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Commit ecd4d7aa authored by Feng Shuai's avatar Feng Shuai 💬
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Deleted gehong/__pycache__/__init__.cpython-38.pyc,... 

Deleted gehong/__pycache__/__init__.cpython-38.pyc, gehong/__pycache__/config.cpython-38.pyc, gehong/__pycache__/cube3d.cpython-38.pyc, gehong/__pycache__/map2d.cpython-38.pyc, gehong/__pycache__/spec1d.cpython-38.pyc, gehong/data/EMILES/model/HST_ch_iPp0.00.MAG, gehong/data/EMILES/model/HST_mag.dat, gehong/data/EMILES/model/out_mass_CH_PADOVA00, gehong/data/EMILES/.DS_Store, gehong/data/EMILES/Ech1.30Zm0.40T00.0631_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.0708_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.0794_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.0891_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1000_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1122_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1259_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1413_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1585_iPp0.00_baseFe.fits, gehong/data/EMILES/Ech1.30Zm0.40T00.1778_iPp0.00_baseFe.fits, 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parent 50fc43d6
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src/gehong/config.py deleted 100644 → 0
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import numpy as np
class config():
"""
The configuration of spectral modeling. The default value is used for ETC calculation.
Parameters
----------
wave_min : float, optional
Minimum value of wavelength coverage, by default 3500.0A
wave_max : float, optional
Minimum value of wavelength coverage, by default 10000.0A
dlam : float, optional
Wavelength width of each spaxel, by default 2.0A
inst_fwhm : float, optional
Spectral resolution, by default 0.1A
nx : int, optional
Number of spaxel in a spatial direction, by default 30
ny : int, optional
Number of spaxel in a spatial direction, by default 30
dpix : float, optional
Pixel size in the spatial direction, by default 0.2arcsec
"""
def __init__(self, wave_min = 3500.0, wave_max = 10000.0,
dlam = 2.0, inst_fwhm = 0.1,
nx = 30, ny = 30, dpix = 0.2):
self.dlam = dlam
self.wave = np.arange(wave_min, wave_max, dlam)
self.wave_min = wave_min
self.inst_fwhm = inst_fwhm
self.nx = nx
self.ny = ny
self.dpix = dpix
self.fov_x = nx * dpix
self.fov_y = ny * dpix
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src/gehong/cube3d.py deleted 100644 → 0
View file @ 50fc43d6
import astropy.units as u
import numpy as np
from scipy.interpolate import interp1d
from .spec1d import *
import astropy.wcs
class Cube3D():
"""
Class of 3-dimentional spectral cube
"""
def __init__(self, config, stellar_map = None, gas_map = None):
self.config= config
self.nx = config.nx
self.ny = config.ny
self.dpix = config.dpix
self.fov_x = config.fov_x
self.fov_y = config.fov_y
self.wave = config.wave
self.nz = len(self.wave)
self.wave0 = np.min(self.wave)
self.inst_fwhm = config.inst_fwhm
self.flux = np.zeros((self.nx,self.ny,self.nz))
self.stellar_map = stellar_map
self.gas_map = gas_map
def make_cube(self, stellar_tem = None, hii_tem = None):
for i in range(self.nx):
for j in range(self.ny):
if self.stellar_map is not None:
ss = StellarContinuum(self.config, stellar_tem, mag = self.stellar_map.mag[i,j],
age = self.stellar_map.age[i,j], feh = self.stellar_map.feh[i,j],
vel = self.stellar_map.vel[i,j], vdisp = self.stellar_map.vdisp[i,j],
ebv = self.stellar_map.ebv[i,j])
if self.gas_map is not None:
gg = HII_Region(self.config, hii_tem, halpha = self.gas_map.halpha[i,j],
logz = self.gas_map.zh[i,j], vel = self.gas_map.vel[i,j],
vdisp = self.gas_map.vdisp[i,j], ebv = self.gas_map.ebv[i,j])
self.flux[i,j,:] = ss.flux + gg.flux
else:
self.flux[i,j,:] = ss.flux
def wcs_info(self):
wcs = fits.Header()
wcs_dict = {'CTYPE1': 'WAVE ',
'CUNIT1': 'Angstrom',
'CDELT1': self.config.dlam,
'CRPIX1': 1,
'CRVAL1': np.min(self.wave),
'CTYPE2': 'RA---TAN',
'CUNIT2': 'deg',
'CDELT2': self.dpix / 3600.,
'CRPIX2': np.round(self.ny / 2.),
'CRVAL2': 0.5,
'CTYPE3': 'DEC--TAN',
'CUNIT3': 'deg',
'CDELT3': self.dpix / 3600.,
'CRPIX3': np.round(self.nx / 2.),
'CRVAL3': 1,
'BUNIT' : '10**(-17)*erg/s/cm**2/Angstrom'}
input_wcs = astropy.wcs.WCS(wcs_dict)
self.wcs_header = input_wcs.to_header()
def insert_spec(self, spec, dx = 0, dy = 0):
x0 = np.int(np.round(self.config.nx / 2.))
y0 = np.int(np.round(self.config.ny / 2.))
