Apply suggestions from code review

Co-authored-by: Chris Markiewicz <[email protected]>

Author: larsoner

nibabel/casting.py

@@ -45,7 +45,6 @@ class CastingError(Exception):
     ],
     'others': [bool, object, bytes, str, np.void],
 }
-# fmt: off
 sctypes_aliases = {
     getattr(np, dtype)
     for dtype in (
@@ -59,8 +58,7 @@ class CastingError(Exception):
         'object_', 'void',
     )
     if hasattr(np, dtype)
-}
-# fmt: on
+}  # fmt:skip
 
 
 def float_to_int(arr, int_type, nan2zero=True, infmax=False):
@@ -492,9 +490,6 @@ def int_to_float(val, flt_type):
         return flt_type(val)
     # The following works around a nasty numpy 1.4.1 bug such that:
     # >>> int(np.uint32(2**32-1)
-    # -1
-    if not isinstance(val, Integral):
-        val = int(str(val))
     val = int(val)
     faval = np.longdouble(0)
     while val != 0:

nibabel/conftest.py

@@ -6,7 +6,8 @@
     import nibabel.nicom
 
 
-def pytest_configure(config):
-    """Configure pytest options."""
-    if int(np.__version__[0]) >= 2:
-        np.set_printoptions(legacy=125)
+@pytest.fixture(scope='session', autouse=True)
+def legacy_printoptions():
+    from packaging.version import Version
+    if Version(np.__version__) >= Version('1.22'):
+        np.set_printoptions(legacy='1.21')

nibabel/quaternions.py

@@ -29,7 +29,9 @@
 
 import numpy as np
 
-MAX_FLOAT = np.finfo(float).max
+from .casting import sctypes
+
+MAX_FLOAT = sctypes['float'][-1]
 FLOAT_EPS = np.finfo(float).eps
 
 

nibabel/tests/test_volumeutils.py

@@ -543,9 +543,12 @@ def test_a2f_scaled_unscaled():
         NUMERIC_TYPES, NUMERIC_TYPES, (0, 0.5, -1, 1), (1, 0.5, 2)
     ):
         mn_in, mx_in = _dt_min_max(in_dtype)
-        nan_val = np.nan if in_dtype in CFLOAT_TYPES else 10
-        mn = 0 if np.dtype(in_dtype).kind == "u" else 1
-        arr = np.array([mn_in, mn, 0, 1, mx_in, nan_val], dtype=in_dtype)
+        vals = [mn_in, 0, 1, mx_in]
+        if np.dtype(in_dtype).kind != 'u':
+            vals.append(-1)
+        if in_dtype in CFLOAT_TYPES:
+            vals.append(np.nan)
+        arr = np.array(vals, dtype=in_dtype)
         mn_out, mx_out = _dt_min_max(out_dtype)
         # 0 when scaled to output will also be the output value for NaN
         nan_fill = -intercept / divslope