def make_observed_isochrone_hst_test(logAge,
AKs=defaultAKs,
k=3,
distance=defaultDist,
verbose=False):
redlaw_k = RedLawNishiyama09(k=k)
isoDir = 'iso_k{0}'.format(k)
startTime = time.time()
print('Making isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
(logAge, AKs, distance))
print(' Starting at: ', datetime.datetime.now(),
' Usually takes ~5 minutes')
outFile = '/u/jlu/work/wd1/models/' + isoDir + '/'
outFile += 'iso_%.2f_hst_%4.2f_%4s.pickle' % (logAge, AKs,
str(distance).zfill(4))
c = constants
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
# Lets do some trimming down to get rid of repeat masses or
# mass resolutions higher than 1/1000. We will just use the first
# unique mass after rounding by the nearest 0.001.
mass_rnd = np.copy(evol.mass)
idx = np.where(evol.mass > 10)[0]
mass_rnd[idx] = np.round(evol.mass[idx], decimals=0)
print(mass_rnd[0:10])
mass_rnd = np.round(mass_rnd, decimals=1)
print(mass_rnd[0:10])
tmp, idx = np.unique(mass_rnd, return_index=True)
print('Number of stars {0}'.format(len(idx)))
mass = evol.mass[idx]
logT = evol.logT[idx]
logg = evol.logg[idx]
logL = evol.logL[idx]
isWR = logT != evol.logT_WR[idx]
temp = 10**logT
# Output magnitudes for each temperature and extinction value.
mag814w = np.zeros(len(temp), dtype=float)
mag125w = np.zeros(len(temp), dtype=float)
mag139m = np.zeros(len(temp), dtype=float)
mag160w = np.zeros(len(temp), dtype=float)
filt814w = get_filter_info('acs,f814w,wfc1')
filt125w = get_filter_info('wfc3,ir,f125w')
filt139m = get_filter_info('wfc3,ir,f139m')
filt160w = get_filter_info('wfc3,ir,f160w')
# Make reddening
red814w = redlaw_k.reddening(AKs).resample(filt814w.wave)
red125w = redlaw_k.reddening(AKs).resample(filt125w.wave)
red139m = redlaw_k.reddening(AKs).resample(filt139m.wave)
red160w = redlaw_k.reddening(AKs).resample(filt160w.wave)
# Convert luminosity to erg/s
L_all = 10**(logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * temp**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = logg[ii]
L = L_all[ii] # in erg/s
T = temp[ii] # in Kelvin
R = R_all[ii] # in pc
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_merged_atmosphere(temperature=T, gravity=gravity)
# Trim wavelength range down to JHKL range (0.5 - 4.25 microns)
star = spectrum.trimSpectrum(star, 5000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
# ----------
# Now to the filter integrations
# ----------
mag814w[ii] = mag_in_filter(star, filt814w, red814w)
mag125w[ii] = mag_in_filter(star, filt125w, red125w)
mag139m[ii] = mag_in_filter(star, filt139m, red139m)
mag160w[ii] = mag_in_filter(star, filt160w, red160w)
if verbose:
print('M = %7.3f Msun T = %5d K R = %2.1f Rsun logg = %4.2f F814W = %4.2f F125W = %4.2f F139M = %4.2f F160W = %4.2f' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii], mag814w[ii], mag125w[ii], mag139m[ii], mag160w[ii]))
iso = objects.DataHolder()
iso.M = mass
iso.T = temp
iso.logg = logg
iso.logL = logL
iso.mag814w = mag814w
iso.mag125w = mag125w
iso.mag139m = mag139m
iso.mag160w = mag160w
iso.isWR = isWR
_out = open(outFile, 'wb')
pickle.dump(mass, _out)
pickle.dump(temp, _out)
pickle.dump(logg, _out)
pickle.dump(logL, _out)
pickle.dump(mag814w, _out)
pickle.dump(mag125w, _out)
pickle.dump(mag139m, _out)
pickle.dump(mag160w, _out)
pickle.dump(isWR, _out)
_out.close()
endTime = time.time()
print(' Time taken: %d seconds' % (endTime - startTime))
def nearIR(distance, logAge, redlawClass=RedLawNishiyama09, AKsGrid=None):
"""
For a sampling of effective temperatures and extinctions, calculate the
J, H, K, Kp, Ks, Lp magnitudes for a population at the specified
distance and age. All output is stored in a pickle file.
