TST: Add more ScaledFloatDType tests

These tests are not quite as interesting, but also improve
code coverage.

Author: seberg

numpy/core/tests/test_custom_dtypes.py

@@ -2,59 +2,91 @@
 
 import numpy as np
 from numpy.testing import assert_array_equal
-
-
-SF = np.core._multiarray_umath._get_sfloat_dtype()
-
-
-@pytest.mark.parametrize("scaling", [1., -1., 2.])
-def test_scaled_float_from_floats(scaling):
-    a = np.array([1., 2., 3.], dtype=SF(scaling))
-
-    assert a.dtype.get_scaling() == scaling
-    assert_array_equal(scaling * a.view(np.float64), np.array([1., 2., 3.]))
-
-
-@pytest.mark.parametrize("scaling", [1., -1., 2.])
-def test_sfloat_from_float(scaling):
-    a = np.array([1., 2., 3.]).astype(dtype=SF(scaling))
-
-    assert a.dtype.get_scaling() == scaling
-    assert_array_equal(scaling * a.view(np.float64), np.array([1., 2., 3.]))
-
-
-def _get_array(scaling, aligned=True):
-    if not aligned:
-        a = np.empty(3*8 + 1, dtype=np.uint8)[1:]
-        a = a.view(np.float64)
-        a[:] = [1., 2., 3.]
-    else:
-        a = np.array([1., 2., 3.])
-
-    a *= 1./scaling  # the casting code also uses the reciprocal.
-    return a.view(SF(scaling))
-
-
-@pytest.mark.parametrize("aligned", [True, False])
-def test_sfloat_casts(aligned):
-    a = _get_array(1., aligned)
-
-    assert np.can_cast(a, SF(-1.), casting="equiv")
-    assert not np.can_cast(a, SF(-1.), casting="no")
-    na = a.astype(SF(-1.))
-    assert_array_equal(-1 * na.view(np.float64), a.view(np.float64))
-
-    assert np.can_cast(a, SF(2.), casting="same_kind")
-    assert not np.can_cast(a, SF(2.), casting="safe")
-    a2 = a.astype(SF(2.))
-    assert_array_equal(2 * a2.view(np.float64), a.view(np.float64))
-
-
-@pytest.mark.parametrize("aligned", [True, False])
-def test_sfloat_cast_internal_errors(aligned):
-    a = _get_array(2e300, aligned)
-
-    with pytest.raises(TypeError,
-            match="error raised inside the core-loop: non-finite factor!"):
-        a.astype(SF(2e-300))
-
+from numpy.core._multiarray_umath import (
+    _discover_array_parameters as discover_array_params, _get_sfloat_dtype)
+
+
+SF = _get_sfloat_dtype()
+
+
+class TestSFloat:
+    def _get_array(self, scaling, aligned=True):
+        if not aligned:
+            a = np.empty(3*8 + 1, dtype=np.uint8)[1:]
+            a = a.view(np.float64)
+            a[:] = [1., 2., 3.]
+        else:
+            a = np.array([1., 2., 3.])
+
+        a *= 1./scaling  # the casting code also uses the reciprocal.
+        return a.view(SF(scaling))
+
+    def test_sfloat_rescaled(self):
+        sf = SF(1.)
+        sf2 = sf.scaled_by(2.)
+        assert sf2.get_scaling() == 2.
+        sf6 = sf2.scaled_by(3.)
+        assert sf6.get_scaling() == 6.
+
+    def test_class_discovery(self):
+        # This does not test much, since we always discover the scaling as 1.
+        # But most of NumPy (when writing) does not understand DType classes
+        dt, _ = discover_array_params([1., 2., 3.], dtype=SF)
+        assert dt == SF(1.)
+
+    @pytest.mark.parametrize("scaling", [1., -1., 2.])
+    def test_scaled_float_from_floats(self, scaling):
+        a = np.array([1., 2., 3.], dtype=SF(scaling))
+
+        assert a.dtype.get_scaling() == scaling
+        assert_array_equal(scaling * a.view(np.float64), [1., 2., 3.])
+
+    def test_repr(self):
+        # Check the repr, mainly to cover the code paths:
+        assert repr(SF(scaling=1.)) == "_ScaledFloatTestDType(scaling=1.0)"
+
+    @pytest.mark.parametrize("scaling", [1., -1., 2.])
+    def test_sfloat_from_float(self, scaling):
+        a = np.array([1., 2., 3.]).astype(dtype=SF(scaling))
+
+        assert a.dtype.get_scaling() == scaling
+        assert_array_equal(scaling * a.view(np.float64), [1., 2., 3.])
+
+    @pytest.mark.parametrize("aligned", [True, False])
+    @pytest.mark.parametrize("scaling", [1., -1., 2.])
+    def test_sfloat_getitem(self, aligned, scaling):
+        a = self._get_array(1., aligned)
+        assert a.tolist() == [1., 2., 3.]
+
+    @pytest.mark.parametrize("aligned", [True, False])
+    def test_sfloat_casts(self, aligned):
+        a = self._get_array(1., aligned)
+
+        assert np.can_cast(a, SF(-1.), casting="equiv")
+        assert not np.can_cast(a, SF(-1.), casting="no")
+        na = a.astype(SF(-1.))
+        assert_array_equal(-1 * na.view(np.float64), a.view(np.float64))
+
+        assert np.can_cast(a, SF(2.), casting="same_kind")
+        assert not np.can_cast(a, SF(2.), casting="safe")
+        a2 = a.astype(SF(2.))
+        assert_array_equal(2 * a2.view(np.float64), a.view(np.float64))
+
+    @pytest.mark.parametrize("aligned", [True, False])
+    def test_sfloat_cast_internal_errors(self, aligned):
+        a = self._get_array(2e300, aligned)
+
+        with pytest.raises(TypeError,
+                match="error raised inside the core-loop: non-finite factor!"):
+            a.astype(SF(2e-300))
+
+    def test_sfloat_promotion(self):
+        assert np.result_type(SF(2.), SF(3.)) == SF(3.)
+        assert np.result_type(SF(3.), SF(2.)) == SF(3.)
+        # Float64 -> SF(1.) and then promotes normally, so both of this work:
+        assert np.result_type(SF(3.), np.float64) == SF(3.)
+        assert np.result_type(np.float64, SF(0.5)) == SF(1.)
+
+        # Test an undefined promotion:
+        with pytest.raises(TypeError):
+            np.result_type(SF(1.), np.int64)