def source_plot(ax, hdu, j):
telescope = telescopes.KeckTelescope()
fobos_fratio = 3.
fobos_platescale = telescope.platescale*fobos_fratio/telescope.fratio
fiber_diameter_limits = numpy.array([48., 252.])
ax.set_xlim(fiber_diameter_limits)
ax.set_ylim([0.0, 6.])
ax.minorticks_on()
ax.tick_params(which='major', length=8, direction='in')
ax.tick_params(which='minor', length=4, direction='in')
ax.grid(True, which='major', color='0.9', linestyle=':')
for i in range(4):
ax.plot(hdu['DIAM'].data[i,j,:]*1000, hdu['TOTAL'].data[i,j,:],
color='C{0}'.format(i), linestyle='-', zorder=2)
ax.plot(hdu['DIAM'].data[i,j,:]*1000, hdu['SKYSUB'].data[i,j,:],
color='C{0}'.format(i), linestyle='--', zorder=2)
m = numpy.array([numpy.argmax(hdu['TOTAL'].data[i,j,:]),
numpy.argmax(hdu['SKYSUB'].data[i,j,:])])
maxsnr = numpy.array([hdu['TOTAL'].data[i,j,m[0]],
hdu['SKYSUB'].data[i,j,m[1]]])
ax.scatter(hdu['DIAM'].data[i,j,m]*1000, maxsnr,
marker='.', color='C{0}'.format(i), lw=0, s=100, zorder=3)
ax2 = ax.twiny()
ax2.minorticks_on()
ax2.tick_params(which='major', length=8, direction='in')
ax2.tick_params(which='minor', length=4, direction='in')
ax2.set_xlim(fiber_diameter_limits/fobos_platescale/1000)
return ax, ax2
def test(mag, seeing, on_sky_fiber_diameter, sersic=None, exptime=1.):
telescope = telescopes.KeckTelescope()
detector_noise_ratio = 0. #1/3.
re, sersicn = (None,None) if sersic is None else sersic
size = 5.0 if re is None else max(5.0, 8*re)
sampling = 0.05 if re is None else min(0.05, re/20) # arcsec/pixel
return onsky_aperture_snr(telescope, mag, seeing, on_sky_fiber_diameter,
detector_noise_ratio=detector_noise_ratio, sampling=sampling,
size=size, re=re, sersicn=sersicn, exptime=exptime, quiet=True)
def fiddles_data_table(seeing, exptime, airmass, dmag_sky, data_file):
mag = numpy.linspace(15, 22, 15, dtype=float)[::-1]
apf_snr_pix = numpy.zeros_like(mag)
apf_snr = numpy.zeros_like(mag)
apf_saturated = numpy.zeros_like(mag, dtype=bool)
keck_snr_pix = numpy.zeros_like(mag)
keck_snr = numpy.zeros_like(mag)
keck_saturated = numpy.zeros_like(mag, dtype=bool)
keck = telescopes.KeckTelescope()
apf = telescopes.APFTelescope()
for i in range(mag.size):
apf_snr_pix[i], apf_snr[i], apf_saturated[i], sky_mag \
= fiddles_snr(mag[i], apf, seeing, exptime, airmass=airmass,
dmag_sky=dmag_sky)
keck_snr_pix[i], keck_snr[i], keck_saturated[i], sky_mag \
= fiddles_snr(mag[i], keck, seeing, exptime, airmass=airmass,
dmag_sky=dmag_sky)
numpy.savetxt(
data_file,
numpy.transpose([
mag, apf_snr_pix, apf_snr,
apf_saturated.astype(int), keck_snr_pix, keck_snr,
keck_saturated.astype(int)
]),
fmt=['%5.1f', '%14.7e', '%14.7e', '%2d', '%14.7e', '%14.7e', '%2d'],
header='Sky surface brightness: {0:.2f}'.format(sky_mag) +
'Exposure time: {0:.1f}\n'.format(exptime) +
'Airmass: {0:.1f}\n'.format(airmass) +
