration/lmfit_param_handling.py

@@ -34,7 +34,7 @@
 # First is the axes_order, second is extrinsic
 DEFAULT_EULER_CONVENTION = ('zxz', False)
 
-EULER_CONVENTION_TYPES = dict | tuple | None
+EULER_CONVENTION_TYPES = dict | tuple
 
 
 def create_instr_params(
@@ -177,9 +177,6 @@ def _add_constrained_detector_parameters(
     if euler_convention is not None:
         # Convert the tilt to the specified Euler convention
         normalized = normalize_euler_convention(euler_convention)
-        if normalized is None:
-            raise ValueError('euler_convention cannot be None in this context.')
-
         rme = RotMatEuler(
             np.zeros(
                 3,
@@ -509,9 +506,6 @@ def _tilt_to_rmat(
         return rotMatOfExpMap(tilt)
 
     normalized = normalize_euler_convention(euler_convention)
-    if normalized is None:
-        raise ValueError('euler_convention cannot be None in this context.')
-
     return make_rmat_euler(
         np.radians(tilt),
         axes_order=normalized[0],
@@ -735,9 +729,7 @@ def validate_params_list(params_list):
 }
 
 
-def normalize_euler_convention(
-    euler_convention: EULER_CONVENTION_TYPES,
-) -> tuple | None:
+def normalize_euler_convention(euler_convention: EULER_CONVENTION_TYPES) -> tuple:
     if isinstance(euler_convention, dict):
         return (
             euler_convention['axes_order'],

hexrd/core/instrument/hedm_instrument.py

@@ -315,6 +315,7 @@ def _parse_imgser_dict(
         elif isinstance(images_in, np.ndarray):
             # 2- or 3-d array of images
             ndim = images_in.ndim
+            images_in = np.asarray(images_in)
             if ndim == 2:
                 ims = images_in[roi[0][0] : roi[0][1], roi[1][0] : roi[1][1]]
             elif ndim == 3:

hexrd/core/material/crystallography.py

@@ -33,7 +33,18 @@
 import csv
 import os
 from math import pi
-from typing import Literal, Sequence, Optional, Union, List, Tuple, TypedDict, overload
+from typing import (
+    Any,
+    Literal,
+    Mapping,
+    Optional,
+    Union,
+    Dict,
+    List,
+    Tuple,
+    TypedDict,
+    overload,
+)
 
 import numpy as np
 from numpy.typing import NDArray
@@ -1471,7 +1482,7 @@ def getMultiplicity(self, allHKLs: Optional[bool] = False) -> np.ndarray:
 
     def getHKLID(
         self,
-        hkl: int | Sequence[int, int, int] | NDArray[np.int_],
+        hkl: int | tuple[int, int, int] | NDArray[np.int_],
         master: bool = False,
     ) -> List[int] | int:
         """
@@ -1507,10 +1518,9 @@ def getHKLID(
         if isinstance(hkl, np.ndarray):
             # if is ndarray, assume is 3xN
             return [self._getHKLID(x, master=master) for x in hkl.T]
-        elif isinstance(hkl, list):
+        elif isinstance(hkl, tuple) or isinstance(hkl, list):
             return [self._getHKLID(x, master=master) for x in hkl]
         else:
-            assert isinstance(hkl, (int, tuple))
             return self._getHKLID(hkl, master=master)
 
     def _getHKLID(
@@ -2232,8 +2242,8 @@ def lorentz_factor(tth: np.ndarray) -> np.ndarray:
 
 def polarization_factor(
     tth: NDArray[np.float64],
-    unpolarized: Optional[bool] = True,
     eta: Optional[NDArray[np.float64]] = None,
+    unpolarized: Optional[bool] = True,
     f_hor: Optional[float] = None,
     f_vert: Optional[float] = None,
 ) -> NDArray[np.float64]: