cal_spec = []
for i in range(len(cal_stars_init)):
for order in order_info:
if order[0] == cal_stars_init[i][0] and order[
1] == 'order35' and order[4] != 2:
#print cal_stars_init[i]
cal_spec += [[
cal_stars_init[i][0], cal_stars_init[i][1], order[2]
]]
for i in range(len(cal_stars_init)):
fitting_res = MultiNestResult.from_hdf5(
'/u/rbentley/metallicity/spectra_fits/' + cal_stars_init[i][0] +
'order35_test_results.h5')
print fitting_res
print fitting_res.median['vrad_2']
cal_spec[i] += [fitting_res.median['vrad_2']]
'''
passwd=raw_input('Sql pwd: ')
con = mdb.connect(host='galaxy1.astro.ucla.edu',user='******',passwd=passwd,db='gcg')
cur = con.cursor()
sql_query = 'SELECT name, date, teff_peak, teff, teff_err,' +\
'logg_peak,logg,logg_err,logg_err_upper,logg_err_lower,'+\
'vz_peak,vz,vz_err,vz_err_upper,vz_err_lower,'+\
'mh_peak,mh,mh_err,mh_err_upper,mh_err_lower,'+\
'alpha_peak,alpha,alpha_err,alpha_err_upper,alpha_err_lower,'+\
'order_ FROM metallicity'
def update_starkit_db(name,
date,
ddate,
mjd,
h5file,
snr=None,
original_location=None,
spectrum_file=None,
vlsr=None,
passwd=None,
vsys=0.0,
source=None,
vhelio=None,
order=None):
# update the starkit database with the info from the hdf5 file and the star
print h5file
if os.path.exists(h5file):
results = MultiNestResult.from_hdf5(h5file)
m = results.maximum
med = results.median
sig = results.calculate_sigmas(1)
if 'add_err_6' in m.keys():
p = [
'teff_0', 'logg_0', 'mh_0', 'alpha_0', 'vrot_1', 'vrad_2',
'R_3', 'add_err_6'
]
else:
p = [
'teff_0', 'logg_0', 'mh_0', 'alpha_0', 'vrot_1', 'vrad_2',
'R_3'
]
temp = []
for k in p:
temp.append([
m[k], med[k], (sig[k][1] - sig[k][0]) / 2.0, sig[k][1],
sig[k][0]
])
values = [name,date,ddate,mjd]+ temp[0] + temp[1]+ temp[5] + temp[2] + temp[3] + temp[4] + temp[6] + \
[original_location,spectrum_file,h5file,str(datetime.datetime.today())]
# if 'add_err_6' in m.keys():
# values = [name,date,ddate,mjd]+ temp[0] + temp[1]+ temp[5] + temp[2] + temp[3] + temp[4] + temp[6] + \
# temp[7] + [original_location,spectrum_file,h5file,str(datetime.datetime.today())]
# else:
# values = [name,date,ddate,mjd]+ temp[0] + temp[1]+ temp[5] + temp[2] + temp[3] + temp[4] + temp[6] + \
# [None,None,None,None] + [original_location,spectrum_file,h5file,str(datetime.datetime.today())]
if vlsr is None:
values = values + [None, None]
else:
values = values + [temp[5][1] + vlsr, temp[5][0] + vlsr]
if vhelio is None:
values = values + [None, None]
else:
values = values + [temp[5][1] + vhelio, temp[5][0] + vhelio]
inf = open('fitting_params_' + name + '.dat', 'r')
lines = inf.readlines()
for l in lines:
lcont = l.split()
if int(lcont[0]) == order:
resmean = lcont[1]
resmax = lcont[2]
chi2 = lcont[3]
inf.close()
values = values + [vsys, snr, source]
con = mdb.connect(host='galaxy1.astro.ucla.edu',
user='******',
passwd=passwd,
db='gcg')
cur = con.cursor()
values = values + [None, None, None, None, None, None, None, None]
values = values + [resmean, resmax, chi2, order, 'PHOENIX']
