How to use autoarray - 10 common examples

def test__signal_to_noise_limit_above_max_signal_to_noise__signal_to_noise_map_unchanged(
            self
        ):
            image = aa.array.full(fill_value=20.0, shape_2d=(2,2))
            image[3] = 5.0

            noise_map_array = aa.array.full(fill_value=5.0, shape_2d=(2,2))
            noise_map_array[3] = 2.0

            imaging_data = al.ImagingData(
                image=image,
                psf=aa.kernel.zeros(shape_2d=(3,3)),
                noise_map=noise_map_array,
                background_noise_map=aa.array.full(fill_value=1.0, shape_2d=(2,2)),
                exposure_time_map=aa.array.full(fill_value=2.0, shape_2d=(2,2)),
                background_sky_map=aa.array.full(fill_value=3.0, shape_2d=(2,2)),
            )

            imaging_data = imaging_data.signal_to_noise_limited_data_from_signal_to_noise_limit(
                signal_to_noise_limit=100.0
            )

            assert (imaging_data.image.in_2d == np.array([[20.0, 20.0], [20.0, 5.0]])).all()

            assert (
                imaging_data.noise_map.in_2d == np.array([[5.0, 5.0], [5.0, 2.0]])
            ).all()

            assert (
                imaging_data.signal_to_noise_map.in_2d
                == np.array([[4.0, 4.0], [4.0, 2.5]])

def test__signal_to_noise_limit_below_max_signal_to_noise__signal_to_noise_map_capped_to_limit(
            self
        ):
            image = aa.array.full(fill_value=20.0, shape_2d=(2,2))
            image[3] = 5.0

            noise_map_array = aa.array.full(fill_value=5.0, shape_2d=(2,2))
            noise_map_array[3] = 2.0

            imaging_data = al.ImagingData(
                image=image,
                psf=aa.kernel.zeros(shape_2d=(3,3)),
                noise_map=noise_map_array,
                background_noise_map=aa.array.full(fill_value=1.0, shape_2d=(2,2)),
                exposure_time_map=aa.array.full(fill_value=2.0, shape_2d=(2,2)),
                background_sky_map=aa.array.full(fill_value=3.0, shape_2d=(2,2)),
            )

            imaging_data_capped = imaging_data.signal_to_noise_limited_data_from_signal_to_noise_limit(
                signal_to_noise_limit=2.0
            )

            assert (
                imaging_data_capped.image.in_2d == np.array([[20.0, 20.0], [20.0, 5.0]])
            ).all()

            assert (
                imaging_data_capped.noise_map.in_2d
                == np.array([[10.0, 10.0], [10.0, 2.5]])
            ).all()

def test__signal_to_noise_limit_above_max_signal_to_noise__signal_to_noise_map_unchanged(
            self
        ):
            image = aa.array.full(fill_value=20.0, shape_2d=(2,2))
            image[3] = 5.0

            noise_map_array = aa.array.full(fill_value=5.0, shape_2d=(2,2))
            noise_map_array[3] = 2.0

            imaging_data = al.ImagingData(
                image=image,
                psf=aa.kernel.zeros(shape_2d=(3,3)),
                noise_map=noise_map_array,
                background_noise_map=aa.array.full(fill_value=1.0, shape_2d=(2,2)),
                exposure_time_map=aa.array.full(fill_value=2.0, shape_2d=(2,2)),
                background_sky_map=aa.array.full(fill_value=3.0, shape_2d=(2,2)),
            )

            imaging_data = imaging_data.signal_to_noise_limited_data_from_signal_to_noise_limit(
                signal_to_noise_limit=100.0
            )

            assert (imaging_data.image.in_2d == np.array([[20.0, 20.0], [20.0, 5.0]])).all()

def test__signal_to_noise_limit_below_max_signal_to_noise__signal_to_noise_map_capped_to_limit(
            self
        ):
            image = aa.array.full(fill_value=20.0, shape_2d=(2,2))
            image[3] = 5.0

            noise_map_array = aa.array.full(fill_value=5.0, shape_2d=(2,2))
            noise_map_array[3] = 2.0

            imaging_data = al.ImagingData(
                image=image,
                psf=aa.kernel.zeros(shape_2d=(3,3)),
                noise_map=noise_map_array,
                background_noise_map=aa.array.full(fill_value=1.0, shape_2d=(2,2)),
                exposure_time_map=aa.array.full(fill_value=2.0, shape_2d=(2,2)),
                background_sky_map=aa.array.full(fill_value=3.0, shape_2d=(2,2)),
            )

            imaging_data_capped = imaging_data.signal_to_noise_limited_data_from_signal_to_noise_limit(
                signal_to_noise_limit=2.0

def test__image_and_exposure_times_and_background_are_all_ranges_of_values__noise_estimates_correct(
            self
        ):
            # Use same pattern as above, noting that we are now also using a variable background signal_to_noise_ratio map.

            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.manual_2d(
                array=[[5.0, 6.0], [7.0, 8.0]]
            )

            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(

def test__via_edges__5x5_image_simple_gaussian_two_edges__ignores_central_pixel(
            self
        ):
            image = aa.array.manual_2d(
                [
                    [1, 1, 1, 1, 1],
                    [1, 1, 1, 1, 1],
                    [1, 1, 100, 1, 1],
                    [1, 1, 1, 1, 1],
                    [1, 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=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__4x4_image_simple_gaussian__ignores_central_pixels(self):
            image = aa.array.manual_2d(
                [[1, 1, 1, 1], [1, 100, 100, 1], [1, 100, 100, 1], [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

def test__setup_with_noise(self, transformer_7x7_7):

        image = aa.array.manual_2d([[2.0, 0.0, 0.0], [0.0, 1.0, 0.0], [3.0, 0.0, 0.0]])

        exposure_time_map = aa.array.full(
            fill_value=20.0, pixel_scales=0.1, shape_2d=image.shape_2d
        )

        uv_plane_data_simulated = al.SimulatedUVPlaneData.from_image_and_exposure_arrays(
            image=image,
            pixel_scales=0.1,
            exposure_time=20.0,
            exposure_time_map=exposure_time_map,
            transformer=transformer_7x7_7,
            noise_sigma=0.1,
            noise_seed=1,
        )

        simulated_visibilities = transformer_7x7_7.visibilities_from_image(

def test__setup_with_psf_blurring_on__blurs_image_and_trims_psf_edge_off(self):
        image = aa.array.manual_2d(
            array=np.array(
                [
                    [0.0, 0.0, 0.0, 0.0, 0.0],
                    [0.0, 0.0, 0.0, 0.0, 0.0],
                    [0.0, 0.0, 1.0, 0.0, 0.0],
                    [0.0, 0.0, 0.0, 0.0, 0.0],
                    [0.0, 0.0, 0.0, 0.0, 0.0],
                ]
            ),
        )

        psf = aa.kernel.manual_2d(
            array=np.array([[0.0, 1.0, 0.0], [1.0, 2.0, 1.0], [0.0, 1.0, 0.0]]),
        )

        exposure_time_map = aa.array.ones(shape_2d=image.mask.shape