def test__constructor_inputs(self):
psf_0 = im.PSF(np.ones((7, 7)), 1)
ccd_0 = im.CCDData(np.ones((51, 51)),
pixel_scale=3.,
psf=psf_0,
noise_map=np.ones((51, 51)))
mask_0 = msk.Mask.unmasked_for_shape_and_pixel_scale(shape=(51, 51),
pixel_scale=1.0,
invert=True)
mask_0[26, 26] = False
psf_1 = im.PSF(np.ones((7, 7)), 1)
ccd_1 = im.CCDData(np.ones((51, 51)),
pixel_scale=3.,
psf=psf_1,
noise_map=np.ones((51, 51)))
mask_1 = msk.Mask.unmasked_for_shape_and_pixel_scale(shape=(51, 51),
pixel_scale=1.0,
invert=True)
mask_1[26, 26] = False
lens_data_stack = lis.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=8,
image_psf_shape=(5, 5),
mapping_matrix_psf_shape=(3, 3),
positions=[np.array([[1.0, 1.0]])])
assert lens_data_stack.sub_grid_size == 8
assert lens_data_stack.convolvers_image[0].psf_shape == (5, 5)
assert lens_data_stack.convolvers_mapping_matrix[0].psf_shape == (3, 3)
assert lens_data_stack.convolvers_image[1].psf_shape == (5, 5)
assert lens_data_stack.convolvers_mapping_matrix[1].psf_shape == (3, 3)
assert (lens_data_stack.positions[0] == np.array([[1.0, 1.0]])).all()
def make_li_blur_stack():
image_0 = np.array([[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]])
psf_0 = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])),
pixel_scale=1.0,
renormalize=False)
ccd_0 = ccd.CCDData(image_0,
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((4, 4)))
mask_0 = np.array([[True, True, True, True], [True, False, False, True],
[True, False, False, True], [True, True, True, True]])
mask_0 = msk.Mask(array=mask_0, pixel_scale=1.0)
image_1 = np.array([[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]])
psf_1 = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])),
pixel_scale=1.0,
renormalize=False)
ccd_1 = ccd.CCDData(image_1,
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((4, 4)))
mask_1 = np.array([[True, True, True, True], [True, False, False, True],
[True, False, False, True], [True, True, True, True]])
mask_1 = msk.Mask(array=mask_1, pixel_scale=1.0)
return lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
def make_image_1():
image = scaled_array.ScaledSquarePixelArray(array=10.0 * np.ones((4, 4)),
pixel_scale=6.0)
psf = im.PSF(array=11.0 * np.ones((3, 3)),
pixel_scale=6.0,
renormalize=False)
noise_map = im.NoiseMap(array=12.0 * np.ones((4, 4)), pixel_scale=6.0)
background_noise_map = im.NoiseMap(array=13.0 * np.ones((4, 4)),
pixel_scale=6.0)
poisson_noise_map = im.PoissonNoiseMap(array=14.0 * np.ones((4, 4)),
pixel_scale=6.0)
exposure_time_map = im.ExposureTimeMap(array=15.0 * np.ones((4, 4)),
pixel_scale=6.0)
background_sky_map = scaled_array.ScaledSquarePixelArray(array=16.0 *
np.ones((4, 4)),
pixel_scale=6.0)
return im.CCDData(image=image,
pixel_scale=6.0,
psf=psf,
noise_map=noise_map,
background_noise_map=background_noise_map,
poisson_noise_map=poisson_noise_map,
exposure_time_map=exposure_time_map,
background_sky_map=background_sky_map)
def test__1x2_image__tracing_fits_data_with_chi_sq_5_and_chi_sq_4(
self):
psf_0 = ccd.PSF(array=(np.array([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]])),
pixel_scale=1.0)
ccd_0 = ccd.CCDData(5.0 * np.ones((3, 4)),
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((3, 4)))
ccd_0.image[1, 2] = 4.0
mask_0 = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