Input Parameters:
distance in pc
logAge
Optional Input Parameters:
redlawClass - default = RedLawNishiyama09
AKsGrid -- default [0 - 5; 0.1 steps]
Output stored in a pickle file named syn_nir_d#####_a####.dat.
"""
pickleFile = 'syn_nir_d' + str(distance).zfill(5) + '_a' \
+ str(int(round(logAge*100))).zfill(3) + '.dat'
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
mass = evol.mass
logT = evol.logT
logg = evol.logg
logL = evol.logL
temp = 10**logT
isWR = evol.logT != evol.logT_WR
print 'nearIR: Getting rid of Wolf-Rayet stars, we cannot model their atmospheres'
# First get rid of the WR stars, we can't connect atmospheres
# to them anyhow.
idx = np.where(isWR == False)[0]
mass = mass[idx]
logT = logT[idx]
logg = logg[idx]
logL = logL[idx]
temp = temp[idx]
isWR = isWR[idx]
# Sample only 100 points along the whole isochrone
interval = int(math.floor(len(mass) / 100.0))
idx = np.arange(0, len(mass), interval, dtype=int)
# Make sure to get the last point
if idx[-1] != (len(mass) - 1):
idx = np.append(idx, len(mass) - 1)
mass = mass[idx]
logT = logT[idx]
logg = logg[idx]
logL = logL[idx]
temp = temp[idx]
# We will also run through a range of extinctions
if AKsGrid == None:
AKsGrid = np.arange(0, 5, 0.1)
# Fetch earth, vega, and extinction objects
earth = EarthAtmosphere()
vega = Vega()
redlaw = redlawClass()
# Get the transmission curve for NIRC2 filters and atmosphere.
J_filter, J_flux0, J_mag0 = get_filter_info('J', earth, vega)
H_filter, H_flux0, H_mag0 = get_filter_info('H', earth, vega)
K_filter, K_flux0, K_mag0 = get_filter_info('K', earth, vega)
Kp_filter, Kp_flux0, Kp_mag0 = get_filter_info('Kp', earth, vega)
Ks_filter, Ks_flux0, Ks_mag0 = get_filter_info('Ks', earth, vega)
Lp_filter, Lp_flux0, Lp_mag0 = get_filter_info('Lp', earth, vega)
# Output magnitudes for each temperature and extinction value.
J = np.zeros((len(temp), len(AKsGrid)), dtype=float)
H = np.zeros((len(temp), len(AKsGrid)), dtype=float)
K = np.zeros((len(temp), len(AKsGrid)), dtype=float)
Kp = np.zeros((len(temp), len(AKsGrid)), dtype=float)
Ks = np.zeros((len(temp), len(AKsGrid)), dtype=float)
Lp = np.zeros((len(temp), len(AKsGrid)), dtype=float)
# For each filter, lets pre-make reddening curves so we only
# have to do the calculation once.
J_red = []
H_red = []
K_red = []
Kp_red = []
Ks_red = []
Lp_red = []
print 'Making extinction curves'
for aa in range(len(AKsGrid)):
red = redlaw.reddening(AKsGrid[aa])
J_red.append(red.resample(J_filter.wave))
H_red.append(red.resample(H_filter.wave))
K_red.append(red.resample(K_filter.wave))
Kp_red.append(red.resample(Kp_filter.wave))
Ks_red.append(red.resample(Ks_filter.wave))
Lp_red.append(red.resample(Lp_filter.wave))
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = 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
print 'M = %6.2f Msun T = %5d R = %2.1f Rsun logg = %4.2f' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii])
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_merged_atmosphere(temperature=T, gravity=gravity)
# Trim wavelength range down to JHKL range (1.0 - 4.25 microns)
star = spectrum.trimSpectrum(star, 10000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
for aa in range(len(AKsGrid)):
print 'Photometry for T = %5d, AKs = %3.1f' % \
(temp[ii], AKsGrid[aa])
# ----------
# Now to the filter integrations
# ----------
# K
J[ii, aa] = mag_in_filter(star, J_filter, J_red[aa], J_flux0,
J_mag0)
H[ii, aa] = mag_in_filter(star, H_filter, H_red[aa], H_flux0,
H_mag0)
K[ii, aa] = mag_in_filter(star, K_filter, K_red[aa], K_flux0,
K_mag0)
Kp[ii, aa] = mag_in_filter(star, Kp_filter, Kp_red[aa], Kp_flux0,
Kp_mag0)
Ks[ii, aa] = mag_in_filter(star, Ks_filter, Ks_red[aa], Ks_flux0,
Ks_mag0)
Lp[ii, aa] = mag_in_filter(star, Lp_filter, Lp_red[aa], Lp_flux0,
Lp_mag0)
# Save output to pickle file
pf = open(pickleFile, 'w')
pickle.dump(mass, pf)
pickle.dump(logg, pf)