'Seeing: {0:.1f}\n'.format(seeing) +
'{0:>3s} {1:^32} {2:^32}\n'.format('', 'APF', 'Keck') +
'{0:>3s} {1:^32} {2:^32}\n'.format('', '-' * 32, '-' * 32) +
'{0:>3s} {1:>14s} {2:>14s} {3:>2s} {4:>14s} {5:>14s} {6:>2s}'.format(
'Mg', 'SNRp', 'SNRt', 'S', 'SNRp', 'SNRt', 'S'))
def fiddles_realization_plot(plot_file=None):
telescope = telescopes.KeckTelescope()
on, off = fiddles_realization(19.,
telescope,
0.8,
60,
airmass=2.,
dmag_sky=-3)
vmin = -100
vmax = 3500
font = {'size': 10}
rc('font', **font)
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
ax = fig.add_axes([0.07, 0.35, 0.3, 0.3])
cax = fig.add_axes([0.07, 0.72, 0.2, 0.01])
ax.minorticks_on()
ax.tick_params(which='major',
length=6,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=3,
direction='in',
top=True,
right=True)
# ax.grid(True, which='major', color='0.9', zorder=0, linestyle='-')
im = ax.imshow(on,
origin='lower',
interpolation='nearest',
vmin=vmin,
vmax=vmax)
pyplot.colorbar(im, cax=cax, orientation='horizontal')
cax.text(1.1,
0.5,
r'Counts (e$-$)',
ha='left',
va='center',
transform=cax.transAxes)
ax.text(-0.15,
0.5,
r'Y (pixels)',
ha='center',
va='center',
rotation='vertical',
transform=ax.transAxes)
ax.text(0.5,
-0.14,
r'X (pixels)',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.5,
1.05,
r'On Source',
ha='center',
va='center',
transform=ax.transAxes)
ax = fig.add_axes([0.37, 0.35, 0.3, 0.3])
ax.minorticks_on()
ax.tick_params(which='major',
length=6,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=3,
direction='in',
top=True,
right=True)
# ax.grid(True, which='major', color='0.9', zorder=0, linestyle='-')
ax.yaxis.set_major_formatter(ticker.NullFormatter())
ax.imshow(off,
origin='lower',
interpolation='nearest',
vmin=vmin,
vmax=vmax)
ax.text(0.5,
-0.14,
r'X (pixels)',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.5,
1.05,
r'Off Source',
ha='center',
va='center',
transform=ax.transAxes)
ax = fig.add_axes([0.67, 0.35, 0.3, 0.3])
ax.minorticks_on()
ax.tick_params(which='major',
length=6,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=3,
direction='in',
top=True,
right=True)
# ax.grid(True, which='major', color='0.9', zorder=0, linestyle='-')
ax.yaxis.set_major_formatter(ticker.NullFormatter())
ax.imshow(on - off,
origin='lower',
interpolation='nearest',
vmin=vmin,
vmax=vmax)
ax.text(0.5,
-0.14,
r'X (pixels)',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(0.5,
1.05,
r'On-Off',
ha='center',
va='center',
transform=ax.transAxes)
if plot_file is None:
pyplot.show()
else:
fig.canvas.print_figure(plot_file, bbox_inches='tight')
fig.clear()
pyplot.close(fig)
def main(args):
t = time.perf_counter()
# Constants:
resolution = 3500. # lambda/dlambda
fiber_diameter = 0.8 # Arcsec
rn = 2. # Detector readnoise (e-)
dark = 0.0 # Detector dark-current (e-/s)
