def buildMirror(mirrorDir, addLosses=True):
"""
Build a mirror throughput curve.
Assumes there are *Losses.dat subdirectory with loss files
and a m*_Ideal.dat file with the mirror throughput.
Returns a bandpass object.
If addLosses is True, the *_Ideal.dat file is multiplied by the *_Losses/*.dat files.
"""
# Read the mirror reflectance curve.
mirrorfile = glob(os.path.join(mirrorDir, 'm*Ideal.dat'))
if len(mirrorfile) != 1:
raise ValueError('Expected a single mirror file in directory %s, found: ' %mirrorDir, mirrorfile)
mirrorfile = mirrorfile[0]
mirror = Bandpass()
mirror.readThroughput(mirrorfile)
if addLosses:
loss = _readLosses(mirrorDir)
wavelen, sb = mirror.multiplyThroughputs(loss.wavelen, loss.sb)
mirror.setBandpass(wavelen, sb)
# Verify that no values go significantly below zero.
belowzero = np.where(mirror.sb < 0)
# If there are QE values significantly < 0, raise an exception.
if mirror.sb[belowzero] < belowZeroThreshhold:
raise ValueError('Found values in mirror response significantly below zero')
# If they are just small errors in interpolation, set to zero.
mirror.sb[belowzero] = 0
return mirror
def buildVendorDetector(vendorDir, addLosses=True):
"""
Builds a detector response from the files in vendorDir, by reading the *_QE.dat
and *_Losses subdirectory for a single version of the detector.
Returns a Bandpass object.
If addLosses is True, the QE curve is multiplied by the losses in the *Losses.dat files.
If addLosses is False, the QE curve does not have any losses included.
"""
# Read the QE file.
qefile = glob(os.path.join(vendorDir, '*_QE.dat'))
if len(qefile) != 1:
raise ValueError('Expected a single QE file in this directory, found: ', qefile)
qefile = qefile[0]
qe = Bandpass()
qe.readThroughput(qefile)
if addLosses:
loss = _readLosses(vendorDir)
wavelength, sb = qe.multiplyThroughputs(loss.wavelen, loss.sb)
qe.setBandpass(wavelength, sb)
# Verify that no values go significantly below zero.
belowzero = np.where(qe.sb < 0)
# If there are QE values significantly < 0, raise an exception.
if qe.sb[belowzero] < belowZeroThreshhold:
raise ValueError('Found values in QE response significantly below zero.')
# If they are just small errors in interpolation, set to zero.
qe.sb[belowzero] = 0
return qe
def buildVendorDetector(vendorDir, addLosses=True):
"""
Builds a detector response from the files in vendorDir, by reading the *_QE.dat
and *_Losses subdirectory for a single version of the detector.
Returns a Bandpass object.
If addLosses is True, the QE curve is multiplied by the losses in the *Losses.dat files.
If addLosses is False, the QE curve does not have any losses included.
"""
# Read the QE file.
qefile = glob(os.path.join(vendorDir, '*_QE.dat'))
if len(qefile) != 1:
raise ValueError('Expected a single QE file in this directory, found: ', qefile)
qefile = qefile[0]
qe = Bandpass()
qe.readThroughput(qefile)
if addLosses:
loss = _readLosses(vendorDir)
wavelength, sb = qe.multiplyThroughputs(loss.wavelen, loss.sb)
qe.setBandpass(wavelength, sb)
# Verify that no values go significantly below zero.
belowzero = np.where(qe.sb < 0)
# If there are QE values significantly < 0, raise an exception.
if qe.sb[belowzero] < belowZeroThreshhold:
raise ValueError('Found values in QE response significantly below zero.')
# If they are just small errors in interpolation, set to zero.
qe.sb[belowzero] = 0
return qe
def buildMirror(mirrorDir, addLosses=True):
"""
Build a mirror throughput curve.
Assumes there are *Losses.dat subdirectory with loss files
and a m*_Ideal.dat file with the mirror throughput.
Returns a bandpass object.
If addLosses is True, the *_Ideal.dat file is multiplied by the *_Losses/*.dat files.
"""
# Read the mirror reflectance curve.
mirrorfile = glob(os.path.join(mirrorDir, 'm*Ideal.dat'))
if len(mirrorfile) != 1:
raise ValueError('Expected a single mirror file in directory %s, found: ' %mirrorDir, mirrorfile)
mirrorfile = mirrorfile[0]
mirror = Bandpass()
mirror.readThroughput(mirrorfile)
if addLosses:
loss = _readLosses(mirrorDir)
wavelen, sb = mirror.multiplyThroughputs(loss.wavelen, loss.sb)
mirror.setBandpass(wavelen, sb)
# Verify that no values go significantly below zero.
belowzero = np.where(mirror.sb < 0)
# If there are QE values significantly < 0, raise an exception.
if mirror.sb[belowzero] < belowZeroThreshhold:
raise ValueError('Found values in mirror response significantly below zero')
# If they are just small errors in interpolation, set to zero.
mirror.sb[belowzero] = 0
return mirror
def buildLens(lensDir, addLosses=True):
"""
Build the lens throughput curve from the files in lensDir.