pickle.dump(logL, pf)
pickle.dump(temp, pf)
pickle.dump(AKsGrid, pf)
pickle.dump(J, pf)
pickle.dump(H, pf)
pickle.dump(K, pf)
pickle.dump(Kp, pf)
pickle.dump(Ks, pf)
pickle.dump(Lp, pf)
return pickleFile
def make_observed_isochrone_hst_test(logAge, AKs=defaultAKs, k=3,
distance=defaultDist, verbose=False):
redlaw_k = RedLawNishiyama09(k=k)
isoDir = 'iso_k{0}'.format(k)
startTime = time.time()
print('Making isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
(logAge, AKs, distance))
print(' Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes')
outFile = '/u/jlu/work/wd1/models/' + isoDir + '/'
outFile += 'iso_%.2f_hst_%4.2f_%4s.pickle' % (logAge, AKs,
str(distance).zfill(4))
c = constants
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
# Lets do some trimming down to get rid of repeat masses or
# mass resolutions higher than 1/1000. We will just use the first
# unique mass after rounding by the nearest 0.001.
mass_rnd = np.copy(evol.mass)
idx = np.where(evol.mass > 10)[0]
mass_rnd[idx] = np.round(evol.mass[idx], decimals=0)
print(mass_rnd[0:10])
mass_rnd = np.round(mass_rnd, decimals=1)
print(mass_rnd[0:10])
tmp, idx = np.unique(mass_rnd, return_index=True)
print('Number of stars {0}'.format(len(idx)))
mass = evol.mass[idx]
logT = evol.logT[idx]
logg = evol.logg[idx]
logL = evol.logL[idx]
isWR = logT != evol.logT_WR[idx]
temp = 10**logT
# Output magnitudes for each temperature and extinction value.
mag814w = np.zeros(len(temp), dtype=float)
mag125w = np.zeros(len(temp), dtype=float)
mag139m = np.zeros(len(temp), dtype=float)
mag160w = np.zeros(len(temp), dtype=float)
filt814w = get_filter_info('acs,f814w,wfc1')
filt125w = get_filter_info('wfc3,ir,f125w')
filt139m = get_filter_info('wfc3,ir,f139m')
filt160w = get_filter_info('wfc3,ir,f160w')
# Make reddening
red814w = redlaw_k.reddening(AKs).resample(filt814w.wave)
red125w = redlaw_k.reddening(AKs).resample(filt125w.wave)
red139m = redlaw_k.reddening(AKs).resample(filt139m.wave)
red160w = redlaw_k.reddening(AKs).resample(filt160w.wave)
# Convert luminosity to erg/s
L_all = 10**(logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * temp**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = logg[ii]
L = L_all[ii] # in erg/s
T = temp[ii] # in Kelvin
R = R_all[ii] # in pc
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_merged_atmosphere(temperature=T,
gravity=gravity)
# Trim wavelength range down to JHKL range (0.5 - 4.25 microns)
star = spectrum.trimSpectrum(star, 5000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
# ----------
# Now to the filter integrations
# ----------
mag814w[ii] = mag_in_filter(star, filt814w, red814w)
mag125w[ii] = mag_in_filter(star, filt125w, red125w)
mag139m[ii] = mag_in_filter(star, filt139m, red139m)
mag160w[ii] = mag_in_filter(star, filt160w, red160w)
if verbose:
print('M = %7.3f Msun T = %5d K R = %2.1f Rsun logg = %4.2f F814W = %4.2f F125W = %4.2f F139M = %4.2f F160W = %4.2f' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii], mag814w[ii], mag125w[ii], mag139m[ii], mag160w[ii]))
iso = objects.DataHolder()
iso.M = mass
iso.T = temp
iso.logg = logg
iso.logL = logL
iso.mag814w = mag814w
iso.mag125w = mag125w
iso.mag139m = mag139m
iso.mag160w = mag160w
iso.isWR = isWR
_out = open(outFile, 'wb')
pickle.dump(mass, _out)
pickle.dump(temp, _out)
pickle.dump(logg, _out)
pickle.dump(logL, _out)
pickle.dump(mag814w, _out)
pickle.dump(mag125w, _out)
pickle.dump(mag139m, _out)
pickle.dump(mag160w, _out)
pickle.dump(isWR, _out)
_out.close()
endTime = time.time()
print(' Time taken: %d seconds' % (endTime - startTime))
def nearIR(distance, logAge, redlawClass=RedLawNishiyama09, AKsGrid=None):
"""
For a sampling of effective temperatures and extinctions, calculate the
J, H, K, Kp, Ks, Lp magnitudes for a population at the specified
distance and age. All output is stored in a pickle file.
