def plot_orig_data(datafile=None, micron=True, alpha=0.8):
# plot the original text files
if datafile is None:
#datafile = os.path.join(os.path.dirname(__file__),'data/ab4475.')
datafile = os.path.join(os.path.dirname(__file__), 'data/ab10900.')
wave_num, obs_s, tel_s, rat_s, obs_w, tel_w, rat_w = np.loadtxt(
datafile, unpack=True)
summer_wave = wave_num * 1.0000478
winter_wave = wave_num * .9999129
if micron:
# convert from wavenumber (1/cm) into microns
summer_wave = (1.0 / summer_wave) * 1e4
winter_wave = (1.0 / winter_wave) * 1e4
#plt.plot(summer_wave,rat_s)
plt.plot(winter_wave, rat_w, label='Arcturus')
atomic_file = os.path.join(os.path.dirname(__file__),
'data/arcturus_atomic_lines.txt')
molecular_file = os.path.join(os.path.dirname(__file__),
'data/arcturus_molecular_lines.txt')
atomic_lines, atomic_line_names = np.loadtxt(atomic_file,
delimiter=',',
unpack=True,
dtype=str)
molecular_lines, molecular_line_names = np.loadtxt(molecular_file,
delimiter=',',
unpack=True,
dtype=str)
atomic_lines = np.array(atomic_lines, dtype=float) - .00001
molecular_lines = np.array(molecular_lines, dtype=float) - 0.00001
molecular_lines = np.array(molecular_lines, dtype=float)
molecular_line_names = ['$' + lamb + '$' for lamb in molecular_line_names]
atomic_line_names = ['$' + lamb + '$' for lamb in atomic_line_names]
plt.ylim(0, 1.1)
plt.xlim(np.min(winter_wave), np.max(winter_wave))
#plotlines.oplotlines(spec_wave=winter_wave,spec_flux=rat_w,lines=atomic_lines,line_names=atomic_line_names)
plotlines.oplotlines(lines=atomic_lines,
line_names=atomic_line_names,
label=True,
color='r',
linestyle=':')
plotlines.oplotlines(lines=molecular_lines,
line_names=molecular_line_names,
label=True,
alpha=alpha)
def plot_spectrum(wave_range=[2.15, 2.2]):
# plot the Arcturus spectrum
datadir = os.path.join(os.path.dirname(__file__), 'data')
# plot all the data into a PDF form
# get the line list
atomic_file = os.path.join(datadir, 'arcturus_atomic_lines.txt')
molecular_file = os.path.join(datadir, 'arcturus_molecular_lines.txt')
atomic_lines, atomic_line_names = np.loadtxt(atomic_file,
delimiter=',',
unpack=True,
dtype=str)
molecular_lines, molecular_line_names = np.loadtxt(molecular_file,
delimiter=',',
unpack=True,
dtype=str)
atomic_lines = np.array(atomic_lines, dtype=float) - .00001
molecular_lines = np.array(molecular_lines, dtype=float) - 0.00001
molecular_line_names = ['$' + lamb + '$' for lamb in molecular_line_names]
atomic_line_names = ['$' + lamb + '$' for lamb in atomic_line_names]
sns.set_context('paper', font_scale=1.0, rc={"lines.linewidth": 0.8})
sns.set_style('white')
sns.set_style('ticks')
fontscale = 0.75
wave, flux = spectrum(wave_range=wave_range)
plt.clf()
plt.plot(wave, flux)
plt.ylim(0.2, 1.1)
plt.xlim(np.min(wave), np.max(wave))
plotlines.oplotlines(label=True,
lines=molecular_lines,
line_names=molecular_line_names,
size=12 * fontscale,
alpha=0.6)
plotlines.oplotlines(label=True,
lines=atomic_lines,
line_names=atomic_line_names,
size=12 * fontscale,
color='r',
alpha=0.9,
linestyle=':')
def plot_orders(star='NE_1_002',
orders=[37, 36, 35, 34],
ylim=[0, 1.5],
noclear=False,
vel=0):
# plot all the orders for a star
if not noclear:
plt.clf()
for i in xrange(len(orders)):
file1 = os.path.join(spectra_dir,
star + '_order' + str(orders[i]) + '_nod1.dat')
tab1 = pd.read_csv(
file1,
delim_whitespace=True,
skiprows=3,
names=['pixel', 'wave', 'flux', 'nod1', 'nod2', 'diff'])
plt.subplot(len(orders), 1, i + 1)
plt.plot(tab1['wave'],
tab1['flux'],
label=star + ' order ' + str(orders[i]))
plt.ylim(ylim[0], ylim[1])
plotlines.oplotlines(vel=vel)
plt.legend(loc=3)
label="Data")
ax.plot(w / (unmasked_median_fits['vrad_2'] / 3e5 + 1.0),
f,
label="Unmasked model with best fit unmasked values")
sl_ax.plot(w / (unmasked_median_fits['vrad_2'] / 3e5 + 1.0),
logsl,
label="Log($S_{\lambda}$)",
color="green")
ax.plot(w / (unmasked_median_fits['vrad_2'] / 3e5 + 1.0),
logsl + 100.,
label="Log($S_{\lambda}$)",
color="green")
ax.set_xlabel("Wavelength (angstroms)", size=12)
ax.set_ylabel("Flux")
sl_ax.set_ylabel("Log($S_{\lambda}$)", size=12)
ax.set_ylim(-0.3, 1.3)
sl_ax.set_ylim(-4, 6.)
