def test_center_of_image():
img = np.zeros((5, 5))
assert center_of_image(img) == (2, 2)
img = np.zeros((5, 7))
xcenter, ycenter = center_of_image(img)
assert xcenter == 3
assert ycenter == 2
def test_centroid_sym():
y, x = centered_grid_quadratic(51)
img = Gaussian2D()(x, y)
assert np.all(np.isclose(centroid(img), center_of_image(img)))
y, x = centered_grid((51, 71))
img = Gaussian2D()(x, y)
assert np.all(np.isclose(centroid(img), center_of_image(img)))
def test_centroid_asym(xy):
y_grid, x_grid = centered_grid_quadratic(101)
xy = np.array(xy)
xy_expected = xy + center_of_image(x_grid)
img = Gaussian2D(x_mean=xy[0], y_mean=xy[1])(x_grid, y_grid)
assert np.all(np.isclose(centroid(img), xy_expected))
def test_anisocado_model_centered(anisocado_model):
data = anisocado_model.render()
actual = centroid_quadratic(data)
expected = center_of_image(data)
assert np.all(np.abs(np.array(actual) - np.array(expected)) < 1e-8)
def test_anisocado_psf_even(psf_effect_even):
data = psf_effect_even.data
actual = centroid_quadratic(data, fit_boxsize=6)
expected = center_of_image(data)
# TODO ideally this should be a lot tighter, but honestly even arrays are probably bad anyway
# TODO does this work wih <0.001 on different computer?
assert np.all(np.abs(np.array(actual) - np.array(expected)) < 0.05)
def test_single_star_image_scopesim_shift(single_star):
"""This test should not fail after scopesim fixes the coordinate transformation"""
img, table = single_star
xcenter, ycenter = center_of_image(img)
xref, yref = table[INPUT_TABLE_NAMES[X]][0], table[INPUT_TABLE_NAMES[Y]][0]
assert np.abs(xcenter-xref) < 0.01
assert np.abs(ycenter-yref) < 0.01
def test_single_star_image(single_star):
img, table = single_star
xcenter, ycenter = center_of_image(img)
xcentroid, ycentroid = centroid_quadratic(img, fit_boxsize=5)
xref, yref = table[INPUT_TABLE_NAMES[X]][0], table[INPUT_TABLE_NAMES[Y]][0]
assert np.abs(xref-xcentroid) < 0.005
assert np.abs(yref-ycentroid) < 0.005
grid_result_baseline['flux_fit'])
grid_result_epsf_plot['flux_fit'] /= offset
plot_dev_vs_mag(grid_result_baseline,
create_figure=False,
label='known PSF',
alpha=0.4)
plot_dev_vs_mag(grid_result_epsf_plot,
create_figure=False,
label='with epsf',
alpha=0.4)
plt.tight_layout()
save_plot(out_dir, 'synthetic_grid_magdevepsf')
# %%
center_of_image(epsf.data), centroid_quadratic(epsf.data, fit_boxsize=5)
# %%
plt.figure()
plt.imshow(pre_epsf.data, norm=LogNorm())
plt.colorbar()
save_plot(out_dir, 'failed_epsf_example')
# %%
img_grid, tab_grid = read_or_generate_image(
'scopesim_grid_16_perturb2_mag18_24_subpixel')
guess_table = prepare_table(tab_grid)
photometry = grid_photometry_epsf()
photometry.psf_model = epsf
def make_delta_model():
from photutils import FittableImageModel
data = np.zeros((401, 401))
ycenter, xcenter = center_of_image(data)
data[int(ycenter),int(xcenter)] = 1
return FittableImageModel(data, oversampling=2, degree=5)
def test_lowpass(xsize, ysize):
img = np.ones((ysize, xsize))
transform = lowpass(5)
assert np.all(np.isclose(centroid(transform(img)), center_of_image(img)))
def test_anisocado_psf_odd(psf_effect_odd):
data = psf_effect_odd.data
actual = centroid_quadratic(data)
expected = center_of_image(data)
assert np.all(np.abs(np.array(actual) - np.array(expected)) < 1e-8)
