def __init__(self, spec, velscale, pklfile=None):
""" Load the pkl file from previous ppxf fit and define some atributes.
"""
if pklfile == None:
pklfile = spec.replace(".fits", ".pkl")
with open(pklfile) as f:
pp = pickle.load(f)
self.__dict__ = pp.__dict__.copy()
self.spec = spec
if not os.path.exists(os.path.join(os.getcwd(), spec)):
self.spec = os.path.join(data_dir, spec)
self.velscale = velscale
self.w = wavelength_array(os.path.join(data_dir, spec))
self.flux = pf.getdata(self.spec)
self.flux_log, self.logw, velscale = util.log_rebin(
[self.w[0], self.w[-1]], self.flux, velscale=velscale)
self.w_log = np.exp(self.logw)
self.header = pf.getheader(os.path.join(data_dir, spec))
self.lam = self.header['CRVAL1'] + np.array(
[0., self.header['CDELT1'] * (self.header['NAXIS1'] - 1)])
######################################################################
# # Read templates
star_templates, self.logLam2, self.delta, miles = stellar_templates(
velscale)
######################################################################
# Convolve our spectra to match MILES resolution
FWHM_tem = 2.51
FWHM_spec = 2.1
FWHM_dif = np.sqrt(FWHM_tem**2 - FWHM_spec**2)
sigma = FWHM_dif / 2.355 / self.delta # Sigma difference in pixels
######################################################################
spec_lin = ndimage.gaussian_filter1d(self.flux, sigma)
# Rebin to logarithm scale
galaxy, self.logLam1, velscale = util.log_rebin(self.lam,
spec_lin,
velscale=velscale)
self.dv = (self.logLam2[0] - self.logLam1[0]) * c
# if self.sky != None:
# sky = self.weights[-1] * self.sky.T[0]
# self.bestfit -= sky
# self.galaxy -= sky
# skyspec = os.path.join(sky_data_dir, spec.replace("fin1", "sky1"))
# sky_lin = pf.getdata(skyspec)
return
def check_ppxf(spec, velscale):
""" Checking if velocity os star is zero"""
templates, logLam2, delta, miles= stellar_templates(velscale)
FWHM_dif = np.sqrt(FWHM_tem**2 - FWHM_spec**2)
sigma = FWHM_dif/2.355/delta # Sigma difference in pixels
star = pf.getdata(spec)
star1 = ndimage.gaussian_filter1d(star, sigma)
w = wavelength_array(spec)
lamRange= np.array([w[0], w[-1]])
galaxy, logLam1, velscale = util.log_rebin(lamRange, star1, \
velscale=velscale)
sn = snr(star1)
noise = np.ones_like(galaxy) / sn
dv = (logLam2[0]-logLam1[0])*c
pp = ppxf(templates, galaxy, noise, velscale, [0,50],
plot=True, moments=2, degree=5, mdegree=-1, vsyst=dv)
plt.show()
return
def __init__(self, spec, velscale, pklfile=None):
""" Load the pkl file from previous ppxf fit and define some atributes.
"""
if pklfile == None:
pklfile = spec.replace(".fits", ".pkl")
with open(pklfile) as f:
pp = pickle.load(f)
self.__dict__ = pp.__dict__.copy()
self.spec = spec
if not os.path.exists(os.path.join(os.getcwd(), spec)):
self.spec = os.path.join(data_dir, spec)
self.velscale = velscale
self.w = wavelength_array(os.path.join(data_dir, spec))
self.flux = pf.getdata(self.spec)
self.flux_log, self.logw, velscale = util.log_rebin(
[self.w[0], self.w[-1]], self.flux, velscale=velscale)
self.w_log = np.exp(self.logw)
self.header = pf.getheader(os.path.join(data_dir, spec))
self.lam = self.header['CRVAL1'] + np.array([0.,
self.header['CDELT1']*(self.header['NAXIS1']-1)])
######################################################################
# # Read templates
star_templates, self.logLam2, self.delta, miles= stellar_templates(velscale)
######################################################################
# Convolve our spectra to match MILES resolution
FWHM_tem = 2.51
FWHM_spec = 2.1
FWHM_dif = np.sqrt(FWHM_tem**2 - FWHM_spec**2)
sigma = FWHM_dif/2.355/self.delta # Sigma difference in pixels
######################################################################
spec_lin = ndimage.gaussian_filter1d(self.flux,sigma)
# Rebin to logarithm scale
galaxy, self.logLam1, velscale = util.log_rebin(self.lam, spec_lin,
velscale=velscale)
self.dv = (self.logLam2[0]-self.logLam1[0])*c
# if self.sky != None:
# sky = self.weights[-1] * self.sky.T[0]
# self.bestfit -= sky
# self.galaxy -= sky
# skyspec = os.path.join(sky_data_dir, spec.replace("fin1", "sky1"))
# sky_lin = pf.getdata(skyspec)
return
def run_ppxf(spectra, velscale, ncomp=2, has_emission=True, mdegree=-1,
degree=20, pkls=None, plot=False, data_sky=None):
""" Run pPXF in a list of spectra"""
if isinstance(spectra, str):
spectra = [spectra]
if isinstance(pkls, str):
pkls = [pkls]
if pkls == None:
pkls = [x.replace(".fits", ".pkl") for x in spectra]
##########################################################################
# Load templates for both stars and gas
star_templates, logLam2, delta, miles= stellar_templates(velscale)
gas_templates,logLam_gas, delta_gas, gas_files=emission_templates(velscale)
