def mkfits_spectrum(name, order, outfile):
# Read in NIRSPEC .dat files for a star and order and combine them
wave = {
34: [2.2406, 2.271],
35: [2.178, 2.20882],
36: [2.11695, 2.14566],
37: [2.0750, 2.0875]
}
if order not in wave.keys():
wave_range = None
else:
wave_range = wave[order]
filename = glob.glob(directory + name + '_order' + str(order) + '*.dat')
starspectrum = read_fits_file.read_nirspec_dat(
filename, wave_range=wave_range, desired_wavelength_units='Angstrom')
write_spectrum.write_txt(starspectrum.wavelength.value,
starspectrum.flux.value, outfile)
"alpha_0": 0.305,
"vrot_1": 0.928,
"vrad_2": 140.6,
"R_3": 24000.
}
sl_cut_val = float(sys.argv[1])
print "Cutting at ", sl_cut_val
specdir = '/u/ghezgroup/data/metallicity/nirspec/spectra/'
testspec_list = specdir + 'NGC6791_J19205+3748282_order35*.dat'
testspec_path = glob.glob(testspec_list)
snr = 50.
starspectrumall = read_fits_file.read_nirspec_dat(
testspec_path, desired_wavelength_units='micron')
#waverange = [np.amin(starspectrumall.wavelength.value[:970]), np.amax(starspectrumall.wavelength.value[:970])]
waverange = [
np.amin(starspectrumall.wavelength.value[:970]),
np.amax(starspectrumall.wavelength.value[:970])
]
starspectrum35 = read_fits_file.read_nirspec_dat(
testspec_path, desired_wavelength_units='Angstrom', wave_range=waverange)
starspectrum35.uncertainty = (np.zeros(len(starspectrum35.flux.value)) +
1.0 / np.float(snr)) * starspectrum35.flux.unit
print testspec_path
g = load_grid(
'/u/rbentley/metallicity/grids/phoenix_t2000_6000_w21500_22220_R40000_o35.h5'
cur.execute(sql_query)
database_vals = cur.fetchall()
cur.close()
con.close()
'''
print cal_spec
for star in cal_spec:
specdir = '/u/rbentley/metallicity/spectra/'
print specdir + star[0]
files = glob.glob(specdir + star[0] + '_order35*.dat')
print files
snr = star[3]
starspectrum = read_fits_file.read_nirspec_dat(
files, desired_wavelength_units='micron')
waverange = [
np.amin(starspectrum.wavelength.value[:970]),
np.amax(starspectrum.wavelength.value[:970])
]
starspectrum = read_fits_file.read_nirspec_dat(
files, desired_wavelength_units='Angstrom', wave_range=waverange)
starspectrum.uncertainty = (np.zeros(len(starspectrum.flux.value)) +
1.0 / np.float(snr)) * starspectrum.flux.unit
star += [starspectrum]
calfig, calax = plt.subplots(len(cal_spec), sharex=True)
print len(cal_spec)
def sl_response_plot_three(starname,
g,
specdir='/group/data/nirspec/spectra/',
snr=30.,
nnorm=2):
file1 = glob.glob(specdir + starname + '_order34*.dat')
file2 = glob.glob(specdir + starname + '_order35*.dat')
file3 = glob.glob(specdir + starname + '_order36*.dat')
starspectrum34 = read_fits_file.read_nirspec_dat(
file1, desired_wavelength_units='Angstrom', wave_range=[2.245, 2.275])
starspectrum34.uncertainty = (
np.zeros(len(starspectrum34.flux.value)) +
1.0 / np.float(snr)) * starspectrum34.flux.unit
starspectrum35 = read_fits_file.read_nirspec_dat(
file2, desired_wavelength_units='Angstrom', wave_range=[2.181, 2.2103])
starspectrum35.uncertainty = (
np.zeros(len(starspectrum35.flux.value)) +
1.0 / np.float(snr)) * starspectrum35.flux.unit
starspectrum36 = read_fits_file.read_nirspec_dat(
file3, desired_wavelength_units='Angstrom', wave_range=[2.1168, 2.145])
starspectrum36.uncertainty = (
np.zeros(len(starspectrum36.flux.value)) +
1.0 / np.float(snr)) * starspectrum36.flux.unit
interp1 = Interpolate(starspectrum34)
convolve1 = InstrumentConvolveGrating.from_grid(g, R=24000)
rot1 = RotationalBroadening.from_grid(g, vrot=np.array([10.0]))
norm1 = Normalize(starspectrum34, nnorm)
interp2 = Interpolate(starspectrum35)
