def main(name, specpath, refpath, outdir, maskpath, suffix):
targ = spectrum.read_hires_fits(specpath, maskfile=maskpath)
ref = spectrum.read_fits(refpath)
# create diagnostic file
outdir = os.path.join(outdir, name)
if not os.path.exists(outdir):
os.makedirs(outdir)
filepath = os.path.join(outdir, name+suffix+'_spec.h5')
f = h5py.File(filepath, 'w')
shifted = shift(targ, ref, store=f)
shifted.to_hdf(f)
# get wavelength limits
w_min = shifted.w[0]
w_max = shifted.w[-1]
ref_trunc = ref.cut(w_min, w_max)
f.create_dataset('s_ref', data=ref_trunc.s)
f.create_dataset('serr_ref', data=ref_trunc.serr)
# store unshifted spectrum
f.create_dataset('s_unshifted', data=targ.s)
f.create_dataset('serr_unshifted', data=targ.serr)
f.create_dataset('w_unshifted', data=targ.w)
# store metadata
f.attrs['cps_name'] = name
f.attrs['obs'] = os.path.basename(specpath)
f.attrs['ref'] = os.path.basename(refpath)
f.close()
def main(name, specpath, refpath, outdir, maskpath, suffix):
targ = spectrum.read_hires_fits(specpath, maskfile=maskpath)
ref = spectrum.read_fits(refpath)
# create diagnostic file
outdir = os.path.join(outdir, name)
if not os.path.exists(outdir):
os.makedirs(outdir)
filepath = os.path.join(outdir, name + suffix + '_spec.h5')
f = h5py.File(filepath, 'w')
shifted = shift(targ, ref, store=f)
shifted.to_hdf(f)
# get wavelength limits
w_min = shifted.w[0]
w_max = shifted.w[-1]
ref_trunc = ref.cut(w_min, w_max)
f.create_dataset('s_ref', data=ref_trunc.s)
f.create_dataset('serr_ref', data=ref_trunc.serr)
# store unshifted spectrum
f.create_dataset('s_unshifted', data=targ.s)
f.create_dataset('serr_unshifted', data=targ.serr)
f.create_dataset('w_unshifted', data=targ.w)
# store metadata
f.attrs['cps_name'] = name
f.attrs['obs'] = os.path.basename(specpath)
f.attrs['ref'] = os.path.basename(refpath)
f.close()
def main():
# Shift the references onto the NSO wavlength scale
spectra_dir = os.path.join(SPECMATCHDIR, 'spectra/')
shifted_dir = os.path.join(SPECMATCHDIR, 'shifted_spectra/')
if not os.path.exists(shifted_dir):
os.mkdir(shifted_dir)
# Save base
r = REFERENCES[0]
infile = os.path.join(spectra_dir, r[0] + '.fits')
base_ref = spectrum.read_fits(infile)
base_w = base_ref.w
outfile = os.path.join(shifted_dir, r[0] + '_adj.fits')
copyfile(infile, outfile)
# Shift each spectrum in order
for r in REFERENCES[1:]:
print("Shifting spectrum {0} onto reference {1}".format(r[0], r[3]))
reffile = os.path.join(shifted_dir, r[3] + '_adj.fits')
ref_spec = spectrum.read_fits(reffile)
maskfile = os.path.join(SPECMATCHDIR, 'hires_telluric_mask.csv')
# Shift each chip
chips = ['b', 'r', 'i']
shifted = []
for c in chips:
infile = os.path.join(spectra_dir, c + r[0] + '.fits')
outfile = os.path.join(shifted_dir, c + r[0] + '_adj.fits')
targ_unshifted = spectrum.read_hires_fits(infile,
maskfile=maskfile)
shift_data = {}
targ_shifted = shift.shift(targ_unshifted,
ref_spec,
store=shift_data)
shift.save_shift_to_fits(outfile,
targ_shifted,
targ_unshifted,
shift_data,
clobber=True)
shifted.append(targ_shifted)
# Flatten spectrum and save
print("Flattening spectra into single file")
outfile = os.path.join(shifted_dir, r[0] + '_adj.fits')
flattened = spectrum.Spectrum.combine_spectra(shifted, base_w)
flattened.to_fits(outfile)
def main(args):
filename = os.path.splitext(args.input_file)[0]
infile = os.path.join(args.dir, filename + '.fits')
spec = spectrum.read_hires_fits(infile)
d_spec = degrade_spec(spec, args.R)
suffix = "_R={0:d}".format(args.R)
outfile = os.path.join(args.dir, filename + suffix + '.fits')
# Save degraded spectrum
d_spec.to_hires_fits(outfile, clobber=True)
def main(args):
filename = os.path.splitext(args.input_file)[0]
