def emission_templates(velscale):
""" Load files with stellar library used as templates. """
current_dir = os.getcwd()
# Template directory is also set in setyp.py
os.chdir(template_dir)
emission = [x for x in os.listdir(".") if x.startswith("emission") and x.endswith(".fits")]
emission.sort()
c = 299792.458
FWHM_tem = 2.1 # MILES library spectra have a resolution FWHM of 2.54A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pf.open(emission[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2["CRVAL1"] + np.array([0.0, h2["CDELT1"] * (h2["NAXIS1"] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates = np.empty((sspNew.size, len(emission)))
for j in range(len(emission)):
hdu = pf.open(emission[j])
ssp = hdu[0].data
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates[:, j] = sspNew
# templates *= 1e5 # Normalize templates
os.chdir(current_dir)
return templates, logLam2, h2["CDELT1"], emission
def emission_templates(velscale):
""" Load files with stellar library used as templates. """
current_dir = os.getcwd()
# Template directory is also set in setup.py
os.chdir(templates_dir)
emission = [x for x in os.listdir(".") if x.startswith("emission") and
x.endswith(".fits") and x not in ["emission_FWHM_2.7.fits",
"emission_FWHM_3.7.fits"]]
emission.sort()
c = 299792.458
FWHM_tem = 2.5 # MILES library spectra have a resolution FWHM of 2.54A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pf.open(emission[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates = np.empty((sspNew.size,len(emission)))
for j in range(len(emission)):
ssp = pf.getdata(emission[j])
w = wavelength_array(emission[j])
dsigma = np.sqrt((3.7**2 - 2.7**2))/2.335/(w[1]-w[0])
ssp = gaussian_filter1d(ssp, dsigma)
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates[:,j] = sspNew
templates *= 1e5 # Normalize templates
os.chdir(current_dir)
return templates, logLam2, h2['CDELT1'], emission
def emission_templates(velscale):
""" Load files with stellar library used as templates. """
current_dir = os.getcwd()
# Template directory is also set in setyp.py
os.chdir(template_dir)
emission = [x for x in os.listdir(".") if x.startswith("emission") and
x.endswith(".fits")]
emission.sort()
c = 299792.458
FWHM_tem = 2.1 # MILES library spectra have a resolution FWHM of 2.54A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pf.open(emission[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates = np.empty((sspNew.size,len(emission)))
for j in range(len(emission)):
hdu = pf.open(emission[j])
ssp = hdu[0].data
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates[:,j] = sspNew
# templates *= 1e5 # Normalize templates
os.chdir(current_dir)
return templates, logLam2, h2['CDELT1'], emission
def load_templates_regul(velscale):
""" Load templates into 2D array for regularization"""
# cd into template folder to make calculations
current_dir = os.getcwd()
os.chdir(os.path.join(home, "miles_models"))
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
miles = [x for x in os.listdir(".") if x.endswith(".fits")]
print miles
raw_input()
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
# Ordered array of metallicities
Zs = set([x.split("Z")[1].split("T")[0] for x in miles])
Zs = [float(x.replace("m", "-").replace("p", "")) for x in Zs]
Zs.sort()
Z2 = Zs[:]
for i in range(len(Zs)):
if Zs[i] < 0:
Zs[i] = "{0:.2f}".format(Zs[i]).replace("-", "m")
else:
Zs[i] = "p{0:.2f}".format(Zs[i])
# Ordered array of ages
Ts = list(set([x.split("T")[1].split(".fits")[0] for x in miles]))
Ts.sort()
# Create a three dimensional array to store the
# two dimensional grid of model spectra
#
nAges = len(Ts)
nMetal = len(Zs)
templates = np.empty((sspNew.size,nAges,nMetal))
# Here we make sure the spectra are sorted in both [M/H]
# and Age along the two axes of the rectangular grid of templates.
# A simple alphabetical ordering of Vazdekis's naming convention
# does not sort the files by [M/H], so we do it explicitly below
miles = []
for k in range(nMetal):
for j in range(nAges):
filename = "Mun1.30Z{0}T{1}.fits".format(Zs[k], Ts[j])
ssp = pf.getdata(filename)
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates[:,j,k] = sspNew # Templates are *not* normalized here
miles.append(filename)
templates /= np.median(templates) # Normalizes templates by a scalar
os.chdir(current_dir)
return templates, logLam2, Ts, Z2, miles, h2['CDELT1']
def setup_bc03_library(wave,
velscale=None,
FWHM_gal=1,
version='N',
in_log=True):
FWHM_tem = 1 # this number is crap as the spectra are synthetic in part...
lam, full, full_data, logAge_grid, metal_grid = load_bc03_library(
version=version)
templates = np.zeros_like(full)
new_ssp = np.interp(wave, lam, ssp, right=0, left=0)
lamRange_temp = [wave[0], wave[-1]]
delta = np.diff(lam)[0]
if in_log:
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp,
ssp,
velscale=0.99999 * velscale)
else:
sspNew = ssp
logLam2 = np.log(lam)
#
templates = np.empty((sspNew.size, nAges, nMetal))
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif / 2.355 / delta # Sigma difference in pixels
for i, d in enumerate(data):
tmp = os.path.join(os.path.dirname(basefile), 'BasesDir',
d['specfile'])
lam, ssp = np.loadtxt(tmp, unpack=True)
full[:, i % nAges, int(i / nAges)] = ssp
new_lam = wave
#new_lam = np.arange(new_lam[0], new_lam[-1],1)
new_ssp = np.interp(new_lam, lam, ssp, right=0, left=0)
# new_lam = np.arange(lam[0], lam[-1],1)
# new_ssp = np.interp(new_lam,lam, ssp, right=0, left=0)
# mask = (new_lam > wave.min()-1) & (new_lam < wave.max()+2)
lam = new_lam #[mask]
ssp = new_ssp #[mask]
ssp = ndimage.gaussian_filter1d(ssp, sigma)
if in_log:
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp,
ssp,
velscale=velscale)
else:
sspNew = ssp
templates[:, i % nAges, int(i / nAges)] = sspNew
logAge_grid[i % nAges, int(i / nAges)] = np.log10(d['age'] / 1.e9)
metal_grid[i % nAges, int(i / nAges)] = np.log10(d['Z'])
return templates, lamRange_temp, logAge_grid, metal_grid #, lam_full, full
def load_templates_regul(velscale):
""" Load templates into 2D array for regularization"""
# cd into template folder to make calculations
current_dir = os.getcwd()
os.chdir(os.path.join(home, "miles_models"))
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
miles = [x for x in os.listdir(".") if x.endswith(".fits")]
print miles
raw_input()
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2["CRVAL1"] + np.array([0.0, h2["CDELT1"] * (h2["NAXIS1"] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
# Ordered array of metallicities
Zs = set([x.split("Z")[1].split("T")[0] for x in miles])
Zs = [float(x.replace("m", "-").replace("p", "")) for x in Zs]
Zs.sort()
Z2 = Zs[:]
for i in range(len(Zs)):
if Zs[i] < 0:
Zs[i] = "{0:.2f}".format(Zs[i]).replace("-", "m")
else:
Zs[i] = "p{0:.2f}".format(Zs[i])
# Ordered array of ages
Ts = list(set([x.split("T")[1].split(".fits")[0] for x in miles]))
Ts.sort()
# Create a three dimensional array to store the
# two dimensional grid of model spectra
#
nAges = len(Ts)
nMetal = len(Zs)
templates = np.empty((sspNew.size, nAges, nMetal))
# Here we make sure the spectra are sorted in both [M/H]
# and Age along the two axes of the rectangular grid of templates.
# A simple alphabetical ordering of Vazdekis's naming convention
# does not sort the files by [M/H], so we do it explicitly below
miles = []
for k in range(nMetal):
for j in range(nAges):
filename = "Mun1.30Z{0}T{1}.fits".format(Zs[k], Ts[j])
ssp = pf.getdata(filename)
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates[:, j, k] = sspNew # Templates are *not* normalized here
miles.append(filename)
templates /= np.median(templates) # Normalizes templates by a scalar
os.chdir(current_dir)
return templates, logLam2, Ts, Z2, miles, h2["CDELT1"]
def load_sky(filenames, velscale, full_output=False):
""" Load and rebin sky files. """
skydata = fits_to_matrix(filenames)
h1 = pf.getheader(filenames[0])
lamRange1 = h1['CRVAL1'] + np.array([0.,h1['CDELT1']*(h1['NAXIS1']-1)])
sky1, logLam1, velscale = util.log_rebin(lamRange1, skydata[:,0],
velscale=velscale)
skylog = np.zeros((sky1.shape[0], len(filenames)))
for i in range(len(filenames)):
skylog[:,i], logLam1, velscale = util.log_rebin(lamRange1,
skydata[:,i], velscale=velscale)
if full_output:
return skylog, logLam1
return skylog
def smooth_and_rebin(bc03, bc03_lambda, gal_lambda, velscale, FWHM_gal=1, FWHM_tem=0):
"""
Smooth and rebin the BC03 spectra based on the sampling
of the galaxy to be fitted.
Input :
- bc03 : the spectral templates
- bc03_lambda : the template wavelength array
- gal_lambda : the galaxy wavelength array, which the rebinning
is going to match. Note tha the galaxy must have already
been log-rebinned.
- velscale : velocity scale of the galaxy, for log rebinning
- FWHM_gal : FWHM of the galaxy resolution
- FWHM_tem : FWHM of the template resolution
Output :
- templates : the smoothed and rebinned version of the BC03 input
"""
templates = np.empty(((gal_lambda.size,)+(bc03.shape[1],bc03.shape[2])))
for i in range(bc03.shape[1]):
for j in range(bc03.shape[2]):
ssp = bc03[:,i,j]
#resize the templates to the log-rebinned galaxy wavelengths
new_ssp = np.interp(gal_lambda, bc03_lambda, ssp, right=0, left=0)
#log-rebin the interpolated templates
#the output velscape should be close to identical to the input
#Likewise, logLam2 == np.log(gal_lambda) to about 1e-5
sspNew, logLam2, velscale = util.log_rebin([gal_lambda[0],gal_lambda[-1]], new_ssp, velscale=velscale)
#smooth the log-rebinned templates
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif/2.355/np.diff(gal_lambda)[0]
smoothed_ssp = ndimage.gaussian_filter1d(new_ssp, sigma)
templates[:, i, j] = smoothed_ssp
return templates
def stellar_templates(velscale):
""" Load files with stellar library used as templates. """
current_dir = os.getcwd()
# Template directory is also set in config.py
os.chdir(templates_dir)
miles = [x for x in os.listdir(".") if x.startswith("Mun") and
x.endswith(".fits")]
# Ordered array of metallicities
Zs = set([x.split("Z")[1].split("T")[0] for x in miles])
Zs = [float(x.replace("m", "-").replace("p", "").replace("_", "."))
for x in Zs]
Zs.sort()
for i in range(len(Zs)):
if Zs[i] < 0:
Zs[i] = "{0:.2f}".format(Zs[i]).replace("-", "m")
else:
Zs[i] = "p{0:.2f}".format(Zs[i])
# Ordered array of ages
Ts = list(set([x.split("T")[1][:7]
for x in miles]))
Ts.sort()
miles = []
metal_ages = []
for m in Zs:
for t in Ts:
filename = "Mun1.30Z{0}T{1}_linear_FWHM_2.7.fits".format(m, t)
if os.path.exists(filename):
miles.append(filename)
metal_ages.append([m.replace("_", ".").replace("p",
"+").replace("m", "-"),t.replace("_", ".")])
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates = np.empty((sspNew.size,len(miles)))
for j in range(len(miles)):
ssp = pf.getdata(miles[j])
w = wavelength_array(miles[j])
dsigma = np.sqrt((3.7**2 - 2.7**2))/2.335/(w[1]-w[0])
ssp = gaussian_filter1d(ssp, dsigma)
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates[:,j] = sspNew
os.chdir(current_dir)
return templates, logLam2, h2['CDELT1'], miles
def stellar_templates(velscale):
""" Load files with stellar library used as templates. """
current_dir = os.getcwd()
# Template directory is also set in config.py
os.chdir(template_dir)
miles = [x for x in os.listdir(".") if x.startswith("Mun") and
x.endswith(".fits")]
# Ordered array of metallicities
Zs = set([x.split("Z")[1].split("T")[0] for x in miles])
Zs = [float(x.replace("m", "-").replace("p", "").replace("_", "."))
for x in Zs]
Zs.sort()
for i in range(len(Zs)):
if Zs[i] < 0:
Zs[i] = "{0:.2f}".format(Zs[i]).replace("-", "m")
else:
Zs[i] = "p{0:.2f}".format(Zs[i])
Zs = [str(x).replace(".", "_") for x in Zs]
# Ordered array of ages
Ts = list(set([x.split("T")[1].split(".fits")[0].replace("_", ".")
for x in miles]))
Ts.sort()
Ts = [str(x).replace(".", "_") for x in Ts]
miles = []
metal_ages = []
for m in Zs:
for t in Ts:
filename = "Mun1_30Z{0}T{1}.fits".format(m, t)
if os.path.exists(filename):
miles.append(filename)
metal_ages.append([m.replace("_", ".").replace("p",
"+").replace("m", "-"),t.replace("_", ".")])
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates = np.empty((sspNew.size,len(miles)))
for j in range(len(miles)):
hdu = pf.open(miles[j])
ssp = hdu[0].data
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp,
velscale=velscale)
templates[:,j] = sspNew
os.chdir(current_dir)
return templates, logLam2, h2['CDELT1'], miles
def build_templates(lam, ssp, wave, velscale, sigma):
new_ssp = np.interp(wave, lam, ssp, right=0, left=0)
lam=wave
ssp=new_ssp
ssp = ndimage.gaussian_filter1d(new_ssp,sigma)
sspNew, logLam2, velscale = util.log_rebin([wave[0],wave[-1]], ssp, velscale=velscale)
return sspNew
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 rebin(self):
self.stellar_templates = get_stellar_templates(
self.FWHM_gal, library=self.params.library)
## smooth spectrum to fit with templates resolution
if self.FWHM_gal < self.stellar_templates.FWHM_tem:
sigma = self.stellar_templates.FWHM_dif / 2.355 / self.CDELT # Change in px
self.bin_lin = ndimage.gaussian_filter1d(self.bin_lin, sigma)
self.bin_lin_noise = np.sqrt(
ndimage.gaussian_filter1d(self.bin_lin_noise**2, sigma))
## rebin spectrum logarthmically
self.bin_log, self.logLam_bin, self.velscale = util.log_rebin(
self.lamRange, self.bin_lin)
bin_log_noise, logLam_bin, _ = util.log_rebin(self.lamRange,
self.bin_lin_noise**2)
self.bin_log_noise = np.sqrt(bin_log_noise)
self.lambdaq = np.exp(self.logLam_bin)
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 setup_bc03_library(wave, velscale=None, FWHM_gal=1, version='N', in_log=True):
FWHM_tem = 1 # this number is crap as the spectra are synthetic in part...
lam, full, full_data, logAge_grid, metal_grid = load_bc03_library(version=version)
templates = np.zeros_like(full)
new_ssp = np.interp(wave,lam, ssp, right=0, left=0)
lamRange_temp = [wave[0],wave[-1]]
delta = np.diff(lam)[0]
if in_log:
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp, ssp, velscale=0.99999*velscale)
else:
sspNew=ssp
logLam2=np.log(lam)
#
templates = np.empty((sspNew.size, nAges, nMetal))
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif/2.355/delta # Sigma difference in pixels
for i,d in enumerate(data):
tmp=os.path.join(os.path.dirname(basefile),'BasesDir',d['specfile'])
lam,ssp = np.loadtxt(tmp, unpack=True)
full[:,i%nAges,int(i/nAges)] = ssp
new_lam = wave
#new_lam = np.arange(new_lam[0], new_lam[-1],1)
new_ssp = np.interp(new_lam,lam, ssp, right=0, left=0)
# new_lam = np.arange(lam[0], lam[-1],1)
# new_ssp = np.interp(new_lam,lam, ssp, right=0, left=0)
# mask = (new_lam > wave.min()-1) & (new_lam < wave.max()+2)
lam=new_lam#[mask]
ssp=new_ssp#[mask]
ssp = ndimage.gaussian_filter1d(ssp,sigma)
if in_log:
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp, ssp, velscale=velscale)
else:
sspNew=ssp
templates[:,i%nAges,int(i/nAges)] = sspNew
logAge_grid[i%nAges,int(i/nAges)] = np.log10(d['age']/1.e9)
metal_grid[i%nAges,int(i/nAges)] = np.log10(d['Z'])
return templates, lamRange_temp, logAge_grid, metal_grid#, lam_full, full
def build_templates(lam, ssp, wave, velscale, sigma):
new_ssp = np.interp(wave, lam, ssp, right=0, left=0)
lam = wave
ssp = new_ssp
ssp = ndimage.gaussian_filter1d(new_ssp, sigma)
sspNew, logLam2, velscale = util.log_rebin([wave[0], wave[-1]],
ssp,
velscale=velscale)
return sspNew
def rebin(self): self.stellar_templates = get_stellar_templates(self.FWHM_gal, library=self.params.library) ## smooth spectrum to fit with templates resolution if self.FWHM_gal < self.stellar_templates.FWHM_tem: sigma = self.stellar_templates.FWHM_dif/2.355/self.CDELT # Change in px self.bin_lin = ndimage.gaussian_filter1d(self.bin_lin, sigma) self.bin_lin_noise = np.sqrt(ndimage.gaussian_filter1d( self.bin_lin_noise**2, sigma)) self.FWHM_gal = self.stellar_templates.FWHM_tem ## rebin spectrum logarthmically self.bin_log, self.logLam_bin, self.velscale = util.log_rebin(self.lamRange, self.bin_lin) bin_log_noise, logLam_bin, _ = util.log_rebin(self.lamRange, self.bin_lin_noise**2) self.bin_log_noise = np.sqrt(bin_log_noise) self.lambdaq = np.exp(self.logLam_bin)
def getdata_NEW(spec, wavelength, l1, l2):
tmp = np.column_stack((wavelength, spec))
mn = tmp[tmp[:, 0] > l1]
mx = tmp[tmp[:, 0] < l2]
lamRange = [mn[0, 0], mx[-1, 0]]
galaxy, logLam1, velscale = util.log_rebin(lamRange, spec)
return galaxy, logLam1, velscale
def stellar_templates(velscale):
""" Load files with stellar library used as templates. """
current_dir = os.getcwd()
# Template directory is also set in config.py
os.chdir(template_dir)
miles = [x for x in os.listdir(".") if x.startswith("Mun") and x.endswith(".fits")]
# Ordered array of metallicities
Zs = set([x.split("Z")[1].split("T")[0] for x in miles])
Zs = [float(x.replace("m", "-").replace("p", "").replace("_", ".")) for x in Zs]
Zs.sort()
for i in range(len(Zs)):
if Zs[i] < 0:
Zs[i] = "{0:.2f}".format(Zs[i]).replace("-", "m")
else:
Zs[i] = "p{0:.2f}".format(Zs[i])
Zs = [str(x).replace(".", "_") for x in Zs]
# Ordered array of ages
Ts = list(set([x.split("T")[1].split(".fits")[0].replace("_", ".") for x in miles]))
Ts.sort()
Ts = [str(x).replace(".", "_") for x in Ts]
miles = []
metal_ages = []
for m in Zs:
for t in Ts:
filename = "Mun1_30Z{0}T{1}.fits".format(m, t)
if os.path.exists(filename):
miles.append(filename)
metal_ages.append([m.replace("_", ".").replace("p", "+").replace("m", "-"), t.replace("_", ".")])