# Temporary numbers that assume a given spectrograph PSF and LSF.
# Assume 3 pixels per spectral and spatial FWHM.
spatial_fwhm = 3.0
spectral_fwhm = 3.0
mags = numpy.arange(args.mag[0], args.mag[1] + args.mag[2], args.mag[2])
times = numpy.arange(args.time[0], args.time[1] + args.time[2],
args.time[2])
# Get source spectrum in 1e-17 erg/s/cm^2/angstrom. Currently, the
# source spectrum is assumed to be
# - normalized by the total integral of the source flux
# - independent of position within the source
wave = get_wavelength_vector(args.wavelengths[0], args.wavelengths[1],
args.wavelengths[2])
spec = get_spectrum(wave, mags[0], resolution=resolution)
# Get the source distribution. If the source is uniform, onsky is None.
onsky = None
# Get the sky spectrum
sky_spectrum = spectrum.MaunakeaSkySpectrum()
# Overplot the source and sky spectrum
# ax = spec.plot()
# ax = sky_spectrum.plot(ax=ax, show=True)
# Get the atmospheric throughput
atmospheric_throughput = efficiency.AtmosphericThroughput(
airmass=args.airmass)
# Set the telescope. Defines the aperture area and throughput
# (nominally 3 aluminum reflections for Keck)
telescope = telescopes.KeckTelescope()
# Define the observing aperture; fiber diameter is in arcseconds,
# center is 0,0 to put the fiber on the target center. "resolution"
# sets the resolution of the fiber rendering; it has nothing to do
# with spatial or spectral resolution of the instrument
fiber = aperture.FiberAperture(0, 0, fiber_diameter, resolution=100)
# Get the spectrograph throughput (circa June 2018; TODO: needs to
# be updated). Includes fibers + foreoptics + FRD + spectrograph +
# detector QE (not sure about ADC). Because this is the total
# throughput, define a generic efficiency object.
thru_db = numpy.genfromtxt(
os.path.join(os.environ['ENYO_DIR'], 'data/efficiency',
'fobos_throughput.db'))
spectrograph_throughput = efficiency.Efficiency(thru_db[:, 1],
wave=thru_db[:, 0])
# System efficiency combines the spectrograph and the telescope
system_throughput = efficiency.SystemThroughput(
wave=spec.wave,
spectrograph=spectrograph_throughput,
telescope=telescope.throughput)
# Instantiate the detector; really just a container for the rn and
# dark current for now. QE is included in fobos_throughput.db file,
# so I set it to 1 here.
det = detector.Detector(rn=rn, dark=dark, qe=1.0)
# Extraction: makes simple assumptions about the detector PSF for
# each fiber spectrum and mimics a "perfect" extraction, including
# an assumption of no cross-talk between fibers. Ignore the
# "spectral extraction".
extraction = extract.Extraction(det,
spatial_fwhm=spatial_fwhm,
spatial_width=1.5 * spatial_fwhm,
spectral_fwhm=spectral_fwhm,
spectral_width=spectral_fwhm)
snr_label = 'S/N per {0}'.format('R element' if args.snr_units ==
'resolution' else args.snr_units)
# SNR
g = efficiency.FilterResponse()
r = efficiency.FilterResponse(band='r')
snr_g = numpy.empty((mags.size, times.size), dtype=float)
snr_r = numpy.empty((mags.size, times.size), dtype=float)
for i in range(mags.size):
spec.rescale_magnitude(mags[i], band=g)
for j in range(times.size):
print('{0}/{1} ; {2}/{3}'.format(i + 1, mags.size, j + 1,
times.size),
end='\r')
# Perform the observation
obs = Observation(telescope,
sky_spectrum,
fiber,
times[j],
det,
system_throughput=system_throughput,
atmospheric_throughput=atmospheric_throughput,
airmass=args.airmass,
onsky_source_distribution=onsky,
source_spectrum=spec,
extraction=extraction,
snr_units=args.snr_units)
# Construct the S/N spectrum
snr_spec = obs.snr(sky_sub=True)
snr_g[i,
j] = numpy.sum(g(snr_spec.wave) * snr_spec.flux) / numpy.sum(
g(snr_spec.wave))
snr_r[i,
j] = numpy.sum(r(snr_spec.wave) * snr_spec.flux) / numpy.sum(
r(snr_spec.wave))
print('{0}/{1} ; {2}/{3}'.format(i + 1, mags.size, j + 1, times.size))
extent = [
args.time[0] - args.time[2] / 2, args.time[1] + args.time[2] / 2,
args.mag[0] - args.mag[2] / 2, args.mag[1] + args.mag[2] / 2
]
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
ax = fig.add_axes([0.15, 0.2, 0.7, 0.7])
img = ax.imshow(snr_g,
origin='lower',
interpolation='nearest',
extent=extent,
aspect='auto',
norm=colors.LogNorm(vmin=snr_g.min(), vmax=snr_g.max()))
cax = fig.add_axes([0.86, 0.2, 0.02, 0.7])
pyplot.colorbar(img, cax=cax)
cax.text(4,
0.5,
snr_label,
ha='center',
va='center',
transform=cax.transAxes,
rotation='vertical')
ax.text(0.5,
-0.08,
'Exposure Time [s]',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(-0.12,
0.5,
r'Surface Brightness [AB mag/arcsec$^2$]',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax.text(0.5,
1.03,
r'$g$-band S/N',
ha='center',
va='center',
transform=ax.transAxes,
fontsize=12)
pyplot.show()
def main(args):
if args.sky_err < 0 or args.sky_err > 1:
raise ValueError(
'--sky_err option must provide a value between 0 and 1.')
