def colorMod():
# Load the Filters
JBandFilterWave, JBandFilterThru = np.loadtxt('2MASS-2MASS.J.dat').T # load the J filter
HBandFilterWave, HBandFilterThru = np.loadtxt('2MASS-2MASS.H.dat').T # load the H filter
KBandFilterWave, KBandFilterThru = np.loadtxt('2MASS-2MASS.Ks.dat').T # load the K filter
# Interpolate the Filters onto the Stellar Model Wavelength Grid
tempModel = S.Icat('phoenix', 5500, 0.0, 4.5) # we are going to use this to setup the filters in the SYNPHOT framework
JBandFilterSpline = CubicSpline(JBandFilterWave, JBandFilterThru)
HBandFilterSpline = CubicSpline(HBandFilterWave, HBandFilterThru)
KBandFilterSpline = CubicSpline(KBandFilterWave, KBandFilterThru)
# For some reason pysnphot stores the Vega spectrum with different number of flux points
JWaveVRange= (S.Vega.wave >= JBandFilterWave.min()) * (S.Vega.wave <= JBandFilterWave.max()) # isolate model region in JBand
HWaveVRange= (S.Vega.wave >= HBandFilterWave.min()) * (S.Vega.wave <= HBandFilterWave.max()) # isolate model region in HBand
KWaveVRange= (S.Vega.wave >= KBandFilterWave.min()) * (S.Vega.wave <= KBandFilterWave.max()) # isolate model region in KBand
VegaJFlux = np.trapz(S.Vega.flux[JWaveVRange] * JBandFilterSpline(S.Vega.wave[JWaveVRange]))
VegaHFlux = np.trapz(S.Vega.flux[HWaveVRange] * HBandFilterSpline(S.Vega.wave[HWaveVRange]))
VegaKFlux = np.trapz(S.Vega.flux[KWaveVRange] * KBandFilterSpline(S.Vega.wave[KWaveVRange]))
# Now to compare the JHK filters to the PHOENIX models
JWaveRange= (tempModel.wave >= JBandFilterWave.min()) * (tempModel.wave <= JBandFilterWave.max()) # isolate model region in JBand
HWaveRange= (tempModel.wave >= HBandFilterWave.min()) * (tempModel.wave <= HBandFilterWave.max()) # isolate model region in HBand
KWaveRange= (tempModel.wave >= KBandFilterWave.min()) * (tempModel.wave <= KBandFilterWave.max()) # isolate model region in KBand
# Loop over the 30 models
nModels = 30
Jmags = np.zeros(nModels)
Hmags = np.zeros(nModels)
Kmags = np.zeros(nModels)
teff_list = [x for x in range(2800,5500+100,100)] + [5800] + [6000]
# check if I did that write
assert(len(teff_list) == nModels)
for kt, teff in enumerate(teff_list):
modelTeff = S.Icat('phoenix', teff, 0.0, 4.5) # load the model
Jmags[kt] = -2.5*np.log10(np.trapz(modelTeff.flux[JWaveRange] * JBandFilterSpline(tempModel.wave[JWaveRange]) / VegaJFlux)) # integrate the flux in the J bandpass
Hmags[kt] = -2.5*np.log10(np.trapz(modelTeff.flux[HWaveRange] * HBandFilterSpline(tempModel.wave[HWaveRange]) / VegaHFlux)) # integrate the flux in the H bandpass
Kmags[kt] = -2.5*np.log10(np.trapz(modelTeff.flux[KWaveRange] * KBandFilterSpline(tempModel.wave[KWaveRange]) / VegaKFlux)) # integrate the flux in the K bandpass
jhmod = Jmags - Hmags
hkmod = Hmags - Kmags
return jhmod, hkmod, teff_list
def test_things_in_cache(self):
fail = False
self.tra = {}
Cache.reset_catalog_cache()
sp = S.Icat('k93models', 6440, 0, 4.3)
self.assertTrue(len(Cache.CATALOG_CACHE) == 1)
k = next(key for key in Cache.CATALOG_CACHE.keys())
from pysynphot.locations import irafconvert
import os.path
f = irafconvert("$PYSYN_CDBS/grid/k93models/catalog.fits")
f = os.path.normpath(f)
f = os.path.normcase(f)
fixed_k = os.path.normpath(k)
fixed_k = os.path.normcase(fixed_k)
if fixed_k != f:
self.tra['cache_found'] = k
self.tra['cache_found_fixed'] = fixed_k
self.tra['cache_expect'] = f
fail = True
if not isinstance(Cache.CATALOG_CACHE[k], list):
self.tra['cache_type_mismatch'] = str(type(Cache.CATALOG_CACHE[k]))
fail = True
if fail:
raise AssertionError()
def get_kurucz_atmosphere(metallicity=0, temperature=20000, gravity=4):
"""
metallicity in [Fe/H] (def = +0.0):
+1.0, +0.5, +0.3, +0.2, +0.1, +0.0, -0.1, -0.2, -0.3, -0.5, -1.0,
-1.5, -2.0, -2.5, -3.0, -3.5, -4.0, -4.5, -5.0.
temperatures (def = 20,000 Kelvin):
Temperature Range Grid Step
K K
3000 - 10000 250
10000 - 13000 500
13000 - 35000 1000
35000 - 50000 2500
log gravity (def = 0.0) in the range of 0.0 - 5.0 in 0.5 increments
"""
sp = pysynphot.Icat('k93models', temperature, metallicity, gravity)
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
print 'Could not find Kurucz 1993 atmosphere model for'
print ' temperature = %d' % temperature
print ' metallicity = %.1f' % metallicity
print ' log gravity = %.1f' % gravity
return sp
def uncached_synthetic_radiation_fn(Teff,
fe_h,
log_g,
mag_v=None,
model='k93models',
lam_min=0,
lam_max=np.inf,
return_sp=False):
sp = None
orig_log_g = log_g
if isinstance(model, tuple):
# give in meters, W/m3
sp = S.spectrum.ArraySourceSpectrum(
np.array(model[0]) * 1e10,
np.array(model[1]) * 1e-4 * 1e-10 / 1e-7, 'angstrom', 'flam')
else:
first_try = True
if Teff < 3500:
print(
'could not init spectral model with given t_eff=%s, using t_eff=3500K instead'
% Teff)
Teff = 3500
for i in range(15):
try:
sp = S.Icat(model, Teff, fe_h,
log_g) # 'ck04models' or 'k93models'
break
except:
first_try = False
log_g = log_g + (0.2 if Teff > 6000 else -0.2)
assert sp is not None, 'could not init spectral model with given params: t_eff=%s, log_g=%s, fe_h=%s' % (
Teff, orig_log_g, fe_h)
if not first_try:
print(
'could not init spectral model with given params (t_eff=%s, log_g=%s, fe_h=%s), changed log_g to %s'
% (Teff, orig_log_g, fe_h, log_g))
if mag_v is not None:
sp = sp.renorm(mag_v, 'vegamag', S.ObsBandpass('johnson,v'))
if return_sp:
return sp
# for performance reasons (caching)
from scipy.interpolate import interp1d
I = np.logical_and(sp.wave >= lam_min * 1e10,
sp.wave <= lam_max * 1e10)
sample_fn = interp1d(sp.wave[I],
sp.flux[I],
kind='linear',
assume_sorted=True)
def phi(lam):
r = sample_fn(
lam * 1e10) # wavelength in Å, result in "flam" (erg/s/cm2/Å)
return r * 1e-7 / 1e-4 / 1e-10 # result in W/m3
return phi
def get_castelli_atmosphere(metallicity=0, temperature=20000, gravity=4):
"""
metallicity in [Fe/H] (def = +0.0):
0.0, -0.5, -1.0, -1.5, -2.0, -2.5.