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2["CRVAL1"] + np.array([0.0, h2["CDELT1"] * (h2["NAXIS1"] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates = np.empty((sspNew.size, len(miles)))
for j in range(len(miles)):
hdu = pf.open(miles[j])
ssp = hdu[0].data
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates[:, j] = sspNew
os.chdir(current_dir)
return templates, logLam2, h2["CDELT1"], miles
def fit_spectra(bin_sci_j, ppxf_bestfit_j, plot=False):
"""
"""
with fits.open(bin_sci_j) as hdu:
odata = hdu[0].data
ohdr = hdu[0].header
bestfit = fits.getdata(ppxf_bestfit_j)
lamRange = ohdr['CRVAL1'] + np.array([1. - ohdr['CRPIX1'], ohdr['NAXIS1'] - ohdr['CRPIX1']]) * ohdr['CD1_1']
x = np.linspace(lamRange[0], lamRange[1], ohdr['NAXIS1'])
# Log bin the spectra to match the best fit absorption template
galaxy, logLam1, velscale = util.log_rebin(lamRange, odata)
log_bins = np.exp(logLam1)
emlns, lnames, lwave = util.emission_lines(logLam1, lamRange, 2.3)
# Hgamma 4340.47, Hbeta 4861.33, OIII [4958.92, 5006.84]
# lwave = [4340.47, 4861.33, 4958.92, 5006.84]
# find the index of the emission lines
iHg = (np.abs(log_bins - lwave[0])).argmin()
iHb = (np.abs(log_bins - lwave[1])).argmin()
iOIII = (np.abs(log_bins - lwave[2])).argmin()
iOIIIb = (np.abs(log_bins - (lwave[2] - 47.92))).argmin()
# There are BOTH absorption and emission features about the wavelength of Hgamma and Hbeta, so we need
# to use a specialized fitting function (convolved Guassian and Lorentzian -> pVoight) to remove the
# emission lines
popt_Hg, pcov_Hg = fit_ems_pvoightcont(log_bins, galaxy, x, odata, iHg, bestfit)
popt_Hb, pcov_Hb = fit_ems_pvoightcont(log_bins, galaxy, x, odata, iHb, bestfit)
# There are only emission features about the OIII doublet so we only fit the emission line with a Gaussian
popt_OIII, pcov_OIII = fit_ems_lincont(x, odata, iOIII, bestfit, x[954], [x[952], x[956]])
popt_OIIIb, pcov_OIIIb = fit_ems_lincont(x, odata, iOIIIb, bestfit)
em_fit = gauss_lorentz(x, popt_Hg[0], popt_Hg[1], popt_Hg[2], popt_Hg[3], popt_Hg[4], popt_Hg[5]) + \
gauss_lorentz(x, popt_Hb[0], popt_Hb[1], popt_Hb[2], popt_Hb[3], popt_Hb[4], popt_Hb[5]) + \
gauss_lorentz(x, popt_OIII[0], popt_OIII[1], popt_OIII[2], popt_OIII[3], popt_OIII[4], popt_OIII[5]) + \
gauss_lorentz(x, popt_OIIIb[0], popt_OIIIb[1], popt_OIIIb[2], popt_OIIIb[3], popt_OIIIb[4], popt_OIIIb[5])
abs_fit = odata - em_fit
if plot:
plt.plot(x, odata, '-k', label="spectra")
# plt.plot(x, bestfit, '--r', label="bestfit absorption line")
plt.plot(x, abs_fit, '-b', label="absorption spectra - gauss")
plt.legend()
plt.show()
return abs_fit, em_fit
def get_templates(velscale, l1, l2):
library = glob('/Volumes/VINCE/OAC/libraries/Valdes/clean_short/*')
ssp = pd.read_table(library[0], header=None)[0].values
FWHM_gal = 13.1
FWHM_tem = 1.35
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
scale = 0.4
sigma = FWHM_dif / 2.355 / scale
lamRange2 = [3465., 9469.]
sspNew, logLam2, _ = util.log_rebin(lamRange2, ssp, velscale=velscale)
library_size = sspNew.size
templates = np.empty((library_size, len(library)))
for j in range(len(library)):
tmp = pd.read_table(library[j], sep=r'\s+', header=None)
ssp = tmp[0].values
ssp = ndimage.gaussian_filter1d(ssp, sigma)
sspNew, logLam2, _ = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates[:, j] = sspNew / np.median(sspNew)
return logLam2, templates
def __init__(self, spec, velscale):
""" Load the pkl file from previous ppxf fit and define some attributes.
"""
with open(spec.replace(".fits", ".pkl")) as f:
pp = pickle.load(f)
self.__dict__ = pp.__dict__.copy()
self.spec = os.path.join(data_dir, spec)
self.vhelio = pf.getval(os.path.join(data_dir, self.spec), "VHELIO")
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.calc_sn()
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 w_temp(velscale):
""" Make templates array"""
current_dir = os.getcwd()
os.chdir(template_dir)
miles = [x for x in os.listdir(".") if x.endswith(".fits")]
miles.sort()
c = 299792.458
FWHM_tem = 2.54 # MILES library spectra have a resolution FWHM of 2.54A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2["CRVAL1"] + np.array([0.0, h2["CDELT1"] * (h2["NAXIS1"] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
os.chdir(current_dir)
return np.exp(logLam2)
def ppxf_gas_kinematics_parallel_loop(bin_sci_j, ppxf_bestfit_j, gal_hdr, templates, velscale, sigma, lamRange1,
logLam2, start, plot, moments, regul_err, reg_dim, component, bias, quiet):
gal_data = fits.getdata(bin_sci_j, 0)
gal_data_new = ndimage.gaussian_filter1d(gal_data, sigma)
galaxy, logLam1, velscale = util.log_rebin(lamRange1, gal_data_new, velscale=velscale)
noise = galaxy * 0 + 1 # Assume constant noise per pixel here
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below as described
# in PPXF_KINEMATICS_EXAMPLE_SAURON.
#
c = 299792.458
dv = (logLam2[0] - logLam1[0]) * c # km/s
t = clock()
pp = ppxf(templates, galaxy, noise, velscale, start, plot=plot, moments=moments, degree=-1, mdegree=10,
vsyst=dv, clean=False, regul=1. / regul_err, reg_dim=reg_dim, component=component, bias=bias,
quiet=quiet)
print ('pPXF sol of bin {}: {}'.format(j, pp.sol))
print ('pPXF error of bin {}: {}'.format(j, pp.error))
hdu_best = fits.PrimaryHDU()
hdu_best.data = pp.bestfit
hdu_best.header['CD1_1'] = gal_hdr['CD1_1']
hdu_best.header['CDELT1'] = gal_hdr['CD1_1']
hdu_best.header['CRPIX1'] = gal_hdr['CRPIX1']
hdu_best.header['CRVAL1'] = gal_hdr['CRVAL1']
hdu_best.header['NAXIS1'] = pp.bestfit.size
hdu_best.header['CTYPE1'] = 'LINEAR' # corresponds to sampling of values above
hdu_best.header['DC-FLAG'] = '1' # 0 = linear, 1= log-linear sampling
hdu_best.writeto(ppxf_bestfit_j, clobber=True)
return pp
def emission_line_template(lines, velscale, res=2.54, intens=None, resamp=15, return_log=True):
lines = np.atleast_1d(lines)
if intens == None:
intens = np.ones_like(lines) * 1e-5
current_dir = os.getcwd()
# Template directory is also set in setyp.py
os.chdir(template_dir)
refspec = [x for x in os.listdir(".") if x.endswith(".fits")][0]
lamb = wavelength_array(refspec)
delta = lamb[1] - lamb[0]
lamb2 = np.linspace(lamb[0] - delta / 2.0, lamb[-1] + delta / 2.0, len(lamb + 1) * resamp)
sigma = res / (2.0 * np.sqrt(2.0 * np.log(2.0)))
spec = np.zeros_like(lamb2)
for line, intensity in zip(lines, intens):
spec += intensity * np.exp(-(lamb2 - line) ** 2 / (2 * sigma * sigma))
spec = np.sum(spec.reshape(len(lamb), resamp), axis=1)
if not return_log:
return spec
specNew, logLam2, velscale = util.log_rebin([lamb[0], lamb[-1]], spec, velscale=velscale)
os.chdir(current_dir)
return specNew
def run_ppxf(spectra, velscale):
""" Run pPXF in a list of spectra"""
print "Loading templates"
templates, logLam2, delta = load_templates(velscale)
for i, spec in enumerate(spectra):
print "pPXF run of spectrum {0} ({1} of {2})".format(spec, i+1,
len(spectra))
print "Preparing files..."
# Read a galaxy spectrum and define the wavelength range
hdu = pf.open(spec)
spec_lin = hdu[0].data
h1 = hdu[0].header
lamRange1 = h1['CRVAL1'] + np.array([0.,h1['CDELT1']*(h1['NAXIS1']-1)])
# Convolve our spectra to match MILES 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 logarithm scale
galaxy, logLam1, velscale = util.log_rebin(lamRange1, spec_lin,
velscale=velscale)
noise = 0. * galaxy + 0.1
dv = (logLam2[0]-logLam1[0])*c
start, goodPixels = read_setup_file(spec, logLam1)
print "First interaction of pPXF.."
pp0 = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodPixels, plot=False, moments=4,
degree=6, mdegree=4, vsyst=dv)
print "Calculating realistic noise for input..."
rms0 = galaxy[goodPixels] - pp0.bestfit[goodPixels]
noise0 = 1.4826 * np.median(np.abs(rms0 - np.median(rms0)))
noise0 = 0. * galaxy + noise0
print "Second run of pPXF..."
pp = ppxf(templates, galaxy, noise0, velscale, pp0.sol,
goodpixels=goodPixels, plot=False, moments=4,
degree=6, mdegree=4, vsyst=dv, lam=lamRange1)
print "Finished! Now saving results..."
with open(spec.replace(".fits", ".pkl"), "w") as f:
pickle.dump(pp, f)
return
def w_temp(velscale):
""" Make templates array"""
current_dir = os.getcwd()
os.chdir(template_dir)
miles = [x for x in os.listdir(".") if x.endswith(".fits")]
miles.sort()
c = 299792.458
FWHM_tem = 2.54 # MILES library spectra have a resolution FWHM of 2.54A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pf.open(miles[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array(
[0., h2['CDELT1'] * (h2['NAXIS1'] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2,
ssp,
velscale=velscale)
os.chdir(current_dir)
return np.exp(logLam2)
def smooth_and_rebin(bc03,
bc03_lambda,
gal_lambda,
velscale,
FWHM_gal=1,
FWHM_tem=0):
"""
Smooth and rebin the BC03 spectra based on the sampling
of the galaxy to be fitted.
Input :
- bc03 : the spectral templates
- bc03_lambda : the template wavelength array
- gal_lambda : the galaxy wavelength array, which the rebinning
is going to match. Note tha the galaxy must have already
been log-rebinned.
- velscale : velocity scale of the galaxy, for log rebinning
- FWHM_gal : FWHM of the galaxy resolution
- FWHM_tem : FWHM of the template resolution
Output :
- templates : the smoothed and rebinned version of the BC03 input
"""
templates = np.empty(
((gal_lambda.size, ) + (bc03.shape[1], bc03.shape[2])))
for i in range(bc03.shape[1]):
for j in range(bc03.shape[2]):
ssp = bc03[:, i, j]
#resize the templates to the log-rebinned galaxy wavelengths
new_ssp = np.interp(gal_lambda, bc03_lambda, ssp, right=0, left=0)
#log-rebin the interpolated templates
#the output velscape should be close to identical to the input
#Likewise, logLam2 == np.log(gal_lambda) to about 1e-5
sspNew, logLam2, velscale = util.log_rebin(
[gal_lambda[0], gal_lambda[-1]], new_ssp, velscale=velscale)
#smooth the log-rebinned templates
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif / 2.355 / np.diff(gal_lambda)[0]
smoothed_ssp = ndimage.gaussian_filter1d(new_ssp, sigma)
templates[:, i, j] = smoothed_ssp
return templates
def emission_line_template(lines, velscale, res=2.54, intens=None, resamp=15,
return_log=True):
lines = np.atleast_1d(lines)
if intens == None:
intens = np.ones_like(lines) * 1e-5
current_dir = os.getcwd()
# Template directory is also set in setyp.py
os.chdir(template_dir)
refspec = [x for x in os.listdir(".") if x.endswith(".fits")][0]
lamb = wavelength_array(refspec)
delta = lamb[1] - lamb[0]
lamb2 = np.linspace(lamb[0]-delta/2., lamb[-1] + delta/2., len(lamb+1)*resamp)
sigma = res / (2. * np.sqrt(2. * np.log(2.)))
spec = np.zeros_like(lamb2)
for line, intensity in zip(lines, intens):
spec += intensity * np.exp(- (lamb2 - line)**2 / (2 * sigma * sigma))
spec = np.sum(spec.reshape(len(lamb), resamp), axis=1)
if not return_log:
return spec
specNew, logLam2, velscale = util.log_rebin([lamb[0], lamb[-1]], spec,
velscale=velscale)
os.chdir(current_dir)
return specNew
def subtract_besftfit(bin_sci, ppxf_bestfit, em_bin_sci):
"""
"""
for j in range(len(glob.glob(bin_sci.format('*')))):
if not os.path.exists(em_bin_sci.format(j)):
with fits.open(bin_sci.format(j)) as hdu:
odata = hdu[0].data
ohdr = hdu[0].header
bestfit = fits.getdata(ppxf_bestfit.format(j))
lamRange = ohdr['CRVAL1'] + np.array([1. - ohdr['CRPIX1'], ohdr['NAXIS1'] - ohdr['CRPIX1']]) * ohdr['CD1_1']
# Log bin the spectra to match the best fit absorption template
galaxy, logLam, velscale = util.log_rebin(lamRange, odata)
ems_fit = np.subtract(galaxy, bestfit)
ems_hdu = fits.PrimaryHDU()
ems_hdu.data = ems_fit
ems_hdu.header = fits.getheader(bin_sci.format(j), 0)
ems_hdu.writeto(em_bin_sci.format(j))
def setup_spectral_library(velscale, FWHM_gal):
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis et al. (2010, MNRAS, 404, 1639) http://miles.iac.es/.
#
# For this example I downloaded from the above website a set of
# model spectra with default linear sampling of 0.9A/pix and default
# spectral resolution of FWHM=2.51A. I selected a Salpeter IMF
# (slope 1.30) and a range of population parameters:
#
# [M/H] = [-1.71, -1.31, -0.71, -0.40, 0.00, 0.22]
# Age = np.linspace(np.log10(1), np.log10(17.7828), 26)
#
# This leads to a set of 156 model spectra with the file names like
#
# Mun1.30Zm0.40T03.9811.fits
#
# IMPORTANT: the selected models form a rectangular grid in [M/H]
# and Age: for each Age the spectra sample the same set of [M/H].
#
# We assume below that the model spectra have been placed in the
# directory "miles_models" under the current directory.
#
vazdekis = glob.glob('miles_models/Mun1.30*.fits')
vazdekis.sort()
FWHM_tem = 2.51 # Vazdekis+10 spectra have a resolution FWHM of 2.51A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SDSS galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = fits.open(vazdekis[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange_temp = h2['CRVAL1'] + np.array(
[0., h2['CDELT1'] * (h2['NAXIS1'] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp,
ssp,
velscale=velscale)
# Create a three dimensional array to store the
# two dimensional grid of model spectra
#
nAges = 26
nMetal = 6
templates = np.empty((sspNew.size, nAges, nMetal))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SDSS and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SDSS
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif / 2.355 / h2['CDELT1'] # Sigma difference in pixels
# These are the array where we want to store
# the characteristics of each SSP model
#
logAge_grid = np.empty((nAges, nMetal))
metal_grid = np.empty((nAges, nMetal))
# These are the characteristics of the adopted rectangular grid of SSP models
#
logAge = np.linspace(np.log10(1), np.log10(17.7828), nAges)
metal = [-1.71, -1.31, -0.71, -0.40, 0.00, 0.22]
# Here we make sure the spectra are sorted in both [M/H]
# and Age along the two axes of the rectangular grid of templates.
# A simple alphabetical ordering of Vazdekis's naming convention
# does not sort the files by [M/H], so we do it explicitly below
#
metal_str = ['m1.71', 'm1.31', 'm0.71', 'm0.40', 'p0.00', 'p0.22']
for k, mh in enumerate(metal_str):
files = [s for s in vazdekis if mh in s]
for j, filename in enumerate(files):
hdu = fits.open(filename)
ssp = hdu[0].data
ssp = ndimage.gaussian_filter1d(ssp, sigma)
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp,
ssp,
velscale=velscale)
templates[:, j, k] = sspNew # Templates are *not* normalized here
logAge_grid[j, k] = logAge[j]
metal_grid[j, k] = metal[k]
return templates, lamRange_temp, logAge_grid, metal_grid
def get_templates(self, galaxy, velscale=None, use_all_temp=False):
import glob # for searching for files
if self.library == 'vazdekis':
templateFiles = glob.glob(
'%s/libraries/python/ppxf/spectra/Rbi1.30z*.fits' %
(cc.home_dir))
f = fits.open(templateFiles[0])
v2 = f[0].data
self.wav = np.arange(f[0].header['NAXIS1'])*f[0].header['CDELT1'] + \
f[0].header['CRVAL1']
# Using same keywords as fits headers
CRVAL_temp = f[0].header['CRVAL1'] # starting wavelength
NAXIS_temp = f[0].header['NAXIS1'] # Number of entries
# wavelength increments (resolution?)
CDELT_temp = f[0].header['CDELT1']
f.close()
self.lamRange_template = CRVAL_temp + np.array(
[0, CDELT_temp * (NAXIS_temp - 1)])
log_temp_template, self.logLam_template, self.velscale = \
util.log_rebin(self.lamRange_template, self.wav)#, velscale=velscale)
self.ntemp = len(templateFiles)
self.templatesToUse = np.arange(self.ntemp)
self.templates = np.zeros((len(log_temp_template), self.ntemp))
self.lin_templates = np.zeros(
(len(f[0].header['NAXIS1']), self.ntemp))
## Reading the contents of the files into the array templates.
## Including rebinning them.
for i in range(self.ntemp):
f = fits.open(templateFiles[i])
self.lin_templates[:, i] = f[0].data
f.close()
if self.FWHM_tem < self.FWHM_gal:
sigma = self.FWHM_dif / 2.355 / CDELT_temp # Sigma difference (px)
conv_temp = ndimage.gaussian_filter1d(
self.lin_templates[:, i], sigma)
else:
conv_temp = self.lin_templates[:, i]
## Rebinning templates logarthmically
log_temp_template, self.logLam_template, _ = util.log_rebin(
self.lamRange_template,
conv_temp) #, velscale=self.velscale)
self.templates[:, i] = log_temp_template
elif self.library == 'Miles': # Miles stars
# Finding the template files
# There is some issue with the memory structure of the university macs
# (HFS+), meaning these templates can only be loaded once if located on
# the home directory, but more if on the Data partition...
if cc.device != 'uni':
templateFiles = glob.glob(
'%s/models/miles_library/m0[0-9][0-9][0-9]V' %
(cc.home_dir))
else:
templateFiles = glob.glob(
'%s/Data/' % (cc.base_dir) +
'idl_libraries/ppxf/MILES_library/m0[0-9][0-9][0-9]V')
# self.wav is wavelength, v2 is spectrum
self.wav, v2 = np.loadtxt(templateFiles[0], unpack='True')
# Using same keywords as fits headers
CRVAL_temp = self.wav[0] # starting wavelength
NAXIS_temp = np.shape(v2)[0] # Number of entries
# wavelength increments (resolution?)
CDELT_temp = (self.wav[NAXIS_temp - 1] -
self.wav[0]) / (NAXIS_temp - 1)
self.lamRange_template = CRVAL_temp + np.array(
[0, CDELT_temp * (NAXIS_temp - 1)])
log_temp_template, self.logLam_template, self.velscale = \
util.log_rebin(self.lamRange_template, self.wav, velscale=velscale)
if use_all_temp or galaxy is None:
self.ntemp = len(templateFiles)
self.templatesToUse = np.arange(self.ntemp)
else:
self.templatesToUse = use_templates(galaxy,
cc.device == 'glamdring')
self.ntemp = len(self.templatesToUse)
self.templates = np.zeros((len(log_temp_template), self.ntemp))
self.lin_templates = np.zeros((len(v2), self.ntemp))
## Reading the contents of the files into the array templates.
## Including rebinning them.
for i in range(self.ntemp):
if use_all_temp:
_, self.lin_templates[:, i] = np.loadtxt(templateFiles[i],
unpack='True')
else:
_, self.lin_templates[:, i] = np.loadtxt(
templateFiles[self.templatesToUse[i]], unpack='True')
if self.FWHM_tem < self.FWHM_gal:
sigma = self.FWHM_dif / 2.355 / CDELT_temp # Sigma difference (px)
conv_temp = ndimage.gaussian_filter1d(
self.lin_templates[:, i], sigma)
else:
conv_temp = self.lin_templates[:, i]
## Rebinning templates logarthmically
log_temp_template, self.logLam_template, _ = util.log_rebin(
self.lamRange_template, conv_temp, velscale=self.velscale)
self.templates[:, i] = log_temp_template
def remove_emission_lines(bin_sci, ppxf_bestfit, abs_bin_sci, plot=True, bad_lines=[]):
"""
Fit the absorption spectra with a pVoight function by parameterizing the bestfit template as output from pPXF,
fit the emission lines over the absorption features with a sum of a gaussian and lorentz and subtract from the
original spectra. Where the emission lines are isolated, just interpolate the continuum values from the best fit
template and replace.