convolve2 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot2 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm2 = Normalize(starspectrum35, nnorm)
interp3 = Interpolate(starspectrum36)
convolve3 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot3 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm3 = Normalize(starspectrum36, nnorm)
model = g | rot1 | Splitter3() | DopplerShift(vrad=0) & DopplerShift(vrad=0) & DopplerShift(vrad=0) | \
convolve1 & convolve2 & convolve3 | interp1 & interp2 & interp3 | \
norm1 & norm2 & norm3
h5_files_us = glob.glob(
'/u/rbentley/metallicity/spectra_fits/masked_fit_results/orders34-35-36-37/masked*.h5'
)
cut_lis = []
for filename in h5_files_us:
print filename.split('_')
cut_lis += [(float(filename.split('_')[6]), filename)]
cut_lis = sorted(cut_lis, key=getKey)
h5_files = [i[1] for i in cut_lis]
sl_val = []
print h5_files
for filename in h5_files:
print filename.split('_')[6]
gc_result = MultiNestResult.from_hdf5(filename)
print gc_result
for a in apogee_vals.keys():
setattr(model, a, apogee_vals[a])
sl_mh1, sl_mh2, sl_mh3 = mtf.s_lambda_three_order(
model, 'mh', model.mh_0.value, 0.1)
w1, f1, w2, f2, w3, f3 = model()
#combine all sl_mh,w,f, lists
sl_mh = np.concatenate((sl_mh1, sl_mh2, sl_mh3))
w = np.concatenate((w1, w2, w3))
f = np.concatenate((f1, f2, f3))
starfluxall = np.concatenate(
(starspectrum34.flux.value, starspectrum35.flux.value,
starspectrum36.flux.value))
starwaveall = np.concatenate(
(starspectrum34.wavelength.value, starspectrum35.wavelength.value,
starspectrum36.wavelength.value))
sigma_bounds = gc_result.calculate_sigmas(1)
sigmas = []
for a in sigma_bounds.keys():
print a
sigmas += [(sigma_bounds[a][1] - sigma_bounds[a][0]) / 2.]
print sigmas
abs_sl_mh = []
mask_sl_f = []
mask_sl_w = []
data_sl_f = []
for i in range(len(sl_mh)):
abs_sl_mh += [np.abs(sl_mh[i])]
if abs(sl_mh[i]) < float(filename.split('_')[6]):
mask_sl_f += [starfluxall[i]]
mask_sl_w += [starwaveall[i]]
else:
data_sl_f += [starfluxall[i]]
print sigmas
sl_val += [(float(filename.split('_')[6]), len(mask_sl_f),
gc_result.median['vrad_3'], gc_result.median['vrad_4'],
gc_result.median['vrad_5'], gc_result.median['logg_0'],
gc_result.median['mh_0'], gc_result.median['alpha_0'],
gc_result.median['teff_0'], sigmas)]
print len(starfluxall)
return sl_val
def sl_response_plot_four(starname,
g,
specdir='/group/data/nirspec/spectra/',
snr=30.,
nnorm=2):
file1 = glob.glob(specdir + starname + '_order34*.dat')
file2 = glob.glob(specdir + starname + '_order35*.dat')
file3 = glob.glob(specdir + starname + '_order36*.dat')
file4 = glob.glob(specdir + starname + '_order37*.dat')
starspectrum34 = read_fits_file.read_nirspec_dat(
file1, desired_wavelength_units='micron')
starspectrum35 = read_fits_file.read_nirspec_dat(
file2, desired_wavelength_units='micron')
starspectrum36 = read_fits_file.read_nirspec_dat(
file3, desired_wavelength_units='micron')
starspectrum37 = read_fits_file.read_nirspec_dat(
file4, desired_wavelength_units='micron')
waverange34 = [
np.amin(starspectrum34.wavelength.value[:970]),
np.amax(starspectrum34.wavelength.value[:970])
]
waverange35 = [
np.amin(starspectrum35.wavelength.value[:970]),
np.amax(starspectrum35.wavelength.value[:970])
]
waverange36 = [
np.amin(starspectrum36.wavelength.value[:970]),
np.amax(starspectrum36.wavelength.value[:970])
]
waverange37 = [
np.amin(starspectrum37.wavelength.value[:970]),
np.amax(starspectrum37.wavelength.value[:970])
]
starspectrum34 = read_fits_file.read_nirspec_dat(
file1, desired_wavelength_units='Angstrom', wave_range=waverange34)