self.flux[x0 + dx, y0 + dy, :] = self.flux[x0 + dx, y0 + dy, :] + spec.flux
def savefits(self, filename, path = './'):
hdr = fits.Header()
hdr['FILETYPE'] = 'SCICUBE'
hdr['CODE'] = 'CSST-IFS-GEHONG'
hdr['VERSION'] = '0.0.1'
hdr['OBJECT'] = 'NGC1234'
hdr['RA'] = 0.0
hdr['DEC'] = 0.0
hdu0 = fits.PrimaryHDU(header = hdr)
self.wcs_info()
hdr = self.wcs_header
hdu1 = fits.ImageHDU(self.flux, header = hdr)
# Output
hdulist = fits.HDUList([hdu0, hdu1])
hdulist.writeto(path + filename, overwrite = True)
\ No newline at end of file
src/gehong/map2d.py deleted 100644 → 0
View file @ 50fc43d6
from __future__ import division
import scipy.special as sp
import numpy as np
from astropy.io import fits
from skimage.transform import resize
def Sersic2D(x, y, mag = 12.0, r_eff = 1.0, n = 2.0, ellip = 0.5,
theta = 0.0, x_0 = 0.0, y_0 = 0.0, pixelscale = 0.01):
"""
Model of 2D - Sersic profile
Parameters
----------
x : float array
_description_
y : _type_
_description_
mag : float, optional
Integral magnitude of sersic model, by default 12.0
r_eff : float, optional
Effective radius in pixel, by default 1.0
n : float, optional
Sersic index, by default 2.0
ellip : float, optional
Ellipticity, by default 0.5
theta : float, optional
Position angle in degree, by default 0.0
x_0 : float, optional
Offset of the center of Sersic model, by default 0.0
y_0 : float, optional
Offset of the center of Sersic model, by default 0.0
pixelscale : float, optional
Size of each pixel in arcsec^2, by default 0.01
Returns
-------
_description_
"""
# Produce Sersic profile
bn = sp.gammaincinv(2. * n, 0.5)
a, b = r_eff, (1 - ellip) * r_eff
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = (abs(x_maj / a) ** 2 + abs(x_min / b) ** 2) ** (1 / 2)
profile = np.exp(-bn * (z ** (1 / n) - 1))
# Normalization
integral = a * b * 2 * np.pi * n * np.exp(bn) / (bn ** (2 * n)) * sp.gamma(2 * n)
prof_norm = profile / integral * pixelscale
# Calibration
total_flux = 10. ** ((22.5 - mag) * 0.4)
sb_mag = 22.5 - 2.5 * np.log10(prof_norm * total_flux / pixelscale)
return sb_mag
def VelMap2D(x, y, vmax = 200.0, rt = 1.0, ellip = 0.5,
theta = 0.0, x_0 = 0.0, y_0 = 0.0):
"""
VelMap2D _summary_
Parameters
----------
x : _type_
_description_
y : _type_
_description_
vmax : int, optional
_description_, by default 200
rt : int, optional
_description_, by default 1
ellip : float, optional
_description_, by default 0.5
theta : int, optional
_description_, by default 0
x_0 : int, optional
_description_, by default 0
y_0 : int, optional
_description_, by default 0
Returns
-------
_type_
_description_
"""
# Produce tanh profile
a, b = rt, (1 - ellip) * rt
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = (abs(x_maj / a) ** 2 + abs(x_min / b) ** 2) ** (1 / 2)
profile = vmax * np.tanh(z) * ((x_maj / a) / z)
return profile
def GradMap2D(x, y, a0 = 10, r_eff = 1, gred = -1, ellip = 0.5,
theta = 0, x_0 = 0, y_0 = 0):
"""
GradMap2D _summary_
Parameters
----------
x : _type_
_description_
y : _type_
_description_
a0 : int, optional
_description_, by default 10
r_eff : int, optional
_description_, by default 1
gred : int, optional
_description_, by default -1
ellip : float, optional
_description_, by default 0.5
theta : int, optional
_description_, by default 0
x_0 : int, optional
_description_, by default 0
y_0 : int, optional
_description_, by default 0
Returns
-------
_type_
_description_
"""
# Produce gradiant profile
a, b = r_eff, (1 - ellip) * r_eff
cos_theta, sin_theta = np.cos(theta), np.sin(theta)
x_maj = (x - x_0) * cos_theta + (y - y_0) * sin_theta
x_min = -(x - x_0) * sin_theta + (y - y_0) * cos_theta
z = (abs(x_maj / a) ** 2 + abs(x_min / b) ** 2) ** (1 / 2)
profile = a0 + z * gred
return profile
class Map2d(object):
def __init__(self, config):
"""
__init__ _summary_
Parameters
----------
inst : _type_
_description_
"""
self.xsamp = config.dpix
self.ysamp = config.dpix
startx = -(config.nx - 1) / 2.0 * self.xsamp
stopx = (config.nx - 1) / 2.0 * self.xsamp
starty = -(config.ny - 1) / 2.0 * self.ysamp
stopy = (config.ny - 1) / 2.0 * self.ysamp
xvals = np.linspace(startx, stopx, num = config.nx)
yvals = np.linspace(starty, stopy, num = config.ny)
ones = np.ones((config.ny, config.nx))
x = ones * xvals
y = np.flipud(ones * yvals.reshape(int(config.ny), 1))
self.nx = config.nx
self.ny = config.ny
self.x = x
self.y = y
self.row = xvals
# flip Y axis because we use Y increasing from bottom to top
self.col = yvals[::-1]
def shift_rotate(self, yoff, xoff, rot):
"""
Return shifted/rotated (y, x) given offsets (yoff, xoff) and rotation, rot (degrees)
Parameters
----------
yoff, xoff: float
yoff, xoff offsets in world coordinates
rot: float
rotation angle in degrees
Returns
-------
ysh_rot, xsh_rot: 2D numpy arrays
rotated and shifted copies of Grid.x and Grid.y
"""
pa_radians = np.pi * rot / 180.0
xsh = self.x - xoff
ysh = self.y - yoff
xsh_rot = xsh * np.cos(pa_radians) + ysh * np.sin(pa_radians)
ysh_rot = -xsh * np.sin(pa_radians) + ysh * np.cos(pa_radians)
return ysh_rot, xsh_rot
def sersic_map(self, mag = 12.0, r_eff = 2.0, n = 2.5, ellip = 0.5, theta = -50.0):
"""
Generate 2D map of Sersic model
Parameters
----------
mag : float, optional
Integral magnitude, by default 12.0
r_eff : float, optional
Effective radius in arcsec, by default 2.0
n : float, optional
Sersic index, by default 2.5
ellip : float, optional
Ellipcity, by default 0.5
theta : float, optional
Position angle in degree, by default -50.0
"""
self.mag = mag
self.reff = r_eff / self.xsamp
self.n = n
self.ellip = ellip
self.theta = theta
self.map = Sersic2D(self.x, self.y, mag = self.mag,
r_eff = self.reff, n = self.n,
ellip = self.ellip, theta = self.theta,
pixelscale = self.xsamp * self.ysamp)
def tanh_map(self, vmax = 200.0, rt = 2.0, ellip = 0.5, theta = -50.0):
"""
Generate 2D velocity map of rotating disk according to tanh rotation curve
Parameters
----------
vmax : float, optional
Maximum rotational velocity, by default 200.0km/s
rt : float, optional
Turn-over radius of rotation curve, by default 2.0 arcsec
ellip : float, optional
Apparent ellipcity of rotating disk, by default 0.5
theta : float, optional
Position angle of rotating disk, by default -50.0
"""
self.vmax = vmax
self.rt = rt / self.xsamp
self.ellip = ellip
self.theta = theta
self.map = VelMap2D(self.x, self.y, vmax = self.vmax, rt = self.rt,
ellip = self.ellip, theta = self.theta)
def gred_map(self, a0 = 10, r_eff = 1, gred = -1, ellip = 0.5, theta = 0):
"""
Generate 2D maps according to the radial gradient form
Parameters
----------
a0 : float, optional
Amplitude at the central pixel, by default 10
r_eff : float, optional
Effective radius, by default 1
gred : float, optional
Gradient of radial profile, by default -1
ellip : float, optional
Ellipcity, by default 0.5
theta : int, optional
Position angle, by default 0
"""
self.a0 = a0
self.reff = r_eff / self.xsamp
self.gred = gred
self.ellip = ellip
self.theta = theta
self.map = GradMap2D(self.x, self.y, a0 = self.a0, r_eff = self.reff,
gred = self.gred, ellip = self.ellip, theta = self.theta)
def load_map(self, image):
"""
Generate 2D map according to input image
Parameters
----------
image : 2d numpy array
The 2d array to be loaded.