infile = os.path.join(args.dir, filename + '.fits')
spec = spectrum.read_hires_fits(infile)
d_spec = degrade_spec(spec, args.R)
suffix = "_R={0:d}".format(args.R)
outfile = os.path.join(args.dir, filename + suffix + '.fits')
# Save degraded spectrum
d_spec.to_hires_fits(outfile, clobber=True)
def main():
# Shift the references onto the NSO wavlength scale
spectra_dir = os.path.join(SPECMATCHDIR, 'spectra/')
shifted_dir = os.path.join(SPECMATCHDIR, 'shifted_spectra/')
if not os.path.exists(shifted_dir):
os.mkdir(shifted_dir)
# Save base
r = REFERENCES[0]
infile = os.path.join(spectra_dir, r[0]+'.fits')
base_ref = spectrum.read_fits(infile)
base_w = base_ref.w
outfile = os.path.join(shifted_dir, r[0]+'_adj.fits')
copyfile(infile, outfile)
# Shift each spectrum in order
for r in REFERENCES[1:]:
print("Shifting spectrum {0} onto reference {1}".format(
r[0], r[3]))
reffile = os.path.join(shifted_dir, r[3]+'_adj.fits')
ref_spec = spectrum.read_fits(reffile)
maskfile = os.path.join(SPECMATCHDIR, 'hires_telluric_mask.csv')
# Shift each chip
chips = ['b', 'r', 'i']
shifted = []
for c in chips:
infile = os.path.join(spectra_dir, c + r[0] + '.fits')
outfile = os.path.join(shifted_dir, c + r[0] + '_adj.fits')
targ_unshifted = spectrum.read_hires_fits(infile, maskfile=maskfile)
shift_data = {}
targ_shifted = shift.shift(targ_unshifted, ref_spec, store=shift_data)
shift.save_shift_to_fits(outfile, targ_shifted, targ_unshifted,
shift_data, clobber=True)
shifted.append(targ_shifted)
# Flatten spectrum and save
print("Flattening spectra into single file")
outfile = os.path.join(shifted_dir, r[0] + '_adj.fits')
flattened = spectrum.Spectrum.combine_spectra(shifted, base_w)
flattened.to_fits(outfile)
def main(args):
filename = os.path.splitext(args.input_file)[0]
infile = os.path.join(args.dir, filename + '.fits')
spec = spectrum.read_hires_fits(infile)
for i in range(args.num):
noised_spec = add_noise(spec, args.snr)
if args.num == 1:
suffix = "_snr={0:.0f}".format(args.snr)
else:
suffix = "_snr={0:.0f}_i={1:d}".format(args.snr, i + 1)
outfile = os.path.join(args.dir, filename + suffix + '.fits')
noised_spec.to_hires_fits(outfile, clobber=True)
def main(args):
filename = os.path.splitext(args.input_file)[0]
infile = os.path.join(args.dir, filename + '.fits')
spec = spectrum.read_hires_fits(infile)
for i in range(args.num):
noised_spec = add_noise(spec, args.snr)
if args.num == 1:
suffix = "_snr={0:.0f}".format(args.snr)
else:
suffix = "_snr={0:.0f}_i={1:d}".format(args.snr, i + 1)
outfile = os.path.join(args.dir, filename + suffix + '.fits')
noised_spec.to_hires_fits(outfile, clobber=True)
# code-stop-spectra-selected-stars
fig.set_tight_layout(True)
fig.savefig('quickstart-spectra-selected-stars.png')
# code-start-pop-library
idx1 = lib.get_index('190406')
G_star = lib.pop(idx1)
idx2 = lib.get_index('GL699')
M_star = lib.pop(idx2)
# code-stop-pop-library
# code-start-read-spectrum-G
from specmatchemp import spectrum
G_spectrum = spectrum.read_hires_fits('../samples/rj59.1923.fits').cut(5130,5210)
G_spectrum.name = 'HD190406'
# code-stop-read-spectrum-G
# code-start-shift-spectrum-G
from specmatchemp.specmatch import SpecMatch
sm_G = SpecMatch(G_spectrum, lib)
sm_G.shift()
# code-stop-shift-spectrum-G
# code-start-plot-shifts-G
fig = plt.figure(figsize=(10,5))
sm_G.target_unshifted.plot(normalize=True, plt_kw={'color':'forestgreen'}, text='Target (unshifted)')
sm_G.target.plot(offset=0.5, plt_kw={'color':'royalblue'}, text='Target (shifted): HD190406')
def shift_spectrum(specpath,
plot_level=0,
indir=None,
outdir="./",
suffix="_adj",
mask=True,
no_bootstrap=False,
flatten=False):
"""Shift a target spectrum given an observation code.