This requires a very good fit between the galaxy spectra and the bestfit spectra. I recommend running this with
plot=True to check for any residuals.
"""
for j in range(len(glob.glob(bin_sci.format('*')))):
bin_sci_j = bin_sci.format(j)
ppxf_bestfit_j = ppxf_bestfit.format(j)
if not os.path.exists(abs_bin_sci.format(j)):
if plot:
print('>>>>> Removing emission lines from spectra {}'.format(j))
with fits.open(bin_sci_j) as hdu:
odata = hdu[0].data
ohdr = hdu[0].header
bestfit = fits.getdata(ppxf_bestfit_j)
lamRange = ohdr['CRVAL1'] + np.array([1. - ohdr['CRPIX1'], ohdr['NAXIS1'] - ohdr['CRPIX1']]) * ohdr['CD1_1']
lin_bins = np.linspace(lamRange[0], lamRange[1], ohdr['NAXIS1'])
# Log bin the spectra to match the best fit absorption template
galaxy, logLam1, velscale = util.log_rebin(lamRange, odata)
log_bins = np.exp(logLam1)
emlns, lnames, lwave = util.emission_lines(logLam1, lamRange, 2.3)
# Hgamma 4340.47, Hbeta 4861.33, OIII [4958.92, 5006.84]
# lwave = [4340.47, 4861.33, 4958.92, 5006.84]
# find the index of the emission lines
iHg = (np.abs(log_bins - lwave[0])).argmin()
iHb = (np.abs(log_bins - lwave[1])).argmin()
iOIII = (np.abs(log_bins - lwave[2])).argmin()
iOIIIb = (np.abs(log_bins - (lwave[2] - 47.92))).argmin()
# There are BOTH absorption and emission features about the wavelength of Hgamma and Hbeta, so we need
# to use a specialized fitting function (convolved Guassian and Lorentzian -> pVoight) to remove the
# emission lines
popt_Hg, pcov_Hg = remove_lines.fit_ems_pvoightcont(log_bins, galaxy, lin_bins, odata, iHg, bestfit)
popt_Hb, pcov_Hb = remove_lines.fit_ems_pvoightcont(log_bins, galaxy, lin_bins, odata, iHb, bestfit)
em_fit = remove_lines.gauss_lorentz(lin_bins, popt_Hg[0], popt_Hg[1], popt_Hg[2], popt_Hg[3], popt_Hg[4],
popt_Hg[5]) + \
remove_lines.gauss_lorentz(lin_bins, popt_Hb[0], popt_Hb[1], popt_Hb[2], popt_Hb[3], popt_Hb[4],
popt_Hb[5])
abs_feat_fit = odata - em_fit
abs_fit = remove_lines.em_chop(lin_bins, abs_feat_fit, iOIII, log_bins, bestfit)
abs_fit = remove_lines.em_chop(lin_bins, abs_fit, iOIIIb, log_bins, bestfit)
for lin in bad_lines:
ibad = (np.abs(lin_bins - lin)).argmin()
abs_fit = remove_lines.em_chop(lin_bins, abs_fit, ibad, log_bins, bestfit)
if plot:
plt.plot(lin_bins, odata, '-k', label="spectra")
# plt.plot(lin_bins, bestfit, '--r', label="bestfit absorption line")
plt.plot(lin_bins, abs_fit, '-b', label="absorption spectra")
plt.legend()
plt.show()
abs_hdu = fits.PrimaryHDU()
abs_hdu.data = abs_fit
abs_hdu.header = fits.getheader(bin_sci.format(j), 0)
abs_hdu.writeto(abs_bin_sci.format(j))
def ppxf_example_simulation():
file_dir = path.dirname(path.realpath(__file__)) # path of this procedure
hdu = fits.open(file_dir + '/veltemps/bd-013097_M2III_rebinflux_rest.fits'
) # BUCKET: which star should I choose???
# '/miles_models/Mun1.30Zp0.00T12.5893_iPp0.00_baseFe_linear_FWHM_2.51.fits') # Solar metallicitly, Age=12.59 Gyr
ssp = hdu[
1].data # hdu[1] is science header for us; hdu[0] is generic header
h = hdu[1].header
lamRange = h['CRVAL1'] + np.array([0., h['CD1_1'] * (h['NAXIS1'] - 1)])
c = 299792.458 # speed of light in km/s
velscale = c * h['CD1_1'] / max(
lamRange) # Do not degrade original velocity sampling
star, logLam, velscale = util.log_rebin(lamRange, ssp, velscale=velscale)
# The finite sampling of the observed spectrum is modeled in detail:
# the galaxy spectrum is obtained by oversampling the actual observed spectrum
# to a high resolution. This represent the true spectrum, which is later resampled
# to lower resolution to simulate the observations on the CCD. Similarly, the
# convolution with a well-sampled LOSVD is done on the high-resolution spectrum,
# and later resampled to the observed resolution before fitting with PPXF.
factor = 10 # Oversampling integer factor for an accurate convolution
starNew = ndimage.interpolation.zoom(
star, factor,
order=3) # This is the underlying spectrum, known at high resolution
star = rebin(
starNew, factor
) # Make sure that the observed spectrum is the integral over the pixels
# find worst SNR, and worst h3, h4 (highest)
h3 = 0.1 # Adopted G-H parameters of the LOSVD
h4 = 0.04 # 0.1
sn = 30. # Adopted S/N of the Monte Carlo simulation
m = 300 # Number of realizations of the simulation
moments = 4
velV = np.random.rand(
m
) # velocity in *pixels* [=V(km/s)/velScale] # (len m array of random on [0, 1)
sigmaV = np.linspace(
0.5, 4, m
) # Range of sigma in *pixels* [=sigma(km/s)/velScale] # m evenly spaced [0.5,...,4]
result = np.zeros((m, moments)) # This will store the results
t = clock()
for j, (vel, sigma) in enumerate(zip(velV, sigmaV)):
dx = int(
abs(vel) +
5 * sigma) # Sample the Gaussian and GH at least to vel+5*sigma
x = np.linspace(
-dx, dx, 2 * dx * factor +
1) # Evaluate the Gaussian using steps of 1/factor pixels.
w = (x - vel) / sigma
w2 = w**2
gauss = np.exp(-0.5 * w2)
gauss /= np.sum(gauss) # Normalized total(gauss)=1
h3poly = w * (2. * w2 - 3.) / np.sqrt(3.) # H3(y)
h4poly = (w2 * (4. * w2 - 12.) + 3.) / np.sqrt(24.) # H4(y)
losvd = gauss * (1. + h3 * h3poly + h4 * h4poly)
galaxy = signal.fftconvolve(
starNew, losvd, mode="same") # Convolve the oversampled spectrum
# FFT convolution: multiplication in the frequency domain corresponds to convolution in the time domain
galaxy = rebin(
galaxy, factor) # Integrate spectrum into original spectral pixels
noise = galaxy / sn # 1sigma error spectrum
galaxy = np.random.normal(galaxy,
noise) # Add noise to the galaxy spectrum
start = np.array([
vel + np.random.uniform(-1, 1),
sigma * np.random.uniform(0.8, 1.2)
]) * velscale # Convert to km/s
pp = ppxf(star,
galaxy,
noise,
velscale,
start,
goodpixels=np.arange(dx, galaxy.size - dx),
plot=False,
moments=moments,
bias=0.4) # 0.2
result[j, :] = pp.sol
print('Calculation time: %.2f s' % (clock() - t))
# large scale kinematics to estimate dark matter fraction within effective radii, and stellar mass-light ratio
# mass distribution discribed by GME and dark matter dstribution different
# work with sarah to use JAM; figure out why we ever use schwarzschild model if JAM is faster (what assumptions are
# we making/what regimes do we have to be in to use JAM?
plt.clf()
plt.subplot(221)
plt.plot(sigmaV * velscale, result[:, 0] - velV * velscale,
'+k') # BUCKET: why is V multiplied by velscale?
plt.axhline(0, color='r')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-20, 20)
plt.xlabel(r'$\sigma_{\rm in}\ (km\ s^{-1})$')
plt.ylabel(r'$V - V_{\rm in}\ (km\ s^{-1})$')
plt.text(2.05 * velscale, -15, r'2$\times$velscale')
plt.subplot(222)
plt.plot(sigmaV * velscale, result[:, 1] - sigmaV * velscale,
'+k') # BUCKET: why is sigma multiplied by velscale?
plt.axhline(0, color='r')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-20, 20)
plt.xlabel(r'$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel(r'$\sigma - \sigma_{\rm in}\ (km\ s^{-1})$')
plt.text(2.05 * velscale, -15, r'2$\times$velscale')
plt.subplot(223)
plt.plot(sigmaV * velscale, result[:, 2], '+k')
plt.axhline(h3, color='r')
plt.axhline(0, linestyle='dotted', color='limegreen')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-0.2 + h3, 0.2 + h3)
plt.xlabel(r'$\sigma_{\rm in}\ (km\ s^{-1})$')
plt.ylabel('$h_3$')
plt.text(2.05 * velscale, h3 - 0.15, r'2$\times$velscale')
plt.subplot(224)
plt.plot(sigmaV * velscale, result[:, 3], '+k')
plt.axhline(h4, color='r')
plt.axhline(0, linestyle='dotted', color='limegreen')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-0.2 + h4, 0.2 + h4)
plt.xlabel(r'$\sigma_{\rm in}\ (km\ s^{-1})$')
plt.ylabel('$h_4$')
plt.text(2.05 * velscale, h4 - 0.15, r'2$\times$velscale')
plt.tight_layout()
plt.show() # pause(1)
def ppxf_example_two_components():
file_dir = path.dirname(path.realpath(__file__)) # path of this procedure
hdu = fits.open(file_dir + '/miles_models/Mun1.30Zp0.00T12.5893.fits'
) # Solar metallicitly, Age=12.59 Gyr
gal_lin = hdu[0].data
h1 = hdu[0].header
lamRange1 = h1['CRVAL1'] + np.array(
[0., h1['CDELT1'] * (h1['NAXIS1'] - 1)])
c = 299792.458 # speed of light in km/s
velscale = c * h1['CDELT1'] / max(
lamRange1) # Do not degrade original velocity sampling
model1, logLam1, velscale = util.log_rebin(lamRange1,
gal_lin,
velscale=velscale)
model1 /= np.median(model1)
hdu = fits.open(file_dir + '/miles_models/Mun1.30Zp0.00T01.0000.fits'
) # Solar metallicitly, Age=1.00 Gyr
gal_lin = hdu[0].data
model2, logLam1, velscale = util.log_rebin(lamRange1,
gal_lin,
velscale=velscale)
model2 /= np.median(model2)
model = np.column_stack([model1, model2])
galaxy = np.empty_like(model)
# These are the input values in spectral pixels
# for the (V,sigma) of the two kinematic components
#
vel = np.array([0., 250.]) / velscale
sigma = np.array([200., 100.]) / velscale
# The synthetic galaxy model consists of the sum of two
# SSP spectra with age of 1Gyr and 13Gyr respectively
# with different velocity and dispersion
#
for j in range(len(vel)):
dx = int(abs(vel[j]) +
4. * sigma[j]) # Sample the Gaussian at least to vel+4*sigma
v = np.linspace(-dx, dx, 2 * dx + 1)
losvd = np.exp(-0.5 * ((v - vel[j]) / sigma[j])**2) # Gaussian LOSVD
losvd /= np.sum(losvd) # normaize LOSVD
galaxy[:, j] = signal.fftconvolve(model[:, j], losvd, mode="same")
galaxy[:, j] /= np.median(model[:, j])
galaxy = np.sum(galaxy, axis=1)
sn = 100.
noise = galaxy / sn
galaxy = np.random.normal(galaxy, noise) # add noise to galaxy
# Adopts two templates per kinematic component
#
templates = np.column_stack([model1, model2, model1, model2])
# Start both kinematic components from the same guess.
# With multiple stellar kinematic components
# a good starting guess is essential
#
start = [np.mean(vel) * velscale, np.mean(sigma) * velscale]
start = [start, start]
goodPixels = np.arange(20, 6000)
t = clock()
plt.clf()
plt.subplot(211)
plt.title("Two components pPXF fit")
print("+++++++++++++++++++++++++++++++++++++++++++++")
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=True,
degree=4,
moments=[2, 2],
component=[0, 0, 1, 1])
plt.subplot(212)
plt.title("Single component pPXF fit")
print("---------------------------------------------")
start = start[0]
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=True,
degree=4,
moments=2)
print("=============================================")
print("Total elapsed time %.2f s" % (clock() - t))
plt.tight_layout()
plt.pause(1)
def run_ppxf(spectra, velscale, ncomp=None, has_emission=True, mdegree=-1,
degree=20, plot=False, sky=None, start=None, moments=None,
log_dir=None, w1=4000., w2=7000.):
""" Run pPXF in a list of spectra"""
if isinstance(spectra, str):
spectra = [spectra]
##########################################################################
# Load templates for both stars and gas
star_templates = pf.getdata(os.path.join(templates_dir,
'miles_FWHM_3.7.fits'), 0)
logLam2 = pf.getdata(os.path.join(templates_dir, 'miles_FWHM_3.7.fits'), 1)
miles = np.loadtxt(os.path.join(templates_dir, 'miles_FWHM_3.7.txt'),
dtype=str).tolist()
gas_templates = pf.getdata(os.path.join(templates_dir,
'emission_FWHM_3.7.fits'), 0)
logLam_gas = pf.getdata(os.path.join(templates_dir, 'emission_FWHM_3.7.fits'),
1)
gas_files = np.loadtxt(os.path.join(templates_dir, 'emission_FWHM_3.7.txt'),
dtype=str).tolist()
ngas = len(gas_files)
ntemplates = len(miles)