"""
if np.ndim(image) == 2:
self.map = resize(image, (self.nx, self.ny))
class StellarPopulationMap():
"""
Class of 2D maps for the parameters of stellar population, such as
surface brightness, median age and metallicity of stellar population,
velocity and velocity dispersion maps, and dust extinction.
Parameters
----------
config : class
Class of configuration
sbright : class, optional
Class of the map of surface brightness of stellar population, by default None
logage : class, optional
Class of the map of stellar age, by default None
feh : class, optional
Class of the map of stellar metellicity, by default None
vel : class, optional
Class of the map of stellar velocity, by default None
vdisp : class, optional
Class of the map of stellar velocity dispersion, by default None
ebv : class, optional
Class of the map of dust extinction, by default None
"""
def __init__(self, config, sbright = None, logage = None,
feh = None, vel = None, vdisp = None, ebv = None):
self.nx = config.nx
self.ny = config.ny
self.dpix = config.dpix
self.fov_x = config.fov_x
self.fov_y = config.fov_y
self.sbright = sbright.map
self.logage = logage.map
self.feh = feh.map
self.vel = vel.map
self.vdisp = vdisp.map
self.ebv = ebv.map
self.mag = self.sbright - 2.5 * np.log10(self.dpix * self.dpix)
self.age = 10 ** self.logage / 1e9
self.vdisp[self.vdisp < 10] = 10
self.ebv[self.ebv < 0] = 0
class IonizedGasMap():
"""
Class of 2D maps for the parameters of ionized gas, such as
Halpha flux map, gas-phase metallicity map,
velocity and velocity dispersion maps, and dust extinction.
Parameters
----------
config : class
Class of configuration
halpha : class, optional
Class of the map of Halpha flux, by default None
zh : class, optional
Class of the map of gas-phase metallicity, by default None
vel : class, optional
Class of the map of gas velocity, by default None
vdisp : class, optional
Class of the map of gas velocity dispersion, by default None
ebv : class, optional
Class of the map of dust extinction, by default None
"""
def __init__(self, config, halpha = None, zh = None, vel = None, vdisp = None, ebv = None):
self.nx = config.nx
self.ny = config.ny
self.dpix = config.dpix
self.fov_x = config.fov_x
self.fov_y = config.fov_y
self.halpha = halpha.map
self.zh = zh.map
self.vel = vel.map
self.vdisp = vdisp.map
self.ebv = ebv.map
self.vdisp[self.vdisp < 10] = 10
self.ebv[self.ebv < 0] = 0
\ No newline at end of file
src/gehong/spec1d.py deleted 100644 → 0
View file @ 50fc43d6
import os
import glob
import sys
from os import path
import numpy as np
import astropy.units as u
from astropy.io import fits
from scipy.stats import norm
from scipy.interpolate import interp1d
data_path = os.getenv('GEHONG_DATA_PATH')
def readcol(filename, **kwargs):
"""
readcol, taken from ppxf.
Parameters
----------
filename : string
The name of input ascii file
Returns
-------
float
The value of each columns
"""
f = np.genfromtxt(filename, dtype=None, **kwargs)
t = type(f[0])
if t == np.ndarray or t == np.void: # array or structured array
f = map(np.array, zip(*f))
# In Python 3.x all strings (e.g. name='NGC1023') are Unicode strings by defauls.
# However genfromtxt() returns byte strings b'NGC1023' for non-numeric columns.
# To have the same behaviour in Python 3 as in Python 2, I convert the Numpy
# byte string 'S' type into Unicode strings, which behaves like normal strings.
# With this change I can read the string a='NGC1023' from a text file and the
# test a == 'NGC1023' will give True as expected.
if sys.version >= '3':
f = [v.astype(str) if v.dtype.char=='S' else v for v in f]
return f
def log_rebin(lamRange, spec, oversample=False, velscale=None, flux=False):
"""
Logarithmically rebin a spectrum, while rigorously conserving the flux.
This function is taken from ppxf.