testval = values
print(values), len(values)
sql_query = 'REPLACE INTO metallicity (name,date,ddate,mjd,'+\
'teff_peak,teff,teff_err,teff_err_upper,teff_err_lower,'+\
'logg_peak,logg,logg_err,logg_err_upper,logg_err_lower,'+\
'vz_peak,vz,vz_err,vz_err_upper,vz_err_lower,'+\
'mh_peak,mh,mh_err,mh_err_upper,mh_err_lower,'+\
'alpha_peak,alpha,alpha_err,alpha_err_upper,alpha_err_lower,'+\
'vrot_peak,vrot,vrot_err,vrot_err_upper,vrot_err_lower,'+\
'r_peak,r,r_err,r_err_upper,r_err_lower,'+\
'original_location,file,chains,edit,vlsr,vlsr_peak,vhelio,vhelio_peak,vsys_err,snr,source,simbad,simbad_err,add_err,add_err_peak,early_type,simbad_rot,simbad_rot_err,vsys_offset,residual_mean,residual_max,reduce_chi2,order_,grid) '+\
'VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,'+\
'%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,'+\
'%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,'+\
'%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,'+\
'%s,%s,%s)'
for x in range(len(values)):
if type(values[x]) is np.float64:
values[x] = float(values[x])
print type(values[x])
print('adding into db', type(values))
#print(sql_query)
#print(values)
#print testval
cur.execute(sql_query, values)
#cur.execute('DELETE FROM starkit_nirspec;')
con.commit()
cur.close()
con.close()
"mh_0":0.447044,
"alpha_0":0.020058,
"vrot_1":0.928,
"vrad_2":-48.4008,
"R_3":24000.}
for a in apogee_vals.keys():
setattr(model,a,apogee_vals[a])
cutoff = 0.05
cutoff_str = str(cutoff)
unmasked_wavelength, unmasked_flux = model()
gc_result = MultiNestResult.from_hdf5("/u/rbentley/metallicity/spectra_fits/masked_fit_results/residual_cut_005_NGC6791_J19205+3748282_order35.h5")
print gc_result
gc_sigmas = gc_result.calculate_sigmas(1)
for a in gc_result.median.keys():
setattr(model,a,gc_result.median[a])
sl_wavelength, sl_flux = model()
data_unmasked_residual = mt.calc_residuals(sl_flux,starspectrum35.flux.value)
masked_unmasked_residual = mt.calc_residuals(sl_flux,unmasked_flux)
residual_mask_flux = []
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
"logg_0":1.653,
"mh_0":0.447044,
"alpha_0":0.020058,
"vrot_1":0.928,
"vrad_2":-48.4008,
"R_3":24000.}
for a in apogee_vals.keys():
setattr(model,a,apogee_vals[a])
unmasked_wavelength, unmasked_flux = model()
sl_mh = mt.s_lambda(model,'mh',model.mh_0.value,0.1)
gc_result = MultiNestResult.from_hdf5("/u/rbentley/metallicity/spectra_fits/masked_fit_results/order34/sensitivity_cut_10.0_"+starname+"_order"+order+".h5")
gc_sigmas = gc_result.calculate_sigmas(1)
print gc_result
for a in gc_result.median.keys():
setattr(model,a,gc_result.median[a])
sl_wavelength, sl_flux = model()
data_unmasked_residual = mt.calc_residuals(sl_flux,starspectrum35.flux.value)
masked_unmasked_residual = mt.calc_residuals(sl_flux,unmasked_flux)
residual_mask_flux = []
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)
