RF: Use np.sctypesDict to source scalar types

np.sctypes does not have a consistent value type, and does not enumerate
all scalar types of a given kind.

Author: effigies

nibabel/spatialimages.py

@@ -284,19 +284,17 @@ def supported_np_types(obj):
         set of numpy types that `obj` supports
     """
     dt = obj.get_data_dtype()
-    supported = []
-    for name, np_types in np.sctypes.items():
-        for np_type in np_types:
-            try:
-                obj.set_data_dtype(np_type)
-            except HeaderDataError:
-                continue
-            # Did set work?
-            if np.dtype(obj.get_data_dtype()) == np.dtype(np_type):
-                supported.append(np_type)
-    # Reset original header dtype
+    supported = set()
+    for np_type in set(np.sctypeDict.values()):
+        try:
+            obj.set_data_dtype(np_type)
+        except HeaderDataError:
+            continue
+        # Did set work?
+        if np.dtype(obj.get_data_dtype()) == np.dtype(np_type):
+            supported.add(np_type)
     obj.set_data_dtype(dt)
-    return set(supported)
+    return supported
 
 
 class ImageDataError(Exception):

nibabel/tests/test_analyze.py

@@ -49,20 +49,20 @@
 PIXDIM0_MSG = 'pixdim[1,2,3] should be non-zero; setting 0 dims to 1'
 
 
-def add_intp(supported_np_types):
-    # Add intp, uintp to supported types as necessary
-    supported_dtypes = [np.dtype(t) for t in supported_np_types]
-    for np_type in (np.intp, np.uintp):
-        if np.dtype(np_type) in supported_dtypes:
-            supported_np_types.add(np_type)
+def add_duplicate_types(supported_np_types):
+    # Update supported numpy types with named scalar types that map to the same set of dtypes
+    dtypes = {np.dtype(t) for t in supported_np_types}
+    supported_np_types.update(
+        scalar for scalar in set(np.sctypeDict.values()) if np.dtype(scalar) in dtypes
+    )
 
 
 class TestAnalyzeHeader(tws._TestLabeledWrapStruct):
     header_class = AnalyzeHeader
     example_file = header_file
     sizeof_hdr = AnalyzeHeader.sizeof_hdr
     supported_np_types = {np.uint8, np.int16, np.int32, np.float32, np.float64, np.complex64}
-    add_intp(supported_np_types)
+    add_duplicate_types(supported_np_types)
 
     def test_supported_types(self):
         hdr = self.header_class()

nibabel/tests/test_nifti1.py

@@ -80,7 +80,7 @@ class TestNifti1PairHeader(tana.TestAnalyzeHeader, tspm.HeaderScalingMixin):
     )
     if have_binary128():
         supported_np_types = supported_np_types.union((np.longdouble, np.longcomplex))
-    tana.add_intp(supported_np_types)
+    tana.add_duplicate_types(supported_np_types)
 
     def test_empty(self):
         tana.TestAnalyzeHeader.test_empty(self)

nibabel/tests/test_spm99analyze.py

@@ -35,10 +35,18 @@
 from ..volumeutils import _dt_min_max, apply_read_scaling
 from . import test_analyze
 
-FLOAT_TYPES = np.sctypes['float']
-COMPLEX_TYPES = np.sctypes['complex']
-INT_TYPES = np.sctypes['int']
-UINT_TYPES = np.sctypes['uint']
+# np.sctypes values are lists of types with unique sizes
+# For testing, we want all concrete classes of a type
+# Key on kind, rather than abstract base classes, since timedelta64 is a signedinteger
+sctypes = {}
+for sctype in set(np.sctypeDict.values()):
+    sctypes.setdefault(np.dtype(sctype).kind, []).append(sctype)
+
+# Sort types to ensure that xdist doesn't complain about test order when we parametrize
+FLOAT_TYPES = sorted(sctypes['f'], key=lambda x: x.__name__)
+COMPLEX_TYPES = sorted(sctypes['c'], key=lambda x: x.__name__)
+INT_TYPES = sorted(sctypes['i'], key=lambda x: x.__name__)
+UINT_TYPES = sorted(sctypes['u'], key=lambda x: x.__name__)
 CFLOAT_TYPES = FLOAT_TYPES + COMPLEX_TYPES
 IUINT_TYPES = INT_TYPES + UINT_TYPES
 NUMERIC_TYPES = CFLOAT_TYPES + IUINT_TYPES