Parameters
----------
lamRange : array
Two elements vector containing the central wavelength
of the first and last pixels in the spectrum
spec : array
Input spectrum
oversample : bool, optional
Oversampling can be done, not to loose spectral resolution,
especally for extended wavelength ranges and to avoid aliasing, by default False
velscale : float, optional
velocity scale in km/s per pixels, by default None
flux : bool, optional
True to preserve total flux, by default False
Returns
-------
specNew : array
Output spectrum
logLam : array
Wavelength array in logarithm
velscale : array
velocity scale in km/s per pixels
"""
lamRange = np.asarray(lamRange)
assert len(lamRange) == 2, 'lamRange must contain two elements'
assert lamRange[0] < lamRange[1], 'It must be lamRange[0] < lamRange[1]'
s = spec.shape
assert len(s) == 1, 'input spectrum must be a vector'
n = s[0]
if oversample:
m = int(n*oversample)
else:
m = int(n)
dLam = np.diff(lamRange)/(n - 1.) # Assume constant dLam
lim = lamRange/dLam + [-0.5, 0.5] # All in units of dLam
borders = np.linspace(*lim, num=n+1) # Linearly
logLim = np.log(lim)
c = 299792.458 # Speed of light in km/s
if velscale is None: # Velocity scale is set by user
velscale = np.diff(logLim)/m*c # Only for output
else:
logScale = velscale/c
m = int(np.diff(logLim)/logScale) # Number of output pixels
logLim[1] = logLim[0] + m*logScale
newBorders = np.exp(np.linspace(*logLim, num=m+1)) # Logarithmically
k = (newBorders - lim[0]).clip(0, n-1).astype(int)
specNew = np.add.reduceat(spec, k)[:-1] # Do analytic integral
specNew *= np.diff(k) > 0 # fix for design flaw of reduceat()
specNew += np.diff((newBorders - borders[k])*spec[k])
if not flux:
specNew /= np.diff(newBorders)
# Output log(wavelength): log of geometric mean
logLam = np.log(np.sqrt(newBorders[1:]*newBorders[:-1])*dLam)
return specNew, logLam, velscale
def gaussian_filter1d(spec, sig):
"""
One-dimensional Gaussian convolution
Parameters
----------
spec : float array
vector with the spectrum to convolve
sig : float
vector of sigma values (in pixels) for every pixel
Returns
-------
float array
Spectrum after convolution
"""
sig = sig.clip(0.01) # forces zero sigmas to have 0.01 pixels
p = int(np.ceil(np.max(3*sig)))
m = 2*p + 1 # kernel size
x2 = np.linspace(-p, p, m)**2
n = spec.size
a = np.zeros((m, n))
for j in range(m): # Loop over the small size of the kernel
a[j, p:-p] = spec[j:n-m+j+1]
gau = np.exp(-x2[:, None]/(2*sig**2))
gau /= np.sum(gau, 0)[None, :] # Normalize kernel
conv_spectrum = np.sum(a*gau, 0)
return conv_spectrum
def calibrate(wave, flux, mag, filtername = 'SLOAN_SDSS.r'):
"""
Flux calibration of spectrum
Parameters
----------
wave : float array
Wavelength of input spectrum
flux : float array
Flux of input spectrum
mag : float
Magnitude used for flux calibration
filtername : str, optional
Filter band name, by default 'SLOAN_SDSS.r'
Returns
-------
float array
Spectrum after flux calibration
"""
# Loading response curve
if filtername == '5100':
wave0 = np.linspace(3000,10000,7000)
response0 = np.zeros(7000)
response0[(wave0 > 5050) & (wave0 < 5150)] = 1.
else:
filter_file = data_path + '/data/filter/' + filtername+'.filter'
wave0, response0 = readcol(filter_file)
# Setting the response
func = interp1d(wave0, response0)
response = np.copy(wave)
ind_extra = (wave > max(wave0)) | (wave < min(wave0))
response[ind_extra] = 0
ind_inside = (wave < max(wave0)) & (wave > min(wave0))
response[ind_inside] = func(wave[ind_inside])
# Flux map of datacube for given filter band
preflux = np.sum(flux * response * np.mean(np.diff(wave))) / np.sum(response * np.mean(np.diff(wave)))
# Real flux from magnitude for given filter
realflux = (mag * u.STmag).to(u.erg/u.s/u.cm**2/u.AA).value
# Normalization
flux_ratio = realflux / preflux
flux_calibrate = flux * flux_ratio * 1e17 # Units: 10^-17 erg/s/A/cm^2
return flux_calibrate
# ----------------
# Reddening Module
def Calzetti_Law(wave, Rv = 4.05):
"""
Dust Extinction Curve of Calzetti et al. (2000)
Parameters
----------
wave : float, or float array
Wavelength
Rv : float, optional
Extinction coefficient, by default 4.05
Returns
-------
float
Extinction value corresponding to the input wavelength
"""
wave_number = 1./(wave * 1e-4)
reddening_curve = np.zeros(len(wave))
idx = (wave >= 1200) & (wave < 6300)
reddening_curve[idx] = 2.659 * ( -2.156 + 1.509 * wave_number[idx] - 0.198 * \
(wave_number[idx] ** 2)) + 0.011 * (wave_number[idx] **3 ) + Rv
idx = (wave >= 6300) & (wave <= 22000)
reddening_curve[idx] = 2.659 * ( -1.857 + 1.040 * wave_number[idx]) + Rv
return reddening_curve
def reddening(wave, flux, ebv = 0.0, law = 'calzetti', Rv = 4.05):
"""
Reddening an input spectra through a given reddening curve.
Parameters
----------
wave : float array
Wavelength of input spectra
flux : float array
Flux of input spectra
ebv : float, optional
E(B-V) value, by default 0
law : str, optional
Extinction curve, by default 'calzetti'
Rv : float, optional
Extinction coefficient, by default 4.05
Returns
-------
float array
Flux of spectra after reddening
"""
curve = Calzetti_Law(wave, Rv = Rv)
fluxNew = flux / (10. ** (0.4 * ebv * curve))
return fluxNew
################
# Emission Lines
################
def SingleEmissinoLine(wave, line_wave, FWHM_inst):
"""
Profile of single emission line (Gaussian profile)
Parameters
----------
wave : float array
Wavelength of spectrum
line_wave : float
Wavelength of emission line at the line center
FWHM_inst : float
Intrinsic broadening of emission line, units: A
Returns
-------
float array
Spectra of single emission line
"""
sigma = FWHM_inst / 2.355
flux = norm.pdf(wave, line_wave, sigma)
return flux
class EmissionLineTemplate():
"""
Template for the emission lines
Parameters
----------
config : class
The class of configuration.