Input Parameters:
distance in pc
logAge
Optional Input Parameters:
redlawClass - default = RedLawNishiyama09
AKsGrid -- default [0 - 5; 0.1 steps]
Output stored in a pickle file named syn_nir_d#####_a####.dat.
"""
pickleFile = 'syn_nir_d' + str(distance).zfill(5) + '_a' \
+ str(int(round(logAge*100))).zfill(3) + '.dat'
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
mass = evol.mass
logT = evol.logT
logg = evol.logg
logL = evol.logL
temp = 10**logT
isWR = evol.logT != evol.logT_WR
print 'nearIR: Getting rid of Wolf-Rayet stars, we cannot model their atmospheres'
# First get rid of the WR stars, we can't connect atmospheres
# to them anyhow.
idx = np.where(isWR == False)[0]
mass = mass[idx]
logT = logT[idx]
logg = logg[idx]
logL = logL[idx]
temp = temp[idx]
isWR = isWR[idx]
# Sample only 100 points along the whole isochrone
interval = int(math.floor(len(mass) / 100.0))
idx = np.arange(0, len(mass), interval, dtype=int)
# Make sure to get the last point
if idx[-1] != (len(mass) - 1):
idx = np.append(idx, len(mass) - 1)
mass = mass[idx]
logT = logT[idx]
logg = logg[idx]
logL = logL[idx]
temp = temp[idx]
# We will also run through a range of extinctions
if AKsGrid == None:
AKsGrid = np.arange(0, 5, 0.1)
# Fetch earth, vega, and extinction objects
earth = EarthAtmosphere()
vega = Vega()
redlaw = redlawClass()
# Get the transmission curve for NIRC2 filters and atmosphere.
J_filter, J_flux0, J_mag0 = get_filter_info('J', earth, vega)
H_filter, H_flux0, H_mag0 = get_filter_info('H', earth, vega)
K_filter, K_flux0, K_mag0 = get_filter_info('K', earth, vega)
Kp_filter, Kp_flux0, Kp_mag0 = get_filter_info('Kp', earth, vega)
Ks_filter, Ks_flux0, Ks_mag0 = get_filter_info('Ks', earth, vega)
Lp_filter, Lp_flux0, Lp_mag0 = get_filter_info('Lp', earth, vega)
# Output magnitudes for each temperature and extinction value.
J = np.zeros((len(temp), len(AKsGrid)), dtype=float)
H = np.zeros((len(temp), len(AKsGrid)), dtype=float)
K = np.zeros((len(temp), len(AKsGrid)), dtype=float)
Kp = np.zeros((len(temp), len(AKsGrid)), dtype=float)
Ks = np.zeros((len(temp), len(AKsGrid)), dtype=float)
Lp = np.zeros((len(temp), len(AKsGrid)), dtype=float)