temperatures (def = 20,000 Kelvin):
Temperature Range Grid Step
K K
2600 - 4000 100
4000 - 10000 200
log gravity (def = 4.0) in the range of 3.5 - 6.0 in 0.5 increments
"""
sp = pysynphot.Icat('ck04models', temperature, metallicity, gravity)
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
print 'Could not find Castelli and Kurucz 2004 atmosphere model for'
print ' temperature = %d' % temperature
print ' metallicity = %.1f' % metallicity
print ' log gravity = %.1f' % gravity
return sp
def get_magnitudes(Teff, FeH=0.0, logg=4.5, Vmag=10, magScale='vegamag'):
# Load the Filters
bp_j = S.ObsBandpass('j')
bp_h = S.ObsBandpass('h')
bp_k = S.ObsBandpass('k')
bp_v = S.ObsBandpass('johnson,v')
# Stellar spectrum normalized to V=10 mags (default Phoenix models)
sp = S.Icat(
'phoenix', Teff, FeH,
logg) #pynrc.stellar_spectrum(stellarType, Vmag, 'vegamag', bp_v)
sp_norm = sp.renorm(Vmag, magScale, bp_v)
sp_norm.name = sp.name
sp = sp_norm
# Observe in J, H, and K
obs_j = S.Observation(sp, bp_j, binset=sp.wave)
obs_h = S.Observation(sp, bp_h, binset=sp.wave)
obs_k = S.Observation(sp, bp_k, binset=sp.wave)
# Magnitudes in each filter
mag_j = obs_j.effstim(magScale)
mag_h = obs_h.effstim(magScale)
mag_k = obs_k.effstim(magScale)
return mag_j, mag_h, mag_k
def test_reset_catalog_cache(self):
sp = S.Icat('k93models', 6440, 0, 4.3)
self.assertTrue(len(Cache.CATALOG_CACHE) != 0)
Cache.reset_catalog_cache()
self.assertTrue(len(Cache.CATALOG_CACHE) == 0)
def testf(fdat, teff, logg, feh):
sp = S.Icat('k93models', float(teff), float(feh), float(logg))\
.renorm(0, 'vegamag', S.ObsBandpass('johnson,v'))
sp_real = S.ArraySpectrum(wave=fdat[0][0], flux=fdat[0][1], fluxunits='flam')\
.renorm(0, 'vegamag', S.ObsBandpass('johnson,v'))
plt.plot(sp_real.wave, sp_real.flux)
plt.plot(sp.wave, sp.flux)
plt.xlim(3000, 10000)
plt.show()
def get_model_spectra(teff, mh, logg, model='ck04models', L=1, ebv=0):
'''
For example: Temperature Teff=10000K, [M/H] = +0.1 and gravity log=3.0.
model is the stellar model:
Castelli-Kurucz - ck04models
Kurucz 1993 Atlas - k93models
Parameters
----------
teff : float
The effective temperature of the star (in K).
mh : string
The metallicity as [M/H].
logg : float
The log g of the stellar model.
model : string
Gride stellar models that can be used to retrieve the models for stellar spectra.
See: https://pysynphot.readthedocs.io/en/latest/appendixa.html
for a description.
Castelli-Kurucz - ck04models
Kurucz 1993 Atlas - k93models
Phoenix (F. Allard et al.) - phoenix
L : float
The bolometric luminosity of the star (in Lsun). It will be used to normalize the spectrum.
ebv : float, default: 0
The reddening to be applied to the model spectrum.
Returns
-------
s: pysynphot ArraySpectrum
The spectrum corresponding to the stellar model, scaled to the luminosity.
Examples
--------
Temperature Teff=10000K, [M/H] = +0.1 and gravity log=3.0 for a star of 1 Lsun
and no extinction.
s = get_model_spectra(10000, 0.1, 3.0, model='ck04models', L=1, ebv=0)
'''
sp = ps.Icat(model, teff, mh, logg)
if ebv != 0:
sp = sp * ps.Extinction(ebv, 'lmcavg')
F_model = sp.trapezoidIntegration(sp.wave, sp.flux)*(u.erg/u.s/u.cm**2) #In erg / s / cm**2
Lmodel = (4 * np.pi * ((10*u.pc).to(u.cm))**2 * F_model ).to(u.Lsun)
norm = L/Lmodel
newflux = sp.flux * norm
s = ps.ArraySpectrum(sp.wave, newflux, fluxunits="flam")
return s
def get_ck04models(temp):
all_sed = []
for log_g in Set_Log_g:
for logz in Set_Log_Z:
sed = S.Icat('ck04models', temp, logz, log_g)
sed.convert('flam') # to be sure every spectrum is in flam unit
if (max(sed.flux) >
0): # remove empty fluxes because of bad parameters
all_sed.append(sed)
return all_sed
def get_fl(system='kic1255', renormBand='Kmag'):
""" Get the fluxes"""
if system == 'kic1255':
## From Rappapor et al. 2012
sp = S.Icat('phoenix', 4400, 0.0, 4.63)
Jmag = 14.021
Kmag = 13.319
elif system == 'k2-22':
## From Sanchis-Ojeda 2016
sp = S.Icat('phoenix', 3830, 0.0, 4.65)
Jmag = 12.726
Kmag = 11.924
else:
print 'No Valid System specified'
## Get the 2MASS bandpasses
tmassdir = os.path.join(os.environ['TEL_DATA'], '2mass')
bpK = S.FileBandpass(os.path.join(tmassdir, '2mass_k_ang.txt'))
bpJ = S.FileBandpass(os.path.join(tmassdir, '2mass_j_ang.txt'))
# Get the IRAC bandpasses
iracdir = os.path.join(os.environ['TEL_DATA'], 'irac')
irac45 = S.FileBandpass(os.path.join(iracdir, 'irac_45_ang.txt'))
irac36 = S.FileBandpass(os.path.join(iracdir, 'irac_36_ang.txt'))
iracnames = ['IRAC 3.6um', 'IRAC 4.5um']
# Renormalize spectrum
if renormBand == 'Kmag':
renormBandpass = bpK
renormMag = Kmag
else:
renormBandpass = bpJ
renormMag = Jmag
sp_norm = sp.renorm(renormMag, 'vegamag', renormBandpass)
for iracInd, iracBP in enumerate([irac36, irac45]):
obs = S.Observation(sp_norm, iracBP)
## Get the flux at the wavelegnth
stim = obs.effstim('uJy')
print "Flux for " + iracnames[iracInd] + " is " + str(stim) + " uJy"
def get_castelli_kurucz_spectrum(teff, metallicity, logg):
'''
A function that returns the pysynphot spectrum given the parameters
based on the Castelli-Kurucz Atlas
Retuns the pysynphot spectrum object with wavelength units of microns
and flux units of photons/s/cm^2/Angstrom
'''
sp = ps.Icat('ck04models', teff, metallicity, logg)
return sp
def getSpectrum(self,Teff=5800,z=0.,logg=4.43,vega=False,): #default: Sun at 10pc Teff=5800, z=0, logg=4.4, johnson_v_abs=4.68
self.johnson_v = S.ObsBandpass('johnson,v')
self.T=Teff
self.z=z
self.g=logg
if vega:
self.star= S.FileSpectrum(S.locations.VegaFile)
print ' Using default Pysynphot Vega spectrum'
else:
print self.getFiltername(), self.getSpectrumInfo()
self.star= S.Icat('ck04models',self.T,self.z,self.g) #FLAM surface flux units, i.e. ergs cm-2 s-1 A-1
#star = S.BlackBody(6000)
#self.star= self.star.renorm(self.j_v_abs,'vegamag',self.johnson_v) #renorm spectrum to (abs_mag, units system, band)
return self.star
def Vega():
# Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0
temperature = 9550
metallicity = -0.5
gravity = 3.95
vega = pysynphot.Icat('k93models', temperature, metallicity, gravity)
vega = spectrum.trimSpectrum(vega, 8000, 25000)