Returns a bandpass object.
The coatings for the lens are in *_Coatings, the loss files are in *_Losses.
The borosilicate glass throughput is in l*_Glass.dat; this file is smoothed using the
savitzsky_golay function.
The glass response is multiplied by the coatings and (if addLosses is True),
also the loss curves.
"""
lens = Bandpass()
# Read the glass base file.
glassfile = glob(os.path.join(lensDir, 'l*_Glass.dat'))
if len(glassfile) != 1:
raise ValueError('Expected a single glass file in this directory, found: ', glassfile)
glassfile = glassfile[0]
glass = Bandpass()
glass.readThroughput(glassfile)
# Smooth the glass response.
smoothSb = savitzky_golay(glass.sb, 31, 3)
lens = Bandpass()
lens.setBandpass(glass.wavelen, smoothSb)
# Read the broad band antireflective (BBAR) coatings files.
bbars = _readCoatings(lensDir)
# Multiply the bbars by the glass.
wavelen, sb = lens.multiplyThroughputs(bbars.wavelen, bbars.sb)
lens.setBandpass(wavelen, sb)
# Add losses.
if addLosses:
loss = _readLosses(lensDir)
wavelen, sb = lens.multiplyThroughputs(loss.wavelen, loss.sb)
lens.setBandpass(wavelen, sb)
# Verify that no values go significantly below zero.
belowzero = np.where(lens.sb < 0)
# If there are QE values significantly < 0, raise an exception.
if lens.sb[belowzero] < belowZeroThreshhold:
raise ValueError('Found values in lens throughput significantly below zero.')
# If they are just small errors in interpolation, set to zero.
lens.sb[belowzero] = 0
return lens
def buildLens(lensDir, addLosses=True):
"""
Build the lens throughput curve from the files in lensDir.
Returns a bandpass object.
The coatings for the lens are in *_Coatings, the loss files are in *_Losses.
The borosilicate glass throughput is in l*_Glass.dat; this file is smoothed using the
savitzsky_golay function.
The glass response is multiplied by the coatings and (if addLosses is True),
also the loss curves.
"""
lens = Bandpass()
# Read the glass base file.
glassfile = glob(os.path.join(lensDir, 'l*_Glass.dat'))
if len(glassfile) != 1:
raise ValueError('Expected a single glass file in this directory, found: ', glassfile)
glassfile = glassfile[0]
glass = Bandpass()
glass.readThroughput(glassfile)
# Smooth the glass response.
smoothSb = savitzky_golay(glass.sb, 31, 3)
lens = Bandpass()
lens.setBandpass(glass.wavelen, smoothSb)
# Read the broad band antireflective (BBAR) coatings files.
bbars = _readCoatings(lensDir)
# Multiply the bbars by the glass.
wavelen, sb = lens.multiplyThroughputs(bbars.wavelen, bbars.sb)
lens.setBandpass(wavelen, sb)
# Add losses.
if addLosses:
loss = _readLosses(lensDir)
wavelen, sb = lens.multiplyThroughputs(loss.wavelen, loss.sb)
lens.setBandpass(wavelen, sb)
# Verify that no values go significantly below zero.
belowzero = np.where(lens.sb < 0)
# If there are QE values significantly < 0, raise an exception.
if lens.sb[belowzero] < belowZeroThreshhold:
raise ValueError('Found values in lens throughput significantly below zero.')