starspectrum34.uncertainty = (
np.zeros(len(starspectrum34.flux.value)) +
1.0 / np.float(snr)) * starspectrum34.flux.unit
starspectrum35 = read_fits_file.read_nirspec_dat(
file2, desired_wavelength_units='Angstrom', wave_range=waverange35)
starspectrum35.uncertainty = (
np.zeros(len(starspectrum35.flux.value)) +
1.0 / np.float(snr)) * starspectrum35.flux.unit
starspectrum36 = read_fits_file.read_nirspec_dat(
file3, desired_wavelength_units='Angstrom', wave_range=waverange36)
starspectrum36.uncertainty = (
np.zeros(len(starspectrum36.flux.value)) +
1.0 / np.float(snr)) * starspectrum36.flux.unit
starspectrum37 = read_fits_file.read_nirspec_dat(
file4, desired_wavelength_units='Angstrom', wave_range=waverange37)
starspectrum37.uncertainty = (
np.zeros(len(starspectrum37.flux.value)) +
1.0 / np.float(snr)) * starspectrum37.flux.unit
interp1 = Interpolate(starspectrum34)
convolve1 = InstrumentConvolveGrating.from_grid(g, R=24000)
rot1 = RotationalBroadening.from_grid(g, vrot=np.array([10.0]))
norm1 = Normalize(starspectrum34, nnorm)
interp2 = Interpolate(starspectrum35)
convolve2 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot2 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm2 = Normalize(starspectrum35, nnorm)
interp3 = Interpolate(starspectrum36)
convolve3 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot3 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm3 = Normalize(starspectrum36, nnorm)
interp4 = Interpolate(starspectrum37)
convolve4 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot3 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm4 = Normalize(starspectrum37, nnorm)
model = g | rot1 | Splitter4() | DopplerShift(vrad=0) & DopplerShift(vrad=0) & DopplerShift(vrad=0) & DopplerShift(vrad=0) | \
convolve1 & convolve2 & convolve3 & convolve4 | interp1 & interp2 & interp3 & interp4 | \
norm1 & norm2 & norm3 & norm4
h5_files_us = glob.glob(
'/u/rbentley/metallicity/spectra_fits/masked_fit_results/orders34-35-36-37/masked*'
+ starname + '_order34-37.h5')
cut_lis = []
for filename in h5_files_us:
print filename.split('_')
cut_lis += [(float(filename.split('_')[6]), filename)]
cut_lis = sorted(cut_lis, key=getKey)
h5_files = [i[1] for i in cut_lis]
sl_val = []
combined_data_mask_model = {}
for filename in h5_files:
print filename
print filename.split('_')[6]
gc_result = MultiNestResult.from_hdf5(filename)
print gc_result
for a in apogee_vals.keys():
setattr(model, a, apogee_vals[a])
w1, f1, w2, f2, w3, f3, w4, f4 = model()
sl_mh1, sl_mh2, sl_mh3, sl_mh4 = mtf.s_lambda_four_order(
model, 'mh', model.mh_0.value, 0.1)
#combine all sl_mh,w,f, lists
sl_mh = np.concatenate((sl_mh1, sl_mh2, sl_mh3, sl_mh4))
w = np.concatenate((w1 / (gc_result.median['vrad_3'] / 3e5 + 1.0),
w2 / (gc_result.median['vrad_4'] / 3e5 + 1.0),
w3 / (gc_result.median['vrad_5'] / 3e5 + 1.0),
w4 / (gc_result.median['vrad_6'] / 3e5 + 1.0)))
f = np.concatenate((f1, f2, f3, f4))
starfluxall = np.concatenate(
(starspectrum34.flux.value, starspectrum35.flux.value,
starspectrum36.flux.value, starspectrum37.flux.value))
starwaveall = np.concatenate(
(starspectrum34.wavelength.value /
(gc_result.median['vrad_3'] / 3e5 + 1.0),
starspectrum35.wavelength.value /
(gc_result.median['vrad_4'] / 3e5 + 1.0),
starspectrum36.wavelength.value /
(gc_result.median['vrad_5'] / 3e5 + 1.0),
starspectrum37.wavelength.value /
(gc_result.median['vrad_6'] / 3e5 + 1.0)))
sigma_bounds = gc_result.calculate_sigmas(1)
sigmas = []
for a in sigma_bounds.keys():
#print a
sigmas += [(sigma_bounds[a][1] - sigma_bounds[a][0]) / 2.]