ax.set_title("Order 35 Observed and Model fluxes, with sensitivity function",
size=15)
ax.legend(loc='upper right', fontsize=11)
plt.tick_params(axis='both', which='major', labelsize=11)
plotlines.oplotlines(angstrom=True, arcturus=True, alpha=0.25, size=12)
plt.show()
def plot_data_pdf(filename='Arcturus_IR_spectrum.pdf',
per_page=2,
micron=True,
plot_both=False,
label_file=False):
datadir = os.path.join(os.path.dirname(__file__), 'data')
# plot all the data into a PDF form
pdf_pages = PdfPages(filename)
# get the line list
atomic_file = os.path.join(datadir, 'arcturus_atomic_lines.txt')
molecular_file = os.path.join(datadir, 'arcturus_molecular_lines.txt')
atomic_lines, atomic_line_names = np.loadtxt(atomic_file,
delimiter=',',
unpack=True,
dtype=str)
molecular_lines, molecular_line_names = np.loadtxt(molecular_file,
delimiter=',',
unpack=True,
dtype=str)
atomic_lines = np.array(atomic_lines, dtype=float) - .00001
molecular_lines = np.array(molecular_lines, dtype=float) - 0.00001
molecular_line_names = ['$' + lamb + '$' for lamb in molecular_line_names]
atomic_line_names = ['$' + lamb + '$' for lamb in atomic_line_names]
sns.set_context('paper', font_scale=1.0, rc={"lines.linewidth": 0.8})
sns.set_style('white')
sns.set_style('ticks')
fontscale = 0.75
# spectra files
specfiles = os.path.join(datadir, 'filenames.txt')
specnames = np.loadtxt(specfiles, dtype=str, unpack=True)
for i in xrange(len(specnames)):
datafile = os.path.join(datadir, specnames[i])
wave_num, obs_s, tel_s, rat_s, obs_w, tel_w, rat_w = np.loadtxt(
datafile, unpack=True)
summer_wave = wave_num * 1.0000478
winter_wave = wave_num * .9999129
if micron:
# convert from wavenumber (1/cm) into microns
summer_wave = (1.0 / summer_wave) * 1e4
winter_wave = (1.0 / winter_wave) * 1e4
if ((i % per_page) == 0):
fig = plt.figure(figsize=(11, 8.5), dpi=100)
plt.clf()
plt.subplot(per_page, 1, (i % per_page) + 1)
plt.ylim(0, 1.1)
plt.xlim(np.min(winter_wave), np.max(winter_wave))
plotlines.oplotlines(label=True,
lines=molecular_lines,
line_names=molecular_line_names,
size=12 * fontscale,
alpha=0.6)
plotlines.oplotlines(label=True,
lines=atomic_lines,
line_names=atomic_line_names,
size=12 * fontscale,
color='r',
alpha=0.9,
linestyle=':')
if plot_both:
plt.plot(summer_wave, rat_s)
plt.plot(winter_wave, rat_w)
else:
plt.plot(winter_wave, rat_w, 'k')
#plotlines.oplotlines(spec_wave=summer_wave,spec_flux=rat_s,lines=atomic_lines,line_names=atomic_line_names,size=12*fontscale)
#plotlines.oplotlines(spec_wave=summer_wave,spec_flux=rat_s,lines=molecular_lines,line_names=molecular_line_names,size=12*fontscale)
plt.xlabel(r'$Wavelength (\mu m)$')
plt.ylabel(r'$Flux$')
if label_file:
plt.text(winter_wave[-2], 1.05, specnames[i], size=9)
if ((i != 0) and (((i + 1) % per_page) == 0)):
pdf_pages.savefig(fig, orientation='landscape')
plt.close(fig)
pdf_pages.close()
plt.close('all')
axpresent.plot(starspectrum35.wavelength.value /
(unmasked_median_fits['vrad_2'] / 3e5 + 1.0),
starspectrum35.flux.value + 1.5,
label="Data")
axpresent.plot(slope_w / (gc_result_masked_slope.median['vrad_2'] / 3e5 + 1.0),
slope_f,
label="Masked model with best fit masked values")
axpresent.plot(slope_w / (gc_result_masked_slope.median['vrad_2'] / 3e5 + 1.0),
masked_data_slope.flux.value - slope_f + 0.5,
label='Masked Model-Masked Data Residuals')
plotlines.oplotlines(angstrom=True,
arcturus=True,
alpha=0.5,