t = time.perf_counter()
# Constants:
resolution = 3500. # lambda/dlambda
fiber_diameter = 0.8 # Arcsec
rn = 2. # Detector readnoise (e-)
dark = 0.0 # Detector dark-current (e-/s)
# Temporary numbers that assume a given spectrograph PSF and LSF.
# Assume 3 pixels per spectral and spatial FWHM.
spatial_fwhm = args.spot_fwhm
spectral_fwhm = args.spot_fwhm
# Get source spectrum in 1e-17 erg/s/cm^2/angstrom. Currently, the
# source spectrum is assumed to be
# - normalized by the total integral of the source flux
# - independent of position within the source
dw = 1 / spectral_fwhm / resolution / numpy.log(10)
wavelengths = [3100, 10000, dw]
wave = get_wavelength_vector(wavelengths[0], wavelengths[1],
wavelengths[2])
emline_db = None if args.emline is None else read_emission_line_database(
args.emline)
spec = get_spectrum(wave,
args.mag,
mag_band=args.mag_band,
mag_system=args.mag_system,
spec_file=args.spec_file,
spec_wave=args.spec_wave,
spec_wave_units=args.spec_wave_units,
spec_flux=args.spec_flux,
spec_flux_units=args.spec_flux_units,
spec_res_indx=args.spec_res_indx,
spec_res_value=args.spec_res_value,
spec_table=args.spec_table,
emline_db=emline_db,
redshift=args.redshift,
resolution=resolution)
# Get the source distribution. If the source is uniform, onsky is None.
onsky = get_source_distribution(args.fwhm, args.uniform, args.sersic)
# Show the rendered source
# if onsky is not None:
# pyplot.imshow(onsky.data, origin='lower', interpolation='nearest')
# pyplot.show()
# Get the sky spectrum
sky_spectrum = get_sky_spectrum(args.sky_mag,
mag_band=args.sky_mag_band,
mag_system=args.sky_mag_system)
# Overplot the source and sky spectrum
# ax = spec.plot()
# ax = sky_spectrum.plot(ax=ax, show=True)
# Get the atmospheric throughput
atmospheric_throughput = efficiency.AtmosphericThroughput(
airmass=args.airmass)
# Set the telescope. Defines the aperture area and throughput
# (nominally 3 aluminum reflections for Keck)
telescope = telescopes.KeckTelescope()
# Define the observing aperture; fiber diameter is in arcseconds,
# center is 0,0 to put the fiber on the target center. "resolution"
# sets the resolution of the fiber rendering; it has nothing to do
# with spatial or spectral resolution of the instrument
fiber = aperture.FiberAperture(0, 0, fiber_diameter, resolution=100)
