def do_specfit(filename, lines=["NIIa + Halp + NIIb", "Hbet", "Hgam", "Hdel",
"Ca H + Ca K",
"Mg", "NaI", "OIIIa + OIIIb"],
fix_lambda=True, smooth_size=3, verbose=False,
verbose_print=True):
'''
Given a FITS file, fit the specified lines to the spectra.
'''
if verbose_print:
print "Running on " + filename
spec_file = fits.open(filename)
flux = spec_file[1].data["flux"]
# Correct for redshift
lam_wav = 10**spec_file[1].data["loglam"] / (1 + spec_file[2].data["Z"])
# Correct for vacuum-to-air
lam_wav = vac_to_air(lam_wav)
# Optionally smooth the spectrum
if smooth_size != 0:
flux = median_filter(flux, smooth_size)
spec = Spectrum(data=flux, xarr=lam_wav,
header=spec_file[1].header,
unit="",
xarrkwargs={'unit': 'Angstroms'})
line_params = []
line_errs = []
for i, line in enumerate(lines):
line_props = line_dict[line]
num_lines = len(line_props) / 3
spec_line = spec.slice(start=line_props[1]-100,
stop=line_props[-2]+100,
units='Angstroms')
if verbose:
spec_line.plotter()
spec_line.baseline(order=1, subract=False, annotate=False)
# Estimate the amplitude of the line after background subtraction
# using the peak of where the line is expected to be
baseline_model = lambda x, p: p[1] + p[0]*x
base_params = spec_line.baseline.baselinepars
for i in range(num_lines):
lam_line = find_nearest(lam_wav, line_props[3*i+1])
line_props[3*i] = float(flux[np.argwhere(lam_wav == lam_line)]) - \
baseline_model(lam_line, base_params)
if fix_lambda:
fix = [False, True, False] * num_lines
else:
fix = [False] * len(line_props)
if num_lines > 1:
multifit = True
else:
multifit = False
try:
spec_line.specfit(guesses=line_props, fixed=fix,
fittype="gaussian",
multifit=multifit)
line_pars = spec_line.specfit.modelpars
line_errors = spec_line.specfit.modelerrs
except ValueError:
line_pars = [0.0] * 3 * num_lines
line_errors = [0.0] * 3 * num_lines
except mpfitException:
line_pars = [np.NaN] * 3 * num_lines
line_errors = [np.NaN] * 3 * num_lines
# For multifits, a bad fit for a line that isn't there increases the
# error on a fit for a line that is. Remove
# if num_lines > 1:
# t_stats = [np.logical_and(np.abs(k)/j < 1, j != 0)
# for k, j in zip(line_pars, line_errors)]
# if np.any(t_stats):
# posn = [f for f, j in enumerate(t_stats) if j]
# bad_line = []
# for pos in posn:
# for n in range(1, num_lines+1):
# if pos < 3*i and pos > 3*(i-1):
# bad_line.append(n)
# break
# # If they're both bad, just continue
# if len(bad_line) == num_lines:
# pass
# else:
# bad_line = np.sort(bad_line)[::-1]
# for bad in bad_line:
# bad = int(bad) - 1
# for p in range(3)[::-1]:
# line_props.pop(3*bad + p)
# fix.pop(3*bad + p)
# if (num_lines - len(bad_line)) == 1:
# multifit = False
# spec_line.specfit(guesses=line_props,
# fixed=fix,
# fittype="gaussian",
# multifit=multifit)
# good_line = \
# np.asarray(list(set(range(1, num_lines+1)) & set(bad_line)))
# good_line -= 1
# for good in good_line:
# line_pars[3*(good-1):3*good] = spec_line.specfit.modelpars
# line_errors[3*(good-1):3*good] = spec_line.specfit.modelerrs
line_params.extend(line_pars)
line_errs.extend(line_errors)
if verbose:
raw_input("Continue?")
rows = len(line_params) / 3
line_params = np.array(line_params).reshape((rows, 3))
line_errs = np.array(line_errs).reshape((rows, 3))
# No point in saving lambda if it is fixed.
if fix_lambda:
line_params = np.hstack([line_params[:, 0], line_params[:, -1]])
line_errs = np.hstack([line_errs[:, 0], line_errs[:, -1]])
line_param_names = ['Amplitude', 'Width', 'Amplitude Error',
'Width Error']
line_names = []
for line in lines:
line = line.split(" + ")
if len(line) > 1:
for l in line:
line_names.append(l)
else:
line_names.append(line[0])
line_and_par_names = []
for par in line_param_names:
for name in line_names:
line_and_par_names.append(name+" "+par)
# Return as a named series, which can be concatenated into a dataframe.
ser = Series(np.hstack([line_params, line_errs]).ravel(),
index=line_and_par_names)
return ser