# For each filter, lets pre-make reddening curves so we only
# have to do the calculation once.
J_red = []
H_red = []
K_red = []
Kp_red = []
Ks_red = []
Lp_red = []
print 'Making extinction curves'
for aa in range(len(AKsGrid)):
red = redlaw.reddening(AKsGrid[aa])
J_red.append( red.resample(J_filter.wave) )
H_red.append( red.resample(H_filter.wave) )
K_red.append( red.resample(K_filter.wave) )
Kp_red.append( red.resample(Kp_filter.wave) )
Ks_red.append( red.resample(Ks_filter.wave) )
Lp_red.append( red.resample(Lp_filter.wave) )
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = 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
print 'M = %6.2f Msun T = %5d R = %2.1f Rsun logg = %4.2f' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii])
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_merged_atmosphere(temperature=T,
gravity=gravity)
# Trim wavelength range down to JHKL range (1.0 - 4.25 microns)
star = spectrum.trimSpectrum(star, 10000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
for aa in range(len(AKsGrid)):
print 'Photometry for T = %5d, AKs = %3.1f' % \
(temp[ii], AKsGrid[aa])
# ----------
# Now to the filter integrations
# ----------
# K
J[ii, aa] = mag_in_filter(star, J_filter, J_red[aa],
J_flux0, J_mag0)
H[ii, aa] = mag_in_filter(star, H_filter, H_red[aa],
H_flux0, H_mag0)
K[ii, aa] = mag_in_filter(star, K_filter, K_red[aa],
K_flux0, K_mag0)
Kp[ii, aa] = mag_in_filter(star, Kp_filter, Kp_red[aa],
Kp_flux0, Kp_mag0)
Ks[ii, aa] = mag_in_filter(star, Ks_filter, Ks_red[aa],
Ks_flux0, Ks_mag0)
Lp[ii, aa] = mag_in_filter(star, Lp_filter, Lp_red[aa],
Lp_flux0, Lp_mag0)
# Save output to pickle file
pf = open(pickleFile, 'w')
pickle.dump(mass, pf)
pickle.dump(logg, pf)
pickle.dump(logL, pf)
pickle.dump(temp, pf)
pickle.dump(AKsGrid, pf)
pickle.dump(J, pf)
pickle.dump(H, pf)
pickle.dump(K, pf)
pickle.dump(Kp, pf)
pickle.dump(Ks, pf)
pickle.dump(Lp, pf)
return pickleFile
def make_observed_isochrone_hst(logAge, AKs=defaultAKs,
distance=defaultDist, verbose=False,
massSampling=10):
"""
massSampling - Sample the raw isochrone every ## steps. The default
is massSampling = 10, which takes every 10th point.
The isochrones are already very finely sampled. Must be
an integer value.
"""
startTime = time.time()
print 'Making isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
(logAge, AKs, distance)
print ' Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes'
# outFile = '/u/mwhosek/Desktop/699-2/isochrones/'
outFile = '/u/jlu/work/arches/models/iso/'
outFile += 'iso_%.2f_hst_%4.2f_%4s.pickle' % (logAge, AKs,
str(distance).zfill(4))
c = constants
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
print 'Elapsed time while getting merged isochrone: ', time.time() - startTime
#Eliminate cases where log g is less than 0
idx = np.where(evol.logg > 0)
mass = evol.mass[idx]
logT = evol.logT[idx]
logg = evol.logg[idx]
logL = evol.logL[idx]
isWR = logT != evol.logT_WR[idx]
mass = mass[::massSampling]
logT = logT[::massSampling]
logg = logg[::massSampling]
logL = logL[::massSampling]
isWR = isWR[::massSampling]
temp = 10**logT
# Output magnitudes for each temperature and extinction value.
mag814w = np.zeros(len(temp), dtype=float)
mag127m = np.zeros(len(temp), dtype=float)
mag139m = np.zeros(len(temp), dtype=float)
mag153m = np.zeros(len(temp), dtype=float)
magJ = np.zeros(len(temp), dtype=float)
magH = np.zeros(len(temp), dtype=float)
magK = np.zeros(len(temp), dtype=float)
magKp = np.zeros(len(temp), dtype=float)
magLp = np.zeros(len(temp), dtype=float)
filt814w = get_filter_info('wfc3,uvis1,f814w')
filt127m = get_filter_info('wfc3,ir,f127m')
filt139m = get_filter_info('wfc3,ir,f139m')
filt153m = get_filter_info('wfc3,ir,f153m')
filtJ = get_filter_info('nirc2,J')
filtH = get_filter_info('nirc2,H')