# This is (R/d)**2 as reported by Girardi et al. 2002, page 198, col 1.
# and is used to convert to flux observed at Earth.
vega *= 6.247e-17
return vega
def get_nextgen_atmosphere(metallicity=0, temperature=5000, gravity=4):
"""
metallicity = [M/H] (def = 0)
temperature = Kelvin (def = 5000)
gravity = log gravity (def = 4.0)
"""
sp = pysynphot.Icat('nextgen', temperature, metallicity, gravity)
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
print('Could not find NextGen atmosphere model for')
print(' temperature = %d' % temperature)
print(' metallicity = %.1f' % metallicity)
print(' log gravity = %.1f' % gravity)
return sp
def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4):
"""
metallicity = [M/H] (def = 0)
temperature = Kelvin (def = 5000)
gravity = log gravity (def = 4.0)
"""
sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity)
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
print 'Could not find AMESdusty Allard+ 2000 atmosphere model for'
print ' temperature = %d' % temperature
print ' metallicity = %.1f' % metallicity
print ' log gravity = %.1f' % gravity
return sp
def get_phoenix_atmosphere(metallicity=0, temperature=5000, gravity=4):
"""
metallicity = [M/H] (def = 0)
temperature = Kelvin (def = 5000)
gravity = log gravity (def = 4.0)
"""
sp = pysynphot.Icat('phoenix', temperature, metallicity, gravity)
# Do some error checking
idx = np.where(sp.flux != 0)[0]
if len(idx) == 0:
print(
'Could not find PHOENIX BT-Settl (Allard+ 2011 atmosphere model for'
)
print(' temperature = %d' % temperature)
print(' metallicity = %.1f' % metallicity)
print(' log gravity = %.1f' % gravity)
return sp
def get_all_ck04models():
all_sed = [] # common SED container
#for key, temp in TypeStar_to_Temperature.iteritems():
for typestar in TypeStar:
temp = TypeStar_to_Temperature[typestar]
all_sub_sed = []
for log_g in Set_Log_g:
for logz in Set_Log_Z:
sed = S.Icat('ck04models', temp, logz, log_g)
sed.convert(
'flam') # to be sure every spectrum is in flam unit
if (max(sed.flux) >
0): # remove empty fluxes because of bad parameters
all_sub_sed.append(sed)
all_sed.append(all_sub_sed)
return all_sed
def get_grid_phoenixmodels():
index = 1
for temp in Temperature_range:
for logg in Set_Log_G:
for logz in Set_Log_Z:
data[index, index_temp] = temp
data[index, index_logg] = logg
data[index, index_logz] = logz
sed = S.Icat('phoenix', temp, logz, logg)
sed.convert(
'flam') # to be sure every spectrum is in flam unit
if (max(sed.flux) >
0): # remove empty fluxes because of bad parameters
data[index, index_val] = 1
func = interp1d(sed.wave, sed.flux, kind='cubic')
flux = func(WL)
data[index, index_spec:] = flux
index += 1
def get_spectrum(self):
"""
Use pysynphot.Icat() to calculate spectrum and convert to microns/mJy. If self.webapp=True,
use key to look up a specified set of parameters. Otherwise, get them from the configured
attributes.
"""
if self.webapp:
if self.key not in self.spectra:
msg = "Provided SED key, %s, not supported." % self.key
raise EngineInputError(value=msg)
pars = self.spectra[self.key]
m = pars['metallicity']
t = pars['teff']
g = pars['log_g']
else:
m = self.metallicity
t = self.teff
g = self.log_g
sp = psyn.Icat("phoenix", t, m, g)
sp.convert("microns")
sp.convert("mjy")
return sp.wave, sp.flux
def make_cluster_rates(self,masses,instrument,filter,bandpass=None,refs=None):
coords = np.load(os.path.join(self.gridpath, 'input.npy'))
m, t, g, i = self.get_star_info()
temps = np.interp(masses,m,t)
gravs = np.interp(masses,m,g)
mags = np.interp(masses,m,i)
metals = np.full_like(mags, self.metallicity)
if os.path.exists(os.path.join(self.gridpath, 'result_{}_{}.npy'.format(instrument.lower(), filter.lower()))):
values = np.load(os.path.join(self.gridpath, 'result_{}_{}.npy'.format(instrument.lower(), filter.lower())))
interpolation_function = RegularGridInterpolator(tuple([x for x in coords]), values)
try:
countrates = interpolation_function(np.array((metals, gravs, temps, mags)).T)
except ValueError as v:
self.log('error', 'Exception caught when interpolating: {}'.format(v))
min_mag = coords[-1][0]
max_mag = coords[-1][-1]
interpolation_function = RegularGridInterpolator(tuple([x for x in coords]), values, bounds_error=False, fill_value=0.)