#print sigmas
abs_sl_mh = []
mask_sl_f = []
mask_sl_w = []
data_sl_f = []
for i in range(len(sl_mh)):
abs_sl_mh += [np.abs(sl_mh[i])]
if abs(sl_mh[i]) < float(filename.split('_')[6]):
mask_sl_f += [starfluxall[i]]
mask_sl_w += [starwaveall[i]]
else:
data_sl_f += [starfluxall[i]]
print sigmas
combined_data_mask_model.update({
filename.split('_')[6]: [(starwaveall, starfluxall), (w, f),
(mask_sl_w, mask_sl_f)]
})
sl_val += [
(float(filename.split('_')[6]), len(mask_sl_f),
gc_result.median['vrad_3'], gc_result.median['vrad_4'],
gc_result.median['vrad_5'], gc_result.median['vrad_6'],
gc_result.median['logg_0'], gc_result.median['mh_0'],
gc_result.median['alpha_0'], gc_result.median['teff_0'], sigmas)
]
print len(starfluxall)
return sl_val, combined_data_mask_model
def plot_multi_order_fit(starname,
g=None,
savefile=None,
specdir='/group/data/nirspec/spectra/',
savedir='../nirspec_fits/',
snr=30.0,
nnorm=2,
save_model=False,
plot_maximum=False):
# plot the results of a multiple order fit on observed spectrum.
file1 = glob.glob(specdir + starname + '_order34*.dat')
file2 = glob.glob(specdir + starname + '_order35*.dat')
file3 = glob.glob(specdir + starname + '_order36*.dat')
if savefile is None:
savefile = os.path.join(
savedir, 'unmasked_' + starname + '_order34-36' + like_str + '.h5')
# restore MultiNest savefile
result = MultiNestResult.from_hdf5(savefile)
starspectrum34 = read_fits_file.read_nirspec_dat(
file1, desired_wavelength_units='Angstrom', wave_range=[2.245, 2.275])
starspectrum34.uncertainty = (
np.zeros(len(starspectrum34.flux.value)) +
1.0 / np.float(snr)) * starspectrum34.flux.unit
starspectrum35 = read_fits_file.read_nirspec_dat(
file2, desired_wavelength_units='Angstrom', wave_range=[2.181, 2.2103])
starspectrum35.uncertainty = (
np.zeros(len(starspectrum35.flux.value)) +
1.0 / np.float(snr)) * starspectrum35.flux.unit
starspectrum36 = read_fits_file.read_nirspec_dat(
file3, desired_wavelength_units='Angstrom', wave_range=[2.1168, 2.145])
starspectrum36.uncertainty = (
np.zeros(len(starspectrum36.flux.value)) +
1.0 / np.float(snr)) * starspectrum36.flux.unit
if g is not None:
interp1 = Interpolate(starspectrum34)
convolve1 = InstrumentConvolveGrating.from_grid(g, R=24000)
rot1 = RotationalBroadening.from_grid(g, vrot=np.array([10.0]))
norm1 = Normalize(starspectrum34, nnorm)
interp2 = Interpolate(starspectrum35)
convolve2 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot2 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm2 = Normalize(starspectrum35, nnorm)
interp3 = Interpolate(starspectrum36)
convolve3 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot3 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm3 = Normalize(starspectrum36, nnorm)
model = g | rot1 | Splitter3() | DopplerShift(vrad=0) & DopplerShift(vrad=0) & DopplerShift(vrad=0) | \
convolve1 & convolve2 & convolve3 | interp1 & interp2 & interp3 | \
norm1 & norm2 & norm3
if plot_maximum:
model.teff_0 = result.maximum['teff_0']
model.logg_0 = result.maximum['logg_0']
model.mh_0 = result.maximum['mh_0']
model.alpha_0 = result.maximum['alpha_0']
model.vrot_1 = result.maximum['vrot_1']
model.vrad_3 = result.maximum['vrad_3']