Saves the shifted spectrum in a fits file.
Args:
specpath (str): Path to spectrum or its CPS observation id jXX.XXXX
plot_level (int, 0-2): Level of plotting to save
indir (str): Directory to look in for target spectrum
outdir (str): Directory to store output files
suffix (str): String to append to output file names
mask (bool): Use a mask to remove telluric lines
no_bootstrap (bool): Shift a spectrum without bootstrapping
flatten (bool): If multiple chips are provided, flatten into a single
spectrum file.
Returns:
shifted, unshifted, shift_data
"""
# Check if specpath is a path or an observation ID
if os.path.exists(specpath):
targ_path = specpath
targid = os.path.splitext(os.path.basename(specpath))[0]
else:
return _multishift_spectrum(specpath, plot_level, indir, outdir,
suffix, mask, no_bootstrap, flatten)
# if a different directory is provided, copy the file into specmatchemp
# working directory
specdir = os.path.join(SPECMATCHDIR, 'spectra')
shiftedspecdir = os.path.join(SPECMATCHDIR, 'shifted_spectra')
if indir != specdir and indir is not None:
copy(targ_path, specdir)
# load target and references
if mask:
maskfile = os.path.join(SPECMATCHDIR, 'hires_telluric_mask.csv')
else:
maskfile = None
targ_spec = spectrum.read_hires_fits(targ_path, maskfile)
if no_bootstrap:
# Shift directly onto NSO spectrum
ref_specs = [
spectrum.read_fits(os.path.join(shiftedspecdir, 'nso_adj.fits'))
]
shift_data = {}
print("Shifting directly against NSO spectrum.")
shifted = shift.shift(targ_spec, ref_specs[0], store=shift_data)
shift_data['shift_reference'] = 0
else:
# Shift spectrum onto boostrapped spectra
ref_specs = [
spectrum.read_fits(os.path.join(shiftedspecdir,
r[0] + '_adj.fits'))
for r in SHIFT_REFERENCES
]
shift_data = {}
shifted = shift.bootstrap_shift(targ_spec, ref_specs, store=shift_data)
# Save shifted spectrum
outpath = os.path.join(shiftedspecdir, targid + suffix + '.fits')
shift.save_shift_to_fits(outpath,
shifted,
targ_spec,
shift_data,
clobber=True)
if outdir != shiftedspecdir:
if not os.path.exists(outdir):
os.mkdir(outdir)
copy(outpath, outdir)
# Generate representative plots
if plot_level == 1:
plotfile = os.path.join(outdir, targid + "_shift_plots.pdf")
print("Saving plots to " + plotfile)
with PdfPages(plotfile) as pdf:
# Get reference used
shift_ref = ref_specs[shift_data['shift_reference']]
# Plot single order
plot_shift_data(targ_spec, shifted, shift_ref, shift_data, pdf, 2)
# Generate individual plots for every order
elif plot_level == 2:
plotfile = os.path.join(outdir, targid + "_shift_plots.pdf")
print("Saving plots to " + plotfile)
with PdfPages(plotfile) as pdf:
# Get reference used
shift_ref = ref_specs[shift_data['shift_reference']]
num_orders = shift_data['num_orders']
for i in range(num_orders):
plot_shift_data(targ_spec,
shifted,
shift_ref,
shift_data,
pdf,
i,
singleorder=True)
return shifted, targ_spec, shift_data
def specmatch_spectrum(specpath,
plot_level=0,
inlib=False,
outdir="./",
num_best=5,
suffix="",
wavlim='all',
lib_subset=None,
name=None,
n_lib_subset=None):
"""Perform the specmatch on a given spectrum
Args:
specpath (str): Path to target spectrum
plot_level (int, 0-2): Level of plots
0 - No plots saved, 1 - Representative plots, 2 - All plots
inlib (str or False): String to search within library for to exclude
from matching process
outdir (str): Output file directory
num_best (int): Number of best matches to use at lincomb stage
suffix (str): String to append to output file names
Returns:
specmatch.SpecMatch object
"""
if not os.path.exists(specpath):
raise ValueError(specpath + " does not exist!")