mags_min = np.full_like(mags, min_mag)
mags_max = np.full_like(mags, max_mag)
countrates = interpolation_function(np.array((metals, gravs, temps, mags)).T)
countrates_min = interpolation_function(np.array((metals, gravs, temps, mags_min)).T)
countrates_min = countrates_min * np.power(10, -(mags-min_mag)/2.512)
countrates_max = interpolation_function(np.array((metals, gravs, temps, mags_max)).T)
countrates_max = countrates_max * np.power(10, -(mags-max_mag)/2.512)
countrates[np.where(mags < mags_min)] = countrates_min[np.where(mags < mags_min)]
countrates[np.where(mags > mags_max)] = countrates_max[np.where(mags > mags_max)]
else:
self.log('warning', 'Could not find result file "result_{}_{}.npy"'.format(instrument.lower(), filter.lower()))
import pysynphot as ps
countrates = np.array(())
ps.setref(**refs)
johnson_i = ps.ObsBandpass('johnson,i')
for te, log_g, z, j_i in zip(temps, gravs, metals, mags):
spectrum = ps.Icat('phoenix', te, z, log_g)
spectrum = spectrum.renorm(j_i, 'vegamag', johnson_i)
obs = ps.Observation(spectrum, bandpass, binset=spectrum.wave)
countrates = np.append(countrates, obs.countrate())
return countrates
def readPhoenixRealtimeTable(self, table, bp, cached=-1):
"""
Converts a set of Phoenix sources specified as (id, ra, dec, T, Z, log(g), apparent) into individual stars, observes them,
and then produces an output catalogue
"""
ps.setref(**self.REFS)
ids = table['id']
ras = table['ra']
decs = table['dec']
temps = table['teff']
gravs = table['log_g']
metallicities = table['metallicity']
apparents = table['apparent']
norm_bp = '{}'.format(apparents.unit)
if norm_bp == '':
norm_bp = 'johnson,i'
rates = np.zeros_like(ras)
for index in range(len(ids)):
t, g, Z, a = temps[index], gravs[index], metallicities[
index], apparents[index]
sp = ps.Icat('phoenix', t, Z, g)
sp = self.normalize(sp, a, norm_bp)
obs = ps.Observation(sp, bp, binset=sp.wave)
rates[index] = obs.countrate()
t = Table()
t['ra'] = Column(data=ras)
t['dec'] = Column(data=decs)
t['flux'] = Column(data=rates)
t['type'] = Column(data=np.full_like(ras, "point", dtype="S6"))
t['n'] = Column(data=np.full_like(ras, "N/A", dtype="S3"))
t['re'] = Column(data=np.full_like(ras, "N/A", dtype="S3"))
t['phi'] = Column(data=np.full_like(ras, "N/A", dtype="S3"))
t['ratio'] = Column(data=np.full_like(ras, "N/A", dtype="S3"))
t['id'] = Column(data=ids)
t['notes'] = Column(data=np.full_like(ras, "None", dtype="S6"))
return t, cached
def create_stellar_obs(aperture, central_obscuration, nwavels, wl_range,
star_dict):
"""
Creates an observation object from pysynphot phoenix models
Parameters:
Aperture (cm): Aperture of the telescope
central_obscuration (cm): diameter of central obscuration of telescope
nwaels: number of wavelengths to sample
wl_range (Angstroms): [first wavelength, last wavelength]
star_dict: dictionary of the following structure
{"mag": float, # vega mag
"Teff": int,
"Z": float,
"log g": float}
Returns:
A pysynphot observation object describing the star being observed through the given telescope architecture
"""
# Set Telescope values
r = (aperture - central_obscuration) / 2
collecting_area = (np.pi * r**2)
S.refs.setref(area=collecting_area) # Takes units of cm^2
wavels = np.linspace(wl_range[0], wl_range[1], nwavels)
throughput = np.ones(nwavels)
bandpass = S.ArrayBandpass(wavels, throughput)
# Create star object
star_obj = S.Icat('phoenix', star_dict["Teff"], star_dict["Z"],
star_dict["log g"])
spec_filt = star_obj.renorm(star_dict["mag"], 'vegamag', bandpass)
# Create observation object
obs = S.Observation(spec_filt, bandpass, binset=wavels)
return obs
def initialize(self):
self.readFilters()
if (self.useSpecModel):
# if (self.T > 3500*units.K):
#for Kurucz
self.specModelName = 'ck04models'
g = np.clip(self.logg, 3, 5.)
T = np.clip(self.T.to(units.K).value, 3500., 50000.0)
MH = np.clip(self.M_H, -2.5, 0.5)
# else:
# #phoenix for cooler stars, but appear to be giving more discrepant results than just using the Kurucz model
# self.specModelName = 'phoenix'
# g = np.clip(self.logg, 0, 4.5)
# T = np.clip(self.T.to(units.K).value, 2000., 7000.0)
# MH = np.clip(self.M_H, -4, 1)
#print("parameters", self.logg,g, self.T.to(units.K).value, T, self.M_H, MH)
self.specModel = pyS.Icat(self.specModelName, T, MH, g)
self.specModel.nu = (constants.c /
(self.specModel.wave * units.angstrom)).to(
units.Hz).value
self.specModel.fnu = (
(((self.specModel.wave * units.angstrom)**2. / constants.c) *
(self.specModel.flux * units.Ba / units.s)).to(
units.g / units.s**2.)).value
def test_atmospheres(distance=6000, logAge=7.0, AKs=None):
"""
Plot synthetic photometry for some main-sequence stars using
various atmospheres. This is useful for checking for smooth overlap
between atmosphere models valid at different temperature ranges.
distance = distance in pc
"""
# Get solar mettalicity models for a population at a specific age.
print 'Loading Geneva Isochrone logAge=%.2f' % logAge
evol = evolution.get_geneva_isochrone(logAge=logAge)
mass = evol.mass
logT = evol.logT
logg = evol.logg
logL = evol.logL
temp = 10**logT
# Trim down to just every 10th entry
idx = np.arange(0, len(mass), 10)
mass = mass[idx]
logT = logT[idx]
logg = logg[idx]
logL = logL[idx]
temp = temp[idx]
# Get the NIRC2 filter throughputs
J_filter = FilterNIRC2('J')
H_filter = FilterNIRC2('H')
Kp_filter = FilterNIRC2('Kp')
Lp_filter = FilterNIRC2('Lp')
# Get the throughput spectrum of the Earth's atmosphere
earth = EarthAtmosphere()
# Get the spectrum of Vega
vega = Vega()
# Get the reddening law
if AKs != None:
redlaw = RedLawNishiyama09()
else:
redlaw = None
J_kurucz = np.zeros(len(mass), dtype=float)
H_kurucz = np.zeros(len(mass), dtype=float)
Kp_kurucz = np.zeros(len(mass), dtype=float)
Lp_kurucz = np.zeros(len(mass), dtype=float)
J_castelli = np.zeros(len(mass), dtype=float)
H_castelli = np.zeros(len(mass), dtype=float)
Kp_castelli = np.zeros(len(mass), dtype=float)
Lp_castelli = np.zeros(len(mass), dtype=float)
J_nextgen = np.zeros(len(mass), dtype=float)
H_nextgen = np.zeros(len(mass), dtype=float)
Kp_nextgen = np.zeros(len(mass), dtype=float)
Lp_nextgen = np.zeros(len(mass), dtype=float)