##########################################################################
# Join templates in case emission lines are used.
if has_emission:
templates = np.column_stack((star_templates, gas_templates))
else:
templates = star_templates
##########################################################################
if ncomp == 1:
components = 0
moments = [4]
templates_names = miles
elif ncomp == 2:
components = np.hstack((np.zeros(len(star_templates[0])),
np.ones(len(gas_templates[0]))))
moments = [4,2]
templates_names = np.hstack((miles, gas_files))
else:
raise Exception("ncomp has to be 1 or 2.")
for i, spec in enumerate(spectra):
print "pPXF run of spectrum {0} ({1} of {2})".format(spec, i+1,
len(spectra))
pkl = pkls[i]
######################################################################
# Read one galaxy spectrum and define the wavelength range
specfile = os.path.join(data_dir, spec)
hdu = pf.open(specfile)
spec_lin = hdu[0].data
h1 = pf.getheader(specfile)
lamRange1 = h1['CRVAL1'] + np.array([0.,h1['CDELT1']*(h1['NAXIS1']-1)])
######################################################################
# Degrade observed spectra to match template resolution
FWHM_dif = np.sqrt(FWHM_tem**2 - FWHM_spec**2)
sigma = FWHM_dif/2.355/delta # Sigma difference in pixels
spec_lin = ndimage.gaussian_filter1d(spec_lin,sigma)
######################################################################
# Rebin to log scale
galaxy, logLam1, velscale = util.log_rebin(lamRange1, spec_lin,
velscale=velscale)
######################################################################
# First guess for the noise
noise = np.ones_like(galaxy) * np.std(galaxy - medfilt(galaxy, 5))
######################################################################
# Calculate difference of velocity between spectrum and templates
# due to different initial wavelength
dv = (logLam2[0]-logLam1[0])*c
######################################################################
# Set first guess from setup files
start, goodPixels = read_setup_file(spec, logLam1, mask_emline=False)
######################################################################
# Expand start variable to include multiple components
if ncomp > 1:
start = [start, [start[0], 30]]
######################################################################
# Read sky in needed
if data_sky == None:
sky = None
else:
sky_lin = pf.getdata(data_sky[i])
sky_lin = ndimage.gaussian_filter1d(sky_lin,sigma)
sky, logLam1, velscale = util.log_rebin(lamRange1, sky_lin,
velscale=velscale)
sky = sky.reshape(-1,1)
######################################################################
# First pPXF interaction
if os.path.exists(spec.replace(".fits", ".pkl")):
pp0 = pPXF(spec, velscale, pklfile=spec.replace(".fits", ".pkl"))
noise0 = pp0.noise
else:
pp0 = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodPixels, plot=False, moments=moments,
degree=12, mdegree=-1, vsyst=dv, component=components,
sky=sky)
rms0 = galaxy[goodPixels] - pp0.bestfit[goodPixels]
noise0 = 1.4826 * np.median(np.abs(rms0 - np.median(rms0)))
noise0 = np.zeros_like(galaxy) + noise0
# Second pPXF interaction, realistic noise estimation
pp = ppxf(templates, galaxy, noise0, velscale, start,
goodpixels=goodPixels, plot=plot, moments=moments,
degree=degree, mdegree=mdegree, vsyst=dv,
component=components, sky=sky)
# pp.template_files = templates_names
# pp.has_emission = has_emission
######################################################################
# Save to output file to keep session
with open(pkl, "w") as f:
pickle.dump(pp, f)
######################################################################
return
def run_ppxf(spectra,
velscale,
ncomp=2,
has_emission=True,
mdegree=-1,
degree=20,
pkls=None,
plot=False,
data_sky=None):
""" Run pPXF in a list of spectra"""
if isinstance(spectra, str):
spectra = [spectra]
if isinstance(pkls, str):
pkls = [pkls]
if pkls == None:
pkls = [x.replace(".fits", ".pkl") for x in spectra]
##########################################################################
# Load templates for both stars and gas
star_templates, logLam2, delta, miles = stellar_templates(velscale)
gas_templates, logLam_gas, delta_gas, gas_files = emission_templates(
velscale)