##########################################################################
# Join templates in case emission lines are used.
if has_emission:
templates = np.column_stack((star_templates, gas_templates))
templates_names = np.hstack((miles, gas_files))
else:
templates = star_templates
templates_names = miles
ngas = 0
##########################################################################
if sky == None:
nsky = 0
else:
nsky = sky.shape[1]
if ncomp == 1:
components = 0
elif ncomp == 2:
components = np.hstack((np.zeros(len(star_templates[0])),
np.ones(len(gas_templates[0]))))
if moments == None:
moments = [4] if ncomp == 1 else [4,2]
for i, spec in enumerate(spectra):
print "pPXF run of spectrum {0} ({1} of {2})".format(spec, i+1,
len(spectra))
plt.clf()
######################################################################
# Read galaxy spectrum and define the wavelength range
hdu = pf.open(spec)
spec_lin = hdu[0].data
h1 = pf.getheader(spec)
lamRange1 = h1['CRVAL1'] + np.array([0.,h1['CDELT1']*(h1['NAXIS1']-1)])
######################################################################
# 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
if start is None:
start = [v0s[spec.split("_")[0]], 50]
goodPixels = None
######################################################################
# Expand start variable to include multiple components
if ncomp > 1:
start = [start, [start[0], 30]]
######################################################################
# Select goodpixels
w = np.exp(logLam1)
goodpixels = np.argwhere((w>w1) & (w<w2)).T[0]
# First pPXF interaction
pp0 = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodpixels, plot=False, moments=moments,
degree=degree, mdegree=mdegree, 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=False, moments=moments,
degree=degree, mdegree=mdegree, vsyst=dv,
component=components, sky=sky)
plt.title(spec.replace("_", "-"))
plt.show(block=False)
plt.savefig("{1}/{0}".format(spec.replace(".fits", ".png"), log_dir))
######################################################################
# Adding other things to the pp object
pp.template_files = templates_names
pp.has_emission = has_emission
pp.dv = dv
pp.w = w
pp.velscale = velscale
pp.spec = spec
pp.ngas = ngas
pp.ntemplates = ntemplates
pp.nsky = nsky
pp.templates = 0
######################################################################
# Save fit to pickles file to keep session
ppsave(pp, "{1}/{0}".format(spec.replace(".fits", ""), log_dir))
pp = ppload("{1}/{0}".format(spec.replace(".fits", ""), log_dir))
######################################################################
ppf = pPXF(spec, velscale, pp)
ppf.plot("{1}/{0}".format(spec.replace(".fits", ".png"), log_dir))
######################################################################
# # Save to output text file
# if ncomp > 1:
# pp.sol = pp.sol[0]
# pp.error = pp.error[0]
# sol = [val for pair in zip(pp.sol, pp.error) for val in pair]
# sol = ["{0:12s}".format("{0:.3g}".format(x)) for x in sol]
# sol.append("{0:12s}".format("{0:.3g}".format(pp0.chi2)))
# sol = ["{0:30s}".format(spec)] + sol
return
def interface_sets(nstart, nend):
full_time = clock()
####### setting up some variables for magnitudes and indices calculation ########
c = 299792.458
filter_list = 'sdss_manga_pipeline.res'
zero_point = 'AB'
redshift = 0.0
indices_list = 'MaNGA_range.def'
print(' > gathering info')
######## setting up directories and files ########
data_dir = '/mnt/lustre/smg/stellar_libraries/MaNGA/'
output_dir = '/mnt/lustre/smg/stellar_libraries/MaNGA/parameters/she-ra/beta_tests/combo_test/'
file_spectra = 'MaStar_spectra_ebv_gaia_rb_isopars'
##### templates information #####
templates_dir = '/mnt/lustre/smg/stellar_libraries/atlas9marcs/'
templates_parameters = 'templates_grid_parameters_R2000.fits'
header = fits.open(templates_dir + templates_parameters)
templ_id, templ_teff, templ_logg, templ_metal = header[1].data[
'NAME'], header[1].data['TEFF'], header[1].data['LOGG'], header[
1].data['METAL']
templ_gmag, templa_rmag, templ_imag = header[1].data['Gmag'], header[
1].data['Rmag'], header[1].data['Imag']
tot_templ = len(templ_id)
estimate_teff = interp1d(templ_gmag - templ_imag,
templ_teff,
bounds_error=False,
fill_value='extrapolate')
######## reading spectra file ########
print(' > reading information from the spectra file')
header = fits.open(data_dir + 'spectra/' + file_spectra + '.fits')
mangaid, mastarid, starid = header[1].data['mangaid'], header[1].data[
'mastarid'], header[1].data['id']
plate, ifudesign, mjd = header[1].data['plate'], header[1].data[
'ifudesign'], header[1].data['mjd']
ra, dec = header[1].data['objra'], header[1].data['objdec']
wave, flux = header[1].data['wave'], header[1].data['flux']
minlogg = header[1].data['minlogg']
bad_info = np.where(minlogg <= -8)
minlogg[bad_info] = 'NaN'
nspectra = int(header[1].header['naxis2'])
naxis1 = int(np.shape(wave)[1])
crval1 = min(wave[0, :])
crval2 = max(wave[0, :])
cdelt1 = abs(crval1 - crval2) / naxis1
new_wave = np.linspace(start=crval1, stop=crval2, num=naxis1)
######## setting up arrays ########
parameters = np.zeros((nspectra, 9))
selected_parameters = np.zeros((nspectra, 3))
ppxf_info = np.zeros((nspectra, 3))
indices = np.zeros((nspectra, 42))
mags = np.zeros((nspectra, 5))
#############################################################################################################################################
######## running things for each spectrum ########
print(' > working on each spectrum')
for i in range(nstart, nend + 1):
start_time = clock()
new_flux = np.interp(new_wave, wave[i, :], flux[i, :])
new_flux = new_flux * 1e-17 # to have 10^-17 erg/s/cm2/Angstrom as units
######## calculating the E(B-V) from Schlegel+98 as a function of coordinates ########
print(' > calculating the E(B-V) from Schlegel+98')
ebv = get_dust_radec(ra[i], dec[i], 'ebv')
######## calculating the reddening as a function of wavelength ########
print(' > calculating the reddening')
reddening = dust_allen_py(ebv, new_wave)
######## in order to correct, the flux needs to be divided by the reddening ########
corrected_flux = new_flux / reddening
bad_flux = np.isnan(corrected_flux) | np.isinf(corrected_flux) | (
corrected_flux <= 0.0)
corrected_flux[bad_flux] = 0.0
######## calculating the indices ########
print(
' > calculating indices within the MaNGA wavelength range'
)
indices[i, :], err_indices = calculate_indices(new_wave,
corrected_flux,
indices_list,
mastarid[i],
ef=0,
rv=0,
plot=False,
sim=False)
######## calculating the magnitudes ########
print(' > calculating SDSS magnitudes')
mags[i, :] = calculate_magnitudes(new_wave, corrected_flux,
filter_list, zero_point, redshift)
######## calculating the first guess for the stellar parameters ########
print(
' > calculating the first set of teff, logg, and metallicity'
)
parameters[i, 0] = estimate_teff(
mags[i, 1] - mags[i, 3]) # Teff as a function of (g-i)
parameters[i, 1] = minlogg[i] # from isoparsfit - isochrone fitting
parameters[i, 2] = -1.0 # open metallicity
bad_data = np.isnan(parameters[i, 0]) | np.isinf(
parameters[i, 0]) | np.isnan(parameters[i, 1]) | np.isinf(
parameters[i, 1]) | np.isnan(parameters[i, 2]) | np.isinf(
parameters[i, 2])
if bad_data:
parameters[i, :] = -999
ppxf_info[i, :] = -999
##### making a preliminary output file #####
print(' > making a preliminary output file')
f = open(output_dir + 'prelim_output/' + mastarid[i] + '_she-ra',
'w')
f.write('%r %r %r %i %i %i %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\n' \
%(mangaid[i], mastarid[i], starid[i], \
plate[i], ifudesign[i], mjd[i], \
ra[i],dec[i], \
parameters[i,0],parameters[i,1],parameters[i,2], \
parameters[i,3],parameters[i,4],parameters[i,5], \
parameters[i,6],parameters[i,7],parameters[i,8], \
ppxf_info[i,0], ppxf_info[i,1], ppxf_info[i,2], \
mags[i,0], mags[i,1], mags[i,2], mags[i,3], mags[i,4], \
indices[i,0], indices[i,1], indices[i,2], indices[i,3], indices[i,4], \
indices[i,5], indices[i,6], indices[i,7], indices[i,8], indices[i,9], \
indices[i,10], indices[i,11], indices[i,12], indices[i,13], indices[i,14], \
indices[i,15], indices[i,16], indices[i,17], indices[i,18], indices[i,19], \
indices[i,20], indices[i,21], indices[i,22], indices[i,23], indices[i,24], \
indices[i,25], indices[i,26], indices[i,27], indices[i,28], indices[i,29], \
indices[i,30], indices[i,31], indices[i,32], indices[i,33], indices[i,34], \
indices[i,35], indices[i,36], indices[i,37], indices[i,38], indices[i,39], \
indices[i,40], indices[i,41]))
f.close()
print(' > spectrum ' + str(i) + ' done')
continue
print(' > Teff = ' + str(parameters[i, 0]) + ' K')
print(' > logg = ' + str(parameters[i, 1]) + ' dex')
print(' > metal = ' + str(parameters[i, 2]) +
' dex') # not a real prior
######## calculating the second guess for the stellar parameters ########
if (parameters[i, 0] >= 12000): teff_guess = 2000
if (parameters[i, 0] < 12000): teff_guess = 1000
logg_guess = 0.80 # fixed value
metal_guess = 2.00 # open metallicity
selected_parameters[i, 0] = parameters[i, 0]
selected_parameters[i, 1] = parameters[i, 1]
selected_parameters[i, 2] = parameters[i, 2]
if (parameters[i, 0] <= min(templ_teff)):
selected_parameters[i, 0] = min(templ_teff) + teff_guess
if (parameters[i, 0] >= max(templ_teff)):
selected_parameters[i, 0] = max(templ_teff) - teff_guess
if ((parameters[i, 0] >= 12000) & (parameters[i, 1] <= 3.5)):
selected_parameters[i, 1] = 3.5
if ((parameters[i, 0] >= 8000) & (parameters[i, 0] <= 12000) &
(parameters[i, 1] <= 2.0)):
selected_parameters[i, 1] = 2.5
if ((parameters[i, 0] >= 6000) & (parameters[i, 0] <= 8000) &
(parameters[i, 1] <= 1.0)):
selected_parameters[i, 1] = 1.5
if ((parameters[i, 0] >= 2500) & (parameters[i, 0] <= 6000) &
(parameters[i, 1] <= 0.0)):
selected_parameters[i, 1] = 0.0
if ((parameters[i, 0] >= 2500) & (parameters[i, 0] <= 12000) &
(parameters[i, 1] >= 5.0)):
selected_parameters[i, 1] = 5.0
if (parameters[i, 2] <= min(templ_metal)):
selected_parameters[i, 2] = min(templ_metal) + metal_guess
if (parameters[i, 2] >= max(templ_metal)):
selected_parameters[i, 2] = max(templ_metal) - metal_guess
ok_set = np.where(
(np.abs(templ_teff - selected_parameters[i, 0]) <= teff_guess)
& (np.abs(templ_logg - selected_parameters[i, 1]) <= logg_guess)
& (np.abs(templ_metal - selected_parameters[i, 2]) <= metal_guess))
templ_set_id = templ_id[ok_set]
templ_set_teff = templ_teff[ok_set]
templ_set_logg = templ_logg[ok_set]
templ_set_metal = templ_metal[ok_set]
if not templ_set_id.size:
parameters[i, 3] = -999
parameters[i, 4] = -999
parameters[i, 5] = -999
parameters[i, 6] = -999
parameters[i, 7] = -999
parameters[i, 8] = -999
##### making a preliminary output file #####
print(' > making a preliminary output file')
f = open(output_dir + 'prelim_output/' + mastarid[i] + '_she-ra',
'w')
f.write('%r %r %r %i %i %i %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\n' \
%(mangaid[i], mastarid[i], starid[i], \
plate[i], ifudesign[i], mjd[i], \
ra[i],dec[i], \
parameters[i,0],parameters[i,1],parameters[i,2], \
parameters[i,3],parameters[i,4],parameters[i,5], \
parameters[i,6],parameters[i,7],parameters[i,8], \
ppxf_info[i,0], ppxf_info[i,1], ppxf_info[i,2], \
mags[i,0], mags[i,1], mags[i,2], mags[i,3], mags[i,4], \
indices[i,0], indices[i,1], indices[i,2], indices[i,3], indices[i,4], \
indices[i,5], indices[i,6], indices[i,7], indices[i,8], indices[i,9], \
indices[i,10], indices[i,11], indices[i,12], indices[i,13], indices[i,14], \
indices[i,15], indices[i,16], indices[i,17], indices[i,18], indices[i,19], \
indices[i,20], indices[i,21], indices[i,22], indices[i,23], indices[i,24], \
indices[i,25], indices[i,26], indices[i,27], indices[i,28], indices[i,29], \
indices[i,30], indices[i,31], indices[i,32], indices[i,33], indices[i,34], \
indices[i,35], indices[i,36], indices[i,37], indices[i,38], indices[i,39], \
indices[i,40], indices[i,41]))
f.close()
print(' > spectrum ' + str(i) + ' done')
continue
##### setting up the MaStar for pPXF #####
velscale = c * cdelt1 / max(
new_wave) # do not degrade original velocity sampling
log_corrected_flux, log_new_wave, velscale = util.log_rebin(
[min(new_wave), max(new_wave)], corrected_flux, velscale=velscale)
##### setting up the selected templates #####
templ_set = len(templ_set_id)
templates = np.zeros((len(log_corrected_flux), templ_set))
for j in range(0, templ_set):
hdu = fits.open(templates_dir + 'grid/' + templ_set_id[j] +
'.fits')
template_wave = np.linspace(start=hdu[0].header['CRVAL1'],
stop=hdu[0].header['CRVAL2'],
num=hdu[0].header['NAXIS1'])
template_flux = np.interp(new_wave, template_wave, hdu[0].data)
bad_flux = np.isnan(template_flux) | np.isinf(template_flux) | (
template_flux <= 0.0)
template_flux[bad_flux] = 0.0
log_template_flux, log_template_wave, velscale = util.log_rebin(
[min(new_wave), max(new_wave)],
template_flux,
velscale=velscale)
log_template_flux /= np.median(log_template_flux)
templates[:, j] = log_template_flux
##### setting up the variables to run pPXF #####
start = [0, 10]
noise = np.ones_like(log_corrected_flux)
sol = ppxf(templates,
log_corrected_flux,
noise,
velscale,
start,
lam=np.exp(log_new_wave),
degree=10,
moments=2,
quiet=True)
sol.plot()
plt.title(mastarid[i])
#plt.show()
plt.savefig(output_dir + 'fsf/' + mastarid[i] + '_bestfit.png')
plt.close()
##### gathering the chi2, vel and sigma from the pPXF output #####
print(
' > gathering the information from the full-spectrum fitting'
)
ppxf_info[i, 0] = sol.chi2
ppxf_info[i, 1] = sol.sol[0]
ppxf_info[i, 2] = sol.sol[1]
print(' > chi2/dof = ' + str(ppxf_info[i, 0]))
print(' > vel = ' + str(ppxf_info[i, 1]) +
' km s-1')
print(' > sigma = ' + str(ppxf_info[i, 2]) +
' km s-1')
prelim_teff = []
prelim_logg = []
prelim_metal = []
ppxf_weights = sol.weights[:]
##### calculating the weighted parameters #####
for j in range(0, templ_set):
prelim_teff.append(sol.weights[j] * templ_set_teff[j] /
np.sum(sol.weights[:]))
prelim_logg.append(sol.weights[j] * templ_set_logg[j] /
np.sum(sol.weights[:]))
prelim_metal.append(sol.weights[j] * templ_set_metal[j] /
np.sum(sol.weights[:]))
parameters[i, 3] = np.sum(prelim_teff[:])
parameters[i, 4] = np.sum(prelim_logg[:])
parameters[i, 5] = np.sum(prelim_metal[:]) + 0.3
bad_data = np.isnan(parameters[i, 3]) | np.isinf(
parameters[i, 3]) | np.isnan(parameters[i, 4]) | np.isinf(
parameters[i, 4]) | np.isnan(parameters[i, 5]) | np.isinf(
parameters[i, 5])
if bad_data:
parameters[i, 3] = -999
parameters[i, 4] = -999
parameters[i, 5] = -999
parameters[i, 6] = -999
parameters[i, 7] = -999
parameters[i, 8] = -999
##### making a preliminary output file #####
print(' > making a preliminary output file')
f = open(output_dir + 'prelim_output/' + mastarid[i] + '_she-ra',
'w')
f.write('%r %r %r %i %i %i %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\n' \
%(mangaid[i], mastarid[i], starid[i], \
plate[i], ifudesign[i], mjd[i], \
ra[i],dec[i], \
parameters[i,0],parameters[i,1],parameters[i,2], \
parameters[i,3],parameters[i,4],parameters[i,5], \
parameters[i,6],parameters[i,7],parameters[i,8], \
ppxf_info[i,0], ppxf_info[i,1], ppxf_info[i,2], \
mags[i,0], mags[i,1], mags[i,2], mags[i,3], mags[i,4], \
indices[i,0], indices[i,1], indices[i,2], indices[i,3], indices[i,4], \
indices[i,5], indices[i,6], indices[i,7], indices[i,8], indices[i,9], \
indices[i,10], indices[i,11], indices[i,12], indices[i,13], indices[i,14], \
indices[i,15], indices[i,16], indices[i,17], indices[i,18], indices[i,19], \
indices[i,20], indices[i,21], indices[i,22], indices[i,23], indices[i,24], \
indices[i,25], indices[i,26], indices[i,27], indices[i,28], indices[i,29], \
indices[i,30], indices[i,31], indices[i,32], indices[i,33], indices[i,34], \
indices[i,35], indices[i,36], indices[i,37], indices[i,38], indices[i,39], \
indices[i,40], indices[i,41]))
f.close()
print(' > spectrum ' + str(i) + ' done')
continue
print(
' > calculating the second set of teff, logg, and metallicity'
)
print(' > Teff = ' + str(parameters[i, 3]) + ' K')
print(' > logg = ' + str(parameters[i, 4]) + ' dex')
print(' > metal = ' + str(parameters[i, 5]) + ' dex')
##### gathering the parameters of the template with the largest weight #####
print(
' > calculating the third set of teff, logg, and metallicity'
)
ok_heavy = np.where(ppxf_weights == max(ppxf_weights))
parameters[i, 6] = templ_set_teff[ok_heavy]
parameters[i, 7] = templ_set_logg[ok_heavy]
parameters[i, 8] = templ_set_metal[ok_heavy] + 0.3
print(' > Teff = ' + str(parameters[i, 6]) + ' K')
print(' > logg = ' + str(parameters[i, 7]) + ' dex')
print(' > metal = ' + str(parameters[i, 8]) + ' dex')
print(' > elapsed time for one MaStar %.2f s' %
(clock() - start_time))
##### making a preliminary output file #####
print(' > making a preliminary output file')
f = open(output_dir + 'prelim_output/' + mastarid[i] + '_she-ra', 'w')
f.write('%r %r %r %i %i %i %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\n' \
%(mangaid[i], mastarid[i], starid[i], \
plate[i], ifudesign[i], mjd[i], \
ra[i],dec[i], \
parameters[i,0],parameters[i,1],parameters[i,2], \
parameters[i,3],parameters[i,4],parameters[i,5], \
parameters[i,6],parameters[i,7],parameters[i,8], \
ppxf_info[i,0], ppxf_info[i,1], ppxf_info[i,2], \
mags[i,0], mags[i,1], mags[i,2], mags[i,3], mags[i,4], \
indices[i,0], indices[i,1], indices[i,2], indices[i,3], indices[i,4], \
indices[i,5], indices[i,6], indices[i,7], indices[i,8], indices[i,9], \
indices[i,10], indices[i,11], indices[i,12], indices[i,13], indices[i,14], \
indices[i,15], indices[i,16], indices[i,17], indices[i,18], indices[i,19], \
indices[i,20], indices[i,21], indices[i,22], indices[i,23], indices[i,24], \
indices[i,25], indices[i,26], indices[i,27], indices[i,28], indices[i,29], \
indices[i,30], indices[i,31], indices[i,32], indices[i,33], indices[i,34], \
indices[i,35], indices[i,36], indices[i,37], indices[i,38], indices[i,39], \
indices[i,40], indices[i,41]))
f.close()
print(' > spectrum ' + str(i) + ' done')
#sys.stdout.write(" > spectrum = %d %s \r" % (i+1,'done'))
#sys.stdout.flush()
print(' > all spectra done')
print(' > elapsed time for all MaStar %.2f s' % (clock() - full_time))
#redshift = 2472. #ngc772 #redshift = 1485. #ngc1022 #redshift = 1032. #vcc784 #redshift = 674. #vcc1146 redshift = 401. #vcc1619 #redshift = 1359 #VCC0355 start = [redshift,10.] #(km/s), starting guess for [V,sigma] specdir='/Users/nluetzge/Science/UCDs/' #----------------------------------Read all files----------------------------------------- # Read the combined spectrum hdu = fits.open(name+'_spec.fits') gal=hdu[0].data hdr=hdu[0].header lamRange1=hdr['CRVAL1'] + np.array([0.,hdr['CDELT1']*(hdr['NAXIS1']-1.)]) galaxy, logLam1, velscale = util.log_rebin(lamRange1, gal) # Read the template if make_temp: temp_files = glob(specdir+Z+'/nifstemp*fits') nfiles=len(temp_files) for i in range(nfiles): hdu = fits.open(temp_files[i]) h_template=hdu[0].header temp_lin=hdu[0].data # the wavelength range calculated from the header data naxis=h_template['NAXIS1'] lamRange2=h_template['CRVAL1'] + np.array([0.,h_template['CDELT1']*(h_template['NAXIS1']-1.)]) tt, logLam2,velscale = util.log_rebin(lamRange2, temp_lin, velscale=velscale)
def ppxf_simulation_example():
dir = 'spectra/'
file = dir + 'Rbi1.30z+0.00t12.59.fits'
hdu = pyfits.open(file)
ssp = hdu[0].data
h = hdu[0].header
lamRange = h['CRVAL1'] + np.array([0., h['CDELT1'] * (h['NAXIS1'] - 1)])
star, logLam, velscale = util.log_rebin(lamRange, ssp)
# The finite sampling of the observed spectrum is modeled in detail:
# the galaxy spectrum is obtained by oversampling the actual observed spectrum
# to a high resolution. This represent the true spectrum, which is later resampled
# to lower resolution to simulate the observations on the CCD. Similarly, the
# convolution with a well-sampled LOSVD is done on the high-resolution spectrum,
# and later resampled to the observed resolution before fitting with PPXF.
factor = 10 # Oversampling integer factor for an accurate convolution
starNew = ndimage.interpolation.zoom(
star, factor,
order=1) # This is the underlying spectrum, known at high resolution
star = rebin(
starNew, factor
) # Make sure that the observed spectrum is the integral over the pixels
vel = 0.3 # velocity in *pixels* [=V(km/s)/velScale]
h3 = 0.1 # Adopted G-H parameters of the LOSVD
h4 = -0.1
sn = 60. # Adopted S/N of the Monte Carlo simulation
m = 300 # Number of realizations of the simulation
sigmaV = np.linspace(
0.8, 4, m) # Range of sigma in *pixels* [=sigma(km/s)/velScale]
result = np.zeros((m, 4)) # This will store the results
t = clock()
np.random.seed(123) # for reproducible results
for j in range(m):
sigma = sigmaV[j]
dx = int(
abs(vel) +
4.0 * sigma) # Sample the Gaussian and GH at least to vel+4*sigma
x = np.linspace(
-dx, dx, 2 * dx * factor +
1) # Evaluate the Gaussian using steps of 1/factor pixels.