# Get the spectrograph throughput (circa June 2018; TODO: needs to
# be updated). Includes fibers + foreoptics + FRD + spectrograph +
# detector QE (not sure about ADC). Because this is the total
# throughput, define a generic efficiency object.
thru_db = numpy.genfromtxt(
os.path.join(os.environ['ENYO_DIR'], 'data/efficiency',
'fobos_throughput.db'))
spectrograph_throughput = efficiency.Efficiency(thru_db[:, 1],
wave=thru_db[:, 0])
# System efficiency combines the spectrograph and the telescope
system_throughput = efficiency.SystemThroughput(
wave=spec.wave,
spectrograph=spectrograph_throughput,
telescope=telescope.throughput)
# Instantiate the detector; really just a container for the rn and
# dark current for now. QE is included in fobos_throughput.db file,
# so I set it to 1 here.
det = detector.Detector(rn=rn, dark=dark, qe=1.0)
# Extraction: makes simple assumptions about the detector PSF for
# each fiber spectrum and mimics a "perfect" extraction, including
# an assumption of no cross-talk between fibers. Ignore the
# "spectral extraction".
extraction = extract.Extraction(det,
spatial_fwhm=spatial_fwhm,
spatial_width=1.5 * spatial_fwhm,
spectral_fwhm=spectral_fwhm,
spectral_width=spectral_fwhm)
# Perform the observation
obs = Observation(telescope,
sky_spectrum,
fiber,
args.time,
det,
system_throughput=system_throughput,
atmospheric_throughput=atmospheric_throughput,
airmass=args.airmass,
onsky_source_distribution=onsky,
source_spectrum=spec,
extraction=extraction,
snr_units=args.snr_units)
# Construct the S/N spectrum
snr = obs.snr(sky_sub=True, sky_err=args.sky_err)
if args.ipython:
embed()
snr_label = 'S/N per {0}'.format('R element' if args.snr_units ==
'resolution' else args.snr_units)
if args.plot:
w, h = pyplot.figaspect(1)
fig = pyplot.figure(figsize=(1.5 * w, 1.5 * h))
ax = fig.add_axes([0.1, 0.5, 0.8, 0.4])
ax.set_xlim([wave[0], wave[-1]])
ax.minorticks_on()
ax.tick_params(which='major',
length=8,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=4,
direction='in',
top=True,
right=True)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax.xaxis.set_major_formatter(ticker.NullFormatter())
ax.set_yscale('log')
ax = spec.plot(ax=ax, label='Object')
ax = sky_spectrum.plot(ax=ax, label='Sky')
ax.legend()
ax.text(-0.1,
0.5,
r'Flux [10$^{-17}$ erg/s/cm$^2$/${\rm \AA}$]',
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
ax = fig.add_axes([0.1, 0.1, 0.8, 0.4])
ax.set_xlim([wave[0], wave[-1]])
ax.minorticks_on()
ax.tick_params(which='major',
length=8,
direction='in',
top=True,
right=True)
ax.tick_params(which='minor',
length=4,
direction='in',
top=True,
right=True)
ax.grid(True, which='major', color='0.8', zorder=0, linestyle='-')
ax = snr.plot(ax=ax)
ax.text(0.5,
-0.1,
r'Wavelength [${\rm \AA}$]',
ha='center',
va='center',
transform=ax.transAxes)
ax.text(-0.1,
0.5,
snr_label,
ha='center',
va='center',
transform=ax.transAxes,
rotation='vertical')
pyplot.show()
# Report
g = efficiency.FilterResponse(band='g')
r = efficiency.FilterResponse(band='r')
iband = efficiency.FilterResponse(band='i')
print('-' * 70)
print('{0:^70}'.format('FOBOS S/N Calculation (v0.2)'))
print('-' * 70)
print('Compute time: {0} seconds'.format(time.perf_counter() - t))
print('Object g- and r-band AB magnitude: {0:.1f} {1:.1f}'.format(
spec.magnitude(band=g), spec.magnitude(band=r)))
print('Sky g- and r-band AB surface brightness: {0:.1f} {1:.1f}'.format(
sky_spectrum.magnitude(band=g), sky_spectrum.magnitude(band=r)))
print('Exposure time: {0:.1f} (s)'.format(args.time))
if not args.uniform:
print('Aperture Loss: {0:.1f}%'.format(
(1 - obs.aperture_factor) * 100))
print('Extraction Loss: {0:.1f}%'.format(
(1 - obs.extraction.spatial_efficiency) * 100))
print('Median {0}: {1:.1f}'.format(snr_label, numpy.median(snr.flux)))
print('g-band weighted mean {0} {1:.1f}'.format(
snr_label,
numpy.sum(g(snr.wave) * snr.flux) / numpy.sum(g(snr.wave))))
print('r-band weighted mean {0} {1:.1f}'.format(
snr_label,
numpy.sum(r(snr.wave) * snr.flux) / numpy.sum(r(snr.wave))))
print('i-band weighted mean {0} {1:.1f}'.format(
snr_label,
numpy.sum(iband(snr.wave) * snr.flux) / numpy.sum(iband(snr.wave))))
def build_data(mag, ofile):