size=6,
highlight=['Sc'])
#plt.plot(sl_w,unmasked_mod_masked_data_removed_flux-sl_f,label="Umasked model-masked model residuals")
axpresent.set_xlabel("Wavelength (angstroms)", size=12)
axpresent.set_ylabel("Flux", size=12)
axpresent.set_ylim(-0.3, 1.2)
axpresent.set_title("Order 35 Observed and Model fluxes (unmasked)", size=15)
axpresent.legend(loc='upper right', fontsize=11)
fit_results_file = open(
"/u/ghezgroup/data/metallicity/nirspec/spectra_fits/pdfs/slope_masked_fit_results_output.lis",
"a+")
fit_results_file.write("Fitting results with slopes with slope < " +
cutoff_str + " removed\n")
m71_021 = 'M71_J19534827+1848021'
tyc_3544 = 'TYC 3544'
sl_vals_ngc6791_282, specs_ngc6791_282 = slp.sl_response_plot_four(
ngc6791_282, mod, specdir=spec_path)
star_data = specs_ngc6791_282['1.0']
plt.plot(star_data[0][0], star_data[0][1], 'g')
plt.plot(star_data[1][0], star_data[1][1], 'g')
plt.plot(star_data[2][0], star_data[2][1], 'g')
plt.plot(star_data[3][0], star_data[3][1], 'g')
plt.plot(star_data[4][0], star_data[4][1], 'b')
plt.plot(star_data[5][0], star_data[5][1], 'b')
plt.plot(star_data[6][0], star_data[6][1], 'b')
plt.plot(star_data[7][0], star_data[7][1], 'b')
plt.plot(star_data[8][0], star_data[8][1], 'r.')
plotlines.oplotlines(
angstrom=True,
arcturus=True,
molecules=False,
alpha=0.5,
size=6,
highlight=['O', 'Ne', 'Mg', 'Si', 'S', 'Ar', 'Ca', 'Ti', 'Cr', 'Fe', 'Ni'])
plt.show()
fitted_params = [str(round(gc_result.median[a],4))+"$\pm$"+str(round((gc_sigmas[a][1]*0.5-gc_sigmas[a][0]*0.5),4)) for a in ["teff_0","logg_0","mh_0","alpha_0"]]
apogee_params = ["4026.56$\pm$46.36","1.653$\pm$0.042","0.4470$\pm$0.0158","0.0201$\pm$0.0086"]
unmasked_params = ["3891.37$\pm$8.85","0.104$\pm$0.005","0.003$\pm$0.043","0.306$\pm$0.020"]
table = tableax.table(cellText=[fitted_params,unmasked_params,apogee_params],rowLabels=rows,colLabels=columns, loc='upper center',fontsize=24)
table.scale(1,1.5)
f.legend([l1, l1b, l2, l3, l3b],["Data","Masked Data Points",'Masked Model','$S_{\lambda}$', '$S_{\lambda}$ = '+cutoff_str], loc=(0.4,0.02))
plt.sca(sl_ax)
plotlines.oplotlines(angstrom=True,arcturus=True,alpha=0.25,molecules=False,size=8)
modelax2.set_xticks([])
l5 = dataresidualax.plot(sl_wavelength/(gc_result.median['vrad_2']/3e5+1.0), data_unmasked_residual, color='0.5')[0]
l5b = dataresidualax.axhline(0.05, color='black',ls='--')
l5c = dataresidualax.axhline(-0.05, color='black',ls='--')
dataresidualax.set_xticks([])
dataresidualax.set_ylabel("Flux",size=14)
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)
gc_result = mt.run_multinest_fit_rv_constrained(fit_model,
apogee_vals['vrad_2'], 0.11)
gc_result.to_hdf(
"/u/rbentley/metallicity/spectra_fits/masked_fit_results/unmasked_rv_constrained_"
+ starname + "_orders34-36.h5")
print "chi squared val ", like1
sigmas = gc_result.calculate_sigmas(1)
#Makes plots of S_lambda masked model
#print gc_result
print gc_result
for a in gc_result.median.keys():
setattr(model, a, gc_result.median[a])
w, f = model()
gc_result.plot_triangle(
parameters=['teff_0', 'logg_0', 'mh_0', 'alpha_0', 'vrot_1', 'vrad_2'])
plt.figure(figsize=(15, 7))
plt.plot(
starspectrum.wavelength.value / (gc_result.median['vrad_2'] / 3e5 + 1.0),
starspectrum.flux)
plt.plot(w / (gc_result.median['vrad_2'] / 3e5 + 1.0), f)
plotlines.oplotlines(angstrom=True)
plt.xlabel('Wavelength (Angstrom)')
plt.ylabel('Flux')
plt.show()