if wavlim == 'all':
target = spectrum.read_hires_fits(specpath)
else:
target = spectrum.read_hires_fits(specpath).cut(*wavlim)
# Determine the name of the target
if inlib:
name = inlib
elif name is None:
name = os.path.basename(specpath)[:-5]
if n_lib_subset is not None:
lib = library.read_hdf(wavlim='none')
lib_subset = lib.library_params.lib_index
lib_subset = np.random.choice(lib_subset,
size=n_lib_subset,
replace=False)
lib = library.read_hdf(wavlim=wavlim, lib_index_subset=lib_subset)
sm = specmatch.SpecMatch(target, lib)
sm.shift()
if inlib:
targ_idx = lib.get_index(inlib)
targ_param, targ_spec = lib[targ_idx]
sm.match(ignore=targ_idx, wavlim=wavlim)
else:
targ_param = None
sm.match(wavlim=wavlim)
sm.target.name = name # attach target name
sm.lincomb(num_best)
# Print results
print("SpecMatch Results for {0}".format(name))
sm.results_to_txt(sys.stdout)
outdir = os.path.join(outdir, name)
if not os.path.exists(outdir):
os.makedirs(outdir)
# Save final results
outpath = os.path.join(outdir, name + suffix + '_results.txt')
with open(outpath, 'w') as f:
if inlib:
f.write('Library Parameters\n')
f.write('------------------\n')
f.write('Teff: {0:.0f} +/- {1:.0f} K\n'.format(
targ_param['Teff'], targ_param['u_Teff']))
f.write('Radius: {0:.3f} +/- {1:.3f} Rsun\n'.format(
targ_param['radius'], targ_param['u_radius']))
f.write('[Fe/H]: {0:.2f} +/- {1:.2f} dex\n'.format(
targ_param['feh'], targ_param['u_feh']))
f.write('\n')
sm.results_to_txt(f, verbose=True)
print("created {}".format(outpath))
# Save full results
outpath = os.path.join(outdir, name + suffix + '_sm.hdf')
sm.to_hdf(outpath)
print("created {}".format(outpath))
# Create representative plots
if plot_level is not None and plot_level > 0:
plotspath = os.path.join(outdir, name + suffix + '_plots.pdf')
with PdfPages(plotspath) as pdf:
region = (5100, 5200)
wavlim = (5160, 5190)
order = 2
plot_shifts(sm, pdf, order, wavlim)
plot_match(sm, pdf, region, wavlim, targ_param)
plot_lincomb(sm, pdf, region, wavlim, targ_param)
print("created {}".format(plotspath))
# Create full plots
if plot_level == 2:
shiftplotspath = os.path.join(outdir,
name + suffix + '_shift_plots.pdf')
with PdfPages(shiftplotspath) as pdf:
for order in range(np.shape(sm.target_unshifted.w)[0]):
plot_shifts(sm, pdf, order, wavlim='all')
matchplotspath = os.path.join(outdir,
name + suffix + '_match_plots.pdf')
with PdfPages(matchplotspath) as pdf:
for reg in sm.regions:
plot_match(sm, pdf, reg, wavlim='all', targ_param=targ_param)
lincombplotspath = os.path.join(outdir,
name + suffix + '_lincomb_plots.pdf')
with PdfPages(lincombplotspath) as pdf:
for reg in sm.lincomb_regions:
plot_lincomb(sm, pdf, reg, wavlim='all', targ_param=targ_param)
print("created {}".format(plotspath))
return sm
def shift_spectrum(specpath, plot_level=0, indir=None, outdir="./",
suffix="_adj", mask=True, no_bootstrap=False,
flatten=False):
"""Shift a target spectrum given an observation code.