lam_range : list, optional
Wavelength range, by default [500, 15000]
dlam : float, optional
Wavelength width per pixel, by default 0.1A
model : str, optional
Emission line model, including 'hii' for HII region and 'nlr' for narrow line region of AGN,
by default 'hii'
"""
def __init__(self, config, lam_range = [500, 15000], dlam = 0.1, model = 'hii'):
self.lam_range = lam_range
self.wave = np.arange(lam_range[0], lam_range[1], 0.1)
self.FWHM_inst = config.inst_fwhm
self.model = model
# HII region model of fsps-cloudy
if model == 'hii':
# Loading emission line flux table
flux_table_file = data_path + '/data/fsps.nebular.fits'
line_table = fits.open(flux_table_file)
# Emission line list
line_list = line_table[1].data
line_wave = line_list['Wave']
line_names = line_list['Name']
w = (line_wave > lam_range[0]) & (line_wave < lam_range[1])
self.line_names = line_names[w]
self.line_wave = line_wave[w]
# Make parameter grid
grid = line_table[2].data
self.logz_grid = grid['logZ']
# Narrow line region model of
if model == 'nlr':
# Loading emission line flux table
flux_table_file = data_path + '/data/AGN.NLR.fits'
line_table = fits.open(flux_table_file)
# Emission line list
line_list = line_table[1].data
line_wave = line_list['Wave']
line_names = line_list['Name']
w = (line_wave > lam_range[0]) & (line_wave < lam_range[1])
self.line_names = line_names[w]
self.line_wave = line_wave[w]
# Make parameter grid
grid = line_table[2].data
self.logz_grid = grid['logZ']
# Flux ratio
flux_ratio = line_table[3].data
self.flux_ratio = flux_ratio
# Make emission line
nline = len(line_wave)
for i in range(nline):
if i==0:
emission_line = SingleEmissinoLine(self.wave, line_wave[i], self.FWHM_inst)
emission_lines = emission_line
else:
emission_line = SingleEmissinoLine(self.wave, line_wave[i], self.FWHM_inst)
emission_lines = np.vstack((emission_lines, emission_line))
self.emission_lines = emission_lines.T
class HII_Region():
"""
Class for the spectra of HII region
Parameters
----------
config : class
Class of configuration
temp : class
Class of emission line template
halpha : float, optional
Integral flux of Halpha emission line, by default 100 * 1e-17 erg/s/cm^2
logz : float, optional
Gas-phase metallicity, by default 0.0
vel : float, optional
Line of sight velocity, by default 100.0km/s
vdisp : float, optional
Velocity dispersion, by default 120.0km/s
ebv : float, optional
Dust extinction, by default 0.1
Raises
------
ValueError
The value of logZ should be between -2 and 0.5.
"""
def __init__(self, config, temp, halpha = 100.0, logz = 0.0,
vel = 100.0, vdisp = 120.0, ebv = 0.1):
if (logz > -2) & (logz < 0.5):
indz = np.argmin(np.abs(logz - temp.logz_grid))
flux_ratio = temp.flux_ratio[indz, :]
else:
raise ValueError('The value of logZ is not in the range of [-2, 0.5]!')
# Make emission line spectra through adding emission lines
emlines = temp.emission_lines * flux_ratio
flux_combine = np.sum(emlines, axis = 1)
flux_calibrate = flux_combine * halpha # Units: erg/s/A/cm^2
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(temp.wave, flux_calibrate, ebv = ebv)
# Broadening caused by Velocity Dispersion
velscale = 10
lam_range = [np.min(temp.wave), np.max(temp.wave)]
flux_logwave, logLam = log_rebin(lam_range, flux_dust, velscale=velscale)[:2]
sigma_gas = vdisp / velscale # in pixel
sigma_LSF = temp.FWHM_inst / (np.exp(logLam)) * 3e5 / velscale # in pixel
if sigma_gas>0:
sigma_dif = np.zeros(len(flux_logwave))
idx = (sigma_gas > sigma_LSF)
sigma_dif[idx] = np.sqrt(sigma_gas ** 2. - sigma_LSF[idx] ** 2.)
idx = (sigma_gas <= sigma_LSF)
sigma_dif[idx] = 0.1
flux_broad = gaussian_filter1d(flux_logwave, sigma_dif)
else:
flux_broad = flux_logwave
# Redshift
redshift = vel / 3e5
wave_r = np.exp(logLam) * (1 + redshift)
flux_red = np.interp(config.wave, wave_r, flux_broad)
self.wave = config.wave
self.flux = flux_red
#############
# AGN Spectra
#############
class AGN_NLR():
"""
Class for narrow line region of AGN
Parameters
----------
config : class
Class of configuration
temp : class
Class of emission line template
halpha : float, optional
Integral flux of Halpha emission line, by default 100 * 1e-17 erg/s/cm^2
logz : float, optional
Gas-phase metallicity, by default 0.0
vel : float, optional
Line of sight velocity, by default 100.0km/s
vdisp : float, optional
Velocity dispersion, by default 120.0km/s
ebv : float, optional
Dust extinction, by default 0.1
Raises
------
ValueError
The value of logZ should be between -2 and 0.5.
"""
def __init__(self, config, temp, halpha = 100.0, logz = 0.0,
vel = 100.0, vdisp = 120.0, ebv = 0.1):
if (logz > -2) & (logz < 0.5):
indz = np.argmin(np.abs(logz - temp.logz_grid))
flux_ratio = temp.flux_ratio[indz, :]
else:
raise ValueError('The value of logZ is not in the range of [-2, 0.5]!')
# Make emission line spectra through adding emission lines
emlines = temp.emission_lines * (flux_ratio / flux_ratio[6] )
flux_combine = np.sum(emlines, axis = 1)
flux_calibrate = flux_combine * halpha # Units: 1e-17 erg/s/A/cm^2
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(temp.wave, flux_calibrate, ebv = ebv)
# Broadening caused by Velocity Dispersion
velscale = 10
lam_range = [np.min(temp.wave), np.max(temp.wave)]
flux_logwave, logLam = log_rebin(lam_range, flux_dust, velscale=velscale)[:2]
sigma_gas = vdisp / velscale # in pixel
sigma_LSF = temp.FWHM_inst / (np.exp(logLam)) * 3e5 / velscale # in pixel
if sigma_gas>0:
sigma_dif = np.zeros(len(flux_logwave))
idx = (sigma_gas > sigma_LSF)
sigma_dif[idx] = np.sqrt(sigma_gas ** 2. - sigma_LSF[idx] ** 2.)
