How to use the autoarray.kernel.ones function in autoarray

# Exposure times = 1.0 s
            # Imaging (counts) = 4.0
            # Background (counts) = 0.0

            # Noise (counts) = sqrt(4.0 + 0.0**2) = 2.0
            # Noise (eps) = 2.0 / 1.0

            image = aa.array.full(fill_value=4.0, shape_2d=(4, 2))

            exposure_time = aa.array.ones(shape_2d=(4,2))
            background_noise = aa.array.zeros(shape_2d=(4,2))

            imaging_data = al.ImagingData(
                image=image,
                pixel_scales=1.0,
                psf=aa.kernel.ones(shape_2d=(3,3)),
                exposure_time_map=exposure_time,
                background_noise_map=background_noise,
            )

            assert (
                imaging_data.estimated_noise_map.in_2d == 2.0 * np.ones((4, 2))
            ).all()

def test__same_as_above_but_different_image_values_in_each_pixel_and_new_background_values(
            self
        ):
            # Can use pattern from previous test_autoarray for values

            image = aa.array.manual_2d(
                array=[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]
            )

            exposure_time = aa.array.ones(shape_2d=(3,2))
            background_noise = aa.array.full(fill_value=12.0, shape_2d=(3,2))

            imaging_data = al.ImagingData(
                image=image,
                pixel_scales=1.0,
                psf=aa.kernel.ones(shape_2d=(3,3)),
                exposure_time_map=exposure_time,
                background_noise_map=background_noise,
            )

            assert imaging_data.estimated_noise_map.in_2d == pytest.approx(
                np.array(
                    [
                        [np.sqrt(1.0 + 144.0), np.sqrt(2.0 + 144.0)],
                        [np.sqrt(3.0 + 144.0), np.sqrt(4.0 + 144.0)],
                        [np.sqrt(5.0 + 144.0), np.sqrt(6.0 + 144.0)],
                    ]
                ),
                1e-2,
            )

# Imaging (counts) = 1.0
            # Background (counts) = 5.0

            # Noise (counts) = sqrt(1.0 + 5**2)
            # Noise (eps) = sqrt(1.0 + 5**2) / 1.0

            image = aa.array.ones(shape_2d=(2,3))

            exposure_time = aa.array.ones(shape_2d=(2,3))

            background_noise = aa.array.full(fill_value=5.0, shape_2d=(2,3))

            imaging_data = al.ImagingData(
                image=image,
                pixel_scales=1.0,
                psf=aa.kernel.ones(shape_2d=(3,3)),
                exposure_time_map=exposure_time,
                background_noise_map=background_noise,
            )

            assert imaging_data.estimated_noise_map.in_2d == pytest.approx(
                np.array(
                    [
                        [np.sqrt(1.0 + 25.0), np.sqrt(1.0 + 25.0), np.sqrt(1.0 + 25.0)],
                        [np.sqrt(1.0 + 25.0), np.sqrt(1.0 + 25.0), np.sqrt(1.0 + 25.0)],
                    ]
                ),
                1e-2,
            )

# Imaging (counts) = 1.0
            # Background (counts) = sqrt(3.0)

            # Noise (counts) = sqrt(1.0 + sqrt(3.0)**2) = sqrt(1.0 + 3.0) = 2.0
            # Noise (eps) = 2.0 / 1.0 = 2.0

            image = aa.array.ones(shape_2d=(3,3))

            exposure_time = aa.array.ones(shape_2d=(3,3))

            background_noise = aa.array.full(fill_value=3.0 ** 0.5, shape_2d=(3,3))

            imaging_data = al.ImagingData(
                image=image,
                pixel_scales=1.0,
                psf=aa.kernel.ones(shape_2d=(3,3)),
                exposure_time_map=exposure_time,
                background_noise_map=background_noise,
            )

            assert imaging_data.estimated_noise_map.in_2d == pytest.approx(
                2.0 * np.ones((3, 3)), 1e-2
            )

def test__via_edges__input_all_ones__sky_bg_level_1(self):
            imaging_data = al.ImagingData(
                image=aa.array.manual_2d(np.ones((3, 3))),
                noise_map=np.ones((3, 3)),
                psf=aa.kernel.ones(shape_2d=(3,3)),
                pixel_scales=0.1,
            )

            sky_noise = imaging_data.background_noise_from_edges(no_edges=1)

            assert sky_noise == 0.0

def test__via_edges__6x5_image_two_edges__values(self):
            image = aa.array.manual_2d(
                [
                    [0, 1, 2, 3, 4],
                    [5, 6, 7, 8, 9],
                    [10, 11, 100, 12, 13],
                    [14, 15, 100, 16, 17],
                    [18, 19, 20, 21, 22],
                    [23, 24, 25, 26, 27],
                ]
            )

            imaging_data = al.ImagingData(
                image=image,
                noise_map=np.ones((3, 3)),
                psf=aa.kernel.ones(shape_2d=(3,3)),
                pixel_scales=0.1,
            )
            sky_noise = imaging_data.background_noise_from_edges(no_edges=2)

            assert sky_noise == np.std(np.arange(28))

def test__via_edges__4x3_image_simple_gaussian__ignores_central_pixels(self):
            image = aa.array.manual_2d([[1, 1, 1], [1, 100, 1], [1, 100, 1], [1, 1, 1]])

            imaging_data = al.ImagingData(
                image=image,
                noise_map=np.ones((3, 3)),
                psf=aa.kernel.ones(shape_2d=(3,3)),
                pixel_scales=0.1,
            )
            sky_noise = imaging_data.background_noise_from_edges(no_edges=1)

            assert sky_noise == 0.0

# Imaging (counts) = 4.0
            # Background (counts) = 0.0

            # Noise (counts) = sqrt(4.0 + 0.0**2) = 2.0
            # Noise (eps) = 2.0 / 4.0 = 0.5

            image = aa.array.ones(shape_2d=(1,5))

            exposure_time = aa.array.full(fill_value=4.0, shape_2d=(1,5))

            background_noise = aa.array.zeros(shape_2d=(1,5))

            imaging_data = al.ImagingData(
                image=image,
                pixel_scales=1.0,
                psf=aa.kernel.ones(shape_2d=(3,3)),
                exposure_time_map=exposure_time,
                background_noise_map=background_noise,
            )

            assert (
                imaging_data.estimated_noise_map.in_2d == 0.5 * np.ones((1, 5))
            ).all()

image = aa.array.manual_2d(
                array=[[5.0, 3.0], [10.0, 20.0]]
            )

            exposure_time = aa.array.manual_2d(
                array=[[1.0, 2.0], [3.0, 4.0]]
            )
            background_noise = aa.array.full(fill_value=9.0, 
                    shape_2d=((2, 2))
            )

            imaging_data = al.ImagingData(
                image=image,
                pixel_scales=1.0,
                psf=aa.kernel.ones(shape_2d=(3,3)),
                exposure_time_map=exposure_time,
                background_noise_map=background_noise,
            )

            assert imaging_data.estimated_noise_map.in_2d == pytest.approx(
                np.array(
                    [
                        [np.sqrt(5.0 + 81.0), np.sqrt(6.0 + 18.0 ** 2.0) / 2.0],
                        [
                            np.sqrt(30.0 + 27.0 ** 2.0) / 3.0,
                            np.sqrt(80.0 + 36.0 ** 2.0) / 4.0,
                        ],
                    ]
                ),
                1e-2,
            )