filtK = get_filter_info('nirc2,K')
filtKp = get_filter_info('nirc2,Kp')
filtLp = get_filter_info('nirc2,Lp')
# Make reddening
red814w = redlaw.reddening(AKs).resample(filt814w.wave)
red127m = redlaw.reddening(AKs).resample(filt127m.wave)
red139m = redlaw.reddening(AKs).resample(filt139m.wave)
red153m = redlaw.reddening(AKs).resample(filt153m.wave)
redJ = redlaw.reddening(AKs).resample(filtJ.wave)
redH = redlaw.reddening(AKs).resample(filtH.wave)
redK = redlaw.reddening(AKs).resample(filtK.wave)
redKp = redlaw.reddening(AKs).resample(filtKp.wave)
redLp = redlaw.reddening(AKs).resample(filtLp.wave)
# Convert luminosity to erg/s
L_all = 10**(logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * temp**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = logg[ii]
L = L_all[ii] # in erg/s
T = temp[ii] # in Kelvin
R = R_all[ii] # in pc
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_phoenix_atmosphere(temperature=T,
gravity=gravity)
# Trim wavelength range down to JHKL range (0.5 - 4.25 microns)
star = spectrum.trimSpectrum(star, 5000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
# ----------
# Now to the filter integrations
# ----------
mag814w[ii] = mag_in_filter(star, filt814w, red814w)
mag127m[ii] = mag_in_filter(star, filt127m, red127m)
mag139m[ii] = mag_in_filter(star, filt139m, red139m)
mag153m[ii] = mag_in_filter(star, filt153m, red153m)
magJ[ii] = mag_in_filter(star, filtJ, redJ)
magH[ii] = mag_in_filter(star, filtH, redH)
magK[ii] = mag_in_filter(star, filtK, redK)
magKp[ii] = mag_in_filter(star, filtKp, redKp)
magLp[ii] = mag_in_filter(star, filtLp, redLp)
if verbose:
print 'M = %7.3f Msun T = %5d K R = %2.1f Rsun logg = %4.2f F127M = %4.2f F139M = %4.2f F153M = %4.2f elapsed time = %4s' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii], mag127m[ii], mag139m[ii], mag153m[ii], time.time() - startTime)
iso = objects.DataHolder()
iso.M = mass
iso.T = temp
iso.logg = logg
iso.logL = logL
iso.mag127m = mag127m
iso.mag139m = mag139m
iso.mag153m = mag153m
iso.magJ = magJ
iso.magH = magH
iso.magK = magK
iso.magKp = magKp
iso.magLp = magLp
iso.isWR = isWR
iso.mag814w = mag814w
_out = open(outFile, 'wb')
pickle.dump(mass, _out)
pickle.dump(temp, _out)
pickle.dump(logg, _out)
pickle.dump(logL, _out)
pickle.dump(mag127m, _out)
pickle.dump(mag139m, _out)
pickle.dump(mag153m, _out)
pickle.dump(magJ, _out)
pickle.dump(magH, _out)
pickle.dump(magK, _out)
pickle.dump(magKp, _out)
pickle.dump(magLp, _out)
pickle.dump(isWR, _out)
pickle.dump(mag814w, _out)
_out.close()
endTime = time.time()
print ' Time taken: %d seconds' % (endTime - startTime)
def make_observed_isochrone_hst(logAge,
AKs=defaultAKs,
distance=defaultDist,
verbose=False,
massSampling=10):
"""
massSampling - Sample the raw isochrone every ## steps. The default
is massSampling = 10, which takes every 10th point.
The isochrones are already very finely sampled. Must be
an integer value.
"""
startTime = time.time()
print 'Making isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
(logAge, AKs, distance)
print ' Starting at: ', datetime.datetime.now(
), ' Usually takes ~5 minutes'
# outFile = '/u/mwhosek/Desktop/699-2/isochrones/'
outFile = '/u/jlu/work/arches/models/iso/'
outFile += 'iso_%.2f_hst_%4.2f_%4s.pickle' % (logAge, AKs,
str(distance).zfill(4))
c = constants
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
print 'Elapsed time while getting merged isochrone: ', time.time(
) - startTime
#Eliminate cases where log g is less than 0
idx = np.where(evol.logg > 0)
mass = evol.mass[idx]
logT = evol.logT[idx]
logg = evol.logg[idx]
logL = evol.logL[idx]
isWR = logT != evol.logT_WR[idx]
mass = mass[::massSampling]
logT = logT[::massSampling]
logg = logg[::massSampling]
logL = logL[::massSampling]
isWR = isWR[::massSampling]