# Loop through the models in the isochrone and derive their
# synthetic photometry.
for ii in range(len(temp)):
print 'Working on Teff=%5d logg=%4.1f' % (temp[ii], logg[ii])
L = 10**(logL[ii]) * c.Lsun # luminosity in erg/s
T = temp[ii] # in Kelvin
# Get the radius
R = math.sqrt(L / (4.0 * math.pi * c.sigma * T**4)) # in cm
R /= (c.cm_in_AU * c.AU_in_pc) # in pc
scaleFactor = (R / distance)**2
# Get the Kurucz atmosphere model
k93 = pysynphot.Icat('k93models', temp[ii], 0, logg[ii])
k93 *= scaleFactor
# Get the Castelli atmosphere model
ck04 = pysynphot.Icat('ck04models', temp[ii], 0, logg[ii])
ck04 *= scaleFactor
# Get the NextGen atmosphere model
ngen = pysynphot.Icat('nextgen', temp[ii], 0, logg[ii])
ngen *= scaleFactor
# Calculate the photometry
J_kurucz[ii] = magnitude_in_filter(J_filter, k93, redlaw, AKs, None,
vega)
H_kurucz[ii] = magnitude_in_filter(H_filter, k93, redlaw, AKs, None,
vega)
Kp_kurucz[ii] = magnitude_in_filter(Kp_filter, k93, redlaw, AKs, None,
vega)
Lp_kurucz[ii] = magnitude_in_filter(Lp_filter, k93, redlaw, AKs, None,
vega)
J_castelli[ii] = magnitude_in_filter(J_filter, ck04, redlaw, AKs, None,
vega)
H_castelli[ii] = magnitude_in_filter(H_filter, ck04, redlaw, AKs, None,
vega)
Kp_castelli[ii] = magnitude_in_filter(Kp_filter, ck04, redlaw, AKs,
None, vega)
Lp_castelli[ii] = magnitude_in_filter(Lp_filter, ck04, redlaw, AKs,
None, vega)
J_nextgen[ii] = magnitude_in_filter(J_filter, ngen, redlaw, AKs, None,
vega)
H_nextgen[ii] = magnitude_in_filter(H_filter, ngen, redlaw, AKs, None,
vega)
Kp_nextgen[ii] = magnitude_in_filter(Kp_filter, ngen, redlaw, AKs,
None, vega)
Lp_nextgen[ii] = magnitude_in_filter(Lp_filter, ngen, redlaw, AKs,
None, vega)
if np.isnan(Kp_nextgen[ii]):
pdb.set_trace()
foo = magnitude_in_filter(Kp_filter, nextgen, redlaw, AKs, None,
vega)
# Now lets plot up some differences vs. effective temp
# Everything is done with reference to Kurucz.
J_diff_cast = J_castelli - J_kurucz
J_diff_ngen = J_nextgen - J_kurucz
J_diff_cast_ngen = J_nextgen - J_castelli
H_diff_cast = H_castelli - H_kurucz
H_diff_ngen = H_nextgen - H_kurucz
H_diff_cast_ngen = H_nextgen - H_castelli
Kp_diff_cast = Kp_castelli - Kp_kurucz
Kp_diff_ngen = Kp_nextgen - Kp_kurucz
Kp_diff_cast_ngen = Kp_nextgen - Kp_castelli
Lp_diff_cast = Lp_castelli - Lp_kurucz
Lp_diff_ngen = Lp_nextgen - Lp_kurucz
Lp_diff_cast_ngen = Lp_nextgen - Lp_castelli
outdir = '/u/jlu/work/models/test/atmospheres/'
outsuffix = '_%dpc_%.1fage' % (distance, logAge)
if AKs != None:
outsuffix += '_AKs%.1f' % (AKs)
outsuffix += '.png'
# Plut luminosity differences:
py.clf()
py.plot(temp, Kp_diff_cast, 'r.', label='Castelli - Kurucz')
py.plot(temp, Kp_diff_ngen, 'b.', label='NextGen - Kurucz')
py.plot(temp, Kp_diff_cast_ngen, 'g.', label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('Kp Magnitude Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_kp' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_kp_zoom' + outsuffix)
py.clf()
py.plot(temp, H_diff_cast, 'r.', label='Castelli - Kurucz')
py.plot(temp, H_diff_ngen, 'b.', label='NextGen - Kurucz')
py.plot(temp, H_diff_cast_ngen, 'g.', label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('H Magnitude Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_h' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_h_zoom' + outsuffix)
py.clf()
py.plot(temp, J_diff_cast, 'r.', label='Castelli - Kurucz')
py.plot(temp, J_diff_ngen, 'b.', label='NextGen - Kurucz')
py.plot(temp, J_diff_cast_ngen, 'g.', label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('J Magnitude Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_j' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_j_zoom' + outsuffix)
py.clf()
py.plot(temp, Lp_diff_cast, 'r.', label='Castelli - Kurucz')
py.plot(temp, Lp_diff_ngen, 'b.', label='NextGen - Kurucz')
py.plot(temp, Lp_diff_cast_ngen, 'g.', label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('Lp Magnitude Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_lp' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_lp_zoom' + outsuffix)
# Now calculate color differences.
JKp_kurucz = J_kurucz - Kp_kurucz
JKp_castelli = J_castelli - Kp_castelli
JKp_nextgen = J_nextgen - Kp_nextgen
HKp_kurucz = H_kurucz - Kp_kurucz
HKp_castelli = H_castelli - Kp_castelli
HKp_nextgen = H_nextgen - Kp_nextgen
KpLp_kurucz = Kp_kurucz - Lp_kurucz
KpLp_castelli = Kp_castelli - Lp_castelli
KpLp_nextgen = Kp_nextgen - Lp_nextgen
py.clf()
py.plot(temp, JKp_castelli - JKp_kurucz, 'r.', label='Castelli - Kurucz')
py.plot(temp, JKp_nextgen - JKp_kurucz, 'b.', label='NextGen - Kurucz')
py.plot(temp, JKp_nextgen - JKp_castelli, 'g.', label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('J - Kp Color Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_j_kp' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_j_kp_zoom' + outsuffix)
py.clf()
py.plot(temp, HKp_castelli - HKp_kurucz, 'r.', label='Castelli - Kurucz')
py.plot(temp, HKp_nextgen - HKp_kurucz, 'b.', label='NextGen - Kurucz')
py.plot(temp, HKp_nextgen - HKp_castelli, 'g.', label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('H - Kp Color Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_h_kp' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_h_kp_zoom' + outsuffix)
py.clf()
py.plot(temp, KpLp_castelli - KpLp_kurucz, 'r.', label='Castelli - Kurucz')
py.plot(temp, KpLp_nextgen - KpLp_kurucz, 'b.', label='NextGen - Kurucz')
py.plot(temp,
KpLp_nextgen - KpLp_castelli,
'g.',
label='NextGen - Castelli')
py.xlabel('Effective Temperature (deg)')
py.ylabel('Kp - Lp Color Difference')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
py.legend(numpoints=1, loc='upper left')
py.savefig(outdir + 'diff_kp_lp' + outsuffix)
py.xlim(3000, 10000)
py.ylim(-0.2, 0.2)
py.savefig(outdir + 'diff_kp_lp_zoom' + outsuffix)