w = (x - vel) / sigma
w2 = w**2
gauss = np.exp(-0.5 * w2) / (np.sqrt(2. * np.pi) * sigma * factor
) # Normalized total(gauss)=1
h3poly = w * (2. * w2 - 3.) / np.sqrt(3.) # H3(y)
h4poly = (w2 * (4. * w2 - 12.) + 3.) / np.sqrt(24.) # H4(y)
losvd = gauss * (1. + h3 * h3poly + h4 * h4poly)
galaxy = signal.fftconvolve(
starNew, losvd, mode="same") # Convolve the oversampled spectrum
galaxy = rebin(
galaxy, factor) # Integrate spectrum into original spectral pixels
noise = galaxy / sn # 1sigma error spectrum
galaxy = np.random.normal(galaxy,
noise) # Add noise to the galaxy spectrum
start = np.array([
vel + np.random.random(), sigma * np.random.uniform(0.85, 1.15)
]) * velscale # Convert to km/s
pp = ppxf(star,
galaxy,
noise,
velscale,
start,
goodpixels=np.arange(dx, galaxy.size - dx),
plot=False,
moments=4,
bias=0.5)
result[j, :] = pp.sol
print('Calculation time: %.2f s' % (clock() - t))
plt.clf()
plt.subplot(221)
plt.plot(sigmaV * velscale, result[:, 0] - vel * velscale, '+k')
plt.plot(sigmaV * velscale, sigmaV * velscale * 0, '-r')
plt.ylim(-40, 40)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$V - V_{in}\ (km\ s^{-1}$)')
plt.subplot(222)
plt.plot(sigmaV * velscale, result[:, 1] - sigmaV * velscale, '+k')
plt.plot(sigmaV * velscale, sigmaV * velscale * 0, '-r')
plt.ylim(-40, 40)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$\sigma - \sigma_{in}\ (km\ s^{-1}$)')
plt.subplot(223)
plt.plot(sigmaV * velscale, result[:, 2], '+k')
plt.plot(sigmaV * velscale, sigmaV * velscale * 0 + h3, '-r')
plt.ylim(-0.2 + h3, 0.2 + h3)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$h_3$')
plt.subplot(224)
plt.plot(sigmaV * velscale, result[:, 3], '+k')
plt.plot(sigmaV * velscale, sigmaV * velscale * 0 + h4, '-r')
plt.ylim(-0.2 + h4, 0.2 + h4)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$h_4$')
plt.tight_layout()
plt.pause(0.01)
def ppxf_simulation_example():
hdu = fits.open('miles_models/Mun1.30Zp0.00T12.5893.fits'
) # Solar metallicitly, Age=12.59 Gyr
ssp = hdu[0].data
h = hdu[0].header
lamRange = h['CRVAL1'] + np.array([0., h['CDELT1'] * (h['NAXIS1'] - 1)])
c = 299792.458 # speed of light in km/s
velscale = c * h['CDELT1'] / max(
lamRange) # Do not degrade original velocity sampling
star, logLam, velscale = util.log_rebin(lamRange, ssp, velscale=velscale)
# The finite sampling of the observed spectrum is modeled in detail:
# the galaxy spectrum is obtained by oversampling the actual observed spectrum
# to a high resolution. This represent the true spectrum, which is later resampled
# to lower resolution to simulate the observations on the CCD. Similarly, the
# convolution with a well-sampled LOSVD is done on the high-resolution spectrum,
# and later resampled to the observed resolution before fitting with PPXF.
factor = 10 # Oversampling integer factor for an accurate convolution
starNew = ndimage.interpolation.zoom(
star, factor,
order=3) # This is the underlying spectrum, known at high resolution
star = rebin(
starNew, factor
) # Make sure that the observed spectrum is the integral over the pixels
np.random.seed(133) # for reproducible results
h3 = 0.1 # Adopted G-H parameters of the LOSVD
h4 = -0.1
sn = 30. # Adopted S/N of the Monte Carlo simulation
m = 300 # Number of realizations of the simulation
moments = 4
velV = np.random.rand(m) # velocity in *pixels* [=V(km/s)/velScale]
sigmaV = np.linspace(
0.5, 4, m) # Range of sigma in *pixels* [=sigma(km/s)/velScale]
result = np.zeros((m, moments)) # This will store the results
t = clock()
for j, (vel, sigma) in enumerate(zip(velV, sigmaV)):
dx = int(
abs(vel) +
4.0 * sigma) # Sample the Gaussian and GH at least to vel+4*sigma
x = np.linspace(
-dx, dx, 2 * dx * factor +
1) # Evaluate the Gaussian using steps of 1/factor pixels.
w = (x - vel) / sigma
w2 = w**2
gauss = np.exp(-0.5 * w2)
gauss /= np.sum(gauss) # Normalized total(gauss)=1
h3poly = w * (2. * w2 - 3.) / np.sqrt(3.) # H3(y)
h4poly = (w2 * (4. * w2 - 12.) + 3.) / np.sqrt(24.) # H4(y)
losvd = gauss * (1. + h3 * h3poly + h4 * h4poly)
galaxy = signal.fftconvolve(
starNew, losvd, mode="same") # Convolve the oversampled spectrum
galaxy = rebin(
galaxy, factor) # Integrate spectrum into original spectral pixels
noise = galaxy / sn # 1sigma error spectrum
galaxy = np.random.normal(galaxy,
noise) # Add noise to the galaxy spectrum
start = np.array([
vel + np.random.random(), sigma * np.random.uniform(0.85, 1.15), 0,
0
]) * velscale # Convert to km/s
pp = ppxf(star,
galaxy,
noise,
velscale,
start,
goodpixels=np.arange(dx, galaxy.size - dx),
plot=False,
moments=moments,
bias=0.3,
oversample=None)
result[j, :] = pp.sol
print('Calculation time: %.2f s' % (clock() - t))
plt.clf()
plt.subplot(221)
plt.plot(sigmaV * velscale, (result[:, 0] / velscale - velV) / sigmaV,
'+k')
plt.axhline(0, color='r')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-0.3, 0.3)
plt.xlabel(r'$\sigma_{\rm in}\ (km\ s^{-1})$')
plt.ylabel(r'$(V - V_{\rm in})/\sigma_{\rm in}$')
plt.text(2.05 * velscale, -0.2, r'2$\times$velscale')
plt.subplot(222)
plt.plot(sigmaV * velscale, np.log10(result[:, 1] / (velscale * sigmaV)),
'+k')
plt.axhline(0, color='r')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-0.15, 0.15)
plt.xlabel(r'$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel(r'$\log(\sigma/\sigma_{\rm in})$')
plt.text(2.05 * velscale, -0.1, r'2$\times$velscale')
plt.subplot(223)
plt.plot(sigmaV * velscale, result[:, 2], '+k')
plt.axhline(h3, color='r')
plt.axhline(0, linestyle='dotted', color='limegreen')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-0.15 + h3, 0.15 + h3)
plt.xlabel(r'$\sigma_{\rm in}\ (km\ s^{-1})$')
plt.ylabel('$h_3$')
plt.text(2.05 * velscale, h3 - 0.1, r'2$\times$velscale')
plt.subplot(224)
plt.plot(sigmaV * velscale, result[:, 3], '+k')
plt.axhline(h4, color='r')
plt.axhline(0, linestyle='dotted', color='limegreen')
plt.axvline(velscale, linestyle='dashed')
plt.axvline(2 * velscale, linestyle='dashed')
plt.ylim(-0.15 + h4, 0.15 + h4)
plt.xlabel(r'$\sigma_{\rm in}\ (km\ s^{-1})$')
plt.ylabel('$h_4$')
plt.text(2.05 * velscale, h4 - 0.1, r'2$\times$velscale')
plt.tight_layout()
plt.pause(0.01)
def ppxf_kinematics_example_sauron():
# Read a galaxy spectrum and define the wavelength range
#
dir = 'spectra/'
file = dir + 'NGC4550_SAURON.fits'
hdu = pyfits.open(file)
gal_lin = hdu[0].data
h1 = hdu[0].header
lamRange1 = h1['CRVAL1'] + np.array([0.,h1['CDELT1']*(h1['NAXIS1']-1)])
FWHM_gal = 4.2 # SAURON has an instrumental resolution FWHM of 4.2A.
# If the galaxy is at a significant redshift (z > 0.03), one would need to apply
# a large velocity shift in PPXF to match the template to the galaxy spectrum.
# This would require a large initial value for the velocity (V > 1e4 km/s)
# in the input parameter START = [V,sig]. This can cause PPXF to stop!
# The solution consists of bringing the galaxy spectrum roughly to the
# rest-frame wavelength, before calling PPXF. In practice there is no
# need to modify the spectrum before the usual LOG_REBIN, given that a
# red shift corresponds to a linear shift of the log-rebinned spectrum.
# One just needs to compute the wavelength range in the rest-frame
# and adjust the instrumental resolution of the galaxy observations.
# This is done with the following three commented lines:
#
# z = 1.23 # Initial estimate of the galaxy redshift
# lamRange1 = lamRange1/(1+z) # Compute approximate restframe wavelength range
# FWHM_gal = FWHM_gal/(1+z) # Adjust resolution in Angstrom
galaxy, logLam1, velscale = util.log_rebin(lamRange1, gal_lin)
galaxy = galaxy/np.median(galaxy) # Normalize spectrum to avoid numerical issues
noise = galaxy*0 + 0.0049 # Assume constant noise per pixel here
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis (1999, ApJ, 513, 224). A subset of the library is included
# for this example with permission. See http://purl.org/cappellari/software
# for suggestions of more up-to-date stellar libraries.
#
vazdekis = glob.glob(dir + 'Rbi1.30z*.fits')
vazdekis.sort()
FWHM_tem = 1.8 # Vazdekis spectra have a resolution FWHM of 1.8A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pyfits.open(vazdekis[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates = np.empty((sspNew.size,len(vazdekis)))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SAURON and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SAURON
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif/2.355/h2['CDELT1'] # Sigma difference in pixels
for j in range(len(vazdekis)):
hdu = pyfits.open(vazdekis[j])
ssp = hdu[0].data
ssp = ndimage.gaussian_filter1d(ssp,sigma)
sspNew, logLam2, velscale = util.log_rebin(lamRange2, ssp, velscale=velscale)
templates[:,j] = sspNew/np.median(sspNew) # Normalizes templates
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below (see above).
#
c = 299792.458
dv = (logLam2[0]-logLam1[0])*c # km/s
vel = 450. # Initial estimate of the galaxy velocity in km/s
z = np.exp(vel/c) - 1 # Relation between velocity and redshift in pPXF
goodPixels = util.determine_goodpixels(logLam1, lamRange2, z)
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
start = [vel, 180.] # (km/s), starting guess for [V,sigma]
t = clock()
pp = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodPixels, plot=True, moments=4,
degree=4, vsyst=dv)
print("Formal errors:")
print(" dV dsigma dh3 dh4")
print("".join("%8.2g" % f for f in pp.error*np.sqrt(pp.chi2)))
print('Elapsed time in PPXF: %.2f s' % (clock() - t))
def __init__(self,
pathname,
velscale,
FWHM_gal,
metal=None,
FWHM_tem=2.51,
normalize=False,
max_age=None):
"""
Produces an array of logarithmically-binned templates by reading
the spectra from the Single Stellar Population (SSP) library by
Vazdekis et al. (2010, MNRAS, 404, 1639) http://miles.iac.es/.
The code checks that the model specctra form a rectangular grid
in age and metallicity and properly sorts them in both parameters.
The code also returns the age and metallicity of each template
by reading these parameters directly from the file names.
The templates are broadened by a Gaussian with dispersion
sigma_diff = np.sqrt(sigma_gal**2 - sigma_tem**2).
Thie script relies on the files naming convention adopted by
the MILES library, where SSP spectra have the form below
Mun1.30Zm0.40T00.0794_iPp0.00_baseFe_linear_FWHM_2.51.fits
This code can be easily adapted by the users to deal with other stellar
libraries, different IMFs or different abundances.
:param pathname: path with wildcards returning the list files to use
(e.g. 'miles_models/Mun1.30*.fits'). The files must form a Cartesian grid
in age and metallicity and the procedure returns an error if they do not.
:param velscale: desired velocity scale for the output templates library in km/s
(e.g. 60). This is generally the same or an integer fraction of the velscale
of the galaxy spectrum.
:param FWHM_gal: vector or scalar with the FWHM of the instrumental resolution
of the galaxy spectrum in Angstrom.
:param normalize: set to True to normalize each template to mean=1.
This is useful to compute light-weighted stellar population quantities.
:param max_age: optional maximum age in Gyr for the MILES models.
This can be useful e.g. to limit the templates age to be younger
than the age of the Universe at a given redshift.
:param metal: optionally select a *single* metallicity [M/H]
(e.g. metal = 0 to select only the spectra with Solar metallicity).
:return: The following variables are stored as attributes of the miles class:
.templates: array has dimensions templates[npixels, n_ages, n_metals];
.log_lam_temp: natural np.log() wavelength of every pixel npixels;
.age_grid: (Gyr) has dimensions age_grid[n_ages, n_metals];
.metal_grid: [M/H] has dimensions metal_grid[n_ages, n_metals].
.n_ages: number of different ages
.n_metal: number of different metallicities
"""
files = glob.glob(pathname)
assert len(files) > 0, "Files not found %s" % pathname
all = [age_metal(f) for f in files]
all_ages, all_metals = np.array(all).T
ages, metals = np.unique(all_ages), np.unique(all_metals)
n_ages, n_metal = len(ages), len(metals)
assert set(all) == set([(a, b) for a in ages for b in metals]), \
'Ages and Metals do not form a Cartesian grid'
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SDSS galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
hdu = fits.open(files[0])
ssp = hdu[0].data
h2 = hdu[0].header
lam_range_temp = h2['CRVAL1'] + np.array(
[0, h2['CDELT1'] * (h2['NAXIS1'] - 1)])
sspNew, log_lam_temp = util.log_rebin(lam_range_temp,
ssp,
velscale=velscale)[:2]
templates = np.empty((sspNew.size, n_ages, n_metal))
age_grid = np.empty((n_ages, n_metal))
metal_grid = np.empty((n_ages, n_metal))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the galaxy and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> galaxy
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif / 2.355 / h2['CDELT1'] # Sigma difference in pixels
# Here we make sure the spectra are sorted in both [M/H] and Age
# along the two axes of the rectangular grid of templates.
for j, age in enumerate(ages):
for k, met in enumerate(metals):
p = all.index((age, met))
hdu = fits.open(files[p])
ssp = hdu[0].data
if np.isscalar(FWHM_gal):
if sigma > 0.1: # Skip convolution for nearly zero sigma
ssp = ndimage.gaussian_filter1d(ssp, sigma)
else:
ssp = util.gaussian_filter1d(
ssp, sigma) # convolution with variable sigma
sspNew = util.log_rebin(lam_range_temp, ssp,
velscale=velscale)[0]
if normalize:
sspNew /= np.mean(sspNew)
templates[:, j, k] = sspNew
age_grid[j, k] = age
metal_grid[j, k] = met
if max_age is not None:
w = age_grid[:, 0] <= max_age
templates = templates[:, w, :]
age_grid = age_grid[w, :]
metal_grid = metal_grid[w, :]
n_ages, n_metal = age_grid.shape
if metal is not None:
w = metal_grid[0, :] == metal
templates = np.squeeze(templates[:, :, w])
age_grid = np.squeeze(age_grid[:, w])
metal_grid = np.squeeze(metal_grid[:, w])
n_ages, n_metal = age_grid.size, 1
self.norm = np.median(templates)
self.templates = templates / self.norm # Normalize by a scalar
self.log_lam_temp = log_lam_temp
self.age_grid = age_grid
self.metal_grid = metal_grid
self.n_ages = n_ages
self.n_metal = n_metal
def __init__(self, galaxy='ngc3557', slit_h=4.5, slit_w=2, slit_pa=30,
method='Rampazzo_aperture', r1=0, r2=None, debug=False):
print galaxy
if 'Rampazzo' in method:
from Rampazzo import rampazzo
# Default is nuclear region: 0<r<R_e/16
if r2 is None:
r2 = get_R_e(galaxy)/16
data = rampazzo(galaxy, method='aperture', slit_h=slit_h, r1=r1, r2=r2,
debug=debug)
if 'gradient' in method:
if '2' in method: data.method = 'gradient2'
else: data.method = 'gradient'
elif method == 'Ogando':
from Ogando import ogando
data = ogando(galaxy, debug=debug, slit_h=slit_h, slit_w=slit_w,
slit_pa=slit_pa)
elif method == 'Miles':
from Miles import miles
data = miles(galaxy)
gal_spec, gal_noise = data.get_spec()
gal_spec, lam, cut = apply_range(gal_spec, window=201, repeats=3,
lam=data.lam, set_range=np.array([4200,10000]), return_cuts=True)
gal_noise = gal_noise[cut]
lamRange = np.array([lam[0],lam[-1]])/(1+data.z)
## ----------================= Templates ====================---------
FWHM_gal = 2.5 # VIMOS documentation (and fits header)
FWHM_gal = FWHM_gal/(1+data.z) # Adjust resolution in Angstrom
stellar_templates = get_stellar_templates(galaxy, FWHM_gal)
velscale = stellar_templates.velscale
e_templates = get_emission_templates(gas, lamRange,
stellar_templates.logLam_template, FWHM_gal)
if gas:
templates = np.column_stack((stellar_templates.templates, e_templates.templates))
else:
templates = stellar_templates.templates
component = [0]*len(stellar_templates.templatesToUse) + e_templates.component
templatesToUse = np.append(stellar_templates.templatesToUse,
e_templates.templatesToUse)
element = ['stellar'] + e_templates.element
start = [[data.vel, data.sig]] * (max(component) + 1)
moments = [stellar_moments] + [gas_moments] * max(component)
## ----------============== Final calibrations ==============---------
## smooth spectrum to fit with templates resolution
if FWHM_gal < stellar_templates.FWHM_tem:
sigma = stellar_templates.FWHM_dif/2.355/data.f[0].header['CDELT3']
gal_spec = ndimage.gaussian_filter1d(gal_spec, sigma)
gal_noise = np.sqrt(ndimage.gaussian_filter1d(gal_noise**2, sigma))
## rebin spectrum logarthmically
gal_spec_log, logLam_bin, _ = util.log_rebin(lamRange, gal_spec, velscale=velscale)
gal_noise_log, logLam_bin, _ = util.log_rebin(lamRange, gal_noise**2,
velscale=velscale)
gal_noise_log = np.sqrt(gal_noise_log)
gal_noise_log = gal_noise_log + 0.0000000000001
dv = (stellar_templates.logLam_template[0]-logLam_bin[0])*c # km/s
# Find the pixels to ignore to avoid being distracted by gas emission
#; lines or atmospheric absorbsion line.
goodPixels = determine_goodpixels(logLam_bin,stellar_templates.lamRange_template,
data.vel, data.z, gas=gas!=0)
lambdaq = np.exp(logLam_bin)
## ----------=================== pPXF =======================---------
pp = ppxf(templates, gal_spec_log, gal_noise_log, velscale, start,
goodpixels=goodPixels, moments=moments, degree=-1, vsyst=dv,
component=component, lam=lambdaq, plot=not quiet, quiet=quiet, mdegree=10)
self.pp = pp
# Only use stellar templates (Remove emission lines)
stellar_spec = gal_spec_log - \
pp.matrix[:, -e_templates.ntemp:].dot(pp.weights[-e_templates.ntemp:])
conv_spec = pp.matrix[:, :-e_templates.ntemp].dot(pp.weights[:-e_templates.ntemp])
# Generate the unconvolved spectra ('0 km/s' resolution)
unc_lam = stellar_templates.wav
unc_spec = stellar_templates.lin_templates.dot(pp.weights[:stellar_templates.ntemp])
unc_spec *= np.polynomial.legendre.legval(np.linspace(-1,1,
len(unc_spec)), np.append(1, pp.mpolyweights))
## ----------============== Absorption Line =================---------
lines = ['G4300', 'Fe4383', 'Ca4455', 'Fe4531', 'H_beta', 'Fe5015', 'Mg_b']
self.result = {}
self.uncert = {}
for line in lines:
ab, ab_uncert = absorption(line, lambdaq, stellar_spec, noise=gal_noise_log,
unc_lam=unc_lam, unc_spec=unc_spec, conv_spec=conv_spec)#,
#lick=True)
if method == 'Ogando':
# Aperture correction
ab_file = '%s/Documents/useful_files/ab_linelist.dat' % (cc.home_dir)
i1, i2, b1, b2, r1, r2, units = np.genfromtxt(ab_file, unpack=True,
usecols=(1,2,3,4,5,6,7), skip_header=2, skip_footer=2)
ls = np.genfromtxt(ab_file, unpack=True, dtype=str, usecols=(8),
skip_header=2, skip_footer=2)
l = np.where(ls == line)[0][0]
index = [i1[l], i2[l]]
# Convert to mag
ab = -2.5 * np.log(1 - ab/(index[1]-index[0]))
# Aperture Correction: beta values taken from paper
beta = {'H_beta':0.002, 'Fe5015':-0.012, 'Mg_b':-0.031}
# If line is not observed by Ogando
if line not in beta.keys():
self.result[line] = np.nan
self.uncert[line] = np.nan
continue
H = 70.0 # value used by Bolonga group.
