Repository: numpy/numpy
File: numpy/lib/tests/test_polynomial.py (Python)

Code before:
    def test_objects(self):
        from decimal import Decimal
        p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
        p2 = p * Decimal('1.333333333333333')
        assert_(p2[1] == Decimal("3.9999999999999990"))
        p2 = p.deriv()
        assert_(p2[1] == Decimal('8.0'))
        p2 = p.integ()
        assert_(p2[3] == Decimal("1.333333333333333333333333333"))
        assert_(p2[2] == Decimal('1.5'))
        assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
        p = np.poly([Decimal(1), Decimal(2)])
        assert_equal(np.poly([Decimal(1), Decimal(2)]),
                     [1, Decimal(-3), Decimal(2)])

    def test_complex(self):
        p = np.poly1d([3j, 2j, 1j])
        p2 = p.integ()
        assert_((p2.coeffs == [1j, 1j, 1j, 0]).all())
        p2 = p.deriv()
        assert_((p2.coeffs == [6j, 2j]).all())

    def test_integ_coeffs(self):
        p = np.poly1d([3, 2, 1])
        p2 = p.integ(3, k=[9, 7, 6])
        expected = [1 / 4. / 5., 1 / 3. / 4., 1 / 2. / 3., 9 / 1. / 2., 7, 6]
        assert_( (p2.coeffs == expected).all())

    def test_zero_dims(self):
        try:
            np.poly(np.zeros((0, 0)))
        except ValueError:
            pass

    def test_poly_int_overflow(self):
        """
        Regression test for gh-5096.
        """
        v = np.arange(1, 21)
        assert_almost_equal(np.poly(v), np.poly(np.diag(v)))

    def test_zero_poly_dtype(self):
        """
        Regression test for gh-16354.
        """
        z = np.array([0, 0, 0])
        p = np.poly1d(z.astype(np.int64))
        assert_equal(p.coeffs.dtype, np.int64)

        p = np.poly1d(z.astype(np.float32))
        assert_equal(p.coeffs.dtype, np.float32)

Review comment (suggestion):
I guess these python 2 remnants aren't needed anymore

```suggestion
        expected = [1 / 4 / 5, 1 / 3 / 4, 1 / 2 / 3, 9 / 1 / 2, 7, 6]
```

Code after:
    def test_objects(self):
        from decimal import Decimal
        p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
        p2 = p * Decimal('1.333333333333333')
        assert_(p2[1] == Decimal("3.9999999999999990"))
        p2 = p.deriv()
        assert_(p2[1] == Decimal('8.0'))
        p2 = p.integ()
        assert_(p2[3] == Decimal("1.333333333333333333333333333"))
        assert_(p2[2] == Decimal('1.5'))
        assert_(np.issubdtype(p2.coeffs.dtype, np.object_))
        p = np.poly([Decimal(1), Decimal(2)])
        assert_equal(np.poly([Decimal(1), Decimal(2)]),
                     [1, Decimal(-3), Decimal(2)])

    def test_complex(self):
        p = np.poly1d([3j, 2j, 1j])
        p2 = p.integ()
        assert_((p2.coeffs == [1j, 1j, 1j, 0]).all())
        p2 = p.deriv()
        assert_((p2.coeffs == [6j, 2j]).all())

    def test_integ_coeffs(self):
        p = np.poly1d([3, 2, 1])
        p2 = p.integ(3, k=[9, 7, 6])
        expected = [1 / 4 / 5, 1 / 3 / 4, 1 / 2 / 3, 9 / 1 / 2, 7, 6]
        assert_((p2.coeffs == expected).all())

    def test_zero_dims(self):
        try:
            np.poly(np.zeros((0, 0)))
        except ValueError:
            pass

    def test_poly_int_overflow(self):
        """
        Regression test for gh-5096.
        """
        v = np.arange(1, 21)
        assert_almost_equal(np.poly(v), np.poly(np.diag(v)))

    def test_zero_poly_dtype(self):
        """
        Regression test for gh-16354.
        """
        z = np.array([0, 0, 0])
        p = np.poly1d(z.astype(np.int64))
        assert_equal(p.coeffs.dtype, np.int64)

        p = np.poly1d(z.astype(np.float32))
        assert_equal(p.coeffs.dtype, np.float32)