# Plot the color-magnitude diagrams for each. Rather than the differences.
py.clf()
py.semilogx(temp, J_kurucz, 'r.', label='Kurucz')
py.plot(temp, J_castelli, 'b.', label='Castelli')
py.plot(temp, J_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('J Magnitude')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.axis([rng[1], rng[0], rng[3], rng[2]])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_j' + outsuffix)
py.clf()
py.semilogx(temp, H_kurucz, 'r.', label='Kurucz')
py.plot(temp, H_castelli, 'b.', label='Castelli')
py.plot(temp, H_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('H Magnitude')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.axis([rng[1], rng[0], rng[3], rng[2]])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_h' + outsuffix)
py.clf()
py.semilogx(temp, Kp_kurucz, 'r.', label='Kurucz')
py.plot(temp, Kp_castelli, 'b.', label='Castelli')
py.plot(temp, Kp_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('Kp Magnitude')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.axis([rng[1], rng[0], rng[3], rng[2]])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_kp' + outsuffix)
py.clf()
py.semilogx(temp, Lp_kurucz, 'r.', label='Kurucz')
py.plot(temp, Lp_castelli, 'b.', label='Castelli')
py.plot(temp, Lp_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('Lp Magnitude')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.axis([rng[1], rng[0], rng[3], rng[2]])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_lp' + outsuffix)
py.clf()
py.semilogx(temp, JKp_kurucz, 'r.', label='Kurucz')
py.plot(temp, JKp_castelli, 'b.', label='Castelli')
py.plot(temp, JKp_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('J - Kp Color')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.xlim(rng[1], rng[0])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_jkp' + outsuffix)
py.clf()
py.semilogx(temp, HKp_kurucz, 'r.', label='Kurucz')
py.plot(temp, HKp_castelli, 'b.', label='Castelli')
py.plot(temp, HKp_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('H - Kp Color')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.xlim(rng[1], rng[0])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_hkp' + outsuffix)
py.clf()
py.semilogx(temp, KpLp_kurucz, 'r.', label='Kurucz')
py.plot(temp, KpLp_castelli, 'b.', label='Castelli')
py.plot(temp, KpLp_nextgen, 'g.', label='NextGen')
py.xlabel('Effective Temperature (deg)')
py.ylabel('Kp - Lp Color')
py.title('Distance = %.1f logAge = %.2f' % (distance / 10**3, logAge))
rng = py.axis()
py.xlim(rng[1], rng[0])
py.legend(numpoints=1, loc='lower left')
py.savefig(outdir + 'temp_kplp' + outsuffix)
def _getWeights(self, source=None, nlambda=5, monochromatic=None, verbose=False):
""" Return the set of discrete wavelengths, and weights for each wavelength,
that should be used for a PSF calculation.
Uses pysynphot (if installed), otherwise assumes simple-minded flat spectrum
"""
if monochromatic is not None:
poppy_core._log.info(" monochromatic calculation requested.")
return (np.asarray([monochromatic]), np.asarray([1]) )
elif _HAS_PYSYNPHOT and (isinstance(source, pysynphot.spectrum.SourceSpectrum) or source is None):
""" Given a pysynphot.SourceSpectrum object, perform synthetic photometry for
nlambda bins spanning the wavelength range of interest.
Because this calculation is kind of slow, cache results for reuse in the frequent
case where one is computing many PSFs for the same spectral source.
"""
poppy_core._log.debug("Calculating spectral weights using pysynphot, nlambda=%d, source=%s" % (nlambda, str(source)))
if source is None:
try:
source = pysynphot.Icat('ck04models',5700,0.0,2.0)
except:
poppy_core._log.error("Could not load Castelli & Kurucz stellar model from disk; falling back to 5700 K blackbody")
source = pysynphot.BlackBody(5700)
poppy_core._log.debug("Computing spectral weights for source = "+str(source))
try:
key = self._getSpecCacheKey(source, nlambda)
if key in self._spectra_cache.keys():
poppy_core._log.debug("Previously computed spectral weights found in cache, just reusing those")
return self._spectra_cache[keys]
except:
pass # in case sourcespectrum lacks a name element so the above lookup fails - just do the below calc.
poppy_core._log.info("Computing wavelength weights using synthetic photometry for %s..." % self.filter)
band = self._getSynphotBandpass(self.filter)
# choose reasonable min and max wavelengths
w_above10 = np.where(band.throughput > 0.10*band.throughput.max())
minwave = band.wave[w_above10].min()
maxwave = band.wave[w_above10].max()
poppy_core._log.debug("Min, max wavelengths = %f, %f" % (minwave/1e4, maxwave/1e4))
wave_bin_edges = np.linspace(minwave,maxwave,nlambda+1)
wavesteps = (wave_bin_edges[:-1] + wave_bin_edges[1:])/2
deltawave = wave_bin_edges[1]-wave_bin_edges[0]
effstims = []
for wave in wavesteps:
poppy_core._log.debug("Integrating across band centered at %.2f microns with width %.2f" % (wave/1e4,deltawave/1e4))
box = pysynphot.Box(wave, deltawave) * band
if box.throughput.max() == 0: # watch out for pathological cases with no overlap (happens with MIRI FND at high nlambda)
result = 0.0
else:
binset = np.linspace(wave-deltawave, wave+deltawave, 30) # what wavelens to use when integrating across the sub-band?
result = pysynphot.Observation(source, box, binset=binset).effstim('counts')
effstims.append(result)
effstims = np.array(effstims)
effstims /= effstims.sum()
wave_m = band.waveunits.Convert(wavesteps,'m') # convert to meters
newsource = (wave_m, effstims)
if verbose: _log.info( " Wavelengths and weights computed from pysynphot: "+str( newsource))
self._spectra_cache[ self._getSpecCacheKey(source,nlambda)] = newsource
return newsource
elif isinstance(source, dict) and ('wavelengths' in source) and ('weights' in source):
# Allow providing directly a set of specific weights and wavelengths, as in poppy.calcPSF source option #2
return (source['wavelengths'], source['weights'])
elif isinstance(source, tuple) and len(source) == 2:
# Allow user to provide directly a tuple, as in poppy.calcPSF source option #3
return source
else: #Fallback simple code for if we don't have pysynphot.
poppy_core._log.warning("Pysynphot unavailable (or invalid source supplied)! Assuming flat # of counts versus wavelength.")
# compute a source spectrum weighted by the desired filter curves.
# The existing FITS files all have wavelength in ANGSTROMS since that is the pysynphot convention...
filterfile = self._filters[self.filter].filename
filterfits = fits.open(filterfile)
filterdata = filterfits[1].data
try:
f1 = filterdata.WAVELENGTH
d2 = filterdata.THROUGHPUT
except:
raise ValueError("The supplied file, {0}, does not appear to be a FITS table with WAVELENGTH and THROUGHPUT columns.".format(filterfile))
if 'WAVEUNIT' in filterfits[1].header.keys():
waveunit = filterfits[1].header['WAVEUNIT']
if re.match(r'[Aa]ngstroms?', waveunit) is None:
raise ValueError("The supplied file, {0}, has WAVEUNIT='{1}'. Only WAVEUNIT = Angstrom supported when Pysynphot is not installed.".format(filterfile, waveunit))
else:
poppy_core._log.warn("CAUTION: no WAVEUNIT keyword found in filter file {0}. Assuming = Angstroms by default".format(filterfile))
waveunit = 'Angstrom'
poppy_core._log.warn("CAUTION: Just interpolating rather than integrating filter profile, over {0} steps".format(nlambda))
wtrans = np.where(filterdata.THROUGHPUT > 0.4)
lrange = filterdata.WAVELENGTH[wtrans] * 1e-10 # convert from Angstroms to Meters
lambd = np.linspace(np.min(lrange), np.max(lrange), nlambda)
filter_fn = scipy.interpolate.interp1d(filterdata.WAVELENGTH * 1e-10, filterdata.THROUGHPUT, kind='cubic', bounds_error=False)
weights = filter_fn(lambd)
return lambd, weights
def airy_model_residuals(params,*args,**kwargs):
""" Residual function for an L-M fit (i.e. scipy.optimize.least_squares).