idx = (sigma_gas <= sigma_LSF)
sigma_dif[idx] = 0.1
flux_broad = gaussian_filter1d(flux_logwave, sigma_dif)
# Redshift
redshift = vel / 3e5
wave_r = np.exp(logLam) * (1 + redshift)
flux_red = np.interp(config.wave, wave_r, flux_broad)
self.wave = config.wave
self.flux = flux_red
class AGN_BLR():
"""
Class for the broad line region of AGN
Parameters
----------
config : class
Class of configuration
hbeta_flux : float, optional
Integral flux of Hbeta broad line, by default 100 * 1e-17 erg/s/cm^2
hbeta_fwhm : float, optional
FWHM of Hbeta broad line, by default 2000.0km/s
vel : float, optional
Line of sight velocity, by default 100.0km/s
ebv : float, optional
Dust extinction, by default 0.1
lam_range : list, optional
Wavelength range, by default [500, 15000]
"""
def __init__(self, config, hbeta_flux = 100.0, hbeta_fwhm = 2000.0, ebv = 0.1,
vel = 0., lam_range = [500, 15000]):
wave_rest = np.arange(lam_range[0], lam_range[1], 0.1)
line_names = ['Hepsilon', 'Hdelta', 'Hgamma', 'Hbeta', 'Halpha']
line_waves = [3970.079, 4101.742, 4340.471, 4861.333, 6562.819]
line_ratio = [0.101, 0.208, 0.405, 1.000, 2.579] # From Ilic et al. (2006)
# Make emission lines
for i in range(len(line_names)):
if i==0:
emission_line = SingleEmissinoLine(wave_rest, line_waves[i],
hbeta_fwhm / 3e5 * line_waves[i])
emission_lines = emission_line
else:
emission_line = SingleEmissinoLine(wave_rest, line_waves[i],
hbeta_fwhm / 3e5 * line_waves[i])
emission_lines = np.vstack((emission_lines, emission_line))
emlines = emission_lines.T * line_ratio
flux_combine = np.sum(emlines, axis = 1)
# Flux callibration
flux_calibrate = flux_combine * hbeta_flux # Units: 1e-17 erg/s/A/cm^2
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(wave_rest, flux_calibrate, ebv = ebv)
else:
flux_dust = flux_calibrate
# Redshift
redshift = vel / 3e5
wave_r = wave_rest * (1 + redshift)
flux_red = np.interp(config.wave, wave_r, flux_dust)
self.wave = config.wave
self.flux = flux_red
class AGN_FeII():
"""
Class for FeII emission lines of AGN
Parameters
----------
config : class
Class of configuration
hbeta_broad : float, optional
Integral flux of Hbeta broad line, by default 100 * 1e-17 erg/s/cm^2
r4570 : float, optional
Flux ratio between Fe4570 flux and Hbeta broad line flux, by default 0.4
vel : float, optional
Line of sight velocity, by default 100.0km/s
ebv : float, optional
Dust extinction, by default 0.1
"""
def __init__(self, config, hbeta_broad = 100.0, r4570 = 0.4, ebv = 0.1, vel = 100.0):
filename = data_path + '/data/FeII.AGN.fits'
# Loading FeII template
hdulist = fits.open(filename)
data = hdulist[1].data
wave_rest = data['WAVE']
flux_model = data['FLUX']
# Determine the flux of FeII
Fe4570_temp = 100
Fe4570_model = hbeta_broad * r4570
Ratio_Fe4570 = Fe4570_model / Fe4570_temp
# Flux calibration
flux_calibrate = flux_model * Ratio_Fe4570
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(wave_rest, flux_calibrate, ebv = ebv)
else:
flux_dust = flux_calibrate
# Redshift
redshift = vel / 3e5
wave_r = wave_rest * (1 + redshift)
flux_red = np.interp(config.wave, wave_r, flux_dust)
self.wave = config.wave
self.flux = flux_red
class AGN_Powerlaw():
"""
The class of power-law spectrum of AGN
Parameters
----------
config : class
Class of configuration
M5100 : float, optional
Magnitude of power law spectrum between 5050A and 5150A, by default 1000.0 * 1e-17 erg/s/cm^2
alpha : float, optional
Index of power law, by default -1.5
vel : float, optional
Line of sight velocity, by default 100.0km/s
Ebv : float, optional
Dust extinction, by default 0.1
"""
def __init__(self, config, m5100 = 1000.0, alpha = -1.5, vel = 100.0, ebv = 0.1):
wave_rest = np.linspace(1000,20000,10000)
flux = wave_rest ** alpha
# Flux calibration
flux_calibrate = calibrate(wave_rest, flux, m5100, filtername='5100')
# Dust attenuation
if np.isscalar(ebv):
flux_dust = reddening(wave_rest, flux_calibrate, ebv = ebv)
else:
flux_dust = flux_calibrate
# Redshift
redshift = vel / 3e5
wave_r = wave_rest * (1 + redshift)
flux_red = np.interp(config.wave, wave_r, flux_dust)
self.wave = config.wave
self.flux = flux_red
class AGN():
"""
Class of singal spectra of AGN
Parameters
----------
config : class
Class of configuration
nlr_template : class
Class of emission line template
bhmass : float, optional
Black hole mass used for calculating the luminosity of power law spectrum at 5100A,
by default 1e6 solar mass
edd_ratio : float, optional
Eddinton ratio used for calculating the luminosity of power law spectrum at 5100A, by default 0.05
halpha_broad : float, optional
Integral flux of Halpha broad line, by default 100.0 * 1e-17 erg/s/cm^2
halpha_narrow : float, optional
Integral flux of Halpha narrow line, by default 100.0 * 1e-17 erg/s/cm^2
vdisp_broad : float, optional
Velocity dispersion of Halpha broad line, by default 5000.0km/s
vdisp_narrow : float, optional
Velocity dispersion of Halpha narrow line, by default 500.0km/s
vel : float, optional
Line of sight velocity, by default 1000.0km/s
logz : float, optional
Gas-phase metallicity of narrow line region, by default 0.0
ebv : float, optional
Dust extinction, by default 0.1
dist : float, optional
Luminosity distance of AGN, by default 20.0Mpc
"""
def __init__(self, config, nlr_template, bhmass = 1e6, edd_ratio = 0.05,
halpha_broad = 100.0, halpha_narrow = 100.0, vdisp_broad = 5000.0, vdisp_narrow = 500.0,
vel = 1000.0, logz = 0.0, ebv = 0.1, dist = 20.0):
NLR = AGN_NLR(config, nlr_template, halpha = halpha_narrow, logz = logz,
vel = vel, vdisp = vdisp_narrow, ebv = ebv)
if halpha_broad > 0:
BLR = AGN_BLR(config, hbeta_flux = halpha_broad / 2.579,
hbeta_fwhm = vdisp_broad / 2.355, ebv = ebv, vel = vel)
m5100 = BHmass_to_M5100(bhmass, edd_ratio = edd_ratio, dist = dist)
PL = AGN_Powerlaw(config, m5100 = m5100, ebv = ebv, vel = vel)
Fe = AGN_FeII(config, hbeta_broad = halpha_broad / 2.579, ebv = ebv, vel = vel)
self.wave = config.wave
self.flux = NLR.flux + PL.flux + Fe.flux
if halpha_broad > 0:
self.flux = self.flux + BLR.flux
def BHmass_to_M5100(bhmass, edd_ratio = 0.05, dist = 21.0):
"""
Caculate magnitude at 5100A according to the black hole mass
Parameters
----------
bhmass : float
Black hole mass, unit: solar mass
edd_ratio : float, optional
Eddtington ratio, by default 0.05
dist : float, optional
Distance to the black hole, by default 21.0Mpc
Returns
-------
float
Magnitude at 5100A
"""
# Calculate bolometric luminosity
Ledd = 3e4 * bhmass
Lbol = Ledd * edd_ratio
# Convert bolometric luminosity to 5100A luminosity (Marconi et al. 2004)
L5100 = Lbol / 10.9
M5100 = 4.86 - 2.5 * np.log10(L5100)
m5100 = M5100 + 5. * np.log10(dist * 1e5)
return m5100
#################
# Stellar Spectra
#################
"""
Extract the age and metallicity from the name of a file of
the MILES library of Single Stellar Population models as
downloaded from http://miles.iac.es/ as of 2016
:param filename: string possibly including full path
(e.g. 'miles_library/Mun1.30Zm0.40T03.9811.fits')
:return: age (Gyr), [M/H]
"""
def age_metal(filename):
"""
Extract the age and metallicity from the name of a file of
the MILES library of Single Stellar Population models.