model.vrad_4 = result.maximum['vrad_4']
model.vrad_5 = result.maximum['vrad_5']
model.R_6 = result.maximum['R_6']
model.R_7 = result.maximum['R_7']
model.R_8 = result.maximum['R_8']
#model.R_9 = result.median['R_9']
else:
model.teff_0 = result.median['teff_0']
model.logg_0 = result.median['logg_0']
model.mh_0 = result.median['mh_0']
model.alpha_0 = result.median['alpha_0']
model.vrot_1 = result.median['vrot_1']
model.vrad_3 = result.median['vrad_3']
model.vrad_4 = result.median['vrad_4']
model.vrad_5 = result.median['vrad_5']
model.R_6 = result.median['R_6']
model.R_7 = result.median['R_7']
model.R_8 = result.median['R_8']
#model.R_9 = result.median['R_9']
w1, f1, w2, f2, w3, f3 = model()
else:
file1 = os.path.join(savedir, starname + '_order34_model.txt')
file2 = os.path.join(savedir, starname + '_order35_model.txt')
file3 = os.path.join(savedir, starname + '_order36_model.txt')
w1, f1 = np.loadtxt(file1, usecols=(0, 1), unpack=True)
w2, f2 = np.loadtxt(file2, usecols=(0, 1), unpack=True)
w3, f3 = np.loadtxt(file3, usecols=(0, 1), unpack=True)
# teff_0 3363.211996
# logg_0 1.691725
# mh_0 0.936003
# alpha_0 -0.027917
# vrot_1 1.378488
# vrad_3 -538.550269
# vrad_4 -239.851862
# vrad_5 -541.044943
# vrad_6 -540.432821
# R_7 20000.000000
# R_8 20000.000000
# R_9 20000.000000
# R_10 20000.000000
plt.clf()
observed_wave = (starspectrum36.wavelength, starspectrum35.wavelength,
starspectrum34.wavelength)
observed_flux = (starspectrum36.flux, starspectrum35.flux,
starspectrum34.flux)
model_wave = (w3, w2, w1)
model_flux = (f3, f2, f1)
max_result = result.maximum
vels = (max_result['vrad_5'], max_result['vrad_4'], max_result['vrad_3'])
print 'maximum likelihood:'
print max_result
print 'median:'
print result.median
print '1 sigma:'
print result.calculate_sigmas(1)
for i in xrange(len(observed_wave)):
plt.subplot(4, 1, i + 1)
velfac = 1.0 / (vels[i] / 3e5 + 1.0)
xwave = observed_wave[i] * velfac
plt.plot(xwave, observed_flux[i])
plt.plot(model_wave[i] * velfac, model_flux[i])
plt.ylim(0.2, 1.2)
plt.xlabel('Wavelength (Angstrom)')
plt.ylabel('Flux')
plt.xlim(np.min(xwave.value), np.max(xwave.value))
plotlines.oplotlines(angstrom=True,
arcturus=True,
alpha=0.5,
molecules=False,
size=12)
plt.tight_layout()
plt.show()
if save_model:
comment1 = 'teff %f,logg %f,mh %f,alpha %f,vrot %f,vrad %f,R %f' % \
(model.teff_0.value,model.logg_0.value,model.mh_0.value,model.alpha_0.value,
model.vrot_1.value,model.vrad_3.value,model.R_6.value)
comment2 = 'teff %f,logg %f,mh %f,alpha %f,vrot %f,vrad %f,R %f' % \
(model.teff_0.value,model.logg_0.value,model.mh_0.value,model.alpha_0.value,
model.vrot_1.value,model.vrad_4.value,model.R_7.value)
comment3 = 'teff %f,logg %f,mh %f,alpha %f,vrot %f,vrad %f,R %f' % \
(model.teff_0.value,model.logg_0.value,model.mh_0.value,model.alpha_0.value,
model.vrot_1.value,model.vrad_5.value,model.R_8.value)
file1 = os.path.join(savedir, starname + '_order34_model.txt')
file2 = os.path.join(savedir, starname + '_order35_model.txt')
file3 = os.path.join(savedir, starname + '_order36_model.txt')
write_spectrum.write_txt(w1, f1, file1, comments=comment1)
write_spectrum.write_txt(w2, f2, file2, comments=comment2)