r_ab = np.degrees(1.19/1000 * H/(c * data.z)) * 60 * 60 # 1.19 kpc -> arcsec
# Correction is def in eq (9) with r_ab and r_norm def in eq (1) - not
# 100% sure if I've got them correct.
ab = ab - beta[line] * np.log(1.025 * np.sqrt(slit_w*r_ab/np.pi)/slit_h)
# Back to Angstroms
ab = (index[1]-index[0]) * (1 - np.exp(ab/-2.5))
elif 'Rampazzo' in method:
self.r = data.r
self.result[line] = ab
self.uncert[line] = ab_uncert
if debug:
for line in lines:
print '%s: %.3f +/- %.3f' % (line, self.result[line], self.uncert[line])
def ppxf_two_components_example():
hdu = fits.open('miles_models/Mun1.30Zp0.00T12.5893.fits') # Solar metallicitly, Age=12.59 Gyr
gal_lin = hdu[0].data
h1 = hdu[0].header
lamRange1 = h1['CRVAL1'] + np.array([0., h1['CDELT1']*(h1['NAXIS1']-1)])
c = 299792.458 # speed of light in km/s
velscale = c*h1['CDELT1']/max(lamRange1) # Do not degrade original velocity sampling
model1, logLam1, velscale = util.log_rebin(lamRange1, gal_lin, velscale=velscale)
model1 /= np.median(model1)
hdu = fits.open('miles_models/Mun1.30Zp0.00T01.0000.fits') # Solar metallicitly, Age=1.00 Gyr
gal_lin = hdu[0].data
model2, logLam1, velscale = util.log_rebin(lamRange1, gal_lin, velscale=velscale)
model2 /= np.median(model2)
model = np.column_stack([model1, model2])
galaxy = np.empty_like(model)
# These are the input values in spectral pixels
# for the (V,sigma) of the two kinematic components
#
vel = np.array([0., 250.])/velscale
sigma = np.array([200., 100.])/velscale
# The synthetic galaxy model consists of the sum of two
# SSP spectra with age of 1Gyr and 13Gyr respectively
# with different velocity and dispersion
#
for j in range(len(vel)):
dx = int(abs(vel[j]) + 4.*sigma[j]) # Sample the Gaussian at least to vel+4*sigma
v = np.linspace(-dx, dx, 2*dx + 1)
losvd = np.exp(-0.5*((v - vel[j])/sigma[j])**2) # Gaussian LOSVD
losvd /= np.sum(losvd) # normaize LOSVD
galaxy[:, j] = signal.fftconvolve(model[:, j], losvd, mode="same")
galaxy[:, j] /= np.median(model[:, j])
galaxy = np.sum(galaxy, axis=1)
sn = 100.
np.random.seed(2) # Ensure reproducible results
noise = galaxy/sn
galaxy = np.random.normal(galaxy, noise) # add noise to galaxy
# Adopts two templates per kinematic component
#
templates = np.column_stack([model1, model2, model1, model2])
# Start both kinematic components from the same guess.
# With multiple stellar kinematic components
# a good starting guess is essential
#
start = [np.mean(vel)*velscale, np.mean(sigma)*velscale]
start = [start, start]
goodPixels = np.arange(20, 6000)
t = clock()
plt.clf()
plt.subplot(211)
plt.title("Two components pPXF fit")
print("+++++++++++++++++++++++++++++++++++++++++++++")
pp = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodPixels, plot=True, degree=4,
moments=[2, 2], component=[0, 0, 1, 1])
plt.subplot(212)
plt.title("Single component pPXF fit")
print("---------------------------------------------")
start = start[0]
pp = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodPixels, plot=True, degree=4, moments=2)
plt.tight_layout()
plt.pause(0.01)
print("=============================================")
print("Total elapsed time %.2f s" % (clock() - t))
for ii in numpy.arange(len(filenames)): print ii, "/", len(filenames) wave, gal_lin, variance_lin, pix = numpy.genfromtxt(filenames[ii], unpack = True, skiprows = 1) Spaxel_coordinates = findCoords_from_string(filenames[ii]) # text = "Blue_Spec_"+str(int(Spaxel_coordinates[0]))+"_"+str(int(Spaxel_coordinates[1])) # noise_lin = numpy.sqrt(variance_lin) out_range = numpy.where(numpy.logical_or(wave< 4020., wave > 5550.)) wave_sel = numpy.delete(wave, out_range) gal_lin_sel = numpy.delete(gal_lin, out_range) noise_lin_sel = numpy.delete(noise_lin, out_range) lamRange1 = numpy.array([wave_sel[0], wave_sel[-1]]) # galaxy, logLam1, velscale = util.log_rebin(lamRange1, gal_lin_sel) fac = numpy.median(galaxy) galaxy = galaxy / fac# Normalize spectrum to avoid numerical issues error, logLam1, velscale = util.log_rebin(lamRange1, noise_lin_sel**2) noise = numpy.sqrt(error) noise = noise / fac # # logLam2, fl_temp = numpy.genfromtxt(directory + 'wise_template.dat', unpack = True, skiprows = 1) wave_temp = numpy.exp(logLam2) lamRange2 = numpy.array([logLam2[0], logLam2[-1]]) # templates = fl_temp # c = 299792.458 dv = (logLam2[0]-logLam1[0])*c # km/s
def ppxf_kinematics_example_sdss():
# Read SDSS DR12 galaxy spectrum taken from here http://dr12.sdss3.org/
# The spectrum is *already* log rebinned by the SDSS DR12
# pipeline and log_rebin should not be used in this case.
file = 'spectra/NGC4636_SDSS_DR12.fits'
hdu = pyfits.open(file)
t = hdu['COADD'].data
z = 0.003129 # SDSS redshift estimate
# Only use the wavelength range in common between galaxy and stellar library.
mask = (t['loglam'] > np.log10(3540)) & (t['loglam'] < np.log10(7409))
flux = t['flux'][mask]
galaxy = flux/np.median(flux) # Normalize spectrum to avoid numerical issues
loglam_gal = t['loglam'][mask]
lam_gal = 10**loglam_gal
noise = galaxy*0 + 0.0166 # Assume constant noise per pixel here
c = 299792.458 # speed of light in km/s
frac = lam_gal[1]/lam_gal[0] # Constant lambda fraction per pixel
dlam_gal = (frac - 1)*lam_gal # Size of every pixel in Angstrom
wdisp = t['wdisp'][mask] # Intrinsic dispersion of every pixel, in pixels units
fwhm_gal = 2.355*wdisp*dlam_gal # Resolution FWHM of every pixel, in Angstroms
velscale = np.log(frac)*c # Constant velocity scale in km/s per pixel
# If the galaxy is at a significant redshift (z > 0.03), one would need to apply
# a large velocity shift in PPXF to match the template to the galaxy spectrum.
# This would require a large initial value for the velocity (V > 1e4 km/s)
# in the input parameter START = [V,sig]. This can cause PPXF to stop!
# The solution consists of bringing the galaxy spectrum roughly to the
# rest-frame wavelength, before calling PPXF. In practice there is no
# need to modify the spectrum in any way, given that a red shift
# corresponds to a linear shift of the log-rebinned spectrum.
# One just needs to compute the wavelength range in the rest-frame
# and adjust the instrumental resolution of the galaxy observations.
# This is done with the following three commented lines:
#
# lam_gal = lam_gal/(1+z) # Compute approximate restframe wavelength
# fwhm_gal = fwhm_gal/(1+z) # Adjust resolution in Angstrom
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset
# of the library is included for this example with permission
vazdekis = glob.glob('miles_models/Mun1.30Z*.fits')
vazdekis.sort()
fwhm_tem = 2.51 # Vazdekis+10 spectra have a constant resolution FWHM of 2.51A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SDSS galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pyfits.open(vazdekis[0])
ssp = hdu[0].data
h2 = hdu[0].header
lam_temp = h2['CRVAL1'] + h2['CDELT1']*np.arange(h2['NAXIS1'])
lamRange_temp = [np.min(lam_temp), np.max(lam_temp)]
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp, ssp, velscale=velscale)
templates = np.empty((sspNew.size, len(vazdekis)))
# Interpolates the galaxy spectral resolution at the location of every pixel
# of the templates. Outside the range of the galaxy spectrum the resolution
# will be extrapolated, but this is irrelevant as those pixels cannot be
# used in the fit anyway.
fwhm_gal = np.interp(lam_temp, lam_gal, fwhm_gal)
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SDSS and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SDSS
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
# In the line below, the fwhm_dif is set to zero when fwhm_gal < fwhm_tem.
# In principle it should never happen and a higher resolution template should be used.
#
fwhm_dif = np.sqrt((fwhm_gal**2 - fwhm_tem**2).clip(0))
sigma = fwhm_dif/2.355/h2['CDELT1'] # Sigma difference in pixels
for j, fname in enumerate(vazdekis):
hdu = pyfits.open(fname)
ssp = hdu[0].data
ssp = util.gaussian_filter1d(ssp, sigma) # perform convolution with variable sigma
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp, ssp, velscale=velscale)
templates[:, j] = sspNew/np.median(sspNew) # Normalizes templates
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below (see above).
#
c = 299792.458
dv = np.log(lam_temp[0]/lam_gal[0])*c # km/s
goodpixels = util.determine_goodpixels(np.log(lam_gal), lamRange_temp, z)
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
vel = c*np.log(1 + z) # Initial estimate of the galaxy velocity in km/s
start = [vel, 200.] # (km/s), starting guess for [V,sigma]
t = clock()
pp = ppxf(templates, galaxy, noise, velscale, start,
goodpixels=goodpixels, plot=True, moments=4,
degree=12, vsyst=dv, clean=False)
print("Formal errors:")
print(" dV dsigma dh3 dh4")
print("".join("%8.2g" % f for f in pp.error*np.sqrt(pp.chi2)))
print('Elapsed time in PPXF: %.2f s' % (clock() - t))
def ppxf_example_sky_and_symmetric_losvd():
file_dir = path.dirname(path.realpath(__file__)) # path of this procedure
# Solar metallicity, Age=12.59 Gyr
hdu = fits.open(file_dir + '/miles_models/Mun1.30Zp0.00T12.5893.fits')
ssp = hdu[0].data
h = hdu[0].header
lamRange = h['CRVAL1'] + np.array([0., h['CDELT1'] * (h['NAXIS1'] - 1)])
velscale = 70. # km/s
star, logLam, velscale = util.log_rebin(lamRange, ssp, velscale=velscale)
star /= np.mean(star)
# Adopted input parameters =================================================
vel = 200. / velscale # Velocity of 1st spectrum in pixels (2nd has -vel)
sigma = 300. / velscale # Dispersion of both spectra in pixels
h3 = 0.1 # h3 of 1st spectrum (2nd has -h3)
h4 = 0.1
sn = 40.
moments = 4
deg = 4
vshift = 10 # Adopted systemic velocity in pixels
vsyst = vshift * velscale # Adopted systemic velocity in km/s
# Generate input Sky =======================================================
# For illustration, the sky is modelled as two Gaussian emission lines
n = star.size
x = np.arange(n)
sky1 = np.exp(-0.5 * (x - 1000)**2 / 100)
sky2 = np.exp(-0.5 * (x - 2000)**2 / 100)
# Generate input LOSVD =====================================================
dx = int(abs(vel) + 5 * sigma)
v = np.linspace(-dx, dx, 2 * dx + 1)
w = (v - vel) / sigma
w2 = w**2
gauss = np.exp(-0.5 * w2)
gauss /= np.sum(gauss)
h3poly = w * (2 * w2 - 3) / np.sqrt(3)
h4poly = (w2 * (4 * w2 - 12) + 3) / np.sqrt(24)
losvd = gauss * (1 + h3 * h3poly + h4 * h4poly)
# Generate first synthetic spectrum ========================================
# The template is convolved with the LOSVD
x = np.linspace(-1, 1, n)
galaxy1 = signal.fftconvolve(star, losvd, mode="same")
galaxy1 = np.roll(galaxy1, vshift) # Mimic nonzero systemic velocity
galaxy1 *= legendre.legval(
x, np.append(1,
np.random.uniform(-0.1, 0.1,
deg - 1))) # Multiplicative polynomials
galaxy1 += legendre.legval(x,
np.random.uniform(-0.1, 0.1,
deg)) # Additive polynomials
galaxy1 += sky1 + 2 * sky2 # Add two sky lines
galaxy1 = np.random.normal(galaxy1, 1 / sn) # Add noise
# Generate symmetric synthetic spectrum ====================================
# The same template is convolved with a reversed LOSVD
# and different polynomials and sky lines are included
galaxy2 = signal.fftconvolve(star, np.flip(losvd, 0), mode="same")
galaxy2 = np.roll(galaxy2, vshift) # Mimic nonzero systemic velocity
galaxy2 *= legendre.legval(
x, np.append(1,
np.random.uniform(-0.1, 0.1,
deg - 1))) # Multiplicative polynomials
galaxy2 += legendre.legval(x,
np.random.uniform(-0.1, 0.1,
deg)) # Additive polynomials
galaxy2 += 2 * sky1 + sky2 # Add two sky lines
galaxy2 = np.random.normal(galaxy2, 1 / sn) # Add noise
# Load spectral templates ==================================================
vazdekis = glob.glob(file_dir + '/miles_models/Mun1.30Z*.fits')
templates = np.empty((n, len(vazdekis)))
for j, file in enumerate(vazdekis):
hdu = fits.open(file)
ssp = hdu[0].data
sspNew, logLam2, velscale = util.log_rebin(lamRange,
ssp,
velscale=velscale)
templates[:, j] = sspNew / np.median(sspNew) # Normalize templates
# Do the fit ===============================================================
# Input both galaxy spectra simultaneously to pPXF
galaxy = np.column_stack([galaxy1, galaxy2])
# Use two sky templates for each galaxy spectrum
sky = np.column_stack([sky1, sky2])
# Randomized starting guess
vel0 = vel + np.random.uniform(-1, 1)
sigma0 = sigma * np.random.uniform(0.8, 1.2)
start = np.array([vel0, sigma0]) * velscale # Convert to km/s
goodpixels = np.arange(50, n - 50)
print(
"\nThe input values are: Vel=%0.0f, sigma=%0.0f, h3=%0.1f, h4=%0.1f\n"
% (vel * velscale, sigma * velscale, h3, h4))
t = clock()
pp = ppxf(templates,
galaxy,
np.full_like(galaxy, 1 / sn),
velscale,
start,
goodpixels=goodpixels,
plot=1,
moments=moments,
vsyst=vsyst,
mdegree=deg,
degree=deg,
sky=sky)
print('Elapsed time in pPXF: %.2f s' % (clock() - t))
plt.pause(1)
def ppxf_kinematics_example_sauron():
# Read a galaxy spectrum and define the wavelength range
#
file = 'spectra/NGC4550_SAURON.fits'
hdu = fits.open(file)
gal_lin = hdu[0].data
h1 = hdu[0].header
lamRange1 = h1['CRVAL1'] + np.array(
[0., h1['CDELT1'] * (h1['NAXIS1'] - 1)])
FWHM_gal = 4.2 # SAURON has an instrumental resolution FWHM of 4.2A.
# If the galaxy is at a significant redshift (z > 0.03), one would need to apply
# a large velocity shift in PPXF to match the template to the galaxy spectrum.
# This would require a large initial value for the velocity (V > 1e4 km/s)
# in the input parameter START = [V,sig]. This can cause PPXF to stop!
# The solution consists of bringing the galaxy spectrum roughly to the
# rest-frame wavelength, before calling PPXF. In practice there is no
# need to modify the spectrum before the usual LOG_REBIN, given that a
# red shift corresponds to a linear shift of the log-rebinned spectrum.
# One just needs to compute the wavelength range in the rest-frame
# and adjust the instrumental resolution of the galaxy observations.
# This is done with the following three commented lines:
#
# z = 1.23 # Initial estimate of the galaxy redshift
# lamRange1 = lamRange1/(1+z) # Compute approximate restframe wavelength range
# FWHM_gal = FWHM_gal/(1+z) # Adjust resolution in Angstrom
galaxy, logLam1, velscale = util.log_rebin(lamRange1, gal_lin)
galaxy = galaxy / np.median(
galaxy) # Normalize spectrum to avoid numerical issues
noise = galaxy * 0 + 0.0047 # Assume constant noise per pixel here
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset
# of the library is included for this example with permission
vazdekis = glob.glob('miles_models/Mun1.30Z*.fits')
FWHM_tem = 2.51 # Vazdekis+10 spectra have a constant resolution FWHM of 2.51A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = fits.open(vazdekis[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange2 = h2['CRVAL1'] + np.array(
[0., h2['CDELT1'] * (h2['NAXIS1'] - 1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange2,
ssp,
velscale=velscale)
templates = np.empty((sspNew.size, len(vazdekis)))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SAURON and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SAURON
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif / 2.355 / h2['CDELT1'] # Sigma difference in pixels
for j, file in enumerate(vazdekis):
hdu = fits.open(file)
ssp = hdu[0].data
ssp = ndimage.gaussian_filter1d(ssp, sigma)
sspNew, logLam2, velscale = util.log_rebin(lamRange2,
ssp,
velscale=velscale)
templates[:, j] = sspNew / np.median(sspNew) # Normalizes templates
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below (see above).