psf_1 = ccd.PSF(array=(np.array([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]])),
pixel_scale=1.0)
ccd_1 = ccd.CCDData(5.0 * np.ones((3, 4)),
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((3, 4)))
mask_1 = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
li_stack = lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
# Setup as a ray trace instance, using a light profile for the lens
g0 = g.Galaxy(light_profile=MockLightProfile(value=1.0, size=2))
tracer = ray_tracing_stack.TracerImagePlaneStack(
lens_galaxies=[g0],
image_plane_grid_stacks=li_stack.grid_stacks)
fit = lens_fit_stack.LensProfileFitStack(lens_data_stack=li_stack,
tracer=tracer)
assert fit.chi_squared == 25.0 + 32.0
assert fit.reduced_chi_squared == (25.0 + 32.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 + 2.0 * np.log(2 * np.pi * 1.0)) +
(32.0 + 2.0 * np.log(2 * np.pi * 1.0)))
def make_lens_image():
ccd_data = ccd.CCDData(np.array(np.zeros(shape)),
pixel_scale=1.0,
psf=ccd.PSF(np.ones((3, 3)), pixel_scale=1.0),
noise_map=ccd.NoiseMap(np.ones(shape),
pixel_scale=1.0))
mask = msk.Mask.circular(shape=shape, pixel_scale=1, radius_arcsec=3.0)
return li.LensData(ccd_data=ccd_data, mask=mask)
def make_image():
image = scaled_array.ScaledSquarePixelArray(array=np.ones((3, 3)),
pixel_scale=1.0)
noise_map = im.NoiseMap(array=2.0 * np.ones((3, 3)), pixel_scale=1.0)
psf = im.PSF(array=3.0 * np.ones((1, 1)), pixel_scale=1.0)
return im.CCDData(image=image,
pixel_scale=1.0,
noise_map=noise_map,
psf=psf)
def make_ccd():
image = scaled_array.ScaledSquarePixelArray(array=np.ones((3, 3)),
pixel_scale=1.0)
noise_map = im.NoiseMap(array=2.0 * np.ones((3, 3)), pixel_scale=1.0)
psf = im.PSF(array=3.0 * np.ones((1, 1)), pixel_scale=1.0)
return im.CCDData(image=image,
pixel_scale=1.0,
noise_map=noise_map,
psf=psf,
exposure_time_map=2.0 * np.ones((3, 3)),
background_sky_map=3.0 * np.ones((3, 3)))
def make_ccd():
image = scaled_array.ScaledSquarePixelArray(array=np.ones((4, 4)), pixel_scale=3.0)
psf = ccd.PSF(array=np.ones((3, 3)), pixel_scale=3.0, renormalize=False)
noise_map = ccd.NoiseMap(array=2.0 * np.ones((4, 4)), pixel_scale=3.0)
background_noise_map = ccd.NoiseMap(array=3.0 * np.ones((4, 4)), pixel_scale=3.0)
poisson_noise_map = ccd.PoissonNoiseMap(array=4.0 * np.ones((4, 4)), pixel_scale=3.0)
exposure_time_map = ccd.ExposureTimeMap(array=5.0 * np.ones((4, 4)), pixel_scale=3.0)
background_sky_map = scaled_array.ScaledSquarePixelArray(array=6.0 * np.ones((4, 4)), pixel_scale=3.0)
return ccd.CCDData(image=image, pixel_scale=3.0, psf=psf, noise_map=noise_map,
background_noise_map=background_noise_map, poisson_noise_map=poisson_noise_map,
exposure_time_map=exposure_time_map, background_sky_map=background_sky_map)
def test__image__tracing_fits_data_perfectly__no_psf_blurring__lh_is_noise_normalization(
self):
psf_0 = ccd.PSF(array=(np.array([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]])),
pixel_scale=1.0)
ccd_0 = ccd.CCDData(np.ones((3, 3)),
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((3, 3)))
mask_0 = msk.Mask(array=np.array([[True, True, True],
[True, False, True],
[True, True, True]]),
pixel_scale=1.0)
psf_1 = ccd.PSF(array=(np.array([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]])),
pixel_scale=1.0)
ccd_1 = ccd.CCDData(np.ones((3, 3)),