Params:
[plate_scale, psf_radius, stellar_teff,flux,ycentre,xcentre]
If extra parameters are given to this function, more things will be fit:
[plate_scale, psf_radius, stellar_teff,flux,ycentre,xcentre,
filter_centre,RV]
Units:
[arcsec/pix, pixels, K, ~max counts in image, pixels, pixels,
metres, km/s]
if kwargs['filter_centre'] or kwargs['RV'] are set, they will _not_ be fit,
and the value in kwargs will be used instead.
Setting kwargs['mask'] will return residuals[mask]
"""
# Unpack everything
image = kwargs['image']
aperture = kwargs['aperture']
wavs = kwargs['wavs']
if 'cutout_sz' in kwargs.keys():
cutout_sz = kwargs['cutout_sz']
else:
cutout_sz = 50
if 'debug' in kwargs.keys():
print(params)
npix = image.shape[0] # assume square
nwavs = wavs.size
plate_scale = params[0]
psf_radius = params[1]
# Use a Phoenix spectrum and the filter
stellar_teff = params[2]
flux = params[3]
if 'filter_centre' in kwargs.keys():
filter_centre = kwargs['filter_centre']
elif len(params) > 6:
filter_centre = params[6]
else:
filter_centre = 550e-9 # m
if 'RV' in kwargs.keys():
rv = kwargs['RV']
elif len(params) > 7:
rv = params[7]
else:
rv = 0. # km/s
source_rest_wavs = wavs/(1+rv*u.km/u.s/constants.c)
acenA = S.Icat('phoenix',stellar_teff,0.2,4.3)
specA = acenA.sample(source_rest_wavs*1e10) # This needs angstroms as input
if 'filter_sigma' in kwargs.keys():
filter_sigma = kwargs['filter_sigma']
else:
filter_sigma = 105e-9
if 'filter_n' in kwargs.keys():
filter_n = kwargs['filter_n']
else:
filter_n = 8
filter_spec = supergaussian(wavs,filter_centre,sigma=filter_sigma,n=filter_n)
spectrum = filter_spec*specA
spectrum *= flux/np.max(spectrum)
# Calculate model image
model = airy_model_image(npix,wavs,spectrum,psf_radius,aperture,plate_scale,offset=params[4:6],cutout_sz=cutout_sz)
resids = image-model
if 'mask' in kwargs.keys():
resids = resids[kwargs['mask']]
return resids.ravel()
def generate_PSF(img, outdir, nruns, x0, y0, grid_step, side, max_step, sigmaTA, sigmaFSM, mask_coron, pupil_stop, lambda0, jitter, rms, fovarcsec):
src=pysynphot.Icat("ck04models",5800, 0.0, 4.44) #temp, z, logg
print src
njitter=50 #number of PSF to use to create the final jittered PSF
for n in xrange(1,nruns+1):
grid = make_ref_grid(n, outdir, x0, y0, side, max_step, grid_step, sigmaTA, sigmaFSM)
#########################################
# GENERATE PSF FOR EACH POSITION IN GRID
#########################################
for isgd in xrange(len(grid)+1):
if isgd==0 : label='Unocculted'
elif isgd==1 : label='ScienceTarget'
elif isgd==2 : label='ReferenceTarget'
else: label='Reference_dither%i' %(isgd-2)
output_name='PSF_%i_run%i_%s_%i.fits' %(np.round(lambda0*100),n,label,rms)
if isgd==0: img.image_mask=None
else: img.image_mask=mask_coron
img.pupil_mask=pupil_stop
if isgd == 0 or isgd == 1: currentPointing=grid[0]
else: currentPointing = grid[isgd-1]
offset= np.sqrt(np.sum( currentPointing**2 ) )/1000
offset_theta= -np.arctan2(currentPointing[0],currentPointing[1])*180/math.pi
img.options['source_offset_r'] = offset # offset in arcseconds
img.options['source_offset_theta'] = offset_theta # degrees counterclockwise from instrumental +Y in the science frame
print isgd,output_name, currentPointing, offset,offset_theta,"\n"
psf = img.calcPSF(source=src, fft_oversample=4, detector_oversample= 1, fov_arcsec=fovarcsec ) #fov_pixels=fovpixels)
finalPSF = psf[0].data
hdr = psf[0].header
string="LAJOIE: optimal source offset for bar occulter: %f arcseconds" %x0
hdr.add_history(string)
string="LAJOIE: actual radial offset is %f arcseconds" %offset
hdr.add_history(string)
string="LAJOIE: actual angular offset is %f degrees" %offset_theta
hdr.add_history(string)
if img.image_mask==None : hdr.set("CORONMSK",img.image_mask,before='PUPIL')
if jitter!=0:
sumPSF=finalPSF
for j in xrange(njitter):
jitter_pos=np.array([np.random.normal(currentPointing[0],jitter),np.random.normal(currentPointing[1],jitter)])
off_jitt = np.sqrt(np.sum( jitter_pos**2 ) )/1000 # arcseconds
off_jitt_theta =-np.arctan2(jitter_pos[0],jitter_pos[1])*180/math.pi # degrees
img.options['source_offset_r'] = off_jitt # offset in arcseconds
img.options['source_offset_theta'] = off_jitt_theta # degrees counterclockwise from instrumental +Y in the science frame
tmp=img.calcPSF(source=src, fft_oversample=4, detector_oversample= 1, fov_arcsec=fovarcsec) #fov_pixels=fovpixels)
sumPSF+=tmp[0].data
finalPSF=sumPSF/(njitter+1)
string="LAJOIE: Jitter (%4.1f mas 1-sigma/axis) included, %d points drawn from Gaussian dist." %(jitter,njitter)
hdr.add_history(string)
else: hdr.add_history("LAJOIE: No jitter included")
##########################
# WRITE OUTPUT IMAGE
##########################
outHDU=astropy.io.fits.PrimaryHDU(data=finalPSF, header=hdr )
outHDU.writeto(outdir+output_name)
return
def outTrans(input) :
"""Compute out of transit spectra
Computes the out of transit spectra by normalizing flux to specified
magnitude and convert to specified Pandeia units of milliJy and microns.