#
c = 299792.458
dv = (logLam2[0] - logLam1[0]) * c # km/s
vel = 450. # Initial estimate of the galaxy velocity in km/s
z = np.exp(vel / c) - 1 # Relation between velocity and redshift in pPXF
goodPixels = util.determine_goodpixels(logLam1, lamRange2, z)
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
start = [vel, 180., 0, 0] # (km/s), starting guess for [V,sigma]
t = clock()
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=True,
moments=4,
degree=4,
vsyst=dv)
print("Formal errors:")
print(" dV dsigma dh3 dh4")
print("".join("%8.2g" % f for f in pp.error * np.sqrt(pp.chi2)))
print('Elapsed time in PPXF: %.2f s' % (clock() - t))
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 VIMOS_ppxf(l1, l2, start, plot=True, nsimulations=False):
spec = getdata(result['ORIGINALFILE'].loc[0], 1)
header = getheader(result['ORIGINALFILE'].loc[0], 1)
quadrant = header['ESO OCS CON QUAD']
lamRange = header['CRVAL1'] + np.array(
[0., header['CD1_1'] * (header['NAXIS1'] - 1)])
wavelength = np.linspace(lamRange[0], lamRange[1], header['NAXIS1'])
ix_start = np.where(wavelength > l1)[0][0]
ix_end = np.where(wavelength < l2)[0][-1]
w_start = wavelength[ix_start]
w_end = wavelength[ix_end]
lamRange = [w_start, w_end]
galaxy, logLam1, velscale = util.log_rebin(lamRange, spec)
wavelength_log = np.exp(logLam1)
logLam2, templates = utilities.get_templates(velscale, l1, l2)
dv = (logLam2[0] - logLam1[0]) * c
df_columns = [
'slit_id', 'rv_ppxf', 'rv_ppxf_err', 'sigma_ppxf', 'sigma_ppxf_err',
'seeing', 'vhelio'
]
final_frame = pd.DataFrame(columns=df_columns)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #
for i in tqdm(range(0, len(result))):
single = result.iloc[i]
hdu = fits.open(single['ORIGINALFILE'])
header = hdu[0].header
galaxy, _, _ = util.log_rebin(lamRange,
hdu[1].data[ix_start:ix_end],
velscale=velscale)
noise, _, _ = util.log_rebin(lamRange,
hdu[2].data[ix_start:ix_end],
velscale=velscale)
noise = noise + 0.001 # Some spectra have noise = 0
twoD = hdu[3].data
thumbnail = hdu[4].data
hdu.close()
seeing = header[
'ESO TEL IA FWHMLINOBS'] # Delivered seeing on IA detector
slit_id = single['ID']
quadrant = slit_id[2]
rv_fxcor = single['VREL']
sg = header['SG_NORM']
sn1 = header['SN1']
sn2 = header['SN2']
RA_g = single['RA_g']
DEC_g = single['DEC_g']
vhelio = utilities.get_vhelio(
header) # Calculate heliocentric velocity
start = [rv_fxcor, 10]
if (quadrant == '1') | (quadrant == '3'):
pixelmask = pixelmask_Q1Q3
else:
pixelmask = pixelmask_normal
goodpixels = utilities.make_goodpixels(pixelmask, logLam1, logLam2)
rv, sigma, error_rv, error_sigma, chi2, bestfit, residuals, zp1, output_wavelengths = run_ppxf(
wavelength_log, templates, galaxy, noise, velscale, start,
goodpixels, dv)
if plot:
kwargs = {
'output_wavelengths': output_wavelengths,
'ix_start': ix_start,
'ix_end': ix_end,
'slit_id': slit_id,
'rv': rv,
'sigma': sigma,
'chi2': chi2,
'pixelmask': pixelmask,
'zp1': zp1,
'rv_fxcor': rv_fxcor,
'sg': sg,
'sn1': sn1,
'sn2': sn2,
'RA_g': RA_g,
'DEC_g': DEC_g
}
figure = ppplot.ppxfplot(twoD, galaxy, noise, bestfit, residuals,
thumbnail, **kwargs)
figure.savefig(
'/Volumes/VINCE/OAC/ppxf/figures/{}.png'.format(slit_id))
plt.close(figure)
df_tmp = pd.DataFrame(
[[slit_id, rv, error_rv, sigma, error_sigma, seeing, vhelio]],
columns=df_columns)
final_frame = final_frame.append(df_tmp, ignore_index=True)
final_frame.to_csv('ppxf_results.csv', index=False)
if nsimulations:
samples = []
rv_mc = []
for j in xrange(nsimulations):
noise_mc = noise * np.random.normal(size=noise.size)
galaxy_mc = bestfit + noise_mc
samples.append((wavelength_log, templates, galaxy_mc, noise,
velscale, start, goodpixels, dv))
rv, sigma, chi2, error_rv, error_sigma, bestfit, residuals, zp1, output_wavelengths = run_ppxf(
wavelength_log, templates, galaxy_mc, noise, velscale,
start, goodpixels, dv)
rv_mc.append(rv)
#workers = min(multiprocessing.cpu_count(), 2)
#pool = multiprocessing.Pool(processes=workers)
#print 'Using', workers, 'workers'
#sample_results = pool.map(_boot_VIMOS_ppxf, samples)
#print 'past sample_results'
return final_frame
def ppxf_nifs_kinematics():
file_dir = path.dirname(path.realpath(__file__)) # path of this procedure
# Read a galaxy spectrum and define the wavelength range
#
file = file_dir + '/spectra/pgc12557_combined.fits' # '/spectra/NGC4550_SAURON.fits' # my current file location
hdu = fits.open(file)
gal_lin = hdu[1].data # gal_lin = hdu[0].data
# h1 = hdu[0].header
h1 = hdu[
1].header # I need to use 1st extension header (0=general, 1=science, 2=variance, 3=data quality flags)
'''
print(h1)
BITPIX = -32 / array data type
NAXIS = 3 / number of array dimensions
NAXIS1 = 71
NAXIS2 = 69
NAXIS3 = 2040
PCOUNT = 0 / number of parameters
GCOUNT = 1 / number of groups
EXTNAME = 'SCI ' / Extension name
EXTVER = 1 / Extension version
INHERIT = F / Inherits global header
ORIGIN = 'NOAO-IRAF FITS Image Kernel July 2003' / FITS file originator
OBJECT = 'PGC12557' / Name of the object observed
DISPAXIS= 3 / Dispersion axis
PIXSCALE= 0.05 / Pixel scale in arcsec/pixel
CTYPE1 = 'LINEAR ' / coordinate type for the dispersion axis
CTYPE2 = 'LINEAR ' / coordinate type for the spatial axis
DC-FLAG = 0 /
CD1_1 = 0.05 /
CD2_2 = 0.05 /
DCLOG1 = 'Transform'
WCSDIM = 3 /
CRVAL1 = 2.987
CRPIX1 = 68.
CRPIX2 = 2.
WAT1_001= 'wtype=linear axtype=xi' /
WAT2_001= 'wtype=linear axtype=eta' /
CTYPE3 = 'LINEAR ' /
WAT3_001= 'wtype=linear axtype=wave' /
CRVAL3 = 19971.869140625 /
CD3_3 = 2.1321439743 /
AIRMASS = 1.374
CRPIX3 = 1.
LTM1_1 = 1.
LTM2_2 = 1.
LTM3_3 = 1.
WAT0_001= 'system=world'
END
'''
'''
# NOTE:
Copied from util.log_rebin():
lamRange: two elements vector containing the central wavelength
of the first and last pixels in the spectrum, which is assumed
to have constant wavelength scale! E.g. from the values in the
standard FITS keywords: LAMRANGE = CRVAL1 + [0, CDELT1*(NAXIS1 - 1)].
It must be LAMRANGE[0] < LAMRANGE[1].
'''
# lamRange1 = h1['CRVAL1'] + np.array([0., h1['CDELT1']*(h1['NAXIS1'] - 1)]) # original
# lamRange1 = h1['CRVAL3'] + np.array([0., h1['CD3_3']*(h1['CRPIX3'])]) # [ 19971.86914062 19974.0012846 ]
# if I use CRPIX3, probably don't want CRPIX3 - 1 because CRPIX3 = 1., and need lamRange1[0] < lamRange1[1]
lamRange1 = h1['CRVAL3'] + np.array(
[0., h1['CD3_3'] *
(h1['NAXIS3'] - 1)]) # [ 19971.86914062 24319.31070422]
print(lamRange1, 'l1')
# print(gal_lin[0][20]) all 0s
# print((gal_lin[300][35])) NOT all 0s!
# print(len(lamRange1)) # len(lamRange1) = 2, # len(gal_lin) = 2040, len(gal_lin[0]) = 69, len(gal_lin[0][1]) = 71
# 2040 --> NAXIS 3, 69 --> NAXIS2, 71 --> NAXIS1
# HMM: want gal_lin to be spectrum itself, which should just be 1d array
# There's a len 2040 spectrum at each gal_lin[:,x,y]
# CUT SPECTRUM TO WAVELENGTH RANGE 2.26 - 2.42
# SO: gal_lin is an array len(2040) starting at lamRange1[0] and finishing at lamRange1[1], with each pixel in
# between separated by h1['CD3_3']. So find [22600 - lamRange1[0]] / CD3_3, and that should give the number of
# pixels between pixel 1 and the pixel corresponding roughly to 2.26 microns. Then find [24200 - lamRange1[1]] /
# CD3_3, which should give the number of pixels between pixel 1 and the pixel corresponding to 2.42 microns.
start = 22600.
stop = 24200.
cut1 = (start - lamRange1[0]) / h1['CD3_3']
cut2 = (stop - lamRange1[0]) / h1['CD3_3']
# print(cut1, cut2, 'cuts') # 1232.62354281, 1983.04191009
gal_lin = gal_lin[int(cut1):int(cut2)] # [1233:1983]
# start1 = h1['CRVAL3'] + h1['CD3_3'] * int(cut1)
# stop1 = h1['CRVAL3'] + h1['CD3_3'] * int(cut2)
# print(start1, stop1, 'me')
lamRange1 = [
h1['CRVAL3'] + h1['CD3_3'] * int(cut1),
h1['CRVAL3'] + h1['CD3_3'] * int(cut2)
]
print(lamRange1, 'l1, cut')
# len(gal_lin) is now NOT 2040 but 1983 - 1233 = 750
FWHM_gal = 4.2 # SAURON has an instrumental resolution FWHM of 4.2A. # BUCKET: do I need this? If so what for?
# If the galaxy is at significant redshift, one should bring the galaxy
# spectrum roughly to the rest-frame wavelength, before calling pPXF
# (See Sec2.4 of Cappellari 2017). In practice there is no
# need to modify the spectrum in any way, given that a red shift
# corresponds to a linear shift of the log-rebinned spectrum.
# One just needs to compute the wavelength range in the rest-frame
# and adjust the instrumental resolution of the galaxy observations.
# This is done with the following three commented lines:
#
# z = 1.23 # Initial estimate of the galaxy redshift
# lamRange1 = lamRange1/(1+z) # Compute approximate restframe wavelength range
# FWHM_gal = FWHM_gal/(1+z) # Adjust resolution in Angstrom
# There's a len 2040 spectrum at each gal_lin[:,x,y]
x = 33
y = 35
galaxy, logLam1, velscale = util.log_rebin(
lamRange1, gal_lin[:, x, y]) # no input velscale --> function returns
print(len(galaxy), 'len gal') # 750 now because of cut to gal_lin!
galaxy = galaxy / np.median(
galaxy) # Normalize spectrum to avoid numerical issues
# print(galaxy)
# BUCKET: constant noise/pix good assumption for me? If so what value do I use? TRY our noise! Maybe trust more?!
# basically bootstrap the noise!
# Do one fit with flat noise, then save the best fit spectrum and the residuals.
# Then, iterate ~200 times. For these iterations, set bias = 0.0. Each iteration, for each pixel, use the spectrum
# value as the center of a gaussian and use the residuals at that pixel value as the width of the gaussian. Draw
# from the resultant distribution to make the new noise. For each iteration, save the output V, sigma, h3, h4, and
# print each spectrum so that we can see it evolving (it should look more like a real spectrum, rather than smooth
# curve without lines)
noise = np.full_like(galaxy,
0.0047) # Assume constant noise per pixel here
# MEANTIME: why is output velocity close to systemic insted of close to 0, if I'm dealing with redshift already?
# SET bias = 0
#
# print(noise.shape) # 751,
# print(galaxy.shape) # 751,
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset
# of the library is included for this example with permission
vazdekis = glob.glob(
file_dir +
'/veltemps/*.fits') # '/miles_models/Mun1.30Z*.fits') # my new
# BUCKET: what are FWHM of spectra in the veltemps library??
FWHM_tem = 2.51 # Vazdekis+10 spectra have a constant resolution FWHM of 2.51A.
# BUCKET: what spectral sampling compared to galaxy do we want for templates??
# velscale_ratio = 2 # 1.23 # 2 # adopts 2x higher spectral sampling for templates than for galaxy
# PROBLEM!! If velscale_ratio is not integer, we get issue later because we slice a list with velscale_ratio
# so need to change velscale? But it's set by util.log_rebin originally! Only need this if oversampling the
# templates, which I'm not doing
# Extract the wavelength range and logarithmically rebin one spectrum
# to a velocity scale 2x smaller than the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
print(vazdekis[0], 'template name')
hdu = fits.open(
vazdekis[0]
) # do for just one template to determine size needed for array containing all templates
ssp = hdu[
1].data # was hdu[0], but that's generic header rather than science header
h2 = hdu[1].header # was hdu[0]
'''
for line in h2:
print(line, h2[line])
XTENSION IMAGE
BITPIX -32
NAXIS 1
NAXIS1 1721
PCOUNT 0
GCOUNT 1
EXTNAME SCI
EXTVER 1
INHERIT False
ORIGIN NOAO-IRAF FITS Image Kernel July 2003
OBJECT BD-01 3097
DATE 2015-02-15T13:32:06
IRAF-TLM 2015-02-15T13:32:30
NFPAD 2015-02-14T14:40:41
GEM-TLM 2015-02-14T14:40:41
DISPAXIS 1
PIXSCALE 0.043
NSCUTSEC [1:2040,1145:1213]
NSCUTSPC 13
FIXPIX Feb 14 8:44 Bad pixel file is tmpdq20234_650
CTYPE1 LINEAR
CD1_1 2.13
CTYPE2 LINEAR
CD2_2 0.04570113
DCLOG1 Transform
DC-FLAG 0
WCSDIM 3
CRVAL1 20628.29
CRPIX1 1.0
CRPIX2 -28.0
WAXMAP01 1 0 0 29 0 0
WAT1_001 wtype=linear axtype=wave
WAT2_001 wtype=linear axtype=eta
CTYPE3 LINEAR
WAT3_001 wtype=linear axtype=xi
CRVAL3 1.751
CD3_3 0.103
LTV2 -29.0
LTM1_1 1.0
LTM2_2 1.0
LTM3_3 1.0
WAT0_001 system=image
EXPTIME 25.0
IMCMB001 xatfbrsnN20070508S0187.fits[SCI]
IMCMB002 xatfbrsnN20070508S0190.fits[SCI]
IMCMB003 xatfbrsnN20070508S0191.fits[SCI]
IMCMB004 xatfbrsnN20070508S0194.fits[SCI]
NCOMBINE 4
GAINORIG 1.0
RONORIG 0.0
GAIN 2.666667
RDNOISE 0.0
'''
# lamRange2 = h2['CRVAL1'] + np.array([0., h2['CDELT1']*(h2['NAXIS1'] - 1)]) # original
lamRange2 = h2['CRVAL1'] + np.array([0., h2['CD1_1'] * (h2['NAXIS1'] - 1)
]) # BUCKET want NAXIS - 1?
# lamRange2 = h2['CRVAL1'] + np.array([0., h2['CD1_1']*(h2['CRPIX1'])]) # or use CRPIX1??
print(lamRange2, 'l2') # [ 20628.29 24291.89]
# print((lamRange2[1] - lamRange2[0])/h2['CD1_1'], 'num of steps in lam2') # 1720.
# print(len(ssp), 'len ssp') # 1721
sspNew, logLam2, velscale_temp = util.log_rebin(
lamRange2, ssp, velscale=velscale) # /velscale_ratio)
# print(len(sspNew), 'len sspnew') # 622 hmmmm NEED THIS TO BE >= (len(galaxy)=750) # FIXED, now 1791
templates = np.empty((sspNew.size, len(vazdekis)))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SAURON and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SAURON
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
# sigma = FWHM_dif/2.355/h2['CDELT1'] # Sigma difference in pixels
sigma = FWHM_dif / 2.355 / h2['CD1_1'] # Sigma difference in pixels
for j, file in enumerate(
vazdekis
): # now for each template file; so why do the thing above with just 1??
hdu = fits.open(file)
# ssp = hdu[0].data
ssp = hdu[1].data
ssp = ndimage.gaussian_filter1d(ssp, sigma)
# ndimage.gaussian_filter takes input array (ssp) and filters it. Sigma = standard deviation of Gaussian kernel
# used to filter the array
# note: discrete convolution is defined (for any 2 arrays a, v): (a*v)[n] == sum m(-inf to inf) of a[m]*v[n-m]
# where a is the original curve and v is the gaussian
# print(len(ssp)) # 1721 --> currently by default being resampled to be 116, want it to be resampled to be 71
sspNew, logLam2, velscale_temp = util.log_rebin(
lamRange2, ssp, velscale=velscale) # /velscale_ratio)
# print(velscale_temp, 'vt') # 78.82967746
# print(velscale, 'v') # 78.82967746
# print(len(sspNew)) # need this to be >= len(galaxy) # now 1791
templates[:, j] = sspNew / np.median(sspNew) # Normalizes templates
# print(len(templates[0])) # len(templates)=29, len(templates[0] = 19)
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below (see above).
#
c = 299792.458
dv = (logLam2[0] - logLam1[0]) * c # km/s
'''
if velscale_ratio > 1:
dv = (np.mean(logLam2[:velscale_ratio]) - logLam1[0])*c # km/s
else:
dv = (logLam2[0] - logLam1[0])*c # km/s
'''
z = 0.016561 # z = 0.0015 # Initial redshift estimate of the galaxy
goodPixels = util.determine_goodpixels(logLam1, lamRange2, z)
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
vel = c * np.log(1 + z) # eq.(8) of Cappellari (2017)
start = [vel, 200.] # [vel, 200.] # (km/s), starting guess for [V, sigma]
t = clock()
# print(galaxy.shape[0]) # = len(galaxy) = 750
# print(goodPixels)
# print(max(noise[:, x, y]), min(noise[:, x, y]), np.median(noise[:, x, y]), np.mean(noise[:, x, y]))
# 0.00106575 -2.77079e-05 4.62628e-05 5.89877e-05
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=True,
moments=4,
degree=4,
vsyst=dv,
velscale_ratio=1) # velscale_ratio=velscale_ratio)
print("Formal errors:")
print(" dV dsigma dh3 dh4")
print("".join("%8.2g" % f for f in pp.error * np.sqrt(pp.chi2)))
print('Elapsed time in pPXF: %.2f s' % (clock() - t))
# If the galaxy is at significant redshift z and the wavelength has been
# de-redshifted with the three lines "z = 1.23..." near the beginning of
# this procedure, the best-fitting redshift is now given by the following
# commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34;
# http://adsabs.harvard.edu/abs/2009ApJ...704L..34C)
#
# print('Best-fitting redshift z:', (z + 1)*(1 + pp.sol[0]/c) - 1)
return pp.bestfit, pp.galaxy, pp.sol # output_spectrum, output_noise # for use in noise_in
def run_stellar_templates(velscale):
""" Run over stellar templates. """
temp_dir = os.path.join(home, "stellar_templates/MILES_FWHM_3.6")
standards_dir = os.path.join(home, "data/standards")
table = os.path.join(tables_dir, "lick_standards.txt")
ids = np.loadtxt(table, usecols=(0,), dtype=str).tolist()
star_pars = np.loadtxt(table, usecols=(26,27,28,))
for night in nights:
cdir = os.path.join(standards_dir, night)
os.chdir(cdir)
standards = sorted([x for x in os.listdir(".") if x.endswith(".fits")])
for standard in standards:
name = standard.split(".")[0].upper()
if name not in ids:
continue
print standard
idx = ids.index(name)
T, logg, FeH = star_pars[idx]
tempfile= "MILES_Teff{0:.2f}_Logg{1:.2f}_MH{2:.2f}" \
"_linear_FWHM_3.6.fits".format(T, logg, FeH )
os.chdir(temp_dir)
template = pf.getdata(tempfile)
htemp = pf.getheader(tempfile)
wtemp = htemp["CRVAL1"] + htemp["CDELT1"] * \
(np.arange(htemp["NAXIS1"]) + 1 - htemp["CRPIX1"])
os.chdir(cdir)
dsigma = np.sqrt((3.7**2 - 3.6**2))/2.335/(wtemp[1]-wtemp[0])
template = broad2hydra(wtemp, template, 3.6)
data = pf.getdata(standard)
w = wavelength_array(standard)
lamRange1 = np.array([w[0], w[-1]])
lamRange2 = np.array([wtemp[0], wtemp[-1]])
# Rebin to log scale
star, logLam1, velscale = util.log_rebin(lamRange1, data,
velscale=velscale)
temp, logLam2, velscale = util.log_rebin(lamRange2, template,
velscale=velscale)
w = np.exp(logLam1)
goodpixels = np.argwhere((w>4700) & (w<6100)).T[0]
noise = np.ones_like(star)
dv = (logLam2[0]-logLam1[0])*c
pp0 = ppxf(temp, star, noise, velscale, [300.,5], plot=False,
moments=2, degree=20, mdegree=-1, vsyst=dv, quiet=True,
goodpixels=goodpixels)
noise = np.ones_like(noise) * np.nanstd(star - pp0.bestfit)
pp0 = ppxf(temp, star, noise, velscale, [0.,5], plot=False,
moments=2, degree=20, mdegree=-1, vsyst=dv,
goodpixels=goodpixels)
pp0.w = np.exp(logLam1)
pp0.wtemp = np.exp(logLam2)
pp0.template_linear = [wtemp, template]
pp0.temp = temp
pp0.ntemplates = 1
pp0.ngas = 0
pp0.has_emission = False
pp0.dv = dv
pp0.velscale = velscale
pp0.ngas = 0
pp0.nsky = 0
if not os.path.exists("logs"):
os.mkdir("logs")
ppsave(pp0, "logs/{0}".format(standard.replace(".fits", "")))
pp = ppload("logs/{0}".format(standard.replace(".fits", "")))
pp = pPXF(standard, velscale, pp)
pp.plot("logs/{0}".format(standard.replace(".fits", ".png")))
# plt.show()
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 ppxf_nifs_kinematics(newgal=None, resid=None, centers=None, coords=[0, 0]):
file_dir = path.dirname(path.realpath(__file__)) # path of this procedure
# Read a galaxy spectrum and define the wavelength range
file = file_dir + '/spectra/pgc12557_combined.fits' # '/spectra/NGC4550_SAURON.fits' # my current file location
hdu = fits.open(file)
gal_lin = hdu[1].data # gal_lin = hdu[0].data
h1 = hdu[
1].header # I need to use 1st extension header (0=general, 1=science, 2=variance, 3=data quality flags)
lamRange1 = h1['CRVAL3'] + np.array(
[0., h1['CD3_3'] *
(h1['NAXIS3'] - 1)]) # [ 19971.86914062 24319.31070422]
print(lamRange1, 'l1')
# print(gal_lin[0][20]) all 0s # print((gal_lin[300][35])) NOT all 0s!
# len(gal_lin) = 2040, len(gal_lin[0]) = 69, len(gal_lin[0][1]) = 71
# 2040 --> NAXIS 3, 69 --> NAXIS2, 71 --> NAXIS1
# There's a len 2040 spectrum at each gal_lin[:,x,y] --> gal_lin[:, x, y] is an array len(2040) starting at
# lamRange1[0] and finishing at lamRange1[1], with each pixel in between separated by h1['CD3_3'].
# CUT SPECTRUM TO WAVELENGTH RANGE 2.26 - 2.42
low_lim = 22600.
up_lim = 24200.
cut1 = int(
(low_lim - lamRange1[0]) / h1['CD3_3']
) # num pixels between pix 1 & pix corresponding to 2.26 microns
cut2 = int(
(up_lim - lamRange1[0]) / h1['CD3_3']
) # num pixels between pix 1 & pix corresponding to 2.42 microns
# print(cut1, cut2, 'cuts') # 1232.62354281, 1983.04191009 --> int(cut1) = 1232, int(cut2) = 1983
gal_lin = gal_lin[cut1:
cut2] # cut gal_lin spectrum to new wavelength range
start = h1['CRVAL3'] + h1['CD3_3'] * cut1
stop = h1['CRVAL3'] + h1['CD3_3'] * cut2
lamRange1 = [start, stop
] # redefine lamRange1 to correspond to new wavelength range
print(lamRange1, 'l1, cut')
# len(gal_lin) is now NOT 2040 but 1983 - 1233 = 750
FWHM_gal = 4.2 # SAURON has an instrumental resolution FWHM of 4.2A. # BUCKET: do I need this? If so what for?