# If they are just small errors in interpolation, set to zero.
lens.sb[belowzero] = 0
return lens
'filter_%s.dat' % bp_name),
dtype=throughput_dtype)
for np_component in np_component_list:
interped_throughput = np.interp(np_filter['wav_nm'],
np_component['wav_nm'],
np_component['throughput'])
np_filter['throughput'] *= interped_throughput
if _LSST_STACK_INSTALLED:
filter_bp = Bandpass()
filter_bp.readThroughput(
os.path.join(bp_dir, 'filter_%s.dat' % bp_name))
wav, sb = optics_bp.multiplyThroughputs(filter_bp.wavelen,
filter_bp.sb)
bp = Bandpass(wavelen=wav, sb=sb)
# integrate the SED over the total system throughput
flambda = np.interp(np_filter['wav_nm'], np_sed['wav_nm'],
np_sed['flambda'])
phys_params = PhysicalParameters()
phot = flambda * np_filter['wav_nm'] / (phys_params.planck *
phys_params.lightspeed * 1.0e9)
integral = 0.5 * (
(phot[1:] * np_filter['throughput'][1:] +
phot[:-1] * np_filter['throughput'][:-1]) *
(np_filter['wav_nm'][1:] - np_filter['wav_nm'][:-1])).sum()
effarea = np.pi * (6.423 * 100.0 / 2.0)**2
def lsst_flare_fluxes_from_u(ju_flux):
"""
Convert from Johnson U band flux to flux in the LSST bands
by assuming the flare is a 9000K black body (see Section 4
of Hawley et al 2003, ApJ 597, 535)
Parameters
----------
flux in Johnson U band (either a float or a numpy array)
Returns
-------
floats/numpy arrays of fluxes in all 6 LSST bands
"""
if not hasattr(lsst_flare_fluxes_from_u, 'johnson_u_raw_flux'):
t_start = time.time()
throughputs_dir = getPackageDir('throughputs')
johnson_dir = os.path.join(throughputs_dir, 'johnson')
johnson_u_hw = Bandpass()
johnson_u_hw.readThroughput(os.path.join(johnson_dir, 'johnson_U.dat'))
atm = Bandpass()
atm.readThroughput(
os.path.join(throughputs_dir, 'baseline', 'atmos_std.dat'))
wv, sb = johnson_u_hw.multiplyThroughputs(atm.wavelen, atm.sb)
johnson_u = Bandpass(wavelen=wv, sb=sb)
boltzmann_k = 1.3807e-16 # erg/K
planck_h = 6.6261e-27 # erg*s
_c = 2.9979e10 # cm/s
hc_over_k = 1.4387e7 # nm*K
temp = 9000.0 # black body temperature in Kelvin
bb_wavelen = np.arange(200.0, 1500.0, 0.1) # in nanometers
exp_arg = hc_over_k / (temp * bb_wavelen)
exp_term = 1.0 / (np.exp(exp_arg) - 1.0)
ln_exp_term = np.log(exp_term)
# the -7.0 np.log(10) will convert wavelen into centimeters
log_bb_flambda = -5.0 * (np.log(bb_wavelen) -
7.0 * np.log(10.0)) + ln_exp_term
log_bb_flambda += np.log(2.0) + np.log(planck_h) + 2.0 * np.log(_c)
# assume these stars all have radii half that of the Sun;
# see Boyajian et al. 2012 (ApJ 757, 112)
r_sun = 6.957e10 # cm
log_bb_flambda += np.log(
4.0 * np.pi * np.pi) + 2.0 * np.log(0.5 * r_sun)
# thee -7.0*np.log(10.0) makes sure we get ergs/s/cm^2/nm
bb_flambda = np.exp(log_bb_flambda - 7.0 * np.log(10))
bb_sed = Sed(wavelen=bb_wavelen, flambda=bb_flambda)
# because we have a flux in ergs/s but need a flux in
# the normalized units of Sed.calcFlux (see eqn 2.1
# of the LSST Science Book), we will calculate the
# ergs/s/cm^2 of a raw, unnormalized blackbody spectrum
# in the Johnson U band, find the factor that converts
# that raw flux into our specified flux, and then
# multiply that factor *by the fluxes calculated for the
# blackbody in the LSST filters using Sed.calcFlux()*.
# This should give us the correct normalized flux for
# the flares in the LSST filters.
lsst_flare_fluxes_from_u.johnson_u_raw_flux = bb_sed.calcErgs(
johnson_u)
lsst_bands = BandpassDict.loadTotalBandpassesFromFiles()
norm_raw = None
lsst_flare_fluxes_from_u.lsst_raw_flux_dict = {}
for band_name in ('u', 'g', 'r', 'i', 'z', 'y'):
bp = lsst_bands[band_name]
flux = bb_sed.calcFlux(bp)
lsst_flare_fluxes_from_u.lsst_raw_flux_dict[band_name] = flux
if norm_raw is None:
norm_raw = flux
print('raw flux in %s = %e; %e; %e' %
(band_name, flux, flux / norm_raw, bb_sed.calcErgs(bp)))
print('sed johnson flux %e' %
lsst_flare_fluxes_from_u.johnson_u_raw_flux)
print('that init took %e' % (time.time() - t_start))
factor = ju_flux / lsst_flare_fluxes_from_u.johnson_u_raw_flux
u_flux_out = factor * lsst_flare_fluxes_from_u.lsst_raw_flux_dict['u']
g_flux_out = factor * lsst_flare_fluxes_from_u.lsst_raw_flux_dict['g']
r_flux_out = factor * lsst_flare_fluxes_from_u.lsst_raw_flux_dict['r']
i_flux_out = factor * lsst_flare_fluxes_from_u.lsst_raw_flux_dict['i']
z_flux_out = factor * lsst_flare_fluxes_from_u.lsst_raw_flux_dict['z']
y_flux_out = factor * lsst_flare_fluxes_from_u.lsst_raw_flux_dict['y']
return (u_flux_out, g_flux_out, r_flux_out, i_flux_out, z_flux_out,
y_flux_out)