def test_psf_gen_psf_raises():
rng = np.random.RandomState(seed=42)
with pytest.raises(ValueError):
gen_psf(
rng=rng, psf_config={"type": "blah"}, gal_config=None,
se_image_shape=None, se_wcs=None
)
def test_psf_gen_psf_wldeblend_ps_rng():
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "wldeblend-ps",
"variation_factor": 8,
}
gs_config, psf = gen_psf(
rng=rng, psf_config=psf_config, gal_config={"type": "lsst-riz"},
se_image_shape=100, se_wcs=galsim.PixelScale(0.2),
)
gmod = psf.getPSF(galsim.PositionD(x=10, y=10))
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "wldeblend-ps",
"variation_factor": 8,
}
gs_config1, psf1 = gen_psf(
rng=rng, psf_config=psf_config, gal_config={"type": "lsst-riz"},
se_image_shape=100, se_wcs=galsim.PixelScale(0.2),
)
gmod1 = psf1.getPSF(galsim.PositionD(x=10, y=10))
assert gmod1 == gmod
assert gs_config1 == gs_config
assert gs_config["beta"] == 2.5
assert gs_config["type"] == "Moffat"
assert psf._variation_factor == 8
assert psf._median_seeing == 0.85
def test_psf_gen_psf_gsobject_rng():
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "galsim.Moffat",
"beta": 2.5,
"fwhm": 2.0,
"fwhm_frac_std": 0.1,
"shear": [0.1, 0.1],
"shear_std": 0.01,
}
gs_config, psf = gen_psf(
rng=rng, psf_config=psf_config, gal_config=None,
se_image_shape=None, se_wcs=None
)
rng = np.random.RandomState(seed=42)
g1 = 0.1 + 0.01 * rng.normal()
g2 = 0.1 + 0.01 * rng.normal()
fwhm = 2.0 * (1 + 0.1 * rng.normal())
assert gs_config == {
"type": "Moffat",
"fwhm": fwhm,
"shear": {"type": "G1G2", "g1": g1, "g2": g2},
"beta": 2.5,
}
assert "Moffat" in repr(psf.gs_object), repr(psf.gs_object)
assert "beta=2.5" in repr(psf.gs_object), repr(psf.gs_object)
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "galsim.Moffat",
"beta": 2.5,
"fwhm": 2.0,
"fwhm_frac_std": 0.1,
"shear": [0.1, 0.1],
"shear_std": 0.01,
}
gs_config1, psf1 = gen_psf(
rng=rng, psf_config=psf_config, gal_config=None,
se_image_shape=None, se_wcs=None
)
assert psf1.gs_object == psf.gs_object
assert gs_config1 == gs_config
def test_psf_gen_psf_wldeblend_rng():
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "wldeblend",
"fwhm": 2.0,
"fwhm_frac_std": 0.1,
"shear": [0.1, 0.1],
"shear_std": 0.01,
}
gs_config, psf = gen_psf(
rng=rng, psf_config=psf_config, gal_config={"type": "lsst-riz"},
se_image_shape=None, se_wcs=None
)
rng = np.random.RandomState(seed=42)
g1 = 0.1 + 0.01 * rng.normal()
g2 = 0.1 + 0.01 * rng.normal()
fwhm = 0.85 * (1 + 0.1 * rng.normal())
assert gs_config == {
"type": "Kolmogorov",
"fwhm": fwhm,
"shear": {"type": "G1G2", "g1": g1, "g2": g2},
}
assert "Kolmogorov" in repr(psf.gs_object), repr(psf.gs_object)
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "wldeblend",
"fwhm": 2.0,
"fwhm_frac_std": 0.1,
"shear": [0.1, 0.1],
"shear_std": 0.01,
}
gs_config1, psf1 = gen_psf(
rng=rng, psf_config=psf_config, gal_config={"type": "lsst-riz"},
se_image_shape=None, se_wcs=None
)
assert psf1.gs_object == psf.gs_object
assert gs_config1 == gs_config
def test_psf_gen_psf_gsobject():
rng = np.random.RandomState(seed=42)
psf_config = {
"type": "galsim.Moffat",
"beta": 2.5,
"fwhm": 2.0,
"fwhm_frac_std": 0,
"shear": [0.1, 0.1],
"shear_std": 0,
}
gs_config, psf = gen_psf(
rng=rng, psf_config=psf_config, gal_config=None,
se_image_shape=None, se_wcs=None
)
assert gs_config == {
"type": "Moffat",
"fwhm": 2.0,
"shear": {"type": "G1G2", "g1": 0.1, "g2": 0.1},
"beta": 2.5,
}
assert "Moffat" in repr(psf.gs_object), repr(psf.gs_object)
assert "beta=2.5" in repr(psf.gs_object), repr(psf.gs_object)