Parameters
----------
input : dict
stellar scene which includes parameters to extract phoenix database or a
filename which points to a stellar spectrum
Return
------
dict
contains wave and flux_out_trans
"""
if input['type'] == 'user':
star = np.genfromtxt(input['starpath'], dtype=(float, float), names='w, f') #pyfits.getdata(input['starpath'],1)
#get flux
flux = star['f'] #star.field(input['logg'])
#get wavelength and reference wavelength for mag normalization
wave = star['w'] #star.field('WAVELENGTH')
#sort if not in ascending order
sort = np.array([wave,flux]).T
sort= sort[sort[:,0].argsort()]
wave = sort[:,0]
flux = sort[:,1]
sp=np.nan
elif input['type'] =='phoenix':
#make sure metal is not out of bounds
if input['metal'] > 0.5: input['metal'] = 0.5
sp = psyn.Icat("phoenix", input['temp'], input['metal'], input['logg'])
sp.convert("microns")
sp.convert("jy")
wave = sp.wave
flux = sp.flux
input['w_unit'] ='um'
input['f_unit'] = 'Jy'
else:
raise Exception('Wrong input type for stellar spectra')
ref_wave = float(input['ref_wave'])
ref_wave = ref_wave*1e3
#Convert evrything to nanometer for converstion based on gemini.edu
if input['w_unit'] == 'um':
wave = wave*1e3
elif input['w_unit'] == 'nm':
wave = wave
elif input['w_unit'] == 'cm' :
wave = wave*1e7
elif input['w_unit'] == 'Angs' :
wave = wave*1e-1
elif input['w_unit'] == 'Hz' :
wave = 3e17/wave
else:
raise Exception('Units are not correct. Pick um, nm, cm or Angs')
#convert to photons/s/nm/m^2 for flux normalization based on
#http://www.gemini.edu/sciops/instruments/integration-time-calculators/itc-help/source-definition
if input['f_unit'] == 'Jy':
flux = flux*1.509e7/wave #eq. C
elif input['f_unit'] == 'W/m2/um':
flux = flux*wave/1.988e-13 #eq. D
elif input['f_unit'] == 'FLAM' :
flux = flux*wave/1.988e-14 #eq. E
elif input['f_unit'] == 'erg/s/cm2/Hz':
flux = flux*1.509e30/wave #*4.0*np.pi
else:
raise Exception('Units are not correct. Pick W/m2/um, FLAM, Jy, or erg/s/cm2/Hz')
#normalize to specific mag
mag = float(input['mag'])
norm_flux = np.interp(ref_wave, wave, flux)
#get zero point for J H and K
if (ref_wave <= 1.3e3) & (ref_wave >= 1.2e3):
zeropoint = 1.97e7
elif (ref_wave <= 1.7e3) & (ref_wave >= 1.6e3):
zeropoint = 9.6e6
elif (ref_wave <= 2.3e3) & (ref_wave >= 2.1e3):
zeropoint = 4.5e6
else:
raise Exception('Only J H and K zeropoints are included')
flux = flux/norm_flux*zeropoint*10**(-mag/2.5)
#return to Pandeia units... milliJy and micron
flux_out_trans = flux*wave/1.509e7*1e3 #inverse of eq. C times 1e3 to get to milliJy instead of Jy
wave = wave*1e-3 #nm to micron
return {'flux_out_trans': flux_out_trans, 'wave': wave,'phoenix':sp}
def outTrans(input):
"""Compute out of transit spectra
Computes the out of transit spectra by normalizing flux to specified
magnitude and convert to specified Pandeia units of milliJy and microns.
Parameters
----------
input : dict
stellar scene which includes parameters to extract phoenix database or a
filename which points to a stellar spectrum
Return
------
dict
contains wave and flux_out_trans
"""
ref_wave = float(input['ref_wave'])
mag = float(input['mag'])
################# USER ####################################
if input['type'] == 'user':
star = np.genfromtxt(
input['starpath'], dtype=(float, float),
names='w, f') #pyfits.getdata(input['starpath'],1)
#get flux
flux = star['f'] #star.field(input['logg'])
#get wavelength and reference wavelength for mag normalization
wave = star['w'] #star.field('WAVELENGTH')
#sort if not in ascending order
sort = np.array([wave, flux]).T
sort = sort[sort[:, 0].argsort()]
wave = sort[:, 0]
flux = sort[:, 1]
if input['w_unit'] == 'um':
PANDEIA_WAVEUNITS = 'um'
elif input['w_unit'] == 'nm':
PANDEIA_WAVEUNITS = 'nm'
elif input['w_unit'] == 'cm':
PANDEIA_WAVEUNITS = 'cm'
elif input['w_unit'] == 'Angs':
PANDEIA_WAVEUNITS = 'angstrom'
elif input['w_unit'] == 'Hz':
PANDEIA_WAVEUNITS = 'Hz'
else:
raise Exception(
'Units are not correct. Pick um, nm, cm, hz, or Angs')
#convert to photons/s/nm/m^2 for flux normalization based on
#http://www.gemini.edu/sciops/instruments/integration-time-calculators/itc-help/source-definition
if input['f_unit'] == 'Jy':
PANDEIA_FLUXUNITS = 'jy'
elif input['f_unit'] == 'FLAM':
PANDEIA_FLUXUNITS = 'FLAM'
else:
raise Exception('Units are not correct. Pick FLAM or Jy')
sp = psyn.ArraySpectrum(
wave,
flux,
waveunits=PANDEIA_WAVEUNITS,
fluxunits=PANDEIA_FLUXUNITS
) #Convert evrything to nanometer for converstion based on gemini.edu
sp.convert("nm")
sp.convert('jy')
############ PHOENIX ################################################
elif input['type'] == 'phoenix':
#make sure metal is not out of bounds
if input['metal'] > 0.5: input['metal'] = 0.5
sp = psyn.Icat("phoenix", input['temp'], input['metal'], input['logg'])
sp.convert("nm")
sp.convert("jy")
wave = sp.wave
flux = sp.flux
input['w_unit'] = 'nm'
input['f_unit'] = 'jy'
else:
raise Exception('Wrong input type for stellar spectra')
############ NORMALIZATION ################################################
refdata = os.environ.get("pandeia_refdata")
all_bps = {
"H": 'bessell_h_004_syn.fits',
"J": 'bessell_j_003_syn.fits',
"K": 'bessell_k_003_syn.fits'
}
if (ref_wave <= 1.3) & (ref_wave >= 1.2):
filt = 'J'
elif (ref_wave <= 1.7) & (ref_wave >= 1.6):
filt = 'H'
elif (ref_wave <= 2.3) & (ref_wave >= 2.1):
filt = 'K'
else:
raise Exception('Only J H and K zeropoints are included')
bp_path = os.path.join(refdata, "normalization", "bandpass", all_bps[filt])
bp = psyn.FileBandpass(bp_path)
sp.convert('angstroms')
bp.convert('angstroms')
rn_sp = sp.renorm(mag, 'vegamag', bp)
rn_sp.convert("microns")
rn_sp.convert("mjy")
flux_out_trans = rn_sp.flux
wave = rn_sp.wave
return {'flux_out_trans': flux_out_trans, 'wave': wave, 'phoenix': sp}