Parameters
----------
filename : string
Full path of template files
Returns
-------
age : float
Age of SSP (Gyr)
FeH : float
Metallicity of SSP
Raises
------
ValueError
This is not a standard MILES filename
"""
s = path.basename(filename)
age = float(s[s.find("T")+1:s.find("_iPp0.00_baseFe.fits")])
metal = s[s.find("Z")+1:s.find("T")]
if "m" in metal:
metal = -float(metal[1:])
elif "p" in metal:
metal = float(metal[1:])
else:
raise ValueError("This is not a standard MILES filename")
return age, metal
class StellarContinuumTemplate(object):
"""
Class of single stellar population template.
Parameters
----------
config : class
Class of configuration
velscale : array
velocity scale in km/s per pixels, by default 50.0km/s
pathname : string, optional
path with wildcards returning the list files to use,
by default data_path+'/data/EMILES/Ech*_baseFe.fits'
normalize : bool, optional
Set to True to normalize each template to mean=1, by default False
"""
def __init__(self, config, velscale = 50,
pathname = data_path + '/data/EMILES/Ech*_baseFe.fits',
normalize = False):
FWHM_inst = config.inst_fwhm
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 = log_rebin(lam_range_temp, ssp, velscale=velscale)[:2]
#wave=((np.arange(hdr['NAXIS1'])+1.0)-hdr['CRPIX1'])*hdr['CDELT1']+hdr['CRVAL1']
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 of Emiles templates
Emile_wave = np.exp(log_lam_temp)
Emile_FWHM = np.zeros(h2['NAXIS1'])
Emile_FWHM[np.where(Emile_wave < 3060)] = 3.
Emile_FWHM[np.where((Emile_wave >= 3060) & (Emile_wave < 3540))] = 3.
Emile_FWHM[np.where((Emile_wave >= 3540) & (Emile_wave < 8950))] = 2.5
Lwave = Emile_wave[np.where(Emile_wave >= 8950)]
Emile_FWHM[np.where(Emile_wave >= 8950)]=60*2.35/3.e5*Lwave # sigma=60km/s at lambda > 8950
LSF = Emile_FWHM
FWHM_eff=Emile_FWHM.copy() # combined FWHM from stellar library and instrument(input)
if np.isscalar(FWHM_inst):
FWHM_eff[Emile_FWHM < FWHM_inst] = FWHM_inst
LSF[Emile_FWHM < FWHM_inst] = FWHM_inst
else:
FWHM_eff[Emile_FWHM < FWHM_inst] = FWHM_inst[Emile_FWHM < FWHM_inst]
LSF[Emile_FWHM < FWHM_inst] = FWHM_inst[Emile_FWHM < FWHM_inst]
FWHM_dif = np.sqrt(FWHM_eff**2 - Emile_FWHM**2)
sigma_dif = 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, metal in enumerate(metals):
p = all.index((age, metal))
hdu = fits.open(files[p])
ssp = hdu[0].data
if np.isscalar(FWHM_dif):
ssp = ndimage.gaussian_filter1d(ssp, sigma_dif)
else:
ssp = gaussian_filter1d(ssp, sigma_dif) # convolution with variable sigma
sspNew = 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] = metal
self.templates = templates/np.median(templates) # 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
self.LSF = log_rebin(lam_range_temp, LSF, velscale=velscale)[0]
self.velscale = velscale
def fmass_ssp(self):
isedpath = data_path + '/data/EMILES/model/'
massfile = isedpath + 'out_mass_CH_PADOVA00'
n_metal = self.n_metal
n_ages = self.n_ages
fage = self.age_grid[:,0]
fMs = np.zeros((n_ages,n_metal))
Metal, Age, Ms = readcol(massfile, usecols=(2, 3, 6))
for i in range(n_metal):
for j in range(self.n_ages):
locmin = np.argmin(abs(Metal - self.metal_grid[j, i])) & np.argmin(abs(Age - self.age_grid[j, i]))
fMs[j,i] = Ms[locmin]
return fMs
class StellarContinuum():
"""
The class of stellar continuum
Parameters
----------
config : class
Class of configuration
template : class
Class of single stellar population template
mag : float, optional
Magnitude in SDSS r-band, by default 15.0
age : float, optional
Median age of stellar continuum, by default 1.0Gyr
feh : float, optional
Metallicity of stellar continuum, by default 0.0
vel : float, optional
Line of sight velocity, by default 100.0km/s
vdisp : float, optional
Velocity dispersion, by default 120.0km/s
ebv : float, optional
Dust extinction, by default 0.1
"""
def __init__(self, config, template, mag = 15.0, age = 1.0, feh = 0.0,
vel = 100.0, vdisp = 100.0, ebv = 0.1):
# -----------------
# Stellar Continuum
SSP_temp = template.templates
# Select metal bins
metals = template.metal_grid[0,:]
minloc = np.argmin(abs(feh - metals))
tpls = SSP_temp[:, :, minloc]
fmass = template.fmass_ssp()[:, minloc]
# Select age bins
Ages = template.age_grid[:,0]
minloc = np.argmin(abs(age-Ages))
Stellar = tpls[:, minloc]
wave = np.exp(template.log_lam_temp)
# Broadening caused by Velocity Dispersion
sigma_gal = vdisp / template.velscale # in pixel
sigma_LSF = template.LSF / template.velscale # in pixel
if sigma_gal>0:
sigma_dif = np.zeros(len(Stellar))
idx = (sigma_gal > sigma_LSF)
sigma_dif[idx] = np.sqrt(sigma_gal ** 2. - sigma_LSF[idx] ** 2.)
