Method test_true_divide

numpy/_core/tests/test_ufunc.py:641–726

(self)

        np.equal(np.array([3]), 2**300, casting="equiv")

    def test_true_divide(self):
        a = np.array(10)
        b = np.array(20)
        tgt = np.array(0.5)

        for tc in 'bhilqBHILQefdgFDG':
            dt = np.dtype(tc)
            aa = a.astype(dt)
            bb = b.astype(dt)

            # Check result value and dtype.
            for x, y in itertools.product([aa, -aa], [bb, -bb]):

                # Check with no output type specified
                if tc in 'FDG':
                    tgt = complex(x) / complex(y)
                else:
                    tgt = float(x) / float(y)

                res = np.true_divide(x, y)
                rtol = max(np.finfo(res).resolution, 1e-15)
                assert_allclose(res, tgt, rtol=rtol)

                if tc in 'bhilqBHILQ':
                    assert_(res.dtype.name == 'float64')
                else:
                    assert_(res.dtype.name == dt.name)

                # Check with output type specified.  This also checks for the
                # incorrect casts in issue gh-3484 because the unary '-' does
                # not change types, even for unsigned types, Hence casts in the
                # ufunc from signed to unsigned and vice versa will lead to
                # errors in the values.
                for tcout in 'bhilqBHILQ':
                    dtout = np.dtype(tcout)
                    assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)

                for tcout in 'efdg':
                    dtout = np.dtype(tcout)
                    if tc in 'FDG':
                        # Casting complex to float is not allowed
                        assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
                    else:
                        tgt = float(x) / float(y)
                        rtol = max(np.finfo(dtout).resolution, 1e-15)
                        # The value of tiny for double double is NaN
                        with warnings.catch_warnings():
                            warnings.simplefilter('ignore', UserWarning)
                            if not np.isnan(np.finfo(dtout).tiny):
                                atol = max(np.finfo(dtout).tiny, 3e-308)
                            else:
                                atol = 3e-308
                        # Some test values result in invalid for float16
                        # and the cast to it may overflow to inf.
                        with np.errstate(invalid='ignore', over='ignore'):
                            res = np.true_divide(x, y, dtype=dtout)
                        if not np.isfinite(res) and tcout == 'e':
                            continue