Saves the shifted spectrum in a fits file.
Args:
specpath (str): Path to spectrum or its CPS observation id jXX.XXXX
plot_level (int, 0-2): Level of plotting to save
indir (str): Directory to look in for target spectrum
outdir (str): Directory to store output files
suffix (str): String to append to output file names
mask (bool): Use a mask to remove telluric lines
no_bootstrap (bool): Shift a spectrum without bootstrapping
flatten (bool): If multiple chips are provided, flatten into a single
spectrum file.
Returns:
shifted, unshifted, shift_data
"""
# Check if specpath is a path or an observation ID
if os.path.exists(specpath):
targ_path = specpath
targid = os.path.splitext(os.path.basename(specpath))[0]
else:
return _multishift_spectrum(specpath, plot_level, indir, outdir,
suffix, mask, no_bootstrap, flatten)
# if a different directory is provided, copy the file into specmatchemp
# working directory
specdir = os.path.join(SPECMATCHDIR, 'spectra')
shiftedspecdir = os.path.join(SPECMATCHDIR, 'shifted_spectra')
if indir != specdir and indir is not None:
copy(targ_path, specdir)
# load target and references
if mask:
maskfile = os.path.join(SPECMATCHDIR, 'hires_telluric_mask.csv')
else:
maskfile = None
targ_spec = spectrum.read_hires_fits(targ_path, maskfile)
if no_bootstrap:
# Shift directly onto NSO spectrum
ref_specs = [spectrum.read_fits(os.path.join(shiftedspecdir,
'nso_adj.fits'))]
shift_data = {}
print("Shifting directly against NSO spectrum.")
shifted = shift.shift(targ_spec, ref_specs[0], store=shift_data)
shift_data['shift_reference'] = 0
else:
# Shift spectrum onto boostrapped spectra
ref_specs = [spectrum.read_fits(os.path.join(shiftedspecdir,
r[0] + '_adj.fits')) for r in SHIFT_REFERENCES]
shift_data = {}
shifted = shift.bootstrap_shift(targ_spec, ref_specs, store=shift_data)
# Save shifted spectrum
outpath = os.path.join(shiftedspecdir, targid + suffix + '.fits')
shift.save_shift_to_fits(outpath, shifted, targ_spec, shift_data,
clobber=True)
if outdir != shiftedspecdir:
if not os.path.exists(outdir):
os.mkdir(outdir)
copy(outpath, outdir)
# Generate representative plots
if plot_level == 1:
plotfile = os.path.join(outdir, targid + "_shift_plots.pdf")
print("Saving plots to " + plotfile)
with PdfPages(plotfile) as pdf:
# Get reference used
shift_ref = ref_specs[shift_data['shift_reference']]
# Plot single order
plot_shift_data(targ_spec, shifted, shift_ref, shift_data, pdf, 2)
# Generate individual plots for every order
elif plot_level == 2:
plotfile = os.path.join(outdir, targid + "_shift_plots.pdf")
print("Saving plots to " + plotfile)
with PdfPages(plotfile) as pdf:
# Get reference used
shift_ref = ref_specs[shift_data['shift_reference']]
num_orders = shift_data['num_orders']
for i in range(num_orders):
plot_shift_data(targ_spec, shifted, shift_ref, shift_data,
pdf, i, singleorder=True)
return shifted, targ_spec, shift_data
def specmatch_spectrum(specpath, plot_level=0, inlib=False, outdir="./",
num_best=5, suffix="", wavlim='all', lib_subset=None,
name=None, n_lib_subset=None):
"""Perform the specmatch on a given spectrum
Args:
specpath (str): Path to target spectrum
plot_level (int, 0-2): Level of plots
0 - No plots saved, 1 - Representative plots, 2 - All plots
inlib (str or False): String to search within library for to exclude
from matching process
outdir (str): Output file directory
num_best (int): Number of best matches to use at lincomb stage
suffix (str): String to append to output file names
Returns:
specmatch.SpecMatch object
"""
if not os.path.exists(specpath):
raise ValueError(specpath + " does not exist!")