##########################################################################
# Join templates in case emission lines are used.
if has_emission:
templates = np.column_stack((star_templates, gas_templates))
else:
templates = star_templates
##########################################################################
if ncomp == 1:
components = 0
moments = [4]
templates_names = miles
elif ncomp == 2:
components = np.hstack(
(np.zeros(len(star_templates[0])), np.ones(len(gas_templates[0]))))
moments = [4, 2]
templates_names = np.hstack((miles, gas_files))
else:
raise Exception("ncomp has to be 1 or 2.")
for i, spec in enumerate(spectra):
print "pPXF run of spectrum {0} ({1} of {2})".format(
spec, i + 1, len(spectra))
pkl = pkls[i]
######################################################################
# Read one galaxy spectrum and define the wavelength range
specfile = os.path.join(data_dir, spec)
hdu = pf.open(specfile)
spec_lin = hdu[0].data
h1 = pf.getheader(specfile)
lamRange1 = h1['CRVAL1'] + np.array(
[0., h1['CDELT1'] * (h1['NAXIS1'] - 1)])
######################################################################
# Degrade observed spectra to match template resolution
FWHM_dif = np.sqrt(FWHM_tem**2 - FWHM_spec**2)
sigma = FWHM_dif / 2.355 / delta # Sigma difference in pixels
spec_lin = ndimage.gaussian_filter1d(spec_lin, sigma)
######################################################################
# Rebin to log scale
galaxy, logLam1, velscale = util.log_rebin(lamRange1,
spec_lin,
velscale=velscale)
######################################################################
# First guess for the noise
noise = np.ones_like(galaxy) * np.std(galaxy - medfilt(galaxy, 5))
######################################################################
# Calculate difference of velocity between spectrum and templates
# due to different initial wavelength
dv = (logLam2[0] - logLam1[0]) * c
######################################################################
# Set first guess from setup files
start, goodPixels = read_setup_file(spec, logLam1, mask_emline=False)
######################################################################
# Expand start variable to include multiple components
if ncomp > 1:
start = [start, [start[0], 30]]
######################################################################
# Read sky in needed
if data_sky == None:
sky = None
else:
sky_lin = pf.getdata(data_sky[i])
sky_lin = ndimage.gaussian_filter1d(sky_lin, sigma)
sky, logLam1, velscale = util.log_rebin(lamRange1,
sky_lin,
velscale=velscale)
sky = sky.reshape(-1, 1)
######################################################################
# First pPXF interaction
if os.path.exists(spec.replace(".fits", ".pkl")):
pp0 = pPXF(spec, velscale, pklfile=spec.replace(".fits", ".pkl"))
noise0 = pp0.noise
else:
pp0 = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=False,
moments=moments,
degree=12,
mdegree=-1,
vsyst=dv,
component=components,
sky=sky)
rms0 = galaxy[goodPixels] - pp0.bestfit[goodPixels]
noise0 = 1.4826 * np.median(np.abs(rms0 - np.median(rms0)))
noise0 = np.zeros_like(galaxy) + noise0
# Second pPXF interaction, realistic noise estimation
pp = ppxf(templates,
galaxy,
noise0,
velscale,
start,
goodpixels=goodPixels,
plot=plot,
moments=moments,
degree=degree,
mdegree=mdegree,
vsyst=dv,
component=components,
sky=sky)
# pp.template_files = templates_names
# pp.has_emission = has_emission
######################################################################
# Save to output file to keep session
with open(pkl, "w") as f:
pickle.dump(pp, f)
######################################################################
return