temp = 10**logT
# Output magnitudes for each temperature and extinction value.
mag814w = np.zeros(len(temp), dtype=float)
mag127m = np.zeros(len(temp), dtype=float)
mag139m = np.zeros(len(temp), dtype=float)
mag153m = np.zeros(len(temp), dtype=float)
magJ = np.zeros(len(temp), dtype=float)
magH = np.zeros(len(temp), dtype=float)
magK = np.zeros(len(temp), dtype=float)
magKp = np.zeros(len(temp), dtype=float)
magLp = np.zeros(len(temp), dtype=float)
filt814w = get_filter_info('wfc3,uvis1,f814w')
filt127m = get_filter_info('wfc3,ir,f127m')
filt139m = get_filter_info('wfc3,ir,f139m')
filt153m = get_filter_info('wfc3,ir,f153m')
filtJ = get_filter_info('nirc2,J')
filtH = get_filter_info('nirc2,H')
filtK = get_filter_info('nirc2,K')
filtKp = get_filter_info('nirc2,Kp')
filtLp = get_filter_info('nirc2,Lp')
# Make reddening
red814w = redlaw.reddening(AKs).resample(filt814w.wave)
red127m = redlaw.reddening(AKs).resample(filt127m.wave)
red139m = redlaw.reddening(AKs).resample(filt139m.wave)
red153m = redlaw.reddening(AKs).resample(filt153m.wave)
redJ = redlaw.reddening(AKs).resample(filtJ.wave)
redH = redlaw.reddening(AKs).resample(filtH.wave)
redK = redlaw.reddening(AKs).resample(filtK.wave)
redKp = redlaw.reddening(AKs).resample(filtKp.wave)
redLp = redlaw.reddening(AKs).resample(filtLp.wave)
# Convert luminosity to erg/s
L_all = 10**(logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * temp**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = logg[ii]
L = L_all[ii] # in erg/s
T = temp[ii] # in Kelvin
R = R_all[ii] # in pc
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_phoenix_atmosphere(temperature=T, gravity=gravity)
# Trim wavelength range down to JHKL range (0.5 - 4.25 microns)
star = spectrum.trimSpectrum(star, 5000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
# ----------
# Now to the filter integrations
# ----------
mag814w[ii] = mag_in_filter(star, filt814w, red814w)
mag127m[ii] = mag_in_filter(star, filt127m, red127m)
mag139m[ii] = mag_in_filter(star, filt139m, red139m)
mag153m[ii] = mag_in_filter(star, filt153m, red153m)
magJ[ii] = mag_in_filter(star, filtJ, redJ)
magH[ii] = mag_in_filter(star, filtH, redH)
magK[ii] = mag_in_filter(star, filtK, redK)
magKp[ii] = mag_in_filter(star, filtKp, redKp)
magLp[ii] = mag_in_filter(star, filtLp, redLp)
if verbose:
print 'M = %7.3f Msun T = %5d K R = %2.1f Rsun logg = %4.2f F127M = %4.2f F139M = %4.2f F153M = %4.2f elapsed time = %4s' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii], mag127m[ii], mag139m[ii], mag153m[ii], time.time() - startTime)
iso = objects.DataHolder()
iso.M = np.array(mass)
iso.T = np.array(temp)
iso.logg = np.array(logg)
iso.logL = np.array(logL)
iso.mag127m = mag127m
iso.mag139m = mag139m
iso.mag153m = mag153m
iso.magJ = magJ
iso.magH = magH
iso.magK = magK
iso.magKp = magKp
iso.magLp = magLp
iso.isWR = isWR
iso.mag814w = mag814w
_out = open(outFile, 'wb')
pickle.dump(mass, _out)
pickle.dump(temp, _out)
pickle.dump(logg, _out)
pickle.dump(logL, _out)
pickle.dump(mag127m, _out)
pickle.dump(mag139m, _out)
pickle.dump(mag153m, _out)
pickle.dump(magJ, _out)
pickle.dump(magH, _out)
pickle.dump(magK, _out)
pickle.dump(magKp, _out)
pickle.dump(magLp, _out)
pickle.dump(isWR, _out)
pickle.dump(mag814w, _out)
_out.close()
pdb.set_trace()
endTime = time.time()
print ' Time taken: %d seconds' % (endTime - startTime)
def make_observed_isochrone_hst(logAge, AKs=defaultAKs,
distance=defaultDist, verbose=False):
startTime = time.time()
print 'Making isochrone: log(t) = %.2f AKs = %.2f dist = %d' % \
(logAge, AKs, distance)
print ' Starting at: ', datetime.datetime.now(), ' Usually takes ~5 minutes'
outFile = '/u/jlu/work/arches/models/iso/'
outFile += 'iso_%.2f_hst_%4.2f_%4s.pickle' % (logAge, AKs,
str(distance).zfill(4))