idx = (sigma_gal <= sigma_LSF)
sigma_dif[idx] = 0.1
flux0 = gaussian_filter1d(Stellar, sigma_dif)
# Dust Reddening
if np.isscalar(ebv):
flux0 = reddening(wave, flux0, ebv = ebv)
# Redshift
redshift = vel / 3e5
wave_r = wave * (1 + redshift)
flux = np.interp(config.wave, wave_r, flux0)
# Calibration
if np.isscalar(mag):
flux = calibrate(config.wave, flux, mag, filtername='SLOAN_SDSS.r')
# Convert to input wavelength
self.wave = config.wave
self.flux = flux
#####################
# Single Star Spectra
#####################
class SingleStarTemplate():
"""
Class of single stellar template
Parameters
----------
config : class
Class of configuration
velscale : float, option
velocity scale in km/s per pixels, by default 20.0km/s
"""
def __init__(self, config, velscale = 20):
FWHM_inst = config.inst_fwhm
filename = data_path + '/data/Starlib.XSL.fits'
hdulist = fits.open(filename)
lam = hdulist[1].data['Wave']
flux = hdulist[2].data
par = hdulist[3].data
lam_range_temp = np.array([3500, 12000])
TemNew, log_lam_temp = log_rebin(lam_range_temp, flux[1, :], velscale = velscale)[:2]
# FWHM of XLS templates
Temp_wave = np.exp(log_lam_temp)
Temp_FWHM = np.zeros(len(log_lam_temp))
Temp_FWHM[(Temp_wave < 5330)] = 13 * 2.35 / 3e5 * Temp_wave[(Temp_wave < 5330)] # sigma = 13km/s at lambda <5330
Temp_FWHM[(Temp_wave >= 5330) & (Temp_wave < 9440)] = 11 * 2.35 / 3e5 * Temp_wave[(Temp_wave >= 5330) & (Temp_wave < 9440)]
# sigma = 13km/s at 5330 < lambda < 9440
Temp_FWHM[(Temp_wave >= 9440)] = 16 * 2.35 / 3e5 * Temp_wave[(Temp_wave >= 9440)] # sigma=16km/s at lambda > 9440
LSF = Temp_FWHM
FWHM_eff = Temp_FWHM.copy() # combined FWHM from stellar library and instrument(input)
if np.isscalar(FWHM_inst):
FWHM_eff[Temp_FWHM < FWHM_inst] = FWHM_inst
LSF[Temp_FWHM < FWHM_inst] = FWHM_inst
else:
FWHM_eff[Temp_FWHM < FWHM_inst] = FWHM_inst[Temp_FWHM < FWHM_inst]
LSF[Temp_FWHM < FWHM_inst] = FWHM_inst[Temp_FWHM < FWHM_inst]
FWHM_dif = np.sqrt(FWHM_eff ** 2 - Temp_FWHM ** 2)
sigma_dif = FWHM_dif / 2.355 / (lam[1] - lam[0]) # Sigma difference in pixels
temp = np.empty((TemNew.size, par.size))
for i in range(par.size):
temp0 = log_rebin(lam_range_temp, flux[i, :], velscale=velscale)[0]
if np.isscalar(FWHM_dif):
temp1 = ndimage.gaussian_filter1d(temp0, sigma_dif)
else:
temp1 = gaussian_filter1d(temp0, sigma_dif) # convolution with variable sigma
tempNew = temp1 / np.mean(temp1)
temp[:, i] = tempNew
self.templates = temp
self.log_lam_temp = log_lam_temp
self.teff_grid = par['Teff']
self.feh_grid = par['FeH']
self.logg_grid = par['logg']
self.LSF = Temp_FWHM
self.velscale = velscale
class SingleStar():
"""
Class of single stelar spectrum
Parameters
----------
config : class
Class of configuration
template : class
Class of single stellar population template
mag : float, optional
Magnitude in SDSS r-band, by default 15.0
Teff : float, optional
Effective tempreture, by default 10000.0K
FeH : float, optional
Metallicity of stellar, by default 0.0
vel : float, optional
Line of sight velocity, by default 100.0km/s
Ebv : float, optional
Dust extinction, by default 0.1
"""
def __init__(self, config, template, mag = 15.0, teff = 10000.0, feh = 0.0, vel = 100.0, ebv = 0.0):
StarTemp = template.templates
# Select metal bins
idx_FeH = (np.abs(template.feh_grid - feh) < 0.5)
tpls = StarTemp[:, idx_FeH]
# Select Teff bins
Teff_FeH = template.teff_grid[idx_FeH]
minloc = np.argmin(abs(teff - Teff_FeH))
starspec = tpls[:, minloc]
wave = np.exp(template.log_lam_temp)
# Dust Reddening
if np.isscalar(ebv):
starspec = reddening(wave, starspec, ebv = ebv)
# Redshift
redshift = vel / 3e5
wave_r = wave * (1 + redshift)
flux = np.interp(config.wave, wave_r, starspec)
# Calibration
if np.isscalar(mag):
flux = calibrate(config.wave, flux, mag, filtername='SLOAN_SDSS.r')
# Convert to input wavelength
self.wave = config.wave
self.flux = flux
\ No newline at end of file
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