if wavlim == 'all':
target = spectrum.read_hires_fits(specpath)
else:
target = spectrum.read_hires_fits(specpath).cut(*wavlim)
# Determine the name of the target
if inlib:
name = inlib
elif name is None:
name = os.path.basename(specpath)[:-5]
if n_lib_subset is not None:
lib = library.read_hdf(wavlim='none')
lib_subset = lib.library_params.lib_index
lib_subset = np.random.choice(
lib_subset, size=n_lib_subset, replace=False
)
lib = library.read_hdf(wavlim=wavlim, lib_index_subset=lib_subset)
sm = specmatch.SpecMatch(target, lib)
sm.shift()
if inlib:
targ_idx = lib.get_index(inlib)
targ_param, targ_spec = lib[targ_idx]
sm.match(ignore=targ_idx, wavlim=wavlim)
else:
targ_param = None
sm.match(wavlim=wavlim)
sm.target.name = name # attach target name
sm.lincomb(num_best)
# Print results
print("SpecMatch Results for {0}".format(name))
sm.results_to_txt(sys.stdout)
outdir = os.path.join(outdir, name)
if not os.path.exists(outdir):
os.makedirs(outdir)
# Save final results
outpath = os.path.join(outdir, name + suffix + '_results.txt')
with open(outpath, 'w') as f:
if inlib:
f.write('Library Parameters\n')
f.write('------------------\n')
f.write('Teff: {0:.0f} +/- {1:.0f} K\n'.format(
targ_param['Teff'], targ_param['u_Teff']))
f.write('Radius: {0:.3f} +/- {1:.3f} Rsun\n'.format(
targ_param['radius'], targ_param['u_radius']))
f.write('[Fe/H]: {0:.2f} +/- {1:.2f} dex\n'.format(
targ_param['feh'], targ_param['u_feh']))
f.write('\n')
sm.results_to_txt(f, verbose=True)
print("created {}".format(outpath))
# Save full results
outpath = os.path.join(outdir, name + suffix + '_sm.hdf')
sm.to_hdf(outpath)
print("created {}".format(outpath))
# Create representative plots
if plot_level is not None and plot_level > 0:
plotspath = os.path.join(outdir, name + suffix + '_plots.pdf')
with PdfPages(plotspath) as pdf:
region = (5100, 5200)
wavlim = (5160, 5190)
order = 2
plot_shifts(sm, pdf, order, wavlim)
plot_match(sm, pdf, region, wavlim, targ_param)
plot_lincomb(sm, pdf, region, wavlim, targ_param)
print("created {}".format(plotspath))
# Create full plots
if plot_level == 2:
shiftplotspath = os.path.join(outdir, name + suffix +
'_shift_plots.pdf')
with PdfPages(shiftplotspath) as pdf:
for order in range(np.shape(sm.target_unshifted.w)[0]):
plot_shifts(sm, pdf, order, wavlim='all')
matchplotspath = os.path.join(outdir, name + suffix +
'_match_plots.pdf')
with PdfPages(matchplotspath) as pdf:
for reg in sm.regions:
plot_match(sm, pdf, reg, wavlim='all', targ_param=targ_param)
lincombplotspath = os.path.join(outdir, name + suffix +
'_lincomb_plots.pdf')
with PdfPages(lincombplotspath) as pdf:
for reg in sm.lincomb_regions:
plot_lincomb(sm, pdf, reg, wavlim='all', targ_param=targ_param)
print("created {}".format(plotspath))
return sm