def test_instrument_throughput_without_atmospheric_bg(self):
cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
metis = sim.OpticalTrain(cmd)
metis["skycalc_atmosphere"].include = True
src = empty_sky()
metis.observe(src)
img = metis.image_planes[0].data
plt.imshow(img)
plt.show()
def test_background_level_is_around_roys_level(self, filter_name, expected_phs):
src = empty_sky()
cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
cmd["!OBS.filter_name"] = filter_name
metis = sim.OpticalTrain(cmd)
metis['detector_linearity'].include = False
metis.observe(src)
img = metis.image_planes[0].data
assert np.median(img) == approx(expected_phs, rel=0.1)
def test_flux_scales_with_pixel_scale(self, filter_name, bg_level):
yaml_text = YAML_TEXT % (WAVE_MIN, WAVE_MAX, PIXEL_SCALE, PIXEL_SCALE,
filter_name)
yamls = [yml for yml in yaml.full_load_all(yaml_text)]
cmd = sim.UserCommands(yamls=yamls)
opt = sim.OpticalTrain(cmd)
opt.cmds["!TEL.area"] = 1 * u.m**2
src = empty_sky()
opt.observe(src)
img = opt.image_planes[0].data
# Ks band photon flux is 1026 ph/s/m2/arcsec2
assert np.median(img) == pytest.approx(bg_level, rel=0.01)
def test_print_background_contributions(self):
cmd = sim.UserCommands(use_instrument="METIS", set_modes=["img_lm"])
metis = sim.OpticalTrain(cmd)
metis["psf"].include = False
metis.observe(empty_sky())
if PLOTS:
plt.figure(figsize=(10, 5))
fov = metis.fov_manager.fovs[0]
for field in fov.fields[1:]:
spec = fov.spectra[field.header["SPEC_REF"]]
wave = spec.waveset
plt.plot(wave, spec(wave), label=field.header["BG_SURF"])
plt.legend()
plt.show()
def test_flux_scales_with_pixel_scale(self, pixel_scale):
yaml_text = YAML_TEXT % (wave_min, wave_max, pixel_scale, pixel_scale,
filter_name)
yamls = [yml for yml in yaml.full_load_all(yaml_text)]
cmd = sim.UserCommands(yamls=yamls)
opt = sim.OpticalTrain(cmd)
opt.cmds["!TEL.area"] = 1 * u.m**2
src = empty_sky()
opt.observe(src)
img = opt.image_planes[0].data
# Ks band photon flux is 1014 ph/s/m2/arcsec2
assert np.median(img) == pytest.approx(1014 * pixel_scale**2, rel=0.01)
if PLOTS:
plt.imshow(img)
plt.show()
def test_integrated_spec_bg_equals_img_bg(self):
src = empty_sky()
toggle_effects = [
# "skycalc_atmosphere",
# "telescope_reflection",
# "common_fore_optics",
# "metis_img_lm_mirror_list",
# "quantum_efficiency",
# "psf",
]
cmds_img = sim.UserCommands(use_instrument="METIS",
set_modes=["img_lm"])
cmds_img["!SIM.spectral.wave_min"] = 3.5
cmds_img["!SIM.spectral.wave_max"] = 4.0
metis_img = sim.OpticalTrain(cmds_img)
for eff in toggle_effects:
metis_img[eff].include = False
metis_img.observe(src)
img = metis_img.image_planes[0].data
cmds_lss = sim.UserCommands(use_instrument="METIS",
set_modes=["lss_l"])
cmds_lss["!SIM.spectral.wave_min"] = 3.5
cmds_lss["!SIM.spectral.wave_max"] = 4.0
metis_lss = sim.OpticalTrain(cmds_lss)
for eff in toggle_effects:
metis_lss[eff].include = False
metis_lss.observe(src)
lss = metis_lss.image_planes[0].data
img_med = np.median(img)
lss_med = np.median(np.sum(lss, axis=0))
# 7x because we need to sum up the overlapping slice images
# and the slit is 7 pixels wide
assert 7 * img_med == approx(lss_med, rel=0.05)
def skycalc_plus_eso_filter_gives_similar_photons_flux_to_ohio():
"""
ohio: http://www.astronomy.ohio-state.edu/~martini/usefuldata.html
skycalc: https://www.eso.org/observing/etc/doc/skycalc/helpskycalc.html#mags
- Use the skycalc background spectrum
- Multiply it by a filter curve
- Sum up the photons
- Divide by FWHM
- Rescale to zero mag using skycalc backgorund magnitude
"""
paranal_bg_mags = {
"U": 20.76,
"B": 21.14,
"V": 20.67,
"R": 20.32,
"I": 19.48,
"Z": 18.67,
"Y": 17.58,
"J": 16.87,
"H": 14.43,
"K": 15.23,
"L": 6.00,
"M": 1.14,
"N": -2.29,
"Q": -7.27
}
ohio_zero_ph = {
"U": 756,
"B": 1393,
"V": 996,
"R": 702,
"I": 452,
"J": 193,
"H": 93,
"K": 43.6
} # ph s-1 cm-2 angstrom-1
ohio_fwhm = {
"U": 0.06,
"B": 0.09,
"V": 0.085,
"R": 0.15,
"I": 0.15,
"J": 0.26,
"H": 0.29,
"K": 0.41
} # micron
for filt_name in ohio_zero_ph:
# load the sky background curve and the filter
print(filt_name)
skycalc_file = "eso_skycalc_r_6000_wave_0.3_15nm_airmass_1_pwv_2.5.dat"
filter_file = f"eso_etc_filter_{filt_name}.dat"
atmo_ter = AtmosphericTERCurve(filename=skycalc_file)
filt_ter = FilterCurve(filename=filter_file)
print(filt_ter.fwhm, filt_ter.centre)
# get skycalc bg flux
src = empty_sky()
src = atmo_ter.apply_to(src)
src = filt_ter.apply_to(src)
wave = np.arange(0.2, 2.5, 0.0001) * u.um
flux = src.spectra[1](wave)
# report normalised flux ratio in ph s-1 cm-2 angstrom-1 arcsec-2
ph = np.trapz(flux, wave) / filt_ter.fwhm.to(u.AA)
ph = ph.to("ph s-1 cm-2 angstrom-1")
ph_zero_mag = ph / 10**(-0.4 * paranal_bg_mags[filt_name])
ohio_ph = ohio_zero_ph[filt_name]
print("Sky BG:", ph)
print(f"Zero mag normalised flux [{ph_zero_mag.unit}]:", "Ohio:",
ohio_zero_ph[filt_name], "ESO:", ph_zero_mag.value)
print("Ratio:", ph_zero_mag.value / ohio_ph)
# report absolute flux ratio in ph s-1 cm-2 arcsec-2 (per filter bandpass)
ph = np.trapz(flux, wave)
ph = ph.to("ph s-1 cm-2")
ph_zero_mag = ph / 10**(-0.4 * paranal_bg_mags[filt_name])
ohio_ph = ohio_zero_ph[filt_name] * ohio_fwhm[filt_name] * 1e4
print("Sky BG:", ph)
print(f"Zero mag absolute flux [{ph_zero_mag.unit}]:", "Ohio:",
ohio_ph, "ESO:", ph_zero_mag.value)
print("Ratio:", ph_zero_mag.value / ohio_ph)