c = constants
# Get solar mettalicity models for a population at a specific age.
evol = evolution.get_merged_isochrone(logAge=logAge)
# Lets do some trimming down to get rid of repeat masses or
# mass resolutions higher than 1/1000. We will just use the first
# unique mass after rounding by the nearest 0.001.
mass_rnd = np.round(evol.mass, decimals=2)
tmp, idx = np.unique(mass_rnd, return_index=True)
mass = evol.mass[idx]
logT = evol.logT[idx]
logg = evol.logg[idx]
logL = evol.logL[idx]
isWR = logT != evol.logT_WR[idx]
temp = 10**logT
# Output magnitudes for each temperature and extinction value.
mag127m = np.zeros(len(temp), dtype=float)
mag139m = np.zeros(len(temp), dtype=float)
mag153m = np.zeros(len(temp), dtype=float)
magH = np.zeros(len(temp), dtype=float)
magKp = np.zeros(len(temp), dtype=float)
magLp = np.zeros(len(temp), dtype=float)
filt127m = get_filter_info('wfc3,ir,f127m')
filt139m = get_filter_info('wfc3,ir,f139m')
filt153m = get_filter_info('wfc3,ir,f153m')
filtH = get_filter_info('nirc2,H')
filtKp = get_filter_info('nirc2,Kp')
filtLp = get_filter_info('nirc2,Lp')
# Make reddening
red127m = redlaw.reddening(AKs).resample(filt127m.wave)
red139m = redlaw.reddening(AKs).resample(filt139m.wave)
red153m = redlaw.reddening(AKs).resample(filt153m.wave)
redH = redlaw.reddening(AKs).resample(filtH.wave)
redKp = redlaw.reddening(AKs).resample(filtKp.wave)
redLp = redlaw.reddening(AKs).resample(filtLp.wave)
# Convert luminosity to erg/s
L_all = 10**(logL) * c.Lsun # luminsoity in erg/s
# Calculate radius
R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma * temp**4))
R_all /= (c.cm_in_AU * c.AU_in_pc)
# For each temperature extract the synthetic photometry.
for ii in range(len(temp)):
gravity = logg[ii]
L = L_all[ii] # in erg/s
T = temp[ii] # in Kelvin
R = R_all[ii] # in pc
# Get the atmosphere model now. Wavelength is in Angstroms
star = atm.get_merged_atmosphere(temperature=T,
gravity=gravity)
# Trim wavelength range down to JHKL range (0.5 - 4.25 microns)
star = spectrum.trimSpectrum(star, 5000, 42500)
# Convert into flux observed at Earth (unreddened)
star *= (R / distance)**2 # in erg s^-1 cm^-2 A^-1
# ----------
# Now to the filter integrations
# ----------
mag127m[ii] = mag_in_filter(star, filt127m, red127m)
mag139m[ii] = mag_in_filter(star, filt139m, red139m)
mag153m[ii] = mag_in_filter(star, filt153m, red153m)
magH[ii] = mag_in_filter(star, filtH, redH)
magKp[ii] = mag_in_filter(star, filtKp, redKp)
magLp[ii] = mag_in_filter(star, filtLp, redLp)
if verbose:
print 'M = %7.3f Msun T = %5d K R = %2.1f Rsun logg = %4.2f F127M = %4.2f F139M = %4.2f F153M = %4.2f' % \
(mass[ii], T, R * c.AU_in_pc / c.Rsun, logg[ii], mag127m[ii], mag139m[ii], mag153m[ii])
iso = objects.DataHolder()
iso.M = mass
iso.T = temp
iso.logg = logg
iso.logL = logL
iso.mag127m = mag127m
iso.mag139m = mag139m
iso.mag153m = mag153m
iso.magH = magH
iso.magKp = magKp
iso.magLp = magLp
iso.isWR = isWR
_out = open(outFile, 'wb')
pickle.dump(mass, _out)
pickle.dump(temp, _out)
pickle.dump(logg, _out)
pickle.dump(logL, _out)
pickle.dump(mag127m, _out)
pickle.dump(mag139m, _out)
pickle.dump(mag153m, _out)
pickle.dump(magH, _out)
pickle.dump(magKp, _out)
pickle.dump(magLp, _out)
pickle.dump(isWR, _out)
_out.close()
endTime = time.time()
print ' Time taken: %d seconds' % (endTime - startTime)