# If the galaxy is at significant redshift, one should bring the galaxy
# spectrum roughly to the rest-frame wavelength, before calling pPXF
# (See Sec2.4 of Cappellari 2017). In practice there is no
# need to modify the spectrum in any way, given that a red shift
# corresponds to a linear shift of the log-rebinned spectrum.
# One just needs to compute the wavelength range in the rest-frame
# and adjust the instrumental resolution of the galaxy observations.
# This is done with the following three commented lines:
#
# z = 1.23 # Initial estimate of the galaxy redshift
# lamRange1 = lamRange1/(1+z) # Compute approximate restframe wavelength range
# FWHM_gal = FWHM_gal/(1+z) # Adjust resolution in Angstrom
# There's a len 2040 spectrum at each gal_lin[:,x,y]
x = coords[0]
y = coords[1]
# if newgal is None:
galaxy, logLam1, velscale = util.log_rebin(
lamRange1, gal_lin[:, x, y]) # no input velscale --> fcn returns it
print(len(galaxy), 'len gal') # 750 now because of cut to gal_lin!
print(np.median(galaxy))
galaxy = galaxy / np.median(
galaxy) # Normalize spectrum to avoid numerical issues
# else:
# galaxy, logLam1, velscale = util.log_rebin(lamRange1, newgal) # newgal is the spectrum at coords x, y
# basically bootstrap the noise!
# Do one fit with flat noise, then save the best fit spectrum and the residuals.
# Then, iterate ~200 times. For these iterations, set bias = 0.0. Each iteration, for each pixel, use the spectrum
# value as the center of a gaussian and use the residuals at that pixel value as the width of the gaussian. Draw
# from the resultant distribution to make the new noise. For each iteration, save the output V, sigma, h3, h4, and
# print each spectrum so that we can see it evolving (it should look more like a real spectrum, rather than smooth
# curve without lines)
noise = np.full_like(galaxy,
0.0047) # Assume constant noise per pixel here
# MEANTIME: why is output velocity close to systemic insted of close to 0, if I'm dealing with redshift already?
# SET bias = 0
#
print('shape', noise.shape) # 751,
# print(galaxy.shape) # 751,
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis (2010, MNRAS, 404, 1639) http://miles.iac.es/. A subset
# of the library is included for this example with permission
vazdekis = glob.glob(
file_dir +
'/veltemps/*.fits') # '/miles_models/Mun1.30Z*.fits') # my new
# BUCKET: what are FWHM of spectra in the veltemps library??
FWHM_tem = 2.51 # Vazdekis+10 spectra have a constant resolution FWHM of 2.51A.
# BUCKET: what spectral sampling compared to galaxy do we want for templates??
# velscale_ratio = 2 # 1.23 # 2 # adopts 2x higher spectral sampling for templates than for galaxy
# PROBLEM!! If velscale_ratio is not integer, we get issue later because we slice a list with velscale_ratio
# so need to change velscale? But it's set by util.log_rebin originally! Only need this if oversampling the
# templates, which I'm not doing
# Extract the wavelength range and logarithmically rebin one spectrum
# to a velocity scale 2x smaller than the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
print(vazdekis[0], 'template name')
hdu = fits.open(
vazdekis[0]
) # do for just one template to determine size needed for array containing all templates
ssp = hdu[
1].data # was hdu[0], but that's generic header rather than science header
h2 = hdu[1].header # was hdu[0]
lamRange2 = h2['CRVAL1'] + np.array([0., h2['CD1_1'] * (h2['NAXIS1'] - 1)
]) # BUCKET want NAXIS - 1?
print(lamRange2, 'l2') # [ 20628.29 24291.89]
# print((lamRange2[1] - lamRange2[0])/h2['CD1_1'], 'num of steps in lam2') # 1720.
# print(len(ssp), 'len ssp') # 1721
sspNew, logLam2, velscale_temp = util.log_rebin(
lamRange2, ssp, velscale=velscale) # /velscale_ratio)
# print(len(sspNew), 'len sspnew') # 622 hmmmm NEED THIS TO BE >= (len(galaxy)=750) # FIXED, now 1791
templates = np.empty((sspNew.size, len(vazdekis)))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SAURON and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SAURON
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
# sigma = FWHM_dif/2.355/h2['CDELT1'] # Sigma difference in pixels
sigma = FWHM_dif / 2.355 / h2['CD1_1'] # Sigma difference in pixels
for j, file in enumerate(
vazdekis
): # now for each template file; so why do the thing above with just 1??
hdu = fits.open(file)
# ssp = hdu[0].data
ssp = hdu[1].data
ssp = ndimage.gaussian_filter1d(ssp, sigma)
# ndimage.gaussian_filter takes input array (ssp) and filters it. Sigma = standard deviation of Gaussian kernel
# used to filter the array
# note: discrete convolution is defined (for any 2 arrays a, v): (a*v)[n] == sum m(-inf to inf) of a[m]*v[n-m]
# where a is the original curve and v is the gaussian
# print(len(ssp)) # 1721 --> currently by default being resampled to be 116, want it to be resampled to be 71
sspNew, logLam2, velscale_temp = util.log_rebin(
lamRange2, ssp, velscale=velscale) # /velscale_ratio)
# print(velscale_temp, 'vt') # 78.82967746
# print(velscale, 'v') # 78.82967746
# print(len(sspNew)) # need this to be >= len(galaxy) # now 1791
templates[:, j] = sspNew / np.median(sspNew) # Normalizes templates
# print(len(templates[0])) # len(templates)=29, len(templates[0] = 19)
# The galaxy and the template spectra do not have the same starting wavelength.
# For this reason an extra velocity shift DV has to be applied to the template
# to fit the galaxy spectrum. We remove this artificial shift by using the
# keyword VSYST in the call to PPXF below, so that all velocities are
# measured with respect to DV. This assume the redshift is negligible.
# In the case of a high-redshift galaxy one should de-redshift its
# wavelength to the rest frame before using the line below (see above).
#
c = 299792.458
dv = (logLam2[0] - logLam1[0]) * c # km/s
'''
if velscale_ratio > 1:
dv = (np.mean(logLam2[:velscale_ratio]) - logLam1[0])*c # km/s
else:
dv = (logLam2[0] - logLam1[0])*c # km/s
'''
z = 0.016561 # z = 0.0015 # Initial redshift estimate of the galaxy
goodPixels = util.determine_goodpixels(logLam1, lamRange2, z)
# Here the actual fit starts. The best fit is plotted on the screen.
# Gas emission lines are excluded from the pPXF fit using the GOODPIXELS keyword.
#
vel = c * np.log(1 + z) # eq.(8) of Cappellari (2017)
start = [vel, 200.] # [vel, 200.] # (km/s), starting guess for [V, sigma]
t = clock()
# print(galaxy.shape[0]) # = len(galaxy) = 750
# print(goodPixels)
# print(max(noise[:, x, y]), min(noise[:, x, y]), np.median(noise[:, x, y]), np.mean(noise[:, x, y]))
# 0.00106575 -2.77079e-05 4.62628e-05 5.89877e-05
if newgal is None:
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=True,
moments=4,
degree=4,
vsyst=dv,
velscale_ratio=1,
bias=0.) # velscale_ratio=velscale_ratio)
stuff = pp.bestfit, pp.galaxy, pp.sol, goodPixels
else:
pp = ppxf(templates,
galaxy,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=True,
moments=4,
degree=4,
vsyst=dv,
velscale_ratio=1,
bias=0.) # velscale_ratio=velscale_ratio)
pp_new = ppxf(templates,
newgal,
noise,
velscale,
start,
goodpixels=goodPixels,
plot=False,
moments=4,
degree=4,
vsyst=dv,
velscale_ratio=1,
bias=0.) # velscale_ratio=velscale_ratio)
# pp.plot() # Plot best fit and gas lines
x_ax = np.arange(galaxy.size)
plt.plot(x_ax, pp.galaxy, 'k')
plt.plot(x_ax, newgal, 'b')
stuff = pp_new.bestfit, pp_new.galaxy, pp_new.sol
print("Formal errors:")
print(" dV dsigma dh3 dh4")
print("".join("%8.2g" % f for f in pp.error * np.sqrt(pp.chi2)))
print('Elapsed time in pPXF: %.2f s' % (clock() - t))
# If the galaxy is at significant redshift z and the wavelength has been
# de-redshifted with the three lines "z = 1.23..." near the beginning of
# this procedure, the best-fitting redshift is now given by the following
# commented line (equation 2 of Cappellari et al. 2009, ApJ, 704, L34;
# http://adsabs.harvard.edu/abs/2009ApJ...704L..34C)
#
# print('Best-fitting redshift z:', (z + 1)*(1 + pp.sol[0]/c) - 1)
return stuff # output_spectrum, output_noise # for use in noise_in
hdulist = fits.open(name + '.fits')
if len(hdulist) > 1:
hdu = hdulist[1]
else:
hdu = hdulist[0]
cube = hdu.data.T #just remember to always transpose with fits files
hdr_cube = hdu.header
xs, ys, naxis = cube.shape
hdulist.close()
xcube, ycube = np.ogrid[-xc:xs - xc, -yc:ys - yc]
rCube = np.sqrt(xcube * xcube + ycube * ycube)
lamRange1 = hdr_cube['CRVAL3'] + np.array(
[0., hdr_cube['CD3_3'] * (hdr_cube['NAXIS3'] - 1.)])
tg, logLam1, velscale = util.log_rebin(lamRange1, cube[10, 10, :])
# Read the template
hdu = fits.open('temp_final_%s.fits' % Z)
h_template = hdu[0].header
temp_lin = hdu[0].data
lamRange2 = h_template['CRVAL1'] + np.array(
[0., h_template['CDELT1'] * (h_template['NAXIS1'] - 1.)])
templates, logLam2, velscale = util.log_rebin(lamRange2,
temp_lin,
velscale=velscale)
# Calculate the offset between the template and the galaxy
c = 299792.458
dv = (logLam2[0] - logLam1[0]) * c
def setup_spectral_library(velscale, FWHM_gal):
# Read the list of filenames from the Single Stellar Population library
# by Vazdekis et al. (2010, MNRAS, 404, 1639) http://miles.iac.es/.
#
# For this example I downloaded from the above website a set of
# model spectra with default linear sampling of 0.9A/pix and default
# spectral resolution of FWHM=2.51A. I selected a Salpeter IMF
# (slope 1.30) and a range of population parameters:
#
# [M/H] = [-1.71, -1.31, -0.71, -0.40, 0.00, 0.22]
# Age = range(1.0, 17.7828, 26, /LOG)
#
# This leads to a set of 156 model spectra with the file names like
#
# Mun1.30Zm0.40T03.9811.fits
#
# IMPORTANT: the selected models form a rectangular grid in [M/H]
# and Age: for each Age the spectra sample the same set of [M/H].
#
# We assume below that the model spectra have been placed in the
# directory "miles_models" under the current directory.
#
vazdekis = glob.glob('miles_models/Mun1.30*.fits')
FWHM_tem = 2.51 # Vazdekis+10 spectra have a resolution FWHM of 2.51A.
# Extract the wavelength range and logarithmically rebin one spectrum
# to the same velocity scale of the SAURON galaxy spectrum, to determine
# the size needed for the array which will contain the template spectra.
#
hdu = pyfits.open(vazdekis[0])
ssp = hdu[0].data
h2 = hdu[0].header
lamRange_temp = h2['CRVAL1'] + np.array([0.,h2['CDELT1']*(h2['NAXIS1']-1)])
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp, ssp, velscale=velscale)
# Create a three dimensional array to store the
# two dimensional grid of model spectra
#
nAges = 26
nMetal = 6
templates = np.empty((sspNew.size,nAges,nMetal))
# Convolve the whole Vazdekis library of spectral templates
# with the quadratic difference between the SAURON and the
# Vazdekis instrumental resolution. Logarithmically rebin
# and store each template as a column in the array TEMPLATES.
# Quadratic sigma difference in pixels Vazdekis --> SAURON
# The formula below is rigorously valid if the shapes of the
# instrumental spectral profiles are well approximated by Gaussians.
#
FWHM_dif = np.sqrt(FWHM_gal**2 - FWHM_tem**2)
sigma = FWHM_dif/2.355/h2['CDELT1'] # Sigma difference in pixels
# Here we make sure the spectra are sorted in both [M/H]
# and Age along the two axes of the rectangular grid of templates.
# A simple alphabetical ordering of Vazdekis's naming convention
# does not sort the files by [M/H], so we do it explicitly below
#
metal = ['m1.71', 'm1.31', 'm0.71', 'm0.40', 'p0.00', 'p0.22']
for k, mh in enumerate(metal):
files = [s for s in vazdekis if mh in s]
for j, filename in enumerate(files):
hdu = pyfits.open(filename)
ssp = hdu[0].data
ssp = ndimage.gaussian_filter1d(ssp,sigma)
sspNew, logLam2, velscale = util.log_rebin(lamRange_temp, ssp, velscale=velscale)
templates[:,j,k] = sspNew # Templates are *not* normalized here
print(np.shape(templates))
return templates, lamRange_temp
def ppxf_simulation_example():
dir = 'spectra/'
file = dir + 'Rbi1.30z+0.00t12.59.fits'
hdu = pyfits.open(file)
ssp = hdu[0].data
h = hdu[0].header
lamRange = h['CRVAL1'] + np.array([0.,h['CDELT1']*(h['NAXIS1']-1)])
star, logLam, velscale = util.log_rebin(lamRange, ssp)
# The finite sampling of the observed spectrum is modeled in detail:
# the galaxy spectrum is obtained by oversampling the actual observed spectrum
# to a high resolution. This represent the true spectrum, which is later resampled
# to lower resolution to simulate the observations on the CCD. Similarly, the
# convolution with a well-sampled LOSVD is done on the high-resolution spectrum,
# and later resampled to the observed resolution before fitting with PPXF.
factor = 10 # Oversampling integer factor for an accurate convolution
starNew = ndimage.interpolation.zoom(star,factor,order=1) # This is the underlying spectrum, known at high resolution
star = rebin(starNew,factor) # Make sure that the observed spectrum is the integral over the pixels
vel = 0.3 # velocity in *pixels* [=V(km/s)/velScale]
h3 = 0.1 # Adopted G-H parameters of the LOSVD
h4 = -0.1
sn = 60. # Adopted S/N of the Monte Carlo simulation
m = 300 # Number of realizations of the simulation
sigmaV = np.linspace(0.8,4,m) # Range of sigma in *pixels* [=sigma(km/s)/velScale]
result = np.zeros((m,4)) # This will store the results
t = clock()
np.random.seed(123) # for reproducible results
for j in range(m):
sigma = sigmaV[j]
dx = int(abs(vel)+4.0*sigma) # Sample the Gaussian and GH at least to vel+4*sigma
x = np.linspace(-dx,dx,2*dx*factor+1) # Evaluate the Gaussian using steps of 1/factor pixels.
w = (x - vel)/sigma
w2 = w**2
gauss = np.exp(-0.5*w2)/(np.sqrt(2.*np.pi)*sigma*factor) # Normalized total(gauss)=1
h3poly = w*(2.*w2 - 3.)/np.sqrt(3.) # H3(y)
h4poly = (w2*(4.*w2 - 12.) + 3.)/np.sqrt(24.) # H4(y)
losvd = gauss *(1. + h3*h3poly + h4*h4poly)
galaxy = signal.fftconvolve(starNew,losvd,mode="same") # Convolve the oversampled spectrum
galaxy = rebin(galaxy,factor) # Integrate spectrum into original spectral pixels
noise = galaxy/sn # 1sigma error spectrum
galaxy = np.random.normal(galaxy, noise) # Add noise to the galaxy spectrum
start = np.array([vel+np.random.random(), sigma*np.random.uniform(0.85,1.15)])*velscale # Convert to km/s
pp = ppxf(star, galaxy, noise, velscale, start,
goodpixels=np.arange(dx,galaxy.size-dx),
plot=False, moments=4, bias=0.5)
result[j,:] = pp.sol
print('Calculation time: %.2f s' % (clock()-t))
plt.clf()
plt.subplot(221)
plt.plot(sigmaV*velscale, result[:,0]-vel*velscale, '+k')
plt.plot(sigmaV*velscale, sigmaV*velscale*0, '-r')
plt.ylim(-40, 40)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$V - V_{in}\ (km\ s^{-1}$)')
plt.subplot(222)
plt.plot(sigmaV*velscale, result[:,1]-sigmaV*velscale, '+k')
plt.plot(sigmaV*velscale, sigmaV*velscale*0, '-r')
plt.ylim(-40, 40)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$\sigma - \sigma_{in}\ (km\ s^{-1}$)')
plt.subplot(223)
plt.plot(sigmaV*velscale, result[:,2], '+k')
plt.plot(sigmaV*velscale, sigmaV*velscale*0+h3, '-r')
plt.ylim(-0.2+h3, 0.2+h3)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$h_3$')
plt.subplot(224)
plt.plot(sigmaV*velscale, result[:,3], '+k')
plt.plot(sigmaV*velscale, sigmaV*velscale*0+h4, '-r')
plt.ylim(-0.2+h4, 0.2+h4)
plt.xlabel('$\sigma_{in}\ (km\ s^{-1})$')
plt.ylabel('$h_4$')
plt.tight_layout()
plt.pause(0.01)