write_spectrum.write_txt(w3, f3, file3, comments=comment3)
def fit_star_multi_order(starname,
g,
specdir='/group/data/nirspec/spectra/',
savedir='../nirspec_fits/',
snr=30.0,
nnorm=2,
teff_range=[2500, 6000],
vrad_range=[-600, 600],
logg_range=[0., 4.5],
mh_range=[-2., 1.0],
vrot_range=[0, 20],
R=40000,
verbose=True,
alpha_range=[-1., 1.],
r_range=[15000.0, 40000.0],
R_fixed=None,
logg_fixed=None,
l1norm=False):
# fit a spectrum of a star with multiple orders that can have different velocities
file1 = glob.glob(specdir + starname + '_order34*.dat')
file2 = glob.glob(specdir + starname + '_order35*.dat')
file3 = glob.glob(specdir + starname + '_order36*.dat')
starspectrum34 = read_fits_file.read_nirspec_dat(
file1, desired_wavelength_units='Angstrom', wave_range=[2.245, 2.275])
starspectrum34.uncertainty = (
np.zeros(len(starspectrum34.flux.value)) +
1.0 / np.float(snr)) * starspectrum34.flux.unit
starspectrum35 = read_fits_file.read_nirspec_dat(
file2, desired_wavelength_units='Angstrom', wave_range=[2.181, 2.2103])
starspectrum35.uncertainty = (
np.zeros(len(starspectrum35.flux.value)) +
1.0 / np.float(snr)) * starspectrum35.flux.unit
starspectrum36 = read_fits_file.read_nirspec_dat(
file3, desired_wavelength_units='Angstrom', wave_range=[2.1168, 2.145])
starspectrum36.uncertainty = (
np.zeros(len(starspectrum36.flux.value)) +
1.0 / np.float(snr)) * starspectrum36.flux.unit
interp1 = Interpolate(starspectrum34)
convolve1 = InstrumentConvolveGrating.from_grid(g, R=24000)
rot1 = RotationalBroadening.from_grid(g, vrot=np.array([10.0]))
norm1 = Normalize(starspectrum34, nnorm)
interp2 = Interpolate(starspectrum35)
convolve2 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot2 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm2 = Normalize(starspectrum35, nnorm)
interp3 = Interpolate(starspectrum36)
convolve3 = InstrumentConvolveGrating.from_grid(g, R=24000)
#rot3 = RotationalBroadening.from_grid(g,vrot=np.array([10.0]))
norm3 = Normalize(starspectrum36, nnorm)
model = g | rot1 | Splitter3() | DopplerShift(vrad=0) & DopplerShift(vrad=0) & DopplerShift(vrad=0) | \
convolve1 & convolve2 & convolve3 | interp1 & interp2 & interp3 | \
norm1 & norm2 & norm3
w1, f1, w2, f2, w3, f3 = model()
plt.clf()
plt.plot(w1, f1)
plt.plot(starspectrum34.wavelength, starspectrum34.flux)
plt.plot(w2, f2)
plt.plot(starspectrum35.wavelength, starspectrum35.flux)
plt.plot(w3, f3)
plt.plot(starspectrum36.wavelength, starspectrum36.flux)
# likelihoods
if l1norm:
like1 = L1Likelihood(starspectrum34)
like2 = L1Likelihood(starspectrum35)
like3 = L1Likelihood(starspectrum36)
else:
like1 = Chi2Likelihood(starspectrum34)
like2 = Chi2Likelihood(starspectrum35)
like3 = Chi2Likelihood(starspectrum36)
fit_model = model | like1 & like2 & like3 | Combiner3()
print fit_model.__class__
print fit_model()
teff_prior = priors.UniformPrior(*teff_range)
if logg_fixed is not None:
logg_prior = priors.FixedPrior(logg_fixed)
else:
logg_prior = priors.UniformPrior(*logg_range)
mh_prior = priors.UniformPrior(*mh_range)
alpha_prior = priors.UniformPrior(*alpha_range)
vrot_prior = priors.UniformPrior(*vrot_range)
vrad_prior1 = priors.UniformPrior(*vrad_range)
vrad_prior2 = priors.UniformPrior(*vrad_range)