# dmag_sky = None # Dark time
#
# dmag_sky = -3.0 # Bright time...
seeing = [0.4, 0.6, 0.8, 1.0]
sersic = [None, (0.1,1), (0.1,4), (0.2,1), (0.2,4), (0.4,1), (0.4,4), (0.8,1), (0.8,4)]
# seeing = [0.4, 0.6]
# sersic = [None]
telescope = telescopes.KeckTelescope()
dmag_sky = None
fobos_fratio = 3.
detector_noise_ratio = 0. #1/3.
fiber_diameter = numpy.linspace(0.05, 0.25, 41)
fobos_platescale = telescope.platescale*fobos_fratio/telescope.fratio
outshape = (len(seeing), len(sersic), len(fiber_diameter))
atmseeing = numpy.zeros(outshape, dtype=float)
profile = numpy.zeros(outshape, dtype=int)
fibdiam = numpy.zeros(outshape, dtype=float)
total_snr = numpy.zeros(outshape, dtype=float)
skysub_snr = numpy.zeros(outshape, dtype=float)
for i in range(len(seeing)):
atmseeing[i,:,:] = seeing[i]
for j in range(len(sersic)):
print('Seeing {0}/{1}'.format(i+1, len(seeing)))
print('Profile {0}/{1}'.format(j+1, len(sersic)))
profile[i,j,:] = j
if sersic[j] is None:
re = None
sersicn = None
else:
re, sersicn = sersic[j]
size = 5.0 if re is None else max(5.0, 8*re)
sampling = 0.05 if re is None else min(0.05, re/20) # arcsec/pixel
for k in range(len(fiber_diameter)):
fibdiam[i,j,k] = fiber_diameter[k]
print('Calculating {0}/{1}'.format(k+1, fiber_diameter.size), end='\r')
total_snr[i,j,k], skysub_snr[i,j,k] = \
aperture_snr(telescope, mag, seeing[i], fiber_diameter[k], dmag_sky=dmag_sky,
fobos_fratio=fobos_fratio,
detector_noise_ratio=detector_noise_ratio, sampling=sampling,
size=size, re=re, sersicn=sersicn, quiet=True)
print('Calculating {0}/{0}'.format(fiber_diameter.size))
fits.HDUList([ fits.PrimaryHDU(),
fits.ImageHDU(data=atmseeing, name='SEEING'),
fits.ImageHDU(data=profile, name='PROF'),
fits.ImageHDU(data=fibdiam, name='DIAM'),
fits.ImageHDU(data=total_snr, name='TOTAL'),
fits.ImageHDU(data=skysub_snr, name='SKYSUB')
]).writeto(ofile, overwrite=True)
return
i = numpy.argmax(total_snr)
print('Max total: {0:.3f} {1:.3f}'.format(fiber_diameter[i], total_snr[i]))
i = numpy.argmax(skysub_snr)
print('Max skysub: {0:.3f} {1:.3f}'.format(fiber_diameter[i], skysub_snr[i]))
print('Calculating {0}/{0}'.format(fiber_diameter.size))
pyplot.plot(fiber_diameter*1000, total_snr)
pyplot.plot(fiber_diameter*1000, skysub_snr)
pyplot.xlabel('Fiber Diameter (micron)')
pyplot.ylabel(r'S/N [(photons/s) / $\sqrt{{\rm photons}^2/{\rm s}^2}$]')
pyplot.show()