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((3, 3)))
mask_1 = msk.Mask(array=np.array([[True, True, True],
[True, False, True],
[True, True, True]]),
pixel_scale=1.0)
li_stack = lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
g0 = g.Galaxy(light_profile=MockLightProfile(value=1.0))
tracer = ray_tracing_stack.TracerImagePlaneStack(
lens_galaxies=[g0],
image_plane_grid_stacks=li_stack.grid_stacks)
fit = lens_fit_stack.LensProfileFitStack(lens_data_stack=li_stack,
tracer=tracer)
assert fit.likelihood == 2.0 * -0.5 * np.log(2 * np.pi * 1.0)
def make_li_manual_stack():
image_0 = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 2.0, 3.0, 0.0],
[0.0, 4.0, 5.0, 6.0, 0.0], [0.0, 7.0, 8.0, 9.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]])
psf_0 = ccd.PSF(array=(np.array([[1.0, 5.0, 9.0], [2.0, 5.0, 1.0],
[3.0, 4.0, 0.0]])),
pixel_scale=1.0)
ccd_0 = ccd.CCDData(image_0,
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((5, 5)))
mask_0 = msk.Mask(array=np.array([[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, True, True, True, True]]),
pixel_scale=1.0)
image_1 = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 2.0, 3.0, 0.0],
[0.0, 4.0, 6.0, 6.0, 0.0], [0.0, 7.0, 8.0, 9.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]])
psf_1 = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [2.0, 1.0, 1.0],
[3.0, 1.0, 0.0]])),
pixel_scale=1.0)
ccd_1 = ccd.CCDData(image_1,
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((5, 5)))
mask_1 = msk.Mask(array=np.array([[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True]]),
pixel_scale=1.0)
return lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
def make_ccd_data():
pixel_scale = 1.0
image = scaled_array.ScaledSquarePixelArray(array=np.array(
np.zeros(shape)),
pixel_scale=pixel_scale)
psf = ccd.PSF(array=np.ones((3, 3)), pixel_scale=pixel_scale)
noise_map = ccd.NoiseMap(np.ones(shape), pixel_scale=1.0)
return ccd.CCDData(image=image,
pixel_scale=pixel_scale,
psf=psf,
noise_map=noise_map)
def test__constructor_inputs(self):
psf = ccd.PSF(np.ones((7, 7)), 1)
image = ccd.CCDData(np.ones((51, 51)), pixel_scale=3., psf=psf, noise_map=np.ones((51, 51)))
mask = msk.Mask.unmasked_for_shape_and_pixel_scale(shape=(51, 51), pixel_scale=1.0, invert=True)
mask[26, 26] = False
lens_data = ld.LensData(image, mask, sub_grid_size=8, image_psf_shape=(5, 5),
mapping_matrix_psf_shape=(3, 3), positions=[np.array([[1.0, 1.0]])])
assert lens_data.sub_grid_size == 8
assert lens_data.convolver_image.psf_shape == (5, 5)
assert lens_data.convolver_mapping_matrix.psf_shape == (3, 3)
assert (lens_data.positions[0] == np.array([[1.0, 1.0]])).all()
assert lens_data.image_psf_shape == (5,5)
assert lens_data.mapping_matrix_psf_shape == (3,3)
def make_lens_data_blur():
psf = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])),
pixel_scale=1.0,
renormalize=False)
im = ccd.CCDData(image=5.0 * np.ones((6, 6)),
pixel_scale=1.0,
psf=psf,
noise_map=np.ones((6, 6)),
exposure_time_map=3.0 * np.ones((6, 6)),
background_sky_map=4.0 * np.ones((6, 6)))
ma = np.array([[True, True, True, True, True, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, True, True, True, True, True]])
ma = mask.Mask(array=ma, pixel_scale=1.0)
return ld.LensData(ccd_data=im, mask=ma, sub_grid_size=2)