vrad_prior3 = priors.UniformPrior(*vrad_range)
# R_prior1 = priors.FixedPrior(R)
# R_prior2 = priors.FixedPrior(R)
# R_prior3 = priors.FixedPrior(R)
# R_prior4 = priors.FixedPrior(R)
if R_fixed is not None:
R_prior1 = priors.FixedPrior(R_fixed)
R_prior2 = priors.FixedPrior(R_fixed)
R_prior3 = priors.FixedPrior(R_fixed)
else:
R_prior1 = priors.UniformPrior(*r_range)
R_prior2 = priors.UniformPrior(*r_range)
R_prior3 = priors.UniformPrior(*r_range)
fitobj = MultiNest(fit_model, [teff_prior, logg_prior, mh_prior, alpha_prior, vrot_prior, \
vrad_prior1,vrad_prior2,vrad_prior3,R_prior1,R_prior2,\
R_prior3])
fitobj.run(verbose=verbose,
importance_nested_sampling=False,
n_live_points=400)
result = fitobj.result
if l1norm:
like_str = '_l1norm'
else:
like_str = ''
result.to_hdf(
os.path.join(savedir, 'unmasked_' + starname + '_order34-36' +
like_str + '.h5'))
print result.calculate_sigmas(1)
print result.median
# save the individual model spectra with the max posterior value
model.teff_0 = result.maximum['teff_0']
model.logg_0 = result.maximum['logg_0']
model.mh_0 = result.maximum['mh_0']
model.alpha_0 = result.maximum['alpha_0']
model.vrot_1 = result.maximum['vrot_1']
model.vrad_3 = result.maximum['vrad_3']
model.vrad_4 = result.maximum['vrad_4']
model.vrad_5 = result.maximum['vrad_5']
model.R_7 = result.maximum['R_6']
model.R_8 = result.maximum['R_7']
model.R_9 = result.maximum['R_8']
w1, f1, w2, f2, w3, f3 = model()
comment1 = 'teff %f,logg %f,mh %f,alpha %f,vrot %f,vrad %f,R %f' % \
(model.teff_0.value,model.logg_0.value,model.mh_0.value,model.alpha_0.value,
model.vrot_1.value,model.vrad_3.value,model.R_7.value)
comment2 = 'teff %f,logg %f,mh %f,alpha %f,vrot %f,vrad %f,R %f' % \
(model.teff_0.value,model.logg_0.value,model.mh_0.value,model.alpha_0.value,
model.vrot_1.value,model.vrad_4.value,model.R_8.value)
comment3 = 'teff %f,logg %f,mh %f,alpha %f,vrot %f,vrad %f,R %f' % \
(model.teff_0.value,model.logg_0.value,model.mh_0.value,model.alpha_0.value,
model.vrot_1.value,model.vrad_5.value,model.R_9.value)
file1 = os.path.join(savedir, starname + '_order34_model.txt')
file2 = os.path.join(savedir, starname + '_order35_model.txt')
file3 = os.path.join(savedir, starname + '_order36_model.txt')
write_spectrum.write_txt(w1, f1, file1, comments=comment1)
write_spectrum.write_txt(w2, f2, file2, comments=comment2)
write_spectrum.write_txt(w3, f3, file3, comments=comment3)
specdir = '/u/ghezgroup/data/metallicity/nirspec/spectra/' snr = 50. testspec_list_o35 = specdir+'NGC6791_J19205+3748282_order35*.dat' testspec_path_o35 = glob.glob(testspec_list_o35) testspec_list_o34 = specdir+'NGC6791_J19205+3748282_order34*.dat' testspec_path_o34 = glob.glob(testspec_list_o34) testspec_list_o36 = specdir+'NGC6791_J19205+3748282_order36*.dat' testspec_path_o36 = glob.glob(testspec_list_o36) testspec_path = testspec_path_o34 + testspec_path_o35 + testspec_path_o36 starspectrum35 = read_fits_file.read_nirspec_dat(testspec_path,desired_wavelength_units='Angstrom') starspectrum35.uncertainty = (np.zeros(len(starspectrum35.flux.value))+1.0/np.float(snr))*starspectrum35.flux.unit print len(starspectrum35.flux.value) ''' spec_f = [] spec_w = [] spec_u = []