# make a MultiNest fitting object with the model and the prior gc_fitobj = MultiNest(fit_model, [teff_prior, logg_prior, mh_prior, alpha_prior,vrot_prior, vrad_prior1,R_prior1]) # Run the fit using the MultiNest sampler. # Will take about 5-10 minutes to run for high resolution Phoenix grid gc_result = gc_fitobj.run() # after fitting, save the results to an HDF5 file gc_result.to_hdf(savefile) print "here" # load from the HDF5 file in case the fit was not run gc_result = MultiNestResult.from_hdf5(savefile) # summary statistics like the mean and median can be accessed as dictionaries print(gc_result.median) print(gc_result.mean), "end" # can also compute 1 sigma intervals (or arbitrary) gc_result.calculate_sigmas(1) # can also make corner plots # be sure to specify parameters if one of them is fixed - if not, corner will crash gc_result.plot_triangle(parameters=['teff_0','logg_0','mh_0','alpha_0','vrot_1','vrad_2'])
model = g | rot1 |DopplerShift(vrad=0.0)| convolve1 | interp1 | norm1
cut_lis = []
for filename in h5_files_us:
cut_lis += [(float(filename.split('_')[5]),filename)]
cut_lis = sorted(cut_lis,key = getKey)
print cut_lis
h5_files = [i[1] for i in cut_lis]
for filename in h5_files:
gc_result = MultiNestResult.from_hdf5(filename)
for a in gc_result.median.keys():
setattr(model,a,gc_result.median[a])
sl_mh = mt.s_lambda(model,'mh',model.mh_0.value,0.1)
mask_sl_f = []
mask_sl_w = []
data_sl_f = []
abs_sl_mh = []
def gc_results_table():
f = open(
'/u/rbentley/localcompute/fitting_plots/plots_for_paper/gc_fit_table_logg.tex',
'w')
teff_offset = 140.
teff_scatter = 157.
logg_offset = 0.15
logg_scatter = 0.14
mh_offset = 0.15
mh_scatter = 0.14
alpha_offset = 0.18
alpha_scatter = 0.16
gc_stars = [
'NE_1_001', 'NE_1_002', 'NE_1_003', 'E7_1_001', 'E7_2_001', 'E7_1_002',
'E7_1_003', 'N2_1_001', 'E5_1_001', 'N2_1_002', 'N2_1_003', 'S1-23'
]
for star in gc_stars:
bosz_result = MultiNestResult.from_hdf5(
'/u/rbentley/metallicity/spectra_fits/masked_fit_results/orders34-35-36/BOSZ_fits/unmasked/'
+ star + '_order34-36_bosz_logg1.2_adderr.h5')
sigmas = bosz_result.calculate_sigmas(1)
teff = [
np.round_(bosz_result.median['teff_0'] + teff_offset),
np.round_(
np.sqrt(((sigmas['teff_0'][1] - sigmas['teff_0'][0]) / 2.)**2 +
teff_scatter**2))
]
logg = [
np.round_(bosz_result.median['logg_0'] + logg_offset, decimals=2),
np.round_(
np.sqrt(((sigmas['logg_0'][1] - sigmas['logg_0'][0]) / 2.)**2 +
logg_scatter**2),
decimals=2)
]
mh = [
np.round_(bosz_result.median['mh_0'] + mh_offset, decimals=2),
np.round_(
np.sqrt(((sigmas['mh_0'][1] - sigmas['mh_0'][0]) / 2.)**2 +
mh_scatter**2),
decimals=2)
]
alpha = [
np.round_(bosz_result.median['alpha_0'] + alpha_offset,
decimals=2),
np.round_(np.sqrt((
(sigmas['alpha_0'][1] - sigmas['alpha_0'][0]) / 2.)**2 +
alpha_scatter**2),
decimals=2)
]
f.write(star + ' & #### & ' + str(teff[0]) + '$\pm$' + str(teff[1]) +
' & ' + str(logg[0]) + '$\pm$' + str(logg[1]) + ' & ' +
str(mh[0]) + '$\pm$' + str(mh[1]) + ' & ' + str(alpha[0]) +
'$\pm$' + str(alpha[1]) + ' \\